The impact of a massive migration flow on the regional population structure: The case of Italy

Low economic growth rates are a common problem in many developed countries in Europe. This paper aims to highlight the possible role of demographic factors. Problems of low growth may be exacerbated by an increase in dependency ratios. However, large-scale migrations have been shown to positively affect the age composition of a population. Focusing on Italy, we estimate the impact of migration on the working age population ratio, population size and gross domestic product. We also show that migration may affect the economic gap between the North and South, posing a new potential problem to policymakers.

Bayesian Modeling of Presence-only Data

This thesis develops models and methods for statistical analysis of presence-only data. Besides constructing new models, the emphasis is on the theoretical characteristics of new models and on Bayesian prediction. Monte Carlo Markov chains algorithms are developed for the new presence-only data models in order to be able to simulate the posterior distribution of the unknowns and the predictive distribution of variable of interest. The new methods are applied to simulated data. One application in ecologic science have been a driving force behind the work

Bayesian logistic regression for presence-only data

Presence-only data are referred to situations in which a censoring mechanism acts on a binary response which can be partially observed only with respect to one outcome, usually denoting the \textit{presence} of an attribute of interest. A typical example is the recording of species presence in ecological surveys. In this work a Bayesian approach to the analysis of presence-only data based on a two levels scheme is presented. A probability law and a case-control design are combined to handle the double source of uncertainty: one due to censoring and the other one due to sampling. In the paper, through the use of a stratified sampling design with non-overlapping strata, a new formulation of the logistic model for presence-only data is proposed. In particular, the logistic regression with linear predictor is considered. Estimation is carried out with a new Markov Chain Monte Carlo algorithm with data augmentation, which does not require the a priori knowledge of the population prevalence. The performance of the new algorithm is validated by means of extensive simulation experiments using three scenarios and comparison with optimal benchmarks. An application to data existing in literature is reported in order to discuss the model behaviour in real world situations together with the results of an original study on termites occurrences data

Bayesian Modeling and MCMC Computation in Linear Logistic Regression for Presence-only Data

Presence-only data are referred to situations in which, given a censoring mechanism, a binary response can be observed only with respect to on outcome, usually called \textit{presence}. In this work we present a Bayesian approach to the problem of presence-only data based on a two levels scheme. A probability law and a case-control design are combined to handle the double source of uncertainty: one due to the censoring and one due to the sampling. We propose a new formalization for the logistic model with presence-only data that allows further insight into inferential issues related to the model. We concentrate on the case of the linear logistic regression and, in order to make inference on the parameters of interest, we present a Markov Chain Monte Carlo algorithm with data augmentation that does not require the a priori knowledge of the population prevalence. A simulation study concerning 24,000 simulated datasets related to different scenarios is presented comparing our proposal to optimal benchmarks.Comment: Affiliations: Fabio Divino - Division of Physics, Computer Science and Mathematics, University of Molise Giovanna jona Lasinio and Natalia Golini - Department of Statistical Sciences, University of Rome "La Sapienza" Antti Penttinen - Department of Mathematics and Statistics, University of Jyv\"{a}skyl\"{a} CONTACT: [email protected], [email protected]

Influence of distance from calving on the trascriptional activity of granulosa cells from preovulatory follicles of dairy cows

Negative energy balance (NEB) in dairy cattle early lactation period is associated with a multitude of endocrine, metabolic and immunological changes that not only influence animal health, but also affect fertility, and in particular ovarian function. There is a lack of information on the the transcriptional activity of granulosa cells during the first month after calving, when dairy cow pass from a severe NEB condition to a correct methabolic omeostasis. In this research, GCs of preovulatory follicles have been collected at 30 (30 d), 60 (60 d), 90 (90 d) and 120 days (120 d) after calving from 12 Holstein Freisian cows. To map the differences in genes expression and cellular functions that occur in the follicular microenvironment during the progressive recovery from NEB condition in dairy cow an analysis of the transcriptome was performed using a global bovine oligo array microarray. Considering that after 4 months from parturition the dairy cows have recovered from the NEB condition, the GC samples collected at 120 d were used as a positive control in microarray analysis. The results obtained allowed the identification of a list of differentially expressed transcripts for each GC group contrast: 30 d vs 120 d, 60 d vs 120 d and 90 d vs 120 d. To provide a comprehensive understanding on the interferences of lacatation on the the processes involved in the maturation of ovarian dominant follicle, different gene pathways and molecular and cellular function by Ingenuity Pathways Analysis (IPA) software were used to reveal the different roles of transcripts. The comparison between 30 d and 120 d groups evidenced up and down regulations differences of transcripts in small molecule biochemistry, DNA replication, recombination and repair, and cellular assembly and organization. The contrast analysis between 90 d and 120 d group revealed modifications in up and down regulations genes activities linked to cell cycle progression, cell proliferation and cell interaction, which are indicative of cells preparing for ovulation. The granulosa cells of 60 d group revealed a significant increase in up and down regulations of genes associated with apoptosis, ovarian cancer and a slow cell follicular development compared to the 120 d group; differences which suggest activaton of apoptotic chain typical of cell in suffering conditions more than those of a normal preovulatory follicular stage. Overall the results and findings of the current study lead to the conclusion that microarray analysis is a useful and valid method for the study of the different gene expression profiles in granulosa cells, from collected preovulatory follicles in dairy cow at different distance from calving. These results offer the opportunity to future studies aimed to the understanding of which molecular mechanisms or external factors negatively influence ovarian development during the time interval between the 30 d and 60 d postpartum period in dairy cow