NEONATAL HANDLING AND THE MATERNAL ODOR PREFERENCE IN RAT PUPS: INVOLVEMENT OF MONOAMINES AND CYCLIC AMP RESPONSE ELEMENT-BINDING PROTEIN PATHWAY IN THE OLFACTORY BULB

Early-life environmental events, such as the handling procedure, can induce long-lasting alterations upon several behavioral and neuroendocrine systems. However, the changes within the pups that could be causally related to the effects in adulthood are still poorly understood. In the present study, we analyzed the effects of neonatal handling on behavioral (maternal odor preference) and biochemical (cyclic AMP response element-binding protein (CREB) phosphorylation, noradrenaline (NA), and serotonin (5-HT) levels in the olfactory bulb (OB)) parameters in 7-day-old male and female rat pups. Repeated handling (RH) abolished preference for the maternal odor in female pups compared with nonhandled (NH) and the single-handled (SH) ones, while in RH males the preference was not different than NH and SH groups. In both male and female pups, RH decreased NA activity in the OB, but 5-HT activity increased only in males. Since preference for the maternal odor involves the synergic action of NA and 5-HT in the OB, the maintenance of the behavior in RH males could be related to the increased 5-HT activity, in spite of reduction in the NA activity in the OB. RH did not alter CREB phosphorylation in the OB of both male and females compared with NH pups. The repeated handling procedure can affect the behavior of rat pups in response to the maternal odor and biochemical parameters related to the olfactory learning mechanism. Sex differences were already detected in 7-day-old pups. Although the responsiveness of the hypothalamic-pituitary-adrenal axis to stressors is reduced in the neonatal period, environmental interventions may impact behavioral and biochemical mechanisms relevant to the animal at that early age. (C) 2009 IBRO. Published by Elsevier Ltd. All rights reserved.

Infeasibility handling in genetic algorithm using nested domains for production planning

In this paper we present a genetic algorithm with new components to tackle capacitated lot sizing and scheduling problems with sequence dependent setups that appear in a wide range of industries, from soft drink bottling to food manufacturing. Finding a feasible solution to highly constrained problems is often a very difficult task. Various strategies have been applied to deal with infeasible solutions throughout the search. We propose a new scheme of classifying individuals based on nested domains to determine the solutions according to the level of infeasibility, which in our case represents bands of additional production hours (overtime). Within each band, individuals are just differentiated by their fitness function. As iterations are conducted, the widths of the bands are dynamically adjusted to improve the convergence of the individuals into the feasible domain. The numerical experiments on highly capacitated instances show the effectiveness of this computational tractable approach to guide the search toward the feasible domain. Our approach outperforms other state-of-the-art approaches and commercial solvers. (C) 2009 Elsevier Ltd. All rights reserved.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

Formalization of an architectural model for exception handling coordination based on CA action concepts

Architectures based on Coordinated Atomic action (CA action) concepts have been used to build concurrent fault-tolerant systems. This conceptual model combines concurrent exception handling with action nesting to provide a general mechanism for both enclosing interactions among system components and coordinating forward error recovery measures. This article presents an architectural model to guide the formal specification of concurrent fault-tolerant systems. This architecture provides built-in Communicating Sequential Processes (CSPs) and predefined channels to coordinate exception handling of the user-defined components. Hence some safety properties concerning action scoping and concurrent exception handling can be proved by using the FDR (Failure Divergence Refinement) verification tool. As a result, a formal and general architecture supporting software fault tolerance is ready to be used and proved as users define components with normal and exceptional behaviors. (C) 2010 Elsevier B.V. All rights reserved.