T-cells are crucial in maintanence of intestinal homeostasis, however, it is still unclear how microbiota metabolites regulate T-effector cells. Here we show gut microbiota-derived short-chain fatty acids (SCFAs) promote microbiota antigen-specific Th1 cell IL-10 production, mediated by G-protein coupled receptors 43 (GPR43). Microbiota antigen-specific Gpr43−/− CBir1 transgenic (Tg) Th1 cells, specific for microbiota antigen CBir1 flagellin, induce more severe colitis compared with wide type (WT) CBir1 Tg Th1 cells in Rag−/− recipient mice. Treatment with SCFAs limits colitis induction by promoting IL-10 production, and administration of anti-IL-10R antibody promotes colitis development. Mechanistically, SCFAs activate Th1 cell STAT3 and mTOR, and consequently upregulate transcription factor B lymphocyte-induced maturation protein 1 (Blimp-1), which mediates SCFA-induction of IL-10. SCFA-treated Blimp1−/− Th1 cells produce less IL-10 and induce more severe colitis compared to SCFA-treated WT Th1 cells. Our studies, thus, provide insight into how microbiota metabolites regulate Th1 cell functions to maintain intestinal homeostasis.

[1] Relativistic (>1 MeV) 'killer electrons' are frequently generated in the Earth's inner magnetosphere during the recovery phase of a typical magnetic storm. We test the hypothesis that the energization of electrons takes place by means of stochastic gyroresonant interaction between lower-energy (several 100 keV) seed electrons and whistler-mode chorus waves. We develop a model kinetic equation for the electron energy distribution, and utilize both electron and whistler-mode wave data at L = 4 for a typical geomagnetic storm (on October 9, 1990) from instruments carried on the Combined Release and Radiation Effects Satellite (CRRES). Our model solutions are found to match well with the CRRES profiles of the electron flux. We conclude that the mechanism of stochastic acceleration by whistler-mode turbulence is a viable candidate for generating killer electrons, not only for the storm considered, but for similar storms with a severalday recovery phase containing prolonged substorm activity.

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As a major challenge and opportunity for traditional manufacturing, intelligent manufacturing is facing the needs of sustainable development in future. Sustainability assessment undoubtedly plays a pivotal role for future development of intelligent manufacturing. Aiming at this, the paper presents the digital twin driven information architecture of sustainability assessment oriented for dynamic evolution under the whole life cycle based on the classic digital twin mapping system. The sustainability assessment method segment of the architecture includes indicator system building, indicator value determination, indicator importance degree determination and intelligent manufacturing project assessing. A novel approach for treating the ambiguity of expert' judgment in indicator value determination by introducing trapezoidal fuzzy number into analytic hierarchy process is proposed, while the complexity of the influence relationship among the indicators is processed by the integration of complex networks modeling and PROMETHEE II for the indicator importance degree determination. A two-stage evidence combination model based on evidence theory is built for intelligent manufacturing project assessing lastly. The presented digital-twin-driven information architecture and the sustainability assessment method is tested and validated on a study of sustainability assessment of 8 intelligent manufacturing projects of an air conditioning enterprise. The results of the presented method were validated by comparing them with the results of the fuzzy and rough extension of the PROMETHEE II, TOPSIS and VIKOR methods, indicator importance degree determining method by entropy and indicator value determining method by accurate expert scoring.