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

Environmental temperature variation affects brain protein expression and cognitive abilities in adult zebrafish (Danio rerio): A proteomic and behavioural study.

Water temperature is an important environmental parameter influencing the distribution and the health of fishes and it plays a central role in ectothermic animals. The aim of this study is to determine the effects of environmental temperature on the brain proteome and the behavioural responses in zebrafish, a widely used animal model for environmental "omics" studies. Adult specimens of wild-type zebrafish were kept at 18 °C, 34 °C and 26 °C (control) for 21 days. Proteomic data revealed that several proteins involved in cytoskeletal organization, mitochondrial regulation and energy metabolism are differently regulated at the extreme temperatures. In particular, the expression of proteins associated to synapses and neurotransmitter release is down-regulated at 18 °C and 34 °C. In both thermal conditions, fish exhibited a reduced interest for the novel environment and an impairment of cognitive abilities during Y-Maze behavioural tests. The observed pathways of protein expression are possibly associated to functional alterations of the synaptic transmission that may result in cognitive functions impairment at central nervous system level as those revealed by behavioural tests. This study indicates that temperature variations can elicit biochemical changes that may affect fish health and behaviour. This combined approach provides insights into mechanisms supporting thermal acclimation and plasticity in fishes. SIGNIFICANCE: Environmental temperature variation may impact on all levels of biological life. Understanding the impact of thermal variation on the nervous system and animal behaviour is of primary importance since the results obtained can be applied from the ecological to the biomedical fields

Brain Proteome and Behavioural Analysis in Wild Type, BDNF+/− and BDNF−/− Adult Zebrafish (Danio rerio) Exposed to Two Different Temperatures

Experimental evidence suggests that environmental stress conditions can alter the expression of BDNF and that the expression of this neurotrophin influences behavioural responses in mammalian models. It has been recently demonstrated that exposure to 34 degrees C for 21 days alters the brain proteome and behaviour in zebrafish. The aim of this work was to investigate the role of BDNF in the nervous system of adult zebrafish under control and heat treatment conditions. For this purpose, zebrafish from three different genotypes (wild type, heterozygous BDNF+/- and knock out BDNF-/-) were kept for 21 days at 26 degrees C or 34 degrees C and then euthanized for brain molecular analyses or subjected to behavioural tests (Y-maze test, novel tank test, light and dark test, social preference test, mirror biting test) for assessing behavioural aspects such as boldness, anxiety, social preference, aggressive behaviour, interest for the novel environment and exploration. qRT-PCR analysis showed the reduction of gene expression of BDNF and its receptors after heat treatment in wild type zebrafish. Moreover, proteomic analysis and behavioural tests showed genotype- and temperature-dependent effects on brain proteome and behavioural responding. Overall, the absent expression of BDNF in KO alters (1) the brain proteome by reducing the expression of proteins involved in synapse functioning and neurotransmitter-mediated transduction; (2) the behaviour, which can be interpreted as bolder and less anxious and (3) the cellular and behavioural response to thermal treatment

Acute environmental temperature variation affects brain protein expression, anxiety and explorative behaviour in adult zebrafish

This study investigated the effect of 4-d acute thermal treatments at 18 \ub0C, 26 \ub0C (control) and 34 \ub0C on the nervous system of adult zebrafish (Danio rerio) using a multidisciplinary approach based on behavioural tests and brain proteomic analysis. The behavioural variations induced by thermal treatment were investigated using five different tests, the novel tank diving, light and dark preference, social preference, mirror biting, and Y-Maze tests, which are standard paradigms specifically tailored for zebrafish to assess their anxiety-like behaviour, boldness, social preference, aggressiveness, and explorative behaviour, respectively. Proteomic data revealed that several proteins involved in energy metabolism, messenger RNA translation, protein synthesis, folding and degradation, cytoskeleton organisation and synaptic vesiculation are regulated differently at extreme temperatures. The results showed that anxiety-like behaviours increase in zebrafish at 18 \ub0C compared to those at 26 \ub0C or 34 \ub0C, whereas anxiety-related protein signalling pathways are downregulated. Moreover, treatments at both 18 \ub0C and 34 \ub0C affect the exploratory behaviour that appears not to be modulated by past experiences, suggesting the impairment of fish cognitive abilities. This study is the continuation of our previous work on the effect of 21-d chronic treatment at the same constant temperature level and will enable the comparison of acute and chronic treatment effects on the nervous system function in adult zebrafish

Data Stream Clustering for Real-Time Anomaly Detection: An Application to Insider Threats

Insider threat detection is an emergent concern for academia, industries, and governments due to the growing number of insider incidents in recent years. The continuous streaming of unbounded data coming from various sources in an organisation, typically in a high velocity, leads to a typical Big Data computational problem. The malicious insider threat refers to anomalous behaviour(s) (outliers) that deviate from the normal baseline of a data stream. The absence of previously logged activities executed by users shapes the insider threat detection mechanism into an unsupervised anomaly detection approach over a data stream. A common shortcoming in the existing data mining approaches to detect insider threats is the high number of false alarms/positives (FPs). To handle the Big Data issue and to address the shortcoming, we propose a streaming anomaly detection approach, namely Ensemble of Random subspace Anomaly detectors In Data Streams (E-RAIDS), for insider threat detection. E-RAIDS learns an ensemble of p established outlier detection techniques [Micro-cluster-based Continuous Outlier Detection (MCOD) or Anytime Outlier Detection (AnyOut)] which employ clustering over continuous data streams. Each model of the p models learns from a random feature subspace to detect local outliers, which might not be detected over the whole feature space. E-RAIDS introduces an aggregate component that combines the results from the p feature subspaces, in order to confirm whether to generate an alarm at each window iteration. The merit of E-RAIDS is that it defines a survival factor and a vote factor to address the shortcoming of high number of FPs. Experiments on E-RAIDS-MCOD and E-RAIDS-AnyOut are carried out, on synthetic data sets including malicious insider threat scenarios generated at Carnegie Mellon University, to test the effectiveness of voting feature subspaces, and the capability to detect (more than one)-behaviour-all-threat in real-time. The results show that E-RAIDS-MCOD reports the highest F1 measure and less number of false alarm = 0 compared to E-RAIDS-AnyOut, as well as it attains to detect approximately all the insider threats in real-time

Selecting promising classes from generated data for an efficient multi-class nearest neighbor classification

The nearest neighbor rule is one of the most considered algorithms for supervised learning because of its simplicity and fair performance in most cases. However, this technique has a number of disadvantages, being the low computational efficiency the most prominent one. This paper presents a strategy to overcome this obstacle in multi-class classification tasks. This strategy proposes the use of Prototype Reduction algorithms that are capable of generating a new training set from the original one to try to gather the same information with fewer samples. Over this reduced set, it is estimated which classes are the closest ones to the input sample. These classes are referred to as promising classes. Eventually, classification is performed using the original training set using the nearest neighbor rule but restricted to the promising classes. Our experiments with several datasets and significance tests show that a similar classification accuracy can be obtained compared to using the original training set, with a significantly higher efficiency.This work has been supported by the Vicerrectorado de Investigación, Desarrollo e Innovación de la Universidad de Alicante through the FPU programme (UAFPU2014–5883), the Spanish Ministerio de Educación, Cultura y Deporte through a FPU Fellowship (Ref. AP2012–0939) and the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R, supported by UE FEDER funds)