• Journal of Microbiology and Biotechnology
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• v.32 no.6
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• pp.718-729
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• 2022

Esophageal squamous cell carcinoma (ESCC) is the most common primary esophageal malignancy with poor prognosis. Here, due to the necessity for exploring potential therapies against ESCC, we obtained the gene expression data on ESCC from the TCGA and GEO databases. Venn diagram analysis was applied to identify common targets. The protein-protein interaction network was constructed by Cytoscape software, and the hub targets were extracted from the network via cytoHubba. The potential hub nodes as drug targets were found by pharmacophore-based virtual screening and molecular modeling, and the antitumor activity was evaluated through in vitro studies. A total of 364 differentially expressed genes (DEGs) in ESCC were identified. Pathway enrichment analyses suggested that most DEGs were mainly involved in the cell cycle. Three hub targets were retrieved, including CENPF, CCNA2 (cyclin A), and CCNB1 (cyclin B1), which were highly expressed in esophageal cancer and associated with prognosis. Moreover, amentoflavone, a promising drug candidate found by pharmacophore-based virtual screening, showed antiproliferative and proapoptotic effects and induced G1 in esophageal squamous carcinoma cells. Taken together, our findings suggested that amentoflavone could be a potential cell cycle inhibitor targeting cyclin B1, and is therefore expected to serve as a great therapeutic agent for treating esophageal squamous cell carcinoma.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2101-2123
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• 2023

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.109-117
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• 2014

In navigation route planning systems using A* algorithms, the cardinality of an Open list, which is a list of candidate nodes through which a terminal node can be accessed, increases as the path length increases. In this paper, a method of alternately utilizing the Dijkstra's algorithm and the A* algorithm to reduce the cardinality of the Open list is investigated. In particular, by employing a depth parameter, named Level, the two algorithms are alternately performed depending on the Level's value. Using the hybrid searching approach, the Open list constructed in the Dijkstra's algorithm is transferred into the Open list of the A* algorithm, and consequently, the unconstricted increase of the cardinality of the Open list of the former algorithm can be avoided and controlled appropriately. In addition, an optimal or nearly optimal path similar to the Dijkstra's route, but not available in the A* algorithm, can be found. The experimental results, obtained with synthetic and real-life benchmark data, demonstrate that the computational cost, measured with the number of nodes to be compared, was remarkably reduced compared to the traditional searching algorithms, while maintaining the similar distance to those of the latter algorithms. Here, the values of Level were empirically selected. Thus, a study on finding the optimal Level values, while taking into consideration the actual road conditions remains open.

• Korean Journal of Clinical Laboratory Science
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• v.40 no.2
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• pp.98-105
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• 2008

Colorectal carcinoma from various cancers is fourth ranked occurred to Korean. Due to western dietary life, this cancer has been increased continuously. Therefore, the further study will be needed to find a candidate gene involved in the development and progression of colorectal carcinoma as well as to diagnose and treatment helpfully. The purpose of this study was designed to find a carcinogenesis gene using microsatellite marker on chromosomes 7th and 20th from 30 colon cancer patients. The amplification was investigated in order of D20S97 57% (17/30), D20S101 57% (17/30), D20S119 53% (16/30), D7S483 50% (15/30), D7S495 47% (14/30), D7S498 47% (14/30). The genetic mutation pattern depends on loci of colorectal carcinoma was shown highly amplified with 3.77 from colon cancer than with 2.08 from right colorectal carcinoma (P<0.018). The genetic mutation with lymph nodes was investigated higher with 4.13 at metastasized group than with 1.93 at non-metastasized group (P<0.001). There was no difference at comparison between histological classfication and serological CEA increase as well as on genetic mutated pattern depends on disease stage. It is suggested that the amplification on chromosomes 7q and 20q determines a pivotal role from first stage to metastasis cancer and also functions as an useful marker on diagnosis and treatment of colorectal carcinoma patients as well as follow-up checkup. Recently, the diagnosis and study using genetic analyzer are necessary for efficient application. Fortunately, several university hospitals run this genetic analyzer currently so it is expected that this method makes full use of clinical application.

• The Journal of the Korea Contents Association
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• v.7 no.4
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• pp.49-55
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• 2007

Recently, with the IPTV services, e-learning, real-time broadcasting and e-contents, many application services need the multicasting routing protocol. In this paper, the performance of the algorithm to assign the rendezvous router (RP: rendezvous point) in the center-based multicasting mesh network is analyzed. The estimated distance to select RP in the candidate nodes is calculated, and the node minimizing the distance is selected as the optimal RP. We estimate the distance by using the maximum distance, average distance, and mean of the maximum and average distance between the RP and members. The performance of the algorithm is compared with the optimal algorithm of all enumeration. With the assumptions of mesh network and randomly positioned for sources and members, the simulations for different parameters are studied. From the simulation results, the performance deviation between the algorithm with minimum cost and optimal method is evaluated as 6.2% average.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.2143-2149
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• 2010

An agent based geographic routing protocol is proposed to improve the well-known geographic routing protocol-GPSR routing protocol. In the proposed scheme, the agent is selected by sink node which concern about the source node's position as well as agent candidate's state. So packets will first be forwarded to agent and next step is to be forwarded to their final goal- sink node from agent. During the next hop selection process, nodes select their neighbors by considering not only position but also their average available buffer size. This results in efficient selection of next hop node in congestion area, and then increases the successful packet delivery ratio. The simulation is conducted for two scenarios: general number of connections and large number of connections in our map. Results show that new method with agent achieves improved performance in successful packet delivery ratio when compares to GPSR without our scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12B
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• pp.1399-1406
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• 2009

In the internet broadcast, efficient and scalable mechanism of multicast is needed for the communication between groups. Furthermore, Optimization of the multicast tree is required to improve the performance of overlay multicast. This optimization is well-known as NP-complete. If a node in the tree has limited out-degree, a user who wants to join the group has to find parent user who has already joined. In this paper, the users who want to join the group need to setup their level using delay test from source node. And then new users can find candidate parent nodes effectively using ACK-SEND approach and take proper position by comparing level. The closer node of the user to root node should be located in lower level. Also, even if a barrier is caused, fast recovery will be guaranteed using ACK-SEND approach. Through this, the newcomer node can fine their location in the multicast tree and join the group fast and effectively.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.11a
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• pp.219-219
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• 2008

Energy harvesting from the environment has been of great interest as a standalone power source of wireless sensor nodes for ubiquitous sensor networks (USN). There are several power generating methods such as thermal gradients, solar cell, energy produced by human action, mechanical vibration energy, and so on. Most of all, mechanical vibration is easily accessible and has no limitation of weather and environment of outdoor or indoor. In particular, the piezoelectric energy harvesting from ambient vibration sources has attracted attention because it has a relative high power density comparing with other energy scavenging methods. Through recent advances in low power consumption RF transmitters and sensors, it is possible to adopt a micro-power energy harvesting system realized by MEMS technology for the system-on-chip. However, the MEMS energy harvesting system hassome drawbacks such as a high natural frequency over 300 Hz and a small power generation due to a small dimension. To overcome these limitations, we devised a novel power generator with a spiral spring structure. In this case, the energy harvester has a lower natural frequency under 200 Hz than a normal cantilever structure. Moreover, it has higher an energy conversion efficient because shear mode ($d_{15}$) is much larger than 33 mode ($d_{33}$) and the energy conversion efficiency is proportional to the piezoelectric constant (d). We expect the spiral type MEMS power generator would be a good candidate as a standalone power generator for USN.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.164-170
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• 2012

A Virtual Reality Modeling Language (VRML) database access in remote virtual reality control platform for dyeing enterprise $MiWi^{TM}$ thermal sensor network is presented in this paper. The VRML-ASP framework is introduced for 3D real-time data plotting in this application. The activities of thermal sensor nodes and sensor area are analyzed. The database access framework is optimized for $MiWi^{TM}$ wireless sensor networks. The experimental results show that VRML-ASP database access framework could be a reliable and competitive data-manage candidate for targeted virtual reality remote industrial visualization application.