Residuals for log-Burr XII regression models in survival analysis

In this paper, we compare three residuals to assess departures from the error assumptions as well as to detect outlying observations in log-Burr XII regression models with censored observations. These residuals can also be used for the log-logistic regression model, which is a special case of the log-Burr XII regression model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to the modified martingale-type residual in log-Burr XII regression models with censored data.

General results for the beta-modified Weibull distribution

We study in detail the so-called beta-modified Weibull distribution, motivated by the wide use of the Weibull distribution in practice, and also for the fact that the generalization provides a continuous crossover towards cases with different shapes. The new distribution is important since it contains as special sub-models some widely-known distributions, such as the generalized modified Weibull, beta Weibull, exponentiated Weibull, beta exponential, modified Weibull and Weibull distributions, among several others. It also provides more flexibility to analyse complex real data. Various mathematical properties of this distribution are derived, including its moments and moment generating function. We examine the asymptotic distributions of the extreme values. Explicit expressions are also derived for the chf, mean deviations, Bonferroni and Lorenz curves, reliability and entropies. The estimation of parameters is approached by two methods: moments and maximum likelihood. We compare by simulation the performances of the estimates from these methods. We obtain the expected information matrix. Two applications are presented to illustrate the proposed distribution.

The exponentiated generalized gamma distribution with application to lifetime data

A four-parameter extension of the generalized gamma distribution capable of modelling a bathtub-shaped hazard rate function is defined and studied. The beauty and importance of this distribution lies in its ability to model monotone and non-monotone failure rate functions, which are quite common in lifetime data analysis and reliability. The new distribution has a number of well-known lifetime special sub-models, such as the exponentiated Weibull, exponentiated generalized half-normal, exponentiated gamma and generalized Rayleigh, among others. We derive two infinite sum representations for its moments. We calculate the density of the order statistics and two expansions for their moments. The method of maximum likelihood is used for estimating the model parameters and the observed information matrix is obtained. Finally, a real data set from the medical area is analysed.

A Log-Linear Regression Model for the Beta-Weibull Distribution

We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes, and censoring percentages, several simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.

Deviance residuals in generalised log-gamma regression models with censored observations

In this article, we compare three residuals based on the deviance component in generalised log-gamma regression models with censored observations. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. For all cases studied, the empirical distributions of the proposed residuals are in general symmetric around zero, but only a martingale-type residual presented negligible kurtosis for the majority of the cases studied. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for the martingale-type residual in generalised log-gamma regression models with censored data. A lifetime data set is analysed under log-gamma regression models and a model checking based on the martingale-type residual is performed.

Assessing influence in survival data with a cure fraction and covariates

Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from error assumptions and the presence of outliers and influential observations with the fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting algorithms steps to obtain the posterior summaries of interest. Some influence methods, such as the local influence, total local influence of an individual, local influence on predictions and generalized leverage were derived, analyzed and discussed in survival data with a cure fraction and covariates. The relevance of the approach was illustrated with a real data set, where it is shown that, by removing the most influential observations, the decision about which model best fits the data is changed.

Origins of the Xylella fastidiosa Prophage-Like Regions and Their Impact in Genome Differentiation

Xylella fastidiosa is a Gram negative plant pathogen causing many economically important diseases, and analyses of completely sequenced X. fastidiosa genome strains allowed the identification of many prophage-like elements and possibly phage remnants, accounting for up to 15% of the genome composition. To better evaluate the recent evolution of the X. fastidiosa chromosome backbone among distinct pathovars, the number and location of prophage-like regions on two finished genomes (9a5c and Temecula1), and in two candidate molecules (Ann1 and Dixon) were assessed. Based on comparative best bidirectional hit analyses, the majority (51%) of the predicted genes in the X. fastidiosa prophage-like regions are related to structural phage genes belonging to the Siphoviridae family. Electron micrograph reveals the existence of putative viral particles with similar morphology to lambda phages in the bacterial cell in planta. Moreover, analysis of microarray data indicates that 9a5c strain cultivated under stress conditions presents enhanced expression of phage anti-repressor genes, suggesting switches from lysogenic to lytic cycle of phages under stress-induced situations. Furthermore, virulence-associated proteins and toxins are found within these prophage-like elements, thus suggesting an important role in host adaptation. Finally, clustering analyses of phage integrase genes based on multiple alignment patterns reveal they group in five lineages, all possessing a tyrosine recombinase catalytic domain, and phylogenetically close to other integrases found in phages that are genetic mosaics and able to perform generalized and specialized transduction. Integration sites and tRNA association is also evidenced. In summary, we present comparative and experimental evidence supporting the association and contribution of phage activity on the differentiation of Xylella genomes.

Prognostic index expression of cyclin-D1, cerbB-2 and VEGF: metastases vs corresponding primary cancers and metastatic vs non-metastatic adenocarcinomas

The prognostic relevance of different molecular markers in lung cancer is a crucial issue still worth investigating, and the specimens collected and analyzed represent a valuable source of material. Cyclin-D1, c-erbB-2 and vascular endothelial growth factor (VEGF) have shown to be promising as prognosticators in human cancer. In this study, we sought to examine the importance of Cyclin-D1, c-erbB-2 and VEGF, and to study the quantitative relationship among these factors and disease progression in metastases vs corresponding primary cancer, and metastatic vs non metastatic cancers. Material and Methods: We used immunohistochemistry and morphometric analysis to evaluate the amount of tumour staining for Cyclin-D1, c-erbB-2 and VEGF in 52 patients with surgically excised ademocarcinoma of the lung, and the outcome for our study was survival time until death from hematogenic metastases. Results: Metastasis presented lower c-erbB-2 expression than corresponding primary cancers (p=0.02). Cyclin-D1 and VEGF expression were also lower in metastases than in corresponding primary cancers, but this difference did not achieve statistical significance. Non-metastatic cancers also presented significantly lower Cyclin-D1 and c-erbB-2 expression than metastatic cancers (p<0.01 and p<0.01, respectively). Equally significant was the difference between higher c-erbB-2 expression by metastatic cancers compared to non-metastatic cancers (p=0.02). Considering survival in Kaplan-Maier analysis, Cyclin-D1 (p=0.04), c-erbB-2 (p=0.04) and VEGF (p<0.01) were important predictors of survival in metastatic cancers.

Abnormal collagen deposition in synovia after collagen type V immunization in rabbits

Sinovitis in Scleroderma (SSc) is rare, usually aggressive and fully resembles rheumatoid arthritis. Experimental models of SSc have been used in an attempt to understand its pathogenesis. Previous studies done in our laboratory had already revealed the presence of a synovial remodeling process in rabbits immunized with collagen V. To validate the importance of collagen type V and to explore the quantitative relationship between this factor and synovia remodeling as well as the relationship between collagen type V and other collagens, we studied the synovial tissue in immunized rabbits. Rabbits (N= 10) were immunized with collagen V plus Freund's adjuvant and compared with animals inoculated with adjuvant only (N= 10). Synovial tissues were submitted to histological analysis, immunolocalization to collagen I, III and V and biochemical analysis by eletrophoresis, immunoblot and densitometric method. The synovial tissue presented an intense remodeling process with deposits of collagen types I, III and V after 75 and 120 days of immunization, mainly distributed around the vessels and interstitium of synovial extracellular matrix. Densitometric analysis confirmed the increased synthesis of collagen I, III and V chains (407.69 +/- 80.31; 24.46 +/- 2.58; 70.51 +/- 7.66, respectively) in immunized rabbits when compared with animals from control group (164.91 +/- 15.67; 12.89 +/- 1.05; 32 +/- 3.57) (p<0.0001). We conclude that synovial remodeling observed in the experimental model can reflect the articular compromise present in patients with scleroderma. Certainly, this experimental model induced by collagen V immunization will bring new insights in to pathogenic mechanisms and allow the testing of new therapeutic strategies to ameliorate the prognosis for scleroderma patients.

Collagen V-induced nasal tolerance downregulates pulmonary collagen mRNA gene and TGF-beta expression in experimental systemic sclerosis

Background: The purpose of this study was to evaluate collagen deposition, mRNA collagen synthesis and TGFbeta expression in the lung tissue in an experimental model of scleroderma after collagen V-induced nasal tolerance. Methods: Female New Zealand rabbits (N = 12) were immunized with 1 mg/ml of collagen V in Freund's adjuvant (IM). After 150 days, six immunized animals were tolerated by nasal administration of collagen V ( 25 mu g/day) (IM-TOL) daily for 60 days. The collagen content was determined by morphometry, and mRNA expressions of types I, III and V collagen were determined by Real-time PCR. The TGF-beta expression was evaluated by immunostaining and quantified by point counting methods. To statistic analysis ANOVA with Bonferroni test were employed for multiple comparison when appropriate and the level of significance was determined to be p < 0.05. Results: IM-TOL, when compared to IM, showed significant reduction in total collagen content around the vessels (0.371 +/- 0.118 vs. 0.874 +/- 0.282, p < 0.001), bronchioles (0.294 +/- 0.139 vs. 0.646 +/- 0.172, p < 0.001) and in the septal interstitium (0.027 +/- 0.014 vs. 0.067 +/- 0.039, p = 0.026). The lung tissue of IM-TOL, when compared to IM, showed decreased immunostaining of types I, III and V collagen, reduced mRNA expression of types I (0.10 +/- 0.07 vs. 1.0 +/- 0.528, p = 0.002) and V (1.12 +/- 0.42 vs. 4.74 +/- 2.25, p = 0.009) collagen, in addition to decreased TGF-beta expression ( p < 0.0001). Conclusions: Collagen V-induced nasal tolerance in the experimental model of SSc regulated the pulmonary remodeling process, inhibiting collagen deposition and collagen I and V mRNA synthesis. Additionally, it decreased TGF-beta expression, suggesting a promising therapeutic option for scleroderma treatment.

Relationship between maxillary and mandibular base lengths and dental crowding in patients with complete Class II malocclusions

Objective: To verify the relationship between maxillary and mandibular effective lengths and dental crowding in patients with Class II malocclusions. Materials and Methods: The sample comprised 80 orthodontic patients with complete Class II malocclusions in the permanent dentition (47 male, 33 female) who were divided into two groups according to the amount of mandibular tooth-arch size discrepancy. The maxillary and mandibular effective lengths (Co-A and Co-Gn) and tooth-arch size discrepancies were measured on the initial cephalograms and dental casts, respectively. Intergroup comparisons of apical base lengths were performed with independent t-tests. Correlation between base length and dental crowding was examined by means of Pearson's correlation coefficient (P < .05). Results: Patients with Class II malocclusion and moderate to severe crowding had significantly smaller maxillary and mandibular effective lengths than subjects with the same malocclusion and slight mandibular crowding. A weak inverse correlation was also found between maxillary and mandibular effective lengths and the severity of dental crowding. Conclusion: Decreased maxillary and mandibular effective lengths constitute an important factor associated with dental crowding in patients with complete Class II malocclusion. (Angle Orthod. 2011;81:217-221.)

Formation of atomic carbon chains from graphene nanoribbons

The formation of one-dimensional carbon chains from graphene nanoribbons is investigated using ab initio molecular dynamics. We show under what conditions it is possible to obtain a linear atomic chain via pulling of the graphene nanoribbons. The presence of dimers composed of two-coordinated carbon atoms at the edge of the ribbons is necessary for the formation of the linear chains, otherwise there is simply the full rupture of the structure. The presence of Stone-Wales defects close to these dimers may lead to the formation of longer chains. The local atomic configuration of the suspended atoms indicates the formation of single and triple bonds, which is a characteristic of polyynes.

Modeling associations between genetic markers using Bayesian networks

Motivation: Understanding the patterns of association between polymorphisms at different loci in a population ( linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging. Results: We present a more practical method to build GM that describe LD. The method is based on learning weighted Bayesian network structures from haplotype data, extracting equivalence structure classes and using them to model LD. The results obtained in public data from the HapMap database showed that the method is a promising tool for modeling LD. The associations represented by the learned models are correlated with the traditional measure of LD D`. The method was able to represent LD blocks found by standard tools. The granularity of the association blocks and the readability of the models can be controlled in the method. The results suggest that the causality information gained by our method can be useful to tell about the conservability of the genetic markers and to guide the selection of subset of representative markers.

The generalized inverse Weibull distribution

The inverse Weibull distribution has the ability to model failure rates which are quite common in reliability and biological studies. A three-parameter generalized inverse Weibull distribution with decreasing and unimodal failure rate is introduced and studied. We provide a comprehensive treatment of the mathematical properties of the new distribution including expressions for the moment generating function and the rth generalized moment. The mixture model of two generalized inverse Weibull distributions is investigated. The identifiability property of the mixture model is demonstrated. For the first time, we propose a location-scale regression model based on the log-generalized inverse Weibull distribution for modeling lifetime data. In addition, we develop some diagnostic tools for sensitivity analysis. Two applications of real data are given to illustrate the potentiality of the proposed regression model.

The Kumaraswamy Weibull distribution with application to failure data

For the first time, we introduce and study some mathematical properties of the Kumaraswamy Weibull distribution that is a quite flexible model in analyzing positive data. It contains as special sub-models the exponentiated Weibull, exponentiated Rayleigh, exponentiated exponential, Weibull and also the new Kumaraswamy exponential distribution. We provide explicit expressions for the moments and moment generating function. We examine the asymptotic distributions of the extreme values. Explicit expressions are derived for the mean deviations, Bonferroni and Lorenz curves, reliability and Renyi entropy. The moments of the order statistics are calculated. We also discuss the estimation of the parameters by maximum likelihood. We obtain the expected information matrix. We provide applications involving two real data sets on failure times. Finally, some multivariate generalizations of the Kumaraswamy Weibull distribution are discussed. (C) 2010 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.