Rapid identification of chemical constituents and dynamic metabolic profile of Shenqi-Tiaoshen formula in rat plasma based on UPLC-Q-TOF/MSE.

Shenqi-Tiaoshen formula (SQTSF) is a traditional Chinese medicine (TCM) prescription that has been employed in the treatment of chronic obstructive pulmonary disease (COPD). Clinical practice has demonstrated that SQTSF is an effective prescription for stable COPD. However, owing to the complexity of TCM prescription, there is a lack of in-depth understanding of the chemical components of SQTSF and its in vivo metabolism studies. In this study, a comprehensive analytical strategy based on ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS) was established to identify the chemical components, the absorbed components, and the metabolites of SQTSF given by gavage in rats, and analyze their dynamic changes. As a result, 86 chemical components of SQTSF were characterized, which were mainly categorized into flavonoids, saponins, organic acids, terpenoids, etc. Among them, 13 compounds were confirmed unambiguously by reference standards. Furthermore, 20 prototype components and 46 metabolites were detected in rat plasma at different time points. It was found that one prototype component and thirteen metabolites could be detected during the entire 24 h, indicating that these compounds were slowly eliminated and thus accumulated in vivo over a prolonged duration. Interestingly, the phenomenon that three prototype components and fourteen metabolites reappeared after a period of disappearance from the plasma was found. It was also observed that different prototype components may generate the same metabolite. The metabolic processes of SQTSF in rats mainly included oxidation, reduction, hydration, demethylation, deglycosylation, methylation, acetylation, glucuronidation, glutathionylation, and associated combination reactions. Overall, the present study identified the chemical components of SQTSF and their dynamic metabolic profile in rat plasma, which provided a systematic and applicable strategy for screening and characterization of the prototype components and metabolites of TCM compound preparations.

Modeling and Control of a Wheeled Biped Robot.

It is difficult to realize the stable control of a wheeled biped robot (WBR), as it is an underactuated nonlinear system. To improve the balance and dynamic locomotion capabilities of a WBR, a decoupled control framework is proposed. First, the WBR is decoupled into a variable-length wheeled inverted pendulum and a five-link multi-rigid body system. Then, for the above two simplified models, a time-varying linear quadratic regulator and a model predictive controller are designed, respectively. In addition, in order to improve the accuracy of the feedback information of the robot, the Kalman filter is used to optimally estimate the system state. The control framework can enable the WBR to realize changing height, resisting external disturbances, velocity tracking and jumping. The results obtained by simulations and physical experiments verify the effectiveness of the framework.

[Research on coronavirus disease 2019 field disinfection technology for prevention and control].

The coronavirus disease 2019 (COVID-19) epidemic has had a serious impact in the world. In the absence of vaccines and therapeutic drugs, disinfection has become an important technical means to block the spread of the virus. By analyzing the characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we studied a series of disinfection technologies for COVID-19. During the outbreak of COVID-19, Jinan Second Center for Disease Control and Prevention disinfected the houses of the cases to be investigated in a community. The effectiveness of the disinfection technology was verified through the process of disinfection preparation, sampling before disinfection, field disinfection, sampling after disinfection and evaluation of disinfection effect. Compared the data before and after disinfection, the killing rate of the total bacterial colonies in the air and on the surface of the object was more than or equal to 90%, and no SARS-CoV-2 was detected after disinfection. The results show that the disinfection effect of the disinfection technology meets the standard. Finally, through the analysis of the wrong way of disinfection and the harm of over disinfection, the importance of scientific disinfection and precise disinfection are emphasized, and the research has a good guiding value for prevention and control of the epidemic.

An improved kernel based extreme learning machine for robot execution failures.

Robot execution failures prediction (classification) in the robot tasks is a difficult learning problem due to partially corrupted or incomplete measurements of data and unsuitable prediction techniques for this prediction problem with little learning samples. Therefore, how to predict the robot execution failures problem with little (incomplete) or erroneous data deserves more attention in the robot field. For improving the prediction accuracy of robot execution failures, this paper proposes a novel KELM learning algorithm using the particle swarm optimization approach to optimize the parameters of kernel functions of neural networks, which is called the AKELM learning algorithm. The simulation results with the robot execution failures datasets show that, by optimizing the kernel parameters, the proposed algorithm has good generalization performance and outperforms KELM and the other approaches in terms of classification accuracy. Other benchmark problems simulation results also show the efficiency and effectiveness of the proposed algorithm.