• Journal of fish pathology
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• v.6 no.2
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• pp.111-122
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• 1993

The mechanism by which $V_H$ region gene segments is selected in B lymphocyte is not known. Moreover, evidence for both random and nonrandom expression of $V_H$ genes in matured B cells has been presented previously. In this report, the technique of in situ hybridization allowed us to analyze expressed $V_H$ gene families in normal B lymphocyte at the single cell level. The analysis of normal B cells in this study eliminated any posssible bias resulting from transformation protocols used previously and minimized limitation associated with sampling size. Therefore, an accurate measure of the functional and expressed $V_H$ gene repertoire in B lymphocyte could be made. One of the most important controls for the optimization of in situ hybridization is to establish probe concentration and washing stringency due to the degree of nucleotide sequence similarlity between different families which in some cases can be as high as 70%. When the radioactive $C{\mu}$ and $V_{H}J558$ RNA probes are tested on LPS-stimulated adult spleen cells, $2{\sim}4{\times}106cpm$/slide shows low background and reasonable frequency of specific positive cells. For the washing condition. 40~50% formamide at $54^{\circ}C$ is found to be optimum for the $C{\mu}$. $V_{H}S107$ and $V_{H}J558$ probes. The analyzed results clearly demonstrate that the level of each different $V_H$ gene family expression is dependent upon the complexity or size of that family. These findings are also extended to the level of $V_H$ gene family expression in separated bone marrow B cells depend upon the various stage of differentiation and conclude no preferential utilization of specific $V_H$ gene family. Thus, the utilization of VH gene segments in B lymphocyte of adult BALB/c mice is random and is not regulated or changed during the differentiation of B cells.

• Journal of the Korean Society of Food Science and Nutrition
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• v.38 no.2
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• pp.244-251
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• 2009

Extraction characteristics of dried jujube and functional properties of corresponding extract were monitored by response surface methodology. Maximum extraction yield of 53.69% was obtained at extraction temperature of $50.35^{\circ}C$, extraction time of 16.69 hr, and ethanol concentration of 72.88%. At extraction temperature, extraction time, and ethanol concentration of $45.80^{\circ}C$, 15.47 hr, and 73.12%, respectively, maximum cyclic adenosine monophosphate content was 8.20 mg/100 g. Maximum total polyphenol content was 18.85 mg/g at extraction temperature, extraction time, and ethanol concentration of $64.91^{\circ}C$, 20.84 hr, and 66.91%, respectively. Maximum total flavonoid content was 0.48 mg/g at extraction temperature, extraction time, and ethanol concentration of $57.36^{\circ}C$, 15.14 hr, and 71.08%, respectively. $IC_{50}$ value of electron donating ability showed maximum level of 32.34 mg/mL at extraction temperature of $48.46^{\circ}C$, extraction time of 19.25 hr, and ethanol concentration of 65.36%. Maximum ascorbic acid equivalent antioxidant capacity was 3.58 mg ascorbic acid equivalent per gram sample at extraction temperature, extraction time, and ethanol concentration of $56.09^{\circ}C$, 21.86 hr, and 65.36%, respectively.

• Korean Journal of Food Science and Technology
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• v.41 no.2
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• pp.173-178
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• 2009

Epidemiological studies showed that high trans-fat consumption is closely associated with getting the risks of cardiovascular disease. The objective of this study was to produce trans-free fat through lipase-catalyzed interesterification, as a substitute for the cream margarine commonly used in industry. Optimum conditions for interesterification in a packed bed enzyme bioreactor (PBEB) were determined using response surface methodology (RSM) based on central composite design. Three kinds of reaction variables were chosen, such as substrate flow rate (0.4-1.2 mL/min), reaction temperature (60-70$^{\circ}C$), and ratio of fully hydrogenated canola oil (FHCO, 35-45%) to evaluate their effects on the degree of interesterification. Optimum conditions from the standpoint of solid fat content (SFC) were found to be as follows: 0.4 mL/min flow rate, 64.7$^{\circ}C$ reaction temperate, and 42.8% (w/w) ratio of FHCO, respectively. The half-life of immobilized lipase in PBEB with two stages at 60$^{\circ}C$ ($1^{st}$ stage) and 55$^{\circ}C$ ($2^{nd}$ stage) was about more than 30 days as estimated by extrapolating the incubation time course of tristearoyl glycerol (TS) conversion, whereas the half-life of the enzyme in PBEB with single stage at 65$^{\circ}C$ was only about 15 days. Finally, the results from SFC analysis suggest that trans-free fat produced in this study seems to be a suitable substitute for the cream margarine commonly used in industry.

• Food Science and Preservation
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• v.20 no.5
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• pp.668-683
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• 2013

In this study, we investigated the contents of total polyphenol (TP), total flavonoid, and absorbance at 475 nm ($OD_{475}$) which may produced in solid-fermented leaf of Smilax china L. by Aspergillus oryzae as a new functional components with reddish brown color, contents of water soluble substance (WSS), electron donating ability (EDA), Hunter $L^*$, $a^*$, $b^*$ values, sensory overall acceptability (OA) and also, the inhibitory activities (XOI and AOI) against partial purified xanthine oxidase (XO) and aldehyde oxidase (AO) from rabbit liver which were well known to relate the gout, and alcoholic liver disease, respectively in order to optimize water extraction using response surface methodology (RSM). All the $R^2$ values of the second-order polymonials ranged from 0.85 to 0.98, except for the EDA (0.69) and the XOI (0.78). However, the activities of the EDA and XOI were relatively high in the lower concentration of the fermented Smilax china L. leaf. The effects on the water extraction were highest in the concentration, among the dependent variables, and showed significant differences at the 1% level in the TP, TF and WSS contents and the $a^*$, $b^*$ and $OD_{475}$ values, but the OA showed significant differences at the 5% level. The optimal values of AOI, which was the most important functionality in the Smilax china L. that was predicted via RSM, were 59.48% at the 2.19% concentration, a $90.02^{\circ}C$ extraction temperature and a 4.03 minute extraction time ($R^2$: 0.93, p<0.007). The ranges of all the dependent variables of the optimal water extraction were 1.6~1.8% for the concentration, $83{\sim}93^{\circ}C$ for the temperature and 3.4~4.4 minutes for the extraction time; and the optimal water extraction conditions were a 1.7% concentration, an $88^{\circ}C$ extraction temperature and a 3.9-min extraction time.

• Food Science and Preservation
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• v.24 no.5
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• pp.623-630
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• 2017

Functional compounds including flavonoids, anthocyanins, polyphneols and antioxidants were extracted from blue honeysuckle (Lonicera caerulea L.) using highly efficient microwave-assisted extraction. And extraction process was modeled and optimized according to response surface methodology (RSM). The independent variables ($X_n$) were ethanol concentration ($X_1$: 0, 25, 50, 75, 100%), irradiation time ($X_2$: 1, 3, 5, 7, 9 min), and microwave power ($X_3$: 60, 120, 180, 240, 300 W). Dependent variables ($Y_n$) were total flavonoid contents ($Y_1$), total anthocyanin contents ($Y_2$), total polyphenol contents ($Y_3$) and antioxidant activity ($Y_4$). Four-dimensional response surface plots were generated based on the fitted second-order polynomial models to get optimal conditions. Estimated optimal conditions for 4 responses were ethanol concentration of 54-72%, irradiation time of 7.1-7.6 min, and microwave power of 243-251 W. Ridge analysis predicted the maximal responses of total flavonoid content, total anthocyanin content, total polyphenol content and antioxidant activity were 38.00 mg RE/g, 6.80 mg CGE/g, 14.90 mg GAE/g, 89.10%, respectively. Verification experiment was carried out at predicted optimal conditions and experimental values for total flavonoid content, total anthocyanin content, total polyphenol content and antioxidant activity were 38.10 mg RE/g, 6.72 mg CGE/g, 14.91 mg GAE/g and 89.13%, respectively. No significant difference was observed between predicted and experimental values, indicating good fitness of fitted model and successful application of RSM.

• Culinary science and hospitality research
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• v.16 no.4
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• pp.286-296
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• 2010

The purpose of this study is to develop fresh pasta added with mulberry leaf powder as functional fresh pasta. Through previous research, the mixture of 40% of flour and 60% of rice powder was optimum for making noodles with mulberry leaf powder. Making fresh pasta with 40% of wheat flour, 60% of rice powder (optimum moo for making noodles) and mulberry leaf powder(0.5% 1.0% 1.5% 2.0%) was done, followed by the mechanical test(moisture content, color value, texture, tension) and the sensory analysis(quantitative descriptive analysis, preference test). Moisture contents of raw pasta and cooked pasta were the highest in control; scores for moisture contents of cooked pasta were higher than those of raw pasta. The result indicated that the more mulberry leaf powder was, the lower L-value and a-value were in raw pasta and cooked pasta. While the b-vale(yellowness) of raw pasta was the highest in control(9.81), 1.0% of mulberry powder addition sample was the highest in cooked pasta. For hardness, the 2.0% of mulberry leaf powder addition sample has high scores, and adhesiveness and chewiness were no significant difference. The 0.5% of mulberry leaf powder addition sample was the longest in tension distance, which was resulted from the lack of water contents in mulberry leaf powder. In cooked pasta, tension distance had no significant difference between the samples, and force showed the highest score in control. The quantitative descriptive analysis showed that color intensity, savory taste, bitterness were the highest in the 2.0% of mulberry leaf powder addition sample. Gloss and chewiness were no significant difference between the samples. Grassy flavor, savory flavor, bitterness and grainess were intense as mulberry leaf powder was added The preference test showed that MRP 1.5 containing 1.5% of mulberry leaf powder was the most preferable for color, texture and overall quality. In conclusion, 40% of wheat flour, 60% of rice powder and 1.5% of mulberry leaf powder made the best formula of fresh pasta with mulberry leaves.

• Applied Biological Chemistry
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• v.43 no.3
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• pp.218-224
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• 2000

The efforts were made to optimite ethanol extraction from persimmon leaf with the time of extraction$(1.5{\sim}2.5\;hrs)$, the temperature of extraction$(70{\sim}90^{\circ}C)$, and the concentration of ethanol$(0{\sim}40%)$ as three primary variables together with several functional characteristics of persimmon leaf as reaction variables. The conditions of extraction was best fitted by using response surface methodology through the center synthesis plan, and the optimal conditions of extraction were established. The contents of soluble solid and soluble tannin went up as the concentration of ethanol went up and the temperature of extraction went down, and the turbidity went down as the concentration of ethanol went down. Electron donation ability was hardly affected by the extraction temperature and had the tendency to go up as the concentration of ethanol went up. The inhibitory activity of xanthine oxidase(XOase) had the tendency to go up as both the concentration of ethanol and the temperature of extraction went up. The inhibitory activity of angiotensin converting enzyme(ACE), the significance of which still was not recognized, showed the maximum when the concentration of ethanol was 27%. In result, the optimal conditions of extraction was the extraction time of two hours, the extraction temperature of $75{\sim}81^{\circ}C$, and the ethanol concentration of $33{\sim}35%$.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.49 no.3
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• pp.231-245
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• 2023

In the cosmetics industry, it is important to develop new materials for functional cosmetics such as whitening, wrinkles, anti-oxidation, and anti-aging, as well as technology to increase absorption when applied to the skin. Therefore, in this study, we tried to optimize the nanoemulsion formulation by utilizing response surface methodology (RSM), an experimental design method. A nanoemulsion was prepared by a high-pressure emulsification method using Glabridin as an active ingredient, and finally, the optimized skin absorption rate of the nanoemulsion was evaluated. Nanoemulsions were prepared by varying the surfactant content, cholesterol content, oil content, polyol content, high-pressure homogenization pressure, and cycling number of high-pressure homogenization as RSM factors. Among them, surfactant content, oil content, high-pressure homogenization pressure, and cycling number of high-pressure homogenization, which are factors that have the greatest influence on particle size, were used as independent variables, and particle size and skin absorption rate of nanoemulsion were used as response variables. A total of 29 experiments were conducted at random, including 5 repetitions of the center point, and the particle size and skin absorption of the prepared nanoemulsion were measured. Based on the results, the formulation with the minimum particle size and maximum skin absorption was optimized, and the surfactant content of 5.0 wt%, oil content of 2.0 wt%, high-pressure homogenization pressure of 1,000 bar, and the cycling number of high-pressure homogenization of 4 pass were derived as the optimal conditions. As the physical properties of the nanoemulsion prepared under optimal conditions, the particle size was 111.6 ± 0.2 nm, the PDI was 0.247 ± 0.014, and the zeta potential was -56.7 ± 1.2 mV. The skin absorption rate of the nanoemulsion was compared with emulsion as a control. As a result of the nanoemulsion and general emulsion skin absorption test, the cumulative absorption of the nanoemulsion was 79.53 ± 0.23%, and the cumulative absorption of the emulsion as a control was 66.54 ± 1.45% after 24 h, which was 13% higher than the emulsion.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.149-161
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• 2014

The export of domestic public services to overseas markets contains many potential obstacles, stemming from different export procedures, the target services, and socio-economic environments. In order to alleviate these problems, the business incubation platform as an open business ecosystem can be a powerful instrument to support the decisions taken by participants and stakeholders. In this paper, we propose an ontology model and its implementation processes for the business incubation platform with an open and pervasive architecture to support public service exports. For the conceptual model of platform ontology, export case studies are used for requirements analysis. The conceptual model shows the basic structure, with vocabulary and its meaning, the relationship between ontologies, and key attributes. For the implementation and test of the ontology model, the logical structure is edited using Prot$\acute{e}$g$\acute{e}$ editor. The core engine of the business incubation platform is the simulator module, where the various contexts of export businesses should be captured, defined, and shared with other modules through ontologies. It is well-known that an ontology, with which concepts and their relationships are represented using a shared vocabulary, is an efficient and effective tool for organizing meta-information to develop structural frameworks in a particular domain. The proposed model consists of five ontologies derived from a requirements survey of major stakeholders and their operational scenarios: service, requirements, environment, enterprise, and county. The service ontology contains several components that can find and categorize public services through a case analysis of the public service export. Key attributes of the service ontology are composed of categories including objective, requirements, activity, and service. The objective category, which has sub-attributes including operational body (organization) and user, acts as a reference to search and classify public services. The requirements category relates to the functional needs at a particular phase of system (service) design or operation. Sub-attributes of requirements are user, application, platform, architecture, and social overhead. The activity category represents business processes during the operation and maintenance phase. The activity category also has sub-attributes including facility, software, and project unit. The service category, with sub-attributes such as target, time, and place, acts as a reference to sort and classify the public services. The requirements ontology is derived from the basic and common components of public services and target countries. The key attributes of the requirements ontology are business, technology, and constraints. Business requirements represent the needs of processes and activities for public service export; technology represents the technological requirements for the operation of public services; and constraints represent the business law, regulations, or cultural characteristics of the target country. The environment ontology is derived from case studies of target countries for public service operation. Key attributes of the environment ontology are user, requirements, and activity. A user includes stakeholders in public services, from citizens to operators and managers; the requirements attribute represents the managerial and physical needs during operation; the activity attribute represents business processes in detail. The enterprise ontology is introduced from a previous study, and its attributes are activity, organization, strategy, marketing, and time. The country ontology is derived from the demographic and geopolitical analysis of the target country, and its key attributes are economy, social infrastructure, law, regulation, customs, population, location, and development strategies. The priority list for target services for a certain country and/or the priority list for target countries for a certain public services are generated by a matching algorithm. These lists are used as input seeds to simulate the consortium partners, and government's policies and programs. In the simulation, the environmental differences between Korea and the target country can be customized through a gap analysis and work-flow optimization process. When the process gap between Korea and the target country is too large for a single corporation to cover, a consortium is considered an alternative choice, and various alternatives are derived from the capability index of enterprises. For financial packages, a mix of various foreign aid funds can be simulated during this stage. It is expected that the proposed ontology model and the business incubation platform can be used by various participants in the public service export market. It could be especially beneficial to small and medium businesses that have relatively fewer resources and experience with public service export. We also expect that the open and pervasive service architecture in a digital business ecosystem will help stakeholders find new opportunities through information sharing and collaboration on business processes.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.