Variation in Environmental Parameters in Research and Aquaculture: Effects on Behaviour, Physiology and Cell Biology of Teleost Fish

Over the last few years the increasing use of fish as animal models in scientific research and the increased fish breeding for human consumption have stressed the need for more knowledge on the effect of variations in environmental parameters on fish biology and on the welfare of specimens used both in research and aquaculture contexts. Experimental evidence shows that environmental variations can affect fish biology at various levels, from the molecular to that of the population, sometimes in a different way depending on the species considered. In order to achieve reproducible results in experiments involving fish it is necessary to set and maintain all environmental parameters constant at the optimal value to guarantee the wellness of the animal. The effects of the variation in environmental parameters on the behaviour, physiology and cell biology of teleosts are here discussed in order to provide useful information for research based on fish models

Uncertain distance-based outlier detection with arbitrarily shaped data objects

AbstractEnabling information systems to face anomalies in the presence of uncertainty is a compelling and challenging task. In this work the problem of unsupervised outlier detection in large collections of data objects modeled by means of arbitrary multidimensional probability density functions is considered. We present a novel definition ofuncertain distance-based outlierunder the attribute level uncertainty model, according to which an uncertain object is an object that always exists but its actual value is modeled by a multivariate pdf. According to this definition an uncertain object is declared to be an outlier on the basis of the expected number of its neighbors in the dataset. To the best of our knowledge this is the first work that considers the unsupervised outlier detection problem on data objects modeled by means of arbitrarily shaped multidimensional distribution functions. We present the UDBOD algorithm which efficiently detects the outliers in an input uncertain dataset by taking advantages of three optimized phases, that are parameter estimation, candidate selection, and the candidate filtering. An experimental campaign is presented, including a sensitivity analysis, a study of the effectiveness of the technique, a comparison with related algorithms, also in presence of high dimensional data, and a discussion about the behavior of our technique in real case scenarios

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

Detecting and repairing anomalous evolutions in noisy environments: logic programming formalization and complexity results

In systems where agents are required to interact with a partially known and dynamic world, sensors can be used to obtain further knowledge about the environment. However, sensors may be unreliable, that is, they may deliver wrong information (due, e.g., to hardware or software malfunctioning) and, consequently, they may cause agents to take wrong decisions, which is a scenario that should be avoided. The paper considers the problem of reasoning in noisy environments in a setting where no (either certain or probabilistic) data is available in advance about the reliability of sensors. Therefore, assuming that each agent is equipped with a background theory (in our setting, an extended logic program) encoding its general knowledge about the world, we define a concept of detecting an anomaly perceived in sensor data and the related concept of agent recovering to a coherent status of information. In this context, the complexities of various anomaly detection and anomaly recovery problems are studied.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 1Red de Universidades con Carreras en Informática (RedUNCI

Exploiting n-gram location for intrusion detection

Signature-based and protocol-based intrusion detection systems (IDS) are employed as means to reveal content-based network attacks. Such systems have proven to be effective in identifying known intrusion attempts and exploits but they fail to recognize new types of attacks or carefully crafted variants of well known ones. This paper presents the design and the development of an anomaly-based IDS technique which is able to detect content-based attacks carried out over application level protocols, like HTTP and FTP. In order to identify anomalous packets, the payload is split up in chunks of equal length and the n-gram technique is used to learn which byte sequences usually appear in each chunk. The devised technique builds a different model for each pair and uses them to classify the incoming traffic. Models are build by means of a semi-supervised approach. Experimental results witness that the technique achieves an excellent accuracy with a very low false positive rate