• Journal of Microbiology and Biotechnology
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• v.27 no.9
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• pp.1586-1592
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• 2017

Some promoters were isolated and characterized from the genome of Leuconostoc mesenteroides SY2, an isolate from kimchi, a Korean traditional fermented vegetable. Chromosomal DNA of L. mesenteroides SY2 was digested with Sau3AI and ligated with BamHI-cut pBV5030, a promoter screening vector containing a promoterless cat-86. Among E. coli transformants (TFs) resistant against Cm (chloramphenicol), 17 were able to grow in the presence of $1,000{\mu}g/ml$ Cm and their inserts were sequenced. Transcription start sites were examined for three putative promoters (P04C, P25C, and P33C) by primer extension. Four putative promoters were inserted upstream of a promoterless ${\alpha}$-amylase reporter gene in $pJY15{\alpha}$. ${\alpha}$-Amylase activities of E. coli TFs containing $pJY15{\alpha}$ (control, no promoter), $pJY03{\alpha}$ ($pJY15{\alpha}$ with P03C), $pJY04{\alpha}$ (with P04C), $pJY25{\alpha}$ (with P25C), and $pJY33{\alpha}$ (with P33C) were 66.9, 78.7, 122.1, 70.8, and 99.3 U, respectively. Cells harboring $pJY04{\alpha}$ showed 1.8 times higher activity than the control. Some promoters characterized in this study might be useful for construction of food-grade expression vectors for Leuconostoc sp. and related lactic acid bacteria.

• Journal of Plant Biotechnology
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• v.45 no.4
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• pp.347-356
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• 2018

Chionanthus retusus is a small deciduous tree that is widely used in landscaping due to its beautiful white spring flowers and ornamental value. Conventional propagation through seeds requires one to two years of breaking dormancy. The objective of this study was to determine the conditions of in vitro germination in C. retusus. In vitro embryo culture was carried out to investigate the effects of six factors: basal media (McCown Woody Plant Medium (WPM) and Murashige and Skoog (MS)); plant growth regulators (different combinations and concentrations of naphthaleneacetic acid (NAA), 6-Benzylaminopurine (BA), and gibberellic acid ($GA_3$)); embryo age (collected weekly beginning 36 days after fruit setting); low temperature pretreatment (storing $4^{\circ}C$ for 1, 2, 3, and 4 weeks); coconut additives (100, 200, and $300ml{\cdot}L^{-1}$); and genotype (grouping plants depending on their flowering nature). The basal medium used in this study was WPM with $2mg{\cdot}L^{-1-1}\;GA_3$, $20g{\cdot}L^{-1}$ sucrose, and $6g{\cdot}L^{-1}$ Agar. WPM medium mixed with $GA_3$, resulted in higher germination rate as compared to when using a combination of auxin and cytokinin. $GA_3$ at $2mg{\cdot}L^{-1}$ was the most effective of all combinations and concentrations of PGRs. WPM medium with $2mg{\cdot}L^{-1}GA_3$ resulted in better and faster germination (75.93%). Embryos collected at 57 days after fruit setting had the highest percent of germinated seeds (87.04%) while low-temperature pretreatment of fruits at $4^{\circ}C$ for two weeks produced the highest germination (95.37%). These results of this study could be an open ground for development of an efficient protocol for commercial production of the ornamental tree.

• Convergence Security Journal
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• v.20 no.4
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• pp.81-90
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• 2020

In the real world, new types of attacks or variants are constantly emerging, but attack traffic classification models developed through artificial neural networks and supervised learning do not properly detect new types of attacks that have not been trained. Most of the previous studies overlooked this problem and focused only on improving the structure of their artificial neural networks. As a result, a number of new attacks were frequently classified as normal traffic, and attack traffic classification performance was severly degraded. On the other hand, the softmax function, which outputs the probability that each class is correctly classified in the multi-class classification as a result, also has a significant impact on the classification performance because it fails to calculate the softmax score properly for a new type of attack traffic that has not been trained. In this paper, based on this characteristic of softmax function, we propose an efficient method to improve the classification performance against new types of attacks by classifying traffic with a probability below a certain level as attacks, and demonstrate the efficiency of our approach through experiments.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Development and Reproduction
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• v.5 no.1
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• pp.47-52
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• 2001

Predetermination of sex in livestock of offpring is in great demand and is of critical importance to providing for the most efficient production of the animal ariculture. Such a sexing techlology would also enhance the economy of conventional artificial insemination(AI) and aid the porcine industry. The purpose of this study was to evaluate the efficiency of enriching X-bearing porcine sperm using discontinuous percoll gradients and PCR mefhod. Semen was collected from mature boars of proven fertility center (AI center KimHae). Sperm was leaded on the isotonic discontinuous percoll gradient and then it was centrifuged at 120 ${\times}$ g for 20 minutes. After centrifugation, sperm included in each fraction were recovered (7${\times}$10$^6$ sperms/ml) and then sperm genomic DNA was extractedfor the PCR. SRY gene was used to evaluate the ratio between X and Y sperm in the separated fractions. Ju viro ffrtilization wascarried out by adding the unseparated sperm (control) or separated (experimental poop) to the matured oocytes in TCM-199. Embryos for sex determination were obtained at 2 cell stage and then was used for SRY gene amplification. After centrifugation of discontinuous percoll gradient, the most motile sperm was obtained at 95% fiaction (94.4% ${\pm}$ 5.1%, p < 0.01). The PCR analysis evaluated that 30%, 50% and 65% fractions were Y sperm rich, whereas 80% and 95% fractions were X sperm rich. PCR analysis with each porcineembryo showed that 33.3% of control and 66.7% of experimental group were determined as female embryos. In conclusion, in vitro matured oocytes inseminated with sperms (95% fraction) prepared by percoll gradient centrifugation showed high fertilization rates and female embryos than control sperms.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• Journal of the Korean Geotechnical Society
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• v.40 no.1
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• pp.15-28
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• 2024

This paper proposes a method to acquire image data inside tunnel structures and a method to structure the acquired image data. By improving the conditions by which image data are acquired inside the tunnel structure, high-quality image data can be obtained from area type tunnel scanning. To improve the data acquisition conditions, a longitudinal rail of the tunnel can be installed on the tunnel ceiling, and image data of the entire tunnel structure can be acquired by moving the installed rail. This study identified 0.5 mm cracked simulation lines under a distance condition of 20 m at resolutions of 3,840 × 2,160 and 720 × 480 pixels. In addition, the proposed image-data-structuring method could acquire image data in image tile units. Here, the image data of the tunnel can be structured by substituting the application factors (resolution of the acquired image and the tunnel size) into a relationship equation. In an experiment, the image data of a tunnel with a length of 1,000 m and a width of 20 m were obtained with a minimum overlap rate of 0.02% to 8.36% depending on resolution and precision, and the size of the local coordinate system was found to be (14 × 15) to (36 × 34) pixels.

• Journal of Life Science
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• v.22 no.10
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• pp.1407-1414
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• 2012

Hormone replacement therapy (HRT) is an effective regimen that has been found to prevent these diseases in postmenopausal women. However, HRT is accompanied by an increased risk of unfavorable outcomes. This study was conducted to evaluate the effects of Eisenia Bicyclis extract on lipids in ovariectomized rats. Fifty 7-week-old female Sprague-Dawley rats were randomly assigned to four groups: sham-operated rats (SHAM), ovariectomized rats (OVX-CON), and ovariectomized rats that were treated with Eisenia bicyclis extracts. The extract-treated diets were fed to the rats for 6 weeks after operation. Antioxidant effects were measured by DPPH free radical scavenging activity. Antioxidant activity of the ethanol extract increased in a dose-dependent manner and was about 55.9% in a concentration of 100 ${\mu}g/ml$. We measured the total cholesterol content, triglyceride content, HDL-cholesterol content, LDL-cholesterol content, atherosclerotic index, cardiac risk factor in serum, and anti-platelet aggregation and blood rheology. The total cholesterol and triglyceride concentration in serum increased for the OVX-control group, but supplementation with the E. bicyclis extract caused these factors to decrease. Notably, the serum LDL-cholesterol concentration in the OVX-EB200 group was significantly lower than the OVX-CON group. In addition, the blood passage times in rats that received the E. bicyclis extract were more rapid than the times in the untreated group (OVX-CON). Microscopic evaluation revealed that whole blood passed more smoothly through the microchannels in rats in the E. bicyclis extract supplement groups. Our results clarified the effects of E. bicyclis extract on serum lipid content in ovariectomized rats, and consequently we expect positive effects from providing E. bicyclis extract to postmenopausal women with cardiovascular disease.