<i>Toxoplasma gondii</i> protects against H₂O₂‐induced apoptosis in ARPE‐19 cells through the transcriptional regulation of apoptotic elements and downregulation of the p38 MAPK pathway

Choi, Si‐,Hwan,Park, Sung Jun,Cha, Guang‐,Ho,Quan, Juan Hua,Chang, Nam‐,Sik,Ahn, Myoung‐,Hee,Shin, Dae‐,Whan,Lee, Young‐,Ha Blackwell Publishing Ltd 2011 Acta ophthalmologica Vol.89 No.4

<P><B>Abstract.</B></P><P><B>Purpose: </B> Toxoplasmosis, which is caused by the protozoan parasite <I>Toxoplasma gondii</I>, can lead to severe visual impairment. <I>T.?gondii</I> inhibits or delays programmed cell death caused by various apoptotic triggers; however, the mechanisms involved in the <I>T</I>.?<I>gondii</I>‐induced suppression of apoptosis in retinal cells have not been analysed in detail.</P><P><B>Methods: </B> We investigated the role of <I>T</I>.<I>?gondii</I> infection in H<SUB>2</SUB>O<SUB>2</SUB>‐induced apoptosis in human retinal pigment epithelial cells (ARPE‐19) by monitoring the activities of apoptosis‐regulating molecules and mitogen‐activated protein kinases (MAPKs), including p38 MAPK. We also examined the gene downstream from p38 MAPK.</P><P><B>Results: </B> <I>T.?gondii</I> infection significantly inhibited the cellular toxicity of H<SUB>2</SUB>O<SUB>2</SUB> (500 μ<SMALL>m</SMALL>) and increased cell viability in a multiplicity of infection (MOI)‐dependent manner by reducing DNA fragmentation and reactive oxygen species (ROS) generation in ARPE‐19 cells. Western blot analysis also showed that <I>T</I>.?<I>gondii</I> infection prevented the host cell expression of pro‐apoptotic factors, such as Bad and Bax, and the activation of caspase‐3. Infection with <I>T</I>.?<I>gondii</I> increased the expression of the anti‐apoptotic factor Bcl‐2 in ARPE‐19 cells under oxidative stress. In accordance with these findings, <I>Toxoplasma</I> infection was protective enough to suppress the phosphorylation of p38 MAPK following H<SUB>2</SUB>O<SUB>2</SUB> treatment. Exposure to H<SUB>2</SUB>O<SUB>2</SUB> increased the expression of heme oxygenase‐1 (HO‐1) in ARPE‐19 cells, and its expression was significantly inhibited in H<SUB>2</SUB>O<SUB>2</SUB>‐treated infected cells.</P><P><B>Conclusion: </B> The protective function of <I>T</I>.?<I>gondii</I> infection against ROS‐induced apoptosis results from changes in the expression of apoptotic molecules and the downregulation of stress‐induced intracellular signalling.</P>

Content-Based Indexing and Retrieval in Large Image Databases

Guang-Ho Cha,Chin-Wan Chung 대한전기학회 1996 Journal of Electical Engineering and information S Vol.1 No.2

Performance Analysis for Multidimensional Indexing Methods Supporting Sequential Accesses

Cha, Guang-Ho 서울産業大學校 2005 논문집 Vol.54 No.3

This paper presents a cost model that estimates the performance of indexing methods that support sequential disk accesses. The cost model makes a distinction of disk access costs depending on whether the disk is accessed sequentially or randomly. Extensive experiments demonstrate that the cost model shows almost the same performance curve as that of the experimental results.

Insulin-Induced Protein Phosphorylation in Imaginal Wing Discs of Drosophila melanogaster

Guang Ho Cha 한국유전학회 2008 Genes & Genomics Vol.30 No.2

Insulin or insulin-like signal has been well-known for their roles in regulating glucose uptake, cell size, organ size, and cell proliferation in many multicellular organisms. In order to study its working mechanism in Drosophila, insulin-induced protein phosphorylation in the imaginal wing disc cells was monitored by measuring radioactive orthophosphate incorporation to the endogenous proteins. Insulin stimulated the phosphorylation of 34 kDa protein, a putative ribosomal protein S6, in the imaginal wing discs. Phosphorylation of the 34 kDa protein was observed as early as 15 min after insulin treatment and the increment of phosphorylation level was insulin dose-dependent. In vitro S6 kinase assay showed that the kinase activity in the imaginal wing discs was dramatically increased by insulin stimulation. Specific kinase inhibitors, rapamycin for TOR (target of rapamycin) and wortmannin for PI3K (phosphoinositide 3-kinase), strongly inhibited the insulin-stimulated phosphorylation of 34 kDa protein and S6 kinase activity. In contrast, okadaic acid, a phosphatase 1 and 2A inhibitor, was able to partially overcome the inhibition of 34 kDa protein phosphorylation by rapamycin and wortmannin. These data suggest the conservation of the insulin-dependent S6 kinase signaling pathway in the imaginal wing discs of Drosophila.

The Kernel Trick for Content-Based Media Retrieval in Online Social Networks

( Guang-Ho Cha ) 한국정보처리학회 2021 Journal of information processing systems Vol.17 No.5

Nowadays, online or mobile social network services (SNS) are very popular and widely spread in our society and daily lives to instantly share, disseminate, and search information. In particular, SNS such as YouTube, Flickr, Facebook, and Amazon allow users to upload billions of images or videos and also provide a number of multimedia information to users. Information retrieval in multimedia-rich SNS is very useful but challenging task. Content-based media retrieval (CBMR) is the process of obtaining the relevant image or video objects for a given query from a collection of information sources. However, CBMR suffers from the dimensionality curse due to inherent high dimensionality features of media data. This paper investigates the effectiveness of the kernel trick in CBMR, specifically, the kernel principal component analysis (KPCA) for dimensionality reduction. KPCA is a nonlinear extension of linear principal component analysis (LPCA) to discovering nonlinear embeddings using the kernel trick. The fundamental idea of KPCA is mapping the input data into a high-dimensional feature space through a nonlinear kernel function and then computing the principal components on that mapped space. This paper investigates the potential of KPCA in CBMR for feature extraction or dimensionality reduction. Using the Gaussian kernel in our experiments, we compute the principal components of an image dataset in the transformed space and then we use them as new feature dimensions for the image dataset. Moreover, KPCA can be applied to other many domains including CBMR, where LPCA has been used to extract features and where the nonlinear extension would be effective. Our results from extensive experiments demonstrate that the potential of KPCA is very encouraging compared with LPCA in CBMR.

Local Polar Coordinate Based Indexing for High-Dimensional Image Databases

Cha, Guang-Ho 동명정보대학교 2000 東明情報大學校論文集 Vol.3 No.-

Nearest neighbor search is emerging as an important search paradigm in a variety of applications, usually involving similarity searching, in which objects are represented as vectors of d numeric feature. However, despite decades of efforts, except for the filtering approach such as the VA-file [31], the current solutions to find exact k nearest neighbors are far from satisfactory for large d. To overcome the dimensionality curse [24], the filtering approach represents vectors as compact approximations, and by first scanning these smaller approximations, only a small fraction of the real vectors are visited. The performance of the filtering approach depends largely on the filtering capability of the approximation. In this paper, we introduce the local polar coordinate file (LPC-file) based on the filtering approach for nearest-neighbor searches in high-dimensional image databases. The basic idea is to partition the vector space into hyper-rectangular cells and then to approximate vectors by polar coordinates on the partitioned local cells. The local polar coordinate information significantly enhances the discriminatory power of the approximation. To demonstrate the effectiveness of the LPC-file, we conducted extensive experiments and compared the performance with the VA-file and the sequential scan by using synthetic and real data sets. The experimental results demonstrate that the LPC-file outperforms significantly both of the VA-file and the sequential scan in total elapsed time and in the number of random disk accesses, and that the LPC-file is robust in both "good" distributions (such as random) and "bad" distributions(such as skewed and clustered).

Design and Implementation of XML-Based Lecture Database System for Distance Learning

Cha, Guang Ho 동명정보대학교 2002 저널 정보공학기술 Vol.1 No.-

Education and training are expected to change dramatically due to the combined impact of the Internet, database, and multimedia technologies. However, the distance learning is often impeded by the lack of effective methods to retrieve specific parts of a lecture by contents. This paper introduces a new approach to realize the content-based lecture retrieval on the Web. The approach involves: (1) The XML (eXtensibleMarkup Language)-based semistructured model not only to represent lecture contents but also to exchange them on the Web; (2) The technique to build structural summaries, i.e., schemas, of XML lecture databases. The structural summaries are useful for browsing and querying the database, building indexes, and enabling query optimization; (3) Index structures to speed up the search to find appropriate lecture contents.

A Framework for Content Based Image Retrieval Based on A Nonlinear Similarity Model

Cha, Guang-Ho 서울産業大學校 2005 논문집 Vol.54 No.3

In this paper, we propose a new nonlinear paradigm to clustering, indexing and searching for content-based image retrieval (CBIR). The scheme is designed for approximate searches and all the work is performed in a transformed feature space. We first (1) map the input space into a feature space via a nonlinear map, (2) compute the top eigenvectors in that feature space, and (3) capture cluster structure based on the eigenvectors. We (4) describe each cluster with a minimal hyper sphere containing all objects in the cluster, (5) derive the similarity measure for each cluster individually and (6) construct a bitmap index for each cluster. Finally we (7) model the similarity query as a hyper-rectangular range query and search the clusters near the query point. Our preliminary experimental results for our new framework demonstrate considerable effectiveness and efficiency in CBIR.

A New Bitmap Indexing Technique for Complex Similarity Queries

Cha, Guang-Ho 서울産業大學校 2005 논문집 Vol.54 No.3

The similarity indexing and searching is well known to be a difficult one for high-dimensional applications such as multimedia databases. Especially, it becomes more difficult when multiple features have to be indexed together. Moreover, few indexing methods are currently available to effectively support disjunctive queries for relevance feedback. In this paper, we propose a novel indexing method that is designed to efficiently handle complex similarity queries as well as relevance feedback in high-dimensional image and video databases. In order to provide the indexing method with Hie. flexibility in control multiple features and multiple query objects, our method treats every dimension independently. The efficiency of our method is realized by a specialized bitmap indexing that represents all objects in a database as a set of bitmaps. The percentage of data accessed in our indexing method is inversely proportional to the overall dimensionality, and thus the performance deterioration with the increasing dimensionality does not occur. Our main contributions are three-fold: (1) We provide a novel way to index high-dimensional data; (2) Our method efficiently handles complex similarity queries; and (3) Disjunctive queries driven by relevance feedback are efficiently treated. Our empirical results demonstrate that our indexing method achieves speedups of 10 to 15 over the linear scan.

Effective Similarity Measure and Ranking Algorithm for Content-Based Image Retrieval

Cha, Guang-Ho 서울産業大學校 2005 논문집 Vol.54 No.3

Over many years, almost all research work in the content-based image retrieval has used Minkowski-like functions to measure similarity between images. However those functions cannot adequately capture the aspects of the characteristics of the human visual system. In this paper, we present a new similarity measure reflecting both of the nonlinearity of human perception and the intrinsic structure of a dataset. Based on this similarity measure, we develop a new similarity ranking algorithm for effective image retrieval. This algorithm exploits the inherent cluster structure revealed by an image dataset. Our scheme yields encouraging experimental results on a real image database.