Long-term monitoring of the ecological environment changes is helpful for the protection of the ecological environment. Based on the ecological environment of the Sahel region in Africa, we established a remote sensing ecological index (RSEI) model for this region by combining dryness, moisture, greenness, and desertification indicators. Using the Moderate-resolution Imaging Spectroradiometer (MODIS) data in Google Earth Engine (GEE) platform, this study analyzed the ecological environment quality of the Sahel region during the period of 2001–2020. We used liner regression and fluctuation analysis methods to study the trend and fluctuation of RSEI, and utilized the stepwise regression approach to analyze the contribution of each indicator to the RSEI. Further, the correlation analysis was used to analyze the correlation between RSEI and precipitation, and Hurst index was applied to evaluate the change trend of RSEI in the future. The results show that RSEI of the Sahel region exhibited spatial heterogeneity. Specifically, it exhibited a decrease in gradient from south to north of the Sahel region. Moreover, RSEI in parts of the Sahel region presented non-zonal features. Different land-cover types demonstrated different RSEI values and changing trends. We found that RSEI and precipitation were positively correlated, suggesting that precipitation is the controlling factor of RSEI. The areas where RSEI values presented an increasing trend were slightly less than the areas where RSEI values presented a decreasing trend. In the Sahel region, the areas with the ecological environment characterized by continuous deterioration and continuous improvement accounted for 44.02% and 28.29% of the total study area, respectively, and the areas in which the ecological environment was changing from improvement to deterioration and from deterioration to improvement accounted for 12.42% and 15.26% of the whole area, respectively. In the face of the current ecological environment and future change trends of RSEI in the Sahel region, the research results provide a reference for the construction of the ''Green Great Wall'' (GGW) ecological environment project in Africa.
摘要：H1N1 swine influenza A virus (H1N1 SwIV) is one key subtype of influenza viruses with pandemic potential. MicroRNAs (miRNAs) are endogenous small RNA molecules that regulate gene expression. MiRNAs relevant with H1N1 SwIV have rarely been reported. To understand the biological functions of miRNAs during H1N1 SwIV infection, this study profiled differentially expressed (DE) miRNAs in pulmonary alveolar macrophages from piglets during the H1N1 SwIV infection using a deep sequencing approach, which was validated by quantitative real-time PCR. Compared to control group, 70 and 16 DE miRNAs were respectively identified on post-infection day (PID) 4 and PID 7. 56 DE miRNAs were identified between PID 4 and PID 7. Our results suggest that most host miRNAs are down-regulated to defend the H1N1 SwIV infection during the acute phase of swine influenza whereas their expression levels gradually return to normal during the recovery phase to avoid the occurrence of too severe porcine lung damage. In addition, targets of DE miRNAs were also obtained, for which bioinformatics analyses were performed. Our results would be useful for investigating the functions and regulatory mechanisms of miRNAs in human influenza because pig serves as an excellent animal model to study the pathogenesis of human influenza.