• Proceedings of the KIPE Conference
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• 2018.11a
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• pp.36-38
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• 2018

As the battery capacity requirement increases, battery cells are connected in a parallel configuration. However, the sharing current of each battery cell becomes unequal due to the imbalance between cell's impedance which results the mismatched states of charge (SOC). The conventional fixed-resistance balancing methods have a limitation in battery equalization performance and system efficiency. This paper proposes a battery equalization method based on dynamic resistance technique, which can improve equalization performance and reduce the loss dissipation. Based on the SOC rate of parallel connected battery cells, the switches in the equalization circuit are controlled to change the equivalent series impedance of the parallel branch, which regulates the current flow to maximize SOC utilization. To verify the method, operations of 4 parallel-connected 18650 Li-ion battery cells with 3.7V-2.6Ah individually are simulated on Matlab/Simulink. The results show that the SOCs are balanced within 1% difference with less power dissipation over the conventional method.

• Journal of Yeungnam Medical Science
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• v.30 no.1
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• pp.4-9
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• 2013

Leptin, a 16-kDa cytokine, is secreted by adipose tissue in response to the surplus of fat store. Thereby, the brain is informed about the body's energy status. In the hypothalamus, leptin triggers specific neuronal subpopulations (e.g., POMC and NPY neurons) and activates several intracellular signaling events, including the JAK/STAT, MAPK, PI3K, and mTOR pathway, which eventually translates into decreased food intake and increased energy expenditure. Leptin signal is inhibited by a feedback inhibitory pathway mediated by SOCS3. PTP1B involves another inhibitory pathway of leptin. Leptin potently promotes fat mass loss and body weight reduction in lean subjects. However, it is not widely used in the clinical field because of leptin resistance, which is a common feature of obesity characterized by hyperleptinemia and the failure of exogenous leptin administration to provide therapeutic benefit in rodents and humans. The potential mechanisms of leptin resistance include the following: 1) increases in circulating leptin-binding proteins, 2) reduced transport of leptin across the blood-brain barrier, 3) decreased leptin receptor-B (LRB), and/or 4) the provocation of processes that diminish cellular leptin signaling (inflammation, endoplasmic reticulum stress, feedback inhibition, etc.). Thus, interference of the cellular mechanisms that attenuate leptin signaling improves leptin action in cells and animal models, suggesting the potential utility of these processes as points of therapeutic intervention. Various experimental trials and compounds that improve leptin resistance are introduced in this paper.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.10
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• pp.1861-1867
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• 2000

Granular activated carbon(GAC) adsorption is one of the best available technology to remove synthetic organic chemicals(SOCs) from water supplies and wastewater. In order to satisfy enviromental criteria and reduce GAC treatment cost, optimal study of reverse stratified tapered adsorber(RSTA) has been conducted. The RSTA was found to provide on increase in breakthrough time when compared to a conventional cylindrical adsorber(CA). Through the RSTA optimal experiment, optimal mean bed velocity was decided 19.10cm/min and optimal angle was decided RSTA($3.0^{\circ}$). Adsorption efficiency was increased with increasing activated carbon doses and the number of activated carbon layers.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.363-373
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• 2022

Multi-omics data is difficult to interpret due to the heterogeneity of information by the volume of data, the complexity of characteristics of each data, and the diversity of omics platforms. There is not yet a system for interpreting to visualize research data on environmental diseases concerning environmental harmful substances. We provide MEE, a web-based visualization tool, to comprehensively explore the complexity of data due to the interconnected characteristics of high-dimensional data sets according to exposure to various environmental harmful substances. MEE visualizes omics data of correlation between omics data, subjects and samples by keyword searches of meta data, multi-omics data, and harmful substances. MEE has been demonstrated the versatility by two examples. We confirmed the correlation between smoking and asthma with RNA-seq and Methylation-Chip data, it was visualized that genes (P HACTR3, PXDN, QZMB, SOCS3 etc.) significantly related to autoimmune or inflammatory diseases. To visualize the correlation between atopic dermatitis and heavy metals, we selected 32 genes related immune response by integrated analysis of multi-omics data. However, it did not show a significant correlation between mercury in blood and atopic dermatitis. In the future, should continuously collect an appropriate level of multi-omics data in MEE system, will obtain data to analyze environmental substances and diseases.

• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.70-72
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• 2019

For the safe and reliable operation of Lithium-ion batteries in Electric Vehicles (EVs) or Energy Storage Systems (ESSs), it is essential to have accurate information of the battery such as State of Charge (SOC). Many kinds of different techniques to estimate the SOC of the batteries have been developed so far such as the Kalman Filter. However, when it is applied to the multiple number of batteries it is difficult to maintain the accuracy of the estimation over all cells due to the difference in parameter value of each cell. Moreover the difference in the parameter of each cell may become larger as the operation time accumulates due to aging. In this paper a novel Deep Neural Network (DNN) based SOC estimation method for multi cell application is proposed. In the proposed method DNN is implemented to learn non-linear relationship of the voltage and current of the lithium-ion battery at different SOCs and different temperatures. In the training the voltage and current data of the Lithium battery at charge and discharge cycles obtained at different temperatures are used. After the comprehensive training with the data obtained with a cell resulting estimation algorithm is applied to the other cells. The experimental results show that the Mean Absolute Error (MAE) of the estimation is 0.56% at 25℃, and 3.16% at 60℃ with the proposed SOC estimation algorithm.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.5
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• pp.365-371
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• 2019

This study shows the feasibility of the parameter of the 1st RC parallel equivalent circuit as a factor of the heat generation of lithium-ion cell. The internal resistance of a lithium-ion cell consists of ohmic and polarization resistances. The internal resistances at various SOCs of the lithium-ion cell are obtained via an electrical characteristic test. The internal resistance is inversely obtained through the amount of heat generated during the experiment. By comparing the resistances obtained using the two methods, the summation of ohmic and polarization resistances is identified as the heating factor of lithium-ion battery. Finally, the amounts of heat generated from the 2C, 3C, and 4C-rate discharge experiments and the COMSOL multiphysics simulation using the summation of ohmic and polarization resistances as the heating parameter are compared. The comparison shows the feasibility of the electrical parameters of the 1st RC parallel equivalent circuit as the heating factor.

• Biomolecules & Therapeutics
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• v.17 no.4
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• pp.370-378
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• 2009

N-myc downstream-regulated gene 2 (NDRG2) has recently been found to be a tumor suppressor gene. Although it has been reported that NDRG2 expression in breast cancer cells decreases cell proliferation by inhibiting STAT3 activation via SOCS1 induction, the molecular mechanism of chemotherapeutic agent-induced apoptosis is not well known. To elucidate the effect of NDRG2 on the apoptotic pathway induced by doxorubicin, we established stable cell lines expressing NDRG2 and investigated the effect of NDRG2 expression on the doxorubicin-induced apoptosis. While STAT3 activation was remarkably inhibited by NDRG2 overexpression, the expression level of p21 was increased by NDRG2 expression. We confirmed that NDRG2-expressing cells treated with doxorubicin suppressed STAT3 activation and upregulated p21 expression. NDRG2 expression considerably enhanced TUNEL positive apoptotic cells, poly-ADP ribose polymerase (PARP) cleavage, release of cytochrome c to cytosol, and caspase-3 activity in doxorubicin-induced apoptosis. Bid expression in a resting state and after treatment with doxorubicin increased in MDA-MB-231-NDRG2 cells compared to MDA-MB-231-mock cells. Meanwhile, Bcl-$x_L$ expression decreased in MDA-MB-231-NDRG2 cells compared to MDA-MB-231-mock cells in a resting state and in doxorubicin-treated cells. Collectively, these data suggest that suppression of STAT3 activation by NDRG2 influences the sensitivity to doxorubicin-induced apoptosis of breast cancer cells and this may provide a potential therapeutic benefit to overcome the resistance against doxorubicin in breast cancer.

• Journal of Korean Neurosurgical Society
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• v.65 no.5
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• pp.697-709
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• 2022

Objective : The present study aimed to identify the function of ischemic stroke (IS) patients' peripheral blood and its role in IS, explore the pathogenesis, and provide direction for clinical research progress by comprehensive bioinformatics analysis. Methods : Two datasets, including GSE58294 and GSE22255, were downloaded from Gene Expression Omnibus database. GEO2R was utilized to obtain differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using the database annotation, visualization and integrated discovery database. The protein-protein interaction (PPI) network of DEGs was constructed by search tool of searching interactive gene and visualized by Cytoscape software, and then the Hub gene was identified by degree analysis. The microRNA (miRNA) and miRNA target genes closely related to the onset of stroke were obtained through the miRNA gene regulatory network. Results : In total, 36 DEGs, containing 27 up-regulated and nine down-regulated DEGs, were identified. GO functional analysis showed that these DEGs were involved in regulation of apoptotic process, cytoplasm, protein binding and other biological processes. KEGG enrichment analysis showed that these DEGs mediated signaling pathways, including human T-cell lymphotropic virus (HTLV)-I infection and microRNAs in cancer. The results of PPI network and cytohubba showed that there was a relationship between DEGs, and five hub genes related to stroke were obtained : SOCS3, KRAS, PTGS2, EGR1, and DUSP1. Combined with the visualization of DEG-miRNAs, hsa-mir-16-5p, hsa-mir-181a-5p and hsa-mir-124-3p were predicted to be the key miRNAs in stroke, and three miRNAs were related to hub gene. Conclusion : Thirty-six DEGs, five Hub genes, and three miRNA were obtained from bioinformatics analysis of IS microarray data, which might provide potential targets for diagnosis and treatment of IS.

• Molecules and Cells
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• v.37 no.11
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• pp.827-832
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• 2014

The balance between bone formation by osteoblasts and destruction of mineralized bone matrix by osteoclasts is important for bone homeostasis. The increase of osteoclast differentiation by RANKL induces bone diseases such as osteoporosis. Recent studies have shown that insulin is one of main factors mediating the cross-talk between bone remodeling and energy metabolism. However, the systemic examination of insulin-induced differential gene expression profiles in osteoclasts has not been extensively studied. Here, we investigated the global effects of insulin on osteoclast precursors at the level of gene transcription by microarray analysis. The number of genes that were up-regulated by ${\geq}1.5$ fold after insulin treatment for 6 h, 12 h, or 24 h was 76, 73, and 39; and 96, 83, and 54 genes were down-regulated, respectively. The genes were classified by 20 biological processes or 24 molecular functions and the number of genes involved in 'development processes' and 'cell proliferation and differentiation' was 25 and 18, respectively, including Inhba, Socs, Plk3, Tnfsf4, and Plk1. The microarray results of these genes were verified by real-time RT-PCR analysis. We also compared the effects of insulin and RANKL on the expression of these genes. Most genes had a very similar pattern of expressions in insulin- and RANKL-treated cells. Interestingly, Tnfsf4 and Inhba genes were affected by insulin but not by RANKL. Taken together, these results suggest a potential role for insulin in osteoclast biology, thus contributing to the understanding of the pathogenesis and development of therapeutics for numerous bone and metabolic diseases.

• Molecules and Cells
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• v.40 no.3
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• pp.211-221
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• 2017

Transforming growth factor ${\beta}1$ $(TGF{\beta}1)/Smad4$ signaling plays a pivotal role in maintenance of the dynamic balance between bone formation and resorption. The microRNA miR-155 has been reported to exert a significant role in the differentiation of macrophage and dendritic cells. The goal of this study was to determine whether miR-155 regulates osteoclast differentiation through $TGF{\beta}1/Smad4$ signaling. Here, we present that $TGF{\beta}1$ elevated miR-155 levels during osteoclast differentiation through the stimulation of M-CSF and RANKL. Additionally, we found that silencing Smad4 attenuated the upregulation of miR-155 induced by $TGF{\beta}1$. The results of luciferase reporter experiments and ChIP assays demonstrated that $TGF{\beta}1$ promoted the binding of Smad4 to the miR-155 promoter at a site located in 454 bp from the transcription start site in vivo, further verifying that miR-155 is a transcriptional target of the $TGF{\beta}1/Smad4$ pathway. Subsequently, TRAP staining and qRT-PCR analysis revealed that silencing Smad4 impaired the $TGF{\beta}1$-mediated inhibition on osteoclast differentiation. Finally, we found that miR-155 may target SOCS1 and MITF to suppress osteoclast differentiation. Taken together, we provide the first evidence that $TGF{\beta}1/Smad4$ signaling affects osteoclast differentiation by regulation of miR-155 expression and the use of miR-155 as a potential therapeutic target for osteoclast-related diseases shows great promise.