• Title/Summary/Keyword: Node Similarity

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Studies on the Construction of Mutant Diversity Pool (MDP) lines, and their Genomic Characterization in Soybean

  • Dong-Gun Kim;Sang Hoon Kim;Chang-Hyu Bae;Soon-Jae Kwon
    • Proceedings of the Plant Resources Society of Korea Conference
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    • 2021.04a
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    • pp.9-9
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    • 2021
  • Mutation breeding is useful for improving agronomic characteristics of various crops. In this study, we constructed soybean Mutant Diversity Pool (MDP) from 1,695 gamma-irradiated mutants through two selection phases over M1 to M12 generations; we selected 523 mutant lines exhibiting at least 30% superior agricultural characteristics, and, second, we eliminated redundant morphological phenotypes in the M12 generation. Finally, we constructed 208 MDP lines and investigated 11 agronomic traits. We then assessed the genetic diversity and inter-relationships of these MDP lines using target region amplification polymorphism (TRAP) markers. Among the different TRAP primer combinations, polymorphism levels and PIC values averaged 59.71% and 0.15, respectively. Dendrogram and population structure analyses divided the MDP lines into four major groups. According to an analysis of AMOVA, the percentage of inter-population variation among mutants was 11.320 (20.6%), whereas mutant inter-population variation ranged from 0.231 (0.4%) to 14.324 (26.1%). Overall, the genetic similarity of each cultivar and its mutants were higher than within other mutant populations. In an analysis of the genome-wide association study (GWAS) using based on the genotyping-by-sequencing (GBS), we detected 66 SNPs located on 13 different chromosomes were found to be highly associated with four agronomic traits: days of flowering (33 SNPs), flower color (16 SNPs), node number (6 SNPs), and seed coat color (11 SNPs). These results are consistent with those previously reported for other genetic resource populations, including natural accessions and recombinant inbred line. Our observations suggest that genomic changes in mutant individuals induced by gamma rays occurred at the same loci as those of natural soybean population. This study has demonstrated that the integration of GBS and GWAS can serve as a powerful complementary approach to gamma-ray mutation for the dissection of complex traits in soybean.

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Multiple Cause Model-based Topic Extraction and Semantic Kernel Construction from Text Documents (다중요인모델에 기반한 텍스트 문서에서의 토픽 추출 및 의미 커널 구축)

  • 장정호;장병탁
    • Journal of KIISE:Software and Applications
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    • v.31 no.5
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    • pp.595-604
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    • 2004
  • Automatic analysis of concepts or semantic relations from text documents enables not only an efficient acquisition of relevant information, but also a comparison of documents in the concept level. We present a multiple cause model-based approach to text analysis, where latent topics are automatically extracted from document sets and similarity between documents is measured by semantic kernels constructed from the extracted topics. In our approach, a document is assumed to be generated by various combinations of underlying topics. A topic is defined by a set of words that are related to the same topic or cooccur frequently within a document. In a network representing a multiple-cause model, each topic is identified by a group of words having high connection weights from a latent node. In order to facilitate teaming and inferences in multiple-cause models, some approximation methods are required and we utilize an approximation by Helmholtz machines. In an experiment on TDT-2 data set, we extract sets of meaningful words where each set contains some theme-specific terms. Using semantic kernels constructed from latent topics extracted by multiple cause models, we also achieve significant improvements over the basic vector space model in terms of retrieval effectiveness.

Analysis of Spatial Trip Regularity using Trajectory Data in Urban Areas (도시부 경로자료를 이용한 통행의 공간적 규칙성 분석)

  • Lee, Su jin;Jang, Ki tae
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.17 no.6
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    • pp.96-110
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    • 2018
  • As the development of ICT has made it easier to collect various traffic information, research on creating new traffic attributes is drawing attention. Estimation and forecasts of demand and traffic volume are one of the main indicators that are essential to traffic operation, assuming that the traffic pattern at a particular node or link is repeated. Traditionally, a survey method was used to demonstrate this similarity on trip behavior. However, the method was limited to achieving high accuracy with high costs and responses that relied on the respondents' memory. Recently, as traffic data has become easier to gather through ETC system, smart card, studies are performed to identify the regularity of trip in various ways. In, this study, route-level trip data collected in Daegu metropolitan city were analyzed to confirm that individual traveler forms a spatially similar trip chain over several days. For this purpose, we newly define the concept of spatial trip regularity and assess the spatial difference between daily trip chains using the sequence alignment algorithm, Dynamic Time Warping. In addition, we will discuss the applications as the indicators of fixed traffic demand and transportation services.