• 제목/요약/키워드: P2P Network

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천연물로부터 Quorum Sensing 저해제의 탐색 (Detection of a Quorum-Sensing Inhibitor from the Natural Products)

  • 김태우;차지영;이준승;민복기;백형석
    • 생명과학회지
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    • 제18권2호
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    • pp.206-212
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    • 2008
  • 인간이 서로 간의 의사소통을 위해 언어를 사용하듯이, 세균의 경우도 외부 환경 변화를 신속히 감지하여 서로 효과적으로 대응하기 위해서 주변 세포들과 소통할 수 있는 세균만의 독특한 화학적 언어를 사용하는 것으로 알려져 있다. 특히, 일정 세포 농도에 도달했을 때 자체적으로 생산된 화학적 신호를 통해 개체 수를 인지하고 그에 따라 특정 유전자의 발현을 동시에 조절하는 quorum sensing (QS) 기작은 다양한 세균 종들에서 광범위하게 존재한다. 본 연구는 다양한 천연물 추출물들을 대상으로 QS 저해 활성을 확인하였는데 QS 지시균주인 Agrobacterium tumefaciens NT1과 화학적으로 합성한 QS autoinducers을 사용한 bioassay를 수행하였다. 그 결과 양배추, 파, 양파의 추출물들에서 QS 저해 활성을 확인하였고, recycling preparative HPLC (prep-HPLC)를 통한 정제 과정을 통해, 83분 지점의 peak에 해당하는 성분들이 공통으로 QS 저해 활성을 가지고 있음을 확인하였다. 따라서 그 QS 저해 성분을 QSI-83으로 지정하고 thin layer chromatography (TLC)를 통해 P. syringae pv. tabaci의 autoinducers 합성을 저해하는 활성을 가지고 있음을 확인하였다. 또한 열에 대한 안정성과 세균 생장에서의 영향을 조사하였는데, 그 결과 QSI-83은 열에 안정하며 세균의 생장에는 영향을 끼치지 않는 물질임을 확인하였다. 따라서 우리는 천연물로부터 분리된 새로운 성분이 QS 저해제로서 이용될 수 있음을 제안한다.

소셜 네트워크 기반 공유경제 서비스에 관한 밀레니얼스 소비자 세분화 연구: 사이코그래픽 관점에서 (Segmenting Korean Millennial Consumers of Sharing Economy Services on Social Networking: A Psychographic-based Approach)

  • 이재헌;최재원;김기연
    • 인터넷정보학회논문지
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    • 제16권6호
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    • pp.109-121
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    • 2015
  • 본 질적연구의 목적은 이머징 소셜 네트워크에 익숙한 밀레니얼스 세대 소비자들의 소셜 네트워크 기반 공유경제 서비스를 대하는 소비자 행동학적 동향, 사이코그래픽적 특성, 다양한 인지적 유형이 어떠한지 탐색하고 발견하는 것이다. 이를 위해, 본 연구는 Q방법론을 적용하여 최신 기술의 ICT 장비, 디바이스 또는 사회 문화적 웹서비스나 네트워킹을 능숙하게 다루는 젊은 밀레니얼 소비자들을 해석적 관점에서 4가지 차별화 된 유형의 이론적 정의를 제시한다. 최근 국내 산학 분야에서 모두 창조경제 정책에 힘입어 공유경제 서비스의 영향력이 증가하고 있지만, 아직 공유경제를 주제로 한 기존 연구들이 본격화 된 것은 그리 오래되지 않았다. 본 연구는 개인의 내재적 관심, 선호, 태도, 의견 등을 포함하는 일명 스키마타(Schemata)라고 불리는 응답자의 사고 구조의 독특한 사이코그래픽적 특성을 발견하는데 초점을 둔다. Q방법론의 연구 절차에 따라, Q모집단과의 인터뷰 및 여러 문헌들의 메타 스터디를 통해 수집한 180개의 진술문으로부터 축약한 최종 40개의 Q샘플(진술문)을 35명의 밀레니얼스 세대(P표본) 응답자이 Q소팅 하여 등급화 하였다. 마지막으로, QUANL PC 분석프로그램을 활용하여 소셜 네트워크 기반 공유경제 서비스에 대한 젊은 층 소비자들의 4가지 시장 세분화를 수행하였다. 도출된 유형들은 제1유형 'Early majority', 제2유형 'Laggard', 제3유형 'Opinion leader', 제4유형 'Late majority' 라고 명명하였다. 본 연구의 결과는 향후 밀레니얼스 신세대 소비자의 행동 및 심리적 특성, 시장 세분화를 깊이 있게 탐구하려는 질적 관점의 후속 연구들의 기초 연구로 활용될 수 있을 것이다.

Implications of Impacts of Climate Change on Forest Product Flows and Forest Dependent Communities in the Western Ghats, India

  • Murthy, Indu K.;Bhat, Savithri;Sathyanarayan, Vani;Patgar, Sridhar;M., Beerappa;Bhat, P.R.;Bhat, D.M.;Gopalakrishnan, Ranjith;Jayaraman, Mathangi;Munsi, Madhushree;N.H., Ravindranath;M.A., Khalid;M., Prashant;Iyer, Sudha;Saxena, Raghuvansh
    • Journal of Forest and Environmental Science
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    • 제30권2호
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    • pp.189-200
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    • 2014
  • The tropical wet evergreen, tropical semi evergreen and moist deciduous forest types are projected to be impacted by climate change. In the Western Ghats region, a biodiversity hotspot, evergreen forests including semi evergreen account for 30% of the forest area and according to climate change impact model projections, nearly a third of these forest types are likely to undergo vegetation type change. Similarly, tropical moist deciduous forests which account for about 28% of the forest area are likely to experience change in about 20% of the area. Thus climate change could adversely impact forest biodiversity and product flow to the forest dependent households and communities in Uttara Kannada district of the Western Ghats. This study analyses the distribution of non-timber forest product yielding tree species through a network of twelve 1-ha permanent plots established in the district. Further, the extent of dependence of communities on forests is ascertained through questionnaire surveys. On an average 21% and 28% of the tree species in evergreen and deciduous forest types, respectively are, non-timber forest product yielding tree species, indicating potential high levels of supply of products to communities. Community dependence on non-timber forest products is significant, and it contributes to Rs. 1199 and Rs. 3561/household in the evergreen and deciduous zones, respectively. Given that the bulk of the forest grids in Uttara Kannada district are projected to undergo change, bulk of the species which provide multiple forest products are projected to experience die back and even mortality. Incorporation of climate change projections and impacts in forest planning and management is necessary to enable forest ecosystems to enhance resilience.

한국인에서의 TNF-α 유전자 다형성과 HLA/TNF-α 일배체형의 분포 (Polymorphisms in the TNF-α Gene and Extended HLA and TNF-α Haplotypes in Koreans)

  • 박윤준;박혜진;박명희
    • IMMUNE NETWORK
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    • 제2권4호
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    • pp.242-247
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    • 2002
  • Background: Tumor necrosis factor-alpha (TNF-$\alpha$) is known to play an important role in various conditions such as inflammation, autoimmunity, apoptosis, insulin resistance and sleep induction. Five single nucleotide polymorphisms (SNPs) have been known to affect the transcriptional activities of TNF-$\alpha$: -1,031T/C, -863C/A, -857C/T, -308G/A and -238G/A. Methods: We have investigated 5 SNPs of the promoter region of TNF-$\alpha$ gene, the distribution of 5-locus TNF-$\alpha$ haplotypes, and their haplotypic associations with previously typed HLA-A, -B and -DRB1 loci in 107 healthy unrelated Koreans. TNF-$\alpha$ SNPs were typed using PCR-single-strand conformation polymorphism (SSCP) and PCR-restriction fragment length polymorphism (RFLP) methods. Results: The allele frequencies of -1,031C, -863A, -857T, -308A, and-238A, which are known as the high-producer-type, were 19.3%, 15.9%, 14.0%, 5.9%, and 2.9%, respectively. The frequency of -308A allele, known to be associated with autoimmune diseases, was 5.9% in Koreans which was lower than Caucasians (14~17%) and somewhat higher than Japanese (1.7%). Five most common TNF-$\alpha$ haplotypes (-1,031/-863/-857/-308/-238) comprised over 95% of total haplotypes: TCCGG (58.4%), CACGG (14.8%), TCTGG (13.7%), TCCAG (5.3%), and CCCGA (3.1%). Strong positive associations (P<0.001) were observed between TCCGG and B62; between CACGG and B51, $DRB1^*0901$; between TCTGG and B35, B54, B59, $DRB1^*1201$; and between TCCAG and A33, B58, $DRB1^*0301$, $DRB1^*1302$. Five most common extended haplotypes (>3%) comprised around 16% of total haplotypes: A33-B58-TCCAG-$DRB1^*1302$, A24-B52-TCCGG-$DRB1^*1502$, A33-B44-TCCGG-$DRB1^*1302$, A24-B7-TCCGG-$DRB1^*0101$, and A11-B62-TCCGG-$DRB1^*0406$. The distribution of extended HLA and TNF-$\alpha$ haplotypes showed that most of HLA haplotypes were almost exclusively associated with particular TNF-$\alpha$ haplotypes. Conclusion: The results obtained in this study would be useful as basic data for anthropologic studies and disease association studies in Koreans.

대규모 발파자료를 이용한 한반도 남부 상부지각의 종파 속도 이방성 (P-wave Velocity Anisotropy in the Upper Crust of the Southern Korean Peninsula Using Seismic Signals from Large Explosions)

  • 홍명호;김기영
    • 지구물리와물리탐사
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    • 제12권3호
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    • pp.225-232
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    • 2009
  • 한반도 지각속도 구조 규명을 위해 실시하고 있는 지각규모 탄성파 실험의 일환으로 한반도 지각구조 연구팀이 2004년도와 2008년도에 조사측선상의 각 4개소와 8개소에서 인공적으로 대규모 지진파 신호를 발생시켰으며, 이 지진파 신호들이 기상청 지진망의 고정관측소 지진계에 기록되었다. 43개 속도센서와 103개 가속도센서로 기록된 고정관측소 자료 중, 신호/잡음비가 양호한 진앙거리 120 km 이내의 156개 자료만을 대상으로 Pg 위상의 속도 이방성을 분석하였다. 상대고도보정은 KCRT-2004 자료분석을 통해 구한 지표부근 속도정보와 고정관측소와 측선상의 이동식 관측소 사이의 고도차이를 이용하여 구하였으며, -101.6 ${\sim}$ 105.3 ms의 범위를 갖는다. 부지효과를 제거하기 위한 관측소 잔여보정은 가용한 모든 방향의 발파자료를 대상으로, 고정관측소 주시와 이동관측소 주시의 차이를 발파점별로 평균한 값을 제거하는 방법으로 구하였으며, -89.6 ${\sim}$ 192.2 ms 범위를 갖는다. 추가령 지구대와 옥천계 구조선 방향으로 나타나는 빠른 속도 이상대와 영덕과 울산 부근 높은 지열대의 느린 속도 이상대를 제외하면, Pg 위상으로 구한 한반도 남부의 상부지각속도는 전반적으로 등방성임을 지시한다.

Sequence variation of necdin gene in Bovidae

  • Peters, Sunday O.;Donato, Marcos De;Hussain, Tanveer;Rodulfo, Hectorina;Babar, Masroor E.;Imumorin, Ikhide G.
    • Journal of Animal Science and Technology
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    • 제60권12호
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    • pp.32.1-32.10
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    • 2018
  • Background: Necdin (NDN), a member of the melanoma antigen family showing imprinted pattern of expression, has been implicated as causing Prader-Willi symptoms, and known to participate in cellular growth, cellular migration and differentiation. The region where NDN is located has been associated to QTLs affecting reproduction and early growth in cattle, but location and functional analysis of the molecular mechanisms have not been established. Methods: Here we report the sequence variation of the entire coding sequence from 72 samples of cattle, yak, buffalo, goat and sheep, and discuss its variation in Bovidae. Median-joining network analysis was used to analyze the variation found in the species. Synonymous and non-synonymous substitution rates were determined for the analysis of all the polymorphic sites. Phylogenetic analysis were carried out among the species of Bovidae to reconstruct their relationships. Results: From the phylogenetic analysis with the consensus sequences of the studied Bovidae species, we found that only 11 of the 26 nucleotide changes that differentiate them produced amino acid changes. All the SNPs found in the cattle breeds were novel and showed similar percentages of nucleotides with non-synonymous substitutions at the N-terminal, MHD and C-terminal (12.3, 12.8 and 12.5%, respectively), and were much higher than the percentage of synonymous substitutions (2.5, 2.6 and 4.9%, respectively). Three mutations in cattle and one in sheep, detected in heterozygous individuals were predicted to be deleterious. Additionally, the analysis of the biochemical characteristics in the most common form of the proteins in each species show very little difference in molecular weight, pI, net charge, instability index, aliphatic index and GRAVY (Table 4) in the Bovidae species, except for sheep, which had a higher molecular weight, instability index and GRAVY. Conclusions: There is sufficient variation in this gene within and among the studied species, and because NDN carry key functions in the organism, it can have effects in economically important traits in the production of these species. NDN sequence is phylogenetically informative in this group, thus we propose this gene as a phylogenetic marker to study the evolution and conservation in Bovidae.

과원 환경과 경관 요소가 사과원 주요 나방류 해충 발생에 미치는 영향 (Effects of Orchard Environments and Landscape Features on the Population Occurrence of Major Lepidopteran Pests in Apple Orchards)

  • 김향미;정철의
    • 한국응용곤충학회지
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    • 제60권1호
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    • pp.79-90
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    • 2021
  • 농업생산생태계 내 경관의 구조와 구성은 해충과 천적을 비롯한 생물다양성을 결정하는 중요한 요소이다. 이 연구는 경남 거창군 80개 사과원을 대상으로 경관 구조가 나비목 해충의 발생에 영향을 미칠 수 있는 지를 조사하였다. 과수원의 지정학적 특징, 농약 사용패턴과 과원 관리 방법 등에 대한 정보는 설문 조사를 통해 추가로 분석하였다. 과수원 주변 경관 구조는 인공위성자료에 바탕하여 추출하였다. 복숭아순나방 발생량이 가장 많았고, 사과굴나방, 복숭아심식나방, 사과잎말이나방 순으로 발생하였다. 농가에서는 살균제와 살충제를 각 12.4회, 살비제는 2.4회 살포하였다. 대부분 사과원 주변 식생은 사과 또는 논이었으며, 자두, 복숭아, 포도 또는 폐과원이 있을 경우 복숭아순나방 밀도가 특히 높았다. 복숭아심식나방 역시 주변에 복숭아나 포도가 있을 경우 그 발생량이 더 높았다. 사과굴나방은 복숭아, 포도, 폐과원 그리고 대추가 있는 지역에서 발생량이 많았다. 이러한 결과는 농업 지역에서 경관 관리는 농촌 어메니티 개선뿐 아니라 병해충 관리의 차원에서 기능적 다양성을 추구하는 방향으로 진행되어야 한다는 점을 시사한다.

DNA 메타바코딩을 이용한 광양만 및 어시장 해양 생물 위 내용물 분석 (Analysis of Stomach Contents of Marine Orgnaisms in Gwangyang Bay and Yeosu Fish Market Using DNA Metabarcoding)

  • 오건희;김용준;김원석;홍철;지창우;곽인실
    • 생태와환경
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    • 제55권4호
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    • pp.368-375
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    • 2022
  • 보구치는 어류와 요각류가 공통 먹이원으로 분석되었다. 광양만에서 채집한 보구치가 가장 많이 먹은 먹이원은 단각류로 ASV 빈도가 62.5%로 나타났으며 여수 어시장에서 구입한 보구치는 어류의 ASV 빈도가 16.6%로 가장 많았다. 광양만에서 채집한 참조기의 우점 먹이원은 십각류로 ASV 빈도가가 99.9%로 나타났으며 어시장의 참조기는 어류의 ASV 빈도가 51.2%로 조사되었다. 광양만과 어시장의 자주새우 우점 먹이원은 각각 요각류로 92.6%와 100%로 조사되었다. 광양만에서 채집한 꼴뚜기는 요각류(91.4%)를 가장 많이 먹었으나 어시장에서 구입한 갑오징어는 어류(96.6%)를 가장 많이 섭식하였다. 계층적 군집 분석 결과, 꼴뚜기 및 채집한 자주새우와 구입한 자주새우는 먹이원이 유사하였으며 보구치와 참조기, 갑오징어와는 차이가 있는 것으로 조사되었다. 네트워크 분석 결과, 요각류는 참조기를 제외한 모든 수서 생물과 연결되어 있어 가장 중요한 먹이원인 것으로 조사되었다. 먹이원 폭 분석 결과 광양만에서 채집한 참조기의 먹이원 폭 값은 0.001로 낮았으나 어시장에서 구입한 참조기의 먹이원 폭 값은 0.886으로 먹이원 다양성이 가장 높았다. 영양단계 분석 결과, 어류를 주로 섭식했던 갑오징어가 3.98로 가장 높았으며, 광양만에서 채집한 보구치가 2.0으로 영양단계가 가장 낮은 것으로 조사되었다. 이를 통해 위 내용물의 DNA 메타바코딩을 활용한 먹이원 분석 연구는 육안을 통한 먹이원 분석 사이에서 상호보완하여 섭식생태 연구에 활용할 수 있을 것이다.

Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation

  • Seul Bi Lee;Youngtaek Hong;Yeon Jin Cho;Dawun Jeong;Jina Lee;Soon Ho Yoon;Seunghyun Lee;Young Hun Choi;Jung-Eun Cheon
    • Korean Journal of Radiology
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    • 제24권4호
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    • pp.294-304
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    • 2023
  • Objective: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. Materials and Methods: We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. Results: The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). Conclusion: Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

다양한 다분류 SVM을 적용한 기업채권평가 (Corporate Bond Rating Using Various Multiclass Support Vector Machines)

  • 안현철;김경재
    • Asia pacific journal of information systems
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    • 제19권2호
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    • pp.157-178
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    • 2009
  • Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.