Mosquitoes are insects of the Diptera, Nematocera, and Culicidae families, some species of which are important disease vectors. Identifying mosquito species based on morphological characteristics is difficult, particularly the identification of specimens collected in the field as part of disease surveillance programs. Because of this difficulty, we constructed DNA barcodes of the cytochrome c oxidase subunit 1, the COI gene, for the more common mosquito species in China, including the major disease vectors. A total of 404 mosquito specimens were collected and assigned to 15 genera and 122 species and subspecies on the basis of morphological characteristics. Individuals of the same species grouped closely together in a Neighborhood-Joining tree based on COI sequence similarity, regardless of collection site. COI gene sequence divergence was approximately 30 times higher for species in the same genus than for members of the same species. Divergence in over 98% of congeneric species ranged from 2.3% to 21.8%, whereas divergence in conspecific individuals ranged from 0% to 1.67%. Cryptic species may be common and a few pseudogenes were detected.

The association of environmental factors with fluctuations in the density of the malaria vector Anopheles minimus (Theobald, 1901) was analyzed in Longtang and Dongfanghong, two villages in southern Yunnan Province, Peoples Republic of China. The distance of trap locations to rice fields and the distance from trap locations to rivulets were negatively correlated to the density of An. minimus. The length of a rivulet within a 1-km buffer around a trap location was positively correlated to the density of An. minimus, whereas the rice field area within a 1-km buffer of a trap location was negatively correlated to An. minimus dominance, but the length of a rivulet within a 1-km buffer around a trap location and the distance of trap locations to rice fields were positively correlated to An. minimus dominance. Linear models relating the density and dominance of An. minimus at trap sites in relation to environmental parameters are described. These results could help in the design of malaria control strategies.

Radio binding assay and dot-virus protein binding assay were carried out to study the difference in binding of the brush border membrane fraction (BBMF) in Aedes albopictus (Skuse) and Culex quinquefasciatus (Say) (Diptera: Culicidae). The BBMF of Ae. albopictus displayed higher saturation and relative binding characteristics, while the BBMF of Cx. quinquefasciatus showed low saturation and non-relativity. The virus overlay protein-binding assay was then adopted to detect virus-binding protein in the midgut of the two mosquitoes. We detected one 35-kDa virus binding protein in the midgut of Ae. albopictus, whereas none was found in the midgut of Cx. quinquefasciatus. The relationship between dengue-2 virus binding to the midgut of mosquitoes, and the mosquito mesenteron infection barrier against dengue 2 virus are discussed.

Background In the eco-epidemiological context of Japanese encephalitis (JE), geo-environmental features influence the spatial spread of the vector (Culex tritaeniorhynchus, Giles 1901) density, vector infection, and JE cases. Methods In Liyi County, Shanxi Province, China, the spatial autocorrelation of mosquito vector density, vector infection indices, and JE cases were investigated at the pigsty and village scales. The map and Enhanced Thematic Mapper (ETM) remote sensing databases on township JE cases and geo-environmental features were combined in a Geographic Information System (GIS), and the connections among these variables were analyzed with regression and spatial analyses. Results At the pigsty level, the vector density but not the infection index of the vector was spatially autocorrelated. For the pigsty vector density, the cotton field area was positively related, whereas the road length and the distance between pigsties and gullies were negatively related. In addition, the vector infection index was correlated with the pigsty vector density (PVD) and the number of pigs. At the village level, the vector density, vector infection index, and number of JE cases were not spatially autocorrelated. In the study area, the geo-environmental features, vector density, vector infection index, and JE case number comprised the Geo-Environment-Vector-JE (GEVJ) intercorrelation net system. In this system, pig abundance and cotton area were positive factors influencing the vector density first. Second, the infection index was primarily influenced by the vector density. Lastly, the JE case number was determined by the vector infection index and the wheat area. Conclusions This study provided quantitative associations among geo-environmental features, vectors, and the incidence of JE in study sties, one typical northern Chinese JE epidemiological area without rice cultivation. The results highlighted the importance of using a diverse range of environmental management methods to control mosquito disease vectors and provided useful information for improving the control of vector mosquitoes and reducing the incidence of JE in the northern Chinese agricultural context. Graphical Abstract