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Background Ergothioneine (EGT) has a unique antioxidant ability and diverse beneficial effects on human health. But the content of EGT is very low in its natural producing organisms such as Mycobacterium smegmatis and mushrooms. Therefore, it is necessary to highly efficient heterologous production of EGT in food-grade yeasts such as Saccharomyces cerevisiae . Results Two EGT biosynthetic genes were cloned from the mushroom Grifola frondosa and successfully heterologously expressed in Saccharomyces cerevisiae EC1118 strain in this study. By optimization of the fermentation conditions of the engineered strain S. cerevisiae EC1118, the 11.80 mg/L of EGT production was obtained. With daily addition of 1% glycerol to the culture medium in the fermentation process, the EGT production of the engineered strain S. cerevisiae EC1118 can reach up to 20.61 mg/L. Conclusion A successful EGT de novo biosynthetic system of S. cerevisiae containing only two genes from mushroom Grifola frondosa was developed in this study. This system provides promising prospects for the large scales production of EGT for human health.

This paper investigates the string stability and traffic flow stability based on an improved quadratic spacing policy for heterogeneous vehicular platoons with actuator faults. Due to the occurrence of actuator faults, the maximum acceleration changes, which may invalid the traditional quadratic spacing policy. To tackle the dilemma, an improved quadratic spacing policy with the lower bound of fault factor is proposed. Furthermore, the improved quadratic spacing policy removes the assumption of zero initial spacing errors. Then, by employing the adaptive fuzzy logic system technique and the PID-type sliding mode control method, an adaptive fuzzy fault-tolerant controller is designed to guarantee individual vehicle stability and string stability after reaching the sliding surface. In addition, traffic flow stability is also ensured thanks to the improved quadratic spacing policy. Finally, simulation results have demonstrated the reliability and effectiveness of the presented method.

This paper is concerned with a robust adaptive fault-tolerant compensation control problem based on sliding mode technique for an unmanned marine vehicle (UMV) with thruster faults and unknown persistent ocean disturbances. A general thruster fault model including partial, total and time-varying stuck is built for the first time. Once the thrusters occur unknown and time-varying stuck faults, the mission of the UMV may be canceled. To avoid it, full-rank decomposition of the thruster configuration matrix is made, based on which a linear sliding surface is constructed and adaptive mechanism is incorporated into sliding mode reaching law. Without the prior knowledge of ocean external disturbances, sliding mode stability is analyzed and a sufficient stability condition through H∞ technique is given. Further the nonlinear unit vector gain of the adaptive sliding mode fault-tolerant compensation controller is designed to ensure the UMV system errors converge to zero independent of fault detection and diagnosis (FDD) mechanism. Finally, the comparison simulation results through a typical floating production ship are shown to testify the feasibility of the presented method.

This paper attempts to develop a unified model predictive control (MPC) method for integrated path planning and trajectory tracking of autonomous underwater vehicles (AUVs). To deal with the computational burden of online path planning, an event-triggered model predictive control (EMPC) method is introduced by using the environmental change as a triggering mechanism. A collision hazard function utilizing the changing rate of hazard as a triggering threshold is proposed to guarantee safety. We further give an illustration of how to calculate this threshold. Then, a Lyapunov-based model predictive control (LMPC) framework is developed for the AUV to solve the trajectory tracking problem. Leveraging a nonlinear integral sliding mode control strategy, we construct the contraction constraint within the formulated LMPC framework, thereby theoretically ensuring closed-loop stability. We derive the necessary and sufficient conditions for recursive feasibility, which are subsequently used to prove the closed-loop stability of the system. In the simulations, the proposed path planning and tracking control are verified separately and integrated and combined with static and dynamic obstacles.

Mucoadhesive microparticles for oromucosal drug delivery offer several advantages, including intimate contact with the mucosa, delivery to less accessible regions, extended residence time, sustained drug release, reduced irritation, and improved patient compliance. In this study, pullulan was used to prepare mucoadhesive spray-dried microparticles for delivering benzydamine hydrochloride (BZH) to oral mucosa. The BZH-pullulan spray-dried microparticles had a mean size of <25 μm with an angle of repose values between 25.8–36.6°. Pullulan markedly extended drug-release time to >180 min, ~9 times greater than the duration (i.e., 20 min) reportedly achieved by chitosan. Kinetic analysis showed the drug-release rate was concentration dependent and jointly controlled by drug diffusion and polymer chain relaxation. Further, pullulan was mucoadhesive and was able to retain up to 78.8% w/w of microencapsulated gold nanoparticle probes at the mucosal membrane. These data strongly suggest that BZH-pullulan microparticles have great potential for oromucosal drug delivery, by providing elongated residence time in situ and sustained drug release for the treatment of local diseases.

Predefined-time control has been extensively implemented in marine control systems due to its capability to enhance transient performance and achieve superior control specifications. However, inaccurate control execution resulting from faulty actuators can compromise this control strategy and critically undermine system performance. To address this challenge, this paper propose a predefined-time model predictive fault-tolerant control strategy for unmanned surface vessels (USVs) while considering actuator failures and ocean disturbances. Firstly, a novel predefined-time model predictive control (PTMPC) strategy is designed by incorporating contraction constraints derived from an auxiliary predefined-time control system into the proposed optimization framework. This ensures that the resulting control variables guarantee predefined-time convergence of tracking errors when applied to the USV system. Furthermore, a long short-term memory-based neural network for disturbance prediction is integrated into the control strategy, leveraging its exceptional capability in modeling temporal sequences to achieve accurate forecasting of ocean disturbances. Thirdly, the proposed control scheme utilizes its integrated fault observation mechanism to actively compensate for actuator failures through real-time fault estimation, ensuring predefined-time convergence performance while providing rigorous guarantees of closed-loop stability and feasibility. Finally, simulation results demonstrate the efficacy and superiority of the proposed algorithm.

Silicon (Si) has been demonstrated to enhance oat resistance to stem rust, caused by Puccinia graminis f. sp. avenae ( Pga ). However, the molecular mechanisms underlying Si-mediated resistance against Pga remain poorly characterized. To address this, we performed transcriptomic and metabolomic analyses on oat plants treated with or without Si and inoculated with Pga . Our results showed that Si treatment increased the activities of catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD) under Pga infection, thereby inhibiting reactive oxygen species (ROS) accumulation. Transcriptomic analysis identified 143 differentially expressed genes (55 upregulated, 88 downregulated) in Si-treated plants. Most of these genes were associated with diterpenoid biosynthesis, zeatin biosynthesis, and phenylpropanoid biosynthesis. Metabolomic profiling revealed 69 significantly enriched metabolites, including carbohydrates, organic acids, amino acids, and secondary metabolites. Based on KEGG database annotation, these metabolites were primarily involved in arginine biosynthesis; alanine, aspartate, and glutamate metabolism; cyanoamino acid metabolism; aminoacyl-tRNA biosynthesis; pyrimidine metabolism; and purine metabolism. Integrative analysis of transcriptome and metabolome data indicated that Si treatment significantly altered key metabolic pathways in oat leaves, including tryptophan metabolism, glyoxylate and dicarboxylate metabolism, porphyrin metabolism, and chlorophyll metabolism. Collectively, these findings provide novel molecular insights into Si-mediated enhancement of oat resistance to stem rust.

Convolutional neural network has shown strong capability to improve performance in vehicle detection, which is one of the main research topics of intelligent transportation system. Aiming to detect the blocked vehicles efficiently in actual traffic scenes, we propose a novel convolutional neural network based on multi-target corner pooling layers. The hourglass network, which could extract local and global information of the vehicles in the images simultaneously, is chosen as the backbone network to provide vehicles’ features. Instead of using the max pooling layer, the proposed multi-target corner pooling (MTCP) layer is used to generate the vehicles’ corners. And in order to complete the blocked corners that cannot be generated by MTCP, a novel matching corners method is adopted in the network. Therefore, the proposed network can detect blocked vehicles accurately. Experiments demonstrate that the proposed network achieves an AP of 43.5% on MS COCO dataset and a precision of 93.6% on traffic videos, which outperforms the several existing detectors.

In the complex ocean environment, the thruster faults may affect the stability of unmanned surface vessel (USV). So it is really important to study the problem of USV fault tolerant control. Under the framework of integral sliding mode technology, this work presents a fault tolerant control problem for USV. First, a comprehensive thruster fault model with total faults, partial faults, hard-over faults, bias faults, and time-varying stuck faults, is established. Then, an integral sliding mode surface and sliding mode controller with fault estimation are designed. Through LMI technology and Lyapunov stability theory, the stability of sliding mode has been proved. Compared with the existing results, the oscillation amplitudes of yaw angle and yaw velocity can be suppressed from the every beginning. Finally, simulation results demonstrate that the proposed integral sliding mode fault tolerant control algorithm is effective.

In this paper, the sliding mode technique is used to study the quantized fault-tolerant control of unmanned marine vehicles with thruster saturation. Firstly, the sliding mode surface is constructed according to the full rank decomposition of input matrix, and the stability of sliding mode is guaranteed by linear matrix inequalities. An improved dynamic adjustment scheme of quantization parameter is proposed. Compared with the original adjustment scheme, the relationship between quantization parameter and desired targets is increased, so that the adjustment range of quantization parameters is more comprehensive. The sliding mode controller is combined with quantization parameter adjustment strategy to ensure the asymptotic stability of unmanned marine vehicles system. In addition, compared with the existing research results of quantitative fault tolerance problem without considering saturation, this paper gives a result of the domain of attraction affected by the fault of the thruster. Finally, the superiority of the proposed method is verified by simulation comparison.