Strategies and New Technologies for Improving the Tolerance of Lactic Acid Bacteria to Processing and Gastrointestinal Environments

The market and demanding of lactic acid bacteria and their products keep increasingly in recent years. However, it is difficult to maintain the activity of lactic acid bacteria during the processing and storage periods due to the stress caused by heating and oxygen exposure conditions for these anaerobic and heat-sensitive bacteria. This also causes a significant reduction in the quantity of live lactic acid bacteria in the end products. The activity of lactic acid bacteria will further decrease under the high acid condition in stomach and the high bile content in gut tract, which greatly reduce the end probiotic efficacy of this kind of probiotic products. Until now, numerous efforts have been made to enhance the tolerance of lactic and bacteria against heat, oxygen and gastrointestinal conditions, resulting in the emergence of several novel technologies. However, it is still challenging to select the most suitable technologies for practical application, as the results from various studies have not been thoroughly summarized and compared. In this study, the currently developed techniques for improving the activity of lactic acid bacteria during processing, under gastrointestinal condition, and in intestinal delivery are comprehensively summarized. The results from different studies are well compared. The application of electrostatic spinning, electrostatic spray, emulsion droplet technology, polyphenol nano armor, and heat induction pretreatment in the process of live lactic acid bacteria is also introduced. The information presented in this study can provide useful guidance for further research and the application of the currently developed techniques

Decomposition-Based Multiobjective Optimization for Variable-Length Mixed-Variable Pareto Optimization and Its Application in Cloud Service Allocation

Ma L, Liu Y, Yu G, et al. Decomposition-Based Multiobjective Optimization for Variable-Length Mixed-Variable Pareto Optimization and Its Application in Cloud Service Allocation. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2023:1-14.In real-world applications, a specific class of multiobjective optimization problems, such as the cloud service allocation problem (CSAOPs), possess the characteristic of variable-length and mixed variables, termed as variable multiobjective optimization problems (VMMOPs). Unfortunately, little research has been reported to solve them. To fill the gap, we propose a tailored enhanced decomposition-based algorithm to handle the VMMOPs. Specifically, a variable-length coding structure is designed to flexibly represent the solutions of VMMOPs. In order to facilitate the solution generation, a simple dimensionality incremental learning strategy is developed to choose representative solutions for the training of two learning models. The one is the fast-clustering-based histogram model, which is built for the sampling of solutions in the continuous decision space, while the other one is the incremental learning-based histogram model, designed to sample solutions in discrete decision space. Following the traditional constructor of the DTLZ test suite and the features of CSAOPs, we present a test suite of VMMOPs for the verification of the performance of the methods in handling VMMOPs. Experimental results on a number of benchmark problems and two real CSAOPs have shown the effectiveness and competitiveness of the proposed method in handling VMMOPs