The initial composition and structure of microbial community determined the yield and quality of Baijiu during the spontaneous fermentation / La composición inicial y la estructura de la comunidad microbiana determinaron el rendimiento y la calidad del Baijiu durante la fermentación espontánea

The microbiota during pit mud fermentation is a crucial factor in Baijiu brewing since it determines the yield and flavor. However, the impact of the microbial community during the initial fermentation stage on Baijiu quality remains uncertain. Herein, high-throughput sequencing was employed to investigate the microbial diversities and distribution during Baijiu fermentation in individual pit mud workshops at both initial and late stages. During the initial fermentation stage, the bacterial community exerted a more pronounced effect on Baijiu quality than the fungal community. And the high-yield pit mud workshop exhibited lower richness and evenness, as well as greater Bray-Curtis dissimilarity during Baijiu fermentation. Lactobacillus was the dominant genus and biomarker in high-yield pit mud, and it constituted the only genus within the bacterial association network during the late fermentation stage. Fungal communities tended to maintain a simple association network with selected core species. Based on the correlation network, Rhizopus and Trichosporon were identified as biomarkers in Baijiu fermentation process. Together, Lactobacillus and Rhizopus could serve as bio-indicators for Baijiu quality during the initial fermentation stage. Therefore, these findings provided novel insights into microbiota interactions during fermentation and the impact of initial microbiota on final Baijiu quality.(AU)

The initial composition and structure of microbial community determined the yield and quality of Baijiu during the spontaneous fermentation.

The microbiota during pit mud fermentation is a crucial factor in Baijiu brewing since it determines the yield and flavor. However, the impact of the microbial community during the initial fermentation stage on Baijiu quality remains uncertain. Herein, high-throughput sequencing was employed to investigate the microbial diversities and distribution during Baijiu fermentation in individual pit mud workshops at both initial and late stages. During the initial fermentation stage, the bacterial community exerted a more pronounced effect on Baijiu quality than the fungal community. And the high-yield pit mud workshop exhibited lower richness and evenness, as well as greater Bray-Curtis dissimilarity during Baijiu fermentation. Lactobacillus was the dominant genus and biomarker in high-yield pit mud, and it constituted the only genus within the bacterial association network during the late fermentation stage. Fungal communities tended to maintain a simple association network with selected core species. Based on the correlation network, Rhizopus and Trichosporon were identified as biomarkers in Baijiu fermentation process. Together, Lactobacillus and Rhizopus could serve as bio-indicators for Baijiu quality during the initial fermentation stage. Therefore, these findings provided novel insights into microbiota interactions during fermentation and the impact of initial microbiota on final Baijiu quality.

Femtosecond Yb-doped tapered fiber pulse amplifiers with peak power of over hundred megawatts.

Ultrafast fiber lasers combining high peak power and excellent beam quality in the 1-µm wavelength range have been explored to applications in industry, medicine and fundamental science. Here, we report generation of a high-energy sub 300 fs polarization maintaining fiber chirped pulse amplification (CPA) system by using a Yb-doped large mode area tapered polarization maintaining (PM) optical fiber with the core/cladding diameters of 35/250 µm at the thin end and 56/400 µm at the thick end. The taper fiber design features a confined core for selective gain amplification and multi-layer cladding for enhanced suppression of higher order modes. In this regime, we have demonstrated 266 fs pulse amplification with peak power of up to 132 MW at a repetition rate of 2 MHz and high beam quality with measured M2 value of 1.1∼1.3. To the best of our knowledge, it is the highest peak power reported in such tapered Yb-doped fiber (T-YDF) amplifier in the femtosecond regime. This work indicates the great potential of the T-YDF to realize further power scaling, high laser efficiency, and excellent beam quality in high-power femtosecond fiber lasers.

Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay.

This paper addresses the problem of adaptive tracking control for a class of strict-feedback nonlinear state constrained systems with input delay. To alleviate the major challenges caused by the appearances of full state constraints and input delay, an appropriate barrier Lyapunov function and an opportune backstepping design are used to avoid the constraint violation, and the Pade approximation and an intermediate variable are employed to eliminate the effect of the input delay. Neural networks are employed to estimate unknown functions in the design procedure. It is proven that the closed-loop signals are semiglobal uniformly ultimately bounded, and the tracking error converges to a compact set of the origin, as well as the states remain within a bounded interval. The simulation studies are given to illustrate the effectiveness of the proposed control strategy in this paper.

Adaptive Control via Neural Output Feedback for a Class of Nonlinear Discrete-Time Systems in a Nested Interconnected Form.

In this paper, an adaptive output feedback control is framed for uncertain nonlinear discrete-time systems. The considered systems are a class of multi-input multioutput nonaffine nonlinear systems, and they are in the nested lower triangular form. Furthermore, the unknown dead-zone inputs are nonlinearly embedded into the systems. These properties of the systems will make it very difficult and challenging to construct a stable controller. By introducing a new diffeomorphism coordinate transformation, the controlled system is first transformed into a state-output model. By introducing a group of new variables, an input-output model is finally obtained. Based on the transformed model, the implicit function theorem is used to determine the existence of the ideal controllers and the approximators are employed to approximate the ideal controllers. By using the mean value theorem, the nonaffine functions of systems can become an affine structure but nonaffine terms still exist. The adaptation auxiliary terms are skillfully designed to cancel the effect of the dead-zone input. Based on the Lyapunov difference theorem, the boundedness of all the signals in the closed-loop system can be ensured and the tracking errors are kept in a bounded compact set. The effectiveness of the proposed technique is checked by a simulation study.

Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full State Constraints.

This paper deals with the tracking control problem for a class of nonlinear multiple input multiple output unknown time-varying delay systems with full state constraints. To overcome the challenges which cause by the appearances of the unknown time-varying delays and full-state constraints simultaneously in the systems, an adaptive control method is presented for such systems for the first time. The appropriate Lyapunov-Krasovskii functions and a separation technique are employed to eliminate the effect of unknown time-varying delays. The barrier Lyapunov functions are employed to prevent the violation of the full state constraints. The singular problems are dealt with by introducing the signal function. Finally, it is proven that the proposed method can both guarantee the good tracking performance of the systems output, all states are remained in the constrained interval and all the closed-loop signals are bounded in the design process based on choosing appropriate design parameters. The practicability of the proposed control technique is demonstrated by a simulation study in this paper.

Adaptive NN Control Using Integral Barrier Lyapunov Functionals for Uncertain Nonlinear Block-Triangular Constraint Systems.

A neural network (NN) adaptive control design problem is addressed for a class of uncertain multi-input-multi-output (MIMO) nonlinear systems in block-triangular form. The considered systems contain uncertainty dynamics and their states are enforced to subject to bounded constraints as well as the couplings among various inputs and outputs are inserted in each subsystem. To stabilize this class of systems, a novel adaptive control strategy is constructively framed by using the backstepping design technique and NNs. The novel integral barrier Lyapunov functionals (BLFs) are employed to overcome the violation of the full state constraints. The proposed strategy can not only guarantee the boundedness of the closed-loop system and the outputs are driven to follow the reference signals, but also can ensure all the states to remain in the predefined compact sets. Moreover, the transformed constraints on the errors are used in the previous BLF, and accordingly it is required to determine clearly the bounds of the virtual controllers. Thus, it can relax the conservative limitations in the traditional BLF-based controls for the full state constraints. This conservatism can be solved in this paper and it is for the first time to control this class of MIMO systems with the full state constraints. The performance of the proposed control strategy can be verified through a simulation example.

Neural Controller Design-Based Adaptive Control for Nonlinear MIMO Systems With Unknown Hysteresis Inputs.

This paper studies an adaptive neural control for nonlinear multiple-input multiple-output systems in interconnected form. The studied systems are composed of N subsystems in pure feedback structure and the interconnection terms are contained in every equation of each subsystem. Moreover, the studied systems consider the effects of Prandtl-Ishlinskii (PI) hysteresis model. It is for the first time to study the control problem for such a class of systems. In addition, the proposed scheme removes an important assumption imposed on the previous works that the bounds of the parameters in PI hysteresis are known. The radial basis functions neural networks are employed to approximate unknown functions. The adaptation laws and the controllers are designed by employing the backstepping technique. The closed-loop system can be proven to be stable by using Lyapunov theorem. A simulation example is studied to validate the effectiveness of the scheme.

Atorvastatin protects endothelium by decreasing asymmetric dimethylarginine in dyslipidemia rats.

BACKGROUND: Asymmetric Dimethylarginine (ADMA) is an inhibitor of endogenous nitric oxide synthase, which is the key synthase for nitric oxide (NO) production. Whether statins could protect endothelium by reducing ADMA concentration is unclear, and whether this effect is associated with the dose of statins usage is also needed further studied. METHODS: Dyslipidemia rat model was produced by giving high-fat and high-cholesterol diet for 8 weeks. Thereafter, low-dose (5 mg/kg body weight/day) and high-dose (20 mg/kg body weight/day) atorvastatin were orally prescribed for 4 weeks. Parameters of interest including lipid profiles, inflammatory and oxidative markers, NO production and plasma levels of ADMA and ADMA concentration of myocardium were evaluated. Liver enzymes and creatinine kinase (CK) were also detected for safety concern. RESULTS: At baseline, all parameters were comparable between the sham and the dyslipidemia groups. At 8 weeks of dyslipidemia establishment, as compared to the sham group, body weight and lipid profiles were significantly elevated, and plasma levels of C-reactive protein (CRP), malondialdehyde (MDA) and ADMA were concomitantly increased in accompanying with NO reduction in the dyslipidemia groups. With 4 weeks of atorvastatin therapy, as compared to the control group, lipid disorders and NO production were improved, and plasma levels of CRP, MDA and ADMA were significantly decreased in the high-dose atorvastatin group. ADMA concentration of cardiac tissues was also significantly reduced in the high-dose atorvastatin group. Notably, there was a trend to similar effects which did not reach statistical significance in the low-dose atorvastatin group when compared to the control group. Liver enzyme and CK were comparable after 4 weeks of atorvastatin therapy between groups. CONCLUSION: In rats with dyslipidemia, atorvastatin therapy could reduce plasma level of ADMA and ADMA concentration in cardiac tissues, and these effects are associated with the dose of atorvastatin therapy.

Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems.

Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.