• 제목/요약/키워드: target scoring

검색결과 43건 처리시간 0.018초

마을 내 잔존 노거수의 생육현황 및 실태진단 - 경주시 현곡면을 중심으로 - (Diagnosis of the growth status and actual condition of the remaining old trees in the village - Focused on Hyeongok-myeon in Gyeongju-si -)

  • 김영훈;덩베이지아;천겅;유주한
    • 한국환경복원기술학회지
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    • 제23권6호
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    • pp.109-123
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    • 2020
  • The purpose of this study is to provide basic data for the establishment of future countermeasures by diagnosing the growth status and actual condition of undesignated old trees distributed in Hyeongok-myeon, Gyeongju, Gyeongsangbuk-do. The number of old trees surveyed was 2 weeks in Gajeong-ri, 2 weeks in Haguri, 3 weeks in Nae-Tae-ri, 1 week in Nawon-ri, 6 weeks in Oryu-ri, 3 weeks in Sangguri, and 2 weeks in Sohyeon-ri, The trees species composition was 6 trees Celtis sinensis Pers., 1 Diospyros lotus L. trees, 4 trees Salix chaenomeloides Kimura trees, 2 Styphnolobium japonicum L. trees, and 7 Zelkova serrata (Thunb.) Makino trees. Growth status is 7.1~22.0m in height, 14.6~25.1m in long axis, 10.2~19.2m in short axis, root diameter is 76.0~236.4cm, diamter at breast height is 67.0~220.0cm, soil acidity is pH4.9~7.0, soil The hardness was measured to be 4.0-27.0mm. The result grade of the scoring data of health information is represented by monitoring generally, monitoring critically, and absolute monitoring, and it was confirmed that out of the 20 trees population in Hyeongok-myeon, the general monitoring grade was 7 weeks, the major monitoring grade was 13 weeks, and there was no absolute monitoring grade. Accordingly, the number of old trees of the general surveillance level was maintained at the current level, and ecological surgical operations were introduced for the major surveillance level, but the case of village forests should be different, and sequential treatments were the old tree urgently needed. The level and bark of the target tree, the state of the crown, the root exposure, the presence of pests and pests, the vitality and the ground condition, the degree of pollution are normal, poor, or very poor, operation and protection management, soil improvement, removal of cover, and disinfection were urgently needed for the old trees with the surveyed data. In order to compensate for these matters, continuous monitoring and management measures for the old number should be sought.

In silico 약리학적 분석을 통한 티모사포닌 A III의 5-베타 리덕타아제 단백질 및 안드로겐 수용체 단백질 활성 부위에 대한 결합 친화도 비교 연구 (Pharmacological Comparison of Timosaponin A III on the 5-beta Reductase and Androgen Receptor via In Silico Molecular Docking Approach)

  • 김동찬
    • 생명과학회지
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    • 제28권3호
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    • pp.307-313
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    • 2018
  • 탈모증상은 겉으로 보이는 모습으로 인해 정신적인 스트레스로 작용한다. 그래서 탈모 방지관련 제품의 글로벌 시장 규모는 지속적으로 성장하고 있다. Timosaponin A III는 지모 추출물에서 발견되는 대표적인 saponin 계열의 생리 활성 효능 성분이다. 본 연구에서는 5-beta reductase 단백질 길항제(antagonist) finasteride, androgen receptor 단백질 길항제 minoxidil, 그리고 지모 추출물의 효능 성분 timosaponin A III의 각각의 타깃 단백질 활성 부위에 대한 친화도 분석 실험을 in silico 컴퓨터 분자결합 분석 방법을 통해 비교하였다. 5-beta reductase 및 androgen receptor 의 3차원 구조 정보는 PDB database (5-beta reductase PDB ID: 3G1R / androgen receptor PDB ID:4K7A)를 활용하였다. In silico 결합 분석을 수행하기 위해 PyRx, Autodock Vina, Discovery Studio Version 4.5, and NX-QuickPharm 프로그램을 각 분석 조건에 따라 활용하였다. 5-beta reductase 활성 부위에 대한 timosaponin A III의 최대 결합친화도는 -12.20 kcal/mol으로 나왔으며 이는 -11.70 kcal/mol으로 분석된 finasteride의 5-beta reductase 활성부위에 대한 결합 친화도 보다 훨씬 더 높고 효율적인 것으로 분석되었다. Androgen receptor 활성 부위에 대한 timosaponin A III의 최대결합친화도 또한 -9.00 kcal/mol으로 -7.40 kcal/mol의 minoxidil에 비하여 훨씬 우수한 결합친화도 값을 나타내었다. Finasteride와 timosaponin A III의 5-beta reductase 단백질 활성 부위에 대한 X,Y,Z Grid 값은 유사한 좌표로 분석되었으나 minoxidil과 timosaponin A III의 androgen receptor 활성 부위에 대한 X,Y,Z centroid grid 좌표는 상당한 거리를 두고 떨어져 있음이 확인 되었다. 즉, timosaponin A III는 minoxidil이 androgen receptor에 결합하는 부위와는 다른 부위에 결합하여 단백질 활성에 영향을 주는 것으로 사료되었다. 이상의 연구 결과들을 바탕으로 분석해 볼 때, 5-beta reductase 길항제 finasteride와 androgen receptor 길항제 minoxidil보다 지모 추출물 생리 활성 물질인 timosaponin A III가 보다 더 효율적인 길항제로 작용할 수 있음을 확인하였다. 결론적으로 지모 추출물 또는 timosaponin 계열이 함유된 효능 성분은 탈모 방지 효능 및 모발 건강 개선을 위한 의약품, 의약외품 및 신물질 연구 개발 분야에 효율적으로 활용할 수 있을 것으로 사료된다.

Generative Adversarial Network-Based Image Conversion Among Different Computed Tomography Protocols and Vendors: Effects on Accuracy and Variability in Quantifying Regional Disease Patterns of Interstitial Lung Disease

  • Hye Jeon Hwang;Hyunjong Kim;Joon Beom Seo;Jong Chul Ye;Gyutaek Oh;Sang Min Lee;Ryoungwoo Jang;Jihye Yun;Namkug Kim;Hee Jun Park;Ho Yun Lee;Soon Ho Yoon;Kyung Eun Shin;Jae Wook Lee;Woocheol Kwon;Joo Sung Sun;Seulgi You;Myung Hee Chung;Bo Mi Gil;Jae-Kwang Lim;Youkyung Lee;Su Jin Hong;Yo Won Choi
    • Korean Journal of Radiology
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    • 제24권8호
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    • pp.807-820
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    • 2023
  • Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.