Profiling the chemical properties of Foeniculum vulgare Mill. and its flavonoids through comprehensive LC-MS/MS to evaluate their anti-motion sickness effect.

Foeniculum vulgare Mill. is a medicinal and food homologous plant, and it has various biological activities. Yet, no research has explored its anti-motion sickness effects. Chemical properties of fennel extracts (FvE) and flavonoids (Fvf) were analyzed based on UPLC-QTRAP-MS to elucidate its potential anti-motion sickness components in the present study. The mice models of motion sickness were stimulated by biaxial rotational acceleration. Behavioral experiments such as motion sickness index and open field test and the measurement of neurotransmitters were used to evaluate the efficacy of compounds on motion sickness. Results showed that FvE contains terpenes, alkaloids, flavonoids, etc. Eight flavonoids including quercetin-3ß-D-glucoside, rutin, hyperoside, quercetin, miquelianin, trifolin, isorhamnetin and kaempferol were identified in the purified Fvf. FvE and Fvf significantly reduced the motion sickness index of mice by 53.2% and 48.9%, respectively. Fvf also significantly alleviated the anxious behavior of mice after rotational stimulation. Among the eight flavonoids, isorhamnetin had the highest oral bioavailability and moderate drug-likeness index and thus speculated to be the bioactive compound in fennel for its anti-motion sickness effect. It reduced the release of 5-HT and Ach to alleviate the motion sickness response and improve the work completing ability of mice and nervous system dysfunction after rotational stimulation. This study provided in-depth understanding of the anti-motion sickness bioactive chemical properties of fennel and its flavonoids, which will contribute to the new development and utilization of fennel.

Multi-UAV simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments.

Target assignment and path planning are crucial for the cooperativity of multiple unmanned aerial vehicles (UAV) systems. However, it is a challenge considering the dynamics of environments and the partial observability of UAVs. In this article, the problem of multi-UAV target assignment and path planning is formulated as a partially observable Markov decision process (POMDP), and a novel deep reinforcement learning (DRL)-based algorithm is proposed to address it. Specifically, a target assignment network is introduced into the twin-delayed deep deterministic policy gradient (TD3) algorithm to solve the target assignment problem and path planning problem simultaneously. The target assignment network executes target assignment for each step of UAVs, while the TD3 guides UAVs to plan paths for this step based on the assignment result and provides training labels for the optimization of the target assignment network. Experimental results demonstrate that the proposed approach can ensure an optimal complete target allocation and achieve a collision-free path for each UAV in three-dimensional (3D) dynamic multiple-obstacle environments, and present a superior performance in target completion and a better adaptability to complex environments compared with existing methods.