• BMB Reports
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• v.30 no.5
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• pp.362-369
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• 1997

Multiple phosphorylation of the carboxy-terminal domain (CTD) of the largest subunit in RNA polymerase II (RNAP II) is thought to play an important role in the transcription cycle. The preinitiation complex in a partially purified complete transcription system suggested that RNA polymerase IIA containing unphosphorylated CTD is involved in complex assembly, whereas RNA polymerase IIO containing Ser and Thr phosphorylated CTD is not involved in preinitiation complex assembly. Recently a minimal transcription system was developed which requires chemically defined minimal components for its transcription: TBP, TFIIB, TFIIF, RNAP II and a supercoiled adenovirus-2 major late promoter (Ad-2 MLP). It would be using interesting to determine the consequence of CTD phosphorylation on preinitiation complex formation using the minimal transcription system. Contrary to the results from the partially purified complete transcription system, both RNA polymerase IIA and IIO are equally recruited in the preinitiation complex formation. The discrepancy may result from the two different assays used to determine complex formation, the use of chemically undefined complete and defined minimal transcription systems. This implicates that some factors in the complete transcription system are involved in the distinction between RNAP IIA and IIO in complex assembly. In addition multiple tyrosine phosphorylation of the CTD of RNAP II was prepared with c-Abl kinase and its recruiting ability in the preinitiation complex was examined. Compare with Ser and Thr phosphorylated RNAP IIO, Tyr phosphorylated RNAP IlOy forms a stable preinitiation complex in both the minimal and complete transcription systems. Based on these results, it seems that tyrosine phosphorylation of the CTD is important in the transcription cycle on the special subset of class-II promoter or has a different role in the transcription process.

• Journal of KIISE
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• v.43 no.11
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• pp.1281-1297
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• 2016

In recent years, personal videos have seen a tremendous growth due to the substantial increase in the use of smart devices and networking services in which users create and share video content easily without many restrictions. However, taking both into account would significantly improve event detection performance because videos generally have multiple modalities and the frame data in video varies at different time points. This paper proposes an event detection method. In this method, high-level features are first extracted from multiple modalities in the videos, and the features are rearranged according to time sequence. Then the association of the modalities is learned by means of DNN to produce a personal video event detector. In our proposed method, audio and image data are first synchronized and then extracted. Then, the result is input into GoogLeNet as well as Multi-Layer Perceptron (MLP) to extract high-level features. The results are then re-arranged in time sequence, and every video is processed to extract one feature each for training by means of DNN.

• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.432-438
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• 2009

In this paper, we evaluated the performance of the automatic classifier applied for the discrimination of acute alcoholic liver injury and chronic liver fibrosis. The classifier uses the discriminant peaks of the preprocessed Raman spectrum as a feature set. In preprocessing step, we subtract baseline and apply Savitzky-Golay smoothing filter which is known to be useful at preserving peaks. After identifying discriminant peaks from the spectra, we carried out the classification experiments using MAP and neural networks. According to the experimental results, the classifier shows the promising results to diagnosis alcoholic liver injury and chronic liver fibrosis. Classification results over 80% means that the peaks used as a feature set is useful for diagnosing liver disease.

• Journal of Korea Multimedia Society
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• v.4 no.4
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• pp.333-338
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• 2001

In this paper, fuzzy-ARTMAP equalizer is developed mainly for overcoming the obstacles, such as complexity and long training, in implementing the previously developed neural-basis equalizers. The proposed fuzzy-ARTMAP equalizer is fast and easy to train and includes capabilities not found in other neural network approaches a small number of parameters, no requirements for the choice of initial weights, no risk of getting trapped in local minima, and capability of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random from linear channel with Gaussian noise. The performance of the proposed equalizer is compared with other neural net basis equalizers, such as MLP and RBF equalizers. The fuzzy ARTMAP equalizer combines relatively simple structure and fast processing speed; it gives accurate results for nonlinear problems that cannot be solved with a linear equalizer.

• The Korean Journal of Physiology and Pharmacology
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• v.8 no.6
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• pp.319-327
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• 2004

This study was aimed at evaluating the effect of defibrotide on the development of the surgically induced reflux esophagitis, on gastric secretion, lipid peroxidation, polymorphonuclear leukocytes (PMNs) accumulation, polymorphonuclear leukocytes adherence, superoxide anion and hydrogen peroxide production in PMNs, scavenge of hydroxyl radical and hydrogen peroxide, cytokine (interleukin-1 ${\beta}$, tumor necrosis $factor-{\alpha}$) production in blood, and intracelluar calcium mobilization in PMNs. Defibrotide did not inhibit the gastric secretion and not change the gastric pH. Treatment of esophagitis rats with defibrotide inhibited lipid peroxidation, and myeloperoxidase (MPO) in the esophagus in comparison with untreated rats. Defibrotide significantly decreased the PMN adherence to superior mesenteric artery endothelium in a dose-dependent manner, Superoxide anion and hydrogen peroxide production in $1{\mu}M$ formylmethionylleucylphenylalanine (fMLP)- or $0.1{\mu}g/ml$ N-phorbol 12-myristate 13-acetate (PMA)-activated PMNs was inhibited by defibrotide in a dose-dependent fashion. Defibrotide effectively scavenged the hydrogen peroxide but did not scavenge the hydroxyl radical. Treatment of esophagitis rats with defibrotide inhibited interleukin-1 ${\beta}$ production in the blood in comparison with untreated rats, but tumor necrosis $factor-{\alpha}$ production was not affected by defibrotide. The fMLP-induced elevation of intracellular calcium in PMNs was inhibited by defibrotide. The results of this study suggest that defibrotide may have partly beneficial protective effects against reflux esophagitis by the inhibition lipid peroxidation, PMNs accumulation, PMNs adherence to endothelium, reactive oxygen species production in PMNs, inflammatory cytokine production(i.e. interleukin-1 ${\beta}$), and intracellular calcium mobilization in PMNs in rats.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.50-53
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• 2017

Not only growth of importance to understanding economic trends, but also the prediction to overcome the uncertainty is coming up for long-term maritime recession. This paper discussed about the prediction of BDI with artificial neural networks (ANN). ANN is one of emerging applications that can be the finest solution to the knotty problems that may not easy to achieve by humankind. Proposed a prediction by implementing neural networks that have recurrent architecture which are a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). And for the reason of comparison, trained Multi Layer Perceptron (MLP) from 2009.04.01 to 2017.07.31. Also made a comparison with conventional statistics, prediction tools; ARIMA. As a result, recurrent net, especially RNN outperformed and also could discover the applicability of LSTM to specific time-series (BDI).

• Archives of Pharmacal Research
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• v.27 no.9
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• pp.906-911
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• 2004

In order to evaluate estrogenic compounds in natural products, an in vitro detection system was established. For this system, the human breast cancer cell line MCF7 was stably trans-fected using an estrogen responsive chloramphenicol acetyltransferase (CAT) reporter plas-mid yielding MCF7/pDsCAT-ERE119-Ad2MLP cells. To test the estrogenic responsiveness of this in vitro assay system, MCF7/pDsCAT-ERE119-Ad2MLP cells were treated with various concentrations of 17f3-estradiol. Treatments of 10$^{-8}$ to 10$^{-12}$ M 17$\beta$-estradiol revealed significant concentration dependent estrogenic activities compared with ethanol. We used in vitro assay system to detect estrogenic effects in Puerariae radix and Ginseng radix Rubra extracts. Treat-ment of 500 and 50 $\mu\textrm{g}$/ml of Puerariae radix extracts increased the transcriptional activity approximately 4- and 1.5-fold, respectively, compared with the ethanol treatment. Treatment of 500, 50, and 5 $\mu\textrm{g}$/ml of Ginseng radix Rubra extracts increased the transcriptional activity approximately 3.2-,2.7, and 1.4-fold, respectively, compared with the ethanol treatment. These observations suggest that Puerariae radix and Ginseng radix Rubra extracts have effective estrogenic actions and that they could be developed as estrogenic supplements.

• The Journal of the Acoustical Society of Korea
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• v.22 no.2
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• pp.135-144
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• 2003

A method for the automatic segmentation of speech signals is described. The method is dedicated to the construction of a large database for a Text-To-Speech (TTS) synthesis system. The main issue of the work involves the refinement of an initial estimation of phone boundaries which are provided by an alignment, based on a Hidden Market Model(HMM). Multi-layer perceptron (MLP) was used as a phone boundary detector. To increase the performance of segmentation, a technique which individually trains an MLP according to phonetic transition is proposed. The optimum partitioning of the entire phonetic transition space is constructed from the standpoint of minimizing the overall deviation from hand labelling positions. With single speaker stimuli, the experimental results showed that more than 95% of all phone boundaries have a boundary deviation from the reference position smaller than 20 ms, and the refinement of the boundaries reduces the root mean square error by about 25%.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.847-851
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• 2004

In the original mixture density network(MDN), which was introduced by Bishop and Nabney, the parameters of the conditional probability density function are represented by the output vector of a single multi-layer perceptron. Among the recent modification of the MDNs, there is the so-called modified mixture density network, in which each of the priors, conditional means, and covariances is represented via an independent multi-layer perceptron. In this paper, we consider a further simplification of the modified MDN, in which the conditional means are linear with respect to the input variable together with the development of the MATLAB program for the simplification. In this paper, we first briefly review the original mixture density network, then we also review the modified mixture density network in which independent multi-layer perceptrons play an important role in the learning for the parameters of the conditional probability, and finally present a further modification so that the conditional means are linear in the input. The applicability of the presented method is shown via an illustrative simulation example.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.139-146
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• 2014

This paper proposes an efficient method for automatically distinguishing various facial expressions. To recognize the emotions from facial expressions, the facial images are obtained by digital cameras, and a number of feature points were extracted. The extracted feature points are then transformed to 49-dimensional feature vectors which are robust to scale and translational variations, and the facial emotions are recognized by statistical pattern classifiers such Naive Bayes, MLP (multi-layer perceptron), and SVM (support vector machine). Based on the experimental results with 5-fold cross validation, SVM was the best among the classifiers, whose performance was obtained by 50.8% for 6 emotion classification, and 78.0% for 3 emotions.