• Biotechnology and Bioprocess Engineering:BBE
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• v.7 no.5
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• pp.237-251
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• 2002

The recent progress on metabolic systems engineering was reviewed based on our recent research results in terms of (1) metabolic signal flow diagram approach, (2) metabolic flux analysis (MFA) in particular with intracellular isotopomer distribution using NMR and/or GC-MS, (3) synthesis and optimization of metabolic flux distribution (MFD), (4) modification of MFD by gene manipulation and by controlling culture environment, (5) metabolic control analysis (MCA), (6) design of metabolic regulation structure, and (7) identification of unknown pathways with isotope tracing by NMR. The main characteristics of metabolic engineering is to treat metabolism as a network or entirety instead of individual reactions. The applications were made for poly-3-hydroxybutyrate (PHB) production using Ralstonia eutropha and recombinant Escherichia coli, lactate production by recombinant Saccharomyces cerevisiae, pyruvate production by vitamin auxotrophic yeast Toluropsis glabrata, lysine production using Corynebacterium glutamicum, and energetic analysis of photosynthesic microorganisms such as Cyanobateria. The characteristics of each approach were reviewed with their applications. The approach based on isotope labeling experiments gives reliable and quantitative results for metabolic flux analysis. It should be recognized that the next stage should be toward the investigation of metabolic flux analysis with gene and protein expressions to uncover the metabolic regulation in relation to genetic modification and/ or the change in the culture condition.

• Journal of the Korea Society of Computer and Information
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• v.23 no.5
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• pp.1-7
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• 2018

Reference string recognition is to extract individual reference strings from a reference section of an academic article, which consists of a sequence of reference lines. This task has been attacked by heuristic-based, clustering-based, classification-based approaches, exploiting lexical and layout characteristics of reference lines. Most classification-based methods have used sequence labeling to assign labels to either a sequence of tokens within reference lines, or a sequence of reference lines. Unlike the previous token-level sequence labeling approach, this study attempts to assign different labels to the beginning, intermediate and terminating tokens of a reference string. After that, post-processing is applied to identify reference strings by predicting their beginning and/or terminating tokens. Experimental evaluation using English and German reference string recognition datasets shows that the proposed method obtains above 94% in the macro-averaged F1.

• Bulletin of the Korean Chemical Society
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• v.31 no.6
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• pp.1474-1478
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• 2010

An efficient method for labeling individual proteins in live cells is required for investigations into biological mechanisms and cellular processes. Here we describe the preparation of small quantum dots (QDs) that target membrane surface proteins bearing a hexahistidine-tag ($His_6$-tag) via specific binding to an nitrilotriacetic acid complex of nickel(II) ($NTA{\cdot}Ni^{2+}$) on the QD surfaces. We showed that the $NTA{\cdot}Ni^{2+}$-QDs bound to His-tag functionalized beads as a cellular mimic with high specificity and that QDs successfully targeted $His_6$-tagged vesicle-associated membrane proteins (VMAP) on cell surfaces. This strategy provides an efficient approach to monitoring synaptic protein dynamics in spatially restricted and confined biological environments.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.625-635
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• 2005

In this paper, we propose a hybrid approach for segmenting the lungs efficiently and automatically in chest CT images. The proposed method consists of the following three steps. first, lungs and airways are extracted by two- and three-dimensional automatic seeded region growing and connected component labeling in low-resolution. Second, trachea and large airways are delineated from the lungs by two-dimensional morphological operations, and the left and right lungs are identified by connected component labeling in low-resolution. Third, smooth and accurate lung region borders are obtained by refinement based on image subtraction. In experiments, we evaluate our method in aspects of accuracy and efficiency using 10 chest CT images obtained from 5 patients. To evaluate the accuracy, we Present results comparing our automatic method to manually traced borders from radiologists. Experimental results show that proposed method which use connected component labeling in low-resolution reduce processing time by 31.4 seconds and maximum memory usage by 196.75 MB on average. Our method extracts lung surfaces efficiently and automatically without additional processing like hole-filling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2881-2894
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• 2014

In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.111-119
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• 2016

In recent years, the opinion analysis is one of the key research fronts of any domain. Opinion target extraction is an essential process of opinion analysis. Target is usually referred to noun or noun phrase in an entity which is deliberated by the opinion holder. Extraction of opinion target facilitates the opinion analysis more precisely and in addition helps to identify the opinion polarity i.e. users can perceive opinion in detail of a target including all its features. One of the most commonly employed algorithms is a sequence labeling algorithm also called Conditional Random Fields. In present article, recent opinion target extraction approaches are reviewed based on sequence labeling algorithm and it features combinations by analyzing and comparing these approaches. The good selection of features combinations will in some way give a good or better accuracy result. Features combinations are an essential process that can be used to identify and remove unneeded, irrelevant and redundant attributes from data that do not contribute to the accuracy of a predictive model or may in fact decrease the accuracy of the model. Hence, in general this review eventually leads to the contribution for the opinion analysis approach and assist researcher for the opinion target extraction in particular.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.1
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• pp.1-8
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• 2015

In many image processing tasks, connected component labeling (CCL) is performed to extract regions of interest. CCL was usually done in a sequential fashion when image resolution was relatively low and there are small number of input channels. As image resolution gets higher up to HD or Full HD and as the number of input channels increases, sequential CCL is too time-consuming to be used in real time applications. To cope with this situation, parallel CCL framework was introduced where multiple cores are utilized simultaneously. Several parallel CCL methods have been proposed in the literature. Among them are NSZ label equivalence (NSZ-LE) method[1], modified 8 directional label selection (M8DLS) method[2], and HYBRID1 method[3]. Soh [3] showed that HYBRID1 outperforms NSZ-LE and M8DLS, and argued that HYBRID1 is by far the best. In this paper we propose an improved hybrid parallel CCL algorithm termed as HYBRID2 that hybridizes M8DLS with label backtracking (LB) and show that it runs around 20% faster than HYBRID1 for various kinds of images.

• ETRI Journal
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• v.36 no.3
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• pp.429-438
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• 2014

Semantic role labeling (SRL) is a task in natural-language processing with the aim of detecting predicates in the text, choosing their correct senses, identifying their associated arguments, and predicting the semantic roles of the arguments. Developing a high-performance SRL system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Constructing SRL training data for a new domain is very expensive. Therefore, domain adaptation in SRL can be regarded as an important problem. In this paper, we show that domain adaptation for SRL systems can achieve state-of-the-art performance when based on structural learning and exploiting a prior model approach. We provide experimental results with three different target domains showing that our method is effective even if training data of small size is available for the target domains. According to experimentations, our proposed method outperforms those of other research works by about 2% to 5% in F-score.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.5 no.2
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• pp.145-151
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• 2019

Fluorine-18 is considered to be the radionuclide of choice for positron emission tomography (PET). Thus, the development of small molecule-based radiopharmaceuticals for use in diagnostic imaging relies heavily on efficient radiofluorination techniques. Until the early 2000s, diaryliodonium salts and aryliodonium ylides were widely employed as labeling precursors to yield aromatic PET radiotracers with cyclotron-produced [¹⁸F]fluoride ion. Rapid recent progress in the development of efficient borylation methods has led to a paradigm shift in ¹⁸F-labeling methods. In addition, deoxyfluorination has attracted a great deal of interest as an alternative approach to aryl ring activation with ¹⁸F^-. In this review, methods for radiolabel development are discussed with a specific focus on the progress made in the last 5 years. Other interesting ¹⁸F-based protocols are also briefly introduced. New methods for exploiting ¹⁸F^- are expected to increase the number of ¹⁸F-labeling methods, to allow applications in a range of chemical environments.

• Journal of the Korean Magnetic Resonance Society
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• v.15 no.2
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• pp.115-127
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• 2011

A newly developed cost-effective approach to prepare $^{15}N/^{13}C$-labeled protein for NMR studies is presented. This method has been successfully applied to isotopically labeling of PTK6 SH2 domain and MTH 1880 protein. The production method generates cell density using a growing media containing $^{15}NH_4Cl$, $^{12}C_6$-D-glucose. Following a doubling time period for unlabeled metabolite exhaustion and then addition $^{13}C_6$-D-glucose into a M9 growing media, the cells are induced. Our results demonstrate that in order to get full incorporation of $^{13}C$, the isotopes are not totally required during the initial growth phase before induction. The addition of small amounts of $^{13}C_6$-D-glucose to the induction phase is sufficient to obtain more than 95% incorporation of isotopes into the protein. Our optimized protocol is two-thirds less costly than the classical method using $^{13}C$ isotope during the entire growth phase.