Objective To monitor and identify the ticks carried by wool imported from Kyrgyz Republic to China, and to prevent the transmission of harmful vectors through international trade. Methods Sixteen batches of wool imported from Kyrgyz Republic at Turgart port, Xinjiang Uygur Autonomous Region, from September 2017 to June 2018 were examined to detect ticks. The detected ticks were subjected to morphological identification. Genomic DNA was extracted from the ticks, and the mitochondrial 16S rRNA gene, 18S rRNA gene, and cytochrome C oxidase subunitⅠgene were amplified and sequenced to construct a phylogenetic tree using MEGA 7.0 software for molecular biological identification. Results Sixteen batches of wool were imported from Kyrgyz Republic, and ticks were detected in 11 batches of the imported wool. A total of 39 ticks were detected, including 1 live tick. They belonged to 5 species of 4 genera in 2 families as identified by morphological and molecular phylogenetic analysis: Ornithodoros lahorensis, Hyalomma asiaticum, Dermacentor marginatus, Haemaphysalis punctata, and H. sulcata. There were 30 ticks of H. sulcata, as the predominant species (76.9% of the total number) of intercepted ticks carried by the wool imported from Kyrgyz Republic. The live tick was a nymph of O. lahorensis. Conclusion The intercepted ticks are probably detected in wool imported from Kyrgyz Republic, which are mainly H. sulcata. The live nymph of O. lahorensis was intercepted for the first time. Therefore, the quarantine of ticks carried by wool imported from Kyrgyz Republic should be strengthened to prevent vector invasion and transmission of harmful diseases.

【Abstract】 Objective To study the effect of meteorological factors on rats density by Response surface methodology（RSM） based on correlation and regression analysis. Methods The meteorological factors and rats density were monitored continuously. A response surface model was made by the correlation and regression analysis of them. Results Linear regression analysis（P＜0.030）indicated that monthly average minimum temperature， sunshine time and precipitation were the main influence factors， and the multiple correlation coefficient was 0.716. However， RSM suggested that monthly average minimum temperature（P＝0.003）, precipitation square（P＝0.059）， interaction of monthly minimum temperature and sunshine（P＝0.027） affected mostly the density of rats， and its multiple correlation coefficient was 0.761. Conclusion The effect of meteorological factors on the rats density could be evaluated by RSM model.  This model was superior to linear regression model. The effect of meteorological factors on rats density was resulted from multiple factors and their interaction.