• Journal of Ginseng Research
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• v.36 no.2
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• pp.119-134
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• 2012

As much as the popularity of ginseng in herbal prescriptions or remedies, ginseng has become the focus of research in many scientific fields. Analytical methodologies for ginseng, referred to as ginseng analysis hereafter, have been developed for bioactive component discovery, phytochemical profiling, quality control, and pharmacokinetic studies. This review summarizes the most recent advances in ginseng analysis in the past half-decade including emerging techniques and analytical trends. Ginseng analysis includes all of the leading analytical tools and serves as a representative model for the analytical research of herbal medicines.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.207-210
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• 1996

Learning methodologies such as neural networks or genetic algorithms usually require long training times. Case based reasoning, however, attains peak performance swiftly and is often appropriate for learning even with small data sets. Previous work has shown that an extended case reasoning methodology can yield superior performance in the task of predicting financial data series. This paper examines the impact of reasoning procedures on stock price prediction. The following characteristics are evaluated: size of input vector, multiplicity of neighboring states, and a scaling factor for growth. The concepts are illustrated in the context of predicting the price of an individual price.

• Journal of Integrative Natural Science
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• v.17 no.1
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• pp.21-30
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• 2024

Several anti-inflammatory small molecules have been found in the process of the inflammatory response, and these small molecules have been used to treat some inflammatory and autoimmune diseases. Numerous tools for predicting anti-inflammatory peptides (AIPs) have emerged in recent years. However, conducting experimental validations in the lab is both resource-intensive and time-consuming. Current therapies for inflammatory and autoimmune disorders often involve nonspecific anti-inflammatory drugs and immunosuppressants, often with potential side effects. AIPs have been used in treating inflammatory illnesses like Alzheimer's disease and can limit the expression of inflammatory promoters. Recent advances in adverse incident predictions (AIPs) have been made, but it is crucial to acknowledge limitations and imperfections in existing methodologies.

• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1128-1151
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• 2011

With the rapid change in technology, it is important to create business opportunities from technology. Many leading companies have tried to improve their competence from capturing those opportunities, which is called TOD (Technology Opportunity Discovery) activities in this research, by identifying emerging technologies or assessing economic and market potential of technologies. Academics have also been interested in the TOD activities but most of the previous studies have focused on developing methodologies or guidelines for TOD. Little effort was given to understanding the current status of TOD activities, which is indispensible to develop effective methodologies for TOD. Therefore, this study aims to investigate the current states of TOD activities, particularly focusing on SMEs, which are the main actors of national innovation system. For the purpose of investigation, firstly, TOD process was defined, and then, based on the process, three main activities of TOD, which include identifying the motivation of TOD, collecting information for TOD, and analyzing the collected information activities for TOD, were examined using a survey method. Finally, the analysis results were used to understand the SMEs' needs and difficulties on TOD activities. Research findings are expected to provide useful information for the future studies on TOD and also can be used to develop a policy for SMEs.

• Journal of the Ergonomics Society of Korea
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• v.31 no.1
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• pp.23-32
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• 2012

Objective: This research proposes a methodology, which validates a grasp of the customers' emotions in the service design area. Background: As the era of service design has taken its approach, the need for a deliberate design that would reflect the customer's experience had emerged in the area of service. Therefore, a variety of methodologies has been adopted in the field of service design with the purpose of discovery of the customers' needs. Even though the importance of an emotion-sentient research of a service experience increases, its research progress remains to be inadequate in comparison to all the other areas. Method: Having had taken some resources from the emotional studies under other areas of expertise as a base, the concept of volatility of emotions has been introduced as the core element of this research, further followed by an elaboration of its special characteristics. The observation technique under Stakeholder's system: SOMC(Simultaneous Observation Method based on Cooperation) has been proposed in this study as it presents an effective way to grasp the concept of volatile emotions in contrast to the previously existent types of methodologies. Results: The SOMC rather supplements the existing research methods than substitutes the previous ones. In other words, although the existing research system allowed emotion detection, it was difficult to capture the change of momentary and fickle emotions. On the opposite, the SOMC provides a condition allowing a sufficient grasp of the customer's emotions and facilitates emotional capture. Conclusion: For that reason, it is hoped that this piece of research represents a valuable and effective approach in terms of grasping the true needs of the customers on the emotional level, which will in its turn contribute to the improvement of the service quality in the midst of a complicated service condition. Application: Moreover, the purpose of this research is that in its outcome it may serve as a sufficient contribution to the area of emotional studies within the field of service design.

• Journal of Preventive Medicine and Public Health
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• v.55 no.5
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• pp.415-423
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• 2022

Economic evaluations in the healthcare are used to assess economic efficiency of pharmaceuticals and medical interventions such as diagnoses and medical procedures. This study introduces the main concepts of economic evaluation across its key steps: planning, outcome and cost calculation, modeling, cost-effectiveness results, uncertainty analysis, and decision-making. When planning an economic evaluation, we determine the study population, intervention, comparators, perspectives, time horizon, discount rates, and type of economic evaluation. In healthcare economic evaluations, outcomes include changes in mortality, the survival rate, life years, and quality-adjusted life years, while costs include medical, non-medical, and productivity costs. Model-based economic evaluations, including decision tree and Markov models, are mainly used to calculate the total costs and total effects. In cost-effectiveness or costutility analyses, cost-effectiveness is evaluated using the incremental cost-effectiveness ratio, which is the additional cost per one additional unit of effectiveness gained by an intervention compared with a comparator. All outcomes have uncertainties owing to limited evidence, diverse methodologies, and unexplained variation. Thus, researchers should review these uncertainties and confirm their robustness. We hope to contribute to the establishment and dissemination of economic evaluation methodologies that reflect Korean clinical and research environment and ultimately improve the rationality of healthcare policies.

• Interdisciplinary Bio Central
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• v.3 no.4
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• pp.15.1-15.11
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• 2011

Background: Histone deacetylase (HDAC) 8 is one of its family members catalyzes the removal of acetyl groups from N-terminal lysine residues of histone proteins thereby restricts transcription factors from being expressed. Inhibition of HDAC8 has become an emerging and effective anti-cancer therapy for various cancers. Application computational methodologies may result in identifying the key components that can be used in developing future potent HDAC8 inhibitors. Results: Facilitating the discovery of novel and potential chemical scaffolds as starting points in the future HDAC8 inhibitor design, quantitative structure-activity relationship models were generated with 30 training set compounds using genetic function approximation (GFA) and Bayesian algorithms. Six GFA models were selected based on the significant statistical parameters calculated during model development. A Bayesian model using fingerprints was developed with a receiver operating characteristic curve cross-validation value of 0.902. An external test set of 54 diverse compounds was used in validating the models. Conclusions: Finally two out of six models based on their predictive ability over the test set compounds were selected as final GFA models. The Bayesian model has displayed a high classifying ability with the same test set compounds and the positively and negatively contributing molecular fingerprints were also unveiled by the model. The effectively contributing physicochemical properties and molecular fingerprints from a set of known HDAC8 inhibitors were identified and can be used in designing future HDAC8 inhibitors.

• Journal of Ecology and Environment
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• v.46 no.2
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• pp.126-135
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• 2022

Background: Pollinators are important ecological elements due to their role in the maintenance of ecosystem health, wild plant reproduction, crop production and food security. The pollinator-plant interaction supports the preservation of plant and animal populations and it also improves the yield in pollination dependent crops. Having knowledge about the plant-pollinator interaction is necessary for development of pesticide risk assessment of pollinators and conservation of endangering species. Results: Traditional methods to discover the relatedness of insects and plants are based on tracing the visiting pollinators by field observations as well as palynology. These methods are time-consuming and needs expert taxonomists to identify different groups of pollinators such as insects or identify flowering plants through palynology. With pace of technology, using molecular methods become popular in identification and classification of organisms. DNA metabarcoding, which is the combination of DNA barcoding and high throughput sequencing, can be applied as an alternative method in identification of mixed origin environmental samples such as pollen loads attached to the body of insects and has been used in DNA-based discovery of plant-pollinator relationship. Conclusions: DNA metabarcoding is practical for plant-pollinator studies, however, lack of reference sequence in online databases, taxonomic resolution, universality of primers are the most crucial limitations. Using multiple molecular markers is preferable due to the limitations of developed universal primers, which improves taxa richness and taxonomic resolution of the studied community.

• Journal of Microbiology and Biotechnology
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• v.33 no.3
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• pp.277-287
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• 2023

Since the first discovery of antibiotics, introduction of new antibiotics has been coupled with the occurrence of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs). Rapid dissemination of ARB and ARGs in the aquatic environments has become a global concern. ARB and ARGs have been already disseminated in the aquatic environments via various routes. Main hosts of most of ARGs were found to belong to Gammaproteobacteria class, including clinically important potential pathogens. Transmission of ARGs also occurs by horizontal gene transfer (HGT) mechanisms between bacterial strains in the aquatic environments, resulting in ubiquity of ARGs. Thus, a few of ARGs and MGEs (e.g., strA, sul1, int1) have been suggested as indicators for global comparability of contamination level in the aquatic environments. With ARB and ARGs contamination, the occurrence of critical pathogens has been globally issued due to their widespread in the aquatic environments. Thus, active surveillance systems have been launched worldwide. In this review, we described advancement of methodologies for ARGs detection, and occurrence of ARB and ARGs and their dissemination in the aquatic environments. Even though numerous studies have been conducted for ARB and ARGs, there is still no clear strategy to tackle antibiotic resistance (AR) in the aquatic environments. At least, for consistent surveillance, a strict framework should be established for further research in the aquatic environments.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.627-640
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• 2017

A common interest in gene expression data analysis is to identify genes that present significant changes in expression levels among biological experimental conditions. In this paper, we develop a Bayesian approach to make a gene-by-gene comparison in the case with a control and more than one treatment experimental condition. The proposed approach is within a Bayesian framework with a Dirichlet process prior. The comparison procedure is based on a model selection procedure developed using the discreteness of the Dirichlet process and its representation via Polya urn scheme. The posterior probabilities for models considered are calculated using a Gibbs sampling algorithm. A numerical simulation study is conducted to understand and compare the performance of the proposed method in relation to usual methods based on analysis of variance (ANOVA) followed by a Tukey test. The comparison among methods is made in terms of a true positive rate and false discovery rate. We find that proposed method outperforms the other methods based on ANOVA followed by a Tukey test. We also apply the methodologies to a publicly available data set on Plasmodium falciparum protein.