ObjectiveHigh-fat diet (HFD)-induced metabolic disorders can lead to impaired sperm production. We aim to investigate if HFD-induced gut microbiota dysbiosis can functionally influence spermatogenesis and sperm motility.DesignFaecal microbes derived from the HFD-fed or normal diet (ND)-fed male mice were transplanted to the mice maintained on ND. The gut microbes, sperm count and motility were analysed. Human faecal/semen/blood samples were collected to assess microbiota, sperm quality and endotoxin.ResultsTransplantation of the HFD gut microbes into the ND-maintained (HFD-FMT) mice resulted in a significant decrease in spermatogenesis and sperm motility, whereas similar transplantation with the microbes from the ND-fed mice failed to do so. Analysis of the microbiota showed a profound increase in genus Bacteroides and Prevotella, both of which likely contributed to the metabolic endotoxaemia in the HFD-FMT mice. Interestingly, the gut microbes from clinical subjects revealed a strong negative correlation between the abundance of Bacteroides-Prevotella and sperm motility, and a positive correlation between blood endotoxin and Bacteroides abundance. Transplantation with HFD microbes also led to intestinal infiltration of T cells and macrophages as well as a significant increase of pro-inflammatory cytokines in the epididymis, suggesting that epididymal inflammation have likely contributed to the impairment of sperm motility. RNA-sequencing revealed significant reduction in the expression of those genes involved in gamete meiosis and testicular mitochondrial functions in the HFD-FMT mice.ConclusionWe revealed an intimate linkage between HFD-induced microbiota dysbiosis and defect in spermatogenesis with elevated endotoxin, dysregulation of testicular gene expression and localised epididymal inflammation as the potential causes.Trial registration numberNCT03634644.

In this paper, adaptive neural network based dynamic surface control (DSC) is developed for a class of nonlinear strict-feedback systems with unknown direction control gains and input saturation. A Gaussian error function based saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided by utilizing DSC. Based on backstepping combined with DSC, adaptive radial basis function neural network control is developed to guarantee that all the signals in the closed-loop system are globally bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design parameters. Simulation results demonstrate the effectiveness of the proposed approach and the good performance is guaranteed even though both the saturation constraints and the wrong control direction are occurred.

This paper proposes a fault-tolerant control scheme for a hypersonic gliding vehicle to counteract actuator faults and model uncertainties. Starting from the kinematic and aerodynamic models of the hypersonic vehicle, the control-oriented model subject to actuator faults is built. The observers are designed to estimate the information of actuator faults and model uncertainties, and to guarantee the estimation errors for converging to zero in fixed settling time. Subsequently, the finite-time multivariable terminal sliding mode control and composite-loop design are pursued to enable integration into the faulttolerant control, which can ensure the safety of the postfault vehicle in a timely manner. Simulation studies of a six degree-of-freedom nonlinear model of the hypersonic gliding vehicle are carried out to manifest the effectiveness of the investigated fault-tolerant control system. Index Terms-Hypersonic gliding vehicle, actuator faults and model uncertainties, fixed-time observer, finite-time fault-tolerant control.

This paper presents a fault-tolerant aircraft control (FTAC) scheme against actuator faults. Firstly, the upper bounds of the norms of the unknown functions are introduced, which contain actuator faults and model uncertainties. Subsequently, self-constructing fuzzy neural networks (SCFNNs) with adaptive laws are capable of obtaining the bounds. The bound estimation can reduce the computational burden with a lower amount of rules and weights, rather than the dynamic matrix approximation. Moreover, with the aid of SCFNNs, a multivariable sliding mode control (SMC) is developed to guarantee the finite-time stability of the handicapped aircraft. As compared to the existing intelligent FTAC techniques, the proposed method has twofold merits: fault accommodation can be promptly accomplished and decoupled difficulties can be overcome. Finally, simulation results from the nonlinear longitudinal Boeing 747 aircraft model illustrate the capability of the presented FTAC scheme.Index Terms-Fault-tolerant aircraft control; actuator faults; self-constructing fuzzy neural network; finite-time stability; multivariable sliding mode control.

This paper presents a fault-tolerant control (FTC) strategy for a hypersonic gliding vehicle (HGV) subject to actuator malfunctions and model uncertainties. The controloriented model of the HGV is estabilished according to the HGV kinematic and aerodynamic models. A composite-loop design for HGV FTC under actuator faults is subsequently developed, where newly developed multivariable integral terminal sliding mode control (TSMC) and adaptive techniques are integrated. The multivariable integral TSMC is capable of ensuring the finitetime stability of the closed-loop system in the presence of actuator malfunctions and model uncertainties, while the adaptive laws are employed to tune the control parameters in response to the HGV status. Simulation studies based on a six degree-of-freedom (DOF) nonlinear model of the HGV are illustrated to highlight the effectiveness of the developed FTC scheme.

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