The molecular mechanisms by which ovarian hormones stimulate growth of breast tumors are unclear. It has been reported previously that estrogens activate the signal-transducing Src/p21 ras /Erk pathway in human breast cancer cells via an interaction of estrogen receptor (ER) with c-Src. We now show that progestins stimulate human breast cancer T47D cell proliferation and induce a similar rapid and transient activation of the pathway which, surprisingly, is blocked not only by anti-progestins but also by anti-estrogens. In Cos-7 cells transfected with the B isoform of progesterone receptor (PR B ), progestin activation of the MAP kinase pathway depends on co-transfection of ER. A transcriptionally inactive PR B mutant also activates the signaling pathway, demonstrating that this activity is independent of transcriptional effects. PR B does not interact with c-Src but associates via the N-terminal 168 amino acids with ER. This association is required for the signaling pathway activation by progestins. We propose that ER transmits to the Src/p21 ras /Erk pathway signals received from the agonist-activated PR B . These findings reveal a hitherto unrecognized cross-talk between ovarian hormones which could be crucial for their growth-promoting effects on cancer cells.

In a number of practical problems where clustering or choosing from a set of dynamic structures is needed, the introduction of a distance between the data is an early step in the application of multivariate statistical methods. In this paper a parametric approach is proposed in order to introduce a well-defined metric on the class of autoregressive integrated moving-average (ARIMA) invertible models as the Euclidean distance between their autoregressive expansions. Two case studies for clustering economic time series and for assessing the consistency of seasonal adjustment procedures are discussed. Finally, some related proposals are surveyed and some suggestions for further research are made.

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In the food industry, sensory analysis can be useful to direct marketing decisions concerning not only products, for example product positioning with respect to competitors, but also market segmentation, customer relationship management, advertising strategies and price policies. In this paper we show how interesting information useful for marketing management can be obtained by combining the results from cub models and algorithmic data mining techniques (specifically, variable importance measurements from Random Forest). A case study on sensory evaluation of different varieties of Italian espresso is presented

In several applied disciplines, as Economics, Marketing, Business, Sociology, Psychology, Political science, Environmental research and Medicine, it is common to collect data in the form of ordered categorical observations. In this paper, we introduce a class of models based on mixtures of discrete random variables in order to specify a general framework for the statistical analysis of this kind of data. The structure of these models allows the interpretation of the final response as related to feeling, uncertainty and a possible shelter option and the expression of the relationship among these components and subjects' covariates. Such a model may be effectively estimated by maximum likelihood methods leading to asymptotically efficient inference. We present a simulation experiment and discuss a real case study to check the consistency and the usefulness of the approach. Some final considerations conclude the paper.