• Journal of Veterinary Clinics
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• v.33 no.1
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• pp.10-15
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• 2016

The purpose of this study was to establish reproducible ischemic infarction model using allogenic blood clot in beagle dogs and identify induced ischemic lesion after middle cerebral artery occlusion using magnetic resonance imaging (MRI) and histopathologic findings. Twenty eight male beagle dogs with no evidence of neurologic disease were experimented. Allogenic embolus was made using a healthy beagle dog. After internal carotid artery (ICA) was exposure, 16G catheter was introduced through the ICA. The dog was administered 0.3 ml blood clot for 15 seconds followed by 3 ml of saline for 15 seconds. MRI scans were performed with 1.5T to evaluate ischemic lesion at 7 days after middle cerebral artery occlusion procedure. Evaluation parameters of MRI include location, distribution, infarction type, margin, shape, mass effect and intensity of T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), fluid attenuated inversion recovery (FLAIR) sequence, diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC). On MRI, all dogs (28/28) showed focal or multifocal lesion including telencephalon and thalamus lesions, especially caudate nucleus (24/28). These lesions had well-defined margin from adjacent brain parenchyma, none or mild mass effect and various shape. Most of dogs appeared hyperintensity on T1WI, T2WI, FLAIR, and DWI/ADC, corresponding to chronic infarction. These lesions were histopathologically confirmed atrophic changes and unstained lesion. In conclusion, MRI is the useful method to provide information about ischemic infarction in dogs and the best reproducible ischemic infarction model was developed by using allogenic blood clot.

• Journal of Life Science
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• v.27 no.5
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• pp.524-529
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• 2017

This study was aimed to produce a transgenic zebrafish expressing the human KCNE1 gene. Initially, the entire CDS of the human KCNE1 gene was amplified from a human genomic DNA sample by polymerase chain reaction using a primer set engineered with restriction enzyme sites (EcoRI, BamHI) at the 5' end of each primer. The resultant 402 bp KCNE1 amplicon flanked by EcoR1 and BamH1 was obtained and subsequently cloned into a plasmid vector pPB-CMVp-EF1-GreenPuro. The integrity of the cloned CDS sequence was confirmed by DNA sequencing analysis. Next, the recombinant vector containing the human KCNE1 (pPB-CMVp-hKCNE1-EF1-GreenPuro) was introduced into fertilized eggs of zebrafish by microinjection. Successful expression of the recombinant vector in the eggs was confirmed by the expression of the fluorescence protein encoded in the vector. Finally, in order to assure that the stable expression of the human KCNE1 gene occurred in the transgenic animal, RNAs were extracted from the animal and the presence of KCNE1 transcripts was confirmed by RT-PCT as well as DNA sequencing analysis. The study provides a methodology to construct a useful transgenic animal model applicable to the development of diagnostic technologies for gene therapy of LQTS (Long QT Syndrome) as well as tools for cloning of useful genes in fish.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.7
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• pp.259-266
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• 2018

In this paper, we propose a method to classify a document using a Recurrent Neural Network by extracting features considering word sense and contexts. Word2vec method is adopted to include the order and meaning of the words expressing the word in the document as a vector. Doc2vec is applied for considering the context to extract the feature of the document. RNN classifier, which includes the output of the previous node as the input of the next node, is used as the document classification method. RNN classifier presents good performance for document classification because it is suitable for sequence data among neural network classifiers. We applied GRU (Gated Recurrent Unit) model which solves the vanishing gradient problem of RNN. It also reduces computation speed. We used one Hangul document set and two English document sets for the experiments and GRU based document classifier improves performance by about 3.5% compared to CNN based document classifier.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.2
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• pp.213-218
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• 2013

One of the most popular approaches to parameter adaptation in hidden Markov model (HMM) based systems is the maximum likelihood linear regression (MLLR) technique. In our previous study, we proposed factored MLLR (FMLLR) where each MLLR parameter is defined as a function of a control vector. We presented a method to train the FMLLR parameters based on a general framework of the expectation-maximization (EM) algorithm. Using the proposed algorithm, supplementary information which cannot be included in the models is effectively reflected in the adaptation process. In this paper, we apply the FMLLR algorithm to a pitch sequence as well as spectrum parameters. In a series of experiments on artificial generation of expressive speech, we evaluate the performance of the FMLLR technique and also compare with other approaches to parameter adaptation in HMM-based speech synthesis.

• Journal of Wetlands Research
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• v.17 no.2
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• pp.113-117
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• 2015

Even though many researches are conducted for the conservation and restoration of endangered species Menyanthes trifoliata, recently, there is no study on the threatening factors to this plant. This is the first time in Korea to study growth and feeding characteristics of Deilephila elpenor as a threatening factor to Menyanthes trifoliata through an experiment. Experiment was done with 6 Eephant hawk-moth larvae and change of body weight, food preference, and ingestion amount of Bog-bean were investigated. It took 27 days from larva to pupa and maximum body weight of lavae was in the range of 4-7.5g. The food preference sequence of the lavae was Menyanthes trifoliata, Impatiens balsamina, Ampelopsis brevipedunculata var. heterophylla, Parthenocissus tricuspidata. Ingestion model shows the total amount of ingestion by a larva is 11-30g and this amount can be acquired at $0.03-0.08m^2$ of Menyanthes trifoliata pure stand. This study showed Deilephila elpenor as a potential threatening factor and suggests that the conservation and restoration plan of endangered species Menyanthes trifoliata include the control plan of Deilephila elpenor, also.

• Phonetics and Speech Sciences
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• v.11 no.2
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• pp.57-64
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• 2019

In this paper, we construct a Korean text-to-speech system using the Merlin toolkit which is an open source system for speech synthesis. In the text-to-speech system, the HMM-based statistical parametric speech synthesis method is widely used, but it is known that the quality of synthesized speech is degraded due to limitations of the acoustic modeling scheme that includes context factors. In this paper, we propose an acoustic modeling architecture that uses deep neural network technique, which shows excellent performance in various fields. Fully connected deep feedforward neural network (DNN), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), bidirectional LSTM (BLSTM) are included in the architecture. Experimental results have shown that the performance is improved by including sequence modeling in the architecture, and the architecture with LSTM or BLSTM shows the best performance. It has been also found that inclusion of delta and delta-delta components in the acoustic feature parameters is advantageous for performance improvement.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.3
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• pp.67-74
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• 2011

In this paper, we presented a dynamic cyber attack tree which can describe an attack scenario flexibly for an active cyber attack model could be detected complex and transformed attack method. An attack tree provides a formal and methodical route of describing the security safeguard on varying attacks against network system. The existent attack tree can describe attack scenario as using vertex, edge and composition. But an attack tree has the limitations to express complex and new attack due to the restriction of attack tree's attributes. We solved the limitations of the existent attack tree as adding an threat occurrence probability and 2 components of composition in the attributes. Firstly, we improved the flexibility to describe complex and transformed attack method, and reduced the ambiguity of attack sequence, as reinforcing composition. And we can identify the risk level of attack at each attack phase from child node to parent node as adding an threat occurrence probability.

• Journal of Korean society of Dental Hygiene
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• v.23 no.4
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• pp.209-218
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• 2023

Objectives: To confirm the antibacterial effects of each mouth rinse on multiple oral biofilms in vitro. Methods: The antibacterial effects of different mouth rinses were examined by ATP and counted colony forming units (CFU). Preformed oral biofilms on saliva coated hydroxyapatite (sHA) disks were treated with essential oil and saline; then, the multiple oral biofilms were observed by Scanning electron microscope (SEM). RNA sequencing analysis was performed on total RNA isolated from old biofilms of P. intermedia ATCC 49046. Results: In the CFU measured result compared to controls, preformed multiple oral biofilms were reduced from a low of 39.0% to 95.7% (p<0.05). The size of bacterial cells changed after treatment with the essential oil, and some of the cells ruptured into small pieces of cell debris. Gene expression in P. intermedia ATCC 49046 significantly altered in RNA transcribed and protein translated genes after exposure to essential oil. Conclusions: Mouth rinse solutions with different ingredients had different antibacterial effects and may alter surface structure and gene expression as determined by RNA sequencing.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.73-80
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• 2009

G protein-coupled receptors(GPCRs) family is a cell membrane protein, and plays an important role in a signaling mechanism which transmits external signals through cell membranes into cells. But, GPCRs each are known to have various complex control mechanisms and very unique signaling mechanisms. Structural features, and family and subfamily of GPCRs are well known by function. and accordingly, the most fundamental work in studies identifying the previous GPCRs is to classify the GPCRs with given protein sequences. Studies for classifying previously identified GPCRs more easily with mathematical models have been mainly going on. In this paper Considering that functions of proteins are determined by their stereoscopic structures, the present paper proposes a method to compare secondary structures of two GPCRs having different amino acid sequences, and then detect an unknown GPCRs assumed to have a same function in databases of previously identified GPCRs.

• Journal of the Korea Computer Graphics Society
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• v.8 no.2
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• pp.1-7
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• 2002

The fast extraction of the silhouettes of a model is very useful for many applications in computer graphics and animation. In this paper, we present an efficient algorithm to compute a sequence of perspective silhouettes for a polyhedral model from a moving viewpoint. The viewpoint is assumed to move along a trajectory q(t), which is a space curve of a time parameter t. Then, we can compute the time-intervals for each edge of the model to be contained in the silhouette by two major computations: (i) intersecting q(t) with two planes and (ii) a number of dot products. If q(t) is a curve of degree n, then there are at most of n + 1 time-intervals for an edge to be in a silhouette. For each time point $t_i$ we can extract silhouette edges by searching the intervals containing $t_i$ among the computed intervals. For the efficient search, we propose two kinds of data structures for storing the intervals: an interval tree and an array. Our algorithm can be easily extended to compute the parallel silhouettes with minor modifications.