Resveratrol is a natural polyphenol and possesses many biological functions such as anti-inflammatory activity and protection against atherosclerosis and myocardial infraction. Parkinson's disease is a common progressive neurodegenerative disease. 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is the most useful neurotoxin to induce Parkinsonism. The present study was carried out to elucidate the neuroprotective effect and possible mechanism of resveratrol on MPTP-induced striatal neuron loss. Sixty adult Balb/c mice were divided into four groups: sham operation, MPTP treatment (30 mg/kg, i.p.), MPTP combined with resveratrol administration (20 mg/kg, i.v.), and resveratrol treatment alone. Microdialysis and high-performance liquid chromatography were used to analyze dihydroxybenzoic acid (DHBA) that reflected the hydroxyl radical level. In the present study, we found MPTP chronic administration significantly induced motor coordination impairment in mice. After MPTP administration, the hydroxyl radical levels in substantia nigra were also significantly elevated and animals displayed severe neuronal loss. Resveratrol administration significantly protected mice from MPTP-induced motor coordination impairment, hydroxyl radical overloading, and neuronal loss. Our results demonstrated that resveratrol could elicit neuroprotective effects on MPTP-induced Parkinsonism through free radical scavenging.

The application of an unmanned vehicle system allows for accelerating the performance of various tasks. Due to limited capacities, such as battery power, it is almost impossible for a single unmanned vehicle to complete a large-scale mission area. An unmanned vehicle swarm has the potential to distribute tasks and coordinate the operations of many robots/drones with very little operator intervention. Therefore, multiple unmanned vehicles are required to execute a set of well-planned mission routes, in order to minimize time and energy consumption. A two-phase heuristic algorithm was used to pursue this goal. In the first phase, a tabu search and the 2-opt node exchange method were used to generate a single optimal path for all target nodes; the solution was then split into multiple clusters according to vehicle numbers as an initial solution for each. In the second phase, a tabu algorithm combined with a 2-opt path exchange was used to further improve the in-route and cross-route solutions for each route. This diversification strategy allowed for approaching the global optimal solution, rather than a regional one with less CPU time. After these algorithms were coded, a group of three robot cars was used to validate this hybrid path programming algorithm.