• Applied Biological Chemistry
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• v.37 no.3
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• pp.148-153
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• 1994

Rice black-streaked dwarf virus (RBSDV), a member of the plant reoviridae fijivirus group, causes a serious damage for rice production in Korea. To characterize the RBSDV genome, virus particles were produced by feeding of planthopper (Laodelphax striatellus F.) carring RBSDV to maize plants for 2 days. In $30{\sim}40$ days after feeding, the viral particles were purified from the infected maize roots by using $10{\sim}40%$ sucrose gradient centrifugation. After treatment of 10% SDS to remove the viral coat proteins, ten viral double-stranded RNAs were resolved in agrose gel electrophoresis. Total dsRNA was then used to synthesize cDNA by reverse transcriptase and a cDNA library was constructed in the ${\lambda}gt11$ vector. The phages that contain RBSDV cDNA fragments were selected by hybridizing with the random-primed probe prepared from RBSDV dsRNAs. After subcloning of several cDNA fragments into the pUC19 plasmid vector, one clone (pRV3) was chosen for sequencing. The pRV3 clone was shown to be located on the RBSDV genome fragment No.3 by RNA gel-blot analysis. Sequence analysis of the clone revealed that the pRV3 contains two partial open reading frames.

• Korean journal of applied entomology
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• v.13 no.1 s.18
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• pp.25-31
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• 1974

This survey was aimed to accumulate basic data of aphid population at the Horticultural Experiment Station at Suweon. The yellow pan traps were setted at five locations (Fig.1.), and ran from May 1 to October 31. 1970. About one hundred and twenty species of aphids were trapped, including 24 species of plant vims vectors. Of these, dominant species were as follows: (Asterisk shows virus vector) Aphid species No. of catches * Aphis spiraecola PATCH 2,635, * Aphis craccivora KOCH 2,377, * Myzus persicae SULXER 2,111, Capitophorus hippophaes javanicus H.R. LAMBERS 2,051, Anoecia fulviabdominalis SASAKI 1,480, * Aphis gossypii GLOVER 867, * Macrosiphum avenae FABRICIUS 859, Cervaphis quercus TAKAHASHI 692, * Lipaphis erysimi KALTENBACH 645, Pleotrichophorus chrysanthemi THEOBALD 489, The above 10 species consisted $76.5\%$ of total catches and the 24 vector species consisted $55.5\%$. The curve of the seasonal occurrence of flying aphids at Horticultural Experiment Station shows bimodal, typical for the temperate region. The total number of trapped aphids at the Station from May to September, 1970, were less than that of average yearly catches at the College of Agriculture from 1967 to 1970. Thi, low numbers at Horticultural Experiment Station may attribute to the frequent spraying of insecticides from Spring to Summer on growing crops there. But the aphids population increase suddenly in the middle of October. This might be resulted from cease of insecticide applications and migration of aphids from summer host to winter host plants.

• Restorative Dentistry and Endodontics
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• v.20 no.2
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• pp.645-654
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• 1995

Porphyromonas endodontalis is a black-pigmented anaerobic Gram negative rod which is associated with endodontal infections. It has been isolated from infected dental root canals and submucous abscesses of endodontal origin. DNA probe is an available alternative, offering the direct detection of a specific microorganism. Nucleic-acid probes can be off different types: whole different: whole-genomic, cloned or oligonucleotide probes. Wholegenomic probes are the most sensitive because the entire genome is used for possible hybridization sites. However, as genetically similar species of bacteria are likely to be present in specimences, cross-reactions need to be considered. Cloned probes are isolated sequences of DNA that do not show cross-reactivity and are produced in quantity by cloning in a plasmid vector. Cloned probes can approach the sensitivity found with whole-genomic probes while avoiding known cross-reacting species. Porphyromonas endodontalis ATCC 35406 (serotype $O_1K_1$) was selected in this experiment to develop specific cloned DNA probes. EcoR I-digested genomic DNA fragments of P. endodontalis ATCC 35406 were cloned into pUC18 plasmid vector. From the E. coli transformed with the recombinant plasmid 4 clones were selected to be tested as specific DNA probes. Restriction-digested whole-genomic DNAs prepared from P. gingivalis 38(serotype a), W50(serotype b), A7A1-28(serotype C), P. intermedia 9336(serotype b), G8-9K-3(serotype C), P. endodontalis ATCC 35406(serotype $O_1K_1$), A. a Y4(serotype b), 75(serotype a), 67(serotype c), were each seperated on agarose gel electrophoresis, blotted on nylon membranes, and were hybridized with digoxigenin-dUTP labeled probe. The results were as follows: 1. Three clones of 1.6kb(probe e), 1.6kb(probe f), and 0.9kb(probe h) in size, were obtained. These clones were identified to be a part of the genomic DNA of P. endodontalis ATCC 35406 judging from their specific hybridization to the genomic DNA fragments of their own size on Southern blot. 2. The clones of 4.9kb(probe i) was identified to be a part of the genomic DNA of P. endodontalis ATCC 35406. but not to specific for itself. It was hybridized to P. gingivalis A7A1-28, P. intermedia G89K-3.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.91-107
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• 2012

As the Internet becomes more popular, many people use it to communicate. With the increasing number of personal homepages, blogs, and social network services, people often expose their personal information online. Although the necessity of those services cannot be denied, we should be concerned about the negative aspects such as personal information leakage. Because it is impossible to review all of the past records posted by all of the people, an automatic personal information detection method is strongly required. This study proposes a method to detect or classify online documents that contain personal information by analyzing features that are common to personal information related documents and learning that information based on the Na$\ddot{i}$ve Bayes algorithm. To select the document classification algorithm, the Na$\ddot{i}$ve Bayes classification algorithm was compared with the Vector Space classification algorithm. The result showed that Na$\ddot{i}$ve Bayes reveals more excellent precision, recall, F-measure, and accuracy than Vector Space does. However, the measurement level of the Na$\ddot{i}$ve Bayes classification algorithm is still insufficient to apply to the real world. Lewis, a learning algorithm researcher, states that it is important to improve the quality of category features while applying learning algorithms to some specific domain. He proposes a way to incrementally add features that are dependent on related documents and in a step-wise manner. In another experiment, the algorithm learns the additional dependent features thereby reducing the noise of the features. As a result, the latter experiment shows better performance in terms of measurement than the former experiment does.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1281-1289
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• 2022

Objective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. Materials and Methods: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. Results: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. Conclusion: Radiomic models using MRI were able to differentiate JME from HCs.

• Journal of KIISE:Software and Applications
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• v.37 no.6
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• pp.455-463
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• 2010

In this paper, we present an approach to sentence boundary detection for web documents that builds on statistical-based methods and uses rule-based correction. The proposed system uses the classification model learned offline using a training set of human-labeled web documents. The web documents have many word-spacing errors and frequently no punctuation mark that indicates the end of sentence boundary. As sentence boundary candidates, the proposed method considers every Ending Eomis as well as punctuation marks. We optimize engine performance by selecting the best feature, the best training data, and the best classification algorithm. For evaluation, we made two test sets; Set1 consisting of articles and blog documents and Set2 of web community documents. We use F-measure to compare results on a large variety of tasks, Detecting only periods as sentence boundary, our basis engine showed 96.5% in Set1 and 56.7% in Set2. We improved our basis engine by adapting features and the boundary search algorithm. For the final evaluation, we compared our adaptation engine with our basis engine in Set2. As a result, the adaptation engine obtained improvements over the basis engine by 39.6%. We proved the effectiveness of the proposed method in sentence boundary detection.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.245-250
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• 2017

Flash floods is defined as the flooding of intense rainfall over a relatively small area that flows through river and valley rapidly in short time with no advance warning. So that it can cause damage property and casuality. This study is to establish the flash-flood warning system using 38 accident data, reported from the National Disaster Information Center and Land Surface Model(TOPLATS) between 2009 and 2012. Three variables were used in the Land Surface Model: precipitation, soil moisture, and surface runoff. The three variables of 6 hours preceding flash flood were reduced to 3 factors through factor analysis. Decision tree, random forest, Naive Bayes, Support Vector Machine, and logistic regression model are considered as big data methods. The prediction performance was evaluated by comparison of Accuracy, Kappa, TP Rate, FP Rate and F-Measure. The best method was suggested based on reproducibility evaluation at the each points of flash flood occurrence and predicted count versus actual count using 4 years data.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.73-75
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• 2013

Atomic force microscopy (AFM) [1] can now not only image individual atoms but also construct atom letters using atom manipulation method [2]. Therefore, the AFM is the second generation atomic tool following the well-known scanning tunneling microscopy (STM). The AFM, however, has the advantages that it can image even insulating surfaces with atomic resolution and also measure the atomic force itself between the tip-apex outermost atom and the sample surface atom. Noting these advantages, we have been developing a novel bottom-up nanostructuring system, as shown in Fig. 1, based on the AFM. It can identify chemical species of individual atoms [3] and then manipulate selected atom species to the designed site one-by-one [2] to assemble complex nanostructures consisted of many atom species at room temperature (RT). In this invited talk, we will introduce our results toward atom-by-atom assembly of composite nanomaterials based on the AFM at RT. To identify chemical species, we developed the site-specific force spectroscopy at RT by compensating the thermal drift using the atom tracking. By converting the precise site-specific frequency shift curves, we obtained short-range force curves of selected Sn and Si atoms as shown in Fig. 2(a) and 2(b) [4]. Then using the atom-by-atom force spectroscopy at RT, we succeeded in chemical identification of intermixed three atom species in Pb/Sn/Si(111)-(${\surd}3$'${\surd}3$) surface as shown in Fig. 2(c) [3]. To create composite nanostructures, we found the lateral atom interchange phenomenon at RT, which enables us to exchange embedded heterogeneous atoms [2]. By combining this phenomenon with the modified vector scan, we constructed the atom letters "Sn" consisted of substitutional Sn adatoms embedded in Ge adatoms at RT as shown in Fig. 3(a)~(f) [2]. Besides, we found another kind of atom interchange phenomenon at RT that is the vertical atom interchange phenomenon, which directly interchanges the surface selected Sn atoms with the tip apex Si atoms [5]. This method is an advanced interchangeable single atom pen at RT. Then using this method, we created the atom letters "Si" consisted of substituted Si adatoms embedded in Sn adatoms at RT as shown in Fig. 4(a)~(f) [5]. In addition to the above results, we will introduce the simultaneous evaluation of the force and current at the atomic scale using the combined AFM/STM at RT.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.33-41
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• 2018

The purpose of this study is to develop more accurate and robust emotion prediction neural network (EPNN) model by combining heart rate variability (HRV) and neural network. For the sake of improving the prediction performance more reliably, the proposed EPNN model is based on various types of activation functions like hyperbolic tangent, linear, and Gaussian functions, all of which are embedded in hidden nodes to improve its performance. In order to verify the validity of the proposed EPNN model, a number of HRV metrics were calculated from 20 valid and qualified participants whose emotions were induced by using money game. To add more rigor to the experiment, the participants' valence and arousal were checked and used as output node of the EPNN. The experiment results reveal that the F-Measure for Valence and Arousal is 80% and 95%, respectively, proving that the EPNN yields very robust and well-balanced performance. The EPNN performance was compared with competing models like neural network, logistic regression, support vector machine, and random forest. The EPNN was more accurate and reliable than those of the competing models. The results of this study can be effectively applied to many types of wearable computing devices when ubiquitous digital health environment becomes feasible and permeating into our everyday lives.

• Journal of Korean Society of Forest Science
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• v.104 no.1
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• pp.43-49
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• 2015

The production of high quality seedlings is a very important phase in silvicultural systems for successful reforestation or restoration. The purpose of this study was to quantitatively measure both growth performances and nutrient responses of Fraxinus rhynchophylla, Pinus densiflora, and Pinus koraiensis seedlings, which are commercially planted in Korea, according to the different types of soil improvement treatments. We applied soil brought (hereafter 'brought'), subsoil inversion (hereafter 'subsoil'), and mixture of brought soil with soil on nursery bed (hereafter 'mixing') in a permanent national nursery. Silt and clay contents were the highest at the subsoil treatment and organic material, soil nitrogen and phosphorus concentrations were the lowest at the brought treatment. The growth of F. rhynchophylla was the lowest at the subsoil treatment, but there were no significant differences among treatments. There were significant differences in only root nutrient concentrations of F. rhynchophylla among treatments: nitrogen, phosphorus, and potassium concentrations were the lowest at the subsoil or brought treatment. Mixing treatment increased N contents with deduction of N concentrations ('dilution') because of more dry weight increase compared with the amount of N uptake. This study suggested mix of brought soil with soil on a nursery bed in a permanently used nursery can economically be an effective technique to improve soil quality.