• Tuberculosis and Respiratory Diseases
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• v.58 no.1
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• pp.31-42
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• 2005

Background : Peroxiredoxins (Prxs) are a relatively newly recognized, novel family of peroxidases that reduce $H_2O_2$ and alkylhydroperoxide into water and alcohol, respectively. There are 6 known isoforms of Prxs present in human cells. Normally, Prxs exist in a head-to-tail homodimeric state in a reduced form. However, in the presence of excess $H_2O_2$, it can be oxidized on its catalytically active cysteine site into inactive oxidized forms. This study surveyed the types of the Prx isoforms present in the pulmonary epithelial, macrophage, endothelial, and other cell lines and observed their response to oxidative stress. Methods : This study examined the effect of exogenous, excess $H_2O_2$ on the Prxs of established cell lines originating from the pulmonary epithelium, macrophages, and other cell lines, which are known to be exposed to high oxygen partial pressures or are believed to be subject to frequent oxidative stress, using non-reducing SDS polyacrylamide electrophoresis (PAGE) and 2 dimensional electrophoresis. Result : The addition of excess $H_2O_2$ to the culture media of the various cell-lines caused the immediate inactivation of Prxs, as evidenced by their inability to form dimers by a disulfide cross linkage. This was detected as a subsequent shift to its monomeric forms on the non-reducing SDS PAGE. These findings were further confirmed by 2 dimensional electrophoresis and immunoblot analysis by a shift toward a more acidic isoelectric point (pI). However, the subsequent reappearance of the dimeric Prxs with a comparable, corresponding decrease in the monomeric bands was noted on the non-reducing SDS PAGE as early as 30 minutes after the $H_2O_2$ treatment suggesting regeneration after oxidation. The regenerated dimers can again be converted to the inactivated form by a repeated $H_2O_2$ treatment, indicating that the protein is still catalytically active. The recovery of Prxs to the original dimeric state was not inhibited by a pre-treatment with cycloheximide, nor by a pretreatment with inhibitors of protein synthesis, which suggests that the reappearance of dimers occurs via a regeneration process rather than via the de novo synthesis of the active protein. Conclusion : The cells, in general, appeared to be equipped with an established system for regenerating inactivated Prxs, and this system may function as a molecular "on-off switch" in various oxidative signal transduction processes. The same mechanisms might applicable other proteins associated with signal transduction where the active catalytic site cysteines exist.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.