• Food Engineering Progress
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• v.14 no.1
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• pp.65-74
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• 2010

Culture conditions of L. plantarum TJ-LP-002, the garlic resistant strain isolated from pakimchi (green onion kimchi), were investigated for the use of feed additives. Acetic acid, citric acid, lactic acid, and tartaric acid were detected in the culture supernatant, and especially the concentrations of lactic acid and acetic acid significantly increased during cultivation. The antimicrobial activity of L. plantarum TJ-LP-002 was not affected by proteases, calatase or cellulase, which showed that the antimicrobial activity might be due to the production of acids rather than proteinaceous antimicrobial substances. L. plantarum TJ-LP-002 was resistant to neomycin sulfate, spectinomycin dihydrochloride, and lincomycin hydrochloride, sensitive to streptomycin sulfate, and intermediate resistant to ampicillin trihydrate, chloramphenicol, erythromycin, tetracycline hydrochloride, and kanamycin sulfate. The optimum initial pH of medium, fermentation temperature and time for the cell growth and antibacterial activity were pH 7.0, 30${^{\circ}C}$ and 24hr, respectively. The optimal composition of culture medium for the cell growth and antimicrobial activity was 3%(w/v) glucose as a carbon source, 3%(w/v) yeast extract as a nitrogen source, and manganese sulfate and ammonium citrate as inorganic salts. The combinatorial supplementation of these inorganic salts, rather than sole addition as an inorganic salt, resulted in better antibacterial activity.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.768-779
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• 2018

In this paper, we propose a novel video quality function for video processing based on MSSSIM to select an appropriate video processing filter and to accommodate multiple processing filters to each pixel block in a picture frame by a mathematical selection law so as to maintain video quality and to reduce the bitrate of compressed video. In viewpoint of video compression, since the properties of video quality and bitrate is different for each picture of video frames and for each areas in the same frame, it is difficult for the video filter with single property to satisfy the object of increasing video quality and decreasing bitrate. Consequently, to maintain the subjective video quality in spite of decreasing bitrate, we propose the methodology about the MSSSIM as the measure of subjective video quality, the KL-Divergence as the measure of bitrate, and the combination method of those two measurements. Moreover, using the proposed combinatorial measurement, when we use the multiple image filters with mutually different properties as a pre-processing filter for video, we can verify that it is possible to compress video with maintaining the video quality under decreasing the bitrate, as possible.

• Animal Bioscience
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• v.34 no.10
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• pp.1579-1589
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• 2021

Objective: This study was conducted to generate single stranded DNA oligonucleotides with selective affinity to bovine spermatozoa, assess its binding potential and explore its potential utility in trapping spermatozoa from suspensions. Methods: A combinatorial library of 94 mer long oligonucleotide was used for systematic evolution of ligands by exponential enrichment (SELEX) with bovine spermatozoa. The amplicons from sixth and seventh rounds of SELEX were sequenced, and the reads were clustered employing cluster database at high identity with tolerance (CD-HIT) and FASTAptamer. The enriched nucleotides were predicted for secondary structures by Mfold, motifs by Multiple Em for Motif Elicitation and 5' labelled with biotin/6-FAM to determine the binding potential and binding pattern. Results: We generated 14.1 and 17.7 million reads from sixth and seventh rounds of SELEX respectively to bovine spermatozoa. The CD-HIT clustered 78,098 and 21,196 reads in the top ten clusters and FASTAptamer identified 2,195 and 4,405 unique sequences in the top three clusters from the sixth and seventh rounds, respectively. The identified oligonucleotides formed secondary structures with delta G values between -1.17 to -26.18 kcal/mol indicating varied stability. Confocal imaging with the oligonucleotides from the seventh round revealed different patterns of binding to bovine spermatozoa (fluorescence of the whole head, spot of fluorescence in head and mid- piece and tail). Use of a 5'-biotin tagged oligonucleotide from the sixth round at 100 pmol with 4×10⁶ spermatozoa could trap almost 80% from the suspension. Conclusion: The binding patterns and ability of the identified oligonucleotides confirms successful optimization of the SELEX process and generation of aptamers to bovine spermatozoa. These oligonucleotides provide a quick approach for selective capture of spermatozoa from complex samples. Future SELEX rounds with X- or Y- enriched sperm suspension will be used to generate oligonucleotides that bind to spermatozoa of a specific sex type.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.221-233
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• 2021

The purpose of this study is to analyze the effects of determinants of innovation performance on innovation performance in industry-research institute(IR) cooperation for researchers of government-funded research institutes in Daedeok Innopolis. We reviewed the existing literature on the determinants of innovation performance through cooperation, and established a conceptual framework to analyze the combinatorial effect of the determinants of innovation performance on innovation performance in IR cooperation. Data for empirical analysis were collected through a questionnaire survey targeting researchers at a government-funded research institute in Daedeok Innopolis. The fuzzy-set qualitative comparative analysis (fsQCA) was used to analyze the combined effect of determinants of innovation performance. The fsQCA results show that the configuration of high motivation, high trust, high commitment and high satisfaction of researchers of government-funded research institutes improve innovation performance. On the other hand, it shows that the configuration of high motivation, high trust, low commitment and low satisfaction of the researcher improves innovation performance.

• Journal of Life Science
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• v.32 no.5
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• pp.391-410
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• 2022

Human microbiota is a community of microorganisms, including bacteria, fungi, and viruses, that inhabit various locations of the body, such as the gut, oral, and skin. Along with the development of metabolomic analysis and next-generation sequencing techniques for 16S ribosomal RNA, it has become possible to analyze the population for subtypes of microbiota, and with these techniques, it has been demonstrated that bacterial microbiota are involved in the metabolic and immunological processes of the hosts. While specific bacteria of microbiota, called commensal bacteria, positively affect hosts by producing essential nutrients and protecting hosts against other pathogenic microorganisms, dysbiosis, an abnormal microbiota composition, disrupts homeostasis and thereby has a detrimental effect on the development and progression of various types of diseases. Recently, several studies have reported that oral and gut bacteria of microbiota are involved in the carcinogenesis of gastrointestinal tumors and the therapeutic effects of anticancer therapy, such as radiation, chemotherapy, targeted therapy, and immunotherapy. Studying the complex relationships (bacterial microbiota-cancer-immunity) and microbiota-related carcinogenic mechanisms can provide important clues for understanding cancer and developing new cancer treatments. This review provides a summary of current studies focused on how bacterial microbiota affect gastrointestinal cancer and anticancer therapy and discusses compelling possibilities for using microbiota as a combinatorial therapy to improve the therapeutic effects of existing anticancer treatments.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.137-148
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• 2009

The purpose of this study is to implement variable selection algorithm which helps construct a reliable linear regression model. If we use all candidate variables to construct a linear regression model, the significance of the model will be decreased and it will cause 'Curse of Dimensionality'. And if the number of data is less than the number of variables (dimension), we cannot construct the regression model. Due to these problems, we consider the variable selection problem as a combinatorial optimization problem, and apply GA (Genetic Algorithm) to the problem. Typical measures of estimating statistical significance are $R^2$, F-value of regression model, t-value of regression coefficients, and standard error of estimates. We design GA to solve multi-objective functions, because statistical significance of model is not to be estimated by a single measure. We perform experiments using simulation data, designed to consider various kinds of situations. As a result, it shows better performance than LARS (Least Angle Regression) which is an algorithm to solve variable selection problems. We modify algorithm to solve portfolio selection problem which construct portfolio by selecting stocks. We conclude that the algorithm is able to solve real problems.

• IMMUNE NETWORK
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• v.20 no.5
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• pp.43.1-43.11
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• 2020

Capicua (CIC) is a transcriptional repressor that regulates several developmental processes. CIC deficiency results in lymphoproliferative autoimmunity accompanied by expansion of CD44^hiCD62L^lo effector/memory and follicular Th cell populations. Deletion of Cic alleles in hematopoietic stem cells (Vav1-Cre-mediated knockout of Cic) causes more severe autoimmunity than that caused by the knockout of Cic in CD4⁺CD8⁺ double positive thymocytes (Cd4-Cre-mediated knockout of Cic). In this study, we compared splenic CD4⁺ T cell activation and proliferation between whole immune cell-specific Cic-null (Cic^f/f;Vav1-Cre) and T cell-specific Cic-null (Cic^f/f;Cd4-Cre) mice. Hyperactivation and hyperproliferation of CD4⁺ T cells were more apparent in Cic^f/f;Vav1-Cre mice than in Cic^f/f;Cd4-Cre mice. Cic^f/f;Vav1-Cre CD4⁺ T cells more rapidly proliferated and secreted larger amounts of IL-2 upon TCR stimulation than did Cic^f/f;Cd4-Cre CD4⁺ T cells, while the TCR stimulation-induced activation of the TCR signaling cascade and calcium flux were comparable between them. Mixed wild-type and Cic^f/f;Vav1-Cre bone marrow chimeras also exhibited more apparent hyperactivation and hyperproliferation of Cic-deficient CD4⁺ T cells than did mixed wild-type and Cic^f/f;Cd4-Cre bone marrow chimeras. Taken together, our data demonstrate that CIC deficiency at the beginning of T cell development endows peripheral CD4⁺ T cells with enhanced T cell activation and proliferative capability.

• The Korean Journal of Pesticide Science
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• v.5 no.3
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• pp.1-11
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• 2001

New technologies will have a large impact on the discovery of new herbicide site of action. Genomics, combinatorial chemistry, and bioinformatics help take advantage of serendipity through tile sequencing of huge numbers of genes or the synthesis of large numbers of chemical compounds. There are approximately $10^{30}\;to\;10^{50}$ possible molecules in molecular space of which only a fraction have been synthesized. Combining this potential with having access to 50,000 plant genes in the future elevates tile probability of discovering flew herbicidal site of actions. If 0.1, 1.0 or 10% of total genes in a typical plant are valid for herbicide target, a plant with 50,000 genes would provide about 50, 500, and 5,000 targets, respectively. However, only 11 herbicide targets have been identified and commercialized. The successful design of novel herbicides depends on careful consideration of a number of factors including target enzyme selections and validations, inhibitor designs, and the metabolic fates. Biochemical information can be used to identify enzymes which produce lethal phenotypes. The identification of a lethal target site is an important step to this approach. An examination of the characteristics of known targets provides of crucial insight as to the definition of a lethal target. Recently, antisense RNA suppression of an enzyme translation has been used to determine the genes required for toxicity and offers a strategy for identifying lethal target sites. After the identification of a lethal target, detailed knowledge such as the enzyme kinetics and the protein structure may be used to design potent inhibitors. Various types of inhibitors may be designed for a given enzyme. Strategies for the selection of new enzyme targets giving the desired physiological response upon partial inhibition include identification of chemical leads, lethal mutants and the use of antisense technology. Enzyme inhibitors having agrochemical utility can be categorized into six major groups: ground-state analogues, group specific reagents, affinity labels, suicide substrates, reaction intermediate analogues, and extraneous site inhibitors. In this review, examples of each category, and their advantages and disadvantages, will be discussed. The target identification and construction of a potent inhibitor, in itself, may not lead to develop an effective herbicide. The desired in vivo activity, uptake and translocation, and metabolism of the inhibitor should be studied in detail to assess the full potential of the target. Strategies for delivery of the compound to the target enzyme and avoidance of premature detoxification may include a proherbicidal approach, especially when inhibitors are highly charged or when selective detoxification or activation can be exploited. Utilization of differences in detoxification or activation between weeds and crops may lead to enhance selectivity. Without a full appreciation of each of these facets of herbicide design, the chances for success with the target or enzyme-driven approach are reduced.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Asia pacific journal of information systems
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• v.19 no.3
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• pp.51-67
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• 2009

Developing an agile recommender system for nomadic users has been regarded as a promising application in mobile and ubiquitous settings. To increase the quality of personalized recommendation in terms of accuracy and elapsed time, estimating future context of the user in a correct way is highly crucial. Traditionally, time series analysis and Makovian process have been adopted for such forecasting. However, these methods are not adequate in predicting context data, only because most of context data are represented as nominal scale. To resolve these limitations, the alignment-prediction algorithm has been suggested for context prediction, especially for future context from the low-level context. Recently, an ontological approach has been proposed for guided context prediction without context history. However, due to variety of context information, acquiring sufficient context prediction knowledge a priori is not easy in most of service domains. Hence, the purpose of this paper is to propose a novel context prediction methodology, which does not require a priori knowledge, and to increase accuracy and decrease elapsed time for service response. To do so, we have newly developed pattern-based context prediction approach. First of ail, a set of individual rules is derived from each context attribute using context history. Then a pattern consisted of results from reasoning individual rules, is developed for pattern learning. If at least one context property matches, say R, then regard the pattern as right. If the pattern is new, add right pattern, set the value of mismatched properties = 0, freq = 1 and w(R, 1). Otherwise, increase the frequency of the matched right pattern by 1 and then set w(R,freq). After finishing training, if the frequency is greater than a threshold value, then save the right pattern in knowledge base. On the other hand, if at least one context property matches, say W, then regard the pattern as wrong. If the pattern is new, modify the result into wrong answer, add right pattern, and set frequency to 1 and w(W, 1). Or, increase the matched wrong pattern's frequency by 1 and then set w(W, freq). After finishing training, if the frequency value is greater than a threshold level, then save the wrong pattern on the knowledge basis. Then, context prediction is performed with combinatorial rules as follows: first, identify current context. Second, find matched patterns from right patterns. If there is no pattern matched, then find a matching pattern from wrong patterns. If a matching pattern is not found, then choose one context property whose predictability is higher than that of any other properties. To show the feasibility of the methodology proposed in this paper, we collected actual context history from the travelers who had visited the largest amusement park in Korea. As a result, 400 context records were collected in 2009. Then we randomly selected 70% of the records as training data. The rest were selected as testing data. To examine the performance of the methodology, prediction accuracy and elapsed time were chosen as measures. We compared the performance with case-based reasoning and voting methods. Through a simulation test, we conclude that our methodology is clearly better than CBR and voting methods in terms of accuracy and elapsed time. This shows that the methodology is relatively valid and scalable. As a second round of the experiment, we compared a full model to a partial model. A full model indicates that right and wrong patterns are used for reasoning the future context. On the other hand, a partial model means that the reasoning is performed only with right patterns, which is generally adopted in the legacy alignment-prediction method. It turned out that a full model is better than a partial model in terms of the accuracy while partial model is better when considering elapsed time. As a last experiment, we took into our consideration potential privacy problems that might arise among the users. To mediate such concern, we excluded such context properties as date of tour and user profiles such as gender and age. The outcome shows that preserving privacy is endurable. Contributions of this paper are as follows: First, academically, we have improved sequential matching methods to predict accuracy and service time by considering individual rules of each context property and learning from wrong patterns. Second, the proposed method is found to be quite effective for privacy preserving applications, which are frequently required by B2C context-aware services; the privacy preserving system applying the proposed method successfully can also decrease elapsed time. Hence, the method is very practical in establishing privacy preserving context-aware services. Our future research issues taking into account some limitations in this paper can be summarized as follows. First, user acceptance or usability will be tested with actual users in order to prove the value of the prototype system. Second, we will apply the proposed method to more general application domains as this paper focused on tourism in amusement park.