Interleukin-22 (IL-22) is a good candidate to play a critical role in regulating gut microbiota because it is an important inducer of antimicrobial peptides and mucins in the gut. However, whether IL-22 participates into immune homeostasis by way of modulating gut microbiota remains to be elucidated. In this study, we find through 16S rRNA gene pyrosequencing analysis that healthy IL-22 deficient mice had altered colonic microbiota, notably with decreased abundance of some genera including Lactobacillus and increased levels of others. Mice harboring this altered microbiota exhibited more severe disease during experimentally-induced colitis. Interestingly, this altered gut microbiota can be transmitted to co-housed wild-type animals along with the increased susceptibility to this colitis, indicating an important role of IL-22 in shaping the homeostatic balance between immunity and colonic microbiota for host health.

Background: Chronic bronchitis (CB) is closely associated with the frequency and severity of chronic obstructive pulmonary disease (COPD) exacerbation. However, little is known about the impact of CB on COPD exacerbations, severe and non-severe, and on recovery from an exacerbation.

Rolling bearings, an essential fundamental component in machinery and equipment, have been widely used. Predicting the remaining useful life (RUL) of rolling bearings helps maintain the reliability of mechanical systems. Accurate prediction of RUL requires extracting deep features in complex non-linear vibration signals, the prediction results often vary widely. This paper proposes a RUL prediction method based on convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), and bootstrap method (CNN-BiLSTM-Bootstrap) to model the forecasting uncertainty. The first step is to extract the first prediction time (FPT) of the degradation phase of rolling bearings using an adaptive method for the 3𝜎 intervals of rolling bearing vibration signal kurtosis. The model extracts the spatio-temporal features through CNN and BiLSTM, and combines the bootstrap method to quantify the RUL prediction interval (PI) of rolling bearings. The comparison with existing models verified the effectiveness and generalization of the proposed model.

Temperature is an important factor affecting the working efficiency and service life of lithium-ion battery (LIB). This study carried out the experiments on the thermal performances of Sanyo ternary and Sony LiFePO4 batteries under different working conditions including extreme conditions, natural convection cooling and phase change material (PCM) cooling. The results showed that PCM could absorb some heat during the charging and discharging process, effectively reduce the temperature and keep the capacity stable. The average highest temperature of Sanyo LIB under PCM cooling was about 54.4 °C and decreased about 12.3 °C compared with natural convection in the 2 C charging and discharging cycles. It was found that the addition of heat dissipation fins could reduce the surface temperature, but the effect was not obvious. In addition, the charge and discharge cycles of the two kinds of LIBs were compared at the discharge rates of 1 C and 2 C. Compared with natural convection cooling, the highest temperature of Sanyo LIB with PCM cooling decreased about 4.7 °C and 12.8 °C for 1 C and 2 C discharging respectively, and the temperature of Sony LIB highest decreased about 1.1 °C and 2 °C.

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