• Journal of Microbiology and Biotechnology
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• v.22 no.4
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• pp.555-562
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• 2012

PCR was performed to analyze the ${\beta}$-lactamase genes carried by ampicillin-resistant Vibrio spp. strains isolated from marine environments in Korea between 2006 and 2009. All 36 strains tested showed negative results in PCR with the primers designed from the nucleotide sequences of various known ${\beta}$-lactamase genes. This prompted us to screen new ${\beta}$-lactamase genes. A novel ${\beta}$-lactamase gene was cloned from Vibrio alginolyticus KV3 isolated from the aquaculture water of Geoje Island of Korea. The determined nucleotide sequence (VAK-3 ${\beta}$-lactamase) revealed an open reading frame (ORF) of 852 bp, encoding a protein of 283 amino acids (aa), which displayed low homology to any other ${\beta}$-lactamase genes reported in public databases. The deduced 283 aa sequence of VAK-3, consisting of a 19 aa signal peptide and a 264 aa mature protein, contained highly conserved peptide segments specific to class A ${\beta}$-lactamases including the specific amino acid residues STFK (62-65), SDN (122-124), E (158), and RTG (226-228). Results from PCR performed with primers specific to the VAK-3 ${\beta}$-lactamase gene identified 3 of the 36 isolated strains as V. alginolyticus, Vibrio cholerae, and Photobacterium damselae subsp. damselae, indicating the utilization of various ${\beta}$-lactamase genes including unidentified ones in ampicillin-resistant Vibrio spp. strains from the marine environment. In a mating experiment, none of the isolates transfered the VAK-3 ${\beta}$-lactamase gene to the Escherichia coli recipient. This lack of mobility, and the presence of a chromosomal acyl-CoA flanking sequence upstream of the VAK-3 ${\beta}$-lactamase gene, led to the assumption that the location of this new ${\beta}$-lactamase gene was in the chromosome, rather than the mobile plasmid. Antibiotic susceptibility of VAK-3 ${\beta}$-lactamase was indicated by elevated levels of resistance to penicillins, but not to cephalosporins in the wild type and E. coli harboring recombinant plasmid pKV-3, compared with those of the host strain alone. Phylogenetic analysis showed that VAK-3 ${\beta}$-lactamase is a new and separate member of class A ${\beta}$-lactamases.

• Journal of Plant Biotechnology
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• v.37 no.2
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• pp.125-132
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• 2010

Rice is the staple food of more than 50% of the worlds population. Cultivated rice has the AA genome (diploid, 2n=24) and small genome size of only 430 megabase (haploid genome). As the sequencing of rice genome was completed by the International Rice Genome Sequencing Project (IRGSP), many researchers in the world have been working to explore the gene function on rice genome. Insertional mutagenesis has been a powerful strategy for assessing gene function. In maize, well characterized transposable elements have traditionally been used to clone genes for which only phenotypic information is available. In rice endogenous mobile elements such as MITE and Tos (Hirochika. 1997) have been used to generate gene-tagged populations. To date T-DNA and maize transposable element systems has been utilized as main insertional mutagens in rice. A main drawback of a T-DNA scheme is that Agrobacteria-mediated transformation in rice requires extensive facilities, time, and labor. In contrast, the Ac/Ds system offers the advantage of generating new mutants by secondary transposition from a single tagged gene. Revertants can be utilized to correlate phenotype with genotype. To enhance the efficiency of gene detection, advanced gene-tagging systems (i.e. activation, gene or enhancer trap) have been employed for functional genomic studies in rice. Internationally, there have been many projects to develop large scales of insertionally mutagenized populations and databases of insertion sites has been established. Ultimate goals of these projects are to supply genetic materials and informations essential for functional analysis of rice genes and for breeding using agronomically important genes. In this report, we summarize the current status of Ac/Ds-mediated gene tagging systems that has been launched by collaborative works from 2001 in Korea.

• Journal of Acupuncture Research
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• v.22 no.1
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• pp.211-221
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• 2005

Objective : The aim of this study is to review clinical trials on the effectiveness of acupuncture in stroke rehabilitation. Methods : Computerized literature searches was carried out on three electronic databases, and hand-searching on some chinese medical journals in library of Kyung Hee Medical Center. Results : 1) Sixteen articles of clinical trials were collected and reviewed. Among these articles, randomized controlled trials were achieved in nine articles. 2) In three articles, statically significant results in improvement of mobile abilities, activities of daily life and Quality of life were reported after acupuncture treatment applied as a part of stroke rehabilitation. In three articles no statically significant changes were reported. 3) Among two articles about spasticity, One about the upper limbs and the results showed statically significant improvement of the spasticity after acupuncture treatment as stroke rehabilitation, and the other was about the lower limbs and the results showed no statically significant changes. 4) One article about acupuncture and postural control showed statically significant results suggesting that acupuncture promotes normalization of postural control after stroke.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.631-643
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• 2006

Moving objects in a mobile environment to change their position based on the change of time require a query with their position as a basis. Efficient Regional Decision for Continuous Skyline Queries requires objectively pre-calculating the OSR(Optimal Skyline Region) regardless of the speed and direction of the moving objects. It proposes techniques to reduce the frequency of continuous queries by choosing a VCircle(Validity Circle) as safe location which has radius of the distance to the closest region with position on the moving object at center. But, a VCircle's area varies based on the Moving object's position from first marked time of continuous query. Therefore, the frequency of its continuous query is variable and also when the object moves inside of OSR, query can re-occur frequently In this paper, we suggest a technique of selecting an IVCircle(Interior Validity Circle) in a Skyline Region as the static Safe Region using the characteristics of the OSR. An Interior IVCircle can be calculated in advance when the OSR is decided. Our experiment shows that the frequency of using IVcircle as safe region reduced than that of using VCircle as safe region by 52.55%.

• Therapeutic Science for Rehabilitation
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• v.12 no.3
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• pp.7-18
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• 2023

Objective : This study aimed to confirm the research characteristics by analyzing the literature that applied the school readiness programs for children with disabilities. Methods : Studies were collected from the PubMed, Embase, Web of Science, and Research Information Sharing Service databases. The key terms were "School readiness" AND ("Occupational Therapy" OR "Rehabilitation") in English and Korean. Total eight articles were selected through the selection and exclusion criteria. Results : The programs included multi-type training, motor skill training, parent training, and mobile application training. The providers were psychologists, occupational therapists, physical therapists, speech pathologists, community workers, educators, and the psychologists who conducted most of the research. The program factors can be classified into academic function, motor function, social function, parental training, and others. Academic and social functions accounted for the largest proportion of the respondents. The intervention improved multiple skills, literacy, parenting skills, and gross fine motor function. Conclusion : This study aimed to provide basic data for school-based occupational therapy by analyzing school readiness programs for children with disabilities. Recently, interest in and research on school readiness has increased. Occupational therapists should also establish their roles in the field of school-related rehabilitation and provide various school-based occupational therapies.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.109-130
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• 2011

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.600-619
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• 2006

According to recent technical advances on sensors and mobile devices, processing of data streams generated by the devices is becoming an important research issue. The data stream of real values obtained at continuous time points is called streaming time-series. Due to the unique features of streaming time-series that are different from those of traditional time-series, similarity matching problem on the streaming time-series should be solved in a new way. In this paper, we propose an efficient algorithm for streaming time- series matching problem that supports normalization transform. While the existing algorithms compare streaming time-series without any transform, the algorithm proposed in the paper compares them after they are normalization-transformed. The normalization transform is useful for finding time-series that have similar fluctuation trends even though they consist of distant element values. The major contributions of this paper are as follows. (1) By using a theorem presented in the context of subsequence matching that supports normalization transform[4], we propose a simple algorithm for solving the problem. (2) For improving search performance, we extend the simple algorithm to use $k\;({\geq}\;1)$ indexes. (3) For a given k, for achieving optimal search performance of the extended algorithm, we present an approximation method for choosing k window sizes to construct k indexes. (4) Based on the notion of continuity[8] on streaming time-series, we further extend our algorithm so that it can simultaneously obtain the search results for $m\;({\geq}\;1)$ time points from present $t_0$ to a time point $(t_0+m-1)$ in the near future by retrieving the index only once. (5) Through a series of experiments, we compare search performances of the algorithms proposed in this paper, and show their performance trends according to k and m values. To the best of our knowledge, since there has been no algorithm that solves the same problem presented in this paper, we compare search performances of our algorithms with the sequential scan algorithm. The experiment result showed that our algorithms outperformed the sequential scan algorithm by up to 13.2 times. The performances of our algorithms should be more improved, as k is increased.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.