In vitro and in vivo effects of methyl isocyanate on rat brain mitochondrial respiration

The present study deals with the in vitro and in vivo effects of methyl isocyanate (MIC) on rat brain mitochondrial function. Addition of MIC to tightly coupled brain mitochondria in vitro resulted in a mild stimulation of state 4 respiration, abolition of respiratory control, decrease in ADP/0 ratio, and inhibition of state 3 oxidation. The oxidation of NAD+-linked substrates (glutamate + malate) was more sensitive (fourfold) to the inhibitory action of MIC than succinate while cytochrome oxidase was unaffected. Administration of MIC subcutaneously at a lethal dose affected respiration only with glutamate + malate as the substrate (site I) and caused a 20% decrease in state 3 oxidation leading to a significant decrease in respiratory control index while state 4 respiration and ADP/O ratio remained unaffected. As both the malondialdehyde and iron contents of brain mitochondria were not altered, it may be inferred that the observed in vivo inhibition of state 3 oxidation is induced by MIC through systemic stagnant hypoxia leading to ischemia of brain, which further contributes to the cerebral hypoxia.

Regulation of diacylglycerol kinase in rat brain membranes by docosahexaenoic acid

The effect of docosahexaenoic acid (DHA) on the diacylglycerol kinase (DG kinase) activity in rat brain membranes was investigated. DHA at 500 mu M concentration, stimulated the enzyme activity by about 2 fold. This effect was concentration-and time-dependent and was observed after very short periods of incubation (one min). DHA stimulation of DG kinase was observed only with rat brain membranes, and not with rat brain cytosol or rat liver membranes. Treating the rat brain membranes with phospholipase A(2) which released free fatty acids including DHA, significantly stimulated the DG kinase activity. It is concluded that DHA through its stimulatory effect on DG kinase may regulate the signalling events in growth-related situations in the brain such as synaptogenesis.

A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems

Even though dynamic programming offers an optimal control solution in a state feedback form, the method is overwhelmed by computational and storage requirements. Approximate dynamic programming implemented with an Adaptive Critic (AC) neural network structure has evolved as a powerful alternative technique that obviates the need for excessive computations and storage requirements in solving optimal control problems. In this paper, an improvement to the AC architecture, called the �Single Network Adaptive Critic (SNAC)� is presented. This approach is applicable to a wide class of nonlinear systems where the optimal control (stationary) equation can be explicitly expressed in terms of the state and costate variables. The selection of this terminology is guided by the fact that it eliminates the use of one neural network (namely the action network) that is part of a typical dual network AC setup. As a consequence, the SNAC architecture offers three potential advantages: a simpler architecture, lesser computational load and elimination of the approximation error associated with the eliminated network. In order to demonstrate these benefits and the control synthesis technique using SNAC, two problems have been solved with the AC and SNAC approaches and their computational performances are compared. One of these problems is a real-life Micro-Electro-Mechanical-system (MEMS) problem, which demonstrates that the SNAC technique is applicable to complex engineering systems.

Adaptive optimization of continuous bioreactor using neural network model

An adaptive optimization algorithm using backpropogation neural network model for dynamic identification is developed. The algorithm is applied to maximize the cellular productivity of a continuous culture of baker's yeast. The robustness of the algorithm is demonstrated in determining and maintaining the optimal dilution rate of the continuous bioreactor in presence of disturbances in environmental conditions and microbial culture characteristics. The simulation results show that a significant reduction in time required to reach optimal operating levels can be achieved using neural network model compared with the traditional dynamic linear input-output model. The extension of the algorithm for multivariable adaptive optimization of continuous bioreactor is briefly discussed.

Optimal management of beaver population using a reduced-order distributed parameter model and single network adaptive critics

Beavers are often found to be in conflict with human interests by creating nuisances like building dams on flowing water (leading to flooding), blocking irrigation canals, cutting down timbers, etc. At the same time they contribute to raising water tables, increased vegetation, etc. Consequently, maintaining an optimal beaver population is beneficial. Because of their diffusion externality (due to migratory nature), strategies based on lumped parameter models are often ineffective. Using a distributed parameter model for beaver population that accounts for their spatial and temporal behavior, an optimal control (trapping) strategy is presented in this paper that leads to a desired distribution of the animal density in a region in the long run. The optimal control solution presented, imbeds the solution for a large number of initial conditions (i.e., it has a feedback form), which is otherwise nontrivial to obtain. The solution obtained can be used in real-time by a nonexpert in control theory since it involves only using the neural networks trained offline. Proper orthogonal decomposition-based basis function design followed by their use in a Galerkin projection has been incorporated in the solution process as a model reduction technique. Optimal solutions are obtained through a "single network adaptive critic" (SNAC) neural-network architecture.

A novel neural network design for long range prediction of rainfall pattern

The importance of long-range prediction of rainfall pattern for devising and planning agricultural strategies cannot be overemphasized. However, the prediction of rainfall pattern remains a difficult problem and the desired level of accuracy has not been reached. The conventional methods for prediction of rainfall use either dynamical or statistical modelling. In this article we report the results of a new modelling technique using artificial neural networks. Artificial neural networks are especially useful where the dynamical processes and their interrelations for a given phenomenon are not known with sufficient accuracy. Since conventional neural networks were found to be unsuitable for simulating and predicting rainfall patterns, a generalized structure of a neural network was then explored and found to provide consistent prediction (hindcast) of all-India annual mean rainfall with good accuracy. Performance and consistency of this network are evaluated and compared with those of other (conventional) neural networks. It is shown that the generalized network can make consistently good prediction of annual mean rainfall. Immediate application and potential of such a prediction system are discussed.