• Genomics & Informatics
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• v.8 no.3
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• pp.131-137
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• 2010

Genome-wide association studies (GWASs) have greatly contributed to the identification of common variants responsible for numerous complex traits. There are, however, unavoidable limitations in detecting causal and/or rare variants for traits in this approach, which depends on an LD-based tagging SNP microarray chip. In an effort to detect potential casual and/or rare variants for complex traits, such as type 2 diabetes (T2D) and triglycerides (TGs), we conducted a targeted resequencing of loci identified by the Korea Association REsource (KARE) GWAS. The target regions for resequencing comprised whole exons, exon-intron boundaries, and regulatory regions of genes that appeared within 1 Mb of the GWA signal boundary. From 124 individuals selected in population-based cohorts, a total of 0.7 Mb target regions were captured by the NimbleGen sequence capture 385K array. Subsequent sequencing, carried out by the Roche 454 Genome Sequencer FLX, generated about 110,000 sequence reads per individual. Mapping of sequence reads to the human reference genome was performed using the SSAHA2 program. An average of 62.2% of total reads was mapped to targets with an average 22X-fold coverage. A total of 5,983 SNPs (average 846 SNPs per individual) were called and annotated by GATK software, with 96.5% accuracy that was estimated by comparison with Affymetrix 5.0 genotyped data in identical individuals. About 51% of total SNPs were singletons that can be considered possible rare variants in the population. Among SNPs that appeared in exons, which occupies about 20% of total SNPs, 304 nonsynonymous singletons were tested with Polyphen to predict the protein damage caused by mutation. In total, we were able to detect 9 and 6 potentially functional rare SNPs for T2D and triglycerides, respectively, evoking a further step of replication genotyping in independent populations to prove their bona fide relevance to traits.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.1-14
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• 2010

The back-propagation neural network (BPN) has long been successfully applied in bankruptcy prediction problems. Despite its wide application, some major issues must be considered before its use, such as the network topology, learning parameters and normalization methods for the input and output vectors. Previous studies on bankruptcy prediction with BPN have shown that many researchers are interested in how to optimize the network topology and learning parameters to improve the prediction performance. In many cases, however, the benefits of data normalization are often overlooked. In this study, a genetic algorithm (GA)-based normalization transform, which is defined as a linearly weighted combination of several different normalization transforms, will be proposed. GA is used to extract the optimal weight for the generalization. From the results of an experiment, the proposed method was evaluated and compared with other methods to demonstrate the advantage of the proposed method.

• The Plant Pathology Journal
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• v.36 no.6
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• pp.558-569
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• 2020

Fusarium head blight (FHB) is a devastating fungal disease of wheat (Triticum aestivum L.). The lack of genetic resources with stable FHB resistance combined with a reliable and rapid screening method to evaluate FHB resistance is a major limitation to the development of FHB resistant wheat germplasm. The present study utilized an immature wheat spike culture method to screen wheat spike culture derived variants (SCDV) for FHB resistance. Mycotoxin concentrations determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS) correlated significantly (P < 0.01) with FHB severity and disease progression during in vitro spike culture. Selected SCDV lines assessed for FHB resistance in a Fusarium field disease nursery in Carman, Manitoba, Canada in 2016 showed significant (P < 0.01) correlation of disease severity to the in vitro spike culture screening method. Selected resistant SCDV lines were also crossed with an elite cv. CDC Hughes and the progeny of F₂ and BC₁F₂ were screened by high resolution melt curve (HRM) analyses for the wheat UDP-glucosyl transferase gene (TaUGT-3B) single nucleotide polymorphism to identify resistant (T-allele) and susceptible (G-allele) markers. The progeny from the crosses were also screened for FHB severity using the immature spike culture method and identified resistant progeny grouped according to the HRM genotyping data. The results demonstrate a reliable approach using the immature spike culture to screen for FHB resistance in progeny of crosses in early stage of breeding programs.

• Korean Journal of Medicinal Crop Science
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• v.25 no.5
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• pp.290-295
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• 2017

Background: Senna tora is a flowering plant in the legume family Fabaceae. Its seeds are roasted and consumed as tea in Asia, to reduce inflammation in the liver and improve eyesight. Thus, it has been considered as an important medicinal crops in Asia. However, breeding trials to improve its genetic properties are rare. Mutation breeding by gamma ray is known to be an effective and highly successful approach for the generation of agronomically useful cultivars. Here we analyzed the effects of several dosages of gamma ray on the biological conditions of Senna tora seeds. Methods and Results: The germination rate and growth patterns of Senna tora were examined following irradiation with gamma ray at 100, 200, 300 and 400 Gy. The total phenolic compound contents and antioxidant activities of Senna tora were analyzed. Germination increased at 100 and 200 Gy in the M1 and M2 generations compared with that of the control (M0). The total phenolic compound contents and antioxidant activity of the seeds significantly decreased as the radiation dosage increased above 100 Gy in the M1 generation. Conclusions: Senna tora, irradiated with gamma ray at dosages 100, 200, 300, and 400 Gy, showed maximum germination rate at 200 Gy in the M2 generation. Plant height and leaf size gradually decreased with increasing gamma ray intensity in the M2 generation. The total phenolic compound contents decreased significantly at 400 Gy, and the related antioxidant activity was also decreased as the radiation dosage increased.

• Proceedings of the Korean Society of Food Hygiene and Safety Conference
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• 1996.06a
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• pp.33-33
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• 1996

Interests in the field of rapid methods and automation in microbiology have been growing steadily on an international scale in recent years. International meetings concerned this problem have been held in elsewhere in the world countries since the past twenty years. But, unfortunately in the field of microbial examination in food hygiene, this problem have not yet been developed so much as in the field of clinical microbiology. Today, I would like to introduce you here present aspects of rapid and automation technologies, those which are manly carrying in milk and meats industries. My illustration will be given recent improved technologies using automatic apparatus and instruments along with process of microbial count procedure. Recent direct microbiological counting system (ChemeScan \ulcorner) as real time ultrasensitive analysis created by Cheminex Ltd., France is now most evolutional instrument to provide direct microbial counts, down to one cell, within 30 minutes. The results from these evaluations how a good correlation between the ChemScan system and the standard plate count method. This system will be successful application for not only in the field of pharmacology but also food microbiology. In addition, current identification of microbes by sophisticated instruments suitable for food microbiology, one of which Biology is manual system (BIOLOG\ulcorner), provides reference-level capability at a modes price. For the manual system, the color reactions in the microplate are read by eye and manually keyed into personal computer. Species identification appears on the computer screen within seconds, along with biotype patterns, a list of closely related species, and other useful statistics. In present this is useful application for microbial ecology and epidemiological survey. RiboPrinter system newly produced by DuPont is now focusing among microbiologists in the world, and is one of the biggest microbial characterization system using a DNA-based approach. The technology analyzer is bacterial culture for its genetic fingerprint or riboprint pattern. Finally Bio-cellTracer system for automatic measurement of fungal growth and Fukitori-Maseter, a Surface Hygiene Monitoring Kit by using swabe procedure in food processing environment are briefly illustrated in this presentation.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.786-792
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• 2012

A microarray is a collection of thousands of DNAs or RNAs arranged on a substrate, and it enables one to navigate large amounts of gene expression. However, a researcher uses his designed experimental methods to focus on particular phenotypes from the available mass of data. In this paper, we used MicroRNAs(miRNAs) and Protein-Protein Interation(PPI) databases to enhance and expand meanings in microarray data. Further, the expanded data are linked with the Online Mendelian Inheritance in Man(OMIM), and International Statistical Classification of Diseases and Related Health Problems, $10^{th}$ Revision(ICD-10), in order to extract common genetic relationships between diseases. This approach, we expect, should provide new biological views.

• Korean Journal of Breeding Science
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• v.41 no.3
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• pp.205-212
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• 2009

Wheat (Triticum aestivum L.) grain texture is an important determinant of milling properties and end product use. Two linked genes, puroindoline a (PINA) and puroindoline b (PINB), control most of the genetic variation in wheat grain texture. Wheat seed proteins were examined to identify PINA and PINB gene using two pre-harvest sprouting wheat cultivars; Jinpum (resistant) and Keumgang (susceptible).Wheat seed proteins were separated by two-dimensional electrophoresis with IEF gels over pH ranges: pH 3-10. A total of 73 spots were digested with trypsin resulting peptide fragmentation were analyzed by matrix assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF/MS). Mass spectra were automatically processed and searched through NCBInr, SWISS-PORT and MSDB database with mono isotopic masses and complete gene sequence were found by UniProt database. Puroindoline a and puroindoline b that is responsible for grain texture related with baking performance and roughness. Two spots were found Pin b (16.7 kDa) and Pin a (16.3 kDa) in Jinpum compare to seven spots were identified Pin a (16.1 kDa, 16.3 kDa) and Pin b (16.7 kDa, 9.5 kDa and 14.4 kDa) in Keumgang. Some selected spots were identified puroindoline like grain softness protein (16.9 kDa, 17 kDa and 18.1 kDa) in Keumgang. Moreover, to gain a better inferring the identification of puroindoline related proteins using proteomics, we accomplished a complete gene sequence of PINA and PINB gene in pre-harvesting sprouting wheat seeds between resistant (Jinpum) and susceptible (Keumgang).

• Journal of IKEEE
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• v.23 no.2
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• pp.529-533
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• 2019

Combined cycle power plants are often used to produce power. These days prediction of power plant output based on operating parameters is a major concern. This paper presents an approach to using computational intelligence technique to predict the output power of combined cycle power plant. Computational intelligence techniques have been developed and applied to many real world problems. In this paper, tree architectures of fuzzy neural networks are considered to predict the output power. Tree architectures of fuzzy neural networks have an advantage of reducing the number of rules by selecting fuzzy neurons as nodes and relevant inputs as leaves optimally. For the optimization of the networks, two-step optimization method is used. Genetic algorithms optimize the binary structure of the networks by selecting the nodes and leaves as binary, and followed by random signal-based learning further refines the optimized binary connections in the unit interval. To verify the effectiveness of the proposed method, combined cycle power plant dataset obtained from the UCI Machine Learning Repository Database is considered.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1260-1283
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• 2019

Low earth orbit (LEO) satellite broadband network is a crucial part of the space information network. LEO satellite constellation design is a top-level design, which plays a decisive role in the overall performance of the LEO satellite network. However, the existing works on constellation design mainly focus on the coverage criterion and rarely take network performance into the design process. In this article, we develop a unified framework for constellation optimization design in LEO satellite broadband networks. Several design criteria including network performance and coverage capability are combined into the design process. Firstly, the quality of service (QoS) metrics is presented to evaluate the performance of the LEO satellite broadband network. Also, we propose a network stability model for the rapid change of the satellite network topology. Besides, a mathematical model of constellation optimization design is formulated by considering the network cost-efficiency and stability. Then, an optimization algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is provided for the problem of constellation design. Finally, the proposed method is further evaluated through numerical simulations. Simulation results validate the proposed method and show that it is an efficient and effective approach for solving the problem of constellation design in LEO satellite broadband networks.

• Computers and Concrete
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• v.27 no.4
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• pp.333-341
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• 2021

Steel slag, an industrial reject from the steel rolling process, has been identified as one of the suitable, environmentally friendly materials for concrete production. Given that the coarse aggregate portion represents about 70% of concrete constituents, other economic approaches have been found in the use of alternative materials such as steel slag in concrete. Unfortunately, a standard framework for its application is still lacking. Therefore, this study proposed functional model equations for the determination of strength properties (compression and splitting tensile) of steel slag aggregate concrete (SSAC), using gene expression programming (GEP). The study, in the experimental phase, utilized steel slag as a partial replacement of crushed rock, in steps 20%, 40%, 60%, 80%, and 100%, respectively. The predictor variables included in the analysis were cement, sand, granite, steel slag, water/cement ratio, and curing regime (age). For the model development, 60-75% of the dataset was used as the training set, while the remaining data was used for testing the model. Empirical results illustrate that steel aggregate could be used up to 100% replacement of conventional aggregate, while also yielding comparable results as the latter. The GEP-based functional relations were tested statistically. The minimum absolute percentage error (MAPE), and root mean square error (RMSE) for compressive strength are 6.9 and 1.4, and 12.52 and 0.91 for the train and test datasets, respectively. With the consistency of both the training and testing datasets, the model has shown a strong capacity to predict the strength properties of SSAC. The results showed that the proposed model equations are reliably suitable for estimating SSAC strength properties. The GEP-based formula is relatively simple and useful for pre-design applications.