식물에게 있어 화분매개는 필수적인 요소 중 하나인데, 화분매개를 하는 식물 중 50%이상이 곤충에 의해 화분매개가 이루어지고 있다. 화분매개를 하는 곤충에 대한 조사는 주로 농업과 관련되어 있는 과수작물 주변의 화분매개곤충에 대해 조사가 되어있지만, 정작 양봉과 관련되어 있는 밀원식물 주변의 화분매개곤충은 조사된 바가 없다. 이에 연구진은 밀원식물 중 국내에서 가장 많은 양봉생산물을 만드는 아까시나무(Robinia pseudoacacia L.)의 개화시기에 맞춰서 화분매개곤충을 조사하였다. 조사지역은 총 6군데로, 백두대간을 중심으로 RCP 기후변화 시나리오에 의해 지정되었다. 조사 결과, 전체적으로 6목 60과 183종 1,555개체의 화분매개곤충이 채집되었다. 이중, 가장 많이 채집된 종은 노린재목의 애긴노린재(Nysius plebejus)로 약 21.30%가 채집되었다. 채집된 종을 군집분석한 결과, 강릉지역이 가장 안정적인 생태계를 유지하고 있으며, 완주지역이 가장 불완전한 생태계를 유지하고 있는 것으로 확인되었다.

Global climate change and increased international travel have affected the transmission of mosquito-borne diseases. In South Korea, uncommon diseases such as Dengue, chikungunya and Zika virus could be transmitted by potent mediator like Aedes albopictus. In order to cope with the risk of mosquito-borne diseases, rapid mosquito monitoring system is needed. Current mosquito monitoring procedures include installation of outdoor traps-mosquito collection-species classification-analysis of disease detection – upload of information to government research institutes – disease alert. In this process, species classification takes a lot of time, and if we reduce the time, we can cope with the disease outbreak more quickly. In this study, we developed automate species classification system target for 5 mosquito species (Culex pipiens, Cx. tritaeniorhynchus, Ae. albpictus, Ae. vexans, Anopheles spp.) disease vector live in South Korea. After modeling the morphology of each mosquito species, machine learning was carried out using DenseNet (Densely Connected Networks), one of the models of Artificial Neural Network. Using the learned model, we tested the classification of 5 species of mosquitoes and showed the accuracy from 97.35% to 99.48% at the maximum. Future research will focus on increasing the number of identifiable mosquito species and reducing the time spent on species classification. The autonomous classification of mosquito species using Deep Learning technology will contribute to the development of mosquito monitoring system and public health.

Mosquitoes are transmit many dangerous disease such as malaria, yellow fever and dengue fever. So far, chemical insecticides such as DEET have been mainly used to control mosquitoes, but there are many side effects. This study used ultrasonic sounds as an alternative to chemical insecticides. We found that Culex pipiens, which are common in Korea, exhibit avoidance behavior in a specific ultrasonic frequency. Through electrophysiological recording, we have inferred that avoidance behavior is caused by different from each other mechanisms depending on the ultrasonic frequency. Using immunohistochemical staining, we analyzed the expression pattern of auditory related genes in the chordotonal organ. Quantitative real time-PCR was used to compare the expression levels of auditory related gene depending on the time of exposure to ultrasonic sounds.