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Bacterial Osteomyelitis Induced by Morganella morganii in a Bearded Dragon (Pogona vitticeps)
권준,김상화,김상근,김현중,Sib Sankar Giri,박세창 한국임상수의학회 2020 한국임상수의학회지 Vol.37 No.6
Bacterial osteomyelitis—or bacterial infection of the bone—is common in reptiles. Unfortunately, its treatment is challenging despite advances in diagnostic and medical technologies. Herein, we present the case of a sexually mature female bearded dragon (Pogona vitticeps) with left forelimb elbow joint stiffness. We diagnosed the reptile with a eft elbow joint traumatic structural abnormality based on gross examination and evaluation of radiographs. Treatment with clindamycin and cephalexin for bacterial infection failed and the reptile died. Necropsy revealed the causative bacteria as Morganella morganii. Treatment of osteomyelitis is typically focused against Staphylococcus aureus as it the most common cause of traumatic bone infection. However, M. morganii, the causative bacterium in this case, has a natural resistance to clindamycin and cephalexin. Recently, these bacteria have begun to appear in clinical reports, more commonly as the causative organisms of bone infections. M. morganii should be considered as a potential cause of infection. Furthermore, antibiotic treatment in such cases should be based on bacterial culture and susceptibility tests.
자율주행 차량 물체 식별 정확도를 위한 이웃 반사 강도 기반 라이다 점군 눈 입자 제거 필터
권준,배석주 한국신뢰성학회 2023 신뢰성응용연구 Vol.23 No.4
Purpose: This study focuses on developing an algorithm that can sift through noisy LiDAR data in adverse weather and filter out snow points without losing essential details. By achieving this, we can boost the reliability of autonomous navigation systems in snowy conditions. Methods: We developed a novel filtering technique that considers the LiDAR intensity from surrounding points, not just the point of interest. We tested this method using the winter adverse driving dataset (WADS), applying our algorithm to LiDAR data distorted by snowy conditions. Results: This study determined the efficiency of our filter based on the degree of noise it removed and the number of essential points it preserved. The results demonstrated a significant improvement in data quality while keeping the most relevant information intact. Conclusion: The new filtering method offers a significant upgrade over previous studies on LiDAR, especially in maintaining crucial LiDAR data. This breakthrough paves the way for more dependable autonomous vehicle navigation in weather that typically disrupts sensor accuracy.