http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Jihoon Shin,Shinae Park,Jung‑Shin Lee,Eun‑Jin Lee,Hong‑Duk Youn 한국유전학회 2020 Genes & Genomics Vol.42 No.3
Background Streptomyces seoulensis has contributed to the discovery and initiation of extensive research into nickel superoxide dismutase (NiSOD), a unique type of superoxide dismutase found in actinomycetes. Still so far, there is no information about whole genome sequence of this strain. Objective To investigate complete genome sequence and perform bioinformatic analyses for genomic functions related with nickel-associated genes. Methods DNA was extracted using the Wizard Genomic DNA Purification Kit then sequenced using a Pacific Biosciences SMRT cell 8Pac V3 DNA Polymerase Binding Kit P6 with the PacBiov2 RSII platform. We assembled the PacBio longreads with the HGAP3 pipeline. Results We obtained complete genome sequence of S. seoulensis, which comprises a 6,339,363 bp linear chromosome. While analyzing the genome to annotate the genomic function, we discovered the nickel-associated genes. We observed that the sodN gene encoding for NiSOD is located adjacent to the sodX gene, which encodes for the nickel-type superoxide dismutase maturation protease. In addition, several nickel-associated genes and gene clusters-nickel-responsive regulator, nickel uptake transporter, nickel–iron [NiFe]-hydrogenase and other putative genes were also detected. Strain specific genes were discovered through a comparative analysis of S. coelicolor and S. griseus. Further bioinformatic analyses revealed that this strain encodes at least 22 putative biosynthetic gene clusters, thereby implying that S. seoulensis has the potential to produce novel bioactive compounds. Conclusion We annotated the genome and determined nickel-associated genes and gene clusters and discovered biosynthetic gene clusters for secondary metabolites implying that S. seoulensis produces novel types of bioactive compounds.
Spatial Analysis of the Vulnerability to Meteorological Hazards in Korea
Jihoon Jung 건국대학교 기후연구소 2018 기후연구 Vol.13 No.3
The purpose of this research is to provide an objective and accurate regional vulnerability index at a finer resolution with the research period from 1983 to 2012 in Korea. To find the spatial patterns and characteristics of regional vulnerability, this research conducted four different types of analyses. First, the most vulnerable regions in terms of demographic, climatological-geographic, socioeconomic, and technological factors were respectively investigated. Second, total vulnerability index combining all the four factors was examined. Next, the most influential factors deciding vulnerability and common spatial patterns of vulnerability were extracted using empirical orthogonal function (EOF) analysis. Lastly, the degree of clustering for each factor was checked using Moran’s I and local indicators of spatial association (LISA). The result found the most vulnerable provinces were Jeolla and Gyeongsang Province, regarding to demographic and climatological- geographic factors, respectively. In the case of socioeconomic factors, the difference between urban and rural areas was larger than the difference between provinces. In addition, the EOF analysis showed that demographic factors would be the most influential factors which explained 32.2 percent of the total variance of data. Lastly, climatological and geographic factors represented the highest degree of clustering (global Moran’s I: 0.51).
Comparison study of SARIMA and ARGO models for in influenza epidemics prediction
Jung, Jihoon,Lee, Sangyeol The Korean Data and Information Science Society 2016 한국데이터정보과학회지 Vol.27 No.4
The big data analysis has received much attention from the researchers working in various fields because the big data has a great potential in detecting or predicting future events such as epidemic outbreaks and changes in stock prices. Reflecting the current popularity of big data analysis, many authors have proposed methods tracking influenza epidemics based on internet-based information. The recently proposed 'autoregressive model using Google (ARGO) model' (Yang et al., 2015) is one of those influenza tracking models that harness search queries from Google as well as the reports from the Centers for Disease Control (CDC), and appears to outperform the existing method such as 'Google Flu Trends (GFT)'. Although the ARGO predicts well the outbreaks of influenza, this study demonstrates that a classical seasonal autoregressive integrated moving average (SARIMA) model can outperform the ARGO. The SARIMA model incorporates more accurate seasonality of the past influenza activities and takes less input variables into account. Our findings show that the SARIMA model is a functional tool for monitoring influenza epidemics.
우리나라 지역별 고온 극한 현상에 의한 사망 취약도 비교
정지훈(Jihoon Jung),김인겸(In-Gyum Kim),이대근(Dae-Geun Lee),신진호(Jinho Shin),김백조(Baek-Jo Kim) 대한지리학회 2014 대한지리학회지 Vol.49 No.2
본 연구에서는 지구 온난화의 가장 직접적인 영향 중 하나인 폭염에 따른 사망자수 변화를 분석하였다. 지난 17년(1994~2010)간의 기온자료와 사망자수 자료를 바탕으로 각 도시별 사망자가 급증하는 임계온도와 최소사망 온도를 분석하였다. 분석결과 우리나라 전 지역의 최소사망 온도는 평균 23~25℃로 나타났으며, 강원도가 23.0℃로 가장 낮게 나타나고 7대도시와 전라북도가 25.45℃로 가장 높게 나타났다. 사망자가 급증하는 임계온도의 경우 평균 27~30℃로 나타났다. 임계온도가 높은 지역은 대부분 포항, 전주, 원주, 대구와 같은 대도시가 많았으며, 임계온도가 낮은 지역은 금산, 문경, 봉화, 보은 등 상대적으로 작은 규모의 도시였다. 한편, 인구구조 취약성이 높은 지역일수록 최소사망 온도가 낮았으며(r=-0.44, p=0.06), 사회·경제·환경 취약성이 높을수록 최소사망 온도와(r=-0.36, p=0.032) 임계온도(r=-0.29, p=0.081)가 낮다는 점을 보여줬다. 본 연구는 앞으로 지구온난화가 진행됨에 따라 사망자가 급격하게 증가할 수 있으며, 지역별로 다양한 자연적, 사회적, 경제적 요소 등이 복합적으로 작용하여 지역에 따라 큰 편차를 보일 수 있다는 점을 보이고 있다. This study tries to investigate the changes of mortality regarding heat waves which are usually considered as one of the most direct impacts of climate change. Based on 17 years data period (1994- 2010), each city’s threshold temperature and minimum mortality temperature are recognized. According to the results, minimum mortality temperature varies from 23 to 25°C, showing minimum temperature corresponding to 23.0°C in Gangwondo and maximum temperature corresponding to 25.4°C in Jeollabukdo and Major 7 city group. In case of threshold temperature, it ranges from 27 to 30°C. The cities having higher threshold temperatures tend to have large populations and vice versa. In addition, the cities having negative demographic vulnerability relatively have lower temperatures, representing correlation -0.44(p=0.06). The socio-economic-environmental vulnerability shows negative correlation with minimum mortality temperature(r=-0.36, p=0.032) and threshold temperature(r=-0.29, p=0.081). This paper represents that the number of mortality could increase rapidly and show large spatial differences in the number of mortality depending on various factors including natural, social, and economic factors of each region.