• Journal of Plant Biotechnology
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• v.36 no.2
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• pp.106-114
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• 2009

Genetic components introduced into approved GM crops are a key subject for safety assessment and provide a basis for the development of detection methods for GM crops. In order to understand the genetic components in approved GM crops comprehensively, we screened the genetic vector maps of GM crops that had been approved for commercialization around the world. A total of 64 varieties from 5 major GM crop species (maize, canola, cotton, soybean, and tomato) were subjected to analysis. The genetic components included genes, promoters, terminators, and selection marker. This survey may be useful for researchers who develop GM crops and methods for detecting GM crops.

• Journal of Integrative Natural Science
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• v.8 no.2
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• pp.117-127
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• 2015

Lentil (Lens culinaris Medik.) is one of the important legumes and cheaper source of protein in Bangladesh that displays great biological diversity. Isozyme, one of the most important protein markers to detect genetic polymorphism in lentil, whereas we considered thirteen-isozyme in six varieties viz., BARI masur-1, BARI masur-2, BARI masur-3, BARI masur-4, BARI masur-5 and BARI masur-6. The highest polymorphism was found in tyrosinase isozyme system. UPGMA analysis revealed that the highest similarity between BARI masur-5 and BARI masur-6 whereas, the highest genetic distance between BARI masur-1 and BARI masur-5 reflecting higher intervarietal variation. Principal component analysis (PCA) also revealed the similar results that of unweighted pair group method with arithmetic mean (UPGMA). The first, second and third PCs contributed 81.58%, 11.19% and 4.94% variation respectively, with cumulative variation of the first three PCs was 75.45%. Consequently, Isozyme could clearly assed the genetic diversity at intervarietal levels and these two varieties can be considered as valuable gene resources for future breeding and conservation programs.

• Journal of Ecology and Environment
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• v.36 no.3
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• pp.205-210
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• 2013

Cicuta virosa L. (Apiaceae) is a perennial emergent plant designated as an endangered species in South Korea. According to the former records, only four natural habitats remain in South Korea. A former study suggested that three of four populations (Pyeongchang: PC, Hoengseong: HS, Gunsan: GS) would be classified as different ecotypes based on their different morphological characteristics and life cycle under different environmental conditions. To evaluate this suggestion, we estimated genetic diversity in each population and distance among three populations by random amplification of polymorphic DNA. Seven random primers generated a total of 61 different banding positions, 36 (59%) of them were polymorphic. Nei's gene diversity and the Shannon diversity index increased in the order of PC < HS < GS, which is the same order of population size. In the two-dimensional (2D) plot of first two principal components in principal component analysis with the presence of 61 loci, individuals could be grouped as three populations easily (proportion of variance = 0.6125). Nei's genetic distance for the three populations showed the same tendency with the geographical distance within three populations. And it is also similar to the result of discriminant analysis with the morphological or life-cycle factors from the previous study. From the results, we concluded that three different populations of C. virosa should be classified as ecotypes based on not only morphology and phenology but genetic differences in terms of diversity and distance as well.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.4
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• pp.203-214
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• 2017

There are two references of genetic information in Korean soybean cultivar. This study suggested that the new seven genetic information to supplement the uncertainty on prediction of potential yield of two references in soybean, and assessed the availability of two references and seven genetic information for future research. We carried out evaluate the prediction on flowering time and potential yield of the two references of genetic parameters and the new seven genetic parameters (New1~New7); the new seven genetic parameters were calibrated in Jinju, Suwon, Chuncheon during 2003-2006. As a result, in the individual and regional combination genetic parameters, the statistical indicators of the genetic parameters of the each site or the genetic parameters of the participating stations showed improved results, but did not significant. In Daegu, Miryang, and Jeonju, the predictability on flowering time of genetic parameters of New7 was not improved than that of two references. However, the genetic parameters of New7 showed improvement of predictability on potential yield. No predictability on flowering time of genetic parameters of two references as having the coefficient of determination ($R^2$) on flowering time respectively, at 0.00 and 0.01, but the predictability of genetic parameter of New7 was improved as $R^2$ on flowering time of New7 was 0.31 in Miryang. On the other hand, $R^2$ on potential yield of genetic parameters of two references were respectively 0.66 and 0.41, but no predictability on potential yield of genetic parameter of New7 as $R^2$ of New7 showed 0.00 in Jeonju. However, it is expected that the regional combination genetic parameters with the good evaluation can be utilized to predict the flowering timing and potential yields of other regions. Although it is necessary to analyze further whether or not the input data is uncertain.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.58-69
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• 1998

This paper introduces a novel neuro-fuzzy system based on the polynomial fuzzy neural network(PFNN) architecture. The PFNN consists of a set of if-then rules with appropriate membership functions whose parameters are optimized via a hybrid genetic algorithm. A polynomial neural network is employed in the defuzzification scheme to improve output performance and to select appropriate rules. A performance criterion for model selection, based on the Group Method of DAta Handling is defined to overcome the overfitting problem in the modeling procedure. The hybrid genetic optimization method, which combines a genetic algorithm and the Simplex method, is developed to increase performance even if the length of a chromosome is reduced. A novel coding scheme is presented to describe fuzzy systems for a dynamic search rang in th GA. For a performance assessment of the PFNN inference system, three well-known problems are used for comparison with other methods. The results of these comparisons show that the PFNN inference system outperforms the other methods while it exhibits exceptional robustness characteristics.

• Geomechanics and Engineering
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• v.15 no.4
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• pp.937-945
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• 2018

A probabilistic study of a reinforced earth wall in a frictional soil using the surface response methodology (RSM) is presented. A deterministic model based on numerical simulations is used (Abdelouhab et al. 2011, 2012b) and the serviceability limit state (SLS) is considered in the analysis. The model computes the maximum horizontal displacement of the wall. The response surface methodology is utilized for the assessment of the Hasofer-Lind reliability index and is optimized by the use of a genetic algorithm. The soil friction angle and the unit weight are considered as random variables while studying the SLS. The assumption of non-normal distribution for the random variables has an important effect on the reliability index for the practical range of values of the wall horizontal displacement.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.64-66
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• 1995

This paper presents a new approach to evaluate optimal basic reliability indices of electric distribution systems using genetic algorithm. The use of optimal reliability evaluation is an important aspect of distribution system planning and operation to determine adequacy reliability level of each area. In this paper, the reliability model is based on the analytical method, connecting component failure to load point outage in each section. The proposed method applies genetic algorithm to calculate the optimal values of basic reliability indices, ie. failure rate and repair time, for a load point in the power distribution system, subject to minimizing interruption cost. Test results for the model system are reported in the paper compared with a direct optimization method(gradient projection).

• Structural Engineering and Mechanics
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• v.71 no.4
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• pp.329-339
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• 2019

Assessment of failure probability, especially for a complex structure, requires a considerable number of calls to the numerical model. Reliability methods have been developed to decrease the computational time. In this approach, the original numerical model is replaced by a surrogate model which is usually explicit and much faster to evaluate. The current paper proposed an efficient reliability method based on Monte Carlo simulation (MCS) and multi-gene genetic programming (MGGP) as a robust variant of genetic programming (GP). GP has been applied in different fields; however, its application to structural reliability has not been tested. The current study investigated the performance of MGGP as a surrogate model in structural reliability problems and compares it with other surrogate models. An adaptive Metropolis algorithm is utilized to obtain the training data with which to build the MGGP model. The failure probability is estimated by combining MCS and MGGP. The efficiency and accuracy of the proposed method were investigated with the help of five numerical examples.

• Korean Journal of Plant Resources
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• v.25 no.6
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• pp.762-772
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• 2012

The objective of this study was to analyze the genetic diversity using SSR marker and investigate the fatty acid composition of perilla (P. frutescens var. frutescens) germplasm. Genetic diversity among 95 accessions, which consisted of 29 weedy types and 66 landrace accessions, was evaluated based on 12 SSR markers carrying 91 alleles. The mean values of observed ($H_O$) and expected heterozygosities ($H_E$) were 0.574 and 0.640, respectively, indicating a considerable amount of polymorphism within this collection. A genetic distance-based phylogeny grouped into two distinct groups, which were the landrace, moderate and weedy type, genetic distance (GD) value was 0.609. The physicochemical traits about crude oil contents and fatty acid compositions were analyzed using GC. Among tested germplasm, the total average oil contents (%) showed a range from 28.57 to 49.67 %. Five fatty acids and their contents in the crude oils are as follows: ${\alpha}$-linolenic acid (41.12%-51.81%), linoleic acid (15.38%-16.43%), oleic acid (18.93%-27.28%), stearic acid (2.56%-4.01%), and palmitic acid (7.38%-10.77%). The average oil content of wild types was lower than landrace, and the oil content of middle genotype accessions was higher than other germplasm, but no significant variation between landrace and wild types was shown. Nevertheless, IT117174, landrace of Korea, was highest in crude oil content (47.11%) and linolenic acid composition (64.58%) among the used germplasm. These traits of the selected accessions will be helped for new functional plant breeding in perilla crop.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.3
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• pp.331-339
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• 2008

The genetic structure and diversity of 15 Chinese indigenous chicken breeds was investigated using 29 microsatellite markers. The total number of birds examined was 542, on average 36 birds per breed. A total of 277 alleles (mean number 9.55 alleles per locus, ranging from 2 to 25) was observed. All populations showed high levels of heterozygosity with the lowest estimate of 0.440 for the Gushi chickens, and the highest one of 0.644 observed for Wannan Three-yellow chickens. The global heterozygote deficit across all populations (FIT) amounted to 0.180 (p<0.001). About 16% of the total genetic variability originated from differences between breeds, with all loci contributing significantly to this differentiation. An unrooted consensus tree was constructed using the Neighbour-Joining method and pair-wise distances based on marker estimated kinships. Two main groups were found. The heavy-body type populations grouped together in one cluster while the light-body type populations formed the second cluster. The STRUCTURE software was used to assess genetic clustering of these chicken breeds. Similar to the phylogenetic analysis, the heavy-body type and light-body type populations separated first. Clustering analysis provided an accurate representation of the current genetic relations among the breeds. Remarkably similar breed rankings were obtained with all methods.