The archaeal elongation factor EF-2 induces the release of aIF6 from 50S ribosomal subunit

The translation factor IF6 is a protein of about 25 kDa shared by the Archaea and the Eukarya but absent in Bacteria. It acts as a ribosome anti-association factor that binds to the large subunit preventing the joining to the small subunit. It must be released from the large ribosomal subunit to permit its entry to the translation cycle. In Eukarya, this process occurs by the coordinated action of the GTPase Efl1 and the docking protein SBDS. Archaea do not possess a homolog of the former factor while they have a homolog of SBDS. In the past, we have determined the function and ribosomal localization of the archaeal (Sulfolobus solfataricus) IF6 homolog (aIF6) highlighting its similarity to the eukaryotic counterpart. Here, we analyzed the mechanism of aIF6 release from the large ribosomal subunit. We found that, similarly to the Eukarya, the detachment of aIF6 from the 50S subunit requires a GTPase activity which involves the archaeal elongation factor 2 (aEF-2). However, the release of aIF6 from the 50S subunits does not require the archaeal homolog of SBDS, being on the contrary inhibited by its presence. Molecular modeling, using published structural data of closely related homologous proteins, elucidated the mechanistic interplay between the aIF6, aSBDS, and aEF2 on the ribosome surface. The results suggest that a conformational rearrangement of aEF2, upon GTP hydrolysis, promotes aIF6 ejection. On the other hand, aSBDS and aEF2 share the same binding site, whose occupation by SBDS prevents aEF2 binding, thereby inhibiting aIF6 release

Genetic Disruption of Both Tryptophan Hydroxylase Genes Dramatically Reduces Serotonin and Affects Behavior in Models Sensitive to Antidepressants

The neurotransmitter serotonin (5-HT) plays an important role in both the peripheral and central nervous systems. The biosynthesis of serotonin is regulated by two rate-limiting enzymes, tryptophan hydroxylase-1 and -2 (TPH1 and TPH2). We used a gene-targeting approach to generate mice with selective and complete elimination of the two known TPH isoforms. This resulted in dramatically reduced central 5-HT levels in Tph2 knockout (TPH2KO) and Tph1/Tph2 double knockout (DKO) mice; and substantially reduced peripheral 5-HT levels in DKO, but not TPH2KO mice. Therefore, differential expression of the two isoforms of TPH was reflected in corresponding depletion of 5-HT content in the brain and periphery. Surprisingly, despite the prominent and evolutionarily ancient role that 5-HT plays in both vertebrate and invertebrate physiology, none of these mutations resulted in an overt phenotype. TPH2KO and DKO mice were viable and normal in appearance. Behavioral alterations in assays with predictive validity for antidepressants were among the very few phenotypes uncovered. These behavioral changes were subtle in the TPH2KO mice; they were enhanced in the DKO mice. Herein, we confirm findings from prior descriptions of TPH1 knockout mice and present the first reported phenotypic evaluations of Tph2 and Tph1/Tph2 knockout mice. The behavioral effects observed in the TPH2 KO and DKO mice strongly confirm the role of 5-HT and its synthetic enzymes in the etiology and treatment of affective disorders

Mapping temporal expectancies for different rhytmical surfaces: The role of metric structure and phenomenal accents

This study explores the rules regulating the formation of temporal expectancies when we listen to a rhythmic sequence and extract regularities (or invariant temporal information) projecting them in the near future. Our ability to generate these expectancies is widely dependant on the metric structure suggested by the patterns we entrain to. In Experiment 1, we mapped temporal expectancies evoked by three different repeating patterns in which the phenomenal accents strength was manipulated keeping the metric structure constant in all three patterns. Results of the test tone timing evaluation reveal that expectancy waves are quite short (after the stimulus stops) and very dependent on phenomenal accent strength. In Experiment 2, we used four patterns with different metric structures and lengths: two patterns inducing isochronous meters, and a pattern inducing a Non-Isochronous structure. All the patterns were composed following a rhythm complexity evaluation algorithm. The timing evaluation judgment task after entraining to the patterns was identical to Exp. 1. Results confirm the crucial role of phenomenal accents time position and strength, and show that Isochronous meters generate strong and periodic expectancy waves, while Non-Isochronous meters tend to evoke periodicities of a different level. Our results are consistent with the recent oscillator models of attending. Discussion proposes an interpretation of the results with special attention devoted to the interpretation of N-I meters effects

The Bootstrap Discovery Behaviour (BDB): A new outlook on usability evaluation

The value of λ is one of the main issues debated in international usability studies. The debate is centred on the deficiencies of the mathematical return on investment model (ROI model) of Nielsen and Landauer (1993). The ROI model is discussed in order to identify the base of another model that, respecting Nielsen and Landauer's one, tries to consider a large number of variables for the estimation of the number of evaluators needed for an interface. Using the bootstrap model (Efron 1979), we can take into account: (a) the interface properties, as the properties at zero condition of evaluation and (b) the probability that the population discovery behaviour is represented by all the possible discovery behaviours of a sample. Our alternative model, named Bootstrap Discovery Behaviour (BDB), provides an alternative estimation of the number of experts and users needed for a usability evaluation. Two experimental groups of users and experts are involved in the evaluation of a website ( http://www.serviziocivile.it ). Applying the BDB model to the problems identified by the two groups, we found that 13 experts and 20 users are needed to identify 80% of usability problems, instead of 6 experts and 7 users required according to the estimation of the discovery likelihood provided by the ROI model. The consequence of the difference between the results of those models is that in following the BDB the costs of usability evaluation increase, although this is justified considering that the results obtained have the best probability of representing the entire population of experts and users

Archeologia e Reti Neurali Artificiali, teoria e pratica

Intelligenza Artificiale, due parole che evocano tutta una serie di scenari, per lo più fantascientifici, influenzati da film e libri che spesso esplorano i lati più oscuri e inquietanti, descrivendo futuri distopici in cui le "macchine" controllano le nostre coscienze. La realtà è naturalmente ben diversa, iniziando dalla definizione di Intelligenza Artificiale. Le sue applicazioni, legate alla creazione delle cosiddette Reti Neurali Artificiali, diventano sempre più uno strumento per interpretare non solo la realtà che ci circonda, ma anche il nostro passato. Da qualche anno l'archeologia muove i primi passi in questa direzione con risultati incoraggianti. Questo articolo è quindi dedicato ad esplorare il significato dell'Intelligenza Artificiale e i suoi possibili ruoli nel contesto archeologicoArtificial Intelligence, two words that evoke a whole series of scenarios, mostly science fiction, influenced by movies and books that often explore the darkest and most disturbing sides, describing dystopian futures in which "machines" control our consciences. The reality is naturally quite different, starting with the definition of Artificial Intelligence. Its applications, linked to the creation of the so-called Artificial Neural Networks, increasingly become a tool for interpreting not only the reality that surrounds us, but also our past. For some years, archeology has been taking its first steps in this direction with encouraging results. This paper is thus devoted to explore the meaning of Artificial Intelligence and its possible roles in archaeologic context

Cooperative behavior of artificial neural agents based on evolutionary architectures.

Artificial agents modeled by evolutionary neural networks have been diffusely described in the specific case of static architectures and synaptic weights coded in genetic strings. At present, more attractive theories devoted to a general theory of mind consider the biological and structural levels as necessary elements for an appropriate natural information processing. In this paper, an evolutionary approach has been taken into account for the selection of neural architectures Of agents embedded in an artificial environment. Several correspondences between natural and artificial neural behavior has been detected (perception, multimodal integration, memory). Moreover, a cooperative social behavior emerged among the agents for a suitable exploration of the environment and the exploitation of the resources