Ethanol stress is one of the major limiting factors for high-gravity brewing. Breeding of yeast strain with high ethanol tolerance, and revealing the ethanol tolerance mechanism of Saccharomyces cerevisiae is of great significance to the production of high-gravity beer. In this study, the mutant YN81 was obtained by ultraviolet-diethyl sulfate (UV-DES) cooperative mutagenesis from parental strain CS31 used in high-gravity craft beer brewing. The ethanol tolerance experiment results showed that cell growth and viability of YN81 were significantly greater than that of CS31 under ethanol stress. The ethanol tolerance mechanisms of YN81 were studied through observation of cell morphology, intracellular trehalose content, and transcriptomic analysis. Results from scanning electron microscope (SEM) showed alcohol toxicity caused significant changes in the cell morphology of CS31, while the cell morphology of YN81 changed slightly, indicating the cell morphology of CS31 got worse (the formation of hole and cell wrinkle). In addition, compared with ethanol-free stress, the trehalose content of YN81 and CS31 increased dramatically under ethanol stress, but there was no significant difference between YN81 and CS31, whether with or without ethanol stress. GO functional annotation analysis showed that under alcohol stress, the number of membrane-associated genes in YN81 was higher than that without alcohol stress, as well as CS31, while membrane-associated genes in YN81 were expressed more than CS31 under alcohol stress. KEGG functional enrichment analysis showed unsaturated fatty acid synthesis pathways and amino acid metabolic pathways were involved in ethanol tolerance of YN81. The mutant YN81 and its ethanol tolerance mechanism provide an optimal strain and theoretical basis for high-gravity craft beer brewing.
To better balance the reliability and conservativeness of uncertainty sets of robust optimization, the concept of adaptive uncertainty sets is proposed in this paper. There are two processes contained in the proposed adaptive uncertainty sets, which are point prediction and uncertainty sets determination. In the process of point prediction, the Long Short-term Memory Network (LSTM) is used to predict the renewable energy output. In the process of uncertainty sets determination, firstly, the prediction data is granulated based on the Modified Fuzzy Information Granulation (MFIG). Then the adjustable parameters are introduced to modify the upper and lower limit parameters of the information granules. Based on the above, the modeling of adaptive uncertainty sets can be achieved. To verify the performance of the proposed adaptive uncertainty sets, three groups of wind power output data of California are introduced to the contrast experiments. The simulation results demonstrate that, under 90% confidence level, the adaptive uncertainty sets method has a higher prediction interval coverage probability and a smaller prediction interval average width compared to the box uncertainty sets and the ellipsoidal uncertainty sets, which illustrates the good performance of the adaptive uncertainty sets in reliability and conservativeness.
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