• IMMUNE NETWORK
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• v.22 no.4
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• pp.33.1-33.17
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• 2022

Suppressors of cytokine signaling (SOCS) have emerged as potential regulators of macrophage function. We have investigated mechanisms of SOCS3 action on type 2 macrophage (M2) differentiation induced by glucocorticoid using human monocytic cell lines and mouse bone marrow-derived macrophages. Treatment of THP1 monocytic cells with dexamethasone (Dex) induced ROS generation and M2 polarization promoting IL-10 and TGF-β production, while suppressing IL-1β, TNF-α and IL-6 production. SOCS3 over-expression reduced, whereas SOCS3 ablation enhanced IL-10 and TGF-β induction with concomitant regulation of ROS. As a mediator of M2 differentiation, glucocorticoid-induced leucine zipper (GILZ) was down-regulated by SOCS3 and up-regulated by shSOCS3. The induction of GILZ and IL-10 by Dex was dependent on ROS and p38 MAPK activity. Importantly, GILZ ablation led to the inhibition of ROS generation and anti-inflammatory cytokine induction by Dex. Moreover, GILZ knock-down negated the up-regulation of IL-10 production induced by shSOCS3 transduction. Our data suggest that SOCS3 targets ROS- and p38-dependent GILZ expression to suppress Dex-induced M2 polarization.

• BMB Reports
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• v.40 no.1
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• pp.82-87
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• 2007

Our heterologous expression system of the human ferritin H-chain gene (hfH) allowed us to characterize the cellular effects of ferritin in yeasts. The recombinant Saccharomyces cerevisiae (YGH2) evidenced impaired growth as compared to the control, which was correlated with ferritin expression and with the formation of core minerals. Growth was recovered via the administration of iron supplements. The modification of cellular iron metabolism, which involved the increased expression of high-affinity iron transport genes (FET3 and FTR1), was detected via Northern blot analysis. The findings may provide some evidence of cytosolic iron deficiency, as the genes were expressed transcriptionally under iron-deficient conditions. According to our results examining reactive oxygen species (ROS) generation via the fluorescence method, the ROS levels in YGH2 were decreased compared to the control. It suggests that the expression of active H-ferritins reduced the content of free iron in yeast. Therefore, present results may provide new insights into the regulatory network and pathways inherent to iron depletion conditions.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.51-55
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• 1990

In this study, a peripheral auditory model used as a frequency feature extractor and a recurrent network which has recurrent links on input nodes is constructed in order to show the reliability of the recurrent network as a recognizer by executing recognition tests for 4 Korean placenames and syllables. As a result of this study, a refined weight compensation method is proposed and, using this method, it is possible to improve the system operation. The recurrent network in this study reflects well time information of temporal speech signal.

• BMB Reports
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• v.45 no.4
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• pp.213-220
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• 2012

Viruses have evolved to manipulate the host cell machinery for virus propagation, in part by interfering with the host cellular signaling network. Molecular studies of individual pathways have uncovered many viral host-protein targets; however, it is difficult to predict how viral perturbations will affect the signaling network as a whole. Systems biology approaches rely on multivariate, context-dependent measurements and computational analysis to elucidate how viral infection alters host cell signaling at a network level. Here we describe recent advances in systems analyses of signaling networks in both viral and non-viral biological contexts. These approaches have the potential to uncover virus- mediated changes to host signaling networks, suggest new therapeutic strategies, and assess how cell-to-cell variability affects host responses to infection. We argue that systems approaches will both improve understanding of how individual virus-host protein interactions fit into the progression of viral pathogenesis and help to identify novel therapeutic targets.

• Journal of Biomedical Engineering Research
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• v.11 no.1
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• pp.157-162
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• 1990

In this study, a peripheral auditory model is used as a frequency feature extractor and a recurrent network which has recurrent links on input nodes is constructed in order to show the reliability of the recurrent network as a recognizer by executing recognition tests for 4 Korean place names and syllables. In the case of using the general learning rule, it is found that the weights are diverged for a long sequence because of the characteristics of the node function in the hidden and output layers. So, a refined weight compensation method is proposed and, using this method, it is possible to improve the system operation and to use long data. The recognition results are considerably good, even if time worping and endpoint detection are omitted and learning patterns and test patterns are made of average length of data. The recurrent network used in this study reflects well time information of temporal speech signal.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.11
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• pp.47-50
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• 1992

This paper describes pattern classification algorithm of ECG using back-propagation neural network. We presents new feature extractor using second order approximating function as the input signals of neural network. We use 9 significant parameters which were extracted by feature extractor. 5 most characterized ECG signal pattern is classified accurately by neural network. We use AHA database to evaluate the performance ol the proposed pattern classification algorithm.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.73-78
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• 1990

In this study, LPC cepstrum coefficients are used as feature vector extracted from AR model of EMG signal, and a reduced-connection network which has reduced connection between nodes is constructed to classify and recognize EMG functional classes. The proposed network reduces learning time and improves system stability. Therefore it is shown that the proposed network is appropriate in recognizing the function of EMG signal.

• Journal of Biomedical Engineering Research
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• v.16 no.1
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• pp.33-42
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• 1995

A new neural network architecture for the recognition of patterns from images is proposed, which is partially based on the results of physiological studies. The proposed network is composed of multi-layers and the nerve cells in each layer are connected by spatial filters which approximate receptive fields in optic nerve fields. In the proposed method, patterns recognition for complicated images is carried out using global features as well as local features such as lines and end-points. A new generating method of matched filters representing global features is proposed in this network.

• Genomics & Informatics
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• v.18 no.4
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• pp.39.1-39.8
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• 2020

Alzheimer's disease (AD) is a chronic, progressive brain disorder that slowly destroys affected individuals' memory and reasoning faculties, and consequently, their ability to perform the simplest tasks. This study investigated the hub genes of AD. Proteins interact with other proteins and non-protein molecules, and these interactions play an important role in understanding protein function. Computational methods are useful for understanding biological problems, in particular, network analyses of protein-protein interactions. Through a protein network analysis, we identified the following top 10 hub genes associated with AD: PTGER3, C3AR1, NPY, ADCY2, CXCL12, CCR5, MTNR1A, CNR2, GRM2, and CXCL8. Through gene enrichment, it was identified that most gene functions could be classified as integral to the plasma membrane, G-protein coupled receptor activity, and cell communication under gene ontology, as well as involvement in signal transduction pathways. Based on the convergent functional genomics ranking, the prioritized genes were NPY, CXCL12, CCR5, and CNR2.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.48 no.1
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• pp.72-81
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• 2012

Using artificial neural network (ANN) technique, auction prices for common mackerel were forecasted with the daily total sale and auction price data at the Busan Cooperative Fish Market before introducing Total Allowable Catch (TAC) system, when catch data had no limit in Korea. Virtual input data produced from actual data were used to improve the accuracy of prediction and the suitable neural network was induced for the prediction. We tested 35 networks to be retained 10, and found good performance network with regression ratio of 0.904 and determination coefficient of 0.695. There were significant variations between training and verification errors in this network. Ideally, it should require more training cases to avoid over-learning, which leads to improve performance and makes the results more reliable. And the precision of prediction was improved when environmental factors including physical and biological variables were added. This network for prediction of price and catch was considered to be applicable for other fishes.