• 제목/요약/키워드: Res

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Interaction of Resveratrol and Genistein with Nucleic Acids

  • Usha, Subbiah;Johnson, Irudayam Maria;Malathi, Raghunathan
    • BMB Reports
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    • 제38권2호
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    • pp.198-205
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    • 2005
  • Resveratrol (RES) and genistein (GEN) are the dietary natural products known to possess chemopreventive property and also the ability to repair DNA damage induced by mutagens/carcinogens. It is believed that the therapeutic activity of these compounds could be primarily due to their interaction with nucleic acids but detailed reports are not available. We here explore the interaction of these drugs with nucleic acids considering DNA and RNA as a potential therapeutic target. The interaction of RES and GEN has been analysed in buffered solution with DNA [saline sodium citrate (SSC)] and RNA [tris ethylene diammine tetra acetic acid (TE)] using UV-absorption and Fourier transform infrared (FTIR) spectroscopy. The UV analysis revealed lesser binding affinity with nucleic acids at lower concentration of RES (P/D = 5.00 and 10.00), while at higher drug concentration (P/D = 0.75, 1.00 and 2.50) hyperchromic effect with shift in the ${\lambda}_{max}$ is noted for DNA and RNA. A major RES-nucleic acids complexes was observed through base pairs and phosphate backbone groups with K = $35.782\;M^{-1}$ and K = $34.25\;M^{-1}$ for DNA-RES and RNA-RES complexes respectively. At various concentrations of GEN (P/D = 0.25, 0.50, 0.75, 1.00 and 2.50) hyperchromicity with shift in the ${\lambda}_{max}$ from 260 $\rightarrow$ 263 om and 260 $\rightarrow$ 270 nm is observed for DNA-GEN and RNA-GEN complexes respectively. The binding constant (from UV analysis) for GEN-nucleic acids complexes could not be obtained due to GEN absorbance overlap with that of nucleic acids at 260 nm. Nevertheless a detailed analysis with regard to the interaction of these drugs (RES/GEN) with DNA and RNA could feasibly be understood by FTIR spectroscopy. The NH band of free DNA and RNA which appeared at $3550-3100\;cm^{-1}$ and $3650-2700\;cm^{-1}$ shifted to $3450-2950\;cm^{-1}$ and $3550-3000\;cm^{-1}$ in DNA-RES and RNA-RES complexes respectively. Similarly shifts corresponding to $3650-3100\;cm^{-1}$ and $3420-3000\;cm^{-1}$ have been observed in DNA-GEN and RNA-GEN complexes respectively. The observed reduction in NH band of free nucleic acids upon complexation of these drugs is an indication of the involvement of the hydroxyl (OH) and imino (NH) group during the interaction of the drugs and nucleic acids (DNA/RNA) through H-bonded formation. The interaction of RES and GEN with bases appears in the order of G $\geq$ T > C > A and A > C $\geq$ T > G. Further interaction of these natural compounds with DNA and RNA is also supported by changes in the vibrational frequency (shift/intensity) in symmetrical and asymmetrical stretching of aromatic rings of drugs in the complex spectra. No appreciable shift is observed in the DNA and RNA marker bands, indicating that the B-DNA form and A-family conformation of RNA are not altered during their interaction with RES and GEN.

An Experimental Comparison of CNN-based Deep Learning Algorithms for Recognition of Beauty-related Skin Disease

  • Bae, Chang-Hui;Cho, Won-Young;Kim, Hyeong-Jun;Ha, Ok-Kyoon
    • 한국컴퓨터정보학회논문지
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    • 제25권12호
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    • pp.25-34
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    • 2020
  • 본 논문에서는 딥러닝 지도학습 알고리즘을 사용한 학습 모델을 대상으로 미용 관련 피부질환 인식의 효과성을 실험적으로 비교한다. 최근 딥러닝 기술을 산업, 교육, 의료 등 다양한 분야에 적용하고 있으며, 의료 분야에서는 중요 피부질환 중 하나인 피부암 식별의 수준을 전문가 수준으로 높인 성과를 보이고 있다. 그러나 아직 피부미용과 관련된 질환에 적용한 사례가 다양하지 못하다. 따라서 딥러닝 기반 이미지 분류에 활용도가 높은 CNN 알고리즘을 비롯하여 ResNet, SE-ResNet을 적용하여 실험적으로 정확도를 비교함으로써 미용 관련 피부질환을 판단하는 효과성을 평가한다. 각 알고리즘을 적용한 학습 모델을 실험한 결과에서 CNN의 경우 평균 71.5%, ResNet은 평균 90.6%, SE-ResNet은 평균 95.3%의 정확도를 보였다. 특히 학습 깊이를 다르게하여 비교한 결과 50개의 계층 구조를 갖는 SE-ResNet-50 모델이 평균 96.2%의 정확도로 미용 관련 피부질환 식별을 위해 가장 효과적인 결과를 보였다. 본 논문의 목적은 피부 미용과 관련된 질환의 판별을 고려하여 효과적인 딥러닝 알고리즘의 학습과 방법을 연구하기 위한 것으로 이를 통해 미용 관련 피부질환 개선을 위한 서비스 개발로 확장할 수 있을 것이다.

Discrimination of Fall and Fall-like ADL Using Tri-axial Accelerometer and Bi-axial Gyroscope

  • Park, Geun-Chul;Kim, Soo-Hong;Baik, Sung-Wan;Kim, Jae-Hyung;Jeon, Gye-Rok
    • 센서학회지
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    • 제26권1호
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    • pp.7-14
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    • 2017
  • A threshold-based fall recognition algorithm using a tri-axial accelerometer and a bi-axial gyroscope mounted on the skin above the upper sternum was proposed to recognize fall-like activities of daily living (ADL) events. The output signals from the tri-axial accelerometer and bi-axial gyroscope were obtained during eight falls and eleven ADL action sequences. The thresholds of signal vector magnitude (SVM_Acc), angular velocity (${\omega}_{res}$), and angular variation (${\theta}_{res}$) were calculated using MATLAB. When the measured values of SVM_Acc, ${\omega}_{res}$, and ${\theta}_{res}$ were compared to the threshold values (TH1, TH2, and TH3), fall-like ADL events could be distinguished from a fall. When SVM_Acc was larger than 2.5 g (TH1), ${\omega}_{res}$ was larger than 1.75 rad/s (TH2), and ${\theta}_{res}$ was larger than 0.385 rad (TH3), eight falls and eleven ADL action sequences were recognized as falls. When at least one of these three conditions was not satisfied, the action sequences were recognized as ADL. Fall-like ADL events such as jogging and jumping up (or down) have posed a problem in distinguishing ADL events from an actual fall. When the measured values of SVM_Acc, ${\omega}_{res}$, and ${\theta}_{res}$ were applied to the sequential processing algorithm proposed in this study, the sensitivity was determined to be 100% for the eight fall action sequences and the specificity was determined to be 100% for the eleven ADL action sequences.

차광처리가 잔대의 광합성 활성에 미치는 영향 (Effect of Shading Treatments on Photosynthetic Activity of Adenophora triphylla var. japonicum)

  • 김정운;윤준혁;전권석;정재민;정혜란;조민기;문현식
    • 농업생명과학연구
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    • 제46권4호
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    • pp.93-99
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    • 2012
  • 본 연구는 네 가지 서로 다른 비음처리구(무처리, 25%, 50%, 70% 차광처리)에서 2년간 생육한 잔대 묘목의 광합성 특성을 분석하였다. 엽록소 a, b 함량과 마찬가지로 총 엽록소 함량은 처리구간에서 유의적인 차이가 나타나지 않았다. 광합성능력은 50%와 75% 차광처리구 보다 무처리구와 25% 처리구가 더 높은 것으로 나타났다. 무처리구(전광조건) 하에서 생육한 잔대 묘목은 가장 높은 광합성 능력, 기공전도도, 엽육 내 $CO_2$ 농도를 나타냈으며, 수분이용효율은 50%와 75% 차광처리구가 무처리구(전광조건) 보다 더 높은 것으로 나타났다.

개선된 DeepResUNet과 컨볼루션 블록 어텐션 모듈의 결합을 이용한 의미론적 건물 분할 (Semantic Building Segmentation Using the Combination of Improved DeepResUNet and Convolutional Block Attention Module)

  • 예철수;안영만;백태웅;김경태
    • 대한원격탐사학회지
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    • 제38권6_1호
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    • pp.1091-1100
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    • 2022
  • 딥러닝 기술의 진보와 함께 다양한 국내외 고해상도 원격탐사 영상의 활용이 가능함에 따라 딥러닝 기술과 원격탐사 빅데이터를 활용하여 도심 지역 건물 검출과 변화탐지에 활용하고자 하는 관심이 크게 증가하고 있다. 본 논문에서는 고해상도 원격탐사 영상의 의미론적 건물 분할을 위해서 건물 분할에 우수한 성능을 보이는 DeepResUNet 모델을 기본 구조로 하고 잔차 학습 단위를 개선하고 Convolutional Block Attention Module(CBAM)을 결합한 새로운 건물 분할 모델인 CBAM-DRUNet을 제안한다. 제안한 건물 분할 모델은 WHU 데이터셋과 INRIA 데이터셋을 이용한 성능 평가에서 UNet을 비롯하여 ResUNet, DeepResUNet 대비 F1 score, 정확도, 재현율 측면에서 모두 우수한 성능을 보였다.