Increase in environmental temperature affects exploratory behaviour, anxiety and social preference in Danio rerio

The aim of this work is to investigate the effect of a temperature increase on the behaviour of adult zebrafish (Danio rerio) maintained for 21 days at 34 °C (treatment) and 26 °C (control). The temperatures chosen are within the vital range of zebrafish and correspond to temperatures that this species encounters in the natural environment. Previous results showed that the same treatment affects the brain proteome and the behaviour of adult zebrafish by producing alterations in the proteins involved in neurotransmitter release and synaptic function and impairing fish exploratory behaviour. In this study, we have investigated the performance of treated and control zebrafish during environmental exploration by using four behavioural tests (novel tank diving, light and dark preference, social preference and mirror biting) that are paradigms for assessing the state of anxiety, boldness, social preference and aggressive behaviour, respectively. The results showed that heat treatment reduces anxiety and increases the boldness of zebrafish, which spent more time in potentially dangerous areas of the tank such as the top and the uncovered bright area and at a distance from the social group, thus decreasing protection for the zebrafish. These data suggest that the increase in ambient temperature may compromise zebrafish survival rate in the natural environment

Gauge fields, ripples and wrinkles in graphene layers

We analyze elastic deformations of graphene sheets which lead to effective gauge fields acting on the charge carriers. Corrugations in the substrate induce stresses, which, in turn, can give rise to mechanical instabilities and the formation of wrinkles. Similar effects may take place in suspended graphene samples under tension.Comment: contribution to the special issue of Solid State Communications on graphen

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information