• Current Research on Agriculture and Life Sciences
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• v.5
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• pp.52-58
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• 1987

The yeast S. cerevisiae DBY747 was transformed with E. C - S. C shuttle vector YIp5, YEp13 and YRp7 by the method of spheroplast. The transformation frequency of YEp13 and YRp7 in S. cerevisiae DBY747 was $1.2{\times}10^3$ and $1.0{\times}10^2$ per $10{\mu}g$ of DNA, respectively. The transformants with YIp5 plasmid incapable of autonomous replication in S. cerevisiae were not detected in the condition of this experiment, but YIpS plasmid expressed the gene carried on it when cotransformed with a helper plasmid such as YEp13 or YRp7 : autonomously replicating plasmid. When plasmids were used in covalently closed circular form, cotransformation frequency of Ylp5-YEpl3 and Ylp5-YRp7 was 210 and 95 per $10{\mu}g$ of DNA, respectively. In cotransformation of linear plasmids, transformation frequency of the same cohesive ends was similar to that of noncomplementary cohesive ends. Transformants by the cotransformation with circular plasmids have been shown much higher frequency than with linear plasmids in S. cerevisiae DBY 747. The mitotic segregation stability test suggested that the cotransformant of YIpS-YEp13 was more stable than that of YIpS-YRp7.

• Molecules and Cells
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• v.44 no.10
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• pp.770-779
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• 2021

Transgenic Arabidopsis thaliana expressing an anti-rabies monoclonal antibody (mAb), SO57, was obtained using Agrobacterium-mediated floral dip transformation. The endoplasmic reticulum (ER) retention signal Lys-Asp-Glu-Leu (KDEL) was tagged to the C-terminus of the anti-rabies mAb heavy chain to localize the mAb to the ER and enhance its accumulation. When the inaccurately folded proteins accumulated in the ER exceed its storage capacity, it results in stress that can affect plant development and growth. We generated T₁ transformants and obtained homozygous T₃ seeds from transgenic Arabidopsis to investigate the effect of KDEL on plant growth. The germination rate did not significantly differ between plants expressing mAb SO57 without KDEL (SO plant) and mAb SO57 with KDEL (SOK plant). The primary roots of SOK agar media grown plants were slightly shorter than those of SO plants. Transcriptomic analysis showed that expression of all 11 ER stress-related genes were not significantly changed in SOK plants relative to SO plants. SOK plants showed approximately three-fold higher mAb expression levels than those of SO plants. Consequently, the purified mAb amount per unit of SOK plant biomass was approximately three times higher than that of SO plants. A neutralization assay revealed that both plants exhibited efficient rapid fluorescent focus inhibition test values against the rabies virus relative to commercially available human rabies immunoglobulins. KDEL did not upregulate ER stress-related genes; therefore, the enhanced production of the mAb did not affect plant growth. Thus, KDEL fusion is recommended for enhancing mAb production in plant systems.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.3
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• pp.336-343
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• 2019

In order to investigate the density of seasonal incidence of mosquitoes, a vector of infectious diseases in Jeju region, this study collected mosquitoes using a Black light trap (BL) and Biogents' Sentinel 2 Mosquito Trap (BG), dividing the region into cattle sheds, habitats for migratory birds, and the downtown area, twice a month for 9 months from March through November 2018. Then, this study conducted identification and classification and checked for the presence of Flavivirus using reverse transcription polymerase chain reaction (RT-PCR). As for the mosquito population, 1,847 mosquitoes (six genera, 12 species) were collected. The places where most mosquitoes were collected were copses near craft workshops in habitats for migratory birds and Jungang-dong in the Seogwipo downtown area. For the population, Culex pipiens pallens was the dominant species (76.9%), followed by Aedes albopictus (8.9%). Most of the population was collected in June, followed by August and October. This study conducted a RT-PCR test with 1,847 collected mosquitoes, which were divided into 50 pools if they had Flavivirus. All turned out to be negative. However, the results of the investigation show the presence of Culex tritaineniorhychus, Aedes albopictus, and Anopheles sinensis and can be used as a basis for the comprehensive prevention management of mosquitoes.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.745-760
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• 2021

Prediction of a stock price has been a subject of interest for a long time in financial markets, and thus, many studies have been conducted in various directions. As the efficient market hypothesis introduced in the 1970s acquired supports, it came to be the majority opinion that it was impossible to predict stock prices. However, recent advances in predictive models have led to new attempts to predict the future prices. Here, we summarize past studies on the price prediction by evaluation measures, and predict the direction of stock prices of Samsung Electronics, LG Chem, and NAVER by applying various machine learning models. In addition to widely used technical indicator variables, accounting indicators such as Price Earning Ratio and Price Book-value Ratio and outputs of the hidden Markov Model are used as predictors. From the results of our analysis, we conclude that no models show significantly better accuracy and it is not possible to predict the direction of stock prices with models used. Considering that the models with extra predictors show relatively high test accuracy, we may expect the possibility of a meaningful improvement in prediction accuracy if proper variables that reflect the opinions and sentiments of investors would be utilized.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• Journal of Korean Neurosurgical Society
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• v.64 no.5
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• pp.705-715
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• 2021

Objective : Through our previous clinical trials, the demonstrated therapeutic effects of MSC in chronic spinal cord injury (SCI) were found to be not sufficient. Therefore, the need to develop stem cell agent with enhanced efficacy is increased. We transplanted enhanced Wnt3-asecreting human mesenchymal stem cells (hMSC) into injured spines at 6 weeks after SCI to improve axonal regeneration in a rat model of chronic SCI. We hypothesized that enhanced Wnt3a protein expression could augment neuro-regeneration after SCI. Methods : Thirty-six Sprague-Dawley rats were injured using an Infinite Horizon (IH) impactor at the T9-10 vertebrae and separated into five groups : 1) phosphate-buffered saline injection (injury only group, n=7); 2) hMSC transplantation (MSC, n=7); 3) hMSC transfected with pLenti vector (without Wnt3a gene) transplantation (pLenti-MSC, n=7); 4) hMSC transfected with Wnt3a gene transplantation (Wnt3a-MSC, n=7); and 5) hMSC transfected with enhanced Wnt3a gene (1.7 fold Wnt3a mRNA expression) transplantation (1.7 Wnt3a-MSC, n=8). Six weeks after SCI, each 5×10⁵ cells/15 µL at 2 points were injected using stereotactic and microsyringe pump. To evaluate functional recovery from SCI, rats underwent Basso-Beattie-Bresnahan (BBB) locomotor test on the first, second, and third days post-injury and then weekly for 14 weeks. Axonal regeneration was assessed using growth-associated protein 43 (GAP43), microtubule-associated protein 2 (MAP2), and neurofilament (NF) immunostaining. Results : Fourteen weeks after injury (8 weeks after transplantation), BBB score of the 1.7 Wnt3a-MSC group (15.0±0.28) was significantly higher than that of the injury only (10.0±0.48), MSC (12.57±0.48), pLenti-MSC (12.42±0.48), and Wnt3a-MSC (13.71±0.61) groups (p<0.05). Immunostaining revealed increased expression of axonal regeneration markers GAP43, MAP2, and NF in the Wnt3a-MSC and 1.7 Wnt3a-MSC groups. Conclusion : Our results showed that enhanced gene expression of Wnt3a in hMSC can potentiate axonal regeneration and improve functional recovery in a rat model of chronic SCI.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.543-552
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• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.794-807
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• 2022

Although registration of dogs is mandatory according to the revision of the Animal Protection Act, the registration rate is low due to the inconvenience of the current registration method. In this paper, a performance improvement study was conducted on the dog face recognition technology, which is being reviewed as a new registration method. Through deep learning learning, an embedding vector for facial recognition of a dog was created and a method for identifying each dog individual was experimented. We built a dog image dataset for deep learning learning and experimented with InceptionNet and ResNet-50 as backbone networks. It was learned by the triplet loss method, and the experiments were divided into face verification and face recognition. In the ResNet-50-based model, it was possible to obtain the best facial verification performance of 93.46%, and in the face recognition test, the highest performance of 91.44% was obtained in rank-5, respectively. The experimental methods and results presented in this paper can be used in various fields, such as checking whether a dog is registered or not, and checking an object at a dog access facility.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.315-324
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• 2022

The conventional LESS (LEast-Squares Solution) is calculated under the assumption that there is no errors in independent variables. However, the coordinates of a point, either from traditional ground surveying such as slant distances, horizontal and/or vertical angles, or GNSS (Global Navigation Satellite System) positioning, cannot be determined independently (and the components are correlated each other). Therefore, the TLS (Total Least Squares) adjustment should be applied for all applications related to the coordinates. Many approaches were suggested in order to solve this problem, resulting in equivalent solutions except some restrictions. In this study, we calculated the normal vector of the 3D plane determined by the trace of the VLBI targets based on TLS within GHM (Gauss-Helmert Model). Another numerical test was conducted for the estimation of the Helmert transformation parameters. Since the errors in the horizontal components are very small compared to the radius of the circle, the final estimates are almost identical. However, the estimated variance components are significantly reduced as well as show a different characteristic depending on the target location. The Helmert transformation parameters are estimated more precisely compared to the conventional LESS case. Furthermore, the residuals can be predicted on both reference frames with much smaller magnitude (in absolute sense).

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.59-68
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• 2022

This paper presents a method for 1:1 verification by comparing the similarity between the given real product image and the drawing image. The proposed method combines two existing CNN-based deep learning models to construct a Siamese Network. After extracting the feature vector of the image through the FC (Fully Connected) Layer of each network and comparing the similarity, if the real product image and the drawing image (front view, left and right side view, top view, etc) are the same product, the similarity is set to 1 for learning and, if it is a different product, the similarity is set to 0. The test (inference) model is a deep learning model that queries the real product image and the drawing image in pairs to determine whether the pair is the same product or not. In the proposed model, through a comparison of the similarity between the real product image and the drawing image, if the similarity is greater than or equal to a threshold value (Threshold: 0.5), it is determined that the product is the same, and if it is less than or equal to, it is determined that the product is a different product. The proposed model showed an accuracy of about 71.8% for a query to a product (positive: positive) with the same drawing as the real product, and an accuracy of about 83.1% for a query to a different product (positive: negative). In the future, we plan to conduct a study to improve the matching accuracy between the real product image and the drawing image by combining the parameter optimization study with the proposed model and adding processes such as data purification.