Klinefelter syndrome: cardiovascular abnormalities and metabolic disorders

Klinefelter syndrome (KS) is one of the most common genetic causes of male infertility. This condition is associated with much comorbidity and with a lower life expectancy. The aim of this review is to explore more in depth cardiovascular and metabolic disorders associated to KS. KS patients have an increased risk of cerebrovascular disease (standardized mortality ratio, SMR, 2.2; 95% confidence interval, CI, 1.6–3.0), but it is not clear whether the cause of the death is of thrombotic or hemorrhagic nature. Cardiovascular congenital anomalies (SMR, 7.3; 95% CI, 2.4–17.1) and the development of thrombosis or leg ulcers (SMR, 7.9; 95% CI, 2.9–17.2) are also more frequent in these subjects. Moreover, cardiovascular abnormalities may be at least partially reversed by testosterone replacement therapy (TRT). KS patients have also an increased probability of endocrine and/or metabolic disease, especially obesity, metabolic syndrome and type 2 diabetes mellitus. The effects of TRT on these abnormalities are not entirely clear

Filosofia della mente: pensiero, coscienza, emozioni

Collezione di articoli di: M. Di Francesco, M. Frixione, S. Gozzano, J.A. Fodor, J.R. Searle, P.M. e P.S Churchland, G. Tamburrini, G.D. Fischbach, J. Horgan, F. Crick e C. Koch, D.J. Chalmers, G. Trautteur, P. Bieri, A. Oliverio

Simulation models of organism behavior: some lessons from precybernetic and cybernetic approaches

The rise and some more recent developments of the machine-simulation methodology of living-organism behavior are discussed in this paper. In putting forward these issue, my aim is that of isolating recurring themes which help understanding the development of such a machine-simulation methodology, from its, so to speak, discovery during the first half of the twentieth century up to the present time. The machine designed by the engineer S. Bent Russell in 1913 seems to share the core of at least some points of such a methodology. This machine was designed with the aim of embodying certain hypotheses on the plasticity of nervous connections, pointed out at the time by psychologists in order to explain the physical bases of learning. I would like to suggest that this machine might be viewed as a case-study of the discovery of the above mentioned simulative methodology, further on developed by cyberneticians beginning from the 1940s. Certain present-day steps toward such a methodology are briefly touched upon in the concluding section of the paper

Psicologia, fisicalismo e Intelligenza Artificiale

In questo articolo mi propongo di mostrare in quale modo la psicologia abbia trovato una collocazione inedita all’interno della tradizionale gerarchia delle scienze proposta, nel quadro della Scienza Unitaria, dal neopositivismo liberalizzato, passando dallo stato di scienza naturale, prediletto dal fisicalismo e dal comportamentismo, a quello di “scienza dell’artificiale”, proposto dall’IA (Intelligenza Artificiale) fin dalle sue origini

The role of heuristics in automated theorem proving

The aim of this paper is to show how J.A. Robinson's resolution principle was perceived and discussed in the AI community between the mid sixties and the first seventies. During this time the so called "heuristic search paradigm" was still influential in the AI community, and both resolution principle and certain resolution based, apparently human-like, search strategies were matched with those problem solving heuristic procedures which were representative of the AI heuristic search paradigm

Searching in a maze, in search of knowledge. Issues in early Artificial Intelligence

Heuristic programming was the first area in which AI methods were tested. The favourite case-studies were fairly simple toy-problems, such as cryptarithmetic, games, such as checker or chess, and formal problems, such as logic or geometry theorem-proving. These problems are well-defined, roughly speaking, at least in comparison to real-life problems, and as such have played the role of Drosophila in early AI. In this paper I will investigate the origins of heuristic programming and the shift to more knowledge-based and real-life problem solving