Here, we report the association between depressive behavior (anhedonia) and astroglial expression of 5-hydroxytryptamine receptor 2B (5-HT2B) in an animal model of Parkinson’s disease, induced by bilateral injection of 6-hydroxydopamine (6-OHDA) into the striatum. Expression of the 5-HT2B receptor at the mRNA and protein level was decreased in the brain tissue of 6-OHDA-treated animals with anhedonia. Expression of the 5-HT2B receptor was corrected by four weeks treatment with either l-3,4-dihydroxyphenylalanine (l-dopa) or fluoxetine. Simultaneously, treatment with l-dopa abolished 6-OHDA effects on both depressive behavior and motor activity. In contrast, fluoxetine corrected 6-OHDA-induced depression but did not affect 6-OHDA-induced motor deficiency. In addition, 6-OHDA downregulated gene expression of the 5-HT2B receptor in astrocytes in purified cell culture and this downregulation was corrected by both l-dopa and fluoxetine. Our findings suggest that 6-OHDA-induced depressive behavior may be related to the downregulation of gene expression of the 5-HT2B receptor but 6-OHDA-induced motor deficiency reflects, arguably, dopamine depletion. Previously, we demonstrated that fluoxetine regulates gene expression in astrocytes by 5-HT2B receptor-mediated transactivation of epidermal growth factor receptor (EGFR). However, the underlying mechanism of l-dopa action remains unclear. The present work indicates that the decrease of gene expression of the astroglial 5-HT2B receptor may contribute to development of depressive behavior in Parkinson’s disease.

<p>To support the Puerto Rico hurricane disaster scenario, we develop a DroneGo disaster response system by establishing the following models. First, we establish a location analysis model for ISO containers based on the coverage of video reconnaissance and the priority comparison between the two required missions–medical supply delivery and video reconnaissance. According to the locations of 11 harbors in Puerto Rico, we select three suitable harbors to position three cargo containers called CON 1, 2 and 3 to conduct the missions. Second, we build two packing configuration models to design the packing configuration for containers. In one model, we recommend a drone fleet for CON 1 and 3 according to reconnaissance conditions, and then put drones into containers in order. In another model for CON 2, we determine the type of drones according to the medical supply demands of hospitals. For both models, the number of drones of each type is determined by the enumeration method and the packing placement is determined by the greedy algorithm. The algorithms are coded in Visual C++ and MATLAB. The computational results show that the space utilizations for the three containers are all above 80.8%. Third, we design a drone flight plan model based on graph theory. According to the time and space constraints of drones, we devise flight plans as well as delivery routes and schedule. The computational results show that the coverage of video reconnaissance is up to 70.1%. Finally, we carry out the error and sensitivity analysis, discuss the strengths and weaknesses of our models, and design the future work. In addition, a two-page memo that summarizes our modeling results, conclusions, and recommendations is given at the end of the paper.</p>

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