• Journal of Plant Biotechnology
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• v.38 no.3
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• pp.198-202
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• 2011

Agrobacterium tumefaciens-mediated cotyledonary-node explants transformation was used to produce transgenic cucumber. Cotyledonary-node explants of cucumber (Cucumis sativus L. cv., Eunsung) were co-cultivated with Agrobacterium strains (EHA101) containing the binary vector (pPZP211) carrying with CaMV 35S promoter-nptII gene as selectable marker gene and 35S promoter-DQ gene (unpublished data) as target gene. The average of transformation efficiency (4.01%) was obtained from three times experiments and the maximum efficiency was shown at 5.97%. A total of 9 putative transgenic plants resistant to paromomycin were produced from the cultures of cotyledonary-node explants on selection medium. Among them, 6 transgenic plants showed that the nptII gene integrated into each genome of cucumber by Southern blot analysis.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.1
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• pp.68-76
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• 1999

The intuitive understanding of the dynamic pattern generation in asymmetric networks may be useful for developing models of dynamic information processing. In this paper, dynamic behavior of the ring connection neural network in which each neuron is only to its nearest neurons with binary synaptic weights of ±1, has been inconnected vestigated Simulation results show that dynamic behavior of the network can be classified into only three categories: fixed points, limit cycles with basin and limit cycles with no basin. Furthermore, the number and the type of limit cycles generated by the networks have been derived through analytical method. The sufficient conditions for a state vector of n-neuron network to produce a limit cycle of n- or 2n-period are also given The results show that the estimated number of limit cycle is an exponential function of n. On the basis of this study, cyclic connection neural network may be capable of storing a large number of dynamic information.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.266-274
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• 2009

An abdominal aortic aneurysm occurs most commonly in older individuals (between 65 and 75), and more in men and smokers. The most important complication of an abdominal aortic aneurysm is rupture, which is most often a fatal event. An abdominal aortic aneurysm weakens the walls of the blood vessel, leaving it vulnerable to bursting open, or rupturing, and spilling large amounts of blood into the abdominal cavity. surface modeling is very useful to surgery for quantitative analysis of abdominal aortic aneurysm. the 3D representation and surface modeling an abdominal aortic aneurysm structure taken from Multi Detector Computed Tomography. The construction of the 3D model is generally carried out by staking the contours obtained from 2D segmentation of each CT slice, so the quality of the 3D model strongly defends on the precision of segmentation process. In this work we present deformable model algorithm. deformable model is an energy-minimizing spline guided by external constraint force. External force which we call Gradient Vector Flow, is computed as a diffusion of a gradient vectors of gray level or binary edge map derived from the image. Finally, we have used snakes successfully for abdominal aortic aneurysm segmentation the performance of snake was visually and quantitatively validated by experts.

• Mycobiology
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• v.49 no.5
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• pp.491-497
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• 2021

An endolichenic fungus Xylaria grammica EL000614 produces grammicin, a potent nematicidal pyrone derivative that can serve as a new control option for root-knot nematodes. We optimized an Agrobacterium tumefaciens-mediated transformation (ATMT) protocol for X. grammica to support genetic studies. Transformants were successfully generated after co-cultivation of homogenized young mycelia of X. grammica with A. tumefaciens strain AGL-1 carrying a binary vector that contains the bacterial hygromycin B phosphotransferase (hph) gene and the eGFP gene in T-DNA. The resulting transformants were mitotically stable, and PCR analysis showed the integratin of both genes in the genome of transformants. Expression of eGFP was confirmed via fluorescence microscopy. Southern analysis showed that 131 (78.9%) out of 166 transformants contained a single T-DNA insertion. Crucial factors for producing predominantly single T-DNA transformants include 48 h of co-cultivation, pretreatment of A. tumefaciens cells with acetosyringone before co-cultivation, and using freshly prepared mycelia. The established ATMT protocol offers an efficient tool for random insertional mutagenesis and gene transfer in studying the biology and ecology of X. grammica.

• Journal of Biomedical Engineering Research
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• v.40 no.2
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• pp.62-67
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• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.

• Journal of Microbiology and Biotechnology
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• v.29 no.2
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• pp.230-234
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• 2019

Currently, the genetic modification of Aspergillus oryzae is mainly dependent on protoplast-mediated transformation (PMT). In this study, we established a dual selection marker system in an industrial A. oryzae 3.042 strain by using Agrobacterium tumefaciens-mediated transformation (ATMT). We first constructed a uridine/uracil auxotrophic A. oryzae 3.042 strain and a pyrithiamine (PT)-resistance binary vector. Then, we established the ATMT system by using uridine/uracil auxotrophy and PT-resistance genes as selection markers. Finally, a dual selection marker ATMT system was developed. This study demonstrates a useful dual selection marker transformation system for genetic manipulations of A. oryzae 3.042.

• Mycobiology
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• v.47 no.1
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• pp.59-65
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• 2019

Agrobacterium tumefaciens-mediated transformation (ATMT), as a simple and versatile method, achieves successful transformation in the yeast-like cells (YLCs) of Tremella fuciformis with lower efficiency. Establishment of a more efficient transformation system of YLCs is important for functional genomics research and biotechnological application. In this study, an enzymolysis-assisted ATMT method was developed. The degradation degree of YLCs depends on the concentration and digestion time of Lywallzyme. Lower concentration (${\leq}0.1%$) of Lywallzyme was capable of formation of limited wounds on the surface of YLCs and has less influence on their growth. In addition, there is no significant difference of YLCs growth among groups treated with 0.1% Lywallzyme for different time. The binary vector pGEH under the control of T. fuciformis glyceraldehyde-3-phosphate dehydrogenase gene (gpd) promoter was utilized to transform the enzymolytic wounded YLCs with different concentrations and digestion time. The results of PCR, Southern blot, quantitative real-time PCR (qRT-PCR) and fluorescence microscopy revealed that the T-DNA was integrated into the YLCs genome, suggesting an efficient enzymolysis-assisted ATMT method of YLCs was established. The highest transformation frequency reached 1200 transformants per $10^6$ YLCs by 0.05% (w/v) Lywallzyme digestion for 15 min, and the transformants were genetically stable. Compared with the mechanical wounding methods, enzymolytic wounding is thought to be a tender, safer and more effective method.

• Entomological Research
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• v.48 no.5
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• pp.384-389
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• 2018

Aphis gossypii Glover is an important insect pest that functions as a viral vector and mediates approximately 45 different viral diseases. As part of a strategy for control of A. gossypii, we investigated the functions of genes using RNAi. To this end, a cDNA library was constructed for various genes and for selecting appropriate targets for RNAi mediated silencing. The cDNA library was constructed using the Gateway cloning system with site-specific recombination of bacteriophage ${\lambda}$. It was used to carry out single step cloning of A. gossypii cDNAs. As a result, a cDNA library with a titer of $8.4{\times}10^6$ was constructed. Since the sequences in this library carry att sites, they can be cloned into various binary vectors. This library will be of value for various studies. For later screening of selected genes, it is planned to clone the library into virus-induced gene silencing (VIGS) vectors, which makes it possible to analyze gene function and allow subsequent transfection of plants. Such transfection experiments will allow testing of RNAi-induced insecticidal activity or repellent activity to A. gossypii, and result in the identification of target genes. It is also expected that the constructed cDNA library will be useful for analysis of gene functions in A. gossypii.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.603-610
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• 2021

In this paper, we extract the user's power consumption patterns, and model the optimal consumption patterns by applying the user's environment and emotion. Based on the comparative analysis of these two patterns, we present an efficient power consumption method through changes in the user's power consumption behavior. To extract significant consumption patterns, vector standardization and binary data transformation methods are used, and learning about the ensemble's ensemble with k-means clustering is applied, and applying the support factor according to the value of k. The optimal power consumption pattern model is generated by applying forced and emotion-based control based on the learning results for ensemble aggregates with relatively low average consumption. Through experiments, we validate that it can be applied to a variety of windows through the number or size adjustment of clusters to enable forced and emotion-based control according to the user's intentions by identifying the correlation between the number of clusters and the consistency ratios.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.155-166
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• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.