http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Urea와 K<sub>2</sub>SO<sub>4</sub> 처리에 의한 복숭아 '미백도'에서 수확 시 과실의 무기성분 농도 및 과피색 변화
문병우,윤익구,문영지,남기웅,이영철,Moon, B.W.,Yoon, I.K.,Moon, Y.J.,Nam, K.W.,Lee, Y.C. 국립한국농수산대학교 교육개발센터 2013 현장농업연구지 = Journal of practical agricultural resear Vol.15 No.1
This study has been conducted to investigate the effect of Urea and K<sub>2</sub>SO<sub>4</sub> treatment at stone hardening stage and 20 days before harvest on soil chemical properties, mineral nutrient concentration and quality of 'Mibaekdo' fruit peach. K concentration after Urea and K<sub>2</sub>SO<sub>4</sub> treatment in soil was increased significantly by Urea 162g+K<sub>2</sub>SO<sub>4</sub> 188g/tree(standard amount) treatment at stone hardening stage, K<sub>2</sub>SO<sub>4</sub> 1.0% tree-spray, Urea 81g+K<sub>2</sub>SO<sub>4</sub> 94g/tree(half amount), Urea 162g+K<sub>2</sub>SO<sub>4</sub> 188g/tree and Urea 324g+K<sub>2</sub>SO<sub>4</sub> 376g/tree(double amount) soil treatment before harvest 20 days compared to control. T-N, K and Ca concentration in leaf was increased significantly by all treatment. but Na concentration in leaf was increased by Urea 0.5% and K<sub>2</sub>SO<sub>4</sub> 1.0% tree-spray treatment before harvest 20 days. T-N concentration in fruit skin was increased significantly by standard amount soil treatment, which decreased by K<sub>2</sub>SO<sub>4</sub> 1.0% tree-spray and half amount soil treatment. T-N, K and Ca concentration in fruit flesh(1~10mm depth flesh from peel) were increased markedly by all treatment excepted Urea 0.5% tree-spray. The leaf weight at harvest was increased markedly by Urea 0.5% tree-spray, standard amount and double amount treatment before harvest 20 days. Fruit weight was increased significantly by standard amount compared to all treatment. Red fruit skin(Hunter a value) progress was effective by K<sub>2</sub>SO<sub>4</sub> tree-spray, half amount and double amount treatment before harvest 20 days. Fruit SSC was increased significantly by Urea 0.5% and K<sub>2</sub>SO<sub>4</sub> tree-spray before harvest 20 days, standard amount treatment at stone hardening stage compared to control.
Runx3 is required for the differentiation of lung epithelial cells and suppression of lung cancer
Lee, K-S,Lee, Y-S,Lee, J-M,Ito, K,Cinghu, S,Kim, J-H,Jang, J-W,Li, Y-H,Goh, Y-M,Chi, X-Z,Wee, H,Lee, H-W,Hosoya, A,Chung, J-H,Jang, J-J,Kundu, J K,Surh, Y-J,Kim, W-J,Ito, Y,Jung, H-S,Bae, S-C Macmillan Publishers Limited 2010 Oncogene Vol.29 No.23
Human lung adenocarcinoma, the most prevalent form of lung cancer, is characterized by many molecular abnormalities. K-ras mutations are associated with the initiation of lung adenocarcinomas, but K-ras-independent mechanisms may also initiate lung tumors. Here, we find that the runt-related transcription factor Runx3 is essential for normal murine lung development and is a tumor suppressor that prevents lung adenocarcinoma. Runx3−/− mice, which die soon after birth, exhibit alveolar hyperplasia. Importantly, Runx3−/− bronchioli exhibit impaired differentiation, as evidenced by the accumulation of epithelial cells containing specific markers for both alveolar (that is SP-B) and bronchiolar (that is CC10) lineages. Runx3−/− epithelial cells also express Bmi1, which supports self-renewal of stem cells. Lung adenomas spontaneously develop in aging Runx3+/− mice (∼18 months after birth) and invariably exhibit reduced levels of Runx3. As K-ras mutations are very rare in these adenomas, Runx3+/− mice provide an animal model for lung tumorigenesis that recapitulates the preneoplastic stage of human lung adenocarcinoma development, which is independent of K-Ras mutation. We conclude that Runx3 is essential for lung epithelial cell differentiation, and that downregulation of Runx3 is causally linked to the preneoplastic stage of lung adenocarcinoma.
Residual Multi-dilated convolution U-Net을 이용한 다중 심장 영역 분할 알고리즘 연구
임상헌 ( Sang-heon Lim ),최한승 ( H. S. Choi ),배희진 ( S. K. Jung ),정서경 ( J. K. Jung ),정진교 ( Myung-suk Lee ),이명숙 한국정보처리학회 2019 한국정보처리학회 학술대회논문집 Vol.26 No.1
본 연구에서는 딥 러닝을 이용하여 완전 자동화된 다중 클래스 전체 심장 분할 알고리즘을 제안하였다. 제안된 방법은 recurrent convolutional block과 residual multi-dilated block을 삽입하여 기존 U-Net을 개선한 인공신경망 모델을 사용하였다. 평가는 자동화 분석 결과와 수동 평가를 비교하였다. 그 결과 96.88%의 평균 DSC, 95.60%의 정확도, 97.00%의 recall을 얻었다. 이 실험 결과는 제안된 방법이 다양한 심장 구조에서 효과적으로 구분되어 수행되었음을 알 수 있다. 본 연구에서 제안된 알고리즘이 의사와 방사선 의사가 영상을 판독하거나 임상 결정을 내리는데 보조적 역할을 할 것을 기대한다.