Why the Question of Animal Consciousness Might Not Matter Very Much

According to higher-order thought accounts of phenomenal consciousness (e.g. Carruthers, 2000) it is unlikely that many non-human animals undergo phenomenally conscious experiences. Many people believe that this result would have deep and far-reaching consequences. More specifically, they believe that the absence of phenomenal consciousness from the rest of the animal kingdom must mark a radical and theoretically significant divide between ourselves and other animals, with important implications for comparative psychology. I shall argue that this belief is mistaken. Since phenomenal consciousness might be almost epiphenomenal in its functioning within human cognition, its absence in animals may signify only relatively trivial differences in cognitive architecture. Our temptation to think otherwise arises partly as a side-effect of imaginative identification with animal experiences, and partly from mistaken beliefs concerning the aspects of common-sense psychology that carry the main explanatory burden, whether applied to humans or to non-human animals

Bone and joint infections in adults: a comprehensive classification proposal

Ten currently available classifications were tested for their ability to describe a continuous cohort of 300 adult patients affected by bone and joint infections. Each classification only focused, on the average, on 1.3\u2009\ub1\u20090.4 features of a single clinical condition (osteomyelitis, implant-related infections, or septic arthritis), being able to classify 34.8\u2009\ub1\u200924.7% of the patients, while a comprehensive classification system could describe all the patients considered in the study. RESULT AND CONCLUSION: A comprehensive classification system permits more accurate classification of bone and joint infections in adults than any single classification available and may serve for didactic, scientific, and clinical purposes

Multiple-input multiple-output causal strategies for gene selection

Traditional strategies for selecting variables in high dimensional classification problems aim to find sets of maximally relevant variables able to explain the target variations. If these techniques may be effective in generalization accuracy they often do not reveal direct causes. The latter is essentially related to the fact that high correlation (or relevance) does not imply causation. In this study, we show how to efficiently incorporate causal information into gene selection by moving from a single-input single-output to a multiple-input multiple-output setting.Journal ArticleResearch Support, N.I.H. ExtramuralResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe