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Jung, W.Y.,Yu, S.L.,Seo, D.W.,Jung, K.C.,Cho, I.C.,Lim, H.T.,Jin, D.I.,Lee, Jun-Heon Asian Australasian Association of Animal Productio 2012 Animal Bioscience Vol.25 No.10
Pigs may need to be exploited as xenotransplantation donors due to the shortage of human organs, tissues and cells. Porcine endogenous retroviruses (PERVs) are a significant obstacle to xenotransplantation because they can infect human cells in vitro and have the potential for transmission of unexpected pathogens to humans. In this research, 101 pigs, including four commercial breeds (23 Berkshire, 13 Duroc, 22 Landrace and 14 Yorkshire pigs), one native breed (19 Korean native pigs) and one miniature breed (10 NIH miniature pigs) were used to investigate insertional variations for 11 PERV loci (three PERV-A, six PERV-B and two PERV-C). Over 60% of the pigs harbored one PERV-A (907F8) integration and five PERV-B (B3-3G, B3-7G, 742H1, 1155D9 and 465D1) integrations. However, two PERV-A loci (A1-6C and 1347C1) and one PERV-B locus (B3-7F) were absent in Duroc pigs. Moreover, two PERV-C loci (C2-6C and C4-2G) only existed in Korean native pigs and NIH miniature pigs. The results suggest that PERV insertional variations differ among pig breeds as well as among individuals within a breed. Also, the results presented here can be used for the selection of animals that do not have specific PERV integration for xenotransplantation research.
Low Temperature Oligomerization of Ethylene over Ni/Al-KIT-6 Catalysts
Hwang, A.,Kim, S.,Kwak, G.,Kim, S. K.,Park, H. G.,Kang, S. C.,Jun, K. W.,Kim, Y. T. Springer Science + Business Media 2017 Catalysis letters Vol.147 No.6
<P>In this paper, we have studied the oligomerization of ethylene with a liquid heptane solvent over bifunctional Ni catalysts in a continuous flow reactor. We have prepared an Al-containing KIT-6 silica that was used as a support after calcination in the temperature range of 300-900 A degrees C. The Ni/Al-KIT-6 catalysts had uniform mesopores with diameters in the range of 5.4-6.3 nm, excepting Ni/Al-KIT-6 (900). The calcination temperature of Al-KIT-6 support changed the surface acidity as well as the interaction of Ni2+ and acid sites for the Ni catalysts, as determined by temperature-programmed desorption of ammonia, temperature-programmed reduction, infrared spectroscopy after the adsorption of pyridine, solid-state Al-27 magic-angle spinning nuclear magnetic resonance spectroscopy, and X-ray adsorption spectroscopy. Among the tested catalysts, the Ni/Al-KIT-6 (300) showed the highest ethylene conversion because of the increased intimate contact between Ni2+ and acid sites. The strong interaction of Ni2+ species and the support is not effective in increasing active sites for ethylene conversion. The Ni/Al-KIT-6 catalysts produced internal linear C4 and C6 olefins with high selectivity. The Ni/Al-KIT-6 (300) had 2.2-6.1 times lower selectivities toward 2-ethyl-1-butene than other catalysts at similar ethylene conversions. The reaction product mixture showed that the Ni/Al-KIT-6 catalysts shifted the product distribution towards acid-catalyzed oligomerization/cracking/realkylation products (i.e. C3, C7, C7, and C8+ olefins) as the concentration of Bronsted acid sites increased. Among the tested catalysts, the Ni/Al-KIT-6 (300) showed the highest yield of C4 and C6 olefins (78.3%).</P>
한국어 형태소 분석 및 품사 태깅을 위한 딥 러닝 기반 2단계 파이프라인 모델
윤준영(Jun Young Youn),이재성(Jae Sung Lee) 한국정보과학회 2021 정보과학회논문지 Vol.48 No.4
인공신경망을 활용한 최근의 한국어 형태소 분석 및 태깅 연구는 주로 표층형에 대해 형태소 분리와 품사 태깅을 먼저하고, 원형 복원 사전을 이용하여 후처리로 형태소 원형을 복원해왔다. 본 연구에서는 형태소 분석 및 품사 태깅을 두 단계로 나누어, sequence-to-sequence를 이용하여 형태소 원형을 먼저 복원하고, 최근 자연어처리의 다양한 분야에서 우수한 성능을 보이는 BERT를 이용하여 형태소 분리 및 품사 태깅을 하였다. 두 단계를 파이프라인으로 적용한 결과, 별도의 규칙이나 복합 태그 처리 등이 필요한 형태소 원형 복원 사전을 사용하지 않고도 우수한 형태소 분석 및 태깅 결과를 보였다. Recent studies on Korean morphological analysis using artificial neural networks have usually performed morpheme segmentation and part-of-speech tagging as the first step with the restoration of the original form of morphemes by using a dictionary as the postprocessing step. In this study, we have divided the morphological analysis into two steps: the original form of a morpheme is restored first by using the sequence-to-sequence model, and then morpheme segmentation and part-of-speech tagging are performed by using BERT. Pipelining these two steps showed comparable performance to other approaches, even without using a morpheme restoring dictionary that requires rules or compound tag processing.