• Proceedings of the Korean Society for Applied Microbiology Conference
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• 2001.06a
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• pp.155-157
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• 2001

Bifidobacterium spp. is nonpathogenic, gram-positive and anaerobic bacteria, which inhabit the intestinal tract of humans and animals. In breast-fed infants, bifidobacteria comprise morethan 90％ of the gut bacterial population. Bifidobacteria spp. are used in commericial fermented dairy products and have been suggested to exert health promoting effects on the host by maintaining intestinal microflora balances, improving lactose tolerance, reducing serum cholesterol levels, increasing synthesis of vitamins, and aiding the immune enchancement and anticarcinogenic activity for the host. These beneficial effects of Bifidobacterium are strain-related. Therefore continued efforts to improve strain characteristics are warranted. in these respect, development of vector system for Bifidobacterium is very important not only for the strain improvement but also because Bifidobacterium is most promising in serving as a delivery system for the useful gene products, such as vaccine or anticarcinogenic polypeptides, into human intestinal tract. For developing vector system, we have characterized several bifidobacterial plasmids at genetic level and developed several shuttle vectors between E. coli and Bifidobacterium using them. Also, we have cloned and sequenced several metabolic genes and food grade selection marker. Also we have obtained bifidobacterial surface protein, which will be used as the mediator for surface display of foreign genes. Recently we have succeeded in expressing amylase and GFP in Bifidobacterium using our own expression vector system. Now we are in a very exciting stage for the molecular breeding and safe delivery system using probiotic Bifidobacterium strains.

• Journal of Plant Biotechnology
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• v.43 no.3
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• pp.302-310
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• 2016

A study aimed at identifying putative drought responsive genes that confer tolerance to water stress deficit in tea plants was conducted in a 'rain-out shelter' using potted plants. Eighteen months old drought tolerant and susceptible tea cultivars were each separately exposed to water stress or control conditions of 18 or 34% soil moisture content, respectively, for three months. After the treatment period, leaves were harvested from each treatment for isolation of RNA and cDNA synthesis. The cDNA libraries were sequenced on Roche 454 high-throughput pyrosequencing platform to produce 232,853 reads. After quality control, the reads were assembled into 460 long transcripts (contigs). The annotated contigs showed similarity with proteins in the Arabidopsis thaliana proteome. Heat shock proteins (HSP70), superoxide dismutase (SOD), catalase (cat), peroxidase (PoX), calmodulinelike protein (Cam7) and galactinol synthase (Gols4) droughtrelated genes were shown to be regulated differently in tea plants exposed to water stress. HSP70 and SOD were highly expressed in the drought tolerant cultivar relative to the susceptible cultivar under drought conditions. The genes and pathways identified suggest efficient regulation leading to active adaptation as a basal defense response against water stress deficit by tea. The knowledge generated can be further utilized to better understand molecular mechanisms underlying stress tolerance in tea.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1306-1325
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• 2019

As the world is moving towards digitization, data is generated from various sources at a faster rate. It is getting humungous and is termed as big data. The financial sector is one domain which needs to leverage the big data being generated to identify financial risks, fraudulent activities, and so on. The design of predictive models for such financial big data is imperative for maintaining the health of the country's economics. Financial data has many features such as transaction history, repayment data, purchase data, investment data, and so on. The main problem in predictive algorithm is finding the right subset of representative features from which the predictive model can be constructed for a particular task. This paper proposes a correlation-based method using submodular optimization for selecting the optimum number of features and thereby, reducing the dimensions of the data for faster and better prediction. The important proposition is that the optimal feature subset should contain features having high correlation with the class label, but should not correlate with each other in the subset. Experiments are conducted to understand the effect of the various subsets on different classification algorithms for loan data. The IBM Bluemix BigData platform is used for experimentation along with the Spark notebook. The results indicate that the proposed approach achieves considerable accuracy with optimal subsets in significantly less execution time. The algorithm is also compared with the existing feature selection and extraction algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1475-1480
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• 2014

Wellness is IT fused with the user manage and maintain the health of a service can help you. If you are using an existing Fitness Center to yourself by choosing appliances that fit with the risk of injury in order to learn how the efficient movement had existed for a long time was needed. To resolve, use the personal training but more expensive cost of people's problems, and shown again in the habit of exercising alone will have difficulty. This paper provides a variety of smart phones based on a hybrid app with compatibility with the platform and personalized training market system. Users of the Fitness Center is built into smart phones in the history of their movement sensors or transmits to the Web by typing directly. This is based on exercise programs tailored to users via the training market. Personal training marketplace has a variety of users, check the history of this movement he can recommend an exercise program for themselves can be applied by selecting the. This provides users with the right exercise program can do long-term exercise habits can be proactive and goal setting.

• Genomics & Informatics
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• v.14 no.3
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• pp.104-111
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• 2016

Zika virus (ZIKV) is a mosquito borne pathogen, belongs to Flaviviridae family having a positive-sense single-stranded RNA genome, currently known for causing large epidemics in Brazil. Its infection can cause microcephaly, a serious birth defect during pregnancy. The recent outbreak of ZIKV in February 2016 in Brazil realized it as a major health risk, demands an enhanced surveillance and a need to develop novel drugs against ZIKV. Amodiaquine, prochlorperazine, quinacrine, and berberine are few promising drugs approved by Food and Drug Administration against dengue virus which also belong to Flaviviridae family. In this study, we performed molecular docking analysis of these drugs against nonstructural 3 (NS3) protein of ZIKV. The protease activity of NS3 is necessary for viral replication and its prohibition could be considered as a strategy for treatment of ZIKV infection. Amongst these four drugs, berberine has shown highest binding affinity of -5.8 kcal/mol and it is binding around the active site region of the receptor. Based on the properties of berberine, more similar compounds were retrieved from ZINC database and a structure-based virtual screening was carried out by AutoDock Vina in PyRx 0.8. Best 10 novel drug-like compounds were identified and amongst them ZINC53047591 (2-(benzylsulfanyl)-3-cyclohexyl-3H-spiro[benzo[h]quinazoline-5,1'-cyclopentan]-4(6H)-one) was found to interact with NS3 protein with binding energy of -7.1 kcal/mol and formed H-bonds with Ser135 and Asn152 amino acid residues. Observations made in this study may extend an assuring platform for developing anti-viral competitive inhibitors against ZIKV infection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.943-946
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• 2006

XML is the most widely used document format by advantage that self-contained for platform. So, currently the most used among other document format. but XML appeared new problem that memory and transmission. And that be used in environment a request restriction memory and fast transmission as like mobile field. Although discussion of XML binarization is going on progress. And fast Infoset configuration using XML Information Set is receiving attention that a way to lower file size of hold down a existing usage. In this paper, we designed of module using fast Infoset and PER among ASN.1 Encoding Rule for XML binarization. And we implementation of encoder constructed interlace by stage of translation from XML into binary XML.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2012.05a
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• pp.586-591
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• 2012

The solution of maintenance decision support system for the gas turbine engine, which is currently operating in GUNSAN combined cycle power plant, was developed and is consist of online monitoring module, periodic performance trending module, optimal compressor washing interval analysis module and hot component management module. Also, GUI platform was applied to this solution for the user to monitoring the analyzed result of engine performance condition and then to make a decision of the consequent maintenance action. In online condition monitoring module, the performance degradation of engine is provided by the analysis of difference between the real time measurement data compared to exist engine performance. The optimal compressor washing interval module produced the washing interval of maximum net profit value by researching the maintenance expense and the loss profit value corresponds to the performance degradation with economic assessment algorithm. Thus, this solution support the user to enable the optimal maintenance and operation of gas turbine engine with overall analysis of engine condition and main information.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.65-72
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• 2020

Today, the Internet of Things is used in many places, including homes, industrial sites, and hospitals, to give us convenience. Many services generate new value through real-time data collection, storage and analysis as devices are connected to the network. Many of these fields are creating services and applications that utilize sensors and communication functions within IoT devices. However, since everything can be hacked, it causes a huge privacy threat to users who provide data. For example, a variety of sensitive information, such as personal information, lifestyle patters and the existence of diseases, will be leaked if data generated by smarwatches are abused. Development of IoT must be accompanied by the development of security. Recently, Differential Privacy(DP) was adopted to privacy-preserving data processing. So we propose the method that can aggregate health data safely on smartwatch platform, based on DP.

• Smart Structures and Systems
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• v.19 no.1
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• pp.1-10
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• 2017

The operation of subway trains induces secondary structure-borne vibrations in the nearby underground spaces. The vibration, along with the associated noise, can cause annoyance and adverse physical, physiological, and psychological effects on humans in dense urban environments. Traditional tethered instruments restrict the rapid measurement and assessment on such vibration effect. This paper presents a novel approach for Wireless Smart Sensor (WSS)-based autonomous evaluation system for the subway train-induced vibrations. The system was implemented on a MEMSIC's Imote2 platform, using a SHM-H high-sensitivity accelerometer board stacked on top. A new embedded application VibrationLevelCalculation, which determines the International Organization for Standardization defined weighted acceleration level, was added into the Illinois Structural Health Monitoring Project Service Toolsuite. The system was verified in a large underground space, where a nearby subway station is a good source of ground excitation caused by the running subway trains. Using an on-board processor, each sensor calculated the distribution of vibration levels within the testing zone, and sent the distribution of vibration level by radio to display it on the central server. Also, the raw time-histories and frequency spectrum were retrieved from the WSS leaf nodes. Subsequently, spectral vibration levels in the one-third octave band, characterizing the vibrating influence of different frequency components on human bodies, was also calculated from each sensor node. Experimental validation demonstrates that the proposed system is efficient for autonomously evaluating the subway train-induced ambient vibration of underground spaces, and the system holds the potential of greatly reducing the laboring of dynamic field testing.

• Journal of Veterinary Science
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• v.22 no.3
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• pp.37.1-37.14
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• 2021

Background: Ticks are one of the most common external parasites in dogs, and are associated with the transmission of a number of major zoonoses, which result in serious harm to human health and even death. Also, the increasing number of pet dogs and pet owners in China has caused concern regarding human tick-borne illnesses. Accordingly, studies are needed to gain a complete understanding of the bacterial composition and diversity of the ticks that parasitize dogs. Objectives: To date, there have been relatively few reports on the analysis of the bacterial community structure and diversity in ticks that parasitize dogs. The objective of this study was to investigate the microbial composition and diversity of parasitic ticks of dogs, and assessed the effect of tick sex and geographical region on the bacterial composition in two tick genera collected from dogs in China. Methods: A total of 178 whole ticks were subjected to a 16S ribosomal RNA (rRNA) next generation sequencing analysis. The Illumina MiSeq platform targeting the V3-V4 region of the 16S rRNA gene was used to characterize the bacterial communities of the collected ticks. Sequence analysis and taxonomic assignment were performed using QIIME 2 and the GreenGene database, respectively. After clustering the sequences into taxonomic units, the sequences were quality-filtered and rarefied. Results: After pooling 24 tick samples, we identified a total of 2,081 operational taxonomic units, which were assigned to 23 phyla and 328 genera, revealing a diverse bacterial community profile. The high, moderate and low prevalent taxa include 46, 101, and 182 genera, respectively. Among them, dominant taxa include environmental bacterial genera, such as Psychrobacter and Burkholderia. Additionally, some known tick-associated endosymbionts were also detected, including Coxiella, Rickettsia, and Ricketssiella. Also, the potentially pathogenic genera Staphylococcus and Pseudomonas were detected in the tick pools. Moreover, our preliminary study found that the differences in microbial communities are more dependent on the sampling location than tick sex in the tick specimens collected from dogs. Conclusions: The findings of this study support the need for future research on the microbial population present in ticks collected from dogs in China.