• Korean journal of applied entomology
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• v.22 no.1 s.54
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• pp.26-30
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• 1983

Soybean mosaic virus (SMV) infection in seeds wits investigated by an immunochemical method Seven soybean cultivars or lines Bughae No.1, KAS 662-7, Chungbugbaeg, Gwanggyo Clark, Bongeui, and Gangrim were tested using hypocotyls of germinated seeds and presence of SMV was detected in six soybean cultivars but Gangrim. The level of SMV infection in the assayed cultivars varied from 2.1 to $12.5\%$. It seemed that seed coat mottling had no correlation with seeds. SMV infection of the seeds since virus has not always been detected from the mot tled seeds SMV has not been detected in the seeds of variety Gwanggyo which showed necrotic symptoms.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.24 no.3
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• pp.59-65
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• 1979

Severe outbreak of necrotic. soybean mosaic virus. (SMV -N) strain has been observed on soybean (Glycine max) in the central northern pans of Korea. Thirty soybean cultivars developed from Korea. Japan, and USA were tested for the estimation of infection percentages and ratings at 4 different planting dates. SMV-N infection was decreased by delaying the planting dates, but no differences were observed in conmon mosaic virus. Most of the soybean cultivars developed in Korea, and Japan were more susceptible to SMV- N than the US developed cultivars. Infection ratings of SMV-N showed highly significant negative correlations with common SMV regardless of planting dates.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.29 no.3
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• pp.280-284
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• 1984

Association of SMV infection in soybean plants with seedcoat mottling and with seed transmission of SMV was examined. Seedcoat mottling was increased positively in proportion to the SMV severity in variety Clark. No correlation was found between the incidence of mottled seed and the seed's position on the plant. Susceptible variety with virus symptoms gave rise to mottled seeds but only a small percentage of these mottled seeds could transmit the virus. SMV could be transmitted even by nonmottled seeds harvested from SMV infected plants in similar rate with mottled seed. It seemed that the amount of mottled seed could be used as an indicator of the amounts of SMV infection in a seed production field.

• The Plant Pathology Journal
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• v.25 no.1
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• pp.54-61
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• 2009

An infectious full-length clone of Soybean mosaic virus (SMV) strain G5H was constructed under the control of the cauliflower mosaic virus 35S promoter. The cloned SMV G5H established infections upon simple rub-inoculation of soybean leaves with intact plasmid DNA. We demonstrated that this SMV G5H infectious DNA clone caused typical characteristic symptoms and virulence of SMV strain G5H in twelve tested soybean cultivars. Soybean cultivars Lee74, Somyungkong and Sowonkong developed systemic mosaic symptom while Kwanggyo, Taekwangkong, Hwangkeumkong and Geumjeongkong-l showed systemic necrosis. In contrast, Geumjeongkong-2, Jinpumkong-2, L29, V94-5152 and Ogden showed resistant response against SMV-G5H infection. We also determined full-length sequence of cloned SMV-G5H. The phyogenetic analyses reveal that SMV-G5H is most closely related to SMV-G5, and support that SMV-G5H might be derived from SMV-G5 by recombination rather than mutation.

• The KIPS Transactions:PartD
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• v.10D no.5
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• pp.813-820
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• 2003

This paper solves Push-Push game with the model checker NuSMY which exhaustively explores all search space to determine whether a model satisfies a property. In case a model doesn't satisfy properties to be checked, NuSMV generates a counterexample which tells where this unsatisfaction occurs. However, the algorithm for generating counterexample in NuSMV traverses a search space twice so that it is inefficient for solving the game we consider here. To save the time to be required to complete the game, we revise the part of counterexample generation so that it traverses a search space once. As a result, we obtain 62% time improvement and 11% space improvement in solving the game with modified NuSMV.

• The Plant Pathology Journal
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• v.37 no.2
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• pp.162-172
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• 2021

Soybean mosaic virus (SMV) is the predominant viral pathogen that affects the yield and quality of soybean. The natural host range for SMV is very narrow, and generally limited to Leguminosae. However, we found that SMV can naturally infect Pinellia ternata and Atractylodes macrocephala. In order to clarify the molecular mechanisms underlying the cross-family infection of SMV, we used double-stranded RNA extraction, rapid amplification of cDNA ends polymerase chain reaction and Gibson assembly techniques to carry out SMV full-length genome amplification from susceptible soybeans and constructed an infectious cDNA clone for SMV. The genome of the SMV Shanxi isolate (SMV-SX) consists of 9,587 nt and encodes a polyprotein consisting of 3,067 aa. SMV-SX and SMV-XFQ008 had the highest nucleotide and amino acid sequence identities of 97.03% and 98.50%, respectively. A phylogenetic tree indicated that SMV-SX and SMV-XFQ018 were clustered together, sharing the closest relationship. We then constructed a pSMV-SX infectious cDNA clone by Gibson assembly technology and used this clone to inoculate soybean and Ailanthus altissima; the symptoms of these hosts were similar to those caused by the virus isolated from natural infected plant tissue. This method of construction not only makes up for the time-consuming and laborious defect of traditional methods used to construct infectious cDNA clones, but also avoids the toxicity of the Potyvirus special sequence to Escherichia coli, thus providing a useful cloning strategy for the construction of infectious cDNA clones for other viruses and laying down a foundation for the further investigation of SMV cross-family infection mechanisms.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.111-117
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• 2008

In this paper, we propose an approach to improve the performance of voiced/unvoiced (V/UV) decision under background noise environments for the selectable mode vocoder (SMV) of 3GPP2. We first present an effective analysis of the features and the classification method adopted in the SMV. And then feature vectors which are applied to the GMM are selected from relevant parameters of the SMV for the efficient voiced/unvoiced classification. For the purpose of evaluating the performance of the proposed algorithm, different experiments were carried out under various noise environments and yields better results compared with the conventional scheme of the SMV.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.92-99
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• 2008

We propose an effective voice-pishing detection algorithm based on the 3GPP2 selectable mode vocoder (SMV). The detection of voice pishing is performed based on a Gaussian mixture model (GMM) using decoding parameters of the SMV directly extracted from the decoding process of the transmitted speech information in the mobile phone. The experimental results indicate that SMV decoding parameters are effective in discriminating between general voice and phisher's voice and the performance is significantly acceptable when the proposed technique is applied.