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First Record of the Himalayan Swiftlet Aerodramus brevirostris (Aves: Apodiformes) from Korea
Chang-Yong Choi,Jong-Gil Park,Yun-Sun Lee,Mi-Sook Min,Gi-Ghang Bing,Gil-Pyo Hong,Hang Lee 한국동물분류학회 2009 Animal Systematics, Evolution and Diversity Vol.25 No.3
On 28 April 2008, a male Himalayan Swiftlet (Aerodramus brevirostris) was captured and examined at a night roost of swallows on Hongdo Island, Jeollanam-do, Korea. This is the first record of this species from Korea confirmed by specimen examination. We describe morphological features and some phylogenetic notes of the Himalayan Swiftlet found.
Coil Embolization of Ruptured Thrombosed Distal Superior Cerebellar Artery Aneurysm: A Case Report
Min-Cheol Kang,Kil Sung Chae,Seong-Jin Noh,Hak-Gi Choi,Chang-Gu Ghang 대한뇌혈관외과학회 2012 Journal of Cerebrovascular and Endovascular Neuros Vol.14 No.3
Distal thrombosed aneurysm of the superior cerebellar artery (SCA) is extremely rare and is often associated with cerebellar infarction or subarachnoid hemorrhage. We report herein on a case involving a patient with a ruptured thrombosed distal SCA aneurysm which was treated successfully through the endovascular approach.
그래프 합성곱 신경망을 이용한 알츠하이머 치매환자 분류
박재희(Jaehee Park),김대겸(Daegyeom Kim),정현강(Hyun-Ghang Jeong),한철(Cheol E. Han) 대한전자공학회 2021 대한전자공학회 학술대회 Vol.2021 No.6
Alzheimer’s disease is a neuro-degenerative disease with severe memory deficits and cognitive declines. These symptoms are generally along with various structural changes including the weakened connectivity between brain regions, that may lead lowered information processing and thus consequent cognitive declines. In this paper, we developed a deep learning model to classify Alzheimer’s disease. We extracted brain networks from diffusion-weighted MR image (DWI) of each individual, and used a recently developed deep learning algorithm, graph convolutional neural network (GCN). Our model achieved 90.7% accuracy on average. We also investigate which brain regions were used to make decisions. We extracted the brain regions with statistically significant differences between groups through Grad-CAM on the results of GCN. This showed that GCN also considers the brain regions similar to those found in the traditional statistical analyses.