• Journal of Haehwa Medicine
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• v.16 no.2
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• pp.131-145
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• 2007

To evaluate the effects of GP on contact dermatitis, we examined the composition of immune cells from drain lymph node in DNCB-induced contact dermatitis murine model NC/Nga mice. And the amount of pathologic cytokines of spleen and antioxidant activity were investigated. The results were summarized as followers; 1. GP did not show cytotoxic effect on mLFC in vitro. 2. GP did not have hepatotoxicity in vivo in the level of ALT, AST. 3. GP decreased the production of DPPH and in a dose-dependent. 4. GP significantly decreased total cell number of DLN in DNCB-induced NC/Nga mice compared to the untreated control group. 5. GP significantly decreased the number of CD3+, CD19+, CD4+, CD8+, CD3+/CD69+ and CD4+/CD45+ in DLN of DNCB-induced NC/Nga mice compared to the untreated control group. 6. GP significantly reduced the level of IL-4 and IFN-$\gamma$ in splenocytes of DNCB-induced NC/Nga mice compared to the untreated control group. Taken together above results, GP have therapeutic effects on contact dermatitis by regulating T cell activation. This study warranted further investigations of molecular mechanisms of GP on contact dermatitis.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.11a
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• pp.81-87
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• 2000

Beat-to-beat changes in heart period (heart period variability, HPV) are mediated by fluctuations in autonomic activity. Spectral analysis is used to quantify such fluctuations in the range of 0.15-0.40 Hz (high frequency, HF), which are influenced primarily by parasympathetic factors. These fluctuations are often referred to as RSA (respiratory sinus arrhythmia), the physiological phenomenon extracted by spectral analysis and other methods including histograms of heart rate ( HR), deviations of HR etc. Respiratory sinus arrhythmia indexing with peak-to-valley method suggested by Grossman et at., (1981) yields a simple range statistic and is quantified on breath-by-breath basis, thus being quite sensitive and less dependent on recording time as compared to spectral analysis. It is strongly recommended to use at least 1 min epoch to asses HF component of HPV and at least 2 min fer low frequency (LF) of HPV and even 5 min far valid clinical assessment. Peak-to-valley statistic is limited to RSA index only, but has its pragmatic advantages. Most important is possibility of its application far relatively small epoch analysis. We used short periods (20,30, 40 sec only) and off-line analysis of RSA using ECG and respiration curve this method of assessment and proved that this method is more practically effective. The RSA index was not so far dependent on respiration pattern differences and reflected actual vagal control of HR and were accompanied by low HR under some high stress conditions and in an aversive affective visual stimulation experiments. Another factor that might modulate cardiac chronotropic response is the interaction of sympathetic and parasympathetic inputs on sino-atrial (SA) node level, because responses to vagal influences are known to be proportional to ongoing sympathetic activity, that is so called accentuated antagonism. Since sympathetic outflow (increment of influences on SA) under negative emotions or stress was high in almost all physiological responses, vagal effects on HR could be therefore potentiated, leading to masking of output cardiac response seen in HPV, In the case of moderate sympathetic activation, on the other hand, autonomic interactions in cardiac control appear to be minimal. Thus RSA index appears to be an effective alternative method to assess and measure spectral HPV.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.29-36
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• 2013

In wireless sensor networks, energy is the most important consideration because the lifetime of the sensor node is limited by battery. Most of the existing energy efficient routing protocols use the minimum energy path to minimize energy consumption, which causes an unbalanced distribution of residual energy among nodes. As a result, the power of nodes on energy efficient paths is quickly depletes resulting in inactive. To solve these problems, a method to equalize the energy consumption of the nodes has been proposed, but do not consider the link error rate in the wireless environment. In this paper, we propose a uniform energy consumption of cluster-based multi-hop routing mechanism considering the residual energy and the link error rate. This mechanism reduces energy consumption caused by unnecessary retransmissions and distributes traffic evenly over the network because considering the link error rate. The simulation results compared to other mechanisms, the proposed mechanism is energy-efficient by reducing the number of retransmissions and activation time of all nodes involved in the network has been extended by using the energy balanced path.

• Horticultural Science & Technology
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• v.33 no.1
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• pp.114-123
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• 2015

Plants can respond and adapt to cold stress through regulation of gene expression in various biochemical and physiological processes. Cold stress triggers decreased rates of metabolism, modification of cell walls, and loss of membrane function. Hence, this study was conducted to construct coexpression networks for time-based expression pattern analysis of genes related to cold stress in Chinese cabbage (Brassica rapa ssp. pekinensis). B. rapa cold stress networks were constructed with 2,030 nodes, 20,235 edges, and 34 connected components. The analysis suggests that similar genes responding to cold stress may also regulate development of Chinese cabbage. Using this network model, it is surmised that cold tolerance is strongly related to activation of chitinase antifreeze proteins by WRKY transcription factors and salicylic acid signaling, and to regulation of stomatal movement and starch metabolic processes for systemic acquired resistance in Chinese cabbage. Moreover, within 48 h, cold stress triggered transition from vegetative to reproductive phase and meristematic phase transition. In this study, we demonstrated that this network model could be used to precisely predict the functions of cold resistance genes in Chinese cabbage.

• Journal of Korean Medical Science
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• v.33 no.47
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• pp.303.1-303.10
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• 2018

Background: Cell division cycle 6 (CDC6) is an essential regulator of DNA replication and plays important roles in the activation and maintenance of the checkpoint mechanisms in the cell cycle. CDC6 has been associated with oncogenic activities in human cancers; however, the clinical significance of CDC6 in prostate cancer (PCa) remains unclear. Therefore, we investigated whether the CDC6 mRNA expression level is a diagnostic and prognostic marker in PCa. Methods: The study subjects included 121 PCa patients and 66 age-matched benign prostatic hyperplasia (BPH) patients. CDC6 expression was evaluated using real-time polymerase chain reaction and immunohistochemical (IH) staining, and then compared according to the clinicopathological characteristics of PCa. Results: CDC6 mRNA expression was significantly higher in PCa tissues than in BPH control tissues (P = 0.005). In addition, CDC6 expression was significantly higher in patients with elevated prostate-specific antigen (PSA) levels (> 20 ng/mL), a high Gleason score, and advanced stage than in those with low PSA levels, a low Gleason score, and earlier stage, respectively. Multivariate logistic regression analysis showed that high expression of CDC6 was significantly associated with advanced stage (${\geq}T3b$) (odds ratio [OR], 3.005; confidence interval [CI], 1.212-7.450; P = 0.018) and metastasis (OR, 4.192; CI, 1.079-16.286; P = 0.038). Intense IH staining for CDC6 was significantly associated with a high Gleason score and advanced tumor stage including lymph node metastasis stage (linear-by-linear association, P = 0.044 and P = 0.003, respectively). Conclusion: CDC6 expression is associated with aggressive clinicopathological characteristics in PCa. CDC6 may be a potential diagnostic and prognostic marker in PCa patients.

• Journal of Life Science
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• v.34 no.5
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• pp.356-364
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• 2024

Lymph nodes (LNs) are crucial sites where immune responses are initiated to combat invading pathogens in the body. LNs are organized into distinctive compartments by stromal cells. Stromal cell subsets constitute special niches supporting the trafficking, activation, differentiation, and crosstalk of immune cells in LNs. Fibroblastic reticular cells (FRC) are a type of stromal cell that form the three-dimensional structure networks of the T cell-rich zones in LNs, providing guidance paths for immigrating T lymphocytes. FRCs imprint immune responses by supporting LN architecture, recruiting immune cells, coordinating immune cell crosstalk, and presenting antigens. During inflammation, FRCs exert both spatial and molecular regulation on immune cells through their topological and secretory responses, thereby steering immune responses. Here, we propose a model in which FRCs regulate immune responses through a three-part scheme: setting up, supporting, or suppressing immune responses. FRCs engage in bidirectional interactions that enhance T cell biological efficiency. In addition, FRCs have profound effects on the innate immune response through phagocytosis. Thus, FRCs in LNs act as gatekeepers of immune responses. Overall, this study aims to highlight the emerging roles of FRCs in controlling both innate and adaptive immunity. This collaborative feedback loop mediated by FRCs may help maintain tissue function during inflammatory responses.

• Journal of Life Science
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• v.26 no.1
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• pp.101-108
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• 2016

The aim of this study was to evaluate the relationships between the expression of Heat shock protein70 (HSP70), Raf-1 and Bcl-2-associated athanogene-1 (BAG1) protein in diffuse type gastric carcinoma and examine association of HSP70, Raf-1 and BAG1 expression with various clinic-pathological factors and survival. Heat shock protein70 is induced in the cells in response to various stress conditions, including carcinogens. Overexpression of heat shock protein 70 has been observed in many types of cancer. The proto-oncoprotein Raf is pivotal for mitogen-activated protein kinase (MAPK) signaling, and its aberrant activation has been implicated in multiple human cancers. Overexpression of BAG1 protein has been documented in some type of human cancer. BAG1 has been reported to interact with protein involved with a variety of signal pathway, and regulation of cell differentiation, survival and apoptosis. These interaction partners include HSP70 and Raf-1. The percentage of tumors exhibiting HSP70 positivity was significantly in cases of positive lymph node metastasis (64.9%) compared to cases without lymph node metastasis (35.1%, p=0.007). HS70 expression was correlated with pathological N-stage (p=0.006). Expression of BAG1 was detected in the majority of diffuse type gastric carcinoma tissues (71.7%), especially in younger patients (80% vs 52.6%, p=0.035). Furthermore BAG1 expression was correlated with tumor size (p=0.020). Raf-1 expression was found to be significantly associated with tumor size (p=0.005). The result indicate that HSP70 was significantly correlated the progression of diffuse type gastric cancer. Expression of BAG1 and Raf-1 may be used as diagnostic markers for gastric carcinoma.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Clinical and Experimental Pediatrics
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• v.46 no.2
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• pp.128-136
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• 2003

Purpose : Hyper IgM syndrome(HIGM) is characterized by severe recurrent bacterial infections with decreased serum levels of IgG, IgA, and IgE but elevated IgM levels. Recently, it has been classified into three groups; HIGM1, HIGM2 and a rare form of HIGM. HIGM1 is a X-linked form of HIGM and has now been identified as a T-cell deficiency in which mutations occur in the gene that encodes the CD40 ligand molecule. HIGM2 is an autosomal recessive form of HIGM. Molecular studies have shown that the mutation of HIGM2 is in the gene that encodes activation-induced cytidine deaminase(AID). Recently, another rare form of X-linked HIGM syndrome associated with hypohydrotic ectodermal dysplasia has been identified. We encountered a patient with a varient form of HIGM2. To clarify the cause of this form of HIGM, we evaluated the peripheral B cells of this patient. Methods : The lymphocytes of the patient were prepared from peripheral blood. B cells were immortalized with the infection of EBV. Cell cycle analysis was done with the immortalized B cells of the patient. Peripheral mononuclear cells were stained with monoclonal anti-CD40L antibody. Total RNA was extracted from the peripheral mononuclear cells. After RT-PCR, direct sequencing for CD40L gene and HuAID gene were done. Immunostainings of a lymph node for CD3, CD23, CD40, Fas-L, bcl-2, BAX were done. Results : The peripheral B cells of this patient showed normal expression of CD40L molecule and normal sequencing of CD40L gene, and also normal sequencing of AID gene. Interestingly, the peripheral B cells of this patient showed a decreased population of G2/mitosis phase in cell cycles which recovered to normal with the stimulation of IL-4. Conclusion : We suspect that the cause of increased serum IgM in this patient may be from a decrease of G2/mitosis phase of the peripheral B cells, which may be from the decreased production or secretion of IL-4. Therefore, this may be a new form of HIGM.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.