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Simple Summary: Both the jejunum and the colon secrete unique immune factors that interact with the gut microbiota. Investigating the association of gut microbiota and the host immune system, we detected higher populations of Bacteroides, Fibrobacteres and Spirochetes in the colon than in the jejunum of Altay sheep, which is a unique breed in Xinjiang. Levels of IL-6 and IL-12 were lower in the colon than in the jejunum. IL-10 was positively correlated with Ruminococcus_2 in the jejunum. These results indicate a potential interaction between intestinal microbiota and the host immune system that may be considered for the prevention of sheep diseases and the screening of probiotics. Both the jejunum and colon release cytokines that interact with intestinal microbiota. However, it is largely unclear which cytokines and microbial populations are involved in the homeostasis of the intestinal ecosystem for sheep health. To address this, we collected contents for isolating microbiota and tissues for determining cytokines from the jejunum and colon of 7-month-old Altay sheep. We used the techniques of 16S rRNA sequencing and ELISA to detect microbial population and cytokine level, respectively. Correlations between microbial population and cytokines were analyzed by Spearman correlation coefficient. The correlation analysis revealed higher populations of Bacteroides, Fibrobacteres and Spirochetes in the colon than in the jejunum, and IL-6 and IL-12 levels were higher in the jejunum than in the colon. Association analysis further revealed a positive association between IL-10 level and both Ruminococcus_2 and norank_f_Bifidobacteriaceae population in the jejunum. The analysis also revealed positive associations between IL-6 level and Ruminococcaceae_UCG-014 and Ruminococcaceae_UCG-013 population, IL-10 and Prevotellaceae_UCG-004, as well as TNF-α and Prevotellaceae_UCG-003 in the colon. These results indicate a potential interaction between the intestinal microbiota and the host immune system that needs to be further clarified for considering dietary formulations to maintain animal health and disease prevention. [ABSTRACT FROM AUTHOR]

Underground coal fires have become a worldwide disaster, which brings serious environmental pollution and massive energy waste. Xinjiang is one of the regions that is seriously affected by the underground coal fires. After years of extinguishing, the underground coal fire areas in Xinjiang have not been significantly reduced yet. To extinguish underground coal fires, it is critical to identify and monitor them. Recently, remote sensing technologies have been showing great potential in coal fires' identification and monitoring. The thermal infrared technology is usually used to detect thermal anomalies in coal fire areas, and the Differential Synthetic Aperture Radar Interferometry (DInSAR) technology for the detection of coal fires related to ground subsidence. However, non-coal fire thermal anomalies caused by ground objects with low specific heat capacity, and surface subsidence caused by mining and crustal activities have seriously affected the detection accuracy of coal fire areas. To improve coal fires' detection accuracy by using remote sensing technologies, this study firstly obtains temperature, normalized difference vegetation index (NDVI), and subsidence information based on Landsat8 and Sentinel-1 data, respectively. Then, a multi-source information strength and weakness constraint method (SWCM) is proposed for coal fire identification and analysis. The results show that the proposed SWCM has the highest coal fire identification accuracy among the employed methods. Moreover, it can significantly reduce the commission and omission error caused by non-coal fire-related thermal anomalies and subsidence. Specifically, the commission error is reduced by 70.4% on average, and the omission error is reduced by 30.6%. Based on the results, the spatio-temporal change characteristics of the coal fire areas have been obtained. In addition, it is found that there is a significant negative correlation between the time-series temperature and the subsidence rate of the coal fire areas (R2 reaches 0.82), which indicates the feasibility of using both temperature and subsidence to identify and monitor underground coal fires. [ABSTRACT FROM AUTHOR]