• IMMUNE NETWORK
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• v.21 no.4
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• pp.28.1-28.14
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• 2021

Lung-resident memory T cells (T_RM) play an essential role in protecting against pulmonary virus infection. Parenteral administration of DNA vaccine is generally not sufficient to induce lung CD8 T_RM cells. This study investigates whether intramuscularly administered DNA vaccine expressing the nucleoprotein (NP) induces lung T_RM cells and protects against the influenza B virus. The results show that DNA vaccination poorly generates lung T_RM cells and massive secondary effector CD8 T cells entering the lungs after challenge infection do not offer sufficient protection. Nonetheless, intranasal administration of non-replicating adenovirus vector expressing no Ag following priming DNA vaccination deploys NP-specific CD8 T_RM cells in the lungs, which subsequently offers complete protection. This novel 'prime and deploy' strategy could be a promising regimen for a universal influenza vaccine targeting the conserved NP Ag.

• The Korea Journal of Herbology
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• v.38 no.1
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• pp.45-53
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• 2023

Objectives : Network pharmacology-based research is one of useful tool to predict the possible efficacy and molecular mechanisms of natural materials with multi compounds-multi targeting effects. In this study, we investigated the functional underlying mechanisms of Astragalus membranaceus Bunge (AM) on its anti-obesity effects using a network pharmacology analysis. Methods : The constituents of AM were collected from public databases and its target genes were gathered from PubChem database. The target genes of AM were compared with the gene set of obesity to find the correlation. Then, the network was constructed by Cytoscape 3.9.1. and functional enrichment analysis was conducted to predict the most relevant pathway of AM. Results : The result showed that AM network contained the 707 nodes and 6867 edges, and 525 intersecting genes were exhibited between AM and obesity gene set, indicating that high correlation with the effects of AM on obesity. Based on GO biological process and KEGG Pathway, 'Response to lipid', 'Cellular response to lipid', 'Lipid metabolic process', 'Regulation of chemokine production', 'Regulation of lipase activity', 'Chemokine signaling pathway', 'Regulation of lipolysis in adipocytes' and 'PPAR signaling pathway' were predicted as functional pathways of AM on obesity. Conclusions : AM showed high relevance with the lipid metabolism related with the chemokine production and lipolysis pathways. This study could be a basis that AM has promising effects on obesity via network pharmacology analysis.

• IMMUNE NETWORK
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• v.13 no.4
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• pp.157-162
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• 2013

Application of vaccine materials through oral mucosal route confers great economical advantage in animal farming industry due to much less vaccination cost compared with that of injection-based vaccination. In particular, oral administration of recombinant protein antigen against foot-and- mouth disease virus (FMDV) is an ideal strategy because it is safe from FMDV transmission during vaccine production and can induce antigen-specific immune response in mucosal compartments, where FMDV infection has been initiated, which is hardly achievable through parenteral immunization. Given that effective delivery of vaccine materials into immune inductive sites is prerequisite for effective oral mucosal vaccination, M cell-targeting strategy is crucial in successful vaccination since M cells are main gateway for luminal antigen influx into mucosal lymphoid tissue. Here, we applied previously identified M cell-targeting ligand Co1 to VP1 of FMDV in order to test the possible oral mucosal vaccination against FMDV infection. M cell-targeting ligand Co1-conjugated VP1 interacted efficiently with M cells of Peyer's patch. In addition, oral administration of ligand-conjugated VP1 enhanced the induction of VP1-specific IgG and IgA responses in systemic and mucosal compartments, respectively, in comparison with those from oral administration of VP1 alone. In addition, the enhanced VP1-specific immune response was found to be due to antigen-specific Th2-type cytokine production. Collectively, it is suggested that the M cell-targeting strategy could be applied to develop efficient oral mucosal vaccine against FMDV infection.

• BMB Reports
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• v.35 no.2
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• pp.213-219
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• 2002

Malonyl-CoA decarboxylase (E.C.4.1.1.9) catalyzes the conversion of malonyl-CoA to acetyl-CoA. Although the metabolic role of this enzyme has not been fully defined, it has been reported that its deficiency is associated with mild mental retardation, seizures, hypotonia, cadiomyopathy, developmental delay, vomiting, hypoglycemia, metabolic acidosis, and malonic aciduria. Here, we isolated a cDNA clone for malonyl CoA decarboxylase from a rat brain cDNA library, expressed it in E. coli, and characterized its biochemical properties. The full-length cDNA contained a single open-reading frame that encoded 491 amino acid residues with a calculated molecular weight of 54, 762 Da. Its deduced amino acid sequence revealed a 65.6% identity to that from the goose uropigial gland. The sequence of the first 38 amino acids represents a putative mitochondrial targeting sequence, and the last 3 amino acid sequences (SKL) represent peroxisomal targeting ones. The expression of malonyl CoA decarboxylase was observed over a wide range of tissues as a single transcript of 2.0 kb in size. The recombinant protein that was expressed in E. coli was used to characterize the biochemical properties, which showed a typical Michaelis-Menten substrate saturation pattern. The $K_m$ and $V_{max}$ were calculated to be $68\;{\mu}M$ and $42.6\;{\mu}mol/min/mg$, respectively.

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.471-482
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• 2014

Influence maximization is an important problem of finding a small subset of nodes in a social network, such that by targeting this set, one will maximize the expected spread of influence in the network. To improve the efficiency of algorithm KK_Greedy proposed by Kempe et al., we propose two improved algorithms, Lv_NewGreedy and Lv_CELF. By combining all of advantages of these two algorithms, we propose a mixed algorithm Lv_MixedGreedy. We conducted experiments on two synthetically datasets and show that our improved algorithms have a matching influence with their benchmark algorithms, while being faster than them.

• Biomolecules & Therapeutics
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• v.21 no.4
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• pp.258-263
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• 2013

We demonstrate herein that silibinin, a polyphenolic flavonoid compound isolated from milk thistle (Silybum marianum), inhibits LPS-induced activation of macrophages and production of nitric oxide (NO) in RAW 264.7 cells. Western blot analysis showed silibinin inhibits iNOS gene expression. RT-PCR showed that silibinin inhibits iNOS, TNF-${\alpha}$, and $IL1{\beta}$. We also showed that silibinin strongly inhibits p38 MAPK phosphorylation, whereas the ERK1/2 and JNK pathways are not inhibited. The p38 MAPK inhibitor abrogated the LPS-induced nitrite production, whereas the MEK-1 inhibitor did not affect the nitrite production. A molecular modeling study proposed a binding pose for silibinin targeting the ATP binding site of p38 MAPK (1OUK). Collectively, this series of experiments indicates that silibinin inhibits macrophage activation by blocking p38 MAPK signaling.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.48-59
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• 2018

Recently, network technology has been used in various fields due to development of network technology. However, there has been an increase in the number of attacks targeting public institutions and companies by exploiting the evolving network technology. Meanwhile, the existing network intrusion detection system takes much time to process logs as the amount of network log increases. Therefore, in this paper, we propose a Spark-based network log analysis system that detects unstructured network attack pattern. by using Snort. The proposed system extracts and analyzes the elements required for network attack pattern detection from large amount of network log data. For the analysis, we propose a rule to detect network attack patterns for Port Scanning, Host Scanning, DDoS, and worm activity, and can detect real attack pattern well by applying it to real log data. Finally, we show from our performance evaluation that the proposed Spark-based log analysis system is more than two times better on log data processing performance than the Hadoop-based system.

• Journal of Preventive Medicine and Public Health
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• v.43 no.6
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• pp.472-478
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• 2010

Cancer is a leading cause of death around the world. Education is at the core of cancer prevention activities, especially programs targeting empowering existing public health workforce. In the past 10 years, researchers at the University of Pittsburgh have been building the Global Health Network Supercourse project, a library of over 4500 online lectures and a network of nearly 50000 public health professionals in 174 countries. As of November 2010, the overall number of Supercourse participants from Asia exceeds 7000 participants. The Supercourse network has been investigating methods for Internet based recruitment of cancer prevention professionals in order to network cancer experts locally and globally, including the use of mHealth technologies for cancer research education and for NCD registries. Supercourse is a tool that can offer a solution to the challenges of information sharing, especially in the field of NCDs and cancer. In this paper, we highlight the need for the development of Cancer Supercourse with Satellite in Asia and encourage faculty members from Asia to join the network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.454-475
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• 2018

This paper presents a novel Android malware classification model planned to classify and categorize Android malicious code at Drebin dataset. The amount of malicious mobile application targeting Android based smartphones has increased rapidly. In this paper, Restricted Boltzmann Machine and Deep Belief Network are used to classify malware into families of Android application. A texture-fingerprint based approach is proposed to extract or detect the feature of malware content. A malware has a unique "image texture" in feature spatial relations. The method uses information on texture image extracted from malicious or benign code, which are mapped to uncompressed gray-scale according to the texture image-based approach. By studying and extracting the implicit features of the API call from a large number of training samples, we get the original dynamic activity features sets. In order to improve the accuracy of classification algorithm on the features selection, on the basis of which, it combines the implicit features of the texture image and API call in malicious code, to train Restricted Boltzmann Machine and Back Propagation. In an evaluation with different malware and benign samples, the experimental results suggest that the usability of this method---using Deep Belief Network to classify Android malware by their texture images and API calls, it detects more than 94% of the malware with few false alarms. Which is higher than shallow machine learning algorithm clearly.

• Proceedings of the IEEK Conference
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• 2000.11b
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• pp.148-151
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• 2000

In recent years, security is essential factor of our safe network community. Therefore, data encryption/ decryption technology is improving more and more. Elliptic Curve Cryptosystem proposed by N. Koblitz and V. Miller independently in 1985, require fewer bits lot the same security, there is a net reduction in cost, size, and time. In this paper, we design high speed underlying field arithmetic processor for elliptic curve cryptosystem. The targeting device is VIRTEX V1000FG680 and verified by Xilinx simulator.