• Journal of The Korean Association For Science Education
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• v.40 no.4
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• pp.385-398
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• 2020

This study aims to examine genetics problem-solving processes of high school students with different learning approaches. Two second graders in high school participated in a task that required solving the complicated pedigree problem. The participants had similar academic achievements in life science but one had a deep learning approach while the other had a surface learning approach. In order to analyze in depth the students' problem-solving processes, each student's problem-solving process was video-recorded, and each student conducted a think-aloud interview after solving the problem. Although students showed similar errors at the first trial in solving the problem, they showed different problem-solving process at the last trial. Student A who had a deep learning approach voluntarily solved the problem three times and demonstrated correct conceptual framing to the three constraints using rule-based reasoning in the last trial. Student A monitored the consistency between the data and her own pedigree, and reflected the problem-solving process in the check phase of the last trial in solving the problem. Student A's problem-solving process in the third trial resembled a successful problem-solving algorithm. However, student B who had a surface learning approach, involuntarily repeated solving the problem twice, and focused and used only part of the data due to her goal-oriented attitude to solve the problem in seeking for answers. Student B showed incorrect conceptual framing by memory-bank or arbitrary reasoning, and maintained her incorrect conceptual framing to the constraints in two problem-solving processes. These findings can help in understanding the problem-solving processes of students who have different learning approaches, allowing teachers to better support students with difficulties in accessing genetics problems.

• Journal of Nutrition and Health
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• v.43 no.2
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• pp.152-160
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• 2010

The purpose of this study was to investigate the effects of aging process on the immunity in human subjects. In this investigation, nineteen families of three generations (daughters on college age, their mothers, and grandmothers) participated to avoid genetic variation among individuals. Dietary food records, anthropometric measurements and biochemical assessments of serum nutrients were used to evaluate the nutritional status of subjects. The immune parameters of subjects were assessed by the total and differential WBC count. Total B and T lymphocytes, and T cell subsets were quantified by flowcytometer. Serum immunoglobulin G, A, M concentrations were also measured as an index of humoral immunity. The result of this study can be summarized as follows: 1. Along with the aging process, body fat was found to be increased whereas lean body mass and total body water were diminished. Since there were no significant difference in serum vitamin E levels in all age groups, serum retinal concentrations tended to decrease as one gets old. 2. Although total number of T lymphocytes seemed to be unchanged, B lymphocytes and NK cell numbers were increased by aging. The Percentage of CD8 + lymphocytes was lower in the elderly subjects compared with the younger, resulting in higher ratio of CD4 +/CD8 + lymphocytes in the elderly. Serum Ig G and Ig A levels remained unchanged, but IgM levels were significantly decreased as the age processes continue. Taking all together, it could be suggested that the alteration of immune cell population by aging is selective and possibly nonage factors such as nutrition may be attributable to the change of immunity in the elderly. The nutritional status and aging process may selectively affect both the cell-mediated (CD8 +, CD4 + CD8 + ratio, NK cell) and humoral (B lymphocyte, Immunoglobulin M, G) immune parameters in human subjects.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Plant Biotechnology
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• v.33 no.1
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• pp.33-37
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• 2006

Bottle gourd (Lagenaria siceraria Standl.) has been used as a rootstock for the watermelon cultivation because of better growth ability at low temperature and avoidance from contamination of the soil disease. Since the genetic source for the elite rootstock is limited in nature, the genetic engineering method is inevitable to develop new lines especially to obtain the functionally important or multi-disease resistant bottle gourd. Recently, our lab has set up a successful system to transform the bottle gourd. in order to monitor the transformation process, GFP gene is used. Cotyledons of the inbred line 9005, 9006 and G5 were used to induce the shoot under the selection media with MS + 30 g/L sucrose + 3.0 mg/L BAP + 100 mg/L kanamycin + 500 mg/L cefotaxime + 0.5 mg/L $AgNO_3$, pH 5.8. The shoot was developed from the cut side of the explants after 3 weeks on the selection media. The shoot was incubated in the rooting media with 1/2 MS + 30 g/L sucrose + 0.1 mg/L IAA + 50 mg/L kanamycin + 500 mg/L cefotaxime, pH 5.8 and moved to pot for acclimation. Although the shoot development rate was depended on the genotype, the G5 was the best line to be transformed. Monitoring GFP expression from the young shoot under microscope could make the selection much easier to distinguish the transformed shoot from the non-transformed shoots.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.15 no.2
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• pp.178-184
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• 2004

Objectives : This study was conducted to investigate the association of the COMT polymorphism with the TD in Korean sample of families with TD probands. The relationship between risk alleles and specific clinical features (tic severity, comorbidity, drug response) was also explored. Method : Patients were recruited from the Tic Disorder clinic at the Child & Adolescent Psychiatric Division of Seoul National University Hospital and assessed through 2 stage evaluation. Firstly, all the patients and parents received semistructured interview using Korean version of K-SADS-PL. Secondly all the patients received clinical interview and tic severity assessment with Korean version of YGTSS. The subjects in control group were recruited from the health promotion center in out hospital and were evaluated by SCL-90 and SCID-IV. Through these process, total of 42 children and adolescents with TD, their 84 parents and 86 control subjects were finally recruited. Genotyping for The Val158Met polymorphism of the COMT gene was done by standardized method. After collection of genetic data of all the patients, parents and control subjects, case-control comparison and tranmission dysequilibrium test was executed by SPSS version 11. Result : From the case-control comparison, the frequency of L-allele and LL genotype was significantly higher in TD group. However, no differences were found from the TDT. No significant differences were found in in family history of tic, ADHD, OCD, drug response and comorbid conditions among the three different genotypes in patients with TD. Conclusion : Though this study results should be interpreted cautiously due to small sample size and negative finding in TDT test, this study is the first report that there is positive association between the functional polymorphism of COMT gene the TD.

• Journal of Plant Biotechnology
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• v.38 no.4
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• pp.285-292
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• 2011

In plants, heteroblasty reflects the morphological adaptation during leaf development according to the external environmental condition and affects the final shape and size of organ. Among parameters displaying heteroblasty, leaf index is an important and typical one to represent the shape and size of simple leaves. Leaf index factor is eventually determined by cell proliferation and cell expansion in leaf blades. Although several regulators and their mechanisms controlling the cell division and cell expansion in leaf development have been studied, it does not fully provide a blueprint of organ formation and morphogenesis during environmental changes. To investigate genes and their mechanisms controlling leaf index during leaf development, we carried out molecular-genetic and physiological experiments using an Arabidopsis mutant. In this study, we identified macrophylla (mac) which had enlarged leaves. In detail, the mac mutant showed alteration in leaf index and cell expansion in direction of width and length, resulting in not only modification of leaf shape but also disruption of heteroblasty. Molecular-genetic studies indicated that mac mutant had point mutation in ROTUDIFOLIA3 (ROT3) gene involved in brassinosteroid biosynthesis and was an allele of rot3-1 mutant. We named it mac/rot3-5 mutant. The expression of ROT3 gene was controlled by negative feedback inhibition by the treatment of brassinosteroid hormone, suggesting that ROT3 gene was involved in brassinosteroid biosynthesis. In dark condition, in addition, the expression of ROT3 gene was up-regulated and mac/rot3-5 mutant showed lower response, compare to wild type in petiole elongation. This study suggests that ROT3 gene has an important role in control of leaf index during leaf expansion process for proper environmental adaptation, such as shade avoidance syndrome, via the control of brassinosteroid biosynthesis.

• Journal of Animal Science and Technology
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• v.46 no.6
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• pp.917-924
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• 2004

Using PCR-RFLP haplotyping for the mitochondrial DNA(mtDNA) fragment containing the NADH dehydrogenase 2 gene(ND2) and three tRNA genes(tRNA-Met, tRNA-Trp and tRNA-Ala), we characterized the genetic diversity of five pig breeds including Jeju native pigs. mtDNA polymorphisms showing distinct cleavage patterns were found in the pig breeds. Two digestion patterns were detected when HaeIII- and Hinfl-RFLP, and four in the Tsp5091-RFLP analyses. Combining the three restriction enzyme digestion patterns found in five different pig breeds, four mtDNA haplotypes were observed and the haplotype frequencies were significantly different by the pig breeds. A monomorphic haplotype, mtWB, was observed in both Korean wild boars and Large White pigs. Both Duroc and Landrace pigs contained two haplotypes suggesting their multiple maternal lineages. Jeju native pig has two haplotypes(mtJN and mtJD). Of these, mtJN is identified as a Jeju native pig specific haplotype. This study suggested that more than two progenitor populations have been taken part in the domestication process of the Jeju native pig population, and/or probably subsequent crossing with other pig breeds from near east Asia. Unlike with our prediction, there was no direct evidence under molecular levels on the maternal introgression of Korean wild boar in the domestication of Jeju native pigs. In conclusion, specificity of mtDNA haplotypes related to pig breeds win be useful for identifying the maternal lineage as wen as constructing the genealogical pedigree in pigs.

• Journal of Life Science
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• v.27 no.10
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• pp.1111-1120
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• 2017

This study was conducted to select pepper lines that were tolerant to excessive water injury among the pepper germplasm and investigate the genetic characteristics of those lines to contribute to the breeding of pepper cultivars with stable productivity in abnormal weather. Each of the tolerant and susceptible lines went through immersion treatment, and differentially expressed genes between them were analyzed. The tolerant line showed increased expression of the CA02g26670 gene, which is involved in the CONSTANS protein pathway and regulation of flowering by day length, but it exhibited decreased expressions of CA01g21450, CA01g22480, CA01g34470, CA02g00370 and CA02g00380. The susceptible line showed increased gene expressions of CA02g09720, CA02g21290, CA03g16520, CA07g 02110, and CA12g17910, which are involved in the inhibition of proteolytic enzyme activity, DNA binding, inhibition of cell wall-degrading enzyme, and inhibition of nodulin, respectively. Meanwhile the expressions of CA02g02820, CA03g21390, CA06g17700 and CA07g18230 decreased in the susceptible line, in relation to calcium-ion binding, high temperature, synthesis of phosphocholine and cold stress, respectively. The expressions of genes related to apoptosis and peroxidase increased, while that of CA02g16990, which functions as a nucleoside transporter, decreased in both the tolerant and susceptible lines. Based on the different gene expressions between the tolerant and susceptible lines, further studies are needed on breeding abiotic stress-tolerant lines.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.