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Abstract:Climatic data and bioclimatic indexes have been used to study plants, animals and ecosystem distribution. GIS-based maps of climatic and bioclimatic data can be obtained by interpolating values observed at measurement stations. Since no single method can be considered as optimal for all observed regions, a major task is to propose comparisons between results obtained using different methods applied to the same data set of climate variables. We compared three methods that have been proved to be useful at regional scale: 1 -a local interpolation method based on de-trended inverse distance weighting (D-IDW), 2 -universal kriging (i.e. simple kriging with trend function defined on the basis of a set of covariates) which is optimal (i.e. BLUP, best linear unbiased predictor) if spatial association is present, 3 -multilayer neural networks trained with backpropagation (representing a complex nonlinear fitting). Long-term (1955Long-term ( -1990 average monthly data were obtained from weather stations measuring precipitation (201 sites) and temperature (102 sites). We analysed twelve climatic variables (temperature and precipitation) and nine bioclimatic indexes. Terrain variables and geographical location have been used as predictors of the climate variables: longitude, latitude, elevation, aspect, slope, continentality and estimated solar radiation. Based on the root mean square errors from cross-validation tests, we ranked the best method for each variable data set. Universal kriging with external drift obtained the best performances for seventeen variables of the twenty-one analysed, neural network interpolator has proven to be more efficient for three variables and D-IDW for only one. Based on these results, we used the universal kriging estimates to produce the climatic and bioclimatic maps aimed at defining the bioclimatic envelope of species.

Knowledge about the balance between heritable and nonheritable risk in multiple sclerosis (MS) is based on twin studies in high-prevalence areas. In a study that avoided ascertainment limitations and directly compared continental Italy (medium-prevalence) and Sardinia (high-prevalence), we ascertained 216 pairs from 34,549 patients. This gives a twinning rate of 0.62% among MS patients, significantly less than that of the general population. In continental Italy, probandwise concordance was 14.5% (95% confidence interval, 5.1-23.8) for monozygotic and 4.0% (95% confidence interval, 0.8-7.1) for dizygotic twins. Results in Sardinia resemble those in northern populations but in limited numbers. Monozygotic concordance was 22.2% (95% confidence interval, 0-49.3) probandwise, but no concordant dizygotic pairs were identified. A questionnaire on 80 items possibly related to disease cause was administered to 70 twin pairs, 135 sporadic patients, and 135 healthy volunteers. Variables positively (7) or negatively (2) associated with predisposition and concordance in twins largely overlapped and were mainly linked to infection. If compared with previous studies, our data demonstrate that penetrance in twins appears to correlate with MS prevalence. They highlight the relevance of nonheritable variables in Mediterranean areas. The apparent underrepresentation of MS among Italian twins draws attention to protective factors, shared by twins, that may influence susceptibility.

In this paper we define a finite mixture of quan- tile and M-quantile regression models for heterogeneous and /or for dependent/clustered data. Components of the finite mixture represent clusters of individuals with homogeneous values of model parameters. For its flexibility and ease of estimation, the proposed approaches can be extended to ran- dom coefficients with a higher dimension than the simple random intercept case. Estimation of model parameters is obtained through maximum likelihood, by implementing an EM-type algorithm. The standard error estimates for model parameters are obtained using the inverse of the observed information matrix, derived through the Oakes (J R Stat Soc Ser B 61:479–482, 1999) formula in the M-quantile setting, and through nonparametric bootstrap in the quantile case. We present a large scale simulation study to analyse the practical behaviour of the proposed model and to evaluate the empiri- cal performance of the proposed standard error estimates for model parameters. We considered a variety of empirical set- tings in both the random intercept and the random coefficient case. The proposed modelling approaches are also applied to two well-known datasets which give further insights on their empirical behaviour

We investigated the hypothesis that patients developing high-grade erythema of the breast skin during radiation treatment could be more likely to present increased levels of proinflammatory cytokines which may lead, in turn, to associated fatigue. Forty women with early stage breast cancer who received adjuvant radiotherapy were enrolled from 2007 to 2010. Fatigue symptoms, erythema, and cytokine levels (IL-1β, IL-2, IL6, IL-8, TNF-α, and MCP-1) were registered at baseline, during treatment, and after radiotherapy completion. Seven (17.5%) patients presented fatigue without associated depression/anxiety. Grade ≥2 erythema was observed in 5 of these 7 patients. IL-1β, IL-2, IL-6, and TNF-α were statistically increased 4 weeks after radiotherapy (P < 0.05). After the Heckman two-step analysis, a statistically significant influence of skin erythema on proinflammatory markers increase (P = 0.00001) was recorded; in the second step, these blood markers showed a significant impact on fatigue (P = 0.026). A seeming increase of fatigue, erythema, and proinflammatory markers was observed between the fourth and the fifth week of treatment followed by a decrease after RT. There were no significant effects of hormone therapy, breast volume, and anemia on fatigue. Our study seems to suggest that fatigue is related to high-grade breast skin erythema during radiotherapy through the increase of cytokines levels.

A challenge in microarray data analysis concerns discovering local structures composed by sets of genes that show homogeneous expression patterns across subsets of conditions. We present an extension of the mixture of factor analyzers model (MFA) allowing for simultaneous clustering of genes and conditions. The proposed model is rather flexible since it models the density of high-dimensional data assuming a mixture of Gaussian distributions with a particular omponent-specific covariance structure. Specifically, a binary and row stochastic matrix representing tissue membership is used to cluster tissues (experimental conditions), whereas the traditional mixture approach is used to define the gene clustering. An alternating expectation conditional maximization (AECM) algorithm is proposed for parameter estimation; experiments on simulated and real data show the efficiency of our method as a general approach to biclustering. The Matlab code of the algorithm is available upon request from authors.

Quantile regression provides a detailed and robust picture of the distribution of a response variable, conditional on a set of observed covariates. Recently, it has be been extended to the analysis of longitudinal continuous outcomes using either time-constant or time-varying random parameters.However, in real-life data, we frequently observe both temporal shocks in the overall trend and individual-specific heterogeneity in model parameters. A benchmark dataset on HIV progression gives a clear example. Here, the evolution of the CD4 log counts exhibits both sudden temporal changes in the overall trend and heterogeneity in the effect of the time since seroconversion on the response dynamics. To accommodate such situations, we propose a quantile regression model where time-varying and time-constant random coefficients are jointly considered. Since observed data may be incomplete due to early drop-out, we also extend the proposed model in a pattern mixture perspective. We assess the performance of the proposals via a large scale simulation study and the analysis of the CD4 count data.

Two-part models are quite well established in the economic literature, since they resemble accurately a principal-agent type model, where homogeneous, observable, counted outcomes are subject to a (prior, exogenous) selection choice. The first decision can be represented by a binary choice model, modeled using a probit or a logit link; the second can be analyzed through a truncated discrete distribution such as a truncated Poisson, negative binomial, and so on. Only recently, a particular attention has been devoted to the extension of two-part models to handle longitudinal data. The authors discuss a semi-parametric estimation method for dynamic two-part models and propose a comparison with other, well-established alternatives. Heterogeneity sources that influence the first level decision process, that is, the decision to use a certain service, are assumed to influence also the (truncated) distribution of the positive outcomes. Estimation is carried out through an EM algorithm without parametric assumptions on the random effects distribution. Furthermore, the authors investigate the extension of the finite mixture representation to allow for unobservable transition between components in each of these parts. The proposed models are discussed using empirical as well as simulated data. The Canadian Journal of Statistics 38: 197-216; 2010 (C) 2010 Statistical Society of Canad