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Abstract Background Aedes albopictus is an important vector for pathogens such as dengue, Zika, and chikungunya viruses. While insecticides is the mainstay for mosquito control, their widespread and excessive use has led to the increased resistance in Ae. albopictus globally. Gut symbiotic bacteria are believed to play a potential role in insect physiology, potentially linking to mosquitoes’ metabolic resistance against insecticides. Methods We investigated the role of symbiotic bacteria in the development of resistance in Ae. albopictus by comparing gut symbiotic bacteria between deltamethrin-sensitive and deltamethrin-resistant populations. Adults were reared from field-collected larvae. Sensitive and resistant mosquitoes were screened using 0.03% and 0.09% deltamethrin, respectively, on the basis of the World Health Organization (WHO) tube bioassay. Sensitive and resistant field-collected larvae were screened using 5 × LC50 (lethal concentration at 50% mortality) and 20 × LC50 concentration of deltamethrin, respectively. Laboratory strain deltamethrin-sensitive adults and larvae were used as controls. The DNA of gut samples from these mosquitoes were extracted using the magnetic bead method. Bacterial 16S rDNA was sequenced using BGISEQ method. We isolated and cultured gut microorganisms from adult and larvae mosquitoes using four different media: Luria Bertani (LB), brain heart infusion (BHI), nutrient agar (NA), and salmonella shigella (SS). Results Sequencing revealed significantly higher gut microbial diversity in field-resistant larvae compared with field-sensitive and laboratory-sensitive larvae (P

【Objective】Based on the method of SSR molecular markers, this study conducted phylogenetic analysis on 12 germplasms and identified true and false hybrids from F1 hybrid progenies of some germplasms in Gerbera hybrida, in order to offer a technological support for the evaluation and innovative utilization of germplasm resources in G. hybrida.【Method】Twelve Gerbera hybrida germplasm resources were collected and their genomic DNA was extracted. PCR amplification was performed using 50 pairs of SSR primers. Cluster analysis and fingerprint construction were conducted by detecting the results of related polymorphisms, as well as identification of hybrid F1 offspring of some varieties.【Result】A total of 74 polymorphic alleles were amplified by 18 pairs of primers in 12 varieties of G. hybrida, with an average of 4.1 polymorphic alleles amplified by each pair of primers. A phylogenetic tree of 12 germplasm resources was constructed using 74 alleles in G. hybrida. UPGMA clustering analysis results showed that when the genetic coefficient was 0.75, the tested 12 germplasm resources could be divided into 5 groups, with Group Ⅰ including 'Yunnanhong', 'Da088', and 'Qingren'. Group Ⅱ including 'Fenjiaren', 'Fenqiu', 'Zhenai', and 'Fuwa'. Group Ⅲ including 'Xiuse', 'Shunv', and 'Huangqiu', both Group Ⅳ and Group Ⅴ containing only one variety, namely 'Zishuijing' and 'Shenzhen 5', respectively. Four pairs of primers were screened, including GHSSR-1, GHSSR-18, GHSSR-20, and GHSSR-21, which can completely distinguish the 12 germplasms with each other. Among them, the last three pairs of primers identified lines with parental specific heterotopies as Gh-5, Gh-4, and Gh-1, respectively. The identification rate of a single pair of primers was 33%. All three tested individual plants are true hybrids, with a true hybrid rate of 100%.【Conclusion】Twelve fingerprint maps of G.hybrida varieties were successfully constructed. At the same time, 3 hybrid F1 offspring seedlings were identified as true hybrids, providing dependable SSR marker primers for the evaluation and innovative utilization of resources in G. hybrida.

Abstract Background Mosquitoes are vectors of many pathogens, such as malaria, dengue virus, yellow fever virus, filaria and Japanese encephalitis virus. Wolbachia are capable of inducing a wide range of reproductive abnormalities in their hosts, such as cytoplasmic incompatibility. Wolbachia has been proposed as a tool to modify mosquitoes that are resistant to pathogen infection as an alternative vector control strategy. This study aimed to determine natural Wolbachia infections in different mosquito species across Hainan Province, China. Methods Adult mosquitoes were collected using light traps, human landing catches and aspirators in five areas in Hainan Province from May 2020 to November 2021. Species were identified based on morphological characteristics, species-specific PCR and DNA barcoding of cox1 assays. Molecular classification of species and phylogenetic analyses of Wolbachia infections were conducted based on the sequences from PCR products of cox1, wsp, 16S rRNA and FtsZ gene segments. Results A total of 413 female adult mosquitoes representing 15 species were identified molecularly and analyzed. Four mosquito species (Aedes albopictus, Culex quinquefasciatus, Armigeres subalbatus and Culex gelidus) were positive for Wolbachia infection. The overall Wolbachia infection rate for all mosquitoes tested in this study was 36.1% but varied among species. Wolbachia types A, B and mixed infections of A × B were detected in Ae. albopictus mosquitoes. A total of five wsp haplotypes, six FtsZ haplotypes and six 16S rRNA haplotypes were detected from Wolbachia infections. Phylogenetic tree analysis of wsp sequences classified them into three groups (type A, B and C) of Wolbachia strains compared to two groups each for FtsZ and 16S rRNA sequences. A novel type C Wolbachia strain was detected in Cx. gelidus by both single locus wsp gene and the combination of three genes. Conclusion Our study revealed the prevalence and distribution of Wolbachia in mosquitoes from Hainan Province, China. Knowledge of the prevalence and diversity of Wolbachia strains in local mosquito populations will provide part of the baseline information required for current and future Wolbachia-based vector control approaches to be conducted in Hainan Province. Graphical Abstract

The actual energy saving effect of plug-in hybrid vehicles (PHEVs) is usually evaluated by the electricity utility factor (UF) in a standardized charging pattern. To further evaluate the impacts of the charging pattern heterogeneity of PHEV, actual vehicle travel data are adopted to classify the charging pattern in seven typical Chinese cities and derive its impacts on actual UF. Additionally, UF curves are fitted as power exponential functions. The result shows that during daily usage, UF can reach over 0.8 for 50 km all-electric-range PHEVs for the 77% frequently charging adopters, while it is as low as 0.1 for the 3% rarely charging adopters. Comprehensive UF values at an actual charging pattern are 0.53 and 0.68 for the 50 km and 80 km all-electric-range PHEVs, and the values are 0.03 and 0.04 smaller than the standard UF, respectively.

The strapdown inertial navigation systems (SINS) have been widely used for many vehicles, such as commercial airplanes, Unmanned Aerial Vehicles (UAVs), and other types of aircrafts. In order to evaluate the navigation errors precisely and efficiently, a prediction method based on support vector machine (SVM) is proposed for positioning error assessment. Firstly, SINS error models that are used for error calculation are established considering several error resources with respect to inertial units. Secondly, flight paths for simulation are designed. Thirdly, the -SVR based prediction method is proposed to predict the positioning errors of navigation systems, and particle swarm optimization (PSO) is used for the SVM parameters optimization. Finally, 600 sets of error parameters of SINS are utilized to train the SVM model, which is used for the performance prediction of new navigation systems. By comparing the predicting results with the real errors, the latitudinal predicting accuracy is 92.73%, while the longitudinal predicting accuracy is 91.64%, and PSO is effective to increase the prediction accuracy compared with traditional SVM with fixed parameters. This method is also demonstrated to be effective for error prediction for an entire flight process. Moreover, the prediction method can save 75% of calculation time compared with analyses based on error models.