왕피천 수계 내 저서성 대형무척추동물의 군집구조 분석을 위해 총 5개 지점에서 2023년 총 4회(4월, 6월, 8월 11월) 조사를 실시하였다. 조사기간 중 저서성 대형무척추동물은 총 5문 7강 17목 77과 156종 17,179.1개체/㎡가 채집되었다. 수환경 변화에 민감한 E.P.T. 분류군은 전체 156종 중 91종이 출현하여, 전체 출현종의 58.3%를 차지 하였다. 섭식기능군(FFGs) 분석결과, 종 출현 양상은 육식성 포식자(Predator: P)가 51종(32.69%)으로, 개체 출현 양상은 주워먹는 무리(Gathering-collector: GC)가 6,867.2개체/㎡(39.97%)로 높은 비율로 출현하였다. 서식기능 군(FHGs) 분석결과, 붙는 무리(Clinger: CL)가 70종(44.87%), 12,720.6개체/㎡(74.04%)로 가장 높은 비율로 출현 하였다. 군집지수 분석결과, 우점도지수(DI) 0.43, 다양도지수(H′) 3.51, 풍부도지수(R1) 4.59 균등도지수(J′) 0.77 로 나타났다. 생물학적 수질 판정 지수(BMI) 분석결과, 평균 92.36(±0.83)으로 모든 지점에서 “매우 좋음”으로 판정되었다.

PURPOSES : This study uses deep learning image classification models and vehicle-mounted cameras to detect types of pavement distress — such as potholes, spalling, punch-outs, and patching damage — which require urgent maintenance. METHODS : For the automatic detection of pavement distress, the optimal mount location on a vehicle for a regular action camera was first determined. Using the orthogonal projection of obliquely captured surface images, morphological operations, and multi-blob image processing, candidate distressed pavement images were extracted from road surface images of a 16,036 km in-lane distance. Next, the distressed pavement images classified by experts were trained and tested for evaluation by three deep learning convolutional neural network (CNN) models: GoogLeNet, AlexNet, and VGGNet. The CNN models were image classification tools used to identify and extract the combined features of the target images via deep layers. Here, a data augmentation technique was applied to produce big distress data for training. Third, the dimensions of the detected distressed pavement patches were computed to estimate the quantity of repair materials needed. RESULTS : It was found that installing cameras 1.8 m above the ground on the exterior rear of the vehicle could provide clear pavement surface images with a resolution of 1 cm per pixel. The sensitivity analysis results of the trained GoogLeNet, AlexNet, and VGGNet models were 93 %, 86 %, and 72 %, respectively, compared to 62.7 % for the dimensional computation. Following readjustment of the image categories in the GoogLeNet model, distress detection sensitivity increased to 94.6 %. CONCLUSIONS : These findings support urgent maintenance by sending the detected distressed pavement images with the dimensions of the distressed patches and GPS coordinates to local maintenance offices in real-time.

Clear cell odontogenic carcinoma (CCOC) is an extremely rare odontogenic neoplasm; Only a few cases have been reported in the English literatures. It displays a propensity for the mandible, most commonly presenting in the fifth to seventh decades, with a female predilection. The clinical and radiological manifestations are multiple and the diagnosis is histological. Histological feature is of large islands and strands of uniform cells with round or ovoid nuclei and clear cytoplasm. Clinically, painless swelling is the most common symptom, followed by pain, teeth mobility, and paresthesia. CCOC has a good prognosis after surgery. This case report presents the histopathological and clinical features of CCOC excised from the mandible.

The red imported fire ant (RIFA, Solenopsis invicta Buren) is defined as invasive species and one of 100 most threaten alien species in world by IUCN. In Korea, RIFA has been first detected at September 28th, 2017 in Busan Port during surveillance by Animal and Plant Quarantine Agency (APQA). The RIFA colony was revealed as a polygyne and H5 type through epidemiological analysis on Gp-9 gene and mt-DNA haplotype. APQA has developed eradication program for RIFA with enhanced surveillance in all nationwide Ports and Internal Container Depots. There are subsequently 7 times more invasions of RIFA in Ports (Busan, Pyongeaek, and Incheon) including 2 times of inlands (Daegu and Ansan) and completely eradicated so far. International trade and climate change increases possibility of establishment and spreading of RIFA in Korea. Therefore, it must be required an international cooperation for prevention of global spreading of RIFA between countries.

Two infamous ant species, red imported fire ant (RIFA, Solenopsis invicta) and tropical fire ant (TFA, S. geminata), originated from South America are invading and harming many countries around the world. Among them, RIFA, known as one of the 100 world’s worst invasive alien species, has been discovered in South Korea; in Busan (Gamman CT) in 2017, and subsequently in Busan (Hutchison CT), Pyongtaek, Incheon and Daegu in 2018. With these relentless introduction of RIFA, South Korea is not safe no more from these ants and faces a risk of settlement. Our research goal is tracing the country origin of invasive populations of RIFA and TFA based on molecular epidemiological approach. We are conducting both microsatellite and genome-wide SNP analyses in order to derive a complementary interpretation. These two methodologies are expected to result in improved resolution in population genetics analysis. Nevertheless, this work needs collaboration of many countries belonging to the range of origin and invasion of RIFA and TFA. We will briefly introduce our research project and progress of collaboration research.