7,8-Dihydroxyflavone (7,8-DHF), a tyrosine kinase B agonist that mimics the neuroprotective properties of brain-derived neurotrophic factor, which can not efficiently deliver into the brain, has been reported to be useful in ameliorating cognitive impairment in many diseases. Researches have indicated that apolipoprotein E-knockout (ApoE-KO) mouse was associated with cognitive alteration via various mechanisms. Our present study investigated the possible mechanisms of cognitive impairment of ApoE-KO mouse fed with western type diet and the protective effects of 7,8-DHF in improving spatial learning and memory in ApoE-KO mouse. Five-weeks-old ApoE-KO mice and C57BL/6 mice were chronically treated with 7,8-DHF (with a dosage of 5 mg/kg) or vehicles orally for 25 weeks, and then subjected to Morris water maze at the age of 30 weeks to evaluate the cognitive performances. Afterward, histology analysis and western blotting were performed. Spatial learning and memory deficits were observed in ApoE-KO mice, which were consistent with higher expression of active-asparaginyl endopeptidase (active-AEP) as well as AEP-derived truncated tau N368 compared with normal group. In addition to that, long-term treatment of 7,8-DHF dramatically ameliorated cognitive decline in ApoE-KO mice, accompanied by the activation in phosphorylated protein kinase B (Akt)/glycogen synthase kinase-3β (GSK-3β) pathway and down-regulated expression of tau S396 and PHF-tau (phosphorylated tau at ser396 and ser404 epitope). These findings suggested that cognitive impairment of ApoE-KO mouse might associate with tau pathology and 7,8-DHF could activate AKT and then phosphorylate its downstream molecule to inhibit expression of abnormal tau, meanwhile, 7,8-DHF could reduce the expression of active-AEP and then inhibit production of truncated tau N368.
An airport’s terminal area is the bottleneck of the air transport system. Convective weather can seriously affect the normal flight status of arrival and departure flights. At present, pilots take different flight operation strategies to avoid convective weather based on onboard radar, visual information, adverse weather experience, etc. This paper studies trajectory clustering based on the OPTICS algorithm to obtain the arrival of typical flight routes in the terminal area. Based on weather information of the planned typical flight route and flight plan information, Random Forest (RF), K-nearest Neighbor KNN (KNN), and Support Vector Machines (SVM) algorithms were used for training and establishing the Arrival Flight Operation Strategy Prediction Model (AFOSPM). In this paper, case studies of historical arrival flights in the Guangzhou (ZGGG) and Wuhan (ZHHH) terminal area were carried out. The results show that trajectory clustering results based on the OPTICS algorithm can more accurately reflect the regular flight routes of arrival flights in a terminal area. Compared to KNN and SVM, the prediction accuracy of AFOSPM based on RF is better, reaching more than 88%. On this basis, six features—including 90% VIL, weather coverage, weather duration, planned route, max VIL, and planned Arrival Gate (AF)—were used as the input features for AFOSPM, which can effectively predict various arrival flight operation strategies. For the most frequently used arrival flight operation strategies under convective weather conditions—radar guidance, AF changing, and diversion strategy—the prediction accuracy of the ZGGG and ZHHH terminal areas can exceed 95%, 85%, and 80%, respectively.
With the rapid growth of flight volume, the impact of convective weather on flight operations in the terminal area has become more and more serious. In this paper, the typical flight paths (TFPs) are used to replace flight procedures as the routine flight paths in the terminal area, and the TFP of each flight is predicted by Random Forest (RF), Boosting Tree (BT) and K-Nearest Neighbour (KNN) algorithms based on the weather and flight plan characteristics. A multi-flight rerouting optimisation model by bi-level programming is established, which contains a flight flow optimisation model in the upper layer and a single flight path optimisation model in the lower layer. The simulated annealing algorithm and the bidirectional A* algorithm are used to solve the upper and lower models. This paper uses the terminal area of Guangzhou Baiyun Airport (ZGGG) and Wuhan Tianhe Airport (ZHHH) for case analysis. The RF algorithm has better performance in predicting TFPs compared with the BT and KNN algorithms. Compared to the historical radar trajectory, the flight path optimisation results show that for the Guangzhou terminal area, while meeting the Terminal Airspace Availability (TAA) as constraint, the flight flow increases and the flight distance reduces, effectively improving the operational efficiency within the terminal.
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