• Journal of the Optical Society of Korea
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• v.6 no.2
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• pp.44-47
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• 2002

In this paper, we propose and demonstrate a novel channel-selectable multichannel optical add/drop multiplexer consisting of a sampled fiber grating and tunable fiber Bra99 gratings. The device adds and drops not only single channel, but also any predetermined multiple channels simultaneously with low interchannel crosstalk by wavelength tuning as small as 0.5 nm or less. The interchannel crosstalk better than 27㏈ has been shown experimentally.

• Journal of Microbiology and Biotechnology
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• v.31 no.7
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• pp.903-911
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• 2021

Previous studies have modified microbial genomes by introducing gene cassettes containing selectable markers and homologous DNA fragments. However, this requires several steps including homologous recombination and excision of unnecessary DNA regions, such as selectable markers from the modified genome. Further, genomic manipulation often leaves scars and traces that interfere with downstream iterative genome engineering. A decade ago, the CRISPR/Cas system (also known as the bacterial adaptive immune system) revolutionized genome editing technology. Among the various CRISPR nucleases of numerous bacteria and archaea, the Cas9 and Cas12a (Cpf1) systems have been largely adopted for genome editing in all living organisms due to their simplicity, as they consist of a single polypeptide nuclease with a target-recognizing RNA. However, accurate and fine-tuned genome editing remains challenging due to mismatch tolerance and protospacer adjacent motif (PAM)-dependent target recognition. Therefore, this review describes how to overcome the aforementioned hurdles, which especially affect genome editing in higher organisms. Additionally, the biological significance of CRISPR-mediated microbial genome editing is discussed, and future research and development directions are also proposed.

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2003.04a
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• pp.107-107
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• 2003

• Horticultural Science & Technology
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• v.32 no.2
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• pp.277-277
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• 2014

• Journal of Plant Biotechnology
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• v.2 no.2
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• pp.55-60
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• 2000

Experiments initiated 30 years ago to obtain selectable markers have led to a series of studies of Trp biosynthesis and anthranilate synthase (AS) the control enzyme using largely plant tissue cultures since they have experimental properties that can be readily exploited. Enzymological and compound feeding studies provided evidence that AS is the control point in the Trp biosynthesis branch and that altering the AS feedback control by the selection of mutants resistant to the Trp analog 5-methyl-tryptophan (5MT) can lead to the overproduction of this important amino acid. Plants regenerated from these Trp overproducing lines of most species also had high free Trp levels but Nicotiana tabaum (tobacco) plants expressed the feedback altered AS only in cultured cells and not in the regenerated plants. further tests by transient and stable expression of the cloned promoter for the naturally occurring tobacco feedback-insensitive AS, denoted ASA2, confirmed the tissue culture specific nature of the expression control. The 5MT caused by the expression of a feedback-insensitive AS from tobacco has been used to select protoplast fusion hybrids with several species since the resistance is expressed dominantly. Recently the ASA2 gene has been used successfully as a selectable marker to select transformed Astragalus sinicus and Glycine max hairy roots induced by Agrobactetium rhizogenes. These results show that the ASA2y-subunit can interact with the y-subunit of another species to form active feedback-insensitive enzyme that may be useful for selecting transformed cells. Plastid DNA transformation of tobacco has also effectively expressed ASA2 in the compartment in which Trp biosynthesis is localized in the cell.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.102-108
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• 2011

Support vector machines are well known for their outstanding performance in pattern recognition fields. One example of their applications is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel scheme that improves the speech/music classification of support vector machines based on the second-order conditional maximum a priori. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. According to experimental results, the proposed algorithm shows its compatibility and potential for improving the performance of support vector machines.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.141-148
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• 2011

Support vector machines have been extensively studied and utilized in pattern recognition area for years. One of interesting applications of this technique is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel approach that improves the speech/music classification of support vector machines. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. We first analyze the impact of kernel width parameter on the classifications made by support vector machines. From this analysis, we observe that we can fine-tune outputs of support vector machines with the kernel width parameter. To make the most of this capability, we identify strong correlation among neighboring input frames, and use this correlation information as a guide to adjusting kernel width parameter. According to the experimental results, the proposed algorithm is found to have potential for improving the performance of support vector machines.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.142-147
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• 2008

In this paper, we propose a novel a roach to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the support vector machine (SVM). The SVM makes it possible to build on an optimal hyperplane that is separated without the error where the distance between the closest vectors and the hyperplane is maximal. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then feature vectors which are a lied to the SVM are selected from relevant parameters of the SMV for the efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.

• The Journal of the Acoustical Society of Korea
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• v.26 no.8
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• pp.390-396
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• 2007

In this letter, we propose a novel approach to improve the performance of speech/music classification for the selectable mode vocoder(SMV) of 3GPP2 using the Gaussian mixture model(GMM) which is based on the expectation-maximization(EM) algorithm. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then feature vectors which are applied to the GMM are selected from relevant Parameters of the SMV for the efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.

• The Journal of the Acoustical Society of Korea
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• v.27 no.6
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• pp.319-324
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• 2008

In this paper, we propose a novel approach to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the discriminative weight training which is based on the minimum classification error (MCE) algorithm. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then proposed the speech/music decision rule is expressed as the geometric mean of optimally weighted features which are selected from the SMV. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.