Calibration transfer employing univariate correction and robust regression.
Galvão, Roberto Kawakami Harrop; Soares, Sófacles Figueredo Carreiro; Martins, Marcelo Nascimento; Pimentel, Maria Fernanda; Araújo, Mário César Ugulino
2015-03-15
This paper proposes a new method for calibration transfer, which was specifically designed to work with isolated variables, rather than the full spectrum or spectral windows. For this purpose, a univariate procedure is initially employed to correct the spectral measurements of the secondary instrument, given a set of transfer samples. A robust regression technique is then used to obtain a model with low sensitivity with respect to the univariate correction residuals. The proposed method is employed in two case studies involving near infrared spectrometric determination of specific mass, research octane number and naphthenes in gasoline, and moisture and oil in corn. In both cases, better calibration transfer results were obtained in comparison with piecewise direct standardization (PDS). The proposed method should be of a particular value for use with application-targeted instruments that monitor only a small set of spectral variables. PMID:25732421
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
Partial least squares Cox regression for genome-wide data.
Nygård, Ståle; Borgan, Ornulf; Lingjaerde, Ole Christian; Størvold, Hege Leite
2008-06-01
Most methods for survival prediction from high-dimensional genomic data combine the Cox proportional hazards model with some technique of dimension reduction, such as partial least squares regression (PLS). Applying PLS to the Cox model is not entirely straightforward, and multiple approaches have been proposed. The method of Park etal. (Bioinformatics 18(Suppl. 1):S120-S127, 2002) uses a reformulation of the Cox likelihood to a Poisson type likelihood, thereby enabling estimation by iteratively reweighted partial least squares for generalized linear models. We propose a modification of the method of Park et al. (2002) such that estimates of the baseline hazard and the gene effects are obtained in separate steps. The resulting method has several advantages over the method of Park et al. (2002) and other existing Cox PLS approaches, as it allows for estimation of survival probabilities for new patients, enables a less memory-demanding estimation procedure, and allows for incorporation of lower-dimensional non-genomic variables like disease grade and tumor thickness. We also propose to combine our Cox PLS method with an initial gene selection step in which genes are ordered by their Cox score and only the highest-ranking k% of the genes are retained, obtaining a so-called supervised partial least squares regression method. In simulations, both the unsupervised and the supervised version outperform other Cox PLS methods. PMID:18188699
Rovadoscki, Gregori A; Petrini, Juliana; Ramirez-Diaz, Johanna; Pertile, Simone F N; Pertille, Fábio; Salvian, Mayara; Iung, Laiza H S; Rodriguez, Mary Ana P; Zampar, Aline; Gaya, Leila G; Carvalho, Rachel S B; Coelho, Antonio A D; Savino, Vicente J M; Coutinho, Luiz L; Mourão, Gerson B
2016-09-01
Repeated measures from the same individual have been analyzed by using repeatability and finite dimension models under univariate or multivariate analyses. However, in the last decade, the use of random regression models for genetic studies with longitudinal data have become more common. Thus, the aim of this research was to estimate genetic parameters for body weight of four experimental chicken lines by using univariate random regression models. Body weight data from hatching to 84 days of age (n = 34,730) from four experimental free-range chicken lines (7P, Caipirão da ESALQ, Caipirinha da ESALQ and Carijó Barbado) were used. The analysis model included the fixed effects of contemporary group (gender and rearing system), fixed regression coefficients for age at measurement, and random regression coefficients for permanent environmental effects and additive genetic effects. Heterogeneous variances for residual effects were considered, and one residual variance was assigned for each of six subclasses of age at measurement. Random regression curves were modeled by using Legendre polynomials of the second and third orders, with the best model chosen based on the Akaike Information Criterion, Bayesian Information Criterion, and restricted maximum likelihood. Multivariate analyses under the same animal mixed model were also performed for the validation of the random regression models. The Legendre polynomials of second order were better for describing the growth curves of the lines studied. Moderate to high heritabilities (h(2) = 0.15 to 0.98) were estimated for body weight between one and 84 days of age, suggesting that selection for body weight at all ages can be used as a selection criteria. Genetic correlations among body weight records obtained through multivariate analyses ranged from 0.18 to 0.96, 0.12 to 0.89, 0.06 to 0.96, and 0.28 to 0.96 in 7P, Caipirão da ESALQ, Caipirinha da ESALQ, and Carijó Barbado chicken lines, respectively. Results indicate that
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.
Cox Regression Models with Functional Covariates for Survival Data
Gellar, Jonathan E.; Colantuoni, Elizabeth; Needham, Dale M.; Crainiceanu, Ciprian M.
2015-01-01
We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome. PMID:26441487
Diagnostic Measures for the Cox Regression Model with Missing Covariates
Zhu, Hongtu; Ibrahim, Joseph G.; Chen, Ming-Hui
2015-01-01
Summary This paper investigates diagnostic measures for assessing the influence of observations and model misspecification in the presence of missing covariate data for the Cox regression model. Our diagnostics include case-deletion measures, conditional martingale residuals, and score residuals. The Q-distance is proposed to examine the effects of deleting individual observations on the estimates of finite-dimensional and infinite-dimensional parameters. Conditional martingale residuals are used to construct goodness of fit statistics for testing possible misspecification of the model assumptions. A resampling method is developed to approximate the p-values of the goodness of fit statistics. Simulation studies are conducted to evaluate our methods, and a real data set is analyzed to illustrate their use. PMID:26903666
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…
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
Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.
ERIC Educational Resources Information Center
Vidal, Sherry
Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…
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
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
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
Box–Cox Transformation and Random Regression Models for Fecal egg Count Data
da Silva, Marcos Vinícius Gualberto Barbosa; Van Tassell, Curtis P.; Sonstegard, Tad S.; Cobuci, Jaime Araujo; Gasbarre, Louis C.
2012-01-01
Accurate genetic evaluation of livestock is based on appropriate modeling of phenotypic measurements. In ruminants, fecal egg count (FEC) is commonly used to measure resistance to nematodes. FEC values are not normally distributed and logarithmic transformations have been used in an effort to achieve normality before analysis. However, the transformed data are often still not normally distributed, especially when data are extremely skewed. A series of repeated FEC measurements may provide information about the population dynamics of a group or individual. A total of 6375 FEC measures were obtained for 410 animals between 1992 and 2003 from the Beltsville Agricultural Research Center Angus herd. Original data were transformed using an extension of the Box–Cox transformation to approach normality and to estimate (co)variance components. We also proposed using random regression models (RRM) for genetic and non-genetic studies of FEC. Phenotypes were analyzed using RRM and restricted maximum likelihood. Within the different orders of Legendre polynomials used, those with more parameters (order 4) adjusted FEC data best. Results indicated that the transformation of FEC data utilizing the Box–Cox transformation family was effective in reducing the skewness and kurtosis, and dramatically increased estimates of heritability, and measurements of FEC obtained in the period between 12 and 26 weeks in a 26-week experimental challenge period are genetically correlated. PMID:22303406
Tosteson, Tor D.; Morden, Nancy E.; Stukel, Therese A.; O'Malley, A. James
2014-01-01
The estimation of treatment effects is one of the primary goals of statistics in medicine. Estimation based on observational studies is subject to confounding. Statistical methods for controlling bias due to confounding include regression adjustment, propensity scores and inverse probability weighted estimators. These methods require that all confounders are recorded in the data. The method of instrumental variables (IVs) can eliminate bias in observational studies even in the absence of information on confounders. We propose a method for integrating IVs within the framework of Cox's proportional hazards model and demonstrate the conditions under which it recovers the causal effect of treatment. The methodology is based on the approximate orthogonality of an instrument with unobserved confounders among those at risk. We derive an estimator as the solution to an estimating equation that resembles the score equation of the partial likelihood in much the same way as the traditional IV estimator resembles the normal equations. To justify this IV estimator for a Cox model we perform simulations to evaluate its operating characteristics. Finally, we apply the estimator to an observational study of the effect of coronary catheterization on survival. PMID:25506259
Pathway-gene identification for pancreatic cancer survival via doubly regularized Cox regression
2014-01-01
Background Recent global genomic analyses identified 69 gene sets and 12 core signaling pathways genetically altered in pancreatic cancer, which is a highly malignant disease. A comprehensive understanding of the genetic signatures and signaling pathways that are directly correlated to pancreatic cancer survival will help cancer researchers to develop effective multi-gene targeted, personalized therapies for the pancreatic cancer patients at different stages. A previous work that applied a LASSO penalized regression method, which only considered individual genetic effects, identified 12 genes associated with pancreatic cancer survival. Results In this work, we integrate pathway information into pancreatic cancer survival analysis. We introduce and apply a doubly regularized Cox regression model to identify both genes and signaling pathways related to pancreatic cancer survival. Conclusions Four signaling pathways, including Ion transport, immune phagocytosis, TGFβ (spermatogenesis), regulation of DNA-dependent transcription pathways, and 15 genes within the four pathways are identified and verified to be directly correlated to pancreatic cancer survival. Our findings can help cancer researchers design new strategies for the early detection and diagnosis of pancreatic cancer. PMID:24565114
Vatcheva, KP; Lee, M; McCormick, JB; Rahbar, MH
2016-01-01
Objective To demonstrate the adverse impact of ignoring statistical interactions in regression models used in epidemiologic studies. Study design and setting Based on different scenarios that involved known values for coefficient of the interaction term in Cox regression models we generated 1000 samples of size 600 each. The simulated samples and a real life data set from the Cameron County Hispanic Cohort were used to evaluate the effect of ignoring statistical interactions in these models. Results Compared to correctly specified Cox regression models with interaction terms, misspecified models without interaction terms resulted in up to 8.95 fold bias in estimated regression coefficients. Whereas when data were generated from a perfect additive Cox proportional hazards regression model the inclusion of the interaction between the two covariates resulted in only 2% estimated bias in main effect regression coefficients estimates, but did not alter the main findings of no significant interactions. Conclusions When the effects are synergic, the failure to account for an interaction effect could lead to bias and misinterpretation of the results, and in some instances to incorrect policy decisions. Best practices in regression analysis must include identification of interactions, including for analysis of data from epidemiologic studies.
Nie, Z Q; Ou, Y Q; Zhuang, J; Qu, Y J; Mai, J Z; Chen, J M; Liu, X Q
2016-05-10
Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study, but Cox proportional hazard model is often used in survival data analysis. Most literature only refer to main effect model, however, generalized linear model differs from general linear model, and the interaction was composed of multiplicative interaction and additive interaction. The former is only statistical significant, but the latter has biological significance. In this paper, macros was written by using SAS 9.4 and the contrast ratio, attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions, and the confidence intervals of Wald, delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions. PMID:27188374
Lee, Eunjee; Zhu, Hongtu; Kong, Dehan; Wang, Yalin; Giovanello, Kelly Sullivan; Ibrahim, Joseph G
2015-01-01
The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer’s disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM. PMID:26900412
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.
Devarajan, Karthik; Ebrahimi, Nader
2011-01-01
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
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
Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi
2015-01-01
Background. Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. Methods. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Result. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. Conclusion. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients. PMID:26413142
Including network knowledge into Cox regression models for biomarker signature discovery.
Fröhlich, Holger
2014-03-01
Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step toward a better personalized medicine. During the last decade various methods have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Most of these methods focus on classification problems, that is learn a model from data that discriminates patients into distinct clinical groups. Far less has been published on approaches that predict a patient's event risk. In this paper, we investigate eight methods that integrate network information into multivariable Cox proportional hazard models for risk prediction in breast cancer. We compare the prediction performance of our tested algorithms via cross-validation as well as across different datasets. In addition, we highlight the stability and interpretability of obtained gene signatures. In conclusion, we find GeneRank-based filtering to be a simple, computationally cheap and highly predictive technique to integrate network information into event time prediction models. Signatures derived via this method are highly reproducible. PMID:24430933
NASA Astrophysics Data System (ADS)
Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany
2016-07-01
Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.
Hegazy, Maha A; Lotfy, Hayam M; Mowaka, Shereen; Mohamed, Ekram Hany
2016-07-01
Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations. PMID:27070527
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. PMID:26970107
NASA Astrophysics Data System (ADS)
Hegazy, Maha A.; Lotfy, Hayam M.; Rezk, Mamdouh R.; Omran, Yasmin Rostom
2015-04-01
Smart and novel spectrophotometric and chemometric methods have been developed and validated for the simultaneous determination of a binary mixture of chloramphenicol (CPL) and dexamethasone sodium phosphate (DSP) in presence of interfering substances without prior separation. The first method depends upon derivative subtraction coupled with constant multiplication. The second one is ratio difference method at optimum wavelengths which were selected after applying derivative transformation method via multiplying by a decoding spectrum in order to cancel the contribution of non labeled interfering substances. The third method relies on partial least squares with regression model updating. They are so simple that they do not require any preliminary separation steps. Accuracy, precision and linearity ranges of these methods were determined. Moreover, specificity was assessed by analyzing synthetic mixtures of both drugs. The proposed methods were successfully applied for analysis of both drugs in their pharmaceutical formulation. The obtained results have been statistically compared to that of an official spectrophotometric method to give a conclusion that there is no significant difference between the proposed methods and the official ones with respect to accuracy and precision.
Khosravi, Bahareh; Pourahmad, Saeedeh; Bahreini, Amin; Nikeghbalian, Saman; Mehrdad, Goli
2015-01-01
Background: Transplantation is the only treatment for patients with liver failure. Since the therapy imposes high expenses to the patients and community, identification of effective factors on survival of such patients after transplantation is valuable. Objectives: The current study attempted to model the survival of patients (two years old and above) after liver transplantation using neural network and Cox Proportional Hazards (Cox PH) regression models. The event is defined as death due to complications of liver transplantation. Patients and Methods: In a historical cohort study, the clinical findings of 1168 patients who underwent liver transplant surgery (from March 2008 to march 2013) at Shiraz Namazee Hospital Organ Transplantation Center, Shiraz, Southern Iran, were used. To model the one to five years survival of such patients, Cox PH regression model accompanied by three layers feed forward artificial neural network (ANN) method were applied on data separately and their prediction accuracy was compared using the area under the receiver operating characteristic curve (ROC). Furthermore, Kaplan-Meier method was used to estimate the survival probabilities in different years. Results: The estimated survival probability of one to five years for the patients were 91%, 89%, 85%, 84%, and 83%, respectively. The areas under the ROC were 86.4% and 80.7% for ANN and Cox PH models, respectively. In addition, the accuracy of prediction rate for ANN and Cox PH methods was equally 92.73%. Conclusions: The present study detected more accurate results for ANN method compared to those of Cox PH model to analyze the survival of patients with liver transplantation. Furthermore, the order of effective factors in patients’ survival after transplantation was clinically more acceptable. The large dataset with a few missing data was the advantage of this study, the fact which makes the results more reliable. PMID:26500682
Univariate Probability Distributions
ERIC Educational Resources Information Center
Leemis, Lawrence M.; Luckett, Daniel J.; Powell, Austin G.; Vermeer, Peter E.
2012-01-01
We describe a web-based interactive graphic that can be used as a resource in introductory classes in mathematical statistics. This interactive graphic presents 76 common univariate distributions and gives details on (a) various features of the distribution such as the functional form of the probability density function and cumulative distribution…
Wong, May C M; Lam, K F; Lo, Edward C M
2006-02-15
In some controlled clinical trials in dental research, multiple failure time data from the same patient are frequently observed that result in clustered multiple failure time. Moreover, the treatments are often delivered by more than one operator and thus the multiple failure times are clustered according to a multilevel structure when the operator effects are assumed to be random. In practice, it is often too expensive or even impossible to monitor the study subjects continuously, but they are examined periodically at some regular pre-scheduled visits. Hence, discrete or grouped clustered failure time data are collected. The aim of this paper is to illustrate the use of the Monte Carlo Markov chain (MCMC) approach and non-informative prior in a Bayesian framework to mimic the maximum likelihood (ML) estimation in a frequentist approach in multilevel modelling of clustered grouped survival data. A three-level model with additive variance components model for the random effects is considered in this paper. Both the grouped proportional hazards model and the dynamic logistic regression model are used. The approximate intra-cluster correlation of the log failure times can be estimated when the grouped proportional hazards model is used. The statistical package WinBUGS is adopted to estimate the parameter of interest based on the MCMC method. The models and method are applied to a data set obtained from a prospective clinical study on a cohort of Chinese school children that atraumatic restorative treatment (ART) restorations were placed on permanent teeth with carious lesions. Altogether 284 ART restorations were placed by five dentists and clinical status of the ART restorations was evaluated annually for 6 years after placement, thus clustered grouped failure times of the restorations were recorded. Results based on the grouped proportional hazards model revealed that clustering effect among the log failure times of the different restorations from the same child was
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
2014-01-01
Background Large-scale public health interventions with rapid scale-up are increasingly being implemented worldwide. Such implementation allows for a large target population to be reached in a short period of time. But when the time comes to investigate the effectiveness of these interventions, the rapid scale-up creates several methodological challenges, such as the lack of baseline data and the absence of control groups. One example of such an intervention is Avahan, the India HIV/AIDS initiative of the Bill & Melinda Gates Foundation. One question of interest is the effect of Avahan on condom use by female sex workers with their clients. By retrospectively reconstructing condom use and sex work history from survey data, it is possible to estimate how condom use rates evolve over time. However formal inference about how this rate changes at a given point in calendar time remains challenging. Methods We propose a new statistical procedure based on a mixture of binomial regression and Cox regression. We compare this new method to an existing approach based on generalized estimating equations through simulations and application to Indian data. Results Both methods are unbiased, but the proposed method is more powerful than the existing method, especially when initial condom use is high. When applied to the Indian data, the new method mostly agrees with the existing method, but seems to have corrected some implausible results of the latter in a few districts. We also show how the new method can be used to analyze the data of all districts combined. Conclusions The use of both methods can be recommended for exploratory data analysis. However for formal statistical inference, the new method has better power. PMID:24397563
Univariate time series forecasting algorithm validation
NASA Astrophysics Data System (ADS)
Ismail, Suzilah; Zakaria, Rohaiza; Muda, Tuan Zalizam Tuan
2014-12-01
Forecasting is a complex process which requires expert tacit knowledge in producing accurate forecast values. This complexity contributes to the gaps between end users and expert. Automating this process by using algorithm can act as a bridge between them. Algorithm is a well-defined rule for solving a problem. In this study a univariate time series forecasting algorithm was developed in JAVA and validated using SPSS and Excel. Two set of simulated data (yearly and non-yearly); several univariate forecasting techniques (i.e. Moving Average, Decomposition, Exponential Smoothing, Time Series Regressions and ARIMA) and recent forecasting process (such as data partition, several error measures, recursive evaluation and etc.) were employed. Successfully, the results of the algorithm tally with the results of SPSS and Excel. This algorithm will not just benefit forecaster but also end users that lacking in depth knowledge of forecasting process.
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. PMID:25647087
Agogo, George O; van der Voet, Hilko; Van't Veer, Pieter; van Eeuwijk, Fred A; Boshuizen, Hendriek C
2016-07-01
Dietary questionnaires are prone to measurement error, which bias the perceived association between dietary intake and risk of disease. Short-term measurements are required to adjust for the bias in the association. For foods that are not consumed daily, the short-term measurements are often characterized by excess zeroes. Via a simulation study, the performance of a two-part calibration model that was developed for a single-replicate study design was assessed by mimicking leafy vegetable intake reports from the multicenter European Prospective Investigation into Cancer and Nutrition (EPIC) study. In part I of the fitted two-part calibration model, a logistic distribution was assumed; in part II, a gamma distribution was assumed. The model was assessed with respect to the magnitude of the correlation between the consumption probability and the consumed amount (hereafter, cross-part correlation), the number and form of covariates in the calibration model, the percentage of zero response values, and the magnitude of the measurement error in the dietary intake. From the simulation study results, transforming the dietary variable in the regression calibration to an appropriate scale was found to be the most important factor for the model performance. Reducing the number of covariates in the model could be beneficial, but was not critical in large-sample studies. The performance was remarkably robust when fitting a one-part rather than a two-part model. The model performance was minimally affected by the cross-part correlation. PMID:27003183
An Artificial Immune Univariate Marginal Distribution Algorithm
NASA Astrophysics Data System (ADS)
Zhang, Qingbin; Kang, Shuo; Gao, Junxiang; Wu, Song; Tian, Yanping
Hybridization is an extremely effective way of improving the performance of the Univariate Marginal Distribution Algorithm (UMDA). Owing to its diversity and memory mechanisms, artificial immune algorithm has been widely used to construct hybrid algorithms with other optimization algorithms. This paper proposes a hybrid algorithm which combines the UMDA with the principle of general artificial immune algorithm. Experimental results on deceptive function of order 3 show that the proposed hybrid algorithm can get more building blocks (BBs) than the UMDA.
Sneeringer, Stacy
2010-04-01
While a recent paper by Cox in this journal uses as its motivating factor the benefits of quantitative risk assessment, its content is entirely devoted to critiquing Sneeringer's article in the American Journal of Agricultural Economics. Cox's two main critiques of Sneeringer are fundamentally flawed and misrepresent the original article. Cox posits that Sneeringer did A and B, and then argues why A and B are incorrect. However, Sneeringer in fact did C and D; thus critiques of A and B are not applicable to Sneeringer's analysis. PMID:20345577
Graphical Models via Univariate Exponential Family Distributions
Yang, Eunho; Ravikumar, Pradeep; Allen, Genevera I.; Liu, Zhandong
2016-01-01
Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications. Popular instances of this class include Gaussian graphical models and Ising models. In many settings, however, it might not be clear which subclass of graphical models to use, particularly for non-Gaussian and non-categorical data. In this paper, we consider a general sub-class of graphical models where the node-wise conditional distributions arise from exponential families. This allows us to derive multivariate graphical model distributions from univariate exponential family distributions, such as the Poisson, negative binomial, and exponential distributions. Our key contributions include a class of M-estimators to fit these graphical model distributions; and rigorous statistical analysis showing that these M-estimators recover the true graphical model structure exactly, with high probability. We provide examples of genomic and proteomic networks learned via instances of our class of graphical models derived from Poisson and exponential distributions. PMID:27570498
Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin
2013-01-01
In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436
Hieke, Stefanie; Benner, Axel; Schlenk, Richard F.; Schumacher, Martin; Bullinger, Lars; Binder, Harald
2016-01-01
Clinical cohorts with time-to-event endpoints are increasingly characterized by measurements of a number of single nucleotide polymorphisms that is by a magnitude larger than the number of measurements typically considered at the gene level. At the same time, the size of clinical cohorts often is still limited, calling for novel analysis strategies for identifying potentially prognostic SNPs that can help to better characterize disease processes. We propose such a strategy, drawing on univariate testing ideas from epidemiological case-controls studies on the one hand, and multivariable regression techniques as developed for gene expression data on the other hand. In particular, we focus on stable selection of a small set of SNPs and corresponding genes for subsequent validation. For univariate analysis, a permutation-based approach is proposed to test at the gene level. We use regularized multivariable regression models for considering all SNPs simultaneously and selecting a small set of potentially important prognostic SNPs. Stability is judged according to resampling inclusion frequencies for both the univariate and the multivariable approach. The overall strategy is illustrated with data from a cohort of acute myeloid leukemia patients and explored in a simulation study. The multivariable approach is seen to automatically focus on a smaller set of SNPs compared to the univariate approach, roughly in line with blocks of correlated SNPs. This more targeted extraction of SNPs results in more stable selection at the SNP as well as at the gene level. Thus, the multivariable regression approach with resampling provides a perspective in the proposed analysis strategy for SNP data in clinical cohorts highlighting what can be added by regularized regression techniques compared to univariate analyses. PMID:27159447
Riad, Safaa M; Salem, Hesham; Elbalkiny, Heba T; Khattab, Fatma I
2015-04-01
Five, accurate, precise, and sensitive univariate and multivariate spectrophotometric methods were developed for the simultaneous determination of a ternary mixture containing Trimethoprim (TMP), Sulphamethoxazole (SMZ) and Oxytetracycline (OTC) in waste water samples collected from different cites either production wastewater or livestock wastewater after their solid phase extraction using OASIS HLB cartridges. In univariate methods OTC was determined at its λmax 355.7 nm (0D), while (TMP) and (SMZ) were determined by three different univariate methods. Method (A) is based on successive spectrophotometric resolution technique (SSRT). The technique starts with the ratio subtraction method followed by ratio difference method for determination of TMP and SMZ. Method (B) is successive derivative ratio technique (SDR). Method (C) is mean centering of the ratio spectra (MCR). The developed multivariate methods are principle component regression (PCR) and partial least squares (PLS). The specificity of the developed methods is investigated by analyzing laboratory prepared mixtures containing different ratios of the three drugs. The obtained results are statistically compared with those obtained by the official methods, showing no significant difference with respect to accuracy and precision at p=0.05. PMID:25637816
NASA Astrophysics Data System (ADS)
Riad, Safaa M.; Salem, Hesham; Elbalkiny, Heba T.; Khattab, Fatma I.
2015-04-01
Five, accurate, precise, and sensitive univariate and multivariate spectrophotometric methods were developed for the simultaneous determination of a ternary mixture containing Trimethoprim (TMP), Sulphamethoxazole (SMZ) and Oxytetracycline (OTC) in waste water samples collected from different cites either production wastewater or livestock wastewater after their solid phase extraction using OASIS HLB cartridges. In univariate methods OTC was determined at its λmax 355.7 nm (0D), while (TMP) and (SMZ) were determined by three different univariate methods. Method (A) is based on successive spectrophotometric resolution technique (SSRT). The technique starts with the ratio subtraction method followed by ratio difference method for determination of TMP and SMZ. Method (B) is successive derivative ratio technique (SDR). Method (C) is mean centering of the ratio spectra (MCR). The developed multivariate methods are principle component regression (PCR) and partial least squares (PLS). The specificity of the developed methods is investigated by analyzing laboratory prepared mixtures containing different ratios of the three drugs. The obtained results are statistically compared with those obtained by the official methods, showing no significant difference with respect to accuracy and precision at p = 0.05.
Maximum Likelihood and Minimum Distance Applied to Univariate Mixture Distributions.
ERIC Educational Resources Information Center
Wang, Yuh-Yin Wu; Schafer, William D.
This Monte-Carlo study compared modified Newton (NW), expectation-maximization algorithm (EM), and minimum Cramer-von Mises distance (MD), used to estimate parameters of univariate mixtures of two components. Data sets were fixed at size 160 and manipulated by mean separation, variance ratio, component proportion, and non-normality. Results…
Evaluating Univariate, Bivariate, and Multivariate Normality Using Graphical Procedures.
ERIC Educational Resources Information Center
Burdenski, Thomas K., Jr.
This paper reviews graphical and nongraphical procedures for evaluating multivariate normality by guiding the reader through univariate and bivariate procedures that are necessary, but insufficient, indications of a multivariate normal distribution. A data set using three dependent variables for two groups provided by D. George and P. Mallery…
Univariable Optimal Discriminant Analysis: One-Tailed Hypotheses.
ERIC Educational Resources Information Center
Soltysik, Robert C.; Yarnold, Paul R.
1994-01-01
This article describes the theoretical distribution of optima arising from two-category univariable optimal discriminant analysis (UniODA) of continuous random data for a one-tailed (directional) hypothesis. Directional UniODA is illustrated through an investigation of the relationship between depression and brain monoamine turnover. (SLD)
Univariate and Bivariate Loglinear Models for Discrete Test Score Distributions.
ERIC Educational Resources Information Center
Holland, Paul W.; Thayer, Dorothy T.
2000-01-01
Applied the theory of exponential families of distributions to the problem of fitting the univariate histograms and discrete bivariate frequency distributions that often arise in the analysis of test scores. Considers efficient computation of the maximum likelihood estimates of the parameters using Newton's Method and computationally efficient…
Univariate Analysis of Multivariate Outcomes in Educational Psychology.
ERIC Educational Resources Information Center
Hubble, L. M.
1984-01-01
The author examined the prevalence of multiple operational definitions of outcome constructs and an estimate of the incidence of Type I error rates when univariate procedures were applied to multiple variables in educational psychology. Multiple operational definitions of constructs were advocated and wider use of multivariate analysis was…
Univariate normalization of bispectrum using Hölder's inequality.
Shahbazi, Forooz; Ewald, Arne; Nolte, Guido
2014-08-15
Considering that many biological systems including the brain are complex non-linear systems, suitable methods capable of detecting these non-linearities are required to study the dynamical properties of these systems. One of these tools is the third order cummulant or cross-bispectrum, which is a measure of interfrequency interactions between three signals. For convenient interpretation, interaction measures are most commonly normalized to be independent of constant scales of the signals such that its absolute values are bounded by one, with this limit reflecting perfect coupling. Although many different normalization factors for cross-bispectra were suggested in the literature these either do not lead to bounded measures or are themselves dependent on the coupling and not only on the scale of the signals. In this paper we suggest a normalization factor which is univariate, i.e., dependent only on the amplitude of each signal and not on the interactions between signals. Using a generalization of Hölder's inequality it is proven that the absolute value of this univariate bicoherence is bounded by zero and one. We compared three widely used normalizations to the univariate normalization concerning the significance of bicoherence values gained from resampling tests. Bicoherence values are calculated from real EEG data recorded in an eyes closed experiment from 10 subjects. The results show slightly more significant values for the univariate normalization but in general, the differences are very small or even vanishing in some subjects. Therefore, we conclude that the normalization factor does not play an important role in the bicoherence values with regard to statistical power, although a univariate normalization is the only normalization factor which fulfills all the required conditions of a proper normalization. PMID:24975293
Rahman, Ziyaur; Mohammad, Adil; Siddiqui, Akhtar; Khan, Mansoor A
2015-12-01
The focus of the present investigation was to explore the use of solid-state nuclear magnetic resonance ((13)C ssNMR) and X-ray powder diffraction (XRPD) for quantification of nimodipine polymorphs (form I and form II) crystallized in a cosolvent formulation. The cosolvent formulation composed of polyethylene glycol 400, glycerin, water, and 2.5% drug, and was stored at 5°C for the drug crystallization. The (13)C ssNMR and XRPD data of the sample matrices containing varying percentages of nimodipine form I and form II were collected. Univariate and multivariate models were developed using the data. Least square method was used for the univariate model generation. Partial least square and principle component regressions were used for the multivariate models development. The univariate models of the (13)C ssNMR were better than the XRPD as indicated by statistical parameters such as correlation coefficient, R (2), root mean square error, and standard error. On the other hand, the XRPD multivariate models were better than the (13)C ssNMR as indicated by precision and accuracy parameters. Similar values were predicted by the univariate and multivariate models for independent samples. In conclusion, the univariate and multivariate models of (13)C ssNMR and XRPD can be used to quantitate nimodipine polymorphs. PMID:25956485
ERIC Educational Resources Information Center
Johns, Stephanie
2010-01-01
Kathy Cox, the superintendent of schools for Georgia, believes "excellence is not an accident". She made a name for herself by winning $1 million proving she was smarter than a fifth-grader on a popular television show. This article presents a profile of Cox, her family, her role as school superintendent, and her accomplishments. Although she…
Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease
Habeck, Christian; Foster, Norman L.; Perneczky, Robert; Kurz, Alexander; Alexopoulos, Panagiotis; Koeppe, Robert A.; Drzezga, Alexander; Stern, Yaakov
2008-01-01
We performed univariate and multivariate discriminant analysis of FDG-PET scans to evaluate their ability to identify Alzheimer’s disease (AD). FDG-PET scans came from two sources: 17 AD patients and 33 healthy elderly controls were scanned at the University of Michigan; 102 early AD patients and 20 healthy elderly controls were scanned at the Technical University of Munich, Germany. We selected a derivation sample of 20 AD patients and 20 healthy controls matched on age with the remainder divided into 5 replication samples. The sensitivity and specificity of diagnostic AD-markers and threshold criteria from the derivation sample were determined in the replication samples. Although both univariate and multivariate analyses produced markers with high classification accuracy in the derivation sample, the multivariate marker’s diagnostic performance in the replication samples was superior. Further, supplementary analysis showed its performance to be unaffected by the loss of key regions. Multivariate measures of AD utilize the covariance structure of imaging data and provide complementary, clinically relevant information that may be superior to univariate measures. PMID:18343688
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…
The Fallacy of Univariate Solutions to Complex Systems Problems.
Lessov-Schlaggar, Christina N; Rubin, Joshua B; Schlaggar, Bradley L
2016-01-01
Complex biological systems, by definition, are composed of multiple components that interact non-linearly. The human brain constitutes, arguably, the most complex biological system known. Yet most investigation of the brain and its function is carried out using assumptions appropriate for simple systems-univariate design and linear statistical approaches. This heuristic must change before we can hope to discover and test interventions to improve the lives of individuals with complex disorders of brain development and function. Indeed, a movement away from simplistic models of biological systems will benefit essentially all domains of biology and medicine. The present brief essay lays the foundation for this argument. PMID:27375425
The Fallacy of Univariate Solutions to Complex Systems Problems
Lessov-Schlaggar, Christina N.; Rubin, Joshua B.; Schlaggar, Bradley L.
2016-01-01
Complex biological systems, by definition, are composed of multiple components that interact non-linearly. The human brain constitutes, arguably, the most complex biological system known. Yet most investigation of the brain and its function is carried out using assumptions appropriate for simple systems—univariate design and linear statistical approaches. This heuristic must change before we can hope to discover and test interventions to improve the lives of individuals with complex disorders of brain development and function. Indeed, a movement away from simplistic models of biological systems will benefit essentially all domains of biology and medicine. The present brief essay lays the foundation for this argument. PMID:27375425
Cache Complexity and Multicore Implementation for Univariate Real Root Isolation
NASA Astrophysics Data System (ADS)
Chen, Changbo; Moreno Maza, Marc; Xie, Yuzhen
2012-02-01
We present parallel algorithms with optimal cache complexity for the kernel routine of many real root isolation algorithms, namely the Taylor shift by 1. We then report on multicore implementation for isolating the real roots of univariate polynomials with integer coefficients based on a classical algorithm due to Vincent, Collins and Akritas. For processing some well-known benchmark examples with sufficiently large size, our software tool reaches linear speedup on an 8-core machine. In addition, we show that our software is able to fully utilize the many cores and the memory space of a 32-core machine to tackle large problems that are out of reach for a desktop implementation.
Inouye, David I.; Ravikumar, Pradeep; Dhillon, Inderjit S.
2016-01-01
We develop Square Root Graphical Models (SQR), a novel class of parametric graphical models that provides multivariate generalizations of univariate exponential family distributions. Previous multivariate graphical models (Yang et al., 2015) did not allow positive dependencies for the exponential and Poisson generalizations. However, in many real-world datasets, variables clearly have positive dependencies. For example, the airport delay time in New York—modeled as an exponential distribution—is positively related to the delay time in Boston. With this motivation, we give an example of our model class derived from the univariate exponential distribution that allows for almost arbitrary positive and negative dependencies with only a mild condition on the parameter matrix—a condition akin to the positive definiteness of the Gaussian covariance matrix. Our Poisson generalization allows for both positive and negative dependencies without any constraints on the parameter values. We also develop parameter estimation methods using node-wise regressions with ℓ1 regularization and likelihood approximation methods using sampling. Finally, we demonstrate our exponential generalization on a synthetic dataset and a real-world dataset of airport delay times.
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
Forecasting electricity usage using univariate time series models
NASA Astrophysics Data System (ADS)
Hock-Eam, Lim; Chee-Yin, Yip
2014-12-01
Electricity is one of the important energy sources. A sufficient supply of electricity is vital to support a country's development and growth. Due to the changing of socio-economic characteristics, increasing competition and deregulation of electricity supply industry, the electricity demand forecasting is even more important than before. It is imperative to evaluate and compare the predictive performance of various forecasting methods. This will provide further insights on the weakness and strengths of each method. In literature, there are mixed evidences on the best forecasting methods of electricity demand. This paper aims to compare the predictive performance of univariate time series models for forecasting the electricity demand using a monthly data of maximum electricity load in Malaysia from January 2003 to December 2013. Results reveal that the Box-Jenkins method produces the best out-of-sample predictive performance. On the other hand, Holt-Winters exponential smoothing method is a good forecasting method for in-sample predictive performance.
Dynamic treatment of invariant and univariant reactions in metamorphic systems
Lasaga, A.C.; Luettge, A.; Rye, D.M.; Bolton, E.W.
2000-03-01
A simple model is presented that incorporates the essential dynamics of metamorphic processes leading to reactions along univariant curves and up to and beyond the invariant point. The model includes both heat flow by conduction and convection as well as fluid flow in and out of a representative volume. Overall mineral reactions can then take place within this rock volume in response to internal and external factors. The paper derives a simple back-of-the-envelope expression for the steady state reached by the system. The steady state composition of the fluid and the steady state temperature are then compared with the composition and temperature predicted by the assumption of thermodynamic equilibrium. Expressions for the amount of fluid passing through the system based on the kinetic model are compared with previous calculations of the mass of fluid added to the system using the equilibrium assumptions. The approach to this steady state is also analyzed and an analytical solution is obtained for the time evolution up to the steady state. Both the steady state and the time evolution solution are then applied to an understanding of the dynamics involved in obtaining T-X-t paths in nature. The results of the kinetic approach lead to major revisions in many of the previously held concepts used in petrologic fluid flow models. These include the expected reaction pathway, the role of metastable reactions, the calculation of fluid flux, the role of the invariant point, and the interpretation of mineral textures and modal abundances of minerals.
Detecting and Dealing with Outliers in Univariate and Multivariate Contexts.
ERIC Educational Resources Information Center
Wiggins, Bettie Caroline
Because multivariate statistics are increasing in popularity with social science researchers, the challenge of detecting multivariate outliers warrants attention. Outliers are defined as cases which, in regression analyses, generally lie more than three standard deviations from Yhat and therefore distort statistics. There are, however, some…
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
NASA Astrophysics Data System (ADS)
Grégoire, G.
2014-12-01
The logistic regression originally is intended to explain the relationship between the probability of an event and a set of covariables. The model's coefficients can be interpreted via the odds and odds ratio, which are presented in introduction of the chapter. The observations are possibly got individually, then we speak of binary logistic regression. When they are grouped, the logistic regression is said binomial. In our presentation we mainly focus on the binary case. For statistical inference the main tool is the maximum likelihood methodology: we present the Wald, Rao and likelihoods ratio results and their use to compare nested models. The problems we intend to deal with are essentially the same as in multiple linear regression: testing global effect, individual effect, selection of variables to build a model, measure of the fitness of the model, prediction of new values… . The methods are demonstrated on data sets using R. Finally we briefly consider the binomial case and the situation where we are interested in several events, that is the polytomous (multinomial) logistic regression and the particular case of ordinal logistic regression.
External validation of a Cox prognostic model: principles and methods
2013-01-01
Background A prognostic model should not enter clinical practice unless it has been demonstrated that it performs a useful role. External validation denotes evaluation of model performance in a sample independent of that used to develop the model. Unlike for logistic regression models, external validation of Cox models is sparsely treated in the literature. Successful validation of a model means achieving satisfactory discrimination and calibration (prediction accuracy) in the validation sample. Validating Cox models is not straightforward because event probabilities are estimated relative to an unspecified baseline function. Methods We describe statistical approaches to external validation of a published Cox model according to the level of published information, specifically (1) the prognostic index only, (2) the prognostic index together with Kaplan-Meier curves for risk groups, and (3) the first two plus the baseline survival curve (the estimated survival function at the mean prognostic index across the sample). The most challenging task, requiring level 3 information, is assessing calibration, for which we suggest a method of approximating the baseline survival function. Results We apply the methods to two comparable datasets in primary breast cancer, treating one as derivation and the other as validation sample. Results are presented for discrimination and calibration. We demonstrate plots of survival probabilities that can assist model evaluation. Conclusions Our validation methods are applicable to a wide range of prognostic studies and provide researchers with a toolkit for external validation of a published Cox model. PMID:23496923
NASA Astrophysics Data System (ADS)
Coe, Rob; Dalrymple, Brent
More than 1000 friends, students, and colleagues from all over the country filled Stanford Memorial Chapel (Stanford, Calif.) on February 3, 1987, to join in “A Celebration of the Life of Allan Cox.” Allan died early on the morning of January 27 while bicycling, the sport he had come to love the most. Between pieces of his favorite music by Bach and Mozart, Stanford administrators and colleagues spoke in tribute of Allan's unique qualities as friend, scientist, teacher, and dean of the School of Earth Sciences. James Rosse, Vice President and Provost of Stanford University, struck a particularly resonant chord with his personal remarks: "Allan reached out to each person he knew with the warmth and attention that can only come from deep respect and affection for others. I never heard him speak ill of others, and I do not believe he was capable of doing anything that would harm another being. He cared too much to intrude where he was not wanted, but his curiosity about people and the loving care with which he approached them broke down reserve to create remarkable friendships. His enthusiasm and good humor made him a welcome guest in the hearts of the hundreds of students and colleagues who shared the opportunity of knowing Allan Cox as a person."
Pisu, E; Vitelli, F; Coggi, G; Franzone, M; Cavallo, M; Chiara, E; Carta, Q; Pagano, G
1988-12-01
The prevalence of diabetic retinopathy and the evaluation of its risk factors is poorly known in Italian population. Therefore, we have studied 894 diabetic outpatients (420 males, 474 females, 27.6% IDDs, 38.1% insulin-treated) in order to investigate the effect of clinical and metabolic characteristics on the frequency of diabetic retinopathy, classified into six different classes. In univariate analyses age, duration of disease, systolic and diastolic blood pressure, blood urea nitrogen, 24 hr proteinuria and fasting glycemia significantly correlated (p less than 0.001) with severity of retinopathy. The significance was confirmed in multivariate analysis for duration, age and systolic blood pressure (p less than 0.001). Stratification by type of diabetes showed that undefined onset of diabetes probably reduced in NID patients the power of duration as an associated factor of retinopathy. Worsening of this complication in three clinical classes of therapy (diet, oral and insulin-treatment) is evident too. Finally, our 11 variables in the step-wise multiple-regression analysis explain only 16.8% of diabetic retinopathy in all patients, but 36.6% in selected ID subjects. PMID:3246287
Biochemistry of cyclooxygenase (COX)-2 inhibitors and molecular pathology of COX-2 in neoplasia.
Fosslien, E
2000-10-01
Several types of human tumors overexpress cyclooxygenase (COX) -2 but not COX-1, and gene knockout transfection experiments demonstrate a central role of COX-2 in experimental tumorigenesis. COX-2 produces prostaglandins that inhibit apoptosis and stimulate angiogenesis and invasiveness. Selective COX-2 inhibitors reduce prostaglandin synthesis, restore apoptosis, and inhibit cancer cell proliferation. In animal studies they limit carcinogen-induced tumorigenesis. In contrast, aspirin-like nonselective NSAIDs such as sulindac and indomethacin inhibit not only the enzymatic action of the highly inducible, proinflammatory COX-2 but the constitutively expressed, cytoprotective COX-1 as well. Consequently, nonselective NSAIDs can cause platelet dysfunction, gastrointestinal ulceration, and kidney damage. For that reason, selective inhibition of COX-2 to treat neoplastic proliferation is preferable to nonselective inhibition. Selective COX-2 inhibitors, such as meloxicam, celecoxib (SC-58635), and rofecoxib (MK-0966), are NSAIDs that have been modified chemically to preferentially inhibit COX-2 but not COX-1. For instance, meloxicam inhibits the growth of cultured colon cancer cells (HCA-7 and Moser-S) that express COX-2 but has no effect on HCT-116 tumor cells that do not express COX-2. NS-398 induces apoptosis in COX-2 expressing LNCaP prostate cancer cells and, surprisingly, in colon cancer S/KS cells that does not express COX-2. This effect may due to induction of apoptosis through uncoupling of oxidative phosphorylation and down-regulation of Bcl-2, as has been demonstrated for some nonselective NSAIDs, for instance, flurbiprofen. COX-2 mRNA and COX-2 protein is constitutively expressed in the kidney, brain, spinal cord, and ductus deferens, and in the uterus during implantation. In addition, COX-2 is constitutively and dominantly expressed in the pancreatic islet cells. These findings might somewhat limit the use of presently available selective COX-2 inhibitors
Bootstrapping to Test for Nonzero Population Correlation Coefficients Using Univariate Sampling
ERIC Educational Resources Information Center
Beasley, William Howard; DeShea, Lise; Toothaker, Larry E.; Mendoza, Jorge L.; Bard, David E.; Rodgers, Joseph Lee
2007-01-01
This article proposes 2 new approaches to test a nonzero population correlation ([rho]): the hypothesis-imposed univariate sampling bootstrap (HI) and the observed-imposed univariate sampling bootstrap (OI). The authors simulated correlated populations with various combinations of normal and skewed variates. With [alpha[subscript "set"
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
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
Complexity and Performance Results for Non FFT-Based Univariate Polynomial Multiplication
NASA Astrophysics Data System (ADS)
Chowdhury, Muhammad F. I.; Maza, Marc Moreno; Pan, Wei; Schost, Eric
2011-11-01
Today's parallel hardware architectures and computer memory hierarchies enforce revisiting fundamental algorithms which were often designed with algebraic complexity as the main complexity measure and with sequential running time as the main performance counter. This study is devoted to two algorithms of univariate polynomial multiplication; that are independent of the coefficient ring: the plain and the Toom-Cook univariate multiplications. We analyze their cache complexity and report on their parallel implementations in Cilk++ [1].
Decomposition of Variance for Spatial Cox Processes
Jalilian, Abdollah; Guan, Yongtao; Waagepetersen, Rasmus
2012-01-01
Spatial Cox point processes is a natural framework for quantifying the various sources of variation governing the spatial distribution of rain forest trees. We introduce a general criterion for variance decomposition for spatial Cox processes and apply it to specific Cox process models with additive or log linear random intensity functions. We moreover consider a new and flexible class of pair correlation function models given in terms of normal variance mixture covariance functions. The proposed methodology is applied to point pattern data sets of locations of tropical rain forest trees. PMID:23599558
Observational Studies: Matching or Regression?
Brazauskas, Ruta; Logan, Brent R
2016-03-01
In observational studies with an aim of assessing treatment effect or comparing groups of patients, several approaches could be used. Often, baseline characteristics of patients may be imbalanced between groups, and adjustments are needed to account for this. It can be accomplished either via appropriate regression modeling or, alternatively, by conducting a matched pairs study. The latter is often chosen because it makes groups appear to be comparable. In this article we considered these 2 options in terms of their ability to detect a treatment effect in time-to-event studies. Our investigation shows that a Cox regression model applied to the entire cohort is often a more powerful tool in detecting treatment effect as compared with a matched study. Real data from a hematopoietic cell transplantation study is used as an example. PMID:26712591
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
COX-2 gene dosage-dependent defects in kidney development.
Slattery, Patrick; Frölich, Stefanie; Schreiber, Yannik; Nüsing, Rolf M
2016-05-15
Deletion of cyclooxygenase (COX)-2 causes impairment of kidney development, including hypothrophic glomeruli and cortical thinning. A critical role for COX-2 is seen 4-8 days postnatally. The present study was aimed at answering whether different COX-2 gene dosage and partial pharmacological COX-2 inhibition impairs kidney development. We studied kidney development in COX-2(+/+), COX-2(+/-), and COX-2(-/-) mice as well as in C57Bl6 mice treated postnatally with low (5 mg·kg(-1)·day(-1)) and high (10 mg·kg(-1)·day(-1)) doses of the selective COX-2 inhibitor SC-236. COX-2(+/-) mice exhibit impaired kidney development leading to reduced glomerular size but, in contrast to COX-2(-/-) mice, only marginal cortical thinning. Moreover, in COX-2(+/-) and COX-2(-/-) kidneys, juxtamedullary glomeruli, which develop in the very early stages of nephrogenesis, also showed a size reduction. In COX-2(+/-) kidneys at the age of 8 days, we observed significantly less expression of COX-2 mRNA and protein and less PGE2 and PGI2 synthetic activity compared with COX-2(+/+) kidneys. The renal defects in COX-2(-/-) and COX-2(+/-) kidneys could be mimicked by high and low doses of SC-236, respectively. In aged COX-2(+/-) kidneys, glomerulosclerosis was observed; however, in contrast to COX-2(-/-) kidneys, periglomerular fibrosis was absent. COX-2(+/-) mice showed signs of kidney insufficiency, demonstrated by enhanced serum creatinine levels, quite similar to COX-2(-/-) mice, but, in contrast, serum urea remained at the control level. In summary, function of both COX-2 gene alleles is absolutely necessary to ensure physiological development of the mouse kidney. Loss of one copy of the COX-2 gene or partial COX-2 inhibition is associated with distinct renal damage and reduced kidney function. PMID:26984955
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
Analysis of the correlation between P53 and Cox-2 expression and prognosis in esophageal cancer
CHEN, JUN; WU, FANG; PEI, HONG-LEI; GU, WEN-DONG; NING, ZHONG-HUA; SHAO, YING-JIE; HUANG, JIN
2015-01-01
The present study aimed to explore the importance of P53 and Cox-2 protein expression in esophageal cancer and assess their influence on prognosis. The expression of P53 and Cox-2 was assessed in esophageal cancer samples from 195 patients subjected to radical surgery at Changzhou First People's Hospital (Changzhou, China) between May 2010 and December 2011. Expression of P53 and Cox-2 proteins were detected in 60.5% (118/195) and 69.7% (136/195) of the samples, respectively, and were co-expressed in 43.1% (84/195) of the samples. A correlation was identified between P53 expression and overall survival (OS) (P=0.0351) as well as disease-free survival (DFS) (P=0.0307). In addition, the co-expression of P53 and Cox-2 also correlated with OS (P=0.0040) and DFS (P=0.0042). P53 expression (P=0.023), TNM staging (P<0.001) and P53/Cox-2 co-expression (P=0.009) were identified as independent factors affecting OS in patients with esophageal cancer via a Cox multivariate regression model analysis. A similar analysis also identified P53 expression (P=0.020), TNM staging (P<0.001) and P53/Cox-2 co-expression (P=0.008) as independent prognostic factors influencing DFS in these patients. Binary logistic regression analysis demonstrated a correlation between P53 expression (P=0.012), TNM staging (P<0.001), tumor differentiation level (P=0.023) and P53/Cox-2 co-expression (P=0.021), and local recurrence or distant esophageal cancer metastasis. The results of the present study indicate that P53 and Cox-2 proteins may act synergistically in the development of esophageal cancer, and the assessment of P53/Cox-2 co-expression status in esophageal cancer biopsies may become an important diagnostic criterion to evaluate the prognosis of patients with esophageal cancer. PMID:26622818
Select Dietary Phytochemicals Function as Inhibitors of COX-1 but Not COX-2
Li, Haitao; Zhu, Feng; Sun, Yanwen; Li, Bing; Oi, Naomi; Chen, Hanyong; Lubet, Ronald A.; Bode, Ann M.; Dong, Zigang
2013-01-01
Recent clinical trials raised concerns regarding the cardiovascular toxicity of selective cyclooxygenase-2 (COX-2) inhibitors. Many active dietary factors are reported to suppress carcinogenesis by targeting COX-2. A major question was accordingly raised: why has the lifelong use of phytochemicals that likely inhibit COX-2 presumably not been associated with adverse cardiovascular side effects. To answer this question, we selected a library of dietary-derived phytochemicals and evaluated their potential cardiovascular toxicity in human umbilical vein endothelial cells. Our data indicated that the possibility of cardiovascular toxicity of these dietary phytochemicals was low. Further mechanistic studies revealed that the actions of these phytochemicals were similar to aspirin in that they mainly inhibited COX-1 rather than COX-2, especially at low doses. PMID:24098505
Overall, J E; Atlas, R S
1999-04-01
The power of univariate and multivariate tests of significance is compared in relation to linear and nonlinear patterns of treatment effects in a repeated measurement design. Bonferroni correction was used to control the experiment-wise error rate in combining results from univariate tests of significance accomplished separately on average level, linear, quadratic, and cubic trend components. Multivariate tests on these same components of the overall treatment effect, as well as a multivariate test for between-groups difference on the original repeated measurements, were also evaluated for power against the same representative patterns of treatment effects. Results emphasize the advantage of parsimony that is achieved by transforming multiple repeated measurements into a reduced set of mean ngful composite variables representing average levels and rates of change. The Bonferroni correction applied to the separate univariate tests provided experiment-wise protection against Type I error, produced slightly greater experiment-wise power than a multivariate test applied to the same components of the data patterns, and provided substantially greater power than a multivariate test on the complete set of original repeated measurements. The separate univariate tests provide interpretive advantage regarding locus of the treatment effects. PMID:10348408
ERIC Educational Resources Information Center
Moses, Tim; Holland, Paul W.
2009-01-01
In this study, we compared 12 statistical strategies proposed for selecting loglinear models for smoothing univariate test score distributions and for enhancing the stability of equipercentile equating functions. The major focus was on evaluating the effects of the selection strategies on equating function accuracy. Selection strategies' influence…
Epileptic Seizure Prediction based on Ratio and Differential Linear Univariate Features
Rasekhi, Jalil; Mollaei, Mohammad Reza Karami; Bandarabadi, Mojtaba; Teixeira, César A.; Dourado, António
2015-01-01
Bivariate features, obtained from multichannel electroencephalogram recordings, quantify the relation between different brain regions. Studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients suffering from refractory epilepsy. A new bivariate approach using univariate features is proposed here. Differences and ratios of 22 linear univariate features were calculated using pairwise combination of 6 electroencephalograms channels, to create 330 differential, and 330 relative features. The feature subsets were classified using support vector machines separately, as one of the two classes of preictal and nonpreictal. Furthermore, minimum Redundancy Maximum Relevance feature reduction method is employed to improve the predictions and reduce the number of false alarms. The studies were carried out on features obtained from 10 patients. For reduced subset of 30 features and using differential approach, the seizures were on average predicted in 60.9% of the cases (28 out of 46 in 737.9 h of test data), with a low false prediction rate of 0.11 h−1. Results of bivariate approaches were compared with those achieved from original linear univariate features, extracted from 6 channels. The advantage of proposed bivariate features is the smaller number of false predictions in comparison to the original 22 univariate features. In addition, reduction in feature dimension could provide a less complex and the more cost-effective algorithm. Results indicate that applying machine learning methods on a multidimensional feature space resulting from relative/differential pairwise combination of 22 univariate features could predict seizure onsets with high performance. PMID:25709936
ERIC Educational Resources Information Center
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
An approach to the estimation of growth standards: the univariate case.
Fryer, J G; Karlberg, J; Hayes, M
1989-01-01
This paper shows how reference values can be determined when the underlying characteristic (say, weight) follows a distribution that is not too distant from the Gaussian. Application of the normalizing Box-Cox power transformation is the basis of our approach. This transformation is monotonic and hence invertible, so offering the choice of two scales of measurement on which to work--the original and the Gaussian. Modified versions of the procedure are provided allowing use of the basic transformation in the presence of certain deficiencies in the data, principally measurement error and misclassification. It is shown that application of Box-Cox to a cohort at several points in time can be quite revealing. When the data are already symmetrical the Box-Cox transformation has no effect: in this case the John-Draper modulus transformation and modifications of it are shown to be helpful. All of this is illustrated by using data from the Swedish Longitudinal Growth Study. PMID:2801103
Barrientos, Antoni; Gouget, Karine; Horn, Darryl; Soto, Ileana C.; Fontanesi, Flavia
2008-01-01
Eukaryotic cytochrome c oxidase (COX) is the terminal enzyme of the mitochondrial respiratory chain. COX is a multimeric enzyme formed by subunits of dual genetic origin whose assembly is intricate and highly regulated. In addition to the structural subunits, a large number of accessory factors are required to build the holoenzyme. The function of these factors is required in all stages of the assembly process. They are relevant to human health because devastating human disorders have been associated with mutations in nuclear genes encoding conserved COX assembly factors. The study of yeast strains and human cell lines from patients carrying mutations in structural subunits and COX assembly factors has been invaluable to attain the current state of knowledge, even if still fragmentary, of the COX assembly process. After the identification of the genes involved, the isolation and characterization of genetic and metabolic suppressors of COX assembly defects, reviewed here, have become a profitable strategy to gain insight into their functions and the pathways in which they operate. Additionally, they have the potential to provide useful information for devising therapeutic approaches to combat human disorders associated with COX deficiency. PMID:18522805
Oxidative switches in functioning of mammalian copper chaperone Cox17
Voronova, Anastassia; Meyer-Klaucke, Wolfram; Meyer, Thomas; Rompel, Annette; Krebs, Bernt; Kazantseva, Jekaterina; Sillard, Rannar; Palumaa, Peep
2007-01-01
Cox17, a copper chaperone for cytochrome-c oxidase, is an essential and highly conserved protein in eukaryotic organisms. Yeast and mammalian Cox17 share six conserved cysteine residues, which are involved in complex redox reactions as well as in metal binding and transfer. Mammalian Cox17 exists in three oxidative states, each characterized by distinct metal-binding properties: fully reduced mammalian Cox170S–S binds co-operatively to four Cu+; Cox172S–S, with two disulfide bridges, binds to one of either Cu+ or Zn2+; and Cox173S–S, with three disulfide bridges, does not bind to any metal ions. The Em (midpoint redox potential) values for two redox couples of Cox17, Cox173S–S↔Cox172S–S (Em1) and Cox172S–S↔Cox170S–S (Em2), were determined to be −197 mV and −340 mV respectively. The data indicate that an equilibrium exists in the cytosol between Cox170S-S and Cox172S–S, which is slightly shifted towards Cox170S-S. In the IMS (mitochondrial intermembrane space), the equilibrium is shifted towards Cox172S–S, enabling retention of Cox172S–S in the IMS and leading to the formation of a biologically competent form of the Cox17 protein, Cox172S–S, capable of copper transfer to the copper chaperone Sco1. XAS (X-ray absorption spectroscopy) determined that Cu4Cox17 contains a Cu4S6-type copper–thiolate cluster, which may provide safe storage of an excess of copper ions. PMID:17672825
Anandamide and decidual remodelling: COX-2 oxidative metabolism as a key regulator.
Almada, M; Piscitelli, F; Fonseca, B M; Di Marzo, V; Correia-da-Silva, G; Teixeira, N
2015-11-01
Recently, endocannabinoids have emerged as signalling mediators in reproduction. It is widely accepted that anandamide (AEA) levels must be tightly regulated, and that a disturbance in AEA levels may impact decidual stability and regression. We have previously characterized the endocannabinoid machinery in rat decidual tissue and reported the pro-apoptotic action of AEA on rat decidual cells. Cyclooxygenase-2 (COX-2) is an inducible enzyme that plays a crucial role in early pregnancy, and is also a key modulator in the crosstalk between endocannabinoids and prostaglandins. On the other hand, AEA-oxidative metabolism by COX-2 is not merely a mean to inactivate its action, but it yields the formation of a new class of mediators, named prostaglandin-ethanolamides, or prostamides. In this study we found that AEA-induced apoptosis in decidual cells involves COX-2 metabolic pathway. AEA induced COX-2 expression through p38 MAPK, resulting in the formation of prostamide E2 (PME2). Our findings also suggest that AEA-induced effect is associated with NF-kB activation. Finally, we describe the involvement of PME2 in the induction of the intrinsic apoptotic pathway in rat decidual cells. Altogether, our findings highlight the role of COX-2 as a gatekeeper in the uterine environment and clarify the impact of the deregulation of AEA levels on the decidual remodelling process. PMID:26335727
López, M Isabel; Callao, M Pilar; Ruisánchez, Itziar
2015-09-01
This tutorial provides an overview of the validation of qualitative analytical methods, with particular focus on their main performance parameters, for both univariate and multivariate methods. We discuss specific parameters (sensitivity, specificity, false positive and false negative rates), global parameters (efficiency, Youden's index and likelihood ratio) and those parameters that have a quantitative connotation since they are usually associated to concentration values (decision limit, detection capability and unreliability region). Some methodologies that can be used to estimate these parameters are also described: the use of contingency tables for the specific and global parameters and the performance characteristic curve (PCC) for the ones with quantitative connotation. To date, PCC has been less commonly used in multivariate methods. To illustrate the proposals summarized in this tutorial, two cases study are discussed at the end, one for a univariate qualitative analysis and the other for multivariate one. PMID:26388364
Statistical methods for astronomical data with upper limits. I - Univariate distributions
NASA Technical Reports Server (NTRS)
Feigelson, E. D.; Nelson, P. I.
1985-01-01
The statistical treatment of univariate censored data is discussed. A heuristic derivation of the Kaplan-Meier maximum-likelihood estimator from first principles is presented which results in an expression amenable to analytic error analysis. Methods for comparing two or more censored samples are given along with simple computational examples, stressing the fact that most astronomical problems involve upper limits while the standard mathematical methods require lower limits. The application of univariate survival analysis to six data sets in the recent astrophysical literature is described, and various aspects of the use of survival analysis in astronomy, such as the limitations of various two-sample tests and the role of parametric modelling, are discussed.
On one-step worst-case optimal trisection in univariate bi-objective Lipschitz optimization
NASA Astrophysics Data System (ADS)
Žilinskas, Antanas; Gimbutienė, Gražina
2016-06-01
The bi-objective Lipschitz optimization with univariate objectives is considered. The concept of the tolerance of the lower Lipschitz bound over an interval is generalized to arbitrary subintervals of the search region. The one-step worst-case optimality of trisecting an interval with respect to the resulting tolerance is established. The theoretical investigation supports the previous usage of trisection in other algorithms. The trisection-based algorithm is introduced. Some numerical examples illustrating the performance of the algorithm are provided.
Varadhan, Ravi; Wang, Sue-Jane
2016-01-01
Treatment effect heterogeneity is a well-recognized phenomenon in randomized controlled clinical trials. In this paper, we discuss subgroup analyses with prespecified subgroups of clinical or biological importance. We explore various alternatives to the naive (the traditional univariate) subgroup analyses to address the issues of multiplicity and confounding. Specifically, we consider a model-based Bayesian shrinkage (Bayes-DS) and a nonparametric, empirical Bayes shrinkage approach (Emp-Bayes) to temper the optimism of traditional univariate subgroup analyses; a standardization approach (standardization) that accounts for correlation between baseline covariates; and a model-based maximum likelihood estimation (MLE) approach. The Bayes-DS and Emp-Bayes methods model the variation in subgroup-specific treatment effect rather than testing the null hypothesis of no difference between subgroups. The standardization approach addresses the issue of confounding in subgroup analyses. The MLE approach is considered only for comparison in simulation studies as the "truth" since the data were generated from the same model. Using the characteristics of a hypothetical large outcome trial, we perform simulation studies and articulate the utilities and potential limitations of these estimators. Simulation results indicate that Bayes-DS and Emp-Bayes can protect against optimism present in the naïve approach. Due to its simplicity, the naïve approach should be the reference for reporting univariate subgroup-specific treatment effect estimates from exploratory subgroup analyses. Standardization, although it tends to have a larger variance, is suggested when it is important to address the confounding of univariate subgroup effects due to correlation between baseline covariates. The Bayes-DS approach is available as an R package (DSBayes). PMID:26485117
NASA Astrophysics Data System (ADS)
Chek, Mohd Zaki Awang; Ahmad, Abu Bakar; Ridzwan, Ahmad Nur Azam Ahmad; Jelas, Imran Md.; Jamal, Nur Faezah; Ismail, Isma Liana; Zulkifli, Faiz; Noor, Syamsul Ikram Mohd
2012-09-01
The main objective of this study is to forecast the future claims amount of Invalidity Pension Scheme (IPS). All data were derived from SOCSO annual reports from year 1972 - 2010. These claims consist of all claims amount from 7 benefits offered by SOCSO such as Invalidity Pension, Invalidity Grant, Survivors Pension, Constant Attendance Allowance, Rehabilitation, Funeral and Education. Prediction of future claims of Invalidity Pension Scheme will be made using Univariate Forecasting Models to predict the future claims among workforce in Malaysia.
When univariate model-free time series prediction is better than multivariate
NASA Astrophysics Data System (ADS)
Chayama, Masayoshi; Hirata, Yoshito
2016-07-01
The delay coordinate method is known to be a practically useful technique for reconstructing the states of an observed system. While this method is theoretically supported by Takens' embedding theorem concerning observations of a scalar time series, we can extend the method to include a multivariate time series. It is often assumed that a better prediction can be obtained using a multivariate time series than by using a scalar time series. However, multivariate time series contains various types of information, and it may be difficult to extract information that is useful for predicting the states. Thus, univariate prediction may sometimes be superior to multivariate prediction. Here, we compare univariate model-free time series predictions with multivariate ones, and demonstrate that univariate model-free prediction is better than multivariate one when the prediction steps are small, while multivariate prediction performs better when the prediction steps become larger. We show the validity of the former finding by using artificial datasets generated from the Lorenz 96 models and a real solar irradiance dataset. The results indicate that it is possible to determine which method is the best choice by considering how far into the future we want to predict.
Hasstedt, Sandra J; Hanis, Craig L; Elbein, Steven C
2010-01-01
Summary Dyslipidemia frequently co-occurs with type 2 diabetes (T2D) and with obesity. To investigate whether the co-occurrence is due to pleiotropic genes, we performed univariate linkage analysis of lipid levels and bivariate linkage analysis of pairs of lipid levels and of lipid levels paired with T2D, body mass index (BMI), and waist-hip ratio (WHR) in the African American subset of the Genetics of NIDDM (GENNID) sample. We obtained significant evidence for a pleiotropic low density lipoprotein cholesterol (LDL-C)–T2D locus on chromosome 1 at 16–19 megabases (MB) (bivariate lod = 4.41), as well as a non-pleiotropic triglyceride (TG) locus on chromosome 20 at 28–34 MB (univariate lod = 3.57). In addition, near-significant evidence supported TG–T2D loci on chromosome 2 at 81–101 MB (bivariate lod = 4.23) and 232–239 MB (bivariate lod = 4.27) and on chromosome 7 at 147–151 MB (univariate lod = 3.08 for TG with P = 0.041 supporting pleiotropy with T2D), as well as an LDL-C–BMI locus on chromosome 3 at 137–147 MB (bivariate lod score = 4.25). These finding provide evidence that at least some of the co-occurrence of dyslipidemia with T2D and obesity is due to common underlying genes. PMID:20597901
ELASTIC NET FOR COX'S PROPORTIONAL HAZARDS MODEL WITH A SOLUTION PATH ALGORITHM.
Wu, Yichao
2012-01-01
For least squares regression, Efron et al. (2004) proposed an efficient solution path algorithm, the least angle regression (LAR). They showed that a slight modification of the LAR leads to the whole LASSO solution path. Both the LAR and LASSO solution paths are piecewise linear. Recently Wu (2011) extended the LAR to generalized linear models and the quasi-likelihood method. In this work we extend the LAR further to handle Cox's proportional hazards model. The goal is to develop a solution path algorithm for the elastic net penalty (Zou and Hastie (2005)) in Cox's proportional hazards model. This goal is achieved in two steps. First we extend the LAR to optimizing the log partial likelihood plus a fixed small ridge term. Then we define a path modification, which leads to the solution path of the elastic net regularized log partial likelihood. Our solution path is exact and piecewise determined by ordinary differential equation systems. PMID:23226932
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.
Golgi-Cox Staining Step by Step
Zaqout, Sami; Kaindl, Angela M.
2016-01-01
Golgi staining remains a key method to study neuronal morphology in vivo. Since most protocols delineating modifications of the original staining method lack details on critical steps, establishing this method in a laboratory can be time-consuming and frustrating. Here, we describe the Golgi-Cox staining in such detail that should turn the staining into an easily feasible method for all scientists working in the neuroscience field. PMID:27065817
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
Lee, Myung Hee; Liu, Yufeng
2013-12-01
The continuum regression technique provides an appealing regression framework connecting ordinary least squares, partial least squares and principal component regression in one family. It offers some insight on the underlying regression model for a given application. Moreover, it helps to provide deep understanding of various regression techniques. Despite the useful framework, however, the current development on continuum regression is only for linear regression. In many applications, nonlinear regression is necessary. The extension of continuum regression from linear models to nonlinear models using kernel learning is considered. The proposed kernel continuum regression technique is quite general and can handle very flexible regression model estimation. An efficient algorithm is developed for fast implementation. Numerical examples have demonstrated the usefulness of the proposed technique. PMID:24058224
Towards a universal barcode of oomycetes--a comparison of the cox1 and cox2 loci.
Choi, Young-Joon; Beakes, Gordon; Glockling, Sally; Kruse, Julia; Nam, Bora; Nigrelli, Lisa; Ploch, Sebastian; Shin, Hyeon-Dong; Shivas, Roger G; Telle, Sabine; Voglmayr, Hermann; Thines, Marco
2015-11-01
Oomycetes are a diverse group of eukaryotes in terrestrial, limnic and marine habitats worldwide and include several devastating plant pathogens, for example Phytophthora infestans (potato late blight). The cytochrome c oxidase subunit 2 gene (cox2) has been widely used for identification, taxonomy and phylogeny of various oomycete groups. However, recently the cox1 gene was proposed as a DNA barcode marker instead, together with ITS rDNA. The cox1 locus has been used in some studies of Pythium and Phytophthora, but has rarely been used for other oomycetes, as amplification success of cox1 varies with different lineages and sample ages. To determine which out of cox1 or cox2 is best suited as a universal oomycete barcode, we compared these two genes in terms of (i) PCR efficiency for 31 representative genera, as well as for historic herbarium specimens, and (ii) sequence polymorphism, intra- and interspecific divergence. The primer sets for cox2 successfully amplified all oomycete genera tested, while cox1 failed to amplify three genera. In addition, cox2 exhibited higher PCR efficiency for historic herbarium specimens, providing easier access to barcoding-type material. Sequence data for several historic type specimens exist for cox2, but there are none for cox1. In addition, cox2 yielded higher species identification success, with higher interspecific and lower intraspecific divergences than cox1. Therefore, cox2 is suggested as a partner DNA barcode along with ITS rDNA instead of cox1. The cox2-1 spacer could be a useful marker below species level. Improved protocols and universal primers are presented for all genes to facilitate future barcoding efforts. PMID:25728598
Cyclooxygenase-2 (COX-2) expression in canine intracranial meningiomas.
Rossmeisl, J H; Robertson, J L; Zimmerman, K L; Higgins, M A; Geiger, D A
2009-09-01
Meningiomas are the most common canine intracranial tumour. Neurologic disability and death from treatment failure remain problematic despite current surgical and radiotherapeutic treatments for canine intracranial meningiomas. Cyclooxygenase-2 (COX-2) over-expression has been demonstrated in multiple canine malignancies, and COX-2 inhibitory treatment strategies have been shown to have both preventative and therapeutic effects in spontaneous and experimental models of cancer. The purpose of this study was to evaluate COX-2 expression in canine intracranial meningiomas. Immunohistochemical and Western blot (WB) analyses showed COX-2 expression in multiple tissues of the normal canine brain, and 87% (21/24) of intracranial meningiomas studied were immunoreactive to COX-2. No significant associations between COX-2 immunoreactivity and tumour grade were identified. Further studies are required to elucidate the physiologic roles of constitutive COX-2 expression in the central nervous system as well as its participation in meningioma tumourigenesis. PMID:19691646
Validation of a heteroscedastic hazards regression model.
Wu, Hong-Dar Isaac; Hsieh, Fushing; Chen, Chen-Hsin
2002-03-01
A Cox-type regression model accommodating heteroscedasticity, with a power factor of the baseline cumulative hazard, is investigated for analyzing data with crossing hazards behavior. Since the approach of partial likelihood cannot eliminate the baseline hazard, an overidentified estimating equation (OEE) approach is introduced in the estimation procedure. It by-product, a model checking statistic, is presented to test for the overall adequacy of the heteroscedastic model. Further, under the heteroscedastic model setting, we propose two statistics to test the proportional hazards assumption. Implementation of this model is illustrated in a data analysis of a cancer clinical trial. PMID:11878222
Nonlinear reconstruction of single-molecule free-energy surfaces from univariate time series
NASA Astrophysics Data System (ADS)
Wang, Jiang; Ferguson, Andrew L.
2016-03-01
The stable conformations and dynamical fluctuations of polymers and macromolecules are governed by the underlying single-molecule free energy surface. By integrating ideas from dynamical systems theory with nonlinear manifold learning, we have recovered single-molecule free energy surfaces from univariate time series in a single coarse-grained system observable. Using Takens' Delay Embedding Theorem, we expand the univariate time series into a high dimensional space in which the dynamics are equivalent to those of the molecular motions in real space. We then apply the diffusion map nonlinear manifold learning algorithm to extract a low-dimensional representation of the free energy surface that is diffeomorphic to that computed from a complete knowledge of all system degrees of freedom. We validate our approach in molecular dynamics simulations of a C24H50 n -alkane chain to demonstrate that the two-dimensional free energy surface extracted from the atomistic simulation trajectory is - subject to spatial and temporal symmetries - geometrically and topologically equivalent to that recovered from a knowledge of only the head-to-tail distance of the chain. Our approach lays the foundations to extract empirical single-molecule free energy surfaces directly from experimental measurements.
NASA Technical Reports Server (NTRS)
Narkawicz, Anthony J.; Munoz, Cesar A.
2014-01-01
Sturm's Theorem is a well-known result in real algebraic geometry that provides a function that computes the number of roots of a univariate polynomial in a semiopen interval. This paper presents a formalization of this theorem in the PVS theorem prover, as well as a decision procedure that checks whether a polynomial is always positive, nonnegative, nonzero, negative, or nonpositive on any input interval. The soundness and completeness of the decision procedure is proven in PVS. The procedure and its correctness properties enable the implementation of a PVS strategy for automatically proving existential and universal univariate polynomial inequalities. Since the decision procedure is formally verified in PVS, the soundness of the strategy depends solely on the internal logic of PVS rather than on an external oracle. The procedure itself uses a combination of Sturm's Theorem, an interval bisection procedure, and the fact that a polynomial with exactly one root in a bounded interval is always nonnegative on that interval if and only if it is nonnegative at both endpoints.
NASA Astrophysics Data System (ADS)
Abdel-Aziz, Amr; Frey, H. Christopher
Historical data regarding hourly variability in coal-fired power plant unit emissions based upon continuous emission monitoring enables estimation of the likely range of possible values in the near future for purposes of air quality modeling. Analyses were conducted for 32 units for a base case in 1995, an alternative 1998 case, and a 2007 future scenario case. Hourly inter-unit uncertainty was assumed to be independent. Univariate stochastic time series models were employed to quantify hourly uncertainty in capacity and emission factors. Ordinary least-squares regression models were used to quantify hourly uncertainty in heat rate. The models were used to develop an hourly probabilistic emission inventory for a 4-day period. There was significant autocorrelation for time lags 1, 2, 23, and 24 for the capacity and emission factor and a 24 h cyclical pattern for the capacity factor. The uncertainty ranges for hourly emissions were found to vary for different hours of the day, with 95% probability ranges of typically ±20-40% of the mean. For the 1995 case, the 95% confidence interval for the daily inventory was 510-633 t/d, representing approximately ±10% uncertainty with respect to the average value of 576 t/d. Inter-annual changes in the mean and variability were assessed by comparison of 1998 data with 1995 data. The daily inventory for the 2007 scenario had an uncertainty range of ±8% of the average value of 175 t/d. The substantial autocorrelation in capacity and emission factor, and the cyclic effect for capacity factor, indicate the importance of accounting for time series effects in estimation of uncertainty in hourly emissions. Additional work is recommended to account for inter-unit dependence, which is addressed in Part 2.
The Mitochondrial Genome of Conus textile, coxI-coxII Intergenic Sequences and Conoidean Evolution
Bandyopadhyay, Pradip K; Stevenson, Bradford J.; Ownby, John-Paul; Cady, Matthew T.; Watkins, Maren; Olivera, Baldomero M.
2009-01-01
The cone snails belong to the superfamily Conoidea, comprising ∼10,000 venomous marine gastropods. We determined the complete mitochondrial DNA sequence of Conus textile. The gene order is identical in Conus textile, Lophiotoma cerithiformis (another Conoidean gastropod), and the neogastropod Ilyanassa obsoleta, (not in the superfamily Conoidea). However, the intergenic interval between the coxI/coxII genes, was much longer in C. textile (165 bp) than in any other previously analyzed gastropod. We used the intergenic region to evaluate evolutionary patterns. In most neogastropods and three conidean families the intergenic interval is small (<30 nucleotides). Within Conus, the variation is from 130-170 bp, and each different clade within Conus has a narrower size distribution. In Conasprella, a subgenus traditionally assigned to Conus, the intergenic regions vary between 200-500 bp, suggesting that the species in Conasprella are not congeneric with Conus. The intergenic region was used for phylogenetic analysis of a group of fish-hunting Conus, despite the short length resolution was better than using standard markers. Thus, the coxI/coxII intergenic region can be used both to define evolutionary relationships between species in a clade, and to understand broad evolutionary patterns across the large superfamily Conoidea. PMID:17936021
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 Astrophysics Data System (ADS)
Subramaniyam, Narayan Puthanmadam; Hyttinen, Jari
2015-02-01
Recently Andrezejak et al. combined the randomness and nonlinear independence test with iterative amplitude adjusted Fourier transform (iAAFT) surrogates to distinguish between the dynamics of seizure-free intracranial electroencephalographic (EEG) signals recorded from epileptogenic (focal) and nonepileptogenic (nonfocal) brain areas of epileptic patients. However, stationarity is a part of the null hypothesis for iAAFT surrogates and thus nonstationarity can violate the null hypothesis. In this work we first propose the application of the randomness and nonlinear independence test based on recurrence network measures to distinguish between the dynamics of focal and nonfocal EEG signals. Furthermore, we combine these tests with both iAAFT and truncated Fourier transform (TFT) surrogate methods, which also preserves the nonstationarity of the original data in the surrogates along with its linear structure. Our results indicate that focal EEG signals exhibit an increased degree of structural complexity and interdependency compared to nonfocal EEG signals. In general, we find higher rejections for randomness and nonlinear independence tests for focal EEG signals compared to nonfocal EEG signals. In particular, the univariate recurrence network measures, the average clustering coefficient C and assortativity R , and the bivariate recurrence network measure, the average cross-clustering coefficient Ccross, can successfully distinguish between the focal and nonfocal EEG signals, even when the analysis is restricted to nonstationary signals, irrespective of the type of surrogates used. On the other hand, we find that the univariate recurrence network measures, the average path length L , and the average betweenness centrality BC fail to distinguish between the focal and nonfocal EEG signals when iAAFT surrogates are used. However, these two measures can distinguish between focal and nonfocal EEG signals when TFT surrogates are used for nonstationary signals. We also
NASA Astrophysics Data System (ADS)
Ismail, A.; Hassan, Noor I.
2013-09-01
Cancer is one of the principal causes of death in Malaysia. This study was performed to determine the pattern of rate of cancer deaths at a public hospital in Malaysia over an 11 year period from year 2001 to 2011, to determine the best fitted model of forecasting the rate of cancer deaths using Univariate Modeling and to forecast the rates for the next two years (2012 to 2013). The medical records of the death of patients with cancer admitted at this Hospital over 11 year's period were reviewed, with a total of 663 cases. The cancers were classified according to 10th Revision International Classification of Diseases (ICD-10). Data collected include socio-demographic background of patients such as registration number, age, gender, ethnicity, ward and diagnosis. Data entry and analysis was accomplished using SPSS 19.0 and Minitab 16.0. The five Univariate Models used were Naïve with Trend Model, Average Percent Change Model (ACPM), Single Exponential Smoothing, Double Exponential Smoothing and Holt's Method. The overall 11 years rate of cancer deaths showed that at this hospital, Malay patients have the highest percentage (88.10%) compared to other ethnic groups with males (51.30%) higher than females. Lung and breast cancer have the most number of cancer deaths among gender. About 29.60% of the patients who died due to cancer were aged 61 years old and above. The best Univariate Model used for forecasting the rate of cancer deaths is Single Exponential Smoothing Technique with alpha of 0.10. The forecast for the rate of cancer deaths shows a horizontally or flat value. The forecasted mortality trend remains at 6.84% from January 2012 to December 2013. All the government and private sectors and non-governmental organizations need to highlight issues on cancer especially lung and breast cancers to the public through campaigns using mass media, media electronics, posters and pamphlets in the attempt to decrease the rate of cancer deaths in Malaysia.
Eberly, Lynn E
2007-01-01
This chapter describes multiple linear regression, a statistical approach used to describe the simultaneous associations of several variables with one continuous outcome. Important steps in using this approach include estimation and inference, variable selection in model building, and assessing model fit. The special cases of regression with interactions among the variables, polynomial regression, regressions with categorical (grouping) variables, and separate slopes models are also covered. Examples in microbiology are used throughout. PMID:18450050
Orthogonal Regression and Equivariance.
ERIC Educational Resources Information Center
Blankmeyer, Eric
Ordinary least-squares regression treats the variables asymmetrically, designating a dependent variable and one or more independent variables. When it is not obvious how to make this distinction, a researcher may prefer to use orthogonal regression, which treats the variables symmetrically. However, the usual procedure for orthogonal regression is…
Energy Science and Technology Software Center (ESTSC)
2015-09-09
The NCCS Regression Test Harness is a software package that provides a framework to perform regression and acceptance testing on NCCS High Performance Computers. The package is written in Python and has only the dependency of a Subversion repository to store the regression tests.
Unitary Response Regression Models
ERIC Educational Resources Information Center
Lipovetsky, S.
2007-01-01
The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…
Towards a More General Type of Univariate Constrained Interpolation with Fractal Splines
NASA Astrophysics Data System (ADS)
Chand, A. K. B.; Viswanathan, P.; Reddy, K. M.
2015-09-01
Recently, in [Electron. Trans. Numer. Anal. 41 (2014) 420-442] authors introduced a new class of rational cubic fractal interpolation functions with linear denominators via fractal perturbation of traditional nonrecursive rational cubic splines and investigated their basic shape preserving properties. The main goal of the current paper is to embark on univariate constrained fractal interpolation that is more general than what was considered so far. To this end, we propose some strategies for selecting the parameters of the rational fractal spline so that the interpolating curves lie strictly above or below a prescribed linear or a quadratic spline function. Approximation property of the proposed rational cubic fractal spine is broached by using the Peano kernel theorem as an interlude. The paper also provides an illustration of background theory, veined by examples.
Computing Confidence Bounds for Power and Sample Size of the General Linear Univariate Model
Taylor, Douglas J.; Muller, Keith E.
2013-01-01
The power of a test, the probability of rejecting the null hypothesis in favor of an alternative, may be computed using estimates of one or more distributional parameters. Statisticians frequently fix mean values and calculate power or sample size using a variance estimate from an existing study. Hence computed power becomes a random variable for a fixed sample size. Likewise, the sample size necessary to achieve a fixed power varies randomly. Standard statistical practice requires reporting uncertainty associated with such point estimates. Previous authors studied an asymptotically unbiased method of obtaining confidence intervals for noncentrality and power of the general linear univariate model in this setting. We provide exact confidence intervals for noncentrality, power, and sample size. Such confidence intervals, particularly one-sided intervals, help in planning a future study and in evaluating existing studies. PMID:24039272
JOINT STRUCTURE SELECTION AND ESTIMATION IN THE TIME-VARYING COEFFICIENT COX MODEL
Xiao, Wei; Lu, Wenbin; Zhang, Hao Helen
2016-01-01
Time-varying coefficient Cox model has been widely studied and popularly used in survival data analysis due to its flexibility for modeling covariate effects. It is of great practical interest to accurately identify the structure of covariate effects in a time-varying coefficient Cox model, i.e. covariates with null effect, constant effect and truly time-varying effect, and estimate the corresponding regression coefficients. Combining the ideas of local polynomial smoothing and group nonnegative garrote, we develop a new penalization approach to achieve such goals. Our method is able to identify the underlying true model structure with probability tending to one and simultaneously estimate the time-varying coefficients consistently. The asymptotic normalities of the resulting estimators are also established. We demonstrate the performance of our method using simulations and an application to the primary biliary cirrhosis data. PMID:27540275
Mahdi, Chanif; Nurdiana, Nurdiana; Kikuchi, Takheshi; Fatchiyah, Fatchiyah
2014-01-01
To understand the structural features that dictate the selectivity of the two isoforms of the prostaglandin H2 synthase (PGHS/COX), the three-dimensional (3D) structure of COX-1/COX-2 was assessed by means of binding energy calculation of virtual molecular dynamic with using ligand alpha-Patchouli alcohol isomers. Molecular interaction studies with COX-1 and COX-2 were done using the molecular docking tools by Hex 8.0. Interactions were further visualized by using Discovery Studio Client 3.5 software tool. The binding energy of molecular interaction was calculated by AMBER12 and Virtual Molecular Dynamic 1.9.1 software. The analysis of the alpha-Patchouli alcohol isomer compounds showed that all alpha-Patchouli alcohol isomers were suggested as inhibitor of COX-1 and COX-2. Collectively, the scoring binding energy calculation (with PBSA Model Solvent) of alpha-Patchouli alcohol isomer compounds (CID442384, CID6432585, CID3080622, CID10955174, and CID56928117) was suggested as candidate for a selective COX-1 inhibitor and CID521903 as nonselective COX-1/COX-2. PMID:25484897
Raharjo, Sentot Joko; Mahdi, Chanif; Nurdiana, Nurdiana; Kikuchi, Takheshi; Fatchiyah, Fatchiyah
2014-01-01
To understand the structural features that dictate the selectivity of the two isoforms of the prostaglandin H2 synthase (PGHS/COX), the three-dimensional (3D) structure of COX-1/COX-2 was assessed by means of binding energy calculation of virtual molecular dynamic with using ligand alpha-Patchouli alcohol isomers. Molecular interaction studies with COX-1 and COX-2 were done using the molecular docking tools by Hex 8.0. Interactions were further visualized by using Discovery Studio Client 3.5 software tool. The binding energy of molecular interaction was calculated by AMBER12 and Virtual Molecular Dynamic 1.9.1 software. The analysis of the alpha-Patchouli alcohol isomer compounds showed that all alpha-Patchouli alcohol isomers were suggested as inhibitor of COX-1 and COX-2. Collectively, the scoring binding energy calculation (with PBSA Model Solvent) of alpha-Patchouli alcohol isomer compounds (CID442384, CID6432585, CID3080622, CID10955174, and CID56928117) was suggested as candidate for a selective COX-1 inhibitor and CID521903 as nonselective COX-1/COX-2. PMID:25484897
COX, LOX and platelet aggregation inhibitory properties of Lauraceae neolignans.
Coy, Ericsson David; Cuca, Luis Enrique; Sefkow, Michael
2009-12-15
The anti-inflammatory potential of 26 neolignans (14 of the bicyclooctane-type and 12 of the benzofuran-type), isolated from three Lauraceae species (Pleurothyrium cinereum, Ocotea macrophylla and Nectandra amazonum), was evaluated in vitro through inhibition of COX-1, COX-2, 5-LOX and agonist-induced aggregation of rabbit platelets. Benzofuran neolignans were found to be selective COX-2 inhibitors, whereas bicyclooctane neolignans inhibit selectively the PAF-action as well as COX-1 and 5-LOX. The neolignan 9-nor-7,8-dehydro-isolicarin B 15 and cinerin C 7 were found to be the most potent COX-2 inhibitor and PAF-antagonist, respectively. Nectamazin C 10 exhibited dual 5-LOX/COX-2 inhibition. PMID:19880317
COX2 Inhibition Reduces Aortic Valve Calcification In Vivo
Wirrig, Elaine E.; Gomez, M. Victoria; Hinton, Robert B.; Yutzey, Katherine E.
2016-01-01
Objective Calcific aortic valve disease (CAVD) is a significant cause of morbidity and mortality, which affects approximately 1% of the US population and is characterized by calcific nodule formation and stenosis of the valve. Klotho-deficient mice were used to study the molecular mechanisms of CAVD as they develop robust aortic valve (AoV) calcification. Through microarray analysis of AoV tissues from klotho-deficient and wild type mice, increased expression of the gene encoding cyclooxygenase 2/COX2 (Ptgs2) was found. COX2 activity contributes to bone differentiation and homeostasis, thus the contribution of COX2 activity to AoV calcification was assessed. Approach and Results In klotho-deficient mice, COX2 expression is increased throughout regions of valve calcification and is induced in the valvular interstitial cells (VICs) prior to calcification formation. Similarly, COX2 expression is increased in human diseased AoVs. Treatment of cultured porcine aortic VICs with osteogenic media induces bone marker gene expression and calcification in vitro, which is blocked by inhibition of COX2 activity. In vivo, genetic loss of function of COX2 cyclooxygenase activity partially rescues AoV calcification in klotho-deficient mice. Moreover, pharmacologic inhibition of COX2 activity in klotho-deficient mice via celecoxib-containing diet reduces AoV calcification and blocks osteogenic gene expression. Conclusions COX2 expression is upregulated in CAVD and its activity contributes to osteogenic gene induction and valve calcification in vitro and in vivo. PMID:25722432
Mucin 1 Regulates Cox-2 Gene in Pancreatic Cancer
Nath, Sritama; Roy, Lopamudra Das; Grover, Priyanka; Rao, Shanti; Mukherjee, Pinku
2015-01-01
Objective Eighty percent of pancreatic ductal adenocarcinomas (PDAs) overexpress mucin 1 (MUC1), a transmembrane mucin glycoprotein. MUC1high PDA patients also express high levels of cyclooxygenase 2 (COX-2) and show poor prognosis. The cytoplasmic tail of MUC1 (MUC1-CT) partakes in oncogenic signaling, resulting in accelerated cancer progression. Our aim was to understand the regulation of Cox-2 expression by MUC1. Methods Levels of COX-2 and MUC1 were determined in MUC1−/−, MUC1low, and MUC1high PDA cells and tumors using reverse transcriptase–polymerase chain reaction, Western blot, and immunohistochemistry. Proliferative and invasive potential was assessed using MTT and Boyden chamber assays. Chromatin immunoprecipitation was performed to evaluate binding of MUC1-CT to the promoter of COX-2 gene. Results Significantly higher levels of COX-2 mRNA and protein were detected in MUC1high versus MUC1low/null cells, which were recapitulated in vivo. In addition, deletion of MUC1 gene and transient knockdown of MUC1 led to decreased COX-2 level. Also, MUC1-CT associated with the COX-2 promoter at ∼1000 base pairs upstream of the transcription start site, the same gene locus where nuclear factor κB p65 associates with the COX-2 promoter. Conclusions Data supports a novel regulation of COX-2 gene by MUC1 in PDA, the intervention of which may lead to a better therapeutic targeting in PDA patients. PMID:26035123
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
NASA Astrophysics Data System (ADS)
Masud, M. B.; Khaliq, M. N.; Wheater, H. S.
2015-03-01
This study is focused on the Saskatchewan River Basin (SRB) that spans southern parts of Alberta, Saskatchewan and Manitoba, the three Prairie Provinces of Canada, where most of the country's agricultural activities are concentrated. The SRB is confronted with immense water-related challenges and is now one of the ten GEWEX (Global Energy and Water Exchanges) Regional Hydroclimate Projects in the world. In the past, various multi-year droughts have been observed in this part of Canada that impacted agriculture, energy and socio-economic sectors. Therefore, proper understanding of the spatial and temporal characteristics of historical droughts is important for many water resources planning and management related activities across the basin. In the study, observed gridded data of daily precipitation and temperature and conventional univariate and copula-based bivariate frequency analyses are used to characterize drought events in terms of drought severity and duration on the basis of two drought indices, the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Within the framework of univariate and bivariate analyses, drought risk indicators are developed and mapped across the SRB to delineate the most vulnerable parts of the basin. Based on the results obtained, southern parts of the SRB (i.e., western part of the South Saskatchewan River, Seven Persons Creek and Bigstick Lake watersheds) are associated with a higher drought risk, while moderate risk is noted for the North Saskatchewan River (except its eastern parts), Red Deer River, Oldman River, Bow River, Sounding Creek, Carrot River and Battle River watersheds. Lower drought risk is found for the areas surrounding the Saskatchewan-Manitoba border (particularly, the Saskatchewan River watershed). It is also found that the areas characterized with higher drought severity are also associated with higher drought duration. A comparison of SPI- and SPEI
Chen, Gang; Adleman, Nancy E; Saad, Ziad S; Leibenluft, Ellen; Cox, Robert W
2014-10-01
All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance-covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within-subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT) with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse-Geisser and Huynh-Feldt) with MVT-WS. To validate the MVM methodology, we performed simulations to assess the controllability for false positives and power achievement. A real FMRI dataset was analyzed to demonstrate the capability of the MVM approach. The methodology has been implemented into an open source program 3dMVM in AFNI, and all the statistical tests can be performed through symbolic coding with variable names instead of the tedious process of dummy coding. Our data indicates that the severity of sphericity violation varies substantially across brain regions. The differences among various modeling methodologies were addressed through direct comparisons between the MVM approach and some of the GLM implementations in
A Mixed-Effects Model with Time Reparametrization for Longitudinal Univariate Manifold-Valued Data.
Schiratti, J B; Allassonnière, S; Routier, A; Durrleman, S
2015-01-01
Mixed-effects models provide a rich theoretical framework for the analysis of longitudinal data. However, when used to analyze or predict the progression of a neurodegenerative disease such as Alzheimer's disease, these models usually do not take into account the fact that subjects may be at different stages of disease progression and the interpretation of the model may depend on some implicit reference time. In this paper, we propose a generative statistical model for longitudinal data, described in a univariate Riemannian manifold setting, which estimates an average disease progression model, subject-specific time shifts and acceleration factors. The time shifts account for variability in age at disease-onset time. The acceleration factors account for variability in speed of disease progression. For a given individual, the estimated time shift and acceleration factor define an affine reparametrization of the average disease progression model. This statistical model has been used to analyze neuropsychological assessments scores and cortical thickness measurements from the Alzheimer's Disease Neuroimaging Initiative database. The numerical results showed that we can distinguish between slow versus fast progressing and early versus late-onset individuals. PMID:26221703
Chen, Gang; Adleman, Nancy E.; Saad, Ziad S.; Leibenluft, Ellen; Cox, RobertW.
2014-01-01
All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance–covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within- subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT)with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse–Geisser and Huynh–Feldt) with MVT-WS. PMID:24954281
spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models
Finley, Andrew O.; Banerjee, Sudipto; Carlin, Bradley P.
2010-01-01
Scientists and investigators in such diverse fields as geological and environmental sciences, ecology, forestry, disease mapping, and economics often encounter spatially referenced data collected over a fixed set of locations with coordinates (latitude–longitude, Easting–Northing etc.) in a region of study. Such point-referenced or geostatistical data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves computationally intensive Markov chain Monte Carlo (MCMC) methods whose efficiency depends upon the specific problem at hand. This requires extensive coding on the part of the user and the situation is not helped by the lack of available software for such algorithms. Here, we introduce a statistical software package, spBayes, built upon the R statistical computing platform that implements a generalized template encompassing a wide variety of Gaussian spatial process models for univariate as well as multivariate point-referenced data. We discuss the algorithms behind our package and illustrate its use with a synthetic and real data example. PMID:21494410
Inference for correlated effect sizes using multiple univariate meta-analyses.
Chen, Yong; Cai, Yi; Hong, Chuan; Jackson, Dan
2016-04-30
Multivariate meta-analysis, which involves jointly analyzing multiple and correlated outcomes from separate studies, has received a great deal of attention. One reason to prefer the multivariate approach is its ability to account for the dependence between multiple estimates from the same study. However, nearly all the existing methods for analyzing multivariate meta-analytic data require the knowledge of the within-study correlations, which are usually unavailable in practice. We propose a simple non-iterative method that can be used for the analysis of multivariate meta-analysis datasets, that has no convergence problems, and does not require the use of within-study correlations. Our approach uses standard univariate methods for the marginal effects but also provides valid joint inference for multiple parameters. The proposed method can directly handle missing outcomes under missing completely at random assumption. Simulation studies show that the proposed method provides unbiased estimates, well-estimated standard errors, and confidence intervals with good coverage probability. Furthermore, the proposed method is found to maintain high relative efficiency compared with conventional multivariate meta-analyses where the within-study correlations are known. We illustrate the proposed method through two real meta-analyses where functions of the estimated effects are of interest. PMID:26537017
Optimizing the Classification Performance of Logistic Regression and Fisher's Discriminant Analyses.
ERIC Educational Resources Information Center
Yarnold, Paul R.; And Others
1994-01-01
A methodology is proposed to optimize the training classification performance of any suboptimal model. The method, referred to as univariate optimal discriminant analysis (UniODA), is illustrated through application to a two-group logistic regression analysis with 12 empirical examples. Maximizing percentage accuracy in classification is…
Prediction in Multiple Regression.
ERIC Educational Resources Information Center
Osborne, Jason W.
2000-01-01
Presents the concept of prediction via multiple regression (MR) and discusses the assumptions underlying multiple regression analyses. Also discusses shrinkage, cross-validation, and double cross-validation of prediction equations and describes how to calculate confidence intervals around individual predictions. (SLD)
Improved Regression Calibration
ERIC Educational Resources Information Center
Skrondal, Anders; Kuha, Jouni
2012-01-01
The likelihood for generalized linear models with covariate measurement error cannot in general be expressed in closed form, which makes maximum likelihood estimation taxing. A popular alternative is regression calibration which is computationally efficient at the cost of inconsistent estimation. We propose an improved regression calibration…
Gerber, Samuel; Rübel, Oliver; Bremer, Peer-Timo; Pascucci, Valerio; Whitaker, Ross T.
2012-01-01
This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduce 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 paper 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 over-fitting. 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. PMID:23687424
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.
Wang, X; Jiao, Y; Tang, T; Wang, H; Lu, Z
2013-12-19
Intrinsic connectivity networks (ICNs) are composed of spatial components and time courses. The spatial components of ICNs were discovered with moderate-to-high reliability. So far as we know, few studies focused on the reliability of the temporal patterns for ICNs based their individual time courses. The goals of this study were twofold: to investigate the test-retest reliability of temporal patterns for ICNs, and to analyze these informative univariate metrics. Additionally, a correlation analysis was performed to enhance interpretability. Our study included three datasets: (a) short- and long-term scans, (b) multi-band echo-planar imaging (mEPI), and (c) eyes open or closed. Using dual regression, we obtained the time courses of ICNs for each subject. To produce temporal patterns for ICNs, we applied two categories of univariate metrics: network-wise complexity and network-wise low-frequency oscillation. Furthermore, we validated the test-retest reliability for each metric. The network-wise temporal patterns for most ICNs (especially for default mode network, DMN) exhibited moderate-to-high reliability and reproducibility under different scan conditions. Network-wise complexity for DMN exhibited fair reliability (ICC<0.5) based on eyes-closed sessions. Specially, our results supported that mEPI could be a useful method with high reliability and reproducibility. In addition, these temporal patterns were with physiological meanings, and certain temporal patterns were correlated to the node strength of the corresponding ICN. Overall, network-wise temporal patterns of ICNs were reliable and informative and could be complementary to spatial patterns of ICNs for further study. PMID:24042040
Olszowski, Tomasz; Gutowska, Izabela; Baranowska-Bosiacka, Irena; Piotrowska, Katarzyna; Korbecki, Jan; Kurzawski, Mateusz; Chlubek, Dariusz
2015-06-01
The aim of this study was to examine the effects of cadmium in concentrations relevant to those detected in human serum on cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2) expression at mRNA, protein, and enzyme activity levels in THP-1 macrophages. Macrophages were incubated with various cadmium chloride (CdCl2) solutions for 48 h at final concentrations of 5 nM, 20 nM, 200 nM, and 2 μM CdCl2. The mRNA expression and protein levels of COXs were analyzed with RT-PCR and Western blotting, respectively. Prostaglandin E2 (PGE2) and stable metabolite of thromboxane B2 (TXB2) concentrations in culture media were determined using ELISA method. Our study demonstrates that cadmium at the highest tested concentrations modulates COX-1 and COX-2 at mRNA level in THP-1 macrophages; however, the lower tested cadmium concentrations appear to inhibit COX-1 protein expression. PGE2 and TXB2 production is not altered by all tested Cd concentrations; however, the significant stimulation of PGE2 and TXB2 production is observed when macrophages are exposed to both cadmium and COX-2 selective inhibitor, NS-398. The stimulatory effect of cadmium on COXs at mRNA level is not reflected at protein and enzymatic activity levels, suggesting the existence of some posttranscriptional, translational, and posttranslational events that result in silencing of those genes' expression. PMID:25645360
A consistent framework for Horton regression statistics that leads to a modified Hack's law
Furey, P.R.; Troutman, B.M.
2008-01-01
A statistical framework is introduced that resolves important problems with the interpretation and use of traditional Horton regression statistics. The framework is based on a univariate regression model that leads to an alternative expression for Horton ratio, connects Horton regression statistics to distributional simple scaling, and improves the accuracy in estimating Horton plot parameters. The model is used to examine data for drainage area A and mainstream length L from two groups of basins located in different physiographic settings. Results show that confidence intervals for the Horton plot regression statistics are quite wide. Nonetheless, an analysis of covariance shows that regression intercepts, but not regression slopes, can be used to distinguish between basin groups. The univariate model is generalized to include n > 1 dependent variables. For the case where the dependent variables represent ln A and ln L, the generalized model performs somewhat better at distinguishing between basin groups than two separate univariate models. The generalized model leads to a modification of Hack's law where L depends on both A and Strahler order ??. Data show that ?? plays a statistically significant role in the modified Hack's law expression. ?? 2008 Elsevier B.V.
Schmid, Matthias; Wickler, Florian; Maloney, Kelly O.; Mitchell, Richard; Fenske, Nora; Mayr, Andreas
2013-01-01
Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1). Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures. PMID:23626706
Knowles, Emma E. M.; McKay, D. Reese; Kent, Jack W.; Sprooten, Emma; Carless, Melanie A.; Curran, Joanne E.; de Almeida, Marcio A. A.; Dyer, Thomas D.; Göring, Harald H. H.; Olvera, Rene; Duggirala, Ravi; Fox, Peter; Almasy, Laura; Blangero, John; Glahn, David. C.
2014-01-01
The role of the amygdala in emotion recognition is well established and separately each trait has been shown to be highly heritable, but the potential role of common genetic influences on both traits has not been explored. Here we present an investigation of the pleiotropic influences of amygdala and emotion recognition in a sample of randomly selected, extended pedigrees (N = 858). Using a combination of univariate and bivariate linkage we found a pleiotropic region for amygdala and emotion recognition on 4q26 (LOD = 4.34). Association analysis conducted in the region underlying the bivariate linkage peak revealed a variant meeting the corrected significance level (pBonferroni = 5.01×10−05) within an intron of PDE5A (rs2622497, Χ2 =16.67, p = 4.4×10−05) as being jointly influential on both traits. PDE5A has been implicated previously in recognition-memory deficits and is expressed in subcortical structures that are thought to underlie memory ability including the amygdala. The present paper extends our understanding of the shared etiology between amygdala and emotion recognition by showing that the overlap between the two traits is due, at least in part, to common genetic influences. Moreover, the present paper identifies a pleiotropic locus for the two traits and an associated variant, which localizes the genetic signal even more precisely. These results, when taken in the context of previous research, highlight the potential utility of PDE5-inhibitors for ameliorating emotion-recognition deficits in populations including, but not exclusively, those individuals suffering from mental or neurodegenerative illness. PMID:25322361
Calderoni, Sara; Retico, Alessandra; Biagi, Laura; Tancredi, Raffaella; Muratori, Filippo; Tosetti, Michela
2012-01-16
Several studies on structural MRI in children with autism spectrum disorders (ASD) have mainly focused on samples prevailingly consisting of males. Sex differences in brain structure are observable since infancy and therefore caution is required in transferring to females the results obtained for males. The neuroanatomical phenotype of female children with ASD (ASDf) represents indeed a neglected area of research. In this study, we investigated for the first time the anatomic brain structures of a sample entirely composed of ASDf (n=38; 2-7 years of age; mean=53 months; SD=18) with respect to 38 female age and non verbal IQ matched controls, using both mass-univariate and pattern classification approaches. The whole brain volumes of each group were compared using voxel-based morphometry (VBM) with diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL) procedure, allowing us to build a study-specific template. Significantly more gray matter (GM) was found in the left superior frontal gyrus (SFG) in ASDf subjects compared to controls. The GM segments obtained in the VBM-DARTEL preprocessing are also classified with a support vector machine (SVM), using the leave-pair-out cross-validation protocol. Then, the recursive feature elimination (SVM-RFE) approach allows for the identification of the most discriminating voxels in the GM segments and these prove extremely consistent with the SFG region identified by the VBM analysis. Furthermore, the SVM-RFE map obtained with the most discriminating set of voxels corresponding to the maximum Area Under the Receiver Operating Characteristic Curve (AUC(max)=0.80) highlighted a more complex circuitry of increased cortical volume in ASDf, involving bilaterally the SFG and the right temporo-parietal junction (TPJ). The SFG and TPJ abnormalities may be relevant to the pathophysiology of ASDf, since these structures participate in some core atypical features of autism. PMID:21896334
Ahrens, Maike; Turewicz, Michael; Casjens, Swaantje; May, Caroline; Pesch, Beate; Stephan, Christian; Woitalla, Dirk; Gold, Ralf; Brüning, Thomas; Meyer, Helmut E; Rahnenführer, Jörg; Eisenacher, Martin
2013-01-01
Detection of yet unknown subgroups showing differential gene or protein expression is a frequent goal in the analysis of modern molecular data. Applications range from cancer biology over developmental biology to toxicology. Often a control and an experimental group are compared, and subgroups can be characterized by differential expression for only a subgroup-specific set of genes or proteins. Finding such genes and corresponding patient subgroups can help in understanding pathological pathways, diagnosis and defining drug targets. The size of the subgroup and the type of differential expression determine the optimal strategy for subgroup identification. To date, commonly used software packages hardly provide statistical tests and methods for the detection of such subgroups. Different univariate methods for subgroup detection are characterized and compared, both on simulated and on real data. We present an advanced design for simulation studies: Data is simulated under different distributional assumptions for the expression of the subgroup, and performance results are compared against theoretical upper bounds. For each distribution, different degrees of deviation from the majority of observations are considered for the subgroup. We evaluate classical approaches as well as various new suggestions in the context of omics data, including outlier sum, PADGE, and kurtosis. We also propose the new FisherSum score. ROC curve analysis and AUC values are used to quantify the ability of the methods to distinguish between genes or proteins with and without certain subgroup patterns. In general, FisherSum for small subgroups and t-test for large subgroups achieve best results. We apply each method to a case-control study on Parkinson's disease and underline the biological benefit of the new method. PMID:24278130
Multivariate Regression with Calibration*
Liu, Han; Wang, Lie; Zhao, Tuo
2014-01-01
We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smoothed proximal gradient algorithm which has a worst-case iteration complexity O(1/ε), where ε is a pre-specified numerical accuracy. Theoretically, we prove that CMR achieves the optimal rate of convergence in parameter estimation. We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR on a brain activity prediction problem and find that CMR is as competitive as the handcrafted model created by human experts. PMID:25620861
George: Gaussian Process regression
NASA Astrophysics Data System (ADS)
Foreman-Mackey, Daniel
2015-11-01
George is a fast and flexible library, implemented in C++ with Python bindings, for Gaussian Process regression useful for accounting for correlated noise in astronomical datasets, including those for transiting exoplanet discovery and characterization and stellar population modeling.
32. SCIENTISTS ALLAN COX (SEATED), RICHARD DOELL, AND BRENT DALRYMPLE ...
32. SCIENTISTS ALLAN COX (SEATED), RICHARD DOELL, AND BRENT DALRYMPLE AT CONTROL PANEL, ABOUT 1965. - U.S. Geological Survey, Rock Magnetics Laboratory, 345 Middlefield Road, Menlo Park, San Mateo County, CA
Students Speak With Gary Cox, EPIC Project Manager
From NASAâs International Space Station Mission Control Center Gary Cox EPIC Project Manager, participates in a Digital Learning Network (DLN) event with students at South Effingham Middle School...
Akasaka, Hironari; So, Shui-Ping; Ruan, Ke-He
2015-06-16
In vascular inflammation, prostaglandin E2 (PGE₂) is largely biosynthesized by microsomal PGE₂ synthase-1 (mPGES-1), competing with other downstream eicosanoid-synthesizing enzymes, such as PGIS, a synthase of a vascular protector prostacyclin (PGI₂), to isomerize the cyclooxygenase (COX)-2-derived prostaglandin H2 (PGH₂). In this study, we found that a majority of the product from the cells co-expressing human COX-2, mPGES-1, and PGIS was PGE₂. We hypothesize that the molecular and cellular mechanisms are related to the post-translational endoplasmic reticulum (ER) arrangement of those enzymes. A set of fusion enzymes, COX-2-linker [10 amino acids (aa)]-PGIS and COX-2-linker (22 amino acids)-PGIS, were created as "The Bioruler", in which the 10 and 22 amino acids are defined linkers with known helical structures and distances (14.4 and 30.8 Å, respectively). Our experiments have shown that the efficiency of PGI₂ biosynthesis was reduced when the separation distance increased from 10 to 22 amino acids. When COX-2-10aa-PGIS (with a 14.4 Å separation) was co-expressed with mPGES-1 on the ER membrane, a major product was PGE₂, but not PGI₂. However, expression of COX-2-10aa-PGIS and mPGES-1 on a separated ER with a distance of ≫30.8 Å reduced the level of PGE₂ production. These data indicated that the mPGES-1 is "complex-likely" colocalized with COX-2 within a distance of 14.4 Å. In addition, the cells co-expressing COX-1-10aa-PGIS and mPGES-1 produced PGI₂ mainly, but not PGE₂. This indicates that mPGES-1 is expressed much farther from COX-1. These findings have led to proposed models showing the different post-translational ER organization between COX-2 and COX-1 with respect to the topological arrangement of the mPGES-1 during vascular inflammation. PMID:25988363
Regression versus No Regression in the Autistic Disorder: Developmental Trajectories
ERIC Educational Resources Information Center
Bernabei, P.; Cerquiglini, A.; Cortesi, F.; D' Ardia, C.
2007-01-01
Developmental regression is a complex phenomenon which occurs in 20-49% of the autistic population. Aim of the study was to assess possible differences in the development of regressed and non-regressed autistic preschoolers. We longitudinally studied 40 autistic children (18 regressed, 22 non-regressed) aged 2-6 years. The following developmental…
Epigenetic deregulation of the COX pathway in cancer.
Cebola, Inês; Peinado, Miguel A
2012-10-01
Inflammation is a major cause of cancer and may condition its progression. The deregulation of the cyclooxygenase (COX) pathway is implicated in several pathophysiological processes, including inflammation and cancer. Although, its targeting with nonsteroidal antiinflammatory drugs (NSAIDs) and COX-2 selective inhibitors has been investigated for years with promising results at both preventive and therapeutic levels, undesirable side effects and the limited understanding of the regulation and functionalities of the COX pathway compromise a more extensive application of these drugs. Epigenetics is bringing additional levels of complexity to the understanding of basic biological and pathological processes. The deregulation of signaling and biosynthetic pathways by epigenetic mechanisms may account for new molecular targets in cancer therapeutics. Genes of the COX pathway are seldom mutated in neoplastic cells, but a large proportion of them show aberrant expression in different types of cancer. A growing body of evidence indicates that epigenetic alterations play a critical role in the deregulation of the genes of the COX pathway. This review summarizes the current knowledge on the contribution of epigenetic processes to the deregulation of the COX pathway in cancer, getting insights into how these alterations may be relevant for the clinical management of patients. PMID:22580191
Practical Session: Logistic Regression
NASA Astrophysics Data System (ADS)
Clausel, M.; Grégoire, G.
2014-12-01
An exercise is proposed to illustrate the logistic regression. One investigates the different risk factors in the apparition of coronary heart disease. It has been proposed in Chapter 5 of the book of D.G. Kleinbaum and M. Klein, "Logistic Regression", Statistics for Biology and Health, Springer Science Business Media, LLC (2010) and also by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr341.pdf). This example is based on data given in the file evans.txt coming from http://www.sph.emory.edu/dkleinb/logreg3.htm#data.
Multiple Roles of the Cox20 Chaperone in Assembly of Saccharomyces cerevisiae Cytochrome c Oxidase
Elliott, Leah E.; Saracco, Scott A.; Fox, Thomas D.
2012-01-01
The Cox2 subunit of Saccharomyces cerevisiae cytochrome c oxidase is synthesized in the mitochondrial matrix as a precursor whose leader peptide is rapidly processed by the inner membrane protease following translocation to the intermembrane space. Processing is chaperoned by Cox20, an integral inner membrane protein whose hydrophilic domains are located in the intermembrane space, and Cox20 remains associated with mature, unassembled Cox2. The Cox2 C-tail domain is exported post-translationally by the highly conserved translocase Cox18 and associated proteins. We have found that Cox20 is required for efficient export of the Cox2 C-tail. Furthermore, Cox20 interacts by co-immune precipitation with Cox18, and this interaction requires the presence of Cox2. We therefore propose that Cox20 binding to Cox2 on the trans side of the inner membrane accelerates dissociation of newly exported Cox2 from the Cox18 translocase, promoting efficient cycling of the translocase. The requirement for Cox20 in cytochrome c oxidase assembly and respiratory growth is partially bypassed by yme1, mgr1 or mgr3 mutations, each of which reduce i-AAA protease activity in the intermembrane space. Thus, Cox20 also appears to stabilize unassembled Cox2 against degradation by the i-AAA protease. Pre-Cox2 leader peptide processing by Imp1 occurs in the absence of Cox20 and i-AAA protease activity, but is greatly reduced in efficiency. Under these conditions some mature Cox2 is assembled into cytochrome c oxidase allowing weak respiratory growth. Thus, the Cox20 chaperone has important roles in leader peptide processing, C-tail export, and stabilization of Cox2. PMID:22095077
Moustafa, Azza Aziz; Salem, Hesham; Hegazy, Maha; Ali, Omnia
2015-02-25
Simple, accurate, and selective methods have been developed and validated for simultaneous determination of a ternary mixture of Chlorpheniramine maleate (CPM), Pseudoephedrine HCl (PSE) and Ibuprofen (IBF), in tablet dosage form. Four univariate methods manipulating ratio spectra were applied, method A is the double divisor-ratio difference spectrophotometric method (DD-RD). Method B is double divisor-derivative ratio spectrophotometric method (DD-RD). Method C is derivative ratio spectrum-zero crossing method (DRZC), while method D is mean centering of ratio spectra (MCR). Two multivariate methods were also developed and validated, methods E and F are Principal Component Regression (PCR) and Partial Least Squares (PLSs). The proposed methods have the advantage of simultaneous determination of the mentioned drugs without prior separation steps. They were successfully applied to laboratory-prepared mixtures and to commercial pharmaceutical preparation without any interference from additives. The proposed methods were validated according to the ICH guidelines. The obtained results were statistically compared with the official methods where no significant difference was observed regarding both accuracy and precision. PMID:25306132
NASA Astrophysics Data System (ADS)
Moustafa, Azza Aziz; Salem, Hesham; Hegazy, Maha; Ali, Omnia
2015-02-01
Simple, accurate, and selective methods have been developed and validated for simultaneous determination of a ternary mixture of Chlorpheniramine maleate (CPM), Pseudoephedrine HCl (PSE) and Ibuprofen (IBF), in tablet dosage form. Four univariate methods manipulating ratio spectra were applied, method A is the double divisor-ratio difference spectrophotometric method (DD-RD). Method B is double divisor-derivative ratio spectrophotometric method (DD-RD). Method C is derivative ratio spectrum-zero crossing method (DRZC), while method D is mean centering of ratio spectra (MCR). Two multivariate methods were also developed and validated, methods E and F are Principal Component Regression (PCR) and Partial Least Squares (PLSs). The proposed methods have the advantage of simultaneous determination of the mentioned drugs without prior separation steps. They were successfully applied to laboratory-prepared mixtures and to commercial pharmaceutical preparation without any interference from additives. The proposed methods were validated according to the ICH guidelines. The obtained results were statistically compared with the official methods where no significant difference was observed regarding both accuracy and precision.
Bourens, Myriam; Boulet, Aren; Leary, Scot C.; Barrientos, Antoni
2014-01-01
Cytochrome c oxidase (CIV) deficiency is one of the most common respiratory chain defects in patients presenting with mitochondrial encephalocardiomyopathies. CIV biogenesis is complicated by the dual genetic origin of its structural subunits, and assembly of a functional holoenzyme complex requires a large number of nucleus-encoded assembly factors. In general, the functions of these assembly factors remain poorly understood, and mechanistic investigations of human CIV biogenesis have been limited by the availability of model cell lines. Here, we have used small interference RNA and transcription activator-like effector nucleases (TALENs) technology to create knockdown and knockout human cell lines, respectively, to study the function of the CIV assembly factor COX20 (FAM36A). These cell lines exhibit a severe, isolated CIV deficiency due to instability of COX2, a mitochondrion-encoded CIV subunit. Mitochondria lacking COX20 accumulate CIV subassemblies containing COX1 and COX4, similar to those detected in fibroblasts from patients carrying mutations in the COX2 copper chaperones SCO1 and SCO2. These results imply that in the absence of COX20, COX2 is inefficiently incorporated into early CIV subassemblies. Immunoprecipitation assays using a stable COX20 knockout cell line expressing functional COX20-FLAG allowed us to identify an interaction between COX20 and newly synthesized COX2. Additionally, we show that SCO1 and SCO2 act on COX20-bound COX2. We propose that COX20 acts as a chaperone in the early steps of COX2 maturation, stabilizing the newly synthesized protein and presenting COX2 to its metallochaperone module, which in turn facilitates the incorporation of mature COX2 into the CIV assembly line. PMID:24403053
Partial covariate adjusted regression
Şentürk, Damla; Nguyen, Danh V.
2008-01-01
Covariate adjusted regression (CAR) is a recently proposed adjustment method for regression analysis where both the response and predictors are not directly observed (Şentürk and Müller, 2005). The available data has been distorted by unknown functions of an observable confounding covariate. CAR provides consistent estimators for the coefficients of the regression between the variables of interest, adjusted for the confounder. We develop a broader class of partial covariate adjusted regression (PCAR) models to accommodate both distorted and undistorted (adjusted/unadjusted) predictors. The PCAR model allows for unadjusted predictors, such as age, gender and demographic variables, which are common in the analysis of biomedical and epidemiological data. The available estimation and inference procedures for CAR are shown to be invalid for the proposed PCAR model. We propose new estimators and develop new inference tools for the more general PCAR setting. In particular, we establish the asymptotic normality of the proposed estimators and propose consistent estimators of their asymptotic variances. Finite sample properties of the proposed estimators are investigated using simulation studies and the method is also illustrated with a Pima Indians diabetes data set. PMID:20126296
Explorations in Statistics: Regression
ERIC Educational Resources Information Center
Curran-Everett, Douglas
2011-01-01
Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This seventh installment of "Explorations in Statistics" explores regression, a technique that estimates the nature of the relationship between two things for which we may only surmise a mechanistic or predictive connection.…
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…
Webcast entitled Statistical Tools for Making Sense of Data, by the National Nutrient Criteria Support Center, N-STEPS (Nutrients-Scientific Technical Exchange Partnership. The section "Correlation and Regression" provides an overview of these two techniques in the context of nut...
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.
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. PMID:23665468
Mechanisms of neuroblastoma regression
Brodeur, Garrett M.; Bagatell, Rochelle
2014-01-01
Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179
Collision prediction models using multivariate Poisson-lognormal regression.
El-Basyouny, Karim; Sayed, Tarek
2009-07-01
This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate Poisson-lognormal (PLN) models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency. The MVPLN is modeled using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a goodness-of-fit measure for model comparisons. The results indicate that the estimates of the extra Poisson variation parameters were considerably smaller under MVPLN leading to higher precision. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables representing property damage only (PDO) and injuries plus fatalities (I+F). This correlation was estimated at 0.758, which is highly significant, suggesting that higher PDO rates are associated with higher I+F rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in roadway design and/or other unobserved factors. In terms of goodness-of-fit, the MVPLN model provided a superior fit than the independent univariate models. The multivariate hazardous location identification results demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models. PMID:19540972
Cardiovascular hazard of selective COX-2 inhibitors: myth or reality?
Chiolero, Arnaud; Maillard, Marc P; Burnier, Michel
2002-05-01
Since 1998, two selective inhibitors of COX-2 have been approved in many countries for the treatment of rheumatoid arthritis, osteoarthritis and acute pain. These new drugs have a significantly reduced gastrointestinal toxicity when compared with non-selective COX inhibitors. However, the results of two large clinical trials conducted in patients with osteoarthritis and rheumatoid arthritis have recently raised some concerns regarding the cardiovascular safety of these new drugs. The purpose of this paper is to review the potential mechanisms whereby selective COX-2 inhibitors could increase the cardiovascular risk of patients and to analyse the data indicating that this clinical risk indeed exists. The authors' analysis shows that even though there are pathophysiological mechanisms which could explain why selective COX-2 inhibition might increase the cardiovascular risk in patients, the actual level of evidence demonstrating that the risk is indeed increased is weak. Because of the importance of the issue, additional studies must be conducted with this class of agents. Meanwhile, it is crucial to emphasise that neither selective COX-2 inhibitors nor conventional NSAIDs replace aspirin in patients with a high cardiovascular risk. PMID:12904159
Isoxazole-Based-Scaffold Inhibitors Targeting Cyclooxygenases (COXs).
Perrone, Maria Grazia; Vitale, Paola; Panella, Andrea; Ferorelli, Savina; Contino, Marialessandra; Lavecchia, Antonio; Scilimati, Antonio
2016-06-01
A new set of cyclooxygenase (COX) inhibitors endowed with an additional functionality was explored. These new compounds also contained either rhodamine 6G or 6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline, two moieties typical of efflux pump substrates and inhibitors, respectively. Among all the synthesized compounds, two new COX inhibitors with opposite selectivity were discovered: compound 8 [N-(9-{2-[(4-{2-[3-(5-chlorofuran-2-yl)-4-phenylisoxazol-5-yl]acetamido}butyl)carbamoyl]phenyl-6-(ethylamino)-2,7-dimethyl-3H-xanthen-3-ylidene}ethanaminium chloride] was found to be a selective COX-1 inhibitor, whereas 17 (2-[3,4-bis(4-methoxyphenyl)isoxazol-5-yl]-1-[6,7-dimethoxy-3,4-dihydroisoquinolin-2-(1H)-yl]ethanone) was found to be a sub-micromolar selective COX-2 inhibitor. However, both were shown to interact with P-glycoprotein. Docking experiments helped to clarify the molecular aspects of the observed COX selectivity. PMID:27136372
Park, Sun Ah; Chevallier, Nathalie; Tejwani, Karishma; Hung, Mary M; Maruyama, Hiroko; Golde, Todd E; Koo, Edward H
2016-09-01
Epidemiologic studies indicate that chronic use of non-steroidal anti-inflammatory drugs (NSAIDs) is associated with a lower risk for developing Alzheimer's disease (AD). Because the primary mode of action of NSAIDs is to inhibit cyclooxygenase (COX) activity, it has been proposed that perturbed activity of COX-1 or COX-2 contributes to AD pathogenesis. To test the role of COX-1 or COX-2 in amyloid deposition and amyloid-associated inflammatory changes, we examined amyloid precursor protein (APP) transgenic mice in the context of either COX-1 or COX-2 deficiency. Our studies showed that loss of either COX-1 or COX-2 gene did not alter amyloid burden in brains of the APP transgenic mice. However, one marker of microglial activation (CD45) was decreased in brains of COX-1 deficient/APP animals and showed a strong trend in reduction in COX-2 deficient/APP animals. These results suggest that COX activity and amyloid deposition in brain are likely independent processes. Further, if NSAIDs do causally reduce the risks of AD, then our findings indicate that the mechanisms are likely not due primarily to their inhibition on COX or γ-secretase modulation activity, the latter reported recently after acute dosing of ibuprofen in humans and nonhuman primates. PMID:27425247
Multiple Use One-Sided Hypotheses Testing in Univariate Linear Calibration
NASA Technical Reports Server (NTRS)
Krishnamoorthy, K.; Kulkarni, Pandurang M.; Mathew, Thomas
1996-01-01
Consider a normally distributed response variable, related to an explanatory variable through the simple linear regression model. Data obtained on the response variable, corresponding to known values of the explanatory variable (i.e., calibration data), are to be used for testing hypotheses concerning unknown values of the explanatory variable. We consider the problem of testing an unlimited sequence of one sided hypotheses concerning the explanatory variable, using the corresponding sequence of values of the response variable and the same set of calibration data. This is the situation of multiple use of the calibration data. The tests derived in this context are characterized by two types of uncertainties: one uncertainty associated with the sequence of values of the response variable, and a second uncertainty associated with the calibration data. We derive tests based on a condition that incorporates both of these uncertainties. The solution has practical applications in the decision limit problem. We illustrate our results using an example dealing with the estimation of blood alcohol concentration based on breath estimates of the alcohol concentration. In the example, the problem is to test if the unknown blood alcohol concentration of an individual exceeds a threshold that is safe for driving.
Ridge Regression: A Regression Procedure for Analyzing Correlated Independent Variables.
ERIC Educational Resources Information Center
Rakow, Ernest A.
Ridge regression is presented as an analytic technique to be used when predictor variables in a multiple linear regression situation are highly correlated, a situation which may result in unstable regression coefficients and difficulties in interpretation. Ridge regression avoids the problem of selection of variables that may occur in stepwise…
Ridge Regression Signal Processing
NASA Technical Reports Server (NTRS)
Kuhl, Mark R.
1990-01-01
The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.
Fast Censored Linear Regression
HUANG, YIJIAN
2013-01-01
Weighted log-rank estimating function has become a standard estimation method for the censored linear regression model, or the accelerated failure time model. Well established statistically, the estimator defined as a consistent root has, however, rather poor computational properties because the estimating function is neither continuous nor, in general, monotone. We propose a computationally efficient estimator through an asymptotics-guided Newton algorithm, in which censored quantile regression methods are tailored to yield an initial consistent estimate and a consistent derivative estimate of the limiting estimating function. We also develop fast interval estimation with a new proposal for sandwich variance estimation. The proposed estimator is asymptotically equivalent to the consistent root estimator and barely distinguishable in samples of practical size. However, computation time is typically reduced by two to three orders of magnitude for point estimation alone. Illustrations with clinical applications are provided. PMID:24347802
Technology Transfer Automated Retrieval System (TEKTRAN)
Non-linear regression techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks present interesting and alternative features for such modeling purposes. In this work, a univariate hydrothermal-time based Weibull m...
The effects of the Cox maze procedure on atrial function
Voeller, Rochus K.; Zierer, Andreas; Lall, Shelly C.; Sakamoto, Shun–ichiro; Chang, Nai–Lun; Schuessler, Richard B.; Moon, Marc R.; Damiano, Ralph J.
2010-01-01
Objective The effects of the Cox maze procedure on atrial function remain poorly defined. The purpose of this study was to investigate the effects of a modified Cox maze procedure on left and right atrial function in a porcine model. Methods After cardiac magnetic resonance imaging, 6 pigs underwent pericardiotomy (sham group), and 6 pigs underwent a modified Cox maze procedure (maze group) with bipolar radiofrequency ablation. The maze group had preablation and immediate postablation left and right atrial pressure–volume relations measured with conductance catheters. All pigs survived for 30 days. Magnetic resonance imaging was then repeated for both groups, and conductance catheter measurements were repeated for the right atrium in the maze group. Results Both groups had significantly higher left atrial volumes postoperatively. Magnetic resonance imaging–derived reservoir and booster pump functional parameters were reduced postoperatively for both groups, but there was no difference in these parameters between the groups. The maze group had significantly higher reduction in the medial and lateral left atrial wall contraction postoperatively. There was no change in immediate left atrial elastance or in the early and 30-day right atrial elastance after the Cox maze procedure. Although the initial left atrial stiffness increased after ablation, right atrial diastolic stiffness did not change initially or at 30 days. Conclusions Performing a pericardiotomy alone had a significant effect on atrial function that can be quantified by means of magnetic resonance imaging. The effects of the Cox maze procedure on left atrial function could only be detected by analyzing segmental wall motion. Understanding the precise physiologic effects of the Cox maze procedure on atrial function will help in developing less-damaging lesion sets for the surgical treatment of atrial fibrillation. PMID:19026812
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
Orthogonal Regression: A Teaching Perspective
ERIC Educational Resources Information Center
Carr, James R.
2012-01-01
A well-known approach to linear least squares regression is that which involves minimizing the sum of squared orthogonal projections of data points onto the best fit line. This form of regression is known as orthogonal regression, and the linear model that it yields is known as the major axis. A similar method, reduced major axis regression, is…
COX-2 and PGE2-dependent immunomodulation in breast cancer.
Chen, Edward P; Smyth, Emer M
2011-11-01
COX-derived prostanoids play multiple roles in inflammation and cancer. This review highlights research examining COX-2 and PGE(2)-dependent regulation of immune cell polarization and function within the tumor microenvironment, particularly as it pertains to breast cancer. Appreciating PGE(2)-mediated immunomodulation is important in understanding how tumors evade immune surveillance by re-educating infiltrating inflammatory and immune cells to support tumorigenesis. Elucidation of the multiple and complex influences exerted by tumor stromal components may lead to targeted therapies in breast and other cancers that restrain microenvironmental permissiveness and maintain natural defenses against malignancies. PMID:21907301
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
Correlation and simple linear regression.
Eberly, Lynn E
2007-01-01
This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression. PMID:18450049
Incremental hierarchical discriminant regression.
Weng, Juyang; Hwang, Wey-Shiuan
2007-03-01
This paper presents incremental hierarchical discriminant regression (IHDR) which incrementally builds a decision tree or regression tree for very high-dimensional regression or decision spaces by an online, real-time learning system. Biologically motivated, it is an approximate computational model for automatic development of associative cortex, with both bottom-up sensory inputs and top-down motor projections. At each internal node of the IHDR tree, information in the output space is used to automatically derive the local subspace spanned by the most discriminating features. Embedded in the tree is a hierarchical probability distribution model used to prune very unlikely cases during the search. The number of parameters in the coarse-to-fine approximation is dynamic and data-driven, enabling the IHDR tree to automatically fit data with unknown distribution shapes (thus, it is difficult to select the number of parameters up front). The IHDR tree dynamically assigns long-term memory to avoid the loss-of-memory problem typical with a global-fitting learning algorithm for neural networks. A major challenge for an incrementally built tree is that the number of samples varies arbitrarily during the construction process. An incrementally updated probability model, called sample-size-dependent negative-log-likelihood (SDNLL) metric is used to deal with large sample-size cases, small sample-size cases, and unbalanced sample-size cases, measured among different internal nodes of the IHDR tree. We report experimental results for four types of data: synthetic data to visualize the behavior of the algorithms, large face image data, continuous video stream from robot navigation, and publicly available data sets that use human defined features. PMID:17385628
Steganalysis using logistic regression
NASA Astrophysics Data System (ADS)
Lubenko, Ivans; Ker, Andrew D.
2011-02-01
We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.
SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *
Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.
2014-01-01
The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844
Cell-type-specific roles for COX-2 in UVB-induced skin cancer
Herschman, Harvey
2014-01-01
In human tumors, and in mouse models, cyclooxygenase-2 (COX-2) levels are frequently correlated with tumor development/burden. In addition to intrinsic tumor cell expression, COX-2 is often present in fibroblasts, myofibroblasts and endothelial cells of the tumor microenvironment, and in infiltrating immune cells. Intrinsic cancer cell COX-2 expression is postulated as only one of many sources for prostanoids required for tumor promotion/progression. Although both COX-2 inhibition and global Cox-2 gene deletion ameliorate ultraviolet B (UVB)-induced SKH-1 mouse skin tumorigenesis, neither manipulation can elucidate the cell type(s) in which COX-2 expression is required for tumorigenesis; both eliminate COX-2 activity in all cells. To address this question, we created Cox-2 flox/flox mice, in which the Cox-2 gene can be eliminated in a cell-type-specific fashion by targeted Cre recombinase expression. Cox-2 deletion in skin epithelial cells of SKH-1 Cox-2 flox/flox;K14Cre + mice resulted, following UVB irradiation, in reduced skin hyperplasia and increased apoptosis. Targeted epithelial cell Cox-2 deletion also resulted in reduced tumor incidence, frequency, size and proliferation rate, altered tumor cell differentiation and reduced tumor vascularization. Moreover, Cox-2 flox/flox;K14Cre + papillomas did not progress to squamous cell carcinomas. In contrast, Cox-2 deletion in SKH-1 Cox-2 flox/flox; LysMCre + myeloid cells had no effect on UVB tumor induction. We conclude that (i) intrinsic epithelial COX-2 activity plays a major role in UVB-induced skin cancer, (ii) macrophage/myeloid COX-2 plays no role in UVB-induced skin cancer and (iii) either there may be another COX-2-dependent prostanoid source(s) that drives UVB skin tumor induction or there may exist a COX-2-independent pathway(s) to UVB-induced skin cancer. PMID:24469308
On nonsingular potentials of Cox-Thompson inversion scheme
NASA Astrophysics Data System (ADS)
Pálmai, Tamás; Apagyi, Barnabás
2010-02-01
We establish a condition for obtaining nonsingular potentials using the Cox-Thompson inverse scattering method with one phase shift. The anomalous singularities of the potentials are avoided by maintaining unique solutions of the underlying Regge-Newton integral equation for the transformation kernel. As a by-product, new inequality sequences of zeros of Bessel functions are discovered.
COX-2 inhibitors are contraindicated for treatment of combined injury.
Jiao, W; Kiang, J G; Cary, L; Elliott, T B; Pellmar, T C; Ledney, G D
2009-12-01
Casualties of radiation dispersal devices, nuclear detonation or major ionizing radiation accidents, in addition to radiation exposure, may sustain physical and/or thermal trauma. Radiation exposure plus additional tissue trauma is known as combined injury. There are no definitive therapeutic agents. Cyclooxygenase-2 (COX-2), an inducible enzyme expressed in pathological disorders and radiation injury, plays an important role in inflammation and the production of cytokines and prostaglandin E(2) (PGE(2)) and could therefore affect the outcome for victims of combined injury. The COX-2 inhibitors celecoxib and meloxicam were evaluated for their therapeutic value against combined injury in mice. In survival studies, the COX-2 inhibitors had no beneficial effect on 30-day survival, wound healing or body weight gain after radiation injury alone or after combined injury. Meloxicam accelerated death in both wounded and combined injury mice. These drugs also induced severe hepatic toxicity, exaggerated inflammatory processes, and did not enhance hematopoietic cell regeneration. This study points to potential contraindications for use of COX-2 inhibitors in patients undergoing therapy for radiation injury and combined injury. PMID:19929415
COX-2-derived endocannabinoid metabolites as novel inflammatory mediators.
Alhouayek, Mireille; Muccioli, Giulio G
2014-06-01
Cyclooxygenase-2 (COX-2) is an enzyme that plays a key role in inflammatory processes. Classically, this enzyme is upregulated in inflammatory situations and is responsible for the generation of prostaglandins (PGs) from arachidonic acid (AA). One lesser-known property of COX-2 is its ability to metabolize the endocannabinoids, N-arachidonoylethanolamine (AEA) and 2-arachidonoylglycerol (2-AG). Endocannabinoid metabolism by COX-2 is not merely a means to terminate their actions. On the contrary, it generates PG analogs, namely PG-glycerol esters (PG-G) for 2-AG and PG-ethanolamides (PG-EA or prostamides) for AEA. Although the formation of these COX-2-derived metabolites of the endocannabinoids has been known for a while, their biological effects remain to be fully elucidated. Recently, several studies have focused on the role of these PG-G or PG-EA in vivo. In this review we take a closer look at the literature concerning these novel bioactive lipids and their role in inflammation. PMID:24684963
MACRO FOR ESTIMATING THE BOX-COX POWER TRANSFORMATION
In their classic paper, Box and Cox (1964) demonstrated how a dependent variable could be transformed to satisfy simultaneously, assumptions implicit in the analysis of linear models. For the class of analyses in which the response of interest is positive and where no transformat...
English in the National Curriculum: The Cox Report.
ERIC Educational Resources Information Center
Use of English, 1989
1989-01-01
Discusses implications of the Cox Report on English instruction in Great Britain. Questions the definitions of aims and responsibilities outlined in the report. Suggests that the magnitude of what teachers are called upon to teach and assess will diffuse rather than concentrate attention on what matters most in English as a distinctive subject.…
On nonsingular potentials of Cox-Thompson inversion scheme
Palmai, Tamas; Apagyi, Barnabas
2010-02-15
We establish a condition for obtaining nonsingular potentials using the Cox-Thompson inverse scattering method with one phase shift. The anomalous singularities of the potentials are avoided by maintaining unique solutions of the underlying Regge-Newton integral equation for the transformation kernel. As a by-product, new inequality sequences of zeros of Bessel functions are discovered.
Lee, Andy C. H.; Brodersen, Kay H.; Rudebeck, Sarah R.
2013-01-01
Although the role of the hippocampus in spatial cognition is well accepted, it is unclear whether its involvement is restricted to the mnemonic domain or also extends to perception. We used functional magnetic resonance imaging (fMRI) to scan neurologically healthy participants during a scene oddity judgment task that placed no explicit demand on long-term memory. Crucially, a surprise recognition test was administered after scanning so that each trial could be categorized not only according to oddity accuracy but also subsequent memory. Univariate analyses showed significant hippocampal activity in association with correct oddity judgment, whereas greater parahippocampal place area (PPA) activity was observed during incorrect oddity trials, both irrespective of subsequent recognition performance. Consistent with this, multivariate pattern analyses revealed that a linear support vector machine was able to distinguish correct from incorrect oddity trials on the basis of activity in voxels within the hippocampus or PPA. Although no significant regions of activity were identified by univariate analyses in association with memory performance, a classifier was able to predict subsequent memory using voxels in either the hippocampus or PPA. Our findings are consistent with the idea that the hippocampus is important for processes beyond long-term declarative memory and that this structure may also play a role in complex spatial perception. PMID:23016766
Vecchiato, G; De Vico Fallani, F; Astolfi, L; Toppi, J; Cincotti, F; Mattia, D; Salinari, S; Babiloni, F
2010-08-30
This paper presents some considerations about the use of adequate statistical techniques in the framework of the neuroelectromagnetic brain mapping. With the use of advanced EEG/MEG recording setup involving hundred of sensors, the issue of the protection against the type I errors that could occur during the execution of hundred of univariate statistical tests, has gained interest. In the present experiment, we investigated the EEG signals from a mannequin acting as an experimental subject. Data have been collected while performing a neuromarketing experiment and analyzed with state of the art computational tools adopted in specialized literature. Results showed that electric data from the mannequin's head presents statistical significant differences in power spectra during the visualization of a commercial advertising when compared to the power spectra gathered during a documentary, when no adjustments were made on the alpha level of the multiple univariate tests performed. The use of the Bonferroni or Bonferroni-Holm adjustments returned correctly no differences between the signals gathered from the mannequin in the two experimental conditions. An partial sample of recently published literature on different neuroscience journals suggested that at least the 30% of the papers do not use statistical protection for the type I errors. While the occurrence of type I errors could be easily managed with appropriate statistical techniques, the use of such techniques is still not so largely adopted in the literature. PMID:20637802
Kirkby, Nicholas S.; Lundberg, Martina H.; Wright, William R.; Warner, Timothy D.; Paul-Clark, Mark J.; Mitchell, Jane A.
2014-01-01
Cyxlo-oxygenase (COX)-2 inhibitors, including traditional nonsteroidal anti-inflammatory drugs (NSAIDs) are associated with increased cardiovascular side effects, including myocardial infarction. We and others have shown that COX-1 and not COX-2 drives vascular prostacyclin in the healthy cardiovascular system, re-opening the question of how COX-2 might regulate cardiovascular health. In diseased, atherosclerotic vessels, the relative contribution of COX-2 to prostacyclin formation is not clear. Here we have used apoE−/−/COX-2−/− mice to show that, whilst COX-2 profoundly limits atherosclerosis, this protection is independent of local prostacyclin release. These data further illustrate the need to look for new explanations, targets and pathways to define the COX/NSAID/cardiovascular risk axis. Gene expression profiles in tissues from apoE−/−/COX-2−/− mice showed increased lymphocyte pathways that were validated by showing increased T-lymphocytes in plaques and elevated plasma Th1-type cytokines. In addition, we identified a novel target gene, rgl1, whose expression was strongly reduced by COX-2 deletion across all examined tissues. This study is the first to demonstrate that COX-2 protects vessels against atherosclerotic lesions independently of local vascular prostacyclin and uses systems biology approaches to identify new mechanisms relevant to development of next generation NSAIDs. PMID:24887395
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
NASA Technical Reports Server (NTRS)
Kuhl, Mark R.
1990-01-01
Current navigation requirements depend on a geometric dilution of precision (GDOP) criterion. As long as the GDOP stays below a specific value, navigation requirements are met. The GDOP will exceed the specified value when the measurement geometry becomes too collinear. A new signal processing technique, called Ridge Regression Processing, can reduce the effects of nearly collinear measurement geometry; thereby reducing the inflation of the measurement errors. It is shown that the Ridge signal processor gives a consistently better mean squared error (MSE) in position than the Ordinary Least Mean Squares (OLS) estimator. The applicability of this technique is currently being investigated to improve the following areas: receiver autonomous integrity monitoring (RAIM), coverage requirements, availability requirements, and precision approaches.
Schmidtmann, I; Elsäßer, A; Weinmann, A; Binder, H
2014-12-30
For determining a manageable set of covariates potentially influential with respect to a time-to-event endpoint, Cox proportional hazards models can be combined with variable selection techniques, such as stepwise forward selection or backward elimination based on p-values, or regularized regression techniques such as component-wise boosting. Cox regression models have also been adapted for dealing with more complex event patterns, for example, for competing risks settings with separate, cause-specific hazard models for each event type, or for determining the prognostic effect pattern of a variable over different landmark times, with one conditional survival model for each landmark. Motivated by a clinical cancer registry application, where complex event patterns have to be dealt with and variable selection is needed at the same time, we propose a general approach for linking variable selection between several Cox models. Specifically, we combine score statistics for each covariate across models by Fisher's method as a basis for variable selection. This principle is implemented for a stepwise forward selection approach as well as for a regularized regression technique. In an application to data from hepatocellular carcinoma patients, the coupled stepwise approach is seen to facilitate joint interpretation of the different cause-specific Cox models. In conditional survival models at landmark times, which address updates of prediction as time progresses and both treatment and other potential explanatory variables may change, the coupled regularized regression approach identifies potentially important, stably selected covariates together with their effect time pattern, despite having only a small number of events. These results highlight the promise of the proposed approach for coupling variable selection between Cox models, which is particularly relevant for modeling for clinical cancer registries with their complex event patterns. PMID:25345575
Recursive Algorithm For Linear Regression
NASA Technical Reports Server (NTRS)
Varanasi, S. V.
1988-01-01
Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.
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
Analysis of COX2 mutants reveals cytochrome oxidase subassemblies in yeast
2005-01-01
Cytochrome oxidase catalyses the reduction of oxygen to water. The mitochondrial enzyme contains up to 13 subunits, 11 in yeast, of which three, Cox1p, Cox2p and Cox3p, are mitochondrially encoded. The assembly pathway of this complex is still poorly understood. Its study in yeast has been so far impeded by the rapid turnover of unassembled subunits of the enzyme. In the present study, immunoblot analysis of blue native gels of yeast wild-type and Cox2p mutants revealed five cytochrome oxidase complexes or subcomplexes: a, b, c, d and f; a is likely to be the fully assembled enzyme; b lacks Cox6ap; d contains Cox7p and/or Cox7ap; f represents unassembled Cox1p; and c, observed only in the Cox2p mutants, contains Cox1p, Cox3p, Cox5p and Cox6p and lacks the other subunits. The identification of these novel cytochrome oxidase subcomplexes should encourage the reexamination of other yeast mutants. PMID:15921494
Multinomial logistic regression ensembles.
Lee, Kyewon; Ahn, Hongshik; Moon, Hojin; Kodell, Ralph L; Chen, James J
2013-05-01
This article proposes a method for multiclass classification problems using ensembles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit model can be applied to each mutually exclusive subset of the feature space without variable selection. By combining multiple models the proposed method can handle a huge database without a constraint needed for analyzing high-dimensional data, and the random partition can improve the prediction accuracy by reducing the correlation among base classifiers. The proposed method is implemented using R, and the performance including overall prediction accuracy, sensitivity, and specificity for each category is evaluated on two real data sets and simulation data sets. To investigate the quality of prediction in terms of sensitivity and specificity, the area under the receiver operating characteristic (ROC) curve (AUC) is also examined. The performance of the proposed model is compared to a single multinomial logit model and it shows a substantial improvement in overall prediction accuracy. The proposed method is also compared with other classification methods such as the random forest, support vector machines, and random multinomial logit model. PMID:23611203
Bayesian Spatial Quantile Regression
Reich, Brian J.; Fuentes, Montserrat; Dunson, David B.
2013-01-01
Tropospheric ozone is one of the six criteria pollutants regulated by the United States Environmental Protection Agency under the Clean Air Act and has been linked with several adverse health effects, including mortality. Due to the strong dependence on weather conditions, ozone may be sensitive to climate change and there is great interest in studying the potential effect of climate change on ozone, and how this change may affect public health. In this paper we develop a Bayesian spatial model to predict ozone under different meteorological conditions, and use this model to study spatial and temporal trends and to forecast ozone concentrations under different climate scenarios. We develop a spatial quantile regression model that does not assume normality and allows the covariates to affect the entire conditional distribution, rather than just the mean. The conditional distribution is allowed to vary from site-to-site and is smoothed with a spatial prior. For extremely large datasets our model is computationally infeasible, and we develop an approximate method. We apply the approximate version of our model to summer ozone from 1997–2005 in the Eastern U.S., and use deterministic climate models to project ozone under future climate conditions. Our analysis suggests that holding all other factors fixed, an increase in daily average temperature will lead to the largest increase in ozone in the Industrial Midwest and Northeast. PMID:23459794
Luo, Chongliang; Liu, Jin; Dey, Dipak K; Chen, Kun
2016-07-01
In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study. PMID:26861909
Bayesian Spatial Quantile Regression.
Reich, Brian J; Fuentes, Montserrat; Dunson, David B
2011-03-01
Tropospheric ozone is one of the six criteria pollutants regulated by the United States Environmental Protection Agency under the Clean Air Act and has been linked with several adverse health effects, including mortality. Due to the strong dependence on weather conditions, ozone may be sensitive to climate change and there is great interest in studying the potential effect of climate change on ozone, and how this change may affect public health. In this paper we develop a Bayesian spatial model to predict ozone under different meteorological conditions, and use this model to study spatial and temporal trends and to forecast ozone concentrations under different climate scenarios. We develop a spatial quantile regression model that does not assume normality and allows the covariates to affect the entire conditional distribution, rather than just the mean. The conditional distribution is allowed to vary from site-to-site and is smoothed with a spatial prior. For extremely large datasets our model is computationally infeasible, and we develop an approximate method. We apply the approximate version of our model to summer ozone from 1997-2005 in the Eastern U.S., and use deterministic climate models to project ozone under future climate conditions. Our analysis suggests that holding all other factors fixed, an increase in daily average temperature will lead to the largest increase in ozone in the Industrial Midwest and Northeast. PMID:23459794
Yun, Jong Pil; Jeon, Yong-Ju; Choi, Doo-chul; Kim, Sang Woo
2012-05-01
We propose a new defect detection algorithm for scale-covered steel wire rods. The algorithm incorporates an adaptive wavelet filter that is designed on the basis of lattice parameterization of orthogonal wavelet bases. This approach offers the opportunity to design orthogonal wavelet filters via optimization methods. To improve the performance and the flexibility of wavelet design, we propose the use of the undecimated discrete wavelet transform, and separate design of column and row wavelet filters but with a common cost function. The coefficients of the wavelet filters are optimized by the so-called univariate dynamic encoding algorithm for searches (uDEAS), which searches the minimum value of a cost function designed to maximize the energy difference between defects and background noise. Moreover, for improved detection accuracy, we propose an enhanced double-threshold method. Experimental results for steel wire rod surface images obtained from actual steel production lines show that the proposed algorithm is effective. PMID:22561939
Some functional limit theorems for compound Cox processes
NASA Astrophysics Data System (ADS)
Korolev, Victor Yu.; Chertok, A. V.; Korchagin, A. Yu.; Kossova, E. V.; Zeifman, Alexander I.
2016-06-01
An improved version of the functional limit theorem is proved establishing weak convergence of random walks generated by compound doubly stochastic Poisson processes (compound Cox processes) to Lévy processes in the Skorokhod space under more realistic moment conditions. As corollaries, theorems are proved on convergence of random walks with jumps having finite variances to Lévy processes with variance-mean mixed normal distributions, in particular, to stable Lévy processes.
Linear regression in astronomy. I
NASA Technical Reports Server (NTRS)
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
Association Between COX-2 Polymorphisms and Lung Cancer Risk
Wang, Weiwei; Fan, Xinyun; Zhang, Yong; Yang, Yi; Yang, Siyuan; Li, Gaofeng
2015-01-01
Background Multiple relevant risk factors for lung cancer have been reported in different populations, but results of previous studies were not consistent. Therefore, a meta-analysis is necessary to summarize these outcomes and reach a relatively comprehensive conclusion. Material/Methods STATA 12.0 software was used for all statistical of the relationship between COX-2 polymorphisms and lung cancer risk. Inter-study heterogeneity was examined with the Q statistic (significance level at P<0.1). The publication bias among studies in the meta-analysis was analyzed with Begg’s funnel plot and Egger’s test. Hardy-Weinberg equilibrium was tested in all controls of the studies. Results COX-2 rs20417 polymorphism had a significant association with reduced risk of lung cancer under homozygous and recessive models, and similar results were observed in white and population-based subgroups under 2 and 3 contrasts, respectively. Additionally, rs2066826 polymorphism manifested a strong correlation with increased risk of lung cancer under 5 genetic models. Conclusions In COX-2 gene, rs20417 may have a certain relationship with reduced risk of lung cancer, while rs2066826 may increase the risk of lung cancer. PMID:26624903
[New studies of COX-inhibitors, yet issues remain].
Wollheim, Frank A
2003-09-18
Advantages and risks related to the use of selective COX-2 inhibitors when treating arthritis are currently being scrutinized by authorities and public. The discussion tends towards exaggerated claims for or against their usefulness. The issue of cardiovascular safety is still not finally settled. In an experimental study using patients with severe coronary disease, administration of celecoxib resulted in improved endothelial function together with reduced CRP levels. Gastrointestinal tolerance was studied in patients who had recently recovered from peptic ulcer bleeding. In this group of high risk patients, celecoxib was as safe as combined therapy using omeprazol and diclofenac when given for 6 months. However, both COX inhibitors caused hypertension and adverse renal effects. The second generation of selective inhibitors is being launched. Etoricoxib--related to rofecoxib--was shown to be as potent as indomethacin in the treatment of acute gout, but it caused fewer adverse reactions. In general, however, any advantage of second generation as compared to first generation COX-2 inhibitors remains to be proven. The Swedish Council on Technology Assessment in Health Care, in its "SBU Alert", has published an appraisal of celecoxib and rofecoxib, in which the need for further long-term safety studies is emphasized. PMID:14558211
2011-01-01
Background Increased cyclooxygenase activity promotes progression of colorectal cancer, but the mechanisms behind COX-2 induction remain elusive. This study was therefore aimed to define external cell signaling and transcription factors relating to high COX-2 expression in colon cancer tissue. Method Tumor and normal colon tissue were collected at primary curative operation in 48 unselected patients. COX-2 expression in tumor and normal colon tissue was quantified including microarray analyses on tumor mRNA accounting for high and low tumor COX-2 expression. Cross hybridization was performed between tumor and normal colon tissue. Methylation status of up-stream COX-2 promoter region was evaluated. Results Tumors with high COX-2 expression displayed large differences in gene expression compared to normal colon. Numerous genes with altered expression appeared in tumors of high COX-2 expression compared to tumors of low COX-2. COX-2 expression in normal colon was increased in patients with tumors of high COX-2 compared to normal colon from patients with tumors of low COX-2. IL1β, IL6 and iNOS transcripts were up-regulated among external cell signaling factors; nine transcription factors (ATF3, C/EBP, c-Fos, Fos-B, JDP2, JunB, c-Maf, NF-κB, TCF4) showed increased expression and 5 (AP-2, CBP, Elk-1, p53, PEA3) were decreased in tumors with high COX-2. The promoter region of COX-2 gene did not show consistent methylation in tumor or normal colon tissue. Conclusions Transcription and external cell signaling factors are altered as covariates to COX-2 expression in colon cancer tissue, but DNA methylation of the COX-2 promoter region was not a significant factor behind COX-2 expression in tumor and normal colon tissue. PMID:21668942
Evaluating differential effects using regression interactions and regression mixture models
Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung
2015-01-01
Research increasingly emphasizes understanding differential effects. This paper focuses on understanding regression mixture models, a relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their formulation, and their assumptions are compared using Monte Carlo simulations and real data analysis. The capabilities of regression mixture models are described and specific issues to be addressed when conducting regression mixtures are proposed. The paper aims to clarify the role that regression mixtures can take in the estimation of differential effects and increase awareness of the benefits and potential pitfalls of this approach. Regression mixture models are shown to be a potentially effective exploratory method for finding differential effects when these effects can be defined by a small number of classes of respondents who share a typical relationship between a predictor and an outcome. It is also shown that the comparison between regression mixture models and interactions becomes substantially more complex as the number of classes increases. It is argued that regression interactions are well suited for direct tests of specific hypotheses about differential effects and regression mixtures provide a useful approach for exploring effect heterogeneity given adequate samples and study design. PMID:26556903
Supercomplex-associated Cox26 protein binds to cytochrome c oxidase.
Strecker, Valentina; Kadeer, Zibirnisa; Heidler, Juliana; Cruciat, Cristina-Maria; Angerer, Heike; Giese, Heiko; Pfeiffer, Kathy; Stuart, Rosemary A; Wittig, Ilka
2016-07-01
Here we identified a hydrophobic 6.4kDa protein, Cox26, as a novel component of yeast mitochondrial supercomplex comprising respiratory complexes III and IV. Multi-dimensional native and denaturing electrophoretic techniques were used to identify proteins interacting with Cox26. The majority of the Cox26 protein was found non-covalently bound to the complex IV moiety of the III-IV supercomplexes. A population of Cox26 was observed to exist in a disulfide bond partnership with the Cox2 subunit of complex IV. No pronounced growth phenotype for Cox26 deficiency was observed, indicating that Cox26 may not play a critical role in the COX enzymology, and we speculate that Cox26 may serve to regulate or support the Cox2 protein. Respiratory supercomplexes are assembled in the absence of the Cox26 protein, however their pattern slightly differs to the wild type III-IV supercomplex appearance. The catalytic activities of complexes III and IV were observed to be normal and respiration was comparable to wild type as long as cells were cultivated under normal growth conditions. Stress conditions, such as elevated temperatures resulted in mild decrease of respiration in non-fermentative media when the Cox26 protein was absent. PMID:27091403
Upregulation of fibronectin expression by COX-2 is mediated by interaction with ELMO1.
Yang, Chen; Sorokin, Andrey
2011-01-01
Engulfment and cell motility 1 (ELMO1), a bipartite guanine nucleotide exchange factor (GEF) for the small GTPase Rac 1, was identified as a susceptibility gene for glomerular disease. Here, we reported that ELMO1 interacted with COX-2 in human mesangial cells. Furthermore, we identified ELMO1 as a posttranslational regulator of COX-2 activity. We demonstrated that COX-2 cyclooxygenase activity increased fibronectin promoter activity. The protein-protein interaction between ELMO1 and COX-2 increased the cyclooxygenase activity of COX-2 and, correspondingly, fibronectin expression. We also found that ET625, the dominant negative form of ELMO1 lacking Rac1 activity, interacted with COX-2, increased cyclooxygenase activity of COX-2 and enhanced COX-2-mediated fibronectin upregulation. To further rule out Rac1 as an ELMO1-mediated regulator of COX-2 activity, we employed the constitutive active Rac1, Rac1(Q63E), and demonstrated that Rac1 signaling has no effect on COX-2-mediated fibronectin promoter activity. These results suggest that ELMO1 contributes to the development of glomerular injury through serving as a regulator of COX-2 activity. The interaction of ELMO1 with COX-2 could play an important role in the development and progression of renal glomerular injury. PMID:20732417
Naoi, Kazuhisa; Kogure, Suguru; Saito, Masataka; Hamazaki, Tomohito; Watanabe, Shiro
2006-07-01
We have shown that anorexic response is induced by intraperitoneal injection of zymosan in mice, although the role of prostaglandins in this response is relatively unknown as compared with lipopolysaccharide (LPS)-induced anorexic response. Indomethacin (0.5 and 2.0 mg/kg), a non-selective cyclooxygenase (COX) inhibitor, as well as meloxicam (0.5 mg/kg), a selective COX-2 inhibitor, but not FR122047 (2.0 mg/kg), a selective COX-1 inhibitor, attenuated zymosan-induced anorexia. Zymosan injection elevated COX-2 expression in brain and liver but not in small intestine and colon. Meloxicam (0.5 mg/kg) and FR122047 treatment (2.0 mg/kg) similarly suppressed the generation of brain prostaglandin E(2) (PGE(2)) and peritoneal prostacyclin (PGI(2)) upon zymosan injection. PGE(2) generation in liver upon zymosan injection was suppressed by meloxicam (0.5 mg/kg) but not by FR122047 treatment (2.0 mg/kg). Our observations suggest that COX-2 plays an important role in zymosan-induced anorexia, which is a similar feature in LPS-induced anorexic response. However, non-selective inhibition by selective COX-1 and COX-2 inhibitors of brain PGE(2) generation upon zymosan injection does not support the role of COX-2 expressed in brain in zymosan-induced anorexic response. PGE(2) generation in liver may account for peripheral role of COX-2 in zymosan-induced anorexic response. PMID:16819161
Linear regression in astronomy. II
NASA Technical Reports Server (NTRS)
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Quantile regression for climate data
NASA Astrophysics Data System (ADS)
Marasinghe, Dilhani Shalika
Quantile regression is a developing statistical tool which is used to explain the relationship between response and predictor variables. This thesis describes two examples of climatology using quantile regression.Our main goal is to estimate derivatives of a conditional mean and/or conditional quantile function. We introduce a method to handle autocorrelation in the framework of quantile regression and used it with the temperature data. Also we explain some properties of the tornado data which is non-normally distributed. Even though quantile regression provides a more comprehensive view, when talking about residuals with the normality and the constant variance assumption, we would prefer least square regression for our temperature analysis. When dealing with the non-normality and non constant variance assumption, quantile regression is a better candidate for the estimation of the derivative.
Retro-regression--another important multivariate regression improvement.
Randić, M
2001-01-01
We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA. PMID:11410035
Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models
ERIC Educational Resources Information Center
Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung
2015-01-01
Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…
The Role of Prostaglandins and COX-Enzymes in Chondrogenic Differentiation of ATDC5 Progenitor Cells
Caron, Marjolein M. J.; Emans, Pieter J.; Sanen, Kathleen; Surtel, Don A. M.; Cremers, Andy; Ophelders, Daan; van Rhijn, Lodewijk W.; Welting, Tim J. M.
2016-01-01
Objectives NSAIDs are used to relieve pain and decrease inflammation by inhibition of cyclooxygenase (COX)-catalyzed prostaglandin (PG) synthesis. PGs are fatty acid mediators involved in cartilage homeostasis, however the action of their synthesizing COX-enzymes in cartilage differentiation is not well understood. In this study we hypothesized that COX-1 and COX-2 have differential roles in chondrogenic differentiation. Methods ATDC5 cells were differentiated in the presence of COX-1 (SC-560, Mofezolac) or COX-2 (NS398, Celecoxib) specific inhibitors. Specificity of the NSAIDs and inhibition of specific prostaglandin levels were determined by EIA. Prostaglandins were added during the differentiation process. Chondrogenic outcome was determined by gene- and protein expression analyses. Results Inhibition of COX-1 prevented Col2a1 and Col10a1 expression. Inhibition of COX-2 resulted in decreased Col10a1 expression, while Col2a1 remained unaffected. To explain this difference expression patterns of both COX-enzymes as well as specific prostaglandin concentrations were determined. Both COX-enzymes are upregulated during late chondrogenic differentiation, whereas only COX-2 is briefly expressed also early in differentiation. PGD2 and PGE2 followed the COX-2 expression pattern, whereas PGF2α and TXA2 levels remained low. Furthermore, COX inhibition resulted in decreased levels of all tested PGs, except for PGD2 and PGF2α in the COX-1 inhibited condition. Addition of PGE2 and PGF2α resulted in increased expression of chondrogenic markers, whereas TXA2 increased expression of hypertrophic markers. Conclusions Our findings point towards a differential role for COX-enzymes and PG-production in chondrogenic differentiation of ATDC5 cells. Ongoing research is focusing on further elucidating the functional partition of cyclooxygenases and specific prostaglandin production. PMID:27050768
Davis, Tyler; LaRocque, Karen F; Mumford, Jeanette A; Norman, Kenneth A; Wagner, Anthony D; Poldrack, Russell A
2014-08-15
Multi-voxel pattern analysis (MVPA) has led to major changes in how fMRI data are analyzed and interpreted. Many studies now report both MVPA results and results from standard univariate voxel-wise analysis, often with the goal of drawing different conclusions from each. Because MVPA results can be sensitive to latent multidimensional representations and processes whereas univariate voxel-wise analysis cannot, one conclusion that is often drawn when MVPA and univariate results differ is that the activation patterns underlying MVPA results contain a multidimensional code. In the current study, we conducted simulations to formally test this assumption. Our findings reveal that MVPA tests are sensitive to the magnitude of voxel-level variability in the effect of a condition within subjects, even when the same linear relationship is coded in all voxels. We also find that MVPA is insensitive to subject-level variability in mean activation across an ROI, which is the primary variance component of interest in many standard univariate tests. Together, these results illustrate that differences between MVPA and univariate tests do not afford conclusions about the nature or dimensionality of the neural code. Instead, targeted tests of the informational content and/or dimensionality of activation patterns are critical for drawing strong conclusions about the representational codes that are indicated by significant MVPA results. PMID:24768930
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
Bootstrapping a change-point Cox model for survival data.
Xu, Gongjun; Sen, Bodhisattva; Ying, Zhiliang
2014-08-20
This paper investigates the (in)-consistency of various bootstrap methods for making inference on a change-point in time in the Cox model with right censored survival data. A criterion is established for the consistency of any bootstrap method. It is shown that the usual nonparametric bootstrap is inconsistent for the maximum partial likelihood estimation of the change-point. A new model-based bootstrap approach is proposed and its consistency established. Simulation studies are carried out to assess the performance of various bootstrap schemes. PMID:25400719
Room temperature ferromagnetic (Fe₁-xCox)₃BO₅ nanorods.
He, Shuli; Zhang, Hongwang; Xing, Hui; Li, Kai; Cui, Hongfei; Yang, Chenguang; Sun, Shouheng; Zeng, Hao
2014-07-01
Cobalt-doped ferroferriborate ((Fe1-xCox)3BO5) nanorods (NRs) are synthesized by a one-pot high-temperature organic-solution-phase method. The aspect ratios of the NRs are tuned by the heating rate. These NRs form via anisotropic growth along twin boundaries of the multiply twinned nuclei. Magnetic properties are dramatically modified by Co substitutional doping, changing from antiferromagnetic order at low temperatures to ferromagnetic above room temperature, with a greatly enhanced magnetic ordering temperature. These anisotropic ferromagnetic NRs with a high ordering temperature may provide a new platform for understanding nanomagnetism and for magnetic applications. PMID:24905634
Electron paramagnetic resonance in Zn1-xCoxO
NASA Astrophysics Data System (ADS)
Acosta-Humánez, F.; Cogollo Pitalúa, R.; Almanza, O.
2013-03-01
In this paper is reported the Electron Paramagnetic Resonance (EPR) studies in Zn1-xCoxO powder, with 0.01≤x≤0.05, at many temperatures (105-250 K). These samples were synthesized by the sol-gel method (citrate route). Results suggest that the ferromagnetism behavior of the materials is governed by ferromagnetic coupling among cobalt ions. For cobalt concentration higher than 3% were obtained mean size particle higher than 25 nm, measured by X-ray diffraction, and for this were also observed shallow free radical.
Quality Reporting of Multivariable Regression Models in Observational Studies
Real, Jordi; Forné, Carles; Roso-Llorach, Albert; Martínez-Sánchez, Jose M.
2016-01-01
Abstract Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly used MRMs (logistic, linear, and Cox regression) that were applied in analytical observational studies published between 2003 and 2014 by journals indexed in MEDLINE. Review of a representative sample of articles indexed in MEDLINE (n = 428) with observational design and use of MRMs (logistic, linear, and Cox regression). We assessed the quality of reporting about: model assumptions and goodness-of-fit, interactions, sensitivity analysis, crude and adjusted effect estimate, and specification of more than 1 adjusted model. The tests of underlying assumptions or goodness-of-fit of the MRMs used were described in 26.2% (95% CI: 22.0–30.3) of the articles and 18.5% (95% CI: 14.8–22.1) reported the interaction analysis. Reporting of all items assessed was higher in articles published in journals with a higher impact factor. A low percentage of articles indexed in MEDLINE that used multivariable techniques provided information demonstrating rigorous application of the model selected as an adjustment method. Given the importance of these methods to the final results and conclusions of observational studies, greater rigor is required in reporting the use of MRMs in the scientific literature. PMID:27196467
Cox26 is a novel stoichiometric subunit of the yeast cytochrome c oxidase.
Levchenko, Maria; Wuttke, Jan-Moritz; Römpler, Katharina; Schmidt, Bernhard; Neifer, Klaus; Juris, Lisa; Wissel, Mirjam; Rehling, Peter; Deckers, Markus
2016-07-01
The cytochrome c oxidase (COX) is the terminal enzyme of the respiratory chain. The complex accepts electrons from cytochrome c and passes them onto molecular oxygen. This process contributes to energy capture in the form of a membrane potential across the inner membrane. The enzyme complex assembles in a stepwise process from the three mitochondria-encoded core subunits Cox1, Cox2 and Cox3, which associate with nuclear-encoded subunits and cofactors. In the yeast Saccharomyces cerevisiae, the cytochrome c oxidase associates with the bc1-complex into supercomplexes, allowing efficient energy transduction. Here we report on Cox26 as a protein found in respiratory chain supercomplexes containing cytochrome c oxidase. Our analyses reveal Cox26 as a novel stoichiometric structural subunit of the cytochrome c oxidase. A loss of Cox26 affects cytochrome c oxidase activity and respirasome organization. PMID:27083394
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…
Ecological Regression and Voting Rights.
ERIC Educational Resources Information Center
Freedman, David A.; And Others
1991-01-01
The use of ecological regression in voting rights cases is discussed in the context of a lawsuit against Los Angeles County (California) in 1990. Ecological regression assumes that systematic voting differences between precincts are explained by ethnic differences. An alternative neighborhood model is shown to lead to different conclusions. (SLD)
Logistic Regression: Concept and Application
ERIC Educational Resources Information Center
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Yang, Zetian; Zhen, Zonglei; Huang, Lijie; Kong, Xiang-zhen; Wang, Xu; Song, Yiying; Liu, Jia
2016-01-01
Faces contain a variety of information such as one’s identity and expression. One prevailing model suggests a functional division of labor in processing faces that different aspects of facial information are processed in anatomically separated and functionally encapsulated brain regions. Here, we demonstrate that facial identity and expression can be processed in the same region, yet with different neural coding strategies. To this end, we employed functional magnetic resonance imaging to examine two types of coding schemes, namely univariate activity and multivariate pattern, in the posterior superior temporal cortex (pSTS) - a face-selective region that is traditionally viewed as being specialized for processing facial expression. With the individual difference approach, we found that participants with higher overall face selectivity in the right pSTS were better at differentiating facial expressions measured outside of the scanner. In contrast, individuals whose spatial pattern for faces in the right pSTS was less similar to that for objects were more accurate in identifying previously presented faces. The double dissociation of behavioral relevance between overall neural activity and spatial neural pattern suggests that the functional-division-of-labor model on face processing is over-simplified, and that coding strategies shall be incorporated in a revised model. PMID:26997104
Yang, Zetian; Zhen, Zonglei; Huang, Lijie; Kong, Xiang-zhen; Wang, Xu; Song, Yiying; Liu, Jia
2016-01-01
Faces contain a variety of information such as one's identity and expression. One prevailing model suggests a functional division of labor in processing faces that different aspects of facial information are processed in anatomically separated and functionally encapsulated brain regions. Here, we demonstrate that facial identity and expression can be processed in the same region, yet with different neural coding strategies. To this end, we employed functional magnetic resonance imaging to examine two types of coding schemes, namely univariate activity and multivariate pattern, in the posterior superior temporal cortex (pSTS) - a face-selective region that is traditionally viewed as being specialized for processing facial expression. With the individual difference approach, we found that participants with higher overall face selectivity in the right pSTS were better at differentiating facial expressions measured outside of the scanner. In contrast, individuals whose spatial pattern for faces in the right pSTS was less similar to that for objects were more accurate in identifying previously presented faces. The double dissociation of behavioral relevance between overall neural activity and spatial neural pattern suggests that the functional-division-of-labor model on face processing is over-simplified, and that coding strategies shall be incorporated in a revised model. PMID:26997104
[Regression grading in gastrointestinal tumors].
Tischoff, I; Tannapfel, A
2012-02-01
Preoperative neoadjuvant chemoradiation therapy is a well-established and essential part of the interdisciplinary treatment of gastrointestinal tumors. Neoadjuvant treatment leads to regressive changes in tumors. To evaluate the histological tumor response different scoring systems describing regressive changes are used and known as tumor regression grading. Tumor regression grading is usually based on the presence of residual vital tumor cells in proportion to the total tumor size. Currently, no nationally or internationally accepted grading systems exist. In general, common guidelines should be used in the pathohistological diagnostics of tumors after neoadjuvant therapy. In particularly, the standard tumor grading will be replaced by tumor regression grading. Furthermore, tumors after neoadjuvant treatment are marked with the prefix "y" in the TNM classification. PMID:22293790
Fungible weights in logistic regression.
Jones, Jeff A; Waller, Niels G
2016-06-01
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record PMID:26651981
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.
Brochhausen, Christoph; Zehbe, Rolf; Gross, Ulrich; Libera, Jeanette; Schubert, Helmut; Nüsing, Rolf M; Klaus, Günter; Kirkpatrick, C James
2008-01-01
Tissue engineering of articular cartilage remains an ongoing challenge. Since tissue regeneration recapitulates ontogenetic processes the growth plate can be regarded as an innovative model to target suitable signalling molecules and growth factors for the tissue engineering of cartilage. In the present study we analysed the expression of cyclooxygenases (COX) in a short-term chondrocyte culture in gelatin-based scaffolds and in articular cartilage of rats and compared it with that in the growth plate. Our results demonstrate the strong cellular expression of COX-1 but only a focal weak expression of COX-2 in the seeded scaffolds. Articular cartilage of rats expresses homogeneously COX-1 and COX-2 with the exception of the apical cell layer. Our findings indicate a functional role of COX in the metabolism of articular chondrocytes. The expression of COX in articular cartilage and in the seeded scaffolds opens interesting perspectives to improve the proliferation and differentiation of chondrocytes in scaffold materials by addition of specific receptor ligands of the COX system. PMID:18198403
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
Splines for Diffeomorphic Image Regression
Singh, Nikhil; Niethammer, Marc
2016-01-01
This paper develops a method for splines on diffeomorphisms for image regression. In contrast to previously proposed methods to capture image changes over time, such as geodesic regression, the method can capture more complex spatio-temporal deformations. In particular, it is a first step towards capturing periodic motions for example of the heart or the lung. Starting from a variational formulation of splines the proposed approach allows for the use of temporal control points to control spline behavior. This necessitates the development of a shooting formulation for splines. Experimental results are shown for synthetic and real data. The performance of the method is compared to geodesic regression. PMID:25485370
Practical Session: Simple Linear Regression
NASA Astrophysics Data System (ADS)
Clausel, M.; Grégoire, G.
2014-12-01
Two exercises are proposed to illustrate the simple linear regression. The first one is based on the famous Galton's data set on heredity. We use the lm R command and get coefficients estimates, standard error of the error, R2, residuals …In the second example, devoted to data related to the vapor tension of mercury, we fit a simple linear regression, predict values, and anticipate on multiple linear regression. This pratical session is an excerpt from practical exercises proposed by A. Dalalyan at EPNC (see Exercises 1 and 2 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_4.pdf).
The cytochrome c oxidase biogenesis factor AtCOX17 modulates stress responses in Arabidopsis.
Garcia, Lucila; Welchen, Elina; Gey, Uta; Arce, Agustín L; Steinebrunner, Iris; Gonzalez, Daniel H
2016-03-01
COX17 is a soluble protein from the mitochondrial intermembrane space that participates in the transfer of copper for cytochrome c oxidase (COX) assembly in eukaryotic organisms. In this work, we studied the function of both Arabidopsis thaliana AtCOX17 genes using plants with altered expression levels of these genes. Silencing of AtCOX17-1 in a cox17-2 knockout background generates plants with smaller rosettes and decreased expression of genes involved in the response of plants to different stress conditions, including several genes that are induced by mitochondrial dysfunctions. Silencing of either of the AtCOX17 genes does not affect plant development or COX activity but causes a decrease in the response of genes to salt stress. In addition, these plants contain higher reactive oxygen and lipid peroxidation levels after irrigation with high NaCl concentrations and are less sensitive to abscisic acid. In agreement with a role of AtCOX17 in stress and abscisic acid responses, both AtCOX17 genes are induced by several stress conditions, abscisic acid and mutation of the transcription factor ABI4. The results indicate that AtCOX17 is required for optimal expression of a group of stress-responsive genes, probably as a component of signalling pathways that link stress conditions to gene expression responses. PMID:26436309
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
Regional Differences in the Neuronal Expression of Cyclooxygenase-2 (COX-2) in the Newborn Pig Brain
Oláh, Orsolya; Németh, István; Tóth-Szűki, Valéria; Bari, Ferenc; Domoki, Ferenc
2012-01-01
Cyclooxygenase (COX)-2 is the major constitutively expressed COX isoform in the newborn brain. COX-2 derived prostanoids and reactive oxygen species appear to play a major role in the mechanism of perinatal hypoxic-ischemic injury in the newborn piglet, an accepted animal model of the human term neonate. The study aimed to quantitatively determine COX-2 immunopositive neurons in different brain regions in piglets under normoxic conditions (n=15), and 4 hours after 10 min asphyxia (n=11). Asphyxia did not induce significant changes in neuronal COX-2 expression of any studied brain areas. In contrast, there was a marked regional difference in all experimental groups. Thus, significant difference was observed between fronto-parietal and temporo-occipital regions: 59±4% and 67±3% versus 41±2%* and 31±3%* respectively (mean±SEM, data are pooled from all subjects, n=26, *p<0.05, vs. fronto-parietal region). In the hippocampus, COX-2 immunopositivity was rare (highest expression in CA1 region: 14±2%). The studied subcortical areas showed negligible COX-2 staining. Our findings suggest that asphyxia does not significantly alter the pattern of neuronal COX-2 expression in the early reventilation period. Furthermore, based on the striking differences observed in cortical neuronal COX-2 distribution, the contribution of COX-2 mediated neuronal injury after asphyxia may also show region-specific differences. PMID:22829712
COX-2 inhibitor as a radiation enhancer: new strategies for the treatment of lung cancer.
Saha, Debabrata; Pyo, Hongryull; Choy, Hak
2003-08-01
Lung cancer is one of the most common causes of cancer-related mortality throughout the world, and the incidence continues to increase. Smoking is the number one cause of lung cancer. Emerging data have implicated cyclooxygenase-2 (COX-2) and prostanoid production in the pathogenesis of lung carcinoma. In invasive lung tumors, COX-2 upregulation has been reported in up to 90% of cases. COX-2 upregulation is an early event in the development of non-small-cell lung cancer and may be integral to the development of new blood vessels and production of specific proteases that are critical to growth and spread of lung malignancies. COX-2 inhibitors are known to enhance the chemosensitivity in COX-2 overexpressing lung cancer cell lines. Recently, we have demonstrated that selective COX-2 inhibitors also enhance the effect of radiation in COX-2 overexpressed cells. Therefore, inhibitors of COX-2 in combination with chemoradiation therapy may be an alternative strategy that can be tested in clinical trials. The combination of COX-2 inhibitors and radiation suggest a complementary strategy to target angiogenesis while potentially minimizing the impact on quality of life. Currently, several groups are conducting clinical trials in cervix cancer, lung cancer, and brain tumors, using inhibitors of COX-2 in combination with chemotherapy and radiation therapy. These clinical trials will help to elucidate the role of this interesting class. PMID:12902860
Characterization of the Cytochrome C Oxidase Assembly Factor Cox19 of 'Saccharomyces Cerevisiae'
Rigby, K.; Zhang, L.; Cobine, P.A.; George, G.N.; Winge, D.R.; /Utah U. /Saskatchewan U.
2007-07-12
Cox19 is an important accessory protein in the assembly of cytochrome c oxidase in yeast. The protein is functional when tethered to the mitochondrial inner membrane, suggesting its functional role within the intermembrane space. Cox19 resembles Cox17 in having a twin CX{sub 9}C sequence motif that adopts a helical hairpin in Cox17. The function of Cox17 appears to be a Cu(I) donor protein in the assembly of the copper centers in cytochrome c oxidase. Cox19 also resembles Cox17 in its ability to coordinate Cu(I). Recombinant Cox19 binds 1 mol eq of Cu(I) per monomer and exists as a dimeric protein. Cox19 isolated from the mitochondrial intermembrane space contains variable quantities of copper, suggesting that Cu(I) binding may be a transient property. Cysteinyl residues important for Cu(I) binding are also shown to be important for the in vivo function of Cox19. Thus, a correlation exists in the ability to bind Cu(I) and in vivo function.
Ku80 cooperates with CBP to promote COX-2 expression and tumor growth
Qin, Yu; Xuan, Yang; Jia, Yunlu; Hu, Wenxian; Yu, Wendan; Dai, Meng; Li, Zhenglin; Yi, Canhui; Zhao, Shilei; Li, Mei; Du, Sha; Cheng, Wei; Xiao, Xiangsheng; Chen, Yiming; Wu, Taihua; Meng, Songshu; Yuan, Yuhui; Liu, Quentin; Huang, Wenlin; Guo, Wei; Wang, Shusen; Deng, Wuguo
2015-01-01
Cyclooxygenase-2 (COX-2) plays an important role in lung cancer development and progression. Using streptavidin-agarose pulldown and proteomics assay, we identified and validated Ku80, a dimer of Ku participating in the repair of broken DNA double strands, as a new binding protein of the COX-2 gene promoter. Overexpression of Ku80 up-regulated COX-2 promoter activation and COX-2 expression in lung cancer cells. Silencing of Ku80 by siRNA down-regulated COX-2 expression and inhibited tumor cell growth in vitro and in a xenograft mouse model. Ku80 knockdown suppressed phosphorylation of ERK, resulting in an inactivation of the MAPK pathway. Moreover, CBP, a transcription co-activator, interacted with and acetylated Ku80 to co-regulate the activation of COX-2 promoter. Overexpression of CBP increased Ku80 acetylation, thereby promoting COX-2 expression and cell growth. Suppression of CBP by a CBP-specific inhibitor or siRNA inhibited COX-2 expression as well as tumor cell growth. Tissue microarray immunohistochemical analysis of lung adenocarcinomas revealed a strong positive correlation between levels of Ku80 and COX-2 and clinicopathologic variables. Overexpression of Ku80 was associated with poor prognosis in patients with lung cancers. We conclude that Ku80 promotes COX-2 expression and tumor growth and is a potential therapeutic target in lung cancer. PMID:25797267
Theilig, F; Debiec, H; Nafz, B; Ronco, P; Nüsing, R; Seyberth, H W; Pavenstädt, H; Bouby, N; Bachmann, S
2006-11-01
Renal volume regulation is modulated by the action of cyclooxygenases (COX) and the resulting generation of prostanoids. Epithelial expression of COX isoforms in the cortex directs COX-1 to the distal convolutions and cortical collecting duct, and COX-2 to the thick ascending limb. Partly colocalized are prostaglandin E synthase (PGES), the downstream enzyme for renal prostaglandin E(2) (PGE(2)) generation, and the EP receptors type 1 and 3. COX-1 and related components were studied in two kidney-one clip (2K1C) Goldblatt hypertensive rats with combined chronic ANG II or bradykinin B(2) receptor blockade using candesartan (cand) or the B(2) antagonist Hoechst 140 (Hoe). Rats (untreated sham, 2K1C, sham + cand, 2K1C + cand, sham + Hoe, 2K1C + Hoe) were treated to map expression of parameters controlling PGE(2) synthesis. In 2K1C, cortical COX isoforms did not change uniformly. COX-2 changed in parallel with NO synthase 1 (NOS1) expression with a raise in the clipped, but a decrease in the nonclipped side. By contrast, COX-1 and PGES were uniformly downregulated in both kidneys, along with reduced urinary PGE(2) levels, and showed no clear relations with the NO status. ANG II receptor blockade confirmed negative regulation of COX-2 by ANG II but blunted the decrease in COX-1 selectively in nonclipped kidneys. B(2) receptor blockade reduced COX-2 induction in 2K1C but had no clear effect on COX-1. We suggest that in 2K1C, COX-1 and PGES expression may fail to oppose the effects of renovascular hypertension through reduced prostaglandin signaling in late distal tubule and cortical collecting duct. PMID:16788145
NASA Astrophysics Data System (ADS)
Ilgen, Elke; Levsen, Karsten; Angerer, Jürgen; Schneider, Peter; Heinrich, Joachim; Wichmann, H.-Erich
The concentrations of the aromatic hydrocarbons benzene, toluene, ethylbenzene and the isomeric xylenes (BTEX) have been determined in the indoor air of 115 private non-smoker homes (˜380 individual rooms) situated in areas with an extreme traffic situation, i.e. in city streets (street canyons) with high traffic density and in rural areas with hardly any traffic at all. The influence of the traffic on the indoor concentration was apparent in the high traffic area. In order to identify other factors influencing the BTEX concentrations, the data and additional questionnaires were analyzed by univariate and multivariate analysis. The analysis was supplemented by some case studies. It is shown that meteorology (the seasons), the type of room (e.g. living room versus bedroom), the ventilation and, in particular, garages in the house strongly influence the indoor concentration of BTEX. Thus, the indoor BTEX level is significantly higher in winter than in summer. Moreover, garages with a connecting door to the living quarters lead to high indoor concentrations of aromatic hydrocarbons in these rooms. In addition, the storage of solvents and hobby materials, and also the presence of smoking guests increase the BTEX level. If rooms are directly heated by coal or wood, the BTEX level is higher compared to the use of gas heating. Surprisingly, no correlation was found between the building materials used and the BTEX level. Case studies were carried out for two homes with an integrated garage (and a connecting door to the living rooms) and for seven homes where redecoration work was carried out during sampling. In both instances, a pronounced increase was observed in the BTEX concentration.
Multiple Regression and Its Discontents
ERIC Educational Resources Information Center
Snell, Joel C.; Marsh, Mitchell
2012-01-01
Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.
Abstract Expression Grammar Symbolic Regression
NASA Astrophysics Data System (ADS)
Korns, Michael F.
This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.
Time-Warped Geodesic Regression
Hong, Yi; Singh, Nikhil; Kwitt, Roland; Niethammer, Marc
2016-01-01
We consider geodesic regression with parametric time-warps. This allows, for example, to capture saturation effects as typically observed during brain development or degeneration. While highly-flexible models to analyze time-varying image and shape data based on generalizations of splines and polynomials have been proposed recently, they come at the cost of substantially more complex inference. Our focus in this paper is therefore to keep the model and its inference as simple as possible while allowing to capture expected biological variation. We demonstrate that by augmenting geodesic regression with parametric time-warp functions, we can achieve comparable flexibility to more complex models while retaining model simplicity. In addition, the time-warp parameters provide useful information of underlying anatomical changes as demonstrated for the analysis of corpora callosa and rat calvariae. We exemplify our strategy for shape regression on the Grassmann manifold, but note that the method is generally applicable for time-warped geodesic regression. PMID:25485368
Wrong Signs in Regression Coefficients
NASA Technical Reports Server (NTRS)
McGee, Holly
1999-01-01
When using parametric cost estimation, it is important to note the possibility of the regression coefficients having the wrong sign. A wrong sign is defined as a sign on the regression coefficient opposite to the researcher's intuition and experience. Some possible causes for the wrong sign discussed in this paper are a small range of x's, leverage points, missing variables, multicollinearity, and computational error. Additionally, techniques for determining the cause of the wrong sign are given.
Basis Selection for Wavelet Regression
NASA Technical Reports Server (NTRS)
Wheeler, Kevin R.; Lau, Sonie (Technical Monitor)
1998-01-01
A wavelet basis selection procedure is presented for wavelet regression. Both the basis and the threshold are selected using cross-validation. The method includes the capability of incorporating prior knowledge on the smoothness (or shape of the basis functions) into the basis selection procedure. The results of the method are demonstrated on sampled functions widely used in the wavelet regression literature. The results of the method are contrasted with other published methods.
Regression methods for spatial data
NASA Technical Reports Server (NTRS)
Yakowitz, S. J.; Szidarovszky, F.
1982-01-01
The kriging approach, a parametric regression method used by hydrologists and mining engineers, among others also provides an error estimate the integral of the regression function. The kriging method is explored and some of its statistical characteristics are described. The Watson method and theory are extended so that the kriging features are displayed. Theoretical and computational comparisons of the kriging and Watson approaches are offered.
Krawczyk, Michal; Emerson, Beverly M
2014-01-01
Deregulated expression of COX-2 has been causally linked to development, progression, and outcome of several types of human cancer. We describe a novel fundamental level of transcriptional control of COX-2 expression. Using primary human mammary epithelial cells and monocyte/macrophage cell lines, we show that the chromatin boundary/insulator factor CTCF establishes an open chromatin domain and induces expression of a long non-coding RNA within the upstream promoter region of COX-2. Upon induction of COX-2 expression, the lncRNA associates with p50, a repressive subunit of NF-κB, and occludes it from the COX-2 promoter, potentially facilitating interaction with activation-competent NF-κB p65/p50 dimers. This enables recruitment of the p300 histone acetyltransferase, a domain-wide increase in histone acetylation and assembly of RNA Polymerase II initiation complexes. Our findings reveal an unexpected mechanism of gene control by lncRNA-mediated repressor occlusion and identify the COX-2-lncRNA, PACER, as a new potential target for COX-2-modulation in inflammation and cancer. DOI: http://dx.doi.org/10.7554/eLife.01776.001 PMID:24843008
Gaussian estimation for discretely observed Cox-Ingersoll-Ross model
NASA Astrophysics Data System (ADS)
Wei, Chao; Shu, Huisheng; Liu, Yurong
2016-07-01
This paper is concerned with the parameter estimation problem for Cox-Ingersoll-Ross model based on discrete observation. First, a new discretized process is built based on the Euler-Maruyama scheme. Then, the parameter estimators are obtained by employing the maximum likelihood method and the explicit expressions of the error of estimation are given. Subsequently, the consistency property of all parameter estimators are proved by applying the law of large numbers for martingales, Holder's inequality, B-D-G inequality and Cauchy-Schwarz inequality. Finally, a numerical simulation example for estimators and the absolute error between estimators and true values is presented to demonstrate the effectiveness of the estimation approach used in this paper.