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임병휘,김준모 한국동물유전육종학회 2022 한국동물유전육종학회지 Vol.6 No.2
In this review paper, we investigated canine diseases and quantitative traits based on estimation of genetic potential to improve the quality of the companion dog breeding industry, as dogs make up the majority of companion animal. Until now, studies on the use of DNA markers in dogs have largely been related to parentage, breed identification, genetic diseases, and quantitative traits. Testing for parentage and breed often utilizes microsatellite markers, a method which has been shown to be effective in a number of studies. Genetic diseases in dogs are often caused by single mutations which show Mendelian inheritance. Causal genes, mutation types, and inheritance types have mainly been investigated in dog genetic diseases that occur most frequently. The coat color and body size of dogs are quantitative traits and do not follow Mendelian inheritance. The coat color of dogs is determined by a complex mechanism involving the interaction of 5 loci (E, A, K, D, and B). Body size was found to be related to mutations located in 17 genes (ESR1, FGF4, STC2, SMAD2, HMGA2, GHR, R3HCM1, ADAMTS9, ACSL4, IGF1R, LCORL, IRS4, IGSF1, TBX3, MED13L, RNFT2, and IGF1) and 2 loci (ZNF608 and IGF2BP2 loci). In addition, the hair feature is controlled by combinations of alleles at 5 genes (FGF5, RSPO2, KRT71, FOXI3, and SGK3). Overseas, companies (Embark, Wisdom panel, Orivet, etc.) that provide breed identification and screening for genetic diseases through DNA analysis are already available. Typical services include breed identification covering 180 – 250 breeds and risk diagnosis of 140 – 180 genetic diseases. DNA analysis services in the Republic of Korea are relatively inferior in quality/quantity and are under publicized. Therefore, it is necessary to develop a dog DNA analysis system that is easy to access and suitable for customers.
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제주마 보존 및 개량을 위한 연구 동향 및 고찰(총설)
임병휘,김상욱,강다원,박경도,도경탁 강원대학교 동물자원공동연구소 2019 동물자원연구 Vol.30 No.1
Jeju pony, a natural monument no. 347 living in Jeju Island, Korea, has been conserved as a genetic resource and a racing pony. A total of 125 studies written about this breed have been published in various fields, primarily animal science (106), veterinary medicine (13), and others (6), from 1961 to 2018. Specifically, the majority of studies have been published in animal breeding and genetics (84), which are subcategories in the field of animal science. However, most of the research in breeding and genetics for Jeju pony were performed in the early 2000s, and studies using recently developed techniques are inferior to other breeds (thoroughbred etc.). Our current understanding of both the conservation and utilization of the breed is still incomplete. Thus, it is important to use recently developed technologies to improve management and breeding strategies for this pony breed. In this study, we review studies on Jeju ponies in the field of animal breeding and genetics and discuss problems that need to be addressed. Reviewed studies were divided into 6 categories: systematic classification, coat color, body measurement and growth, racing performance, behavior, and effective population size. Finally, we suggest ideas for future research projects that could potentially contribute to the Jeju pony industry. -
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임병휘,장민재,오승미,노진구,이정재,김상은,옥선아,윤익진,김준석,지현근,김완섭,강희정,조가희,오건봉,김준모 한국통합생물학회 2023 Animal cells and systems Vol.27 No.1
Cardiac xenotransplantation is the potential treatment for end-stage heart failure, but the allogenicorgan supply needs to catch up to clinical demand. Therefore, genetically-modified porcine heartxenotransplantation could be a potential alternative. So far, pig-to-monkey heart xenografts havebeen studied using multi-transgenic pigs, indicating various survival periods. However, functionalmechanisms based on survival period-related gene expression are unclear. This study aimed toidentify the differential mechanisms between pig-to-monkey post-xenotransplantation longandshort-term survivals. Heterotopic abdominal transplantation was performed using a donorCD46-expressing GTKO pig and a recipient cynomolgus monkey. RNA-seq was performed usingsamples from POD60 XH from monkey and NH from age-matched pigs, D35 and D95. GeneannotatedDEGs for POD60 XH were compared with those for POD9 XH (Park et al. 2021). DEGswere identified by comparing gene expression levels in POD60 XH versus either D35 or D95 NH. 1,804 and 1,655 DEGs were identified in POD60 XH versus D35 NH and POD60 XH versus D95NH, respectively. Overlapped 1,148 DEGs were annotated and compared with 1,348 DEGs forPOD9 XH. Transcriptomic features for heart failure and inhibition of T cell activation wereobserved in both long (POD60)- and short (POD9)-term survived monkeys. Only short-termsurvived monkey showed heart remodeling and regeneration features, while long-term survivedmonkey indicated multi-organ failure by neural and hormonal signaling as well as suppressionof B cell activation. Our results reveal differential heart failure development and survival at thetranscriptome level and suggest candidate genes for specific signals to control adverse cardiacxenotransplantation effects.
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X-ray 이물검출기의 이물 검출 향상을 위한 딥러닝 방법
임병휘(Byung Hey Lim),정승수(Seung Su Jeong),유윤섭(Yun Seop Yu) 한국정보통신학회 2021 한국정보통신학회 종합학술대회 논문집 Vol.25 No.1
식품은 기본적으로 영양성과 안전성을 반드시 갖추어야 한다. 최근에 식품의 안정성이 의심이 되는 안산의 한 유치원에서 식중독성 유증상자가 다수 발생하였다. 그래서 식품의 안전성은 더욱 요구되는 사항이다. 본 논문에서는 식품의 안전성을 확보하기 위한 이물검출기의 딥러닝모델을 통해 검출율을 향상시키는 방법을 제안한다. 제안방법으로는 CNN(convolution neural network), Faster R-CNN(region convolution neural network)의 네트워크를 통해 학습하고 정상과 이물제품의 영상을 테스트한다. 딥러닝 모델을 통해 테스트한 결과 기존 이물검출기의 알고리즘에 Faster R-CNN을 병행한 방법이 다른 방법보다 검출율이 좋은 성능을 보였다. Food basically must have nutrition and safety. Recently, a number of symptoms of food poisoning occurred in a kindergarten in Ansan, where food safety was suspected. Therefore, the safety of food is more demanding. In this paper, we propose a method to improve the detector to secure food safety. The proposed method is to learn through the network of convolution neural network (CNN) and Faster region-CNN (Faster R-CNN) and test the images of normal and foreign products. As a result of testing through a deep learning model, the method that used Faster R-CNN in parallel with the existing foreign body detector algorithm showed better detection rate than other methods.
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