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Porcine pseudorabies has long existed in China and is a serious threat to the Chinese farming industry. To understand the prevalence and genetic variation of the porcine pseudorabies virus (PRV) and its pathogenicity in Yunnan Province, China, we collected 560 serum samples across seven Yunnan Province regions from 2020 to 2021 and detected anti-gE antibodies in these samples. Sixty-one clinical tissue samples were also collected from pigs with suspected PRV that were vaccinated with Bartha-K61. PRV-gE antibodies were found in 29.6% (166/560) of the serum samples. The PRV positivity rate in clinical tissue samples was 13.1% (8/61). Two isolates, PRV-KM and PRV-QJ, were obtained. The identity of the gB, gD, and gE genes between these isolates and the Chinese mutants exceeded 99.5%. These isolates and the classical Fa strain were used to infect 4-week-old rats intranasally to assess their pathogenicity. All infected rats showed the typical clinical and pathological features of PRV two days post-infection. The viral loads in the organs differed significantly among the infected groups. Viruses were detected in the saliva and feces at 12 h. Significant dynamic changes in total white blood cell counts (WBC), lymphocyte counts (Lym), and neutrophil counts (Gran) occurred in the blood of the infected groups at 24 and 48 h. These results show that mutant PRV strains are prevalent in Bartha-K61-vaccinated pigs in Yunnan Province, China. Moreover, rats shed PRV in their saliva and feces during early infection, indicating the need for rodent control in combatting PRV infections in Yunnan Province, China. Full article

A landslide is one of the most destructive natural disasters in the world. The accurate modeling and prediction of landslide hazards have been used as some of the vital tools for landslide disaster prevention and control. The purpose of this study was to explore the application of coupling models in landslide susceptibility evaluation. This paper used Weixin County as the research object. First, according to the landslide catalog database constructed, there were 345 landslides in the study area. Twelve environmental factors were selected, including terrain (elevation, slope, slope direction, plane curvature, and profile curvature), geological structure (stratigraphic lithology and distance from fault zone), meteorological hydrology (average annual rainfall and distance to rivers), and land cover (NDVI, land use, and distance to roads). Then, a single model (logistic regression, support vector machine, and random forest) and a coupled model (IV–LR, IV–SVM, IV–RF, FR–LR, FR–SVM, and FR–RF) based on information volume and frequency ratio were constructed, and the accuracy and reliability of the models were compared and analyzed. Finally, the influence of environmental factors on landslide susceptibility under the optimal model was discussed. The results showed that the prediction accuracy of the nine models ranged from 75.2% (LR model) to 94.9% (FR–RF model), and the coupling accuracy was generally higher than that of the single model. Therefore, the coupling model could improve the prediction accuracy of the model to a certain extent. The FR–RF coupling model had the highest accuracy. Under the optimal model FR–RF, distance from the road, NDVI, and land use were the three most important environmental factors, ac-counting for 20.15%, 13.37%, and 9.69%, respectively. Therefore, it was necessary for Weixin County to strengthen the monitoring of mountains near roads and areas with sparse vegetation to prevent landslides caused by human activities and rainfall. Full article