• Asian-Australasian Journal of Animal Sciences
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• v.16 no.9
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• pp.1247-1253
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• 2003

Selection for increased milk production in dairy cows has often resulted in a higher incidence of disease and thus incurred a greater health costs. Considerable interests have been shown in breeding dairy cattle for disease resistance in recent years. This paper discusses the limitations of breeding dairy cattle for genetic resistance in six parts: 1) complexity of disease resistance, 2) difficulty in estimating genetic parameters for planning breeding programs against disease, 3) undesirable relationship between production traits and disease, 4) disease as affected by recessive genes, 5) new mutation of the pathogens, and 6) variable environmental factors. The hidden problems of estimating genetic and phenotypic parameters involving disease incidence were examined in terms of categorical nature, non-independence, heterogeneity of error variance, non-randomness, and automatic relationship between disease and production traits. In light of these limitations, the prospect for increasing genetic resistance by conventional breeding methods would not be so bright as we like. Since the phenomenon of disease is the result of a joint interaction among host genotype, pathogen genotype and environment, it becomes essential to adopt an integrated approach of increasing genetic resistance of the host animals, manipulating the pathogen genotypes, developing effective vaccines and drugs, and improving the environmental conditions. The advances in DNA-based technology show considerable promise in directly manipulating host and pathogen genomes for genetic resistance and producing vaccines and drugs for prevention and medication to promote the wellbeing of the animals.

• BMB Reports
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• v.46 no.8
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• pp.416-421
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• 2013

Genome-wide association studies (GWAS) have become popular as an approach for the identification of large numbers of phenotype-associated variants. However, differences in genetic architecture and environmental factors mean that the effect of variants can vary across populations. Understanding population genetic diversity is valuable for the investigation of possible population specific and independent effects of variants. EvoSNP-DB aims to provide information regarding genetic diversity among East Asian populations, including Chinese, Japanese, and Korean. Non-redundant SNPs (1.6 million) were genotyped in 54 Korean trios (162 samples) and were compared with 4 million SNPs from HapMap phase II populations. EvoSNP-DB provides two user interfaces for data query and visualization, and integrates scores of genetic diversity (Fst and VarLD) at the level of SNPs, genes, and chromosome regions. EvoSNP-DB is a web-based application that allows users to navigate and visualize measurements of population genetic differences in an interactive manner, and is available online at [http://biomi.cdc.go.kr/EvoSNP/].

• Korean Journal of Medicinal Crop Science
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• v.13 no.2
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• pp.69-74
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• 2005

Kalopanax pictus is a long-lived woody species mostly distributed in East Asia. K. pictus has been regarded as medically and ecologically important species in Korea. Thornless castor aralia variant, local name 'Cheongsong' is an endemic to Cheongsong province in Korea. Random amplified polymorphic DNA (RAPD) was used to investigate the genetic variation and structure of Korean populations of two species. A high level of genetic variation was found in six K. pictus populations. Twelve primers revealed 49 loci, of which 29 were polymorphic (59.2%). Nei's gene diversity for K.pictus and K. pictus variant were 0.119 and 0.098, respectively. Mean of genetic diversity in K. pictus was higher than average values for species with similar life history traits. The asexual and sexual reproduction, perennial habitat, and longevity are proposed as possible factors contributing to high genetic diversity. An indirect estimate of the number of migrants per generation (Nm=0.857) indicated that gene flow was not extensive among Korean populations of K.pictus. It is suggested that the isolation of geographical distance and reproductive isolation between K.pictus and K.pictus variant populations may have played roles in shaping the population structure of this species.

• International Journal of Oral Biology
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• v.45 no.1
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• pp.1-7
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• 2020

Oral lichen planus (OLP) is a chronic inflammatory disease observed in approximately 0.5-2.2% of the population, and it is recognized as a premalignant lesion that can progress into oral squamous cell carcinoma (OSCC). The rate of malignant transformation is approximately 1.09-2.3%, and the risk factors for malignant transformation are age, female, erosive type, and tongue site location. Malignant transformation of OLP is likely related to the low frequency of apoptotic phenomena. Therefore, apoptosis-related genetic factors, like p53, BCL-2, and BAX are reviewed. Increased p53 expression and altered expression of BCL-2 and BAX were observed in OLP patients, and the malignant transformation rate in these patients was relatively higher. The involvement of microRNA (miRNA) in the malignant transformation of OLP is also reviewed. Because autophagy is involved in cell survival and death through the regulation of various cellular processes, autophagy-related genetic factors may function as factors for malignant transformation. In OLP, decreased levels of ATG9B mRNA and a higher expression of IGF1 were observed, suggesting a reduction in cell death and autophagic response. Activated IGF1-PI3K/AKT/mTor cascade may play an important role in a signaling pathway related to the malignant transformation of OLP to OSCC. Recent research has shown that miRNAs, such as miR-199 and miR-122, activate the cascade, increasing the prosurvival and proproliferative signals.

• Asian-Australasian Journal of Animal Sciences
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• v.22 no.4
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• pp.459-464
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• 2009

Data and pedigree information for Lori-Bakhtiari sheep used in this study were 6,239 records of lamb mortality from 246 sires and 1,721 dams, collected from 1989 through 2007 from a Lori-Bakhtiari flock at Shooli station in Shahrekord. The traits investigated were cumulative lamb mortality from birth up to 7 days, up to 14 days, up to 21 days, and up to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12 months of age. The models included fixed factors that had significant effects and random direct genetic, maternal genetic and maternal permanent environmental effects. Variance components were estimated using the restricted maximum likelihood procedure applying three animal models with and without maternal and common environmental effects. The overall mean of cumulative lamb mortality rate was 22.95% from birth to 1 year of age, while the overall mortality rate up to 3 and from 3 to 6 months of age was 6.14% and 12.76%, respectively. The mortality rate after 6 months of age declined as the lambs grew older. The age of dam had no important effect on lamb mortality. The type of birth was more important during the preweaning period than at later ages, and lamb mortality rate was higher in twins. The year of birth, month of birth and sex of lamb significantly (p${\leq}$0.01) affected the cumulative lamb mortality rate at all ages. The least square mean of mortality during the final one-third of the lambing period was higher than the first and middle onethird of the lambing period. Male lambs were found to be at a higher risk of mortality than females. Birth weight of the lamb had a highly significant (p${\leq}$0.01) effect on lamb mortality at all ages as a quadratic regression. Direct and maternal heritability estimates of lamb mortality ranged from 0.01 to 0.13 and 0.01 to 0.05, respectively. Direct heritability increased with age of lamb, while maternal effects (genetic and common environmental) were important in the preweaning period. These results indicate that lamb mortality can be reduced first through farm management practices and secondly by genetic selection. Both animal and maternal effects should be considered in breeding programmes for reducing lamb mortality at preweaning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5842-5861
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• 2019

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.

• Journal of Veterinary Science
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• v.22 no.5
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• pp.73.1-73.11
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• 2021

Background: Feline calicivirus (FCV) is a common pathogen of felids, and FCV vaccination is regularly practiced. The genetic variability and antigenic diversity of FCV hinder the effective control and prevention of infection by vaccination. Improved knowledge of the epidemiological characteristics of FCV should assist in the development of more effective vaccines. Objectives: This study aims to determine the prevalence of FCV in a population of cats with FCV-suspected clinical signs in Hangzhou and to demonstrate the antigenic and genetic relationships between vaccine status and representative isolated FCV strains. Methods: Cats (n = 516) from Hangzhou were investigated between 2018 and 2020. The association between risk factors and FCV infection was assessed. Phylogenetic analyses based on a capsid coding sequence were performed to identify the genetic relationships between strains. In vitro virus neutralization tests were used to assess antibody levels against isolated FCV strains in client-owned cats. Results: The FCV-positive rate of the examined cats was 43.0%. Risk factors significantly associated with FCV infection were vaccination status and oral symptoms. Phylogenetic analysis revealed a radial phylogeny with no evidence of temporal or countrywide clusters. There was a significant difference in the distribution of serum antibody titers between vaccinated and unvaccinated cats. Conclusions: This study revealed a high prevalence and genetic diversity of FCV in Hangzhou. The results indicate that the efficacy of FCV vaccination is unsatisfactory. More comprehensive and refined vaccination protocols are an urgent and unmet need.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.3
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• pp.31-38
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• 2019

In this paper, we design a green supply chain (gSC) network model. For constructing the gSC network model, environmental and economic factors are taken into consideration in it. Environmental factor is to minimize the $CO_2$ emission amount emitted when transporting products or materials between each stage. For economic factor, the total cost which is composed of total transportation cost, total handling cost and total fixed cost is minimized. To minimize the environmental and economic factors simultaneously, a mathematical formulation is proposed and it is implemented in a genetic algorithm (GA) approach. In numerical experiment, some scales of the gSC network model is presented and its performance is analyzed using the GA approach. Finally, the efficiencies of the gSC network model and the GA approach are proved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1414-1430
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• 2022

Due to the Internet of Things popularity, many agricultural data are collected by sensors automatically. The abundance of agricultural data makes precise prediction of rice yield possible. Because the climate factors have an essential effect on the rice yield, we considered the climate factors in the prediction model. Accordingly, this paper proposes a machine learning model for rice yield prediction in Taiwan, including the genetic algorithm and support vector regression model. The dataset of this study includes the meteorological data from the Central Weather Bureau and rice yield of Taiwan from 2003 to 2019. The experimental results show the performance of the proposed model is nearly 30% better than MARS, RF, ANN, and SVR models. The most important climate factors affecting the rice yield are the total sunshine hours, the number of rainfall days, and the temperature.The proposed model also offers three advantages: (a) the proposed model can be used in different geographical regions with high prediction accuracies; (b) the proposed model has a high explanatory ability because it could select the important climate factors which affect rice yield; (c) the proposed model is more suitable for predicting rice yield because it provides higher reliability and stability for predicting. The proposed model can assist the government in making sustainable agricultural policies.

• 대한생식의학회:학술대회논문집
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• 2004.06a
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• pp.39-42
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• 2004