• Asian Pacific Journal of Cancer Prevention
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• v.15 no.16
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• pp.6899-6904
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• 2014

NGAL (neutrophil gelatinase-associated lipocalin) is a novel cancer-related protein involves multiple functions in many cancers and other diseases. We previously overexpressed NGAL to analyze its role in esophageal squamous cell carcinoma (ESCC). In this study, a protein-protein interaction (PPI) was constructed and the shortest paths from NGAL to transcription factors in the network were analyzed. We found 28 shortest paths from NGAL to RELA, most of them obeying the principle of extracellular to cytoplasm, then nucleus. These shortest paths were also prioritized according to their normalized intensity from the microarray by the order of interaction cascades. A systems approach was developed in this study by linking differentially expressed genes with publicly available PPI data, Gene Ontology and subcellular localizaton for the integrated analyses. These shortest paths from NGAL to DEG transcription factors or other transcription factors in the PPI network provide important clues for future experimental identification of new pathways.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.8
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• pp.4621-4625
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• 2013

The purpose of this study was to construct a protein-protein interaction (PPI) network related to oral squamous cell carcinoma (OSCC). Each protein was ranked and those most associated with OSCC were mined within the network. First, OSCC-related genes were retrieved from the Online Mendelian Inheritance in Man (OMIM) database. Then they were mapped to their protein identifiers and a seed set of proteins was built. The seed proteins were expanded using the nearest neighbor expansion method to construct a PPI network through the Online Predicated Human Interaction Database (OPHID). The network was verified to be statistically significant, the score of each protein was evaluated by algorithm, then the OSCC-related proteins were ranked. 38 OSCC related seed proteins were expanded to 750 protein pairs. A protein-protein interaction nerwork was then constructed and the 30 top-ranked proteins listed. The four highest-scoring seed proteins were SMAD4, CTNNB1, HRAS, NOTCH1, and four non-seed proteins P53, EP300, SMAD3, SRC were mined using the nearest neighbor expansion method. The methods shown here may facilitate the discovery of important OSCC proteins and guide medical researchers in further pertinent studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.751-770
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• 2019

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

• Fashion & Textile Research Journal
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• v.19 no.2
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• pp.194-206
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• 2017

This study analyzed the effect of individualism-collectivism propensity and social capital on participation activity as well as the effect of participation activity on fashion brands relationship orientation of social network service users. Also, this study investigated the difference in participation activity and fashion brands relationship orientation by participation level of social network service. A survey was conducted from October 1 to November 31, 2015, and 476 responses were used in the analysis. As results, the individualism-collectivism propensity was composed of vertical-horizontal individualism and vertical-horizontal collectivism. The social capital was composed of trust, norm, and network. Also, the participation activity was composed of personal interaction, consumer rights, information pursuit, interest pursuit, and economic pursuit. Vertical individualism positively affected information pursuit and economic pursuit, and horizontal individualism positively affected personal interaction, consumer rights, and information pursuit; in addition, vertical collectivism positively affected personal interaction, consumer rights, and interest pursuit. Horizontal collectivism positively affected information pursuit, but it negatively affected consumer rights. Consumer rights, information pursuit, interest pursuit, and economic pursuit of participation activity positively affected fashion brands relationship orientation. Also, there were significant differences in the participation activity and fashion brands relationship orientation by participation level. The study results provide useful information to the marketing strategies using social network service of fashion brands.

• Journal of Korean Elementary Science Education
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• v.39 no.3
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• pp.323-337
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• 2020

To achieve effective learning, knowledge must be shared and developed through interaction with peer learners. Therefore, science education emphasizes small group inquiry activities that solves tasks through the interaction of members. The effect of small group inquiry activities depends on how to compose small groups. The way in which a group is composed is a very important factor for improving interaction. In particular, it has been reported that the gender of peer is a very important factor in the interaction between students in the composition of small groups. Meanwhile, studies are being conducted on the types and stability of verbal interactions in small group inquiry activities using social network analysis. The purpose of this study is to analyze the types and stability of verbal interaction types and stability according to gender-grouping in elementary school students' small group activities using social network analysis. To this end, 60 5th graders in elementary school were conducted with different gender in the composition of the group of male, female and mixed-gender students. The study found that the composition of a group by gender had little impact on the type or stability of verbal interaction. However, the frequency of verbal interactions was higher in mixed-gender groups than in other groups. Through this process, the gender-grouping in elementary school students' small group activities suggests a mixed-gender group.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.45-52
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• 2021

The article proves the relevance of developing conceptual frameworks for managing the quality assurance of logistics activities in the context of socio-economic interaction of their participants. It is established that the fundamental difference of the logistic approach in management from traditional approaches is the allocation of a single management function of previously separated, disparate material flows, as well as economic, technological, information integration of chain links into a single system capable of effective management of these flows. It is substantiated that the functioning of the enterprise as a logistics system can be represented in the form of a triad of logistics components, namely: supply logistics, production logistics, sales logistics. Management of quality assurance processes of logistics activities in the context of socio-economic interaction of their participants is a functional component of the entire logistics system due to the quality of work and interaction of all participants in the implementation of certain activities. The quality of logistics activities will affect the level of economic potential, rationalization and optimization of all logistics flows. It is proved that the management of quality assurance processes of logistics activities in the context of socio-economic interaction of their participants involves the following main areas: the introduction of a quality system of logistics processes; development and implementation of the general strategy of quality improvement at the enterprise; internal integration; controlling. Management of quality assurance processes of logistics activities in the context of socio-economic interaction of its participants requires compliance with the following requirements: systematic and comprehensive management of all flow processes; coordination of criteria and indicators for assessing the effectiveness of the entire logistics system; dissemination of the use and application of information technology; ensuring partnerships and close interaction of all participants in sales networks.

• Molecules and Cells
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• v.27 no.3
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• pp.271-277
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• 2009

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.

• Journal of Family Resource Management and Policy Review
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• v.17 no.4
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• pp.85-100
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• 2013

The purposes of this study are to explore the effect of the network characteristics of Healthy Family Support Center on its performance, and also to investigate the mediating effect of the organizational properties on the performance. We used the data from 148 healthy family support centers in National Survey in Korea. The analytic sample for this study consists of 102 responses.(response rate=68.9%) Multivariate regression model estimated the effects of the network's structural, interactive and functional characteristics and the interaction between the network's characteristics and organizational properties on the performance The findings of this study demonstrate that healthy family support centers with higher closeness centrality and with better functional characteristics reported more performances. Moreover, Centers that are more independent in organizational properties showed higher performances. However, the findings did not show that the interaction between the network's characteristics and organizational properties mediates on the performance.

• Journal of Korean Society on Water Environment
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• v.27 no.5
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• pp.661-669
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• 2011

The purpose of this study is quantitative analysis of the effects of the interactions between stream network and hillslope to hydrologic response functions. To this end general formulation of hydrologic response function is performed based on width function and grid framework. Target basins are Ipyeong and Tanbu basins. From the results of width function estimation even similar sized and closely located basins could have very different hydrologic response function. It is found out that the interactions between stream network and hillslope are essential factors of rainfall-runoff processes because their difference can make the hydrologic response function with positive skewness. The change of velocities of stream network and hillslope might influence the magnitude of peak but time to peak tends to more sensitively respond to velocities of stream network. Lag time of basin would be the result of complex interaction between drainage structures and dynamic properties of river basin.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.677-684
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• 2009

With various sensor network and ubiquitous technologies, we can extend interaction area from a virtual domain to physical space domain. This spatial interaction is differ in that traditional interaction is mainly processed by direct interaction with the computer machine which is a target machine or provides interaction tools and the spatial interaction is performed indirectly between users with smart interaction tools and many distributed components of space. So, this interaction gives methods to users to control whole manageable space components by registering and recognizing objects. Finally, this paper provides an effective spatial interaction method with template-based task mapping algorithm which is sorted by historical interaction data for support of users' intended task. And then, we analyze how much the system performance would be improved with the task mapping algorithm and conclude with an introduction of a GUI method to visualize results of spatial interaction.