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Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen that can cause severe bacterial pneumonia. Amygdalin is the main active pharmaceutical ingredient of bitter almond, which has broad-spectrum antibacterial, anti-inflammatory, anti-oxidation and immunomodulatory effects. It is also the main ingredient of Yinhua Pinggan granule, which is commonly used to moisten the lung and relieve cough. However, little is known about the effects of amygdalin on MRSA. In this study, we found that amygdalin exhibited good antimicrobial activity in vitro against MRSA. Amygdalin has a protective effect on MRSA infected cells, and the effect is better when combined with levofloxacin. It also can reduce the adhesion and invasion of MRSA to cells. Amygdalin has anti-inflammatory and antioxidant effects, which can significantly reduce the increase of inflammatory factors and the production of ROS caused by infection. The protective mechanism of amygdalin on cells may be related to inhibiting the expression of NLRP3, ASC and IL-1β pyroptosis pathways. Taken together, our study suggests that amygdalin exerts antibacterial effects by affecting biofilm formation, the expression of virulence factors, and drug resistance genes. Amygdalin combined with levofloxacin has a protective effect on A549 cells infected with MRSA, and the mechanism may be related to the inhibition of inflammatory response, oxidative damage and pyroptosis. [ABSTRACT FROM AUTHOR]

Reusable launch vehicles need to face complex and diverse environments during flight. The design of rocket recovery control law based on traditional deep reinforcement learning (DRL) makes it difficult to obtain a set of network architectures that can adapt to multiple scenarios and multi-parameter uncertainties, and the performance of deep reinforcement learning algorithm depends on manual trial and error of hyperparameters. To solve this problem, this paper proposes a self-learning control method for launch vehicle recovery based on neural architecture search (NAS), which decouples deep network structure search and reinforcement learning hyperparameter optimization. First, using network architecture search technology based on a multi-objective hybrid particle swarm optimization algorithm, the proximal policy optimization algorithm of deep network architecture is automatically designed, and the search space is lightweight design in the process. Secondly, in order to further improve the landing accuracy of the launch vehicle, the Bayesian optimization (BO) method is used to automatically optimize the hyperparameters of reinforcement learning, and the control law of the landing phase in the recovery process of the launch vehicle is obtained through training. Finally, the algorithm is transplanted to the rocket intelligent learning embedded platform for comparative testing to verify its online deployment capability. The simulation results show that the proposed method can satisfy the landing accuracy of the launch vehicle recovery mission, and the control effect is basically the same as the landing accuracy of the trained rocket model under the untrained condition of model parameter deviation and wind field interference, which verifies the generalization of the proposed method. [ABSTRACT FROM AUTHOR]

During aerial combat, when an aircraft is facing an infrared air-to-air missile strike, infrared baiting technology is an important means of penetration, and the strategy of effective delivery of infrared bait is critical. To address this issue, this study proposes an improved deep deterministic policy gradient (DDPG) algorithm-based intelligent bait-dropping control method. Firstly, by modeling the relative motion between aircraft, bait, and incoming missiles, the Markov decision process of aircraft-bait-missile infrared effect was constructed with visual distance and line of sight angle as states. Then, the DDPG algorithm was improved by means of pre-training and classification sampling. Significantly, the infrared bait-dropping decision network was trained through interaction with the environment and iterative learning, which led to the development of the bait-dropping strategy. Finally, the corresponding environment was transferred to the Nvidia Jetson TX2 embedded platform for comparative testing. The simulation results showed that the convergence speed of this method was 46.3% faster than the traditional DDPG algorithm. More importantly, it was able to generate an effective bait-throwing strategy, enabling the aircraft to successfully evade the attack of the incoming missile. The strategy instruction generation time is only about 2.5 ms, giving it the ability to make online decisions. [ABSTRACT FROM AUTHOR]

Intensive aquaculture in high-density hybrid snakehead [Channa maculata (♀) × Channa argus (♂)] fishponds can lead to toxic conditions for fish. This study investigated nitrogen migration and transformation in these fishponds during different cultivation periods. Using qPCR technology, we analyzed the abundance variation of nitrogen-cycling microorganisms in water and sediment to reveal the nitrogen metabolism characteristics of hybrid snakehead fishponds. The results showed that fish biomass significantly impacts suspended particulate matter (SPM) flux. At the sediment–water interface, inorganic nitrogen fluxes showed predominant NO3−-N absorption by sediments and NH4+-N and NO2−-N release, especially in later cultivation stages. Sediments were rich in nirS and AMX 16S rRNA genes (ranging from 4.04 × 109 to 1.01 × 1010 and 1.19 × 108 to 2.62 × 108 copies/g, respectively) with nirS-type denitrifiers potentially dominating the denitrification process. Ammonia-oxidizing bacteria (AOB) were found to dominate the ammonia oxidation process over ammonia-oxidizing archaea (AOA) in both water and sediment. Redundancy analysis revealed a positive correlation between SPM flux, Chlorophyll a (Chl-a), and denitrification genes in the water, and between nitrogen-cycling genes and NH4+/NO2− fluxes at the interface. These findings provide a scientific basis for nitrogen control in hybrid snakehead fishponds. [ABSTRACT FROM AUTHOR]

TiO2 is a typical anti‐ultraviolet agent in sunscreen cream, but it suffers from a lack of anti‐oxidant activity for scavenging reactive oxygen species. Other traditional sunscreen ingredients, such as C60 and ferulic acid, have moderate antioxidant and UV protection capabilities, and the products do not provide further UV protection. Herein, titanium hydride (TiH1.97) particles with dual functions of anti‐oxidant and anti‐ultraviolet are presented as an active material to remove the highly oxidizing and harmful hydroxide free radicals from the skin surface. Owing to the active hydrogen in TiH2, it shows great potential to eliminate hydroxyl radicals, and the rate is roughly proportional to the exposed surface area (50% /20 min). Together with the biocompatibility of the in situ formed TiO2, which has a strong ability to absorb ultraviolet light during OH radical scavenging. At the same time, adding titanium hydride to sunscreen also enhances sun protection. The SPF value of sunscreen containing 1% titanium hydride and 20% titanium dioxide can reach 31.8, which exceeds the 20% titanium dioxide (15) by more than twofold. Therefore, titanium hydride with anti‐ultraviolet and antioxidant functions can be regarded as a promising and safe ingredient for sunscreen cream. [ABSTRACT FROM AUTHOR]