Structural characterization and innate immunomodulatory effect of glucomannan from Bletilla striata.

The crude polysaccharide of Bletilla striata in this study was extracted by water extraction and alcohol precipitation and further purified by gel column to yield the purified component Bletilla striata polysaccharide (BSP). Its structure and innate immune regulation activity were studied. BSP mainly comprises mannose and glucose, with a monosaccharide molar ratio of 2.9:1 and a weight-average molecular weight of 28,365 Da. It is a new low-molecular-weight water-soluble neutral glucomannan. BSP contains a â†’ 6)-ß-Manp-(1→, →4)-ß-Glcp-(1→, →4)-ß-Manp-(1 â†’ and →3)-α-Manp-(1 â†’ linear main chain, containing ß-Glcp-(1 â†’ and ß-Manp-(1 â†’ two branched chain fragments were connected to the Man residue at position 4. BSP can enhance the anti-infection ability of Caenorhabditis elegans against Pseudomonas aeruginosa, significantly improve the phagocytic ability of RAW264.7 macrophages, stimulate the secretion of NO and TNF-α, and have good innate immune regulation activity. These findings guide the use of Bletilla striata polysaccharides with immunomodulatory action.

Multi-Objective Optimization of Sugarcane Milling System Operations Based on a Deep Data-Driven Model.

The extraction of sugarcane juice is the first step of sugar production. The optimal values of process indicators and the set values of operating parameters in this process are still determined by workers' experience, preventing adaptive adjustment of the production process. To address this issue, a multi-objective optimization framework based on a deep data-driven model is proposed to optimize the operation of sugarcane milling systems. First, the sugarcane milling process is abstracted as the interaction of material flow, energy flow, and information flow (MF-EF-IF) by introducing synergetic theory, and each flow's order parameters and state parameters are obtained. Subsequently, the state parameters of the subsystems are taken as inputs, and the order parameters-including the grinding capacity, electric consumption per ton of sugarcane, and sucrose extraction-are produced as outputs. A collaborative optimization model of the MF-EF-IF of the milling system is established by using a deep kernel extreme learning machine (DK-ELM). The established milling system model is applied for an improved multi-objective chicken swarm optimization (IMOCSO) algorithm to obtain the optimal values of the order parameters. Finally, the milling process is described as a Markov decision process (MDP) with the optimal values of the order parameters as the control objectives, and an improved deep deterministic policy gradient (DDPG) algorithm is employed to achieve the adaptive optimization of the operating parameters under different working conditions of the milling system. Computational experiments indicate that enhanced performance is achieved, with an increase of 3.2 t per hour in grinding capacity, a reduction of 660 W per ton in sugarcane electric consumption, and an increase of 0.03% in the sucrose extraction.

Propofol pretreatment alleviates mast cell degranulation by inhibiting SOC to protect the myocardium from ischemia-reperfusion injury.

Propofol (PPF) has a protective effect on myocardial ischemia-reperfusion (I/R) injury (MIRI). The purpose of this study was to investigate whether the myocardial protective effect of propofol is related to the inhibition of mast cell degranulation and explore the possible mechanisms involved. Our in vivo results showed that compared with the sham group, cardiac function, infarct size, histopathological damage, apoptosis, and markers of myocardial necrosis were significantly increased in the ischemia-reperfusion group, and propofol pretreatment alleviated these effects. In the coculture system, propofol-treated mast cells reduced their tryptase activity, resulting in cardiomyocyte protective effects, such as decreased apoptosis of cardiomyocytes and decreased expression of myocardial necrosis markers. Finally, experimental results in vitro revealed that thapsigargin (TG) can increase mast cell degranulation, tryptase release, calcium ion concentration, and the expression of STIM1 and Orai1 induced by H/R, but propofol pretreatment can partially reverse the above effects. These results suggested that the cardioprotective effect of propofol is achieved in part by inhibiting calcium influx through store-operated Ca2+ channels (SOCs) and thus alleviating mast cell degranulation.

Data-driven modeling based on kernel extreme learning machine for sugarcane juice clarification.

Clarification of sugarcane juice is an important operation in the production process of sugar industry. The gravity purity and the color value of juice are the two most important evaluation indexes in the cane sugar production using the sulphitation clarification method. However, in the actual operation, the measurement of these two indexes is usually obtained by offline experimental titration, which makes it impossible to timely adjust the system indicators. A data-driven modeling based on kernel extreme learning machine is proposed to predict the gravity purity of juice and the color value of clear juice. The model parameters are optimized by particle swarm optimization. Experiments are conducted to verify the effectiveness and superiority of the modeling method. Compared with BP neural network, radial basis neural network, and support vector machine, the model has a good performance, which proves the reliability of the model.