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The effect of rearing temperature on gonad differentiation and growth was evaluated in sea bass ( Dicentrarchus labrax ). One control group (CG, n = 60) and two experimental groups (EG1, n = 30; EG2, n = 30) were selected. CG was reared at 15.0 °C during 10 days post-hatch (dph) and at 19.0 °C throughout the remaining larval and post-larval development. EG1 was exposed to 14.5 °C from 1 to 50 dph, followed by an increase to 20 °C until sampling (176 dph). EG2 was exposed to 14.5 °C from 1 to 37 dph, followed by an increase to 20 °C until sampling (226 dph); 30 fish from CG were randomly sampled at 176 dph (CG1, control for EG1) and 30 fish were sampled at 226 dph (CG2, control for EG2). Weight, total and fork length measurement, and gonad collection were performed. All biometric indices were higher in EG1 and EG2 than in CG1 and CG2, respectively. Histologic analysis reported 100% of undifferentiated gonads in CG1 and; EG1 reported 80% of undifferentiated gonads and 20% of gonads at early differentiation toward the male line. CG2 reported 34% of undifferentiated gonads, 10% of gonads at early differentiation toward the female line, 23% of gonads at early differentiation toward the male line, and 33% of gonads with intratesticular oocytes. EG2 reported 47% of gonads differentiated in testis, 33% gonads differentiated in ovary, and 20% of gonads with intratesticular oocytes. These results suggest that water temperature plays an important role in the process of gonad differentiation and growth of sea bass. [ABSTRACT FROM AUTHOR]

Abstract: Nowadays wireless body sensor networks (WBSNs) have great potential to enable a broad variety of assisted living applications such as human biophysical/biochemical control and activity monitoring for health care, e-fitness, emergency detection, emotional recognition for social networking, security, and highly interactive games. It is therefore important to define design methodologies and programming frameworks which enable rapid prototyping of WBSN applications. Several effective application development frameworks have been already proposed for WBSNs designed for TinyOS-based sensor platforms, e.g. CodeBlue, SPINE, and Titan. In this paper we present an application of MAPS, an agent framework for wireless sensor networks based on the Java-programmable Sun SPOT sensor platform, for the development of a real-time WBSN-based system for human activity monitoring. The agent-oriented programming abstractions provided by MAPS allow effective and rapid prototyping of the sensor-side software. In particular, the architecture of the developed system is a typical star-based WBSN composed of a coordinator node and two sensor nodes located respectively on the waist and the thigh of the monitored assisted living. The coordinator relies on a JADE-based enhancement of the SPINE coordinator and allows configuring sensors, receiving their data, and recognizing pre-defined human activities. On the other hand, each sensor node runs a MAPS-based agent that performs sensing of the 3-axial accelerometer sensor, computation of significant features on the acquired data, feature aggregation and transmission to the coordinator. The experimentation phase of the prototype, which allows evaluating the obtainable monitoring performances and activity recognition accuracy, is described. Moreover, a comparison of the monitoring system based on MAPS, AFME and SPINE in terms of programming effectiveness and system performances is discussed. [Copyright &y& Elsevier]

Machine Learning (ML) techniques are becoming an invaluable support for network intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats. Typically, ML algorithms are exploited to classify/recognize data traffic on the basis of statistical features such as inter-arrival times, packets length distribution, mean number of flows, etc. Dealing with the vast diversity and number of features that typically characterize data traffic is a hard problem. This results in the following issues: (i) the presence of so many features leads to lengthy training processes (particularly when features are highly correlated), while prediction accuracy does not proportionally improve; (ii) some of the features may introduce bias during the classification process, particularly those that have scarce relation with the data traffic to be classified. To this end, by reducing the feature space and retaining only the most significant features, Feature Selection (FS) becomes a crucial pre-processing step in network management and, specifically, for the purposes of network intrusion detection. In this review paper, we complement other surveys in multiple ways: (i) evaluating more recent datasets (updated w.r.t. obsolete KDD 99) by means of a designed-from-scratch Python-based procedure; (ii) providing a synopsis of most credited FS approaches in the field of intrusion detection, including Multi-Objective Evolutionary techniques; (iii) assessing various experimental analyses such as feature correlation, time complexity, and performance. Our comparisons offer useful guidelines to network/security managers who are considering the incorporation of ML concepts into network intrusion detection, where trade-offs between performance and resource consumption are crucial. [ABSTRACT FROM AUTHOR]

Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behavior. This is an important research problem, due to its broad set of application domains, from data analysis to e-health, cybersecurity, predictive maintenance, fault prevention, and industrial automation. Herein, we review state-of-the-art methods that may be employed to detect anomalies in the specific area of sensor systems, which poses hard challenges in terms of information fusion, data volumes, data speed, and network/energy efficiency, to mention but the most pressing ones. In this context, anomaly detection is a particularly hard problem, given the need to find computing-energy-accuracy trade-offs in a constrained environment. We taxonomize methods ranging from conventional techniques (statistical methods, time-series analysis, signal processing, etc.) to data-driven techniques (supervised learning, reinforcement learning, deep learning, etc.). We also look at the impact that different architectural environments (Cloud, Fog, Edge) can have on the sensors ecosystem. The review points to the most promising intelligent-sensing methods, and pinpoints a set of interesting open issues and challenges. • Survey and review of anomaly detection techniques for sensor systems. • Conventional vs data-driven techniques for anomaly detection in sensor systems. • Machine learning in the Internet of Things via Cloud, Fog and Edge computing. • Challenges and open issues for intelligent sensing and anomaly detection. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the possible correlation between biometric indices (length and weight) and some blood parameters in 225 Gilthead sea bream (Sparus aurata)and 225 European sea bass (Dicentrarchus labrax)cultured in onshore tanks with the same physical–chemical conditions of water (temperature, soluble oxygen and salinity). From each fish, blood samples were collected to evaluate Red Blood Cell Count, Haematocrit, Haemoglobin Concentration, White Blood Cell Count, Thrombocyte Count, Mean Corpuscular Volume, Mean Corpuscular Haemoglobin and Mean Corpuscular Haemoglobin Concentration, glucose, lactate and Total Proteins. In both species, the relationship between the biometric indices and blood parameters was evaluated using linear regression analysis that showed a statistically significant correlation between the biometric indices and seven of the studied parameters. These results showed that fish size is an important factor in the interpretation of haematological and biochemical profiles in order to evaluate correctly the health status of the fish. [ABSTRACT FROM AUTHOR]

The article presents a study which focuses on the evaluation of the impact of the three different anticoagulants and storage time on the count of the leukocyte of Mugil cephalus (M.) cephalus. The study uses the two-way analysis of variance (ANOVA). Results show that utilization of the ethylenediamine tetraacetic acid (EDTA) s anticoagulant.