Cellular stress responses can be activated following functional defects in organelles such as mitochondria and the endoplasmic reticulum. Mitochondrial dysfunction caused by loss of the serine protease HtrA2 leads to a progressive movement disorder in mice and has been linked to parkinsonian neurodegeneration in humans. Here, we demonstrate that loss of HtrA2 results in transcriptional upregulation of nuclear genes characteristic of the integrated stress response, including the transcription factor CHOP, selectively in the brain. We also show that loss of HtrA2 results in the accumulation of unfolded proteins in the mitochondria, defective mitochondrial respiration and enhanced production of reactive oxygen species that contribute to the induction of CHOP expression and to neuronal cell death. CHOP expression is also significantly increased in Parkinson's disease patients' brain tissue. We therefore propose that this brain-specific transcriptional response to stress may be important in the advance of neurodegenerative diseases.

A study was done to formulate and solve the multiobjective network design problem with uncertain demand. Various samples of demand are realized for optimal improvements in the network while the objectives of the expected total system travel time and the higher moment for total system travel time are minimized. A formulation is proposed for multi-objective robust network design, and a solution methodology is developed on the basis of a revised fast and elitist nondominated sorting genetic algorithm. The developed methodology has been tested on the Nguyen-Dupuis network, and various Pareto optimal solutions are compared with earlier work on the single-objective robust network design problem. A real medium-size network was solved to prove efficacy of the model. The results show better solutions for the multiobjective robust network design problem with relatively less computational effort.

Existing optimal road-network capacity-expansion models are based on minimizing travel time and rarely consider environmental factors such as vehicular emissions. In this study we attempt to solve such a transportation network design problem when the planner is environment conscious and thereby tries to minimize health-damage cost due to vehicular emissions along with total system travel time while performing optimal capacity expansion. This problem can be formulated as a multiobjective optimization model which minimizes emissions in addition to travel time, and under budget constraints. A prerequisite for this model is an accurate estimation of vehicle emissions due to changes in link capacities. Since the current practice of estimation of vehicular emissions by aggregate emission factors does not account for the improved speeds resulting from capacity improvements, speed-dependent emission functions for various transport modes and pollutants are used in this study. These functions help in calculating emission factors for use in the proposed model. The model uses a nondominated sorting genetic algorithm as the optimization tool to solve the network design problem. The model is tested on a small hypothetical network and solved for a real large-sized network in India taking into account three pollutants and five transport modes. The Pareto-optimal solutions generated can act as trade-offs between total emissions and total system travel time to account for the planner's desired objectives. Also, reduction in travel time as well as in emissions supports the present model compared with the single-objective model.

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