• 제목/요약/키워드: Target Amounts

검색결과 199건 처리시간 0.024초

Evaluation of Amplified-based Target Preparation Strategies for Toxicogenomics Study : cDNA versus cRNA

  • Nam, Suk-Woo;Lee, Jung-Young
    • Molecular & Cellular Toxicology
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    • 제1권2호
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    • pp.92-98
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    • 2005
  • DNA microarray analysis of gene expression in toxicogenomics typically requires relatively large amounts of total RNA. This limits the use of DNA microarray when the sample available is small. To confront this limitation, different methods of linear RNA amplification that generate antisense RNA (aRNA) have been optimized for microarray use. The target preparation strategy using amplified RNA in DNA microarray protocol can be divided into direct-incorporation labeling which resulted in cDNA targets (Cy-dye labeled cDNA from aRNA) and indirect-labeling which resulted in cRNA targets (i.e. Cy-dye labeled aRNA), respectively. However, despite the common use of amplified targets (cDNA or cRNA) from aRNAs, no systemic assessment for the use of amplified targets and bias in terms of hybridization performance has been reported. In this investigation, we have compared the hybridization performance of cRNA targets with cDNA targets from aRNA on a 10 K cDNA microarrays. Under optimized hybridization conditions, we found that 43% of outliers from cDNA technique and 86% from the outlier genes were reproducibly detected by both targets hybridization onto cDNA microarray. This suggests that the cRNA labeling method may have a reduced capacity for detecting the differential gene expression when compared to the cDNA target preparation. However, further validation of this discordant result should be pursued to determine which techniques possesses better accuracy in identifying truly differential genes.

국민연금 지분 투자가 기부금 지출에 미치는 영향 (The Effects of National Pension's Ownership on Corporate Philanthropic Giving)

  • 박철형;조영곤
    • 한국콘텐츠학회논문지
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    • 제20권3호
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    • pp.73-80
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    • 2020
  • 본 연구는 국내 상장기업의 국민연금 지분 투자가 기부금 지출에 미치는 영향을 규명하기 위해 2014년부터 2018년까지 5년간 1,878개 기업의 8,499건의 관측 자료를 대상으로 실증분석을 실시하였다. 실증분석 결과, 첫째, 국민연금 지분율은 투자대상 기업의 기부금 지출(인당 기부금, 매출 대비, 자산 대비)에 정(+)의 유의적인 영향을 미치는 것으로 나타났다. 본 결과는 국민연금은 투자대상 기업의 장기적인 기업가치를 높이기 위해 기부금 지출을 늘리도록 영향력을 행사하는 것으로 해석할 수 있다. 둘째, 국민연금이 5% 이상의 지분을 보유하는 주요주주일 때 투자대상 기업의 기부금 지출(인당 기부금, 자산 대비)이 유의적으로 증가하는 것으로 나타났다. 본 결과는 국민연금이 주요 주주인 경우 투자대상 기업의 장기적인 가치 제고를 위해 기부금 지출을 촉진하도록 감시 활동을 전개하는 것으로 해석할 수 있다. 전체적으로 본 실증결과는 국내 상장기업에서 국민연금 지분투자가 투자대상 기업의 기부금 지출을 촉진하도록 영향력을 행사하고 있음을 보여준다. 이는 환경·사회·지배구조 등의 요소를 고려한 국민연금의 사회적 책임투자(Socially Responsible Investment)가 국내 상장기업의 사회적 책임활동에 긍정적 영향을 미칠 수 있음을 시사한다.

Elimination Effect of Formaldehyde, Acetaldehyde and Total Volatile Organic Compounds from Car Felts using Nano-carbon Materials

  • Cho, Wan-Goo;Park, Seung-Gyu;Kim, Hyung-Man
    • 한국응용과학기술학회지
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    • 제26권1호
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    • pp.38-44
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    • 2009
  • We proposed the new nano-carbon ball (NCB) materials for eliminating the total volatile organic compounds(TVOCs) from the felt which is built in the car. The concentrations of acetaldehyde and formaldehyde of the original felts were varied upon the different production lots. Acetaldehyde in the felt can be eliminated to target level($0.2{\mu}g$) after introducing 0.5 wt% of NCB into the felt. Detector tube method for analyzing formaldehyde gas was more accurate than HPLC method. Formaldehyde can be eliminated to target level (64 ppb) after introducing 0.5 wt% of NCB into the felt. We also found that TVOC can be reduced to target level ($0.32{\mu}g$) after introducing 2.0 wt% of NCB. Upon introducing small amounts of NCB into the felt, it was possible that the level of formaldehyde, acetaldehyde and TVOC formed from the felts can be reduced to the target level. We also suggest the effective analyzing method of TVOCs.

Molecular Strategy for Development of Value-Added Sesame Variety

  • Chung, Chung-Han
    • 동아시아식생활학회:학술대회논문집
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    • 동아시아식생활학회 2004년도 참깨과학 국제학술대회 발표 논문집
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    • pp.13-30
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    • 2004
  • There are two groups of significant functional constituents in sesame seeds on the whole; one is the vegetable oils and another is the anti-oxidative compounds. However, although high amounts of major fatty acids are synthesized in sesame seeds, their composition is unfavorable because the contents of alpha- and gamma-linolenic acid, the essential fatty acids, are very low or do not produced in sesame seeds. So, to increase these fatty acids in sesame seeds, one strategy is to overexpress their genes, ${\omega}$-3 fatty acid desaturase for alpha-linolenic acid and delta-6 fatty acid desaturase for gamma-linolenid acid, in them. Another molecular target is to enhance alpha-tocopherol, vitamin E, because its content is very low in sesame seeds. The enzyme, gamma-tocopherol methyltransferase, catalyzes the conversion of gamma-tocophero to alpha-tocopherol. Overexpression of this enzyme in sesame seeds could be also a good molecular breeding target. Reduction of phytic acid is also another molecular target in sesame seeds because phosphorus pollution may be caused by its high content in sesame seeds. Accordingly, to do so, one of target enzymes could be myo-inositol 1-phosphate synthase which is a key regulatory enzyme in the pathway of phytic aicd biosyntheses. In this lecture, a molecular strategy for development of value-added sesame crop is described in association with some results of our experiments involved in the molecular characterizations of the genes mentioned above.

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Validation of protein refolding via 1-dimensional 1H-15N heteronuclear single quantum correlation experiments

  • Kim, Boram;Choi, Joonhyeok;Ryu, Kyoung-Seok
    • 한국자기공명학회논문지
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    • 제23권4호
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    • pp.104-107
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    • 2019
  • Many proteins are expressed as an insoluble form during the production using Escherichia coli (E. coli) system. Although various methods are applied to increase their amounts of soluble expression, refolding is the only feasible way to obtain a target protein in some cases. Moreover, protein NMR experiments require 13C/15N-labeled proteins that can only be obtained from E. coli systems in terms of cost and technical difficulty. The finding of appropriate refolding conditions for a target protein is a time-consuming process. In particular, it is very difficult to determine whether the refolded protein has a native structure, when a target protein has no enzymatic activity and its refolding yield is very low. Here, we showed that 1-dimensional 1H-15N heteronuclear single quantum correlation (1D 1H-15N HSQC) experiment can be efficiently used to screen an optimal condition for the refolding of a target protein by monitoring both the structure and concentration of the refolded protein.

파래분말 첨가 설기떡의 최적화 (Optimization of Sulgidduk with Green Laver Powder Using a Response Surface Methodology)

  • 김현숙;류은순
    • 한국식품조리과학회지
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    • 제26권1호
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    • pp.54-61
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    • 2010
  • This study was performed to determine the optimal manufacturing conditions adding green laver, which has a rich aroma and unique taste, to Sulgidduk. The variables in green laver Sulgidduk production were amounts of green laver powder and water. Six sensory characteristics were used for sensory evaluations, including color, green laver flavor, green laver taste, moistness, softness, and elasticity. The optimal amounts of the powder and water were found to be 6.84 g for green laver powder and 31.08 g for water, satisfying a target sensory score (7.0/9.0) according to a response surface method. Sulgidduk with these optimal amounts of green laver and water was tasted by 118 consumers and showed a high acceptability score (6.94). Older consumers ($\geq$30 years old) preferred the color and flavor of green laver significantly more than younger consumers ($\leq$29 years old) did (p<0.01). Color analysis results of green laver Sulgidduk were significantly different in brightness, redness, and yellowness from those of Sulgidduk (control)(p<0.01). Texture analysis scores of green laver Sulgidduk were significantly lower than those of Sulgidduk (control) in hardness, adhesiveness, springiness, cohesiveness, gumminess, and chewiness, and they were significantly different in adhesiveness and chewiness (p<0.05).

HMM 기반 한국어 음성합성에서의 화자적응 방식 성능비교 및 지속시간 모델 개선 (Performance Comparison and Duration Model Improvement of Speaker Adaptation Methods in HMM-based Korean Speech Synthesis)

  • 이혜민;김형순
    • 말소리와 음성과학
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    • 제4권3호
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    • pp.111-117
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    • 2012
  • In this paper, we compare the performance of several speaker adaptation methods for a HMM-based Korean speech synthesis system with small amounts of adaptation data. According to objective and subjective evaluations, a hybrid method of constrained structural maximum a posteriori linear regression (CSMAPLR) and maximum a posteriori (MAP) adaptation shows better performance than other methods, when only five minutes of adaptation data are available for the target speaker. During the objective evaluation, we find that the duration models are insufficiently adapted to the target speaker as the spectral envelope and pitch models. To alleviate the problem, we propose the duration rectification method and the duration interpolation method. Both the objective and subjective evaluations reveal that the incorporation of the proposed two methods into the conventional speaker adaptation method is effective in improving the performance of the duration model adaptation.

배경 추정을 통한 수중음향신호의 표적 추출 알고리즘 (An Algorithm of Target Detection of an Underwater Acoustic Signal by Estimating the Background)

  • 최민관;변기원;임재욱;김대동;남기곤;주재흠
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2008년도 하계종합학술대회
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    • pp.881-882
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    • 2008
  • This paper presents an algorithm of target detection of an underwater acoustic signal by estimating the background. At first, subtract the estimated background from the underwater acoustic signal. To estimate the background, this paper uses an algorithm of Denoising. By using Thresholding and Power analysis, we extract targets from the signal to eliminate the background. The proposed method is valuable as an algorithm to reduce calculation amounts of multi frames we will apply.

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적응적 준무손실 영상 부호화 (Adaptive Near-Lossless Image Coding)

  • 김영로;이준환
    • 전자공학회논문지 IE
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    • 제46권4호
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    • pp.42-48
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    • 2009
  • 본 논문에서는 전송 대역폭에 따라 손실을 최소화하며 적응적으로 압축률을 조절하는 준무손실 영상 부호화 알고리즘을 제안한다. 제안하는 방법은 슬라이스 부호화 간격으로 부호화된 비트량과 목표 부호화 비트량을 이용하여 적응적으로 허용 오류 범위를 변경한다. 실험 결과, 제안하는 방법은 기존 알고리즘들에 비하여 효과적으로 전송 대역폭에 맞추어 압축률을 조절할 뿐만 아니라 화질 또한 향상됨을 알 수 있다.

Improving Adversarial Domain Adaptation with Mixup Regularization

  • Bayarchimeg Kalina;Youngbok Cho
    • Journal of information and communication convergence engineering
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    • 제21권2호
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    • pp.139-144
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    • 2023
  • Engineers prefer deep neural networks (DNNs) for solving computer vision problems. However, DNNs pose two major problems. First, neural networks require large amounts of well-labeled data for training. Second, the covariate shift problem is common in computer vision problems. Domain adaptation has been proposed to mitigate this problem. Recent work on adversarial-learning-based unsupervised domain adaptation (UDA) has explained transferability and enabled the model to learn robust features. Despite this advantage, current methods do not guarantee the distinguishability of the latent space unless they consider class-aware information of the target domain. Furthermore, source and target examples alone cannot efficiently extract domain-invariant features from the encoded spaces. To alleviate the problems of existing UDA methods, we propose the mixup regularization in adversarial discriminative domain adaptation (ADDA) method. We validated the effectiveness and generality of the proposed method by performing experiments under three adaptation scenarios: MNIST to USPS, SVHN to MNIST, and MNIST to MNIST-M.