• Journal of Microbiology and Biotechnology
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• v.13 no.3
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• pp.429-436
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• 2003

Pediocin K1 is a bacteriocin produced by Pediococcus sp. KCA 1303-10, isolated from traditionally fermented flatfish in Korea. Pediocin K1-dependent antibiosis and pediocin K1-independent antibiosis against Listeria monocyrogenes were investigated by comparing antibiosis potential of the ped＋ wild-type strain of Pediococcus sp. KCA1303-10 with that of the ped- mutant strain in 3 different media at 3 different temperatures. In the synthetic MRS-APT medium, bacteriocin (pediocin K1)-dependent antibiosis (BDA) acted as the major driving force of overall antibiosis at the initial stage before the pH of the media was not sufficiently lowered, while bacteriocin-independent antibiosis (BIA) took over the major role at the late stage of antibiosis by killing otherwise resistant cells in the modium. The role of BDA increased as the temperature of the system decreased. The antibiosis potential of BDA among the overall antibiosis of Pediococcus against Listeria at $37^{\circ}C$ was calculated as 46%, and as 75% at $25^{\circ}C$. In the skim milk medium, antibiosis of Pediococcus against Listeria was weakened more than 4 log cycles compared to that of the synthetic medium; however, BDA worked as the main antibiosis force regardless of the culturing temperature in the skim milk medium. In the bean soup medium, BDA also worked as the major killing mechanism against Listeria, but BIA played as another suppressing mechanism against otherwise pediocin-resistant Listeria population. These results suggest that a large portion of the inhibitory action of the ped+Pediococcus sp. KCA1303-10 was attributable to the bacteriocin produced by the strain and that viable Pediococcus sp. KCA1303-10 was superior to the purified bacteriocin in suppressing the occurrence of the bacteriocin-resistant Listeria monocytogenes in food systems.

• Journal of Microbiology and Biotechnology
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• v.30 no.8
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• pp.1195-1206
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• 2020

Beringraja pulchra, Cham-hong-eo in Korean, is a mottled skate which is belonging to the cartilaginous fish. Although this species is economically valuable in South Korea as an alkaline-fermented food, there are few microbial studies on such fermentation. Here, we analyzed microbial changes and pH before, during, and after fermentation and examined the effect of inoculation by a skin microbiota mixture on the skate fermentation (control vs. treatment). To analyze microbial community, the V4 regions of bacterial 16S rRNA genes from the skates were amplified, sequenced and analyzed. During the skate fermentation, pH and total number of marine bacteria increased in both groups, while microbial diversity decreased after fermentation. Pseudomonas, which was predominant in the initial skate, declined by fermentation (Day 0: 11.39 ± 5.52%; Day 20: 0.61 ± 0.9%), while the abundance of Pseudoalteromonas increased dramatically (Day 0: 1.42 ± 0.41%; Day 20: 64.92 ± 24.15%). From our co-occurrence analysis, the Pseudoalteromonas was positively correlated with Aerococcaceae (r = 0.638) and Moraxella (r = 0.474), which also increased with fermentation, and negatively correlated with Pseudomonas (r = -0.847) during fermentation. There are no critically significant differences between control and treatment. These results revealed that the alkaline fermentation of skates dramatically changed the microbiota, but the initial inoculation by a skin microbiota mixture didn't show critical changes in the final microbial community. Our results extended understanding of microbial interactions and provided the new insights of microbial changes during alkaline fermentation.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.600-608
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• 2017

Recently data growth rates are growing exponentially according to the rapid expansion of internet. Since users need some of all the information, they carry a heavy workload for examination and discovery of the necessary contents. Therefore information retrieval must provide hierarchical class information and the priority of examination through the evaluation of similarity on query and documents. In this paper we propose an Multi-class support vector machines model based clustering for hierarchical document categorization that make semantic search possible considering the word co-occurrence measures. A combination of hierarchical document categorization and SVM classifier gives high performance for analytical classification of web documents that increase exponentially according to extension of document hierarchy. More information retrieval systems are expected to use our proposed model in their developments and can perform a accurate and rapid information retrieval service.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.861-873
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• 2009

There are many reasons that fail to get appropriate information in information retrieval. Allomorph is one of the reasons for search failure due to keyword mismatch. This research proposes a method to construct alternative word candidates automatically in order to minimize search failure due to keyword mismatch. Assuming that two words have similar meaning if they have similar co-occurrence words, the proposed method uses the concept of concentration, association word set, cosine similarity between association word sets and a filtering technique using confidence. Performance of the proposed method is evaluated using a manually extracted alternative list. Evaluation results show that the proposed method outperforms the context window overlapping in precision and recall.

• Journal of Korean Library and Information Science Society
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• v.47 no.3
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• pp.95-114
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• 2016

The purpose of this preliminary study is to collect specific examples of book reports and understand semantic characteristics of them through semantic network. The analysis was conducted with 23 book reports which classified by three groups. The keywords were selected from the of book reports. Five types of keyword network were composed based on co-occurrence relations with keywords. The result of this study is following these. First, each keyword network of book reports of groups and individuals is shown to have different structural characteristics. Second, each network has different high centrality keywords according to the result analysis of 3 types of centrality(degree centrality, closeness centrality, betweenness centrality). These characteristic means that keyword network analysis is useful in recognizing the characteristics of not only groups' and but also individual's book reports.

• Geomechanics and Engineering
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• v.12 no.1
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• pp.53-69
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• 2017

Rock burst may cause serious casualties and property losses, and how to conduct effective monitoring and warning is the key to avoid this disaster. In this paper, we reviewed both the rock burst mechanism and the principle of using electromagnetic radiation (EMR) from coal rock to monitor and forewarn rock burst, and systematically studied EMR monitored data of 4 rock bursts of Qianqiu Coal Mine, Yima Coal Group, Co. Ltd. Results show that (1) Before rock burst occurrence, there is a breeding process for stress accumulation and energy concentration inside the coal rock mass subject to external stresses, which causes it to crack, emitting a large amount of EMR; when the EMR level reaches a certain intensity, which reveals that deformation and fracture inside the coal rock mass have become serious, rock burst may occur anytime and it's necessary to implement an early warning. (2) Monitored EMR indicators such as its intensity and pulses amount are well and positively correlated before rock bursts occurs, generally showing a rising trend for more than 5 continuous days either slowly or dramatically, and the disaster bursts generally occurs at the lower level within 48 h after reaching its peak intensity. (3) The rank of EMR signals sensitive to rock burst in a descending order is maximum EMR intensity > rate of change in EMR intensity > maximum amount of EMR pulses > rate of change in the amount of EMR pulses.

• Microbiology and Biotechnology Letters
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• v.46 no.2
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• pp.135-144
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• 2018

The rapid increase in number and diversity of Extended Spectrum ${\beta}$-Lactamases (ESBLs) producing Enterobacteriaceae in natural aquatic environment is a major health concern worldwide. This study investigates abundance and distribution of ESBL producing multidrug resistant Enterobacteriaceae and molecular characterization of ESBL genes among isolates from highly polluted stretch of river Yamuna, India. Water samples were collected from ten different sites distributed across Delhi stretch of river Yamuna, during 2014-15. A total of 506 non duplicate Enterobacteriaceae isolates were obtained. Phenotypic detection of ESBL production and antibiotic sensitivity for 15 different antibiotics were performed according to CLSI guidelines (Clinical and Laboratory Standard Institute, 2015). A subset of ESBL positive Enterobacteriaceae isolates were identified by 16S rRNA gene and screened for ESBL genes, such as $bla_{CTX-M}$, $bla_{TEM}$ and $bla_{OXA}$. Out of 506 non-duplicate bacterial isolates obtained, 175 (34.58%) were positive for ESBL production. Susceptibility pattern for fifteen antibiotics used in this study revealed higher resistance to cefazolin, rifampicin and ampicillin. A high proportion (76.57%) of ESBL positive isolates showed multidrug resistance phenotype, with MAR index of 0.39 at Buddha Vihar and Old Delhi Railway bridge sampling site. Identification and PCR based characterization of ESBL genes revealed the prevalence of $bla_{CTX-M}$ and $bla_{TEM}$ genes to be 88.33% and 61.66%, respectively. Co-occurrence of $bla_{CTX-M}$ and $bla_{TEM}$ genes was detected in 58.33% of the resistant bacteria. The $bla_{OXA}$ gene was not detected in any isolates. This study highlights deteriorating condition of urban aquatic environment due to rising level of ESBL producing Enterobacteriaceae with multidrug resistance phenotype.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.375-379
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• 2009

Protein domain is the conserved unit of compact tree-dimensional structure and evolution, which carries specific function. Domains may appear in patterns in proteins, since they have been conserved through the evolution for functional formation of proteins. In this paper, we propose a formulated method for conservation analysis of domain combination based on association rule. Proposed method measures mutual dependency of domains in a combination, as well as co-occurrence frequency of them, which is conventionally used. Based on the method, we extracted conserve domain combinations in S.cerevisiae proteins and analyzed their functions based on Gene Ontology. From the results, we drew conclusions that domains in S.cerevisiae proteins form patterns whose members are highly affiliated to one another, and that extracted patterns tend to be associated with molecular function. Moreover, the results testified to proposed method superior to conventional ones for identifying domain combinations conserved for functional cooperation.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.459-472
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• 2011

The vast number of biomedical literature is an important source of biomedical interaction information discovery. However, it is complicated to obtain interaction information from them because most of them are not easily readable by machine. In this paper, we present a method for extracting biomedical interaction information assuming that the biomedical Named Entities (NEs) are already identified. The proposed method labels all possible pairs of given biomedical NEs as INTERACTION or NO-INTERACTION by using a Maximum Entropy (ME) classifier. The features used for the classifier are obtained by applying various NLP techniques such as POS tagging, base phrase recognition, parsing and predicate-argument recognition. Especially, specific verb predicates (activate, inhibit, diminish and etc.) and their biomedical NE arguments are very useful features for identifying interactive NE pairs. Based on this, we devised a twostep method: 1) an interaction verb extraction step to find biomedically salient verbs, and 2) an argument relation identification step to generate partial predicate-argument structures between extracted interaction verbs and their NE arguments. In the experiments, we analyzed how much each applied NLP technique improves the performance. The proposed method can be completely improved by more than 2% compared to the baseline method. The use of external contextual features, which are obtained from outside of NEs, is crucial for the performance improvement. We also compare the performance of the proposed method against the co-occurrence-based and the rule-based methods. The result demonstrates that the proposed method considerably improves the performance.

• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.55-62
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• 2014

This paper proposes a fault detection method utilizing converted images of acoustic emission signals and texture analysis for identifying bearing's multi-faults which frequently occur in an induction motor. The proposed method analyzes three texture features from the converted images of multi-faults: multi-faults image's entropy, homogeneity, and energy. These extracted features are then used as inputs of a fuzzy-ARTMAP to identify each multi-fault including outer-inner, inner-roller, and outer-roller. The experimental results using ten times trials indicate that the proposed method achieves 100% accuracy in the fault classification.