• Communications of the Korean Mathematical Society
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• v.32 no.2
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• pp.435-445
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• 2017

Let D be a directed graph with p vertices and q arcs. A vertex out-magic total labeling is a bijection f from $V(D){\cup}A(D){\rightarrow}\{1,2,{\ldots},p+q\}$ with the property that for every $v{\in}V(D)$, $f(v)+\sum_{u{\in}O(v)}f((v,u))=k$, for some constant k. Such a labeling is called a V-super vertex out-magic total labeling (V-SVOMT labeling) if $f(V(D))=\{1,2,3,{\ldots},p\}$. A digraph D is called a V-super vertex out-magic total digraph (V-SVOMT digraph) if D admits a V-SVOMT labeling. In this paper, we provide a method to find the most vital nodes in a network by introducing the above labeling and we study the basic properties of such labelings for digraphs. In particular, we completely solve the problem of finding V-SVOMT labeling of generalized de Bruijn digraphs which are used in the interconnection network topologies.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.922-928
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• 2004

We propose a new 3 dimensional labeling method based on slice information for the volume data. This method is named SIL (Slice Information based Labeling). Compare to the conventional algorithms, it has advantages that the use of memory is efficient and it Is possible to combine with a variety of 2 dimensional labeling algorithms for finding an appropriate labeling algorithm to its application. In this study, we applied SIL to confocal microscopy images of cervix cancer cell and compared the results of labeling. According to the measurement, we found that the speed of Sd combined with, CCCL (Contour based Connected Component Labeling) is almost 2 times higher than that of other methods. In conclusion, considering that the performance of labeling depends on a kind of image, we obtained that the proposed method provide better result for the confocal microscopy cell volume data.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.996-1002
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• 2015

In this paper, we present an effective system for the 3D scene labeling of objects from RGB-D videos. Our system uses a Markov Random Field (MRF) over a voxel representation of the 3D scene. In order to estimate the correct label of each voxel, the probabilistic graphical model integrates both scores from sliding window-based object detectors and also from object location prior maps. Both the object detectors and the location prior maps are pre-trained from manually labeled RGB-D images. Additionally, the model integrates the scores from considering the geometric constraints between adjacent voxels in the label estimation. We show excellent experimental results for the RGB-D Scenes Dataset built by the University of Washington, in which each indoor scene contains tabletop objects.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9C
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• pp.555-563
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• 2011

In this paper, we propose an object based 2D image to 3D conversion algorithm by using motion estimation, color labeling and non-local mean filtering methods. In the proposed algorithm, we first extract the motion vector of each object by estimating the motion between frames and then segment a given image frame with color labeling method. Then, combining the results of motion estimation and color labeling, we extract object regions and assign an exact depth value to each object to generate the right image. While generating the right image, occlusion regions occur but they are effectively recovered by using non-local mean filter. Through the experimental results, it is shown that the proposed algorithm performs much better than conventional conversion scheme by removing the eye fatigue effectively.

• BMB Reports
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• v.28 no.5
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• pp.433-436
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• 1995

In order to assign NMR signals, conditions for the specific labeling of cytochrome $c_3$ of D. vulgaris Miyazaki F through the culture in a minimal medium were established. Phenylalanine residue was specifically deuterated at more than 85% efficiency. Cytochrome $c_3$ has two phenylalanine residues. The signals of one phenylalane were missing and this was tentatively assigned to Phe20.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.3
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• pp.1363-1367
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• 2014

This research was conducted to compare differences in colon cancer lymphatic vessel invasion (LVI) with D2-40 antibody labeling and regular HE staining, blood vessel invasion (BVI) with CD34 antibody labeling and HE staining and to assess the possibility of using D2-40-LVI/CD34-BVI in combination for predicting stage II colon cancer prognosis and guiding adjuvant chemotherapy.Anti-D2-40 and anti-CD34 antibodies were applied to tissue samples of 220 cases of stage II colon cancer to label lymphatic vessels and small blood vessels, respectively. LVI and BVI were assessed and multivariate COX regression analysis was performed for associations with colon cancer prognosis. Regular HE staining proved unable to differentiate lymphatic vessels from blood vessels, while D2-40 selectively labeled lymphatic endothelial cell cytosol and CD34 was widely expressed in large and small blood vessels of tumors as well as normal tissues. Compared to regular HE staining, D2-40-labeling for LVI and CD34-labeling for BVI significantly increased positive rate (22.3% vs 10.0% for LVI, and 19.1% vs 9.1% for BVI). Multivariate analysis indicated that TNM stage, pathology tissue type, post-surgery adjuvant chemotherapy, D2-40-LVI, and CD34-BVI were independent factors affecting whole group colon cancer prognosis, while HE staining-BVI, HE staining-LVI were not significantly related. When CD34-BVI/D2-40-LVI were used in combination for detection, the risk of death for patients with two or one positive results was 5.003 times that in the LVI(-)&BVI(-) group (95% CI 2.365 - 9.679). D2-40 antibody LVI labeling and CD34 antibody BVI labeling have higher specificity and accuracy than regular HE staining and can be used as molecular biological indicators for prognosis prediction and guidance of adjuvant chemotherapy for stage II colon cancer.

• Molecules and Cells
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• v.39 no.6
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• pp.439-446
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• 2016

Clearing and labeling techniques for large-scale biological tissues enable simultaneous extraction of molecular and structural information with minimal disassembly of the sample, facilitating the integration of molecular, cellular and systems biology across different scales. Recent years have witnessed an explosive increase in the number of such methods and their applications, reflecting heightened interest in organ-wide clearing and labeling across many fields of biology and medicine. In this review, we provide an overview and comparison of existing clearing and labeling techniques and discuss challenges and opportunities in the investigations of large-scale biological systems.

• ALGAE
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• v.24 no.1
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• pp.39-45
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• 2009

The conjugation processes of a filamentous freshwater green alga Spirogyra varians were examined using FITC-lectins. Conjugation comprised five steps: 1) aligning with adjacent filaments, 2) formation of conjugation protru-sion (papilla), 3) fusion of the protrusions, 4) formation of conjugation tube,and 5) formation of zygotes. Three lectins, ConA, RCA and UEA, showed considerable labeling during the progression of conjuation. FITC-ConA labeled the surfaces of filaments throughout the whole conjugation processes. FITC-RCA labeling was observed at the conjugation protrusions only after the papilla formation. Strong labeling continued until formationg of zygotes at the contacting area where the conjugation tube developed, but no labeling was detected on the surface of vegetative filaments. The labeling decreased gradually over time and disappeared when zygotes were formed. FITC-UEA showed similar labeling pattern with FITC-RCA except that weak labeling remained after zygote formation. Inhibition experiments using RCA, UEA which are complementary to sugars L-fucose and D-galactose, showed considerable decrease of conjugation (<32% vs. 70% in control). These results suggested that the lectin-carbohydrate recognition system might be involved in the conjugation of spirogyra varians.

• Korean Journal of Community Nutrition
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• v.23 no.2
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• pp.116-127
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• 2018

Objectives: The purpose of this study was to examine the coverage of the current mandatory nutrition labeling system on the nutrient intake of Koreans. Methods: KNHANES dietary intake data (2013) of 7,242 subjects were used in the analysis. KNHANES dietary intake data were collected by a 24-hour recall method by trained dietitians. For analysis, all food items consumed by the subjects were classified into two groups (foods with mandatory labeling and other foods). In the next step, all food items were reclassified into four groups according to the food type and nutrition labeling regulations: raw material food, processed food of raw material characteristics, processed foods without mandatory labeling, and processed foods with mandatory labeling. The intake of energy and five nutrients (carbohydrate, protein, fat, saturated fat, and sodium) of subjects from each food group were analyzed to determine the coverage of the mandatory nutrition labeling system among the total nutrient intake of Koreans. Results: The average intake of foods with mandatory labeling were 384g/day, which was approximately one quarter of the total daily food intake (1,544 g/day). The proportion of energy and five nutrients intake from foods with mandatory labeling was 18.1%~47.4%. The average food intake from the 4 food groups were 745 g/day (48.3%) for the raw food materials, 54 g/day (3.5%) for the processed food of raw material characteristics, 391 g/day (25.3%) for the processed foods without mandatory labeling, and 354 g/day (22.9%) for the processed foods with mandatory labeling. Conclusions: Although nutrition labeling is a useful tool for providing nutritional information to consumers, the coverage of current mandatory nutrition labeling system on daily nutrient intake of the Korean population is not high. To encourage informed choices and improve healthy eating habits of the Korean population, the nutrition labeling system should be expanded to include more food items and foodservice menus.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.3
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• pp.151-157
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• 2008

In this paper, we propose a three dimensional distance measurement method for a stereo system by using web cams. Using a parallel stereo system, a robot gets two images from each webcam and equalize brightness of both images. And we suggest an image processing method such as labeling, isolating an object from background and finding center of an object. We also propose a method of calculating the focal distance by using least square algorithm based on triangulation and we can reduce calculation error by this method. From experimental results, we show that the proposed method can be effective for 3D distance measurement.