CPDs and 6-4PPs play different roles in UV-induced cell death in normal and NER-deficient human cells

Ultraviolet (UV) light generates two major DNA lesions: cyclobutane pyrimidine dimers (CPDs) and pyrimidine-(6-4)-pyrimidone photoproducts (6-4PPs), but the specific participation of these two lesions in the deleterious effects of UV is a longstanding question. In order to discriminate the precise role of unrepaired CPDs and 6-4PPs in UV-induced responses triggering cell death, human fibroblasts were transduced by recombinant adenoviruses carrying the CPD-photolyase or 6-4PP-photolyase cDNAs. Both photolyases were able to prevent UV-induced apoptosis in cells deficient for nucleotide excision repair (NER) to a similar extent, while in NER-proficient cells UV-induced apoptosis was prevented only by CPD-photolyase, with no effects observed when 6-4PPs were removed by the specific photolyase. These results strongly suggest that both CPDs and 6-4PPs contribute to UV-induced apoptosis in NER-deficient cells, while in NER-proficient cells, CPDs are the only lesions responsible for UV-killing, probably due to the rapid repair of 6-4PPs by NER. As a consequence, the difference in skin photosensitivity, including carcinogenesis, of most of the xeroderma pigmentosum patients and of normal people is probably not only a quantitative aspect, but depends on the type of DNA damage induced by sunlight and its rate of repair. (c) 2007 Elsevier B.V. All rights reserved.

Essential role of nuclear factor-kappa B for NADPH oxidase activity in normal and anhildrotic ectodermal dysplasia leukocytes

This work investigated the functional role of nuclear factor-kappa B (NF-kappa B) in respiratory burst activity and in expression of the human phagocyte nicotinamide adenine dinucleotide phosphate (NADPH) oxidase genes CYBB, CYBA, NCF1, and NCF2. U937 cells with a stably transfected repressor of NF-kappa B (IKB alpha-S32A/S36A) demonstrated significantly lower superoxide release and lower CYBB and NCF1 gene expression compared with control U937 cells. We further tested Epstein-Barr virus (EBV)-transformed B cells from patients with anhidrotic ectodermal dysplasia with immunodeficiency (EDA-ID), an inherited disorderof NF-kappa B function. Superoxide release and CYBB gene expression by EDA-ID cells were significantly decreased compared with healthy cells and similar to cells from patients with X-linked chronic granulomatous disease (X91 degrees CGD). NCF1 gene expression in EDA-ID S321 cells was decreased compared with healthy control cells and similar to that in autosomal recessive (A47 degrees) CGD cells. Gel shift assays demonstrated loss of recombinant human p50 binding to a NF-kappa B site 5` to the CYBB gene in U937 cells treated with NF-kappa B inhibitors, repressor-transfected U937 cells, and EDA-ID patients cells. Zymosan phagocytosis was not affected by transfection of U937 cells with the NF-kappa B repressor. These studies show that NF-kappa B is necessary for CYBB and NCF1 gene expression and activation of the phagocyte NADPH oxidase in this model system.

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.

A nonlinear regression model with skew-normal errors

In this paper we have discussed inference aspects of the skew-normal nonlinear regression models following both, a classical and Bayesian approach, extending the usual normal nonlinear regression models. The univariate skew-normal distribution that will be used in this work was introduced by Sahu et al. (Can J Stat 29:129-150, 2003), which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini (Scand J Stat 12:171-178, 1985) one and, moreover, it allows easy implementation of the EM-algorithm. As illustration of the proposed methodology, we consider a data set previously analyzed in the literature under normality.

The competing roles of extensional viscosity and normal stress differences in complex flows of elastic liquids

In various attempts to relate the behaviour of highly-elastic liquids in complex flows to their rheometrical behaviour, obvious candidates for study have been the variation of shear viscosity with shear rate, the two normal stress differences N(1) and N(2) especially N(1), and the extensional viscosity eta(E). In this paper, we shall be mainly interested in `constant-viscosity` Boger fluids, and, accordingly, we shall limit attention to N(1) and eta(E). We shall concentrate on two important flows - axisymmetric contraction flow and ""splashing"" (particularly that which arises when a liquid drop falls onto the free Surface of the same liquid). Modem numerical techniques are employed to provide the theoretical predictions. It is shown that the two obvious manifestations of viscoelastic rheometrical behaviour can sometimes be opposing influences in determining flow characteristics. Specifically, in an axisymmetric contraction flow, high eta(E) , can retard the flow, whereas high N(1) can have the opposite effect. In the splashing experiment, high eta(E) can certainly reduce the height of the so-called Worthington jet, thus confirming some early suggestions, but, again, other rheometrical influences can also have a role to play and the overall picture may not be as clear as it was once envisaged.

Robust linear mixed models with skew-normal independent distributions from a Bayesian perspective

Linear mixed models were developed to handle clustered data and have been a topic of increasing interest in statistics for the past 50 years. Generally. the normality (or symmetry) of the random effects is a common assumption in linear mixed models but it may, sometimes, be unrealistic, obscuring important features of among-subjects variation. In this article, we utilize skew-normal/independent distributions as a tool for robust modeling of linear mixed models under a Bayesian paradigm. The skew-normal/independent distributions is an attractive class of asymmetric heavy-tailed distributions that includes the skew-normal distribution, skew-t, skew-slash and the skew-contaminated normal distributions as special cases, providing an appealing robust alternative to the routine use of symmetric distributions in this type of models. The methods developed are illustrated using a real data set from Framingham cholesterol study. (C) 2009 Elsevier B.V. All rights reserved.

Conformal deformation of equilibrium measures in normal random ensembles

We investigate the eigenvalue statistics of ensembles of normal random matrices when their order N tends to infinite. In the model, the eigenvalues have uniform density within a region determined by a simple analytic polynomial curve. We study the conformal deformations of equilibrium measures of normal random ensembles to the real line and give sufficient conditions for it to weakly converge to a Wigner measure.

Inference and local influence assessment in skew-normal null intercept measurement error model

In this article, we discuss inferential aspects of the measurement error regression models with null intercepts when the unknown quantity x (latent variable) follows a skew normal distribution. We examine first the maximum-likelihood approach to estimation via the EM algorithm by exploring statistical properties of the model considered. Then, the marginal likelihood, the score function and the observed information matrix of the observed quantities are presented allowing direct inference implementation. In order to discuss some diagnostics techniques in this type of models, we derive the appropriate matrices to assessing the local influence on the parameter estimates under different perturbation schemes. The results and methods developed in this paper are illustrated considering part of a real data set used by Hadgu and Koch [1999, Application of generalized estimating equations to a dental randomized clinical trial. Journal of Biopharmaceutical Statistics, 9, 161-178].

Bimodal extension based on the skew-normal distribution with application to pollen data

This paper considers an extension to the skew-normal model through the inclusion of an additional parameter which can lead to both uni- and bi-modal distributions. The paper presents various basic properties of this family of distributions and provides a stochastic representation which is useful for obtaining theoretical properties and to simulate from the distribution. Moreover, the singularity of the Fisher information matrix is investigated and maximum likelihood estimation for a random sample with no covariates is considered. The main motivation is thus to avoid using mixtures in fitting bimodal data as these are well known to be complicated to deal with, particularly because of identifiability problems. Data-based illustrations show that such model can be useful. Copyright (C) 2009 John Wiley & Sons, Ltd.

Bayesian inference for a skew-normal IRT model under the centred parameterization

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is interest in studying latent variables. These latent variables are directly considered in the Item Response Models (IRM) and they are usually called latent traits. A usual assumption for parameter estimation of the IRM, considering one group of examinees, is to assume that the latent traits are random variables which follow a standard normal distribution. However, many works suggest that this assumption does not apply in many cases. Furthermore, when this assumption does not hold, the parameter estimates tend to be biased and misleading inference can be obtained. Therefore, it is important to model the distribution of the latent traits properly. In this paper we present an alternative latent traits modeling based on the so-called skew-normal distribution; see Genton (2004). We used the centred parameterization, which was proposed by Azzalini (1985). This approach ensures the model identifiability as pointed out by Azevedo et al. (2009b). Also, a Metropolis Hastings within Gibbs sampling (MHWGS) algorithm was built for parameter estimation by using an augmented data approach. A simulation study was performed in order to assess the parameter recovery in the proposed model and the estimation method, and the effect of the asymmetry level of the latent traits distribution on the parameter estimation. Also, a comparison of our approach with other estimation methods (which consider the assumption of symmetric normality for the latent traits distribution) was considered. The results indicated that our proposed algorithm recovers properly all parameters. Specifically, the greater the asymmetry level, the better the performance of our approach compared with other approaches, mainly in the presence of small sample sizes (number of examinees). Furthermore, we analyzed a real data set which presents indication of asymmetry concerning the latent traits distribution. The results obtained by using our approach confirmed the presence of strong negative asymmetry of the latent traits distribution. (C) 2010 Elsevier B.V. All rights reserved.

On the Skew-Normal-Cauchy Distribution

In this article, we study further properties of a skew normal distribution, called the skew-normal-Cauchy (SNC) distribution by Nadarajah and Kotz (2003). A stochastic representation is obtained which allows alternative derivations for moments, moments generating function, and skewness and kurtosis coefficients. Issues related to singularity of the Fisher information matrix are investigated.

Multivariate measurement error models based on scale mixtures of the skew-normal distribution

Scale mixtures of the skew-normal (SMSN) distribution is a class of asymmetric thick-tailed distributions that includes the skew-normal (SN) distribution as a special case. The main advantage of these classes of distributions is that they are easy to simulate and have a nice hierarchical representation facilitating easy implementation of the expectation-maximization algorithm for the maximum-likelihood estimation. In this paper, we assume an SMSN distribution for the unobserved value of the covariates and a symmetric scale mixtures of the normal distribution for the error term of the model. This provides a robust alternative to parameter estimation in multivariate measurement error models. Specific distributions examined include univariate and multivariate versions of the SN, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.

On the skew-normal calibration model

In this article, we present the EM-algorithm for performing maximum likelihood estimation of an asymmetric linear calibration model with the assumption of skew-normally distributed error. A simulation study is conducted for evaluating the performance of the calibration estimator with interpolation and extrapolation situations. As one application in a real data set, we fitted the model studied in a dimensional measurement method used for calculating the testicular volume through a caliper and its calibration by using ultrasonography as the standard method. By applying this methodology, we do not need to transform the variables to have symmetrical errors. Another interesting aspect of the approach is that the developed transformation to make the information matrix nonsingular, when the skewness parameter is near zero, leaves the parameter of interest unchanged. Model fitting is implemented and the best choice between the usual calibration model and the model proposed in this article was evaluated by developing the Akaike information criterion, Schwarz`s Bayesian information criterion and Hannan-Quinn criterion.

Analysis of Ki-67 and Bcl-2 protein expression in normal colorectal mucosa of women with breast cancer

The aim of this study was to evaluate Ki-67 and Bcl-2 protein expression in the normal colorectal mucosa adjacent to adenomatous polyps in women with breast cancer. A cross-sectional, controlled study was conducted in 35 women with and without breast cancer who had adenomatous colorectal polyps. The patients were divided into two groups: Group A (a control group of women without breast cancer, n = 18) and Group B (a study group of women with breast cancer, n = 17). A sample of normal colonic mucosa was collected at a distance of 5 cm from the polypoid lesion to evaluate immunchistochemical expression of the Ki-67 and Bcl-2 proteins. Student`s t-test and the chi-square test were used to analyse Ki-67 and Bcl-2 expression, respectively. Statistical significance was established at p < 0.05. The mean percentage of Ki-67-stained nuclei in Groups A and B was 25.12 +/- 2.08 and 41.50 +/- 1.85, respectively (p < 0.001), whereas the percentage of cases with cells expressing Bcl-2 in Groups A and B was 17.6% and 82.4%, respectively (p < 0.003). In the present study, greater proliferative activity and greater expression of the antiapoptotic protein Bcl-2 was found in the normal colorectal mucosa of women with breast cancer. (C) 2009 Elsevier Ltd. All rights reserved.

Local Influence Analysis for Skew-Normal Linear Mixed Models

In this article, we consider local influence analysis for the skew-normal linear mixed model (SN-LMM). As the observed data log-likelihood associated with the SN-LMM is intractable, Cook`s well-known approach cannot be applied to obtain measures of local influence. Instead, we develop local influence measures following the approach of Zhu and Lee (2001). This approach is based on the use of an EM-type algorithm and is measurement invariant under reparametrizations. Four specific perturbation schemes are discussed. Results obtained for a simulated data set and a real data set are reported, illustrating the usefulness of the proposed methodology.