• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.5
• /
• pp.2051-2055
• /
• 2012

Background: The cell cycle checkpoint kinase 2 (CHEK2) gene I157T variant may be associated with an increased risk of colorectal cancer, but it is unclear whether the evidence is sufficient to recommend testing for the mutation in clinical practice. Materials and Methods: We systematically searched PubMed, EMBASES, Elsevier and Springer for relevant articles before Apr 2012. Summary odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using a fixed-effects or random-effects models with Review Manager 5.0 software. Results: A total of seven studies including 4,029 cases and 13,844 controls based on the search criteria were included for analysis. A significant association of the CHEK2 I157T C variant with unselected CRC was found (OR = 1.61, 95% CI = 1.40-1.87, P < 0.001). We also found a significant association with sporadic CRC (OR = 1.48, 95% CI = 1.23-1.77, P < 0.001) and separately with familial CRC (OR = 1.97, 95% CI = 1.41-2.74, P < 0.001). Conclusion: This meta-analysis demonstrates that the CHEK2 I157T variant may be another important CRC-predisposing gene, which increases CRC risk, especially in familial CRC.

• Journal of Microbiology and Biotechnology
• /
• v.26 no.12
• /
• pp.2066-2075
• /
• 2016

In this study, an enhanced lipid-producing mutant strain of the microalga Chlamydomonas reinhardtii was developed by gamma irradiation. To induce the mutation, C. reinhardtii was gamma irradiated at a dose of 400 Gy. After irradiation, the surviving cells were stained with Nile red. The mutant (Cr-4013) accumulating 20% more lipid than the wild type was selected. Thin-layer chromatography revealed the triglyceride and free fatty acid contents to be markedly increased in Cr-4013. The major fatty acids identified were palmitic acid, oleic acid, linoleic acid, and linolenic acid. Random amplified polymeric DNA analysis showed partial genetic modifications in Cr-4013. To ascertain the changes of protein expression in the mutant strain, two-dimensional electrophoresis was conducted. These results showed that gamma radiation could be used for the development of efficient microalgal strains for lipid production.

• Journal of Microbiology and Biotechnology
• /
• v.22 no.5
• /
• pp.607-613
• /
• 2012

Improvement of endoglucanase activity was accomplished by utilizing error-prone rolling circle amplification, supplemented with 1.7 mM $MnCl_2$. This procedure generated random mutations in the Bacillus amyloliquefaciens endoglucanase gene with a frequency of 10 mutations per kilobase. Six mutated endoglucanase genes, recovered from six colonies, possessed endoglucanase activity between 2.50- and 3.12-folds higher than wild type. We sequenced these mutants, and the different mutated sites of nucleotides were identified. The mutated endoglucanase sequences had five mutated amino acids: A15T, P24A, P26Q, G27A, and E289V. Among these five substitutions, E289V was determined to be responsible for the improved enzyme activity. This observation was confirmed with site-directed mutagenesis; the introduction of only one mutation (E289V) in the wild-type endoglucanase gene resulted in a 7.93-fold (5.55 U/mg protein) increase in its enzymatic activity compared with that (0.7 U/mg protein) of wild type.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.6 no.2
• /
• pp.43-51
• /
• 1996

Fuzzy rules in fuzzy logic control play a major role in deciding the control dynamics of a fuzzy logic controller. Thus, control performance is mainly determined by the quality of fuzzy rules. This paper introduces an optimization method for fuzzy rules using GAS with adaptive probabilies of crossover and mutation. Also we design two fitness measures to satisfy control objectives by partitioning the response of a plant into two parts. An initial population is generated by an automatic fuzzy rule generation method instead of random selection for fast a.pproaching to the final solution. We employed a nonlinear plant to simulate our method. It is shown through simulation that our method is reasonable and can be useful for optimizing fuzzy rules.

• Proceedings of the KIEE Conference
• /
• 1993.07b
• /
• pp.971-973
• /
• 1993

Deflection Yoke(the following, DY) is the important electric device of CRT which deflects R, G, B beans influencing magnetic field produced by yoke coils. Recently, DY is designed to the saddle/saddle type of coils, being proposed for high-definite and high-efficient CRT. This paper presents the optimization of pin-sectioned saddle coil's shape for minimizing gap between desired and practical deflections of electron beams by using Genetic Algorithm. Evolution Startegy is utilized in this paper, since evolution strategy is a kind of genetic algorithms finding the optimized values by choicing the better generation with comparing the parents and their children. Here, the children are generated by only mutations from the normal random variables. Evolution strategy has shown better powerful converge rate than the other genetic algorithms becuase of using only the mutation-operator.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.20 no.10
• /
• pp.946-952
• /
• 2010

A method for the identification of structural characteristic parameters of a steel bar in the matrices form such as stiffness matrices and mass matrices from frequency response function(FRF) by genetic algorithm is proposed. As the method is based on the finite element method(FEM), the obtained matrices have perfect physical meanings if the FRFs got from the analysis and the FRFs from the experiments were well coincident each other. The identified characteristic matrices from the FRFs with maximun 40 % of random errors by the genetic algorithm are coincident with the characteristic matrices from exact FEM FRFs well each other. The fitted element diameters by using only 2 points experimental FRFs are similar to the actual diameters of the bar. The fitted FRFs are good accordance with the experimental FRFs on the graphs. FRFs of the rest 9 points not used for calculating could be fitted even well.

• The Plant Pathology Journal
• /
• v.39 no.1
• /
• pp.62-74
• /
• 2023

Plant pathogenic Pectobacterium species cause severe soft rot/blackleg diseases in many economically important crops worldwide. Pectobacterium utilizes plant cell wall degrading enzymes (PCWDEs) as the main virulence determinants for its pathogenicity. In this study, we screened a random mutant, M29 is a transposon insertion mutation in the metC gene encoding cystathionine β-lyase that catalyzes cystathionine to homocysteine at the penultimate step in methionine biosynthesis. M29 became a methionine auxotroph and resulted in growth defects in methionine-limited conditions. Impaired growth was restored with exogenous methionine or homocysteine rather than cystathionine. The mutant exhibited reduced soft rot symptoms in Chinese cabbages and potato tubers, maintaining activities of PCWDEs and swimming motility. The mutant was unable to proliferate in both Chinese cabbages and potato tubers. The reduced virulence was partially restored by a complemented strain or 100 µM of methionine, whereas it was fully restored by the extremely high concentration (1 mM). Our transcriptomic analysis showed that genes involved in methionine biosynthesis or transporter were downregulated in the mutant. Our results demonstrate that MetC is important for methionine biosynthesis and transporter and influences its virulence through Pcc21 multiplication in plant hosts.

• Proceedings of the Optical Society of Korea Conference
• /
• 2003.07a
• /
• pp.60-61
• /
• 2003

A new approach to reduce the computation time of genetic algorithm (GA) for making binary phase holograms is described. Synthesized holograms having diffraction efficiency of 75.8% and uniformity of 5.8% are proven in computer simulation and experimentally demonstrated. Recently, computer-generated holograms (CGHs) having high diffraction efficiency and flexibility of design have been widely developed in many applications such as optical information processing, optical computing, optical interconnection, etc. Among proposed optimization methods, GA has become popular due to its capability of reaching nearly global. However, there exits a drawback to consider when we use the genetic algorithm. It is the large amount of computation time to construct desired holograms. One of the major reasons that the GA' s operation may be time intensive results from the expense of computing the cost function that must Fourier transform the parameters encoded on the hologram into the fitness value. In trying to remedy this drawback, Artificial Neural Network (ANN) has been put forward, allowing CGHs to be created easily and quickly (1), but the quality of reconstructed images is not high enough to use in applications of high preciseness. For that, we are in attempt to find a new approach of combiningthe good properties and performance of both the GA and ANN to make CGHs of high diffraction efficiency in a short time. The optimization of CGH using the genetic algorithm is merely a process of iteration, including selection, crossover, and mutation operators [2]. It is worth noting that the evaluation of the cost function with the aim of selecting better holograms plays an important role in the implementation of the GA. However, this evaluation process wastes much time for Fourier transforming the encoded parameters on the hologram into the value to be solved. Depending on the speed of computer, this process can even last up to ten minutes. It will be more effective if instead of merely generating random holograms in the initial process, a set of approximately desired holograms is employed. By doing so, the initial population will contain less trial holograms equivalent to the reduction of the computation time of GA's. Accordingly, a hybrid algorithm that utilizes a trained neural network to initiate the GA's procedure is proposed. Consequently, the initial population contains less random holograms and is compensated by approximately desired holograms. Figure 1 is the flowchart of the hybrid algorithm in comparison with the classical GA. The procedure of synthesizing a hologram on computer is divided into two steps. First the simulation of holograms based on ANN method [1] to acquire approximately desired holograms is carried. With a teaching data set of 9 characters obtained from the classical GA, the number of layer is 3, the number of hidden node is 100, learning rate is 0.3, and momentum is 0.5, the artificial neural network trained enables us to attain the approximately desired holograms, which are fairly good agreement with what we suggested in the theory. The second step, effect of several parameters on the operation of the hybrid algorithm is investigated. In principle, the operation of the hybrid algorithm and GA are the same except the modification of the initial step. Hence, the verified results in Ref [2] of the parameters such as the probability of crossover and mutation, the tournament size, and the crossover block size are remained unchanged, beside of the reduced population size. The reconstructed image of 76.4% diffraction efficiency and 5.4% uniformity is achieved when the population size is 30, the iteration number is 2000, the probability of crossover is 0.75, and the probability of mutation is 0.001. A comparison between the hybrid algorithm and GA in term of diffraction efficiency and computation time is also evaluated as shown in Fig. 2. With a 66.7% reduction in computation time and a 2% increase in diffraction efficiency compared to the GA method, the hybrid algorithm demonstrates its efficient performance. In the optical experiment, the phase holograms were displayed on a programmable phase modulator (model XGA). Figures 3 are pictures of diffracted patterns of the letter "0" from the holograms generated using the hybrid algorithm. Diffraction efficiency of 75.8% and uniformity of 5.8% are measured. We see that the simulation and experiment results are fairly good agreement with each other. In this paper, Genetic Algorithm and Neural Network have been successfully combined in designing CGHs. This method gives a significant reduction in computation time compared to the GA method while still allowing holograms of high diffraction efficiency and uniformity to be achieved. This work was supported by No.mOl-2001-000-00324-0 (2002)) from the Korea Science & Engineering Foundation.

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

• The KIPS Transactions:PartC
• /
• v.10C no.6
• /
• pp.747-754
• /
• 2003

Thia paper suggests a milti user-authentication system comprises that DNA biometric informatiom, owner's RFID(Radio Frequency Identification) smartcard of hardware token, and PKI digital signqture of software. This system improved items proposed in [1] as follows : this mechanism provides one RFID smartcard instead of two user-authentication smartcard(the biometric registered seal card and the DNA personal ID card), and solbers user information exposure as RFID of low proce when the card is lost. In addition, this can be perfect multi user-autentication system to enable identification even in cases such as identical twins, the DNA collected from the blood of patient who has undergone a medical procedure involving blood replacement and the DNA of the blood donor, mutation in the DNA base of cancer cells and other cells. Therefore, the proposed system is applied to terminal log-on with RFID smart card that stores accurate digital DNA biometric information instead of present biometric user-authentication system with the card is lost, which doesn't expose any personal DNA information. The security of PKI digital signature private key can be improved because secure pseudo random number generator can generate infinite one-time pseudo randon number corresponding to a user ID to keep private key of PKI digital signature securely whenever authenticated users access a system. Un addition, this user-authentication system can be used in credit card, resident card, passport, etc. acceletating the use of biometric RFID smart' card. The security of proposed system is shown by statistical anaysis.