What ethologically based models have taught us about the neural systems underlying fear and anxiety

Classical Pavlovian fear conditioning to painful stimuli has provided the generally accepted view of a core system centered in the central amygdala to organize fear responses. Ethologically based models using other sources of threat likely to be expected in a natural environment, such as predators or aggressive dominant conspecifics, have challenged this concept of a unitary core circuit for fear processing. We discuss here what the ethologically based models have told us about the neural systems organizing fear responses. We explored the concept that parallel paths process different classes of threats, and that these different paths influence distinct regions in the periaqueductal gray - a critical element for the organization of all kinds of fear responses. Despite this parallel processing of different kinds of threats, we have discussed an interesting emerging view that common cortical-hippocampal-amygdalar paths seem to be engaged in fear conditioning to painful stimuli, to predators and, perhaps, to aggressive dominant conspecifics as well. Overall, the aim of this review is to bring into focus a more global and comprehensive view of the systems organizing fear responses.

Compulsive features in behavioural addictions: the case of pathological gambling

Aims To describe, in the context of DSM-V, how a focus on addiction and compulsion is emerging in the consideration of pathological gambling (PG). Methods A systematic literature review of evidence for the proposed re-classification of PG as an addiction. Results Findings include: (i) phenomenological models of addiction highlighting a motivational shift from impulsivity to compulsivity associated with a protracted withdrawal syndrome and blurring of the ego-syntonic/ego-dystonic dichotomy; (ii) common neurotransmitter (dopamine, serotonin) contributions to PG and substance use disorders (SUDs); (iii) neuroimaging support for shared neurocircuitries between behavioural and substance addictions and differences between obsessivecompulsive disorder (OCD), impulse control disorders (ICDs) and SUDs; (iv) genetic findings more closely related to endophenotypic constructs such as compulsivity and impulsivity than to psychiatric disorders; (v) psychological measures such as harm avoidance identifying a closer association between SUDs and PG than with OCD; (vi) community and pharmacotherapeutic trials data supporting a closer association between SUDs and PG than with OCD. Adapted behavioural therapies, such as exposure therapy, appear applicable to OCD, PG or SUDs, suggesting some commonalities across disorders. Conclusions PG shares more similarities with SUDs than with OCD. Similar to the investigation of impulsivity, studies of compulsivity hold promising insights concerning the course, differential diagnosis and treatment of PG, SUDs, and OCD.

The role of the ventrolateral caudoputamen in predatory hunting

The ventrolateral caudoputamen (VLCP) is well known to participate in the control of orofacial movements and forepaw usage accompanying feeding behavior. Previous studies from our laboratory have shown that insect hunting is associated with a distinct Fos up-regulation in the VLCP at intermediate rostro-caudal levels. Moreover, using the reversible blockade with lidocaine, we have previously suggested that the VLCP implements the stereotyped actions seen during prey capture and handling, and may influence the motivational drive to start attacking the roaches, as well. However, considering that (1) lidocaine suppresses action potentials not only in neurons, but also in fibers-of-passage, rendering the observed behavioral effect not specific to the ventrolateral caudoputamen; (2) the short lidocaine-induced inactivation period had left a relatively narrow window to observe the behavioral changes; and (3) that the restriction stress to inject the drug could have also disturbed hunting behavior, in the present study, we have examined the role of the VLCP in predatory hunting by placing bilateral NMDA lesions three weeks previous to the behavior testing. We were able to confirm that the VLCP serves to implement the stereotyped sequence of actions seen during prey capture and handling, but the study did not confirm its role in influencing the motivational drive to hunt. Together with other studies from our group, the present work serves as an important piece of information that helps to reveal the neural systems underlying predatory hunting. (C) 2011 Elsevier Inc. All rights reserved.

New approach for power transformer protection based on intelligent hybrid systems

A power transformer needs continuous monitoring and fast protection as it is a very expensive piece of equipment and an essential element in an electrical power system. The most common protection technique used is the percentage differential logic, which provides discrimination between an internal fault and different operating conditions. Unfortunately, there are some operating conditions of power transformers that can mislead the conventional protection affecting the power system stability negatively. This study proposes the development of a new algorithm to improve the protection performance by using fuzzy logic, artificial neural networks and genetic algorithms. An electrical power system was modelled using Alternative Transients Program software to obtain the operational conditions and fault situations needed to test the algorithm developed, as well as a commercial differential relay. Results show improved reliability, as well as a fast response of the proposed technique when compared with conventional ones.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.