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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.
다차원 히스토그램에서 범위 질의의 선택도에 대한 오차 추정
정지훈(Jihoon Jung),홍석진(Seokjin Hong),배진욱(Jinuk Bae),안성준(Seongjoon Ahn),송병호(Byoungho Song),이석호(Sukho Lee) 한국정보과학회 2001 한국정보과학회 학술발표논문집 Vol.28 No.2Ⅰ
히스토그램은 질의 최적화를 위해 사용되는 통계 정보 중 하나이다. 최근에는 방대한 데이타에 대한 범위 질의의 선택도 추정 방법의 하나로 사용되기도 한다. 히스토그램을 통한 범위 질의의 선택도 추정 결과는 항상 오차를 포함한다. 따라서 결과의 신뢰성을 보장하기 위해 선택도에 대한 오차를 추정하는 방법이 요구된다. 추정된 선택도의 오차 추정에 대한 기존의 방법은 1차원 히스토그램만을 고려하여 하나의 애트리뷰트의 값에 따라 빈도의 분포를 반영하므로 애트리뷰트가 많은 다차원 히스토그램에 바로 적용시키는데 문제가 있다. 이 논문에서는 기존의 추정된 선택도에 대한 오차 추정 기법들을 다차원에 적용할 수 있게 확장한 M-Max, M-Sum 기법을 제안하고, 두 기법을 합친 하이브리드 기법을 제안한다. 실험을 통해 M-Sum 기법과 하이브리드 기법이 M-Max 기법보다 정확한 오차 추정 기법임을 보이고, 또한 작은 기억 공간에서도 두 기법이 오차를 보다 정확하게 추정함을 보인다.
2단계 튜닝 방법을 적용한 PHS 용 CMOS Bandpass Complex Filter의 설계
정지훈(JiHoon Jung),김진경(JinKyung Kim),이강윤(Kang-Yoon Lee) 대한전자공학회 2006 대한전자공학회 학술대회 Vol.2006 No.11
This paper presents a baseband complex bandpass filter for PHS applications with a new automatic tuning method. The full-CMOS PHS transceiver is implemented by adopting the Low-IF architecture to overcome the DC-offset problems. To meet the Adjacent Channel Selectivity (ACS) performance, the 3rd-order Chebyshev complex bandpass filter is designed as the baseband channel-select filter. The new comer frequency tuning method is proposed to compensate the process variation. This method can reduce the noise level due to MOS switches. The filter was fabricated using a 0.35㎛ CMOS process, and the power consumption is 12㎽.