Uma caracterização dos imigrantes no distrito de Évora

Uma vez que existe uma grande necessidade de se obter um maior conhecimento das comunidades imigrantes residentes no Distrito, este trabalho tem como principal objectivo caracterizar a população de imigrantes comunitários residentes no distrito de Évora. Em particular, procuramos analisar os casamentos realizados não só entre os não nacionais e portugueses, bem como entre os nãos nacionais entre si. Consideramos os 354 imigrantes que constituem os imigrantes comunitários inscritos legalmente nos Serviços Estrangeiros e Fronteiras de Évora entre 2006 a 2009. Por outro lado, analisamos, para o mesmo período, os casamentos dos imigrantes que casaram com os portugueses num total de 165 indivíduos. Começamos por levar a efeito uma investigação básica de tipo descritivo para uma caracterização dos imigrantes relativamente a diferentes variáveis e investigamos algumas associações entre elas via tabelas de contingência. Refira-se que a maioria dos imigrantes é do sexo feminino, sendo a nacionalidade mais representativa a brasileira. Com base no modelo de regressão de Cox identificamos factores de risco e diferentes perfis associados à rotura do casamento. Mostramos que há evidência estatística para considerar que um menor nível de escolaridade, o pertencer a um país da União Europeia e o ter entrado à procura de trabalho são níveis de factores que aumentam o risco de rotura do casamento. Analisam-se, ainda, de uma forma crítica, a abordagem paramétrica, procurando modelar os dados através dos modelos Exponencial, Weibull, Lognormal e Log-logístico. ABSTRACT: There is a need to obtain a greater understanding of immigrant communities in the district; this work has as main objective to characterize the population of immigrant community residing in the district of Évora. ln particular, we analyze not only marriages between Portuguese and non-nationals, as well as among non-nationals among themselves. We consider the 354 immigrants who are legally registered immigrants in the Community Service of Foreigners and Frontiers of Évora, from 2006 to 2009. Moreover, we analyze, for the same period, the marriages of immigrants who intermarried with the Portuguese for a total of 165 individuals. We start by carrying out a basic descriptive research in order to characterize the immigrants in relation to several variables and investigated some associations between them by contingency tables. It should be noted that most immigrants are women, and the more representative the Brazilian nationality. Based on the Cox regression model, were possible to identify risk factors and identify profiles of high and low risk associated with the rupture of the marriage. We show that there is statistical evidence to conclude that a less educated levels, belonging to a country inside the European Union and have gone looking for work are factors that increase the risk of rupture of the marriage. The parametric approach is analyzed also, in a critical way, seeking to model the data using exponential, Weibull, Lognormal and Log-logistic.

Modelling and classification with quantile-based distributions

In this work, we explore and demonstrate the potential for modeling and classification using quantile-based distributions, which are random variables defined by their quantile function. In the first part we formalize a least squares estimation framework for the class of linear quantile functions, leading to unbiased and asymptotically normal estimators. Among the distributions with a linear quantile function, we focus on the flattened generalized logistic distribution (fgld), which offers a wide range of distributional shapes. A novel naïve-Bayes classifier is proposed that utilizes the fgld estimated via least squares, and through simulations and applications, we demonstrate its competitiveness against state-of-the-art alternatives. In the second part we consider the Bayesian estimation of quantile-based distributions. We introduce a factor model with independent latent variables, which are distributed according to the fgld. Similar to the independent factor analysis model, this approach accommodates flexible factor distributions while using fewer parameters. The model is presented within a Bayesian framework, an MCMC algorithm for its estimation is developed, and its effectiveness is illustrated with data coming from the European Social Survey. The third part focuses on depth functions, which extend the concept of quantiles to multivariate data by imposing a center-outward ordering in the multivariate space. We investigate the recently introduced integrated rank-weighted (IRW) depth function, which is based on the distribution of random spherical projections of the multivariate data. This depth function proves to be computationally efficient and to increase its flexibility we propose different methods to explicitly model the projected univariate distributions. Its usefulness is shown in classification tasks: the maximum depth classifier based on the IRW depth is proven to be asymptotically optimal under certain conditions, and classifiers based on the IRW depth are shown to perform well in simulated and real data experiments.

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model.

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.

Fiber average size and distribution dependence on the electrospinning parameters of poly(vinylidene fluoride trifluoroethylene)) membranes for biomedical applications

Poly(vinylidene fluoride-trifluoethylene) electrospun membranes were obtained from a blend of dimethylformamide (DMF) and methylethylketone (MEK) solvents. The inclusion of the MEK to the solvent system promotes a faster solvent evaporation allowing complete polymer crystallization during the jet travelling between the tip and the grounded collector. Several processing parameters were systematically changed to study their influence on fiber dimensions. Applied voltage and inner needle diameter do not have large influence on the electrospun fiber average diameter but in the fiber diameter distribution. On the other hand, the increase of the distance between the needle tip to collector results in fibers with larger average diameter. Independently on the processing conditions, all mats are produced in the electroactive phase of the polymer. Further, MC-3T3-E1cell adhesion was not inhibited by the fiber mats preparation, indicating their potential use for biomedical applications.

Influence of electrospinning parameters on Poly(hydroxybutyrate) electrospun membranes fiber size and distribution

Poly(hydroxybutyrate) (PHB) obtained from sugar cane was dissolved in a blend of chloroform and dimethylformamide (DMF) and electrospun at 40 ºC. By adding DMF to the solution, the electrospinning process for the PHB polymer becomes more stable, allowing complete polymer crystallization during the jet travelling between the tip and the grounded collector. The influence of processing parameters on fiber size and distribution was systematically studied. It was observed that an increase of tip inner diameter promotes a decrease of the fiber average size and a broader distribution. On the other hand, an increase of the electric field and flow rate produces an increase of fiber diameter until a maximum of ~2.0 m, but for electric fields higher than 1.5 kV.cm-1, a decrease of the fiber diameter was observed. Polymer crystalline phase seems to be independent of the processing conditions and a crystallinity degree of 53 % was found. Moreover, thermal degradation of the as-spun membrane occurs in single step degradation with activation energy of 91 kJ/mol. Furthermore, MC-3T3-E1 cell adhesion was not inhibited by the fiber mats preparation, indicating their potential use for biomedical applications.

Spontaneous viral clearance, viral load, and genotype distribution of hepatitis C virus (HCV) in HIV-infected patients with anti-HCV antibodies in Europe.

BACKGROUND: Variables influencing serum hepatitis C virus (HCV) RNA levels and genotype distribution in individuals with human immunodeficiency virus (HIV) infection are not well known, nor are factors determining spontaneous clearance after exposure to HCV in this population. METHODS: All HCV antibody (Ab)-positive patients with HIV infection in the EuroSIDA cohort who had stored samples were tested for serum HCV RNA, and HCV genotyping was done for subjects with viremia. Logistic regression was used to identify variables associated with spontaneous HCV clearance and HCV genotype 1. RESULTS: Of 1940 HCV Ab-positive patients, 1496 (77%) were serum HCV RNA positive. Injection drug users (IDUs) were less likely to have spontaneously cleared HCV than were homosexual men (20% vs. 39%; adjusted odds ratio [aOR], 0.36 [95% confidence interval {CI}, 0.24-0.53]), whereas patients positive for hepatitis B surface antigen (HBsAg) were more likely to have spontaneously cleared HCV than were those negative for HBsAg (43% vs. 21%; aOR, 2.91 [95% CI, 1.94-4.38]). Of patients with HCV viremia, 786 (53%) carried HCV genotype 1, and 53 (4%), 440 (29%), and 217 (15%) carried HCV genotype 2, 3, and 4, respectively. A greater HCV RNA level was associated with a greater chance of being infected with HCV genotype 1 (aOR, 1.60 per 1 log higher [95% CI, 1.36-1.88]). CONCLUSIONS: More than three-quarters of the HIV- and HCV Ab-positive patients in EuroSIDA showed active HCV replication. Viremia was more frequent in IDUs and, conversely, was less common in HBsAg-positive patients. Of the patients with HCV viremia analyzed, 53% were found to carry HCV genotype 1, and this genotype was associated with greater serum HCV RNA levels.

Effect of sequence distribution on the morphology, crystallization, melting, and biodegradation of poly(epsilon-caprolactone-co-epsilon-caprolactam) copolymers

Two types of poly(epsilon-caprolactone (CLo)-co-poly(epsilon-caprolactam (CLa)) copolymers were prepared by catalyzed hydrolytic ring-opening polymerization. Both cyclic comonomers were added simultaneously in the reaction medium for the First type or materials where copolymers have a random distribution of counits, as evidenced by H-1 and C-13 NMR. For the second type of copolymers, the cyclic comonomers were added sequentially, yielding diblock poly(ester-amides). The materials were characterized by differential scanning calorimetry (DSC), wide- and small-angle X-ray scattering (WAXS and SAXS), and transmission and scanning electron microscopies (TEM and SEM). Their biodegradation in compost was also studied. All copolymers were found to be miscible by the absence of structure in the melt. TEM revealed that all samples exhibited a crystalline lamellar morphology. DSC and WAXS showed that in a wide composition range (CLo contents from 6 to 55%) only the CLa units were capable of crystallization in the random copolymers. The block copolymer samples only experience a small reduction of crystallization and melting temperature with composition, and this was attributed to a dilution effect caused by the miscible noncrystalline CLo units. The comparison between block and random copolymers provided a unique opportunity to distinguish the dilution effect of the CLo units on the crystallization and melting of the polyamide phase from the chemical composition effect in the random copolymers case, where the CLa sequences are interrupted statistically by the CLo units, making the crystallization of the polyamide strongly composition dependent. Finally, the enzymatic degradation of the copolymers in composted soil indicate a synergistic behavior where much faster degradation was obtained for random copolymers witha CLo content larger than 30% than for neat PCL.

Controlled variation of monomer sequence-distribution in the synthesis of aromatic poly(ether ketone)s

The effects of varying the alkali metal cation in the high-temperature nucleophilic synthesis of a semi-crystalline, aromatic poly(ether ketone) have been systematically investigated, and striking variations in the sequence-distributions and thermal characteristics of the resulting polymers were found. Polycondensation of 4,4'-dihydroxybenzophenone with 1,3-bis(4-fluorobenzoyl)benzene in diphenylsulfone as solvent, in the presence of an alkali metal carbonate M2CO3 (M= Li, Na, K, or Rb) as base, affords a range of different polymers that vary in the distribution pattern of 2-ring and 3-ring monomer units along the chain. Lithium carbonate gives an essentially alternating and highly crystalline polymer, but the degree of sequence-randomisation increases progressively as the alkali metal series is descended, with rubidium carbonate giving a fully random and non-thermally-crystallisable polymer. Randomisation during polycondensation is shown to result from reversible cleavage of the ether linkages in the polymer by fluoride ions, and an isolated sample of alternating-sequence polymer is thus converted to a fully randomised material on heating with rubidium fluoride.

Choosing between Cox proportional hazards and logistic models for interval-censored data via bootstrap

This work develops a new methodology in order to discriminate models for interval-censored data based on bootstrap residual simulation by observing the deviance difference from one model in relation to another, according to Hinde (1992). Generally, this sort of data can generate a large number of tied observations and, in this case, survival time can be regarded as discrete. Therefore, the Cox proportional hazards model for grouped data (Prentice & Gloeckler, 1978) and the logistic model (Lawless, 1982) can befitted by means of generalized linear models. Whitehead (1989) considered censoring to be an indicative variable with a binomial distribution and fitted the Cox proportional hazards model using complementary log-log as a link function. In addition, a logistic model can be fitted using logit as a link function. The proposed methodology arises as an alternative to the score tests developed by Colosimo et al. (2000), where such models can be obtained for discrete binary data as particular cases from the Aranda-Ordaz distribution asymmetric family. These tests are thus developed with a basis on link functions to generate such a fit. The example that motivates this study was the dataset from an experiment carried out on a flax cultivar planted on four substrata susceptible to the pathogen Fusarium oxysoprum. The response variable, which is the time until blighting, was observed in intervals during 52 days. The results were compared with the model fit and the AIC values.

A log-linear regression model for the beta-Birnbaum-Saunders distribution with censored data

The beta-Birnbaum-Saunders (Cordeiro and Lemonte, 2011) and Birnbaum-Saunders (Birnbaum and Saunders, 1969a) distributions have been used quite effectively to model failure times for materials subject to fatigue and lifetime data. We define the log-beta-Birnbaum-Saunders distribution by the logarithm of the beta-Birnbaum-Saunders distribution. Explicit expressions for its generating function and moments are derived. We propose a new log-beta-Birnbaum-Saunders regression model that can be applied to censored data and be used more effectively in survival analysis. We obtain the maximum likelihood estimates of the model parameters for censored data and investigate influence diagnostics. The new location-scale regression model is modified for the possibility that long-term survivors may be presented in the data. Its usefulness is illustrated by means of two real data sets. (C) 2011 Elsevier B.V. All rights reserved.

The polarized distribution of poly(A+)-mRNA-induced functional ion channels in the Xenopus oocyte plasma membrane is prevented by anticytoskeletal drugs

Foreign mRNA was expressed in Xenopus laevis oocytes. Newly expressed ion currents localized in defined plasma membrane areas were measured using the two-electrode voltage clamp technique in combination with a specially designed chamber, that exposed only part of the surface on the oocytes to channel agonists or inhibitors. Newly expressed currents were found to be unequally distributed in the surface membrane of the oocyte. This asymmetry was most pronounced during the early phase of expression, when channels could almost exclusively be detected in the animal hemisphere of the oocyte. 4 d after injection of the mRNA, or later, channels could be found at a threefold higher density at the animal than at the vegetal pole area. The pattern of distribution was observed to be similar with various ion channels expressed from crude tissue mRNA and from cRNAs coding for rat GABAA receptor channel subunits. Electron microscopical analysis revealed very similar microvilli patterns at both oocyte pole areas. Thus, the asymmetric current distribution is not due to asymmetric surface structure. Upon incubation during the expression period in either colchicine or cytochalasin D, the current density was found to be equal in both pole areas. The inactive control substance beta-lumicolchicine had no effect on the asymmetry of distribution. Colchicine was without effect on the amplitude of the expressed whole cell current. Our measurements reveal a pathway for plasma membrane protein expression endogenous to the Xenopus oocyte, that may contribute to the formation and maintenance of polarity of this highly organized cell.

Censored Linear Regression Models For Irregularly Observed Longitudinal Data Using The Multivariate-t Distribution.

In acquired immunodeficiency syndrome (AIDS) studies it is quite common to observe viral load measurements collected irregularly over time. Moreover, these measurements can be subjected to some upper and/or lower detection limits depending on the quantification assays. A complication arises when these continuous repeated measures have a heavy-tailed behavior. For such data structures, we propose a robust structure for a censored linear model based on the multivariate Student's t-distribution. To compensate for the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is employed. An efficient expectation maximization type algorithm is developed for computing the maximum likelihood estimates, obtaining as a by-product the standard errors of the fixed effects and the log-likelihood function. The proposed algorithm uses closed-form expressions at the E-step that rely on formulas for the mean and variance of a truncated multivariate Student's t-distribution. The methodology is illustrated through an application to an Human Immunodeficiency Virus-AIDS (HIV-AIDS) study and several simulation studies.