Some species of the Anopheles dirus species complex are considered to be highly competent malaria vectors in Southeast Asia. Anopheles dirus is the primary vector of Plasmodium falciparum and P. vivax while An. cracens is the main vector of P. knowlesi. However, these two species are difficult to distinguish and identify based on morphological characters. Hence, the aim of this study was to investigate the potential use of antennal sensilla to distinguish them. Large sensilla coeloconica borne on the antennae of adult females were counted under a compound light microscope and the different types of antennal sensilla were examined in a scanning electron microscope. The antennae of both species bear five types of sensilla: ampullacea, basiconica, chaetica, coeloconica and trichodea. Observations revealed that the mean numbers of large sensilla coeloconica on antennal flagellomeres 2, 3, 7, 10 and 12 on both antennae of both species were significantly different. This study is the first to describe the types of antennal sensilla and to discover the usefulness of the large coeloconic sensilla for distinguishing the two species. The discovery provides a simple, reliable and inexpensive method for distinguishing them.
Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.
Yearly, huge amounts of sock refuse are discarded into the environment. Socks contain many molecules, and worn ones, which are rich in smell-causing bacteria, have a strong influence on animals' behaviors. But the impacts of sock odor on the oviposition behavior of dengue vectors are unknown. We assessed whether Aedes albopictus changes its oviposition activity in response to the presence of used socks extract (USEx) in potential breeding grounds, using choice and no-choice bioassays (NCB). When furnished even chances to oviposit in two sites holding USEx and two others containing water (control), Ae. albopictus deposited significantly less eggs in USEx than in water sites. A similar pattern of oviposition preference was also observed when there were more oviposition options in water. When there were greater oviposition opportunities in USEx sites, Ae. albopictus oviposited preferentially in water. Females laid significantly more eggs during the NCB involving water than USEx. Also, significantly more mature eggs were retained by females in the NCB with USEx than in that with water. These observations strongly suggest the presence of molecules with either repellent or deterrent activities against Ae. albopictus females and provide an impetus to advocate the integration of used socks in dengue control programs. Such applications could be a realistic end-of-life recourse to reroute this waste from landfills.