• Weed & Turfgrass Science
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• v.2 no.3
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• pp.230-235
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• 2013

Amaranths (Amaranthus sp.), an endemic American crop, are now grown widely across the world. This study used 14 simple sequence repeat (SSR) markers to analyze the genetic diversity of 74 amaranth accessions from the United States, with eight accessions from Australia as controls. One hundred twenty-two alleles, averaging eight alleles per locus, were observed. The average major allele frequency, expected heterozygosity, and polymorphism information content (PIC) were 0.44, 0.69, and 0.65, respectively. The structure analysis based on genetic distance classified 77 accessions (94%) into three clusters, while five accessions (6%) were admixtures. Among the three clusters, Cluster 3 had the highest allele number and PIC values, while Cluster 2 had the lowest. The lowest FST was between Clusters 1 and 3, indicating that these two clusters have higher gene flow between them compared to the others. This finding was reasonable because Cluster 2 included most of the Australian accessions. These results indicated satisfactory genetic diversity among U.S. amaranths. These findings can be used to design effective breeding programs involving different plant characteristics.

• Bulletin of the Korean Chemical Society
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• v.29 no.4
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• pp.833-841
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• 2008

Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm-artificial neural network (PC-GA-ANN) models were applied for prediction of melting point for 323 drug-like compounds. A large number of theoretical descriptors were calculated for each compound. The first 234 principal components (PC’s) were found to explain more than 99.9% of variances in the original data matrix. From the pool of these PC’s, the genetic algorithm was employed for selection of the best set of extracted PC’s for PC-MLR and PC-ANN models. The models were generated using fifteen PC’s as variables. For evaluation of the predictive power of the models, melting points of 64 compounds in the prediction set were calculated. Root-mean square errors (RMSE) for PC-GA-MLR and PC-GA-ANN models are 48.18 and $12.77{^{\circ}C}$, respectively. Comparison of the results obtained by the models reveals superiority of the PC-GA-ANN relative to the PC-GA-MLR and the recently proposed models (RMSE = $40.7{^{\circ}C}$). The improvements are due to the fact that the melting point of the compounds demonstrates non-linear correlations with the principal components.

• Journal of Environmental Health Sciences
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• v.44 no.1
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• pp.31-43
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• 2018

Objectives: Effective genetic toxicology and molecular biology research techniques and strategies that are highly correlated with the carcinogenic inhalation toxicity test and related research are required. The aim of this study was to maximize the utilization of chemical substances to prevent workers' occupational diseases. Methods: We surveyed the literature, domestic and international references, and the status of relevant domestic and foreign professional organizations. Expert advisory opinions were reflected, and experts were consulted by participating in domestic and overseas academic conferences. Results: The current status of domestic and international genotoxic toxicity evaluation was examined through various documents from related organizations. Cell models for in vitro lung toxicology were investigated and summarized, and the human resources and performance results of genetic toxicity studies and pilot projects were compared and analyzed by holding an advisory meeting. We examined domestic and international genotoxicity guidelines and investigated new test methods for the development of genotoxicity and carcinogenicity. Ultimately, we described long-term future predictions, including the implementation of our researchers' recommendations and occupational genetic toxicology forecasts for future worker health protection. Conclusions: This research project aims to establish current genetic toxicology and molecular biology research techniques and strategies that can maximize the linkage with the carcinogenic inhalation toxicity test and research in the future. We expanded the study of genetic toxicity and establish a foundation forgenetic toxicity in accordance with research trends in Korea and abroad.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.406-412
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• 2010

In this paper, we formally define the problem of maximizing the coverage of sensor deployment, which is the optimization problem appeared in real-world sensor deployment, and analyze the properties of its solution space. To solve the problem, we proposed novel genetic algorithms, and we could show their superiority through experiments. When applying genetic algorithms to maximum coverage sensor deployment, the most important issue is how we evaluate the given sensor deployment efficiently. We could resolve the difficulty by using Monte Carlo method. By regulating the number of generated samples in the Monte Carlo evaluation of genetic algorithms, we could also reduce the computing time significantly without loss of solution quality.

• Proceedings of the Korean Society of Crop Science Conference
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• 2007.11a
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• pp.215-215
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• 2007

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.1311-1312
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• 2011

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2002.11a
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• pp.43.2-44
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• 2002

• Horticultural Science & Technology
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• v.34 no.2
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• pp.294-304
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• 2016

Members of the genus Brassica, which are known as oil crops or cruciferous vegetables, are widely cultivated in Canada, Australia, Asian and Europe. Because Brassica species have high yields, are well adapted to their environments, and are self-incompatible, the germplasm is abundant. Previous studies have reported abundant genetic diversity even within Brassica subspecies. In Korea, fresh cabbage leaves are eaten with roast meat, and to meet the current popular demand, new varieties are being increasingly bred. To determine the genetic diversity and relationships among the cabbage vegetables in Korea, we evaluated the genetic variation of 18 accessions based on 5S and 18S ribosomal RNA (rRNA) gene sequences. We detected many variable nucleotide sites, especially in the 5S rRNA gene sequences. Because the length of the 18S rRNA gene might influence the dissimilarity rate statistics, we used both the 5S and 18S sequences to analyze the phylogenetic relationships. S7 (B. oleracea) showed the most distant phylogenetic relationship with the other Brassica species. Interestingly, B2 (B. oleracea), B15, and B18 (B. campestris) have three different types of leaf profiles, and were divided into one group, and the other Brassica species formed another group. Statistical analysis of interspecies and intraspecies genetic distances revealed that B. campestris L. showed higher genetic diversity than B. oleracea L. This work provides additional data that facilitates the evaluation of the genetic variation and relationships among Brassica species. The results could be used in functional plant breeding programs to improve Brassica crops.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.6
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• pp.747-753
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• 2001

In this study, the Italian milk recorded buffalo population from 1974 to 1996 was analysed with the purpose to estimate genetic and environmental variability and provide genetic parameters for the most important economic traits. High variability between herds was evident due to the poor knowledge of feeding requirements and husbandry technology in this species compared to cattle. Age at first calving was reduced by 57 days during the considered years following efforts made in better feeding and management from 1990; on the contrary, calving interval has increased by 17 days as a consequence of forcing buffaloes to calve in spring, in order to have the peak milk yield when milk is much better paid. Average milk yield increased by 1853 kg during these years, while lactation duration was reduced by 30 days. Season of calving has no effect on all traits. Calving order has a positive effect on milk yield especially because older cows produce more milk in shorter lactations. Heritability for the age at first calving and calving interval was 0.26 and 0.05 respectively. Heritability of productive traits, milk yield and duration of the lactation was 0.19 and 0.13 respectively, with repeatabilities of 0.40 and 0.26. Genetic trend for milk yield was 2.1 kg milk/year for the bulls and 1 kg for all population. The high genetic variability of milk production as well as duration of the lactation, indicates that there are good opportunities for genetic improvement when including these traits in a selection scheme. The low genetic trend registered over 15 years of recording activity can be explained by the fact that neither progeny testing was performed or selection schemes were implemented, due to the difficulties to use artificial insemination in buffalo.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.9
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• pp.1226-1234
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• 2015

The development of effective genetic evaluations and selection of sires requires accurate estimates of genetic parameters for all economically important traits in the breeding goal. The main objective of this study was to assess the relative performance of the traditional lactation average model (LAM) against the random regression test-day model (RRM) in the estimation of genetic parameters and prediction of breeding values for Holstein Friesian herds in Ethiopia. The data used consisted of 6,500 test-day (TD) records from 800 first-lactation Holstein Friesian cows that calved between 1997 and 2013. Co-variance components were estimated using the average information restricted maximum likelihood method under single trait animal model. The estimate of heritability for first-lactation milk yield was 0.30 from LAM whilst estimates from the RRM model ranged from 0.17 to 0.29 for the different stages of lactation. Genetic correlations between different TDs in first-lactation Holstein Friesian ranged from 0.37 to 0.99. The observed genetic correlation was less than unity between milk yields at different TDs, which indicated that the assumption of LAM may not be optimal for accurate evaluation of the genetic merit of animals. A close look at estimated breeding values from both models showed that RRM had higher standard deviation compared to LAM indicating that the TD model makes efficient utilization of TD information. Correlations of breeding values between models ranged from 0.90 to 0.96 for different group of sires and cows and marked re-rankings were observed in top sires and cows in moving from the traditional LAM to RRM evaluations.