• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.254-259
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• 2013

A newly developed fall recognition algorithm using gravity weighted 3-axis accelerometer data as the input of HMM (Hidden Markov Model) is introduced. Five types of fall feature parameters including the sum vector magnitude(SVM) and a newly-defined gravity-weighted sum vector magnitude(GSVM) are applied to a HMM to evaluate the accuracy of fall recognition. A GSVM parameter shows the best accuracy of falls which is 100% of sensitivity and 97.96% of specificity, and comparing with SVM, the results archive more improved recognition rate, 5.2% of sensitivity and 4.5% of specificity. GSVM shows higher recognition rate than SVM due to expressing falls characteristics well, whereas SVM expresses the only momentum.

• KSBB Journal
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• v.9 no.1
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• pp.63-71
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• 1994

During the last decade enzyme reaction in organic solvent has been studied to show that specificity in buffer is different from that in organic solvent. The specificity of restriction enzyme was effected by various factors such as ionic strength, salt organic solvent and temperature. In this study, restriction enzyme PvuII which is used most frequently in genetic engineering and the substrate was vector pGEM3 whose sequence was already known were used. As a result the recognition sequence site was changed in the presence of organic solvents whose Log P are -1.5∼0. Their specificities were contrast with activities were contrasted. Specificities were not changed in organic solvent easily in inactivating enzyme. We think that the enzyme recognition site was not changed randomly but by preferential order. A recombinant vector which does not contain typical cleavage site CAG↓CTG was cleaved in 20% ethanol solution. This result might show that restriction enzyme could be used to cleave at unusual sites by changing the reaction conditions.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.167-175
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• 2016

The volatile component analysis of 82 commercialized liquors(44 samples of single malt whisky, 20 samples of blended whisky and 18 samples of brandy) was carried out by gas chromatography after liquid-liquid extraction with dichloromethane. Pattern recognition techniques such as principle component analysis(PCA), cluster analysis(CA), linear discriminant analysis(LDA) and partial least square discriminant analysis(PLSDA) were applied for the discrimination of different liquor categories. Classification rules were validated by considering sensitivity and specificity of each class. Both techniques, LDA and PLSDA, gave 100% sensitivity and specificity for all of the categories. These results suggested that the common characteristics and identities as typification of whiskies and brandys was founded by using multivariate data analysis method.

• KSBB Journal
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• v.11 no.2
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• pp.193-200
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• 1996

In molecular biology, type-II restriction endonuclease, which specifically recognize and cleave DNA at a limited number of sites, have been exploited as a means of characterizing DNA fragments, DNA mapping for genetic engineering. Type-II restriction endonucleases have been found to modulate their substrate specificity under modified conditions such as extreme pH, ionic strength, high enzyme concentration, substitution of metallic cofactors or addition of organic solvents. This study was initiated to investigate the modification of recognition specificity of BamHI according to the different pH and organic solvent under the given buffer condition. The specificity of BamHI is highly depends on the presence of hydrophobicity (LogP: partition coefficient) and pH of reaction solution. The specificity of BamHI is changed in range of LogP -1.03∼-1.35(at pH 7.5), -1.03∼-2.5 (at pH 8.0), -0.75∼-0.25(at pH 8.5), 0.32∼-2.5(at pH 8.9), respectively. Alteration of specificity appears in lower concentration of organic solvent when the reaction occurs in more alkali pH. For example, in DMSO solution, alteration of specificity appears in 20% concentration at pH 7.5 but in 4% concentration at pH 8.9.

• BMB Reports
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• v.31 no.6
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• pp.607-610
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• 1998

Transcription of mitochondrial DNA in the yeast S. cerevisiae depends on recognition of a consensus nonanucleotide promoter sequence by mitochondrial RNA polymerase specificity factor, which is a 43 kDa polypeptide encoded by the nuclear MTF1 gene. Mtf1p has only limited amino acid sequence homology to bacterial sigma factors, but functions in many ways like sigma in that it is required for promoter recognition and initiation of transcription. To analyze the corebinding region of Mtf1p, monoclonal antibodies to this protein were prepared. Recombinant Mtf1p overproduced in E. coli was purified to near homogeneity and used to raise monoclonal antibodies (mAbs). From fused cells screened for Mtf1p mAbs by immunodot blot analysis, 19 positive clones were initially isolated. Further analysis of positive clones by Western blotting resulted in 4 mAbs of Mtf1p.

• BMB Reports
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• v.53 no.12
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• pp.611-621
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• 2020

Bacterial endoribonuclease toxins belong to a protein family that inhibits bacterial growth by degrading mRNA or rRNA sequences. The toxin genes are organized in pairs with its cognate antitoxins in the chromosome and thus the activities of the toxins are antagonized by antitoxin proteins or RNAs during active translation. In response to a variety of cellular stresses, the endoribonuclease toxins appear to be released from antitoxin molecules via proteolytic cleavage of antitoxin proteins or preferential degradation of antitoxin RNAs and cleave a diverse range of mRNA or rRNA sequences in a sequence-specific or codon-specific manner, resulting in various biological phenomena such as antibiotic tolerance and persister cell formation. Given that substrate specificity of each endoribonuclease toxin is determined by its structure and the composition of active site residues, we summarize the biology, structure, and substrate specificity of the updated bacterial endoribonuclease toxins.

• The Plant Pathology Journal
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• v.20 no.1
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• pp.7-12
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• 2004

An important recent advance in the field of plant-microbe interactions has been the cloning of genes that confer resistance to specific viruses, bacteria, fungi or insects. Disease resistance (R) genes encode proteins with predicted structural motifs consistent with them having roles in signal recognition and transduction. Plant disease resistance is the result of an innate host defense mechanism, which relies on the ability of plant to recognize pathogen invasion and efficiently mount defense responses. In tomato, resistance to the pathogen Pseudomonas syringae pv. tomato is mediated by the specific recognition between the tomato serine/threonine kinase Pto and bacterial protein AvrPto or AvrPtoB. This recognition event initiates signaling events that lead to defense responses including an oxidative burst, the hypersensitive response (HR), and expression of pathogenesis- related genes.

• Proceedings of the Microbiological Society of Korea Conference
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• 2004.05a
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• pp.126-126
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• 2004

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.257-264
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• 2005

The number of vehicles are rapidly increased as our society is developed. The vehicle recognition has been studied for a while because many people acknowledged it has critical functions to solve the problems of traffic control or vehicle-related crimes. In this paper a novel method is proposed to recognize vehicle models corresponding makers. Vehicles' models are recognized based on the texture parameters from segmented radiator region above a number plate. A three-layer neural network was built and trained with the texture features for recognition. The proposed method shows $93.7\%$ of recognition rate and $99.7\%$ of specificity for vehicles' model.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.179-182
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• 2003

The Development of promoter recognition systems is a interesting problem in computational biology. In this paper, we introduce a intelligent system fur promoter recognition with multiple decision models using artificial neural networks. We have trained this models with 1871 human promoter sequences and 5230exon and intron sequences. Our system is found to perform better than other promoter finding systems insensitivity and specificity measures. We have tested our system with Chromosome 22 dataset.