• Journal of Microbiology and Biotechnology
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• v.22 no.3
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• pp.343-351
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• 2012

In this paper, the kinetics of a cloned special glucosidase, named ginsenosidase type III hydrolyzing 3-O-glucoside of multi-protopanaxadiol (PPD)-type ginsenosides, were investigated. The gene (bgpA) encoding this enzyme was cloned from a Terrabacter ginsenosidimutans strain and then expressed in E. coli cells. Ginsenosidase type III was able to hydrolyze 3-O-glucoside of multi-PPD-type ginsenosides. For instance, it was able to hydrolyze the 3-O-${\beta}$-D-(1${\rightarrow}$2)-glucopyranosyl of Rb1 to gypenoside XVII, and then to further hydrolyze the 3-O-${\beta}$-D-glucopyranosyl of gypenoside XVII to gypenoside LXXV. Similarly, the enzyme could hydrolyze the glucopyranosyls linked to the 3-O-position of Rb2, Rc, Rd, Rb3, and Rg3. With a larger enzyme reaction $K_m$ value, there was a slower enzyme reaction speed; and the larger the enzyme reaction $V_{max}$ value, the faster the enzyme reaction speed was. The $K_m$ values from small to large were 3.85 mM for Rc, 4.08 mM for Rb1, 8.85 mM for Rb3, 9.09 mM for Rb2, 9.70 mM for Rg3(S), 11.4 mM for Rd and 12.9 mM for F2; and $V_{max}$ value from large to small was 23.2 mM/h for Rc, 16.6 mM/h for Rb1, 14.6 mM/h for Rb3, 14.3 mM/h for Rb2, 1.81mM/h for Rg3(S), 1.40 mM/h for Rd, and 0.41 mM/h for F2. According to the $V_{max}$ and $K_m$ values of the ginsenosidase type III, the hydrolysis speed of these substrates by the enzyme was Rc>Rb1>Rb3>Rb2>Rg3(S)>Rd>F2 in order.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.411-419
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• 2004

When the imaging object rotates in image plane during MRI scan, its rotation causes phase error and non-uniform sampling to MRI signal. The model of the problem including phase error non-uniform sampling of MRI signal showed that the MRI signals corrupted by rotations about an arbitrary center and the origin in image plane are different in their phases. Therefore the following methods are presented to improve the quality of the MR image which includes the artifact. The first, assuming that the angle of 2-D rotational motion is already known and the position of 2-D rotational center is unknown, an algorithm to correct the artifact which is based on the phase correction is presented. The second, in case of 2-D rotational motion with unknown rotational center and unknown rotational angle, an algorithm is presented to correct the MRI artifact. At this case, the energy of an ideal MR image is minimum outside the boundary of the imaging object to estimate unknown motion parameters and the measured energy increases when the imaging object has an rotation. By using this property, an evaluation function is defined to estimate unknown values of rotational angle at each phase encoding step. Finally, the effectiveness of this presented techniques is shown by using a phantom image with simulated motion and a real image with 2-D translational shift and rotation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.15-22
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• 2019

Awireless sensor network utilizes small sensors with a low cost and low power being deployed over a wide area. They monitor the surrounding environment and gather the associated information to transmit it to a base station via multi-hop transmission. Most of the research has mainly focused on static sensors that are located in a fixed position. Unlike a wireless sensor network based on static sensors, we can exploit drone-based technologies for more efficient wireless networks in terms of coverage and energy. In this paper, we introduce a transmission power model and a video encoding power model to design the network environment. We also explain a priority mapping scheme, and deploy drones oriented for network coverage and energy consumption. Through our simulations, this research shows coverage and energy improvements in adrone-based wireless sensor network with fewer sensors, compared to astatic sensor-based wireless sensor network. Concretely, coverage increases by 30% for thedrone-based wireless sensor network with the same number of sensors. Moreover, we save an average of 25% with respect to the total energy consumption of the network while maintaining the coverage required.

• Tuberculosis and Respiratory Diseases
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• v.61 no.3
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• pp.248-255
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• 2006

Background: LOH11A is a region with frequent allele loss (>75%) in lung cancer that is located on the centromeric part of chromosome 11p15.5. Clinical and cell biological studies suggest that this region contains a gene associated with metastatic tumor spread. RRM1 encoding the M1 subunit of ribonucleotide reductase, which is an enzyme that catalyses the rate-limiting step in deoxyribonucleotide synthesis, is located in the LOH11A region. Methods: Polymorphisms were found at nucleotide position (-)37 (C/A) and (-)524 (C/T) from the beginning of exon 1 of the RRM1 gene that might regulate the expression of RRM1. We studied the polymorphisms in 127 Korean individuals (66 lung cancer and 61 normal controls) and compared with those of 140 American patients with lung cancer. Results: CC, AC and AA were found at the (-)37 position in 64(50.4%), 55(43.3%), and 8(6.3%) out of 127 Korean individuals (66 cancer, 61 non-cancer patients), respectively. There was a similar frequency of allele A at (-)37 in the American(27.9%) and Korean population(28.0%). CC, CT and TT was found at the (-)524 position in 24(18.9%), 44(34.6%), and 59(46.5%) out of the 127 Korean individuals, respectively. There was a similar frequency of allele C at (-)524 in the American(34.6%) and Korean population(36.2%). There was no difference in the frequency of the (-)37 and (-)524 genotypes between the cancer and non-cancer group. However there was a significant correlation of the genotypes between (-)37 and (-)524 (p<0.001), which suggests the possible coordination of these polymorphisms in the regulation of the promoter activity of the RRM1 gene. Conclusion: RRM1 promoter polymorphisms were not found to be significant risk factors for lung cancer. However, a further study of the promoter activity and expression of the RRM1 gene according to the pattern of the polymorphism will be needed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12C
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• pp.1173-1183
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• 2006

In this paper, we propose an efficient method for improving visual quality of AR-FGS (Adaptive Reference FGS) which is adopted as a key scheme for SVC (Scalable Video Coding) or H.264 scalable extension. The standard FGS (Fine Granularity Scalability) adopts AR-FGS that introduces temporal prediction into FGS layer by using a high quality reference signal which is constructed by the weighted average between the base layer reconstructed imageand enhancement reference to improve the coding efficiency in the FGS layer. However, when the enhancement stream is truncated at certain bitstream position in transmission, the rest of the data of the FGS layer will not be available at the FGS decoder. Thus the most noticeable problem of using the enhancement layer in prediction is the degraded visual quality caused by drifting because of the mismatch between the reference frame used by the FGS encoder and that by the decoder. To solve this problem, we exploit the principle of cyclical block coding that is used to encode quantized transform coefficients in a cyclical manner in the FGS layer. Encoding block coefficients in a cyclical manner places 'higher-value' bits earlier in the bitstream. The quantized transform coefficients included in the ealry coding cycle of cyclical block coding have higher probability to be correctly received and decoded than the others included in the later cycle of the cyclical block coding. Therefore, we can minimize visual quality degradation caused by bitstream truncation by adjusting weighting factor to control the contribution of the bitstream produced in each coding cycle of cyclical block coding when constructing the enhancement layer reference frame. It is shown by simulations that the improved AR-FGS scheme outperforms the standard AR-FGS by about 1 dB in maximum in the reconstructed visual quality.

• Journal of Magnetics
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• v.20 no.4
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• pp.381-386
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• 2015

The purpose of this study is to improve diagnostic efficiency of clinical study by setting up guidelines for more precise examination with a comparative analysis of signal intensity and image distortion depending on the location of X axial of object when performing magnetic resonance diffusion weighted imaging (MR DWI) examination. We arranged the self-produced phantom with a 45 mm of interval from the core of 44 regent bottles that have a 16 mm of external diameter and 55 mm of height, and were placed in 4 rows and 11 columns in an acrylic box. We also filled up water and margarine to portrait the fat. We used 3T Skyra and 18 Channel Body array coil. We also obtained the coronal image with the direction of RL (right to left) by using scan slice thinkness 3 mm, slice gap: 0mm, field of view (FOV): $450{\times}450mm^2$, repetition time (TR): 5000 ms, echo time (TE): 73/118 ms, Matrix: $126{\times}126$, slice number: 15, scan time: 9 min 45sec, number of excitations (NEX): 3, phase encoding as a diffusion-weighted imaging parameter. In order to scan, we set b-value to $0s/mm^2$, $400s/mm^2$, and $1,400s/mm^2$, and obtained T2 fat saturation image. Then we did a comparative analysis on the differences between image distortion and signal intensity depending on the location of X axial based on iso-center of patient's table. We used "Image J" as a comparative analysis programme, and used SPSS v18.0 as a statistic programme. There was not much difference between image distortion and signal intensity on fat and water from T2 fat saturation image. But, the average value depends on the location of X axial was statistically significant (p < 0.05). From DWI image, when b-value was 0 and 400, there was no significant difference up to $2^{nd}$ columns right to left from the core of patient's table, however, there was a decline in signal intensity and image distortion from the $3^{rd}$ columns and they started to decrease rapidly at the $4^{th}$ columns. When b-value was 1,400, there was not much difference between the $1^{st}$ row right to left from the core of patient's table, however, image distortion started to appear from the $2^{nd}$ columns with no change in signal intensity, the signal was getting decreased from the $3^{rd}$ columns, and both signal intensity and image distortion started to get decreased rapidly. At this moment, the reagent bottles from outside out of 11 reagent bottles were not verified from the image, and only 9 reagent bottles were verified. However, it was not possible to verify anything from the $5^{th}$ columns. But, the average value depends on the location of X axial was statistically significant. On T2 FS image, there was a significant decline in image distortion and signal intensity over 180mm from the core of patient's table. On diffusion-weighted image, there was a significant decline in image distortion and signal intensity over 90 mm, and they became unverifiable over 180 mm. Therefore, we should make an image that has a diagnostic value from examinations that are hard to locate patient's position.

• Korean Journal of Microbiology
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• v.52 no.3
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• pp.278-285
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• 2016

The bifunctional PheA protein, having chorismate mutase and prephenate dehydratase (CMPD) activities, is one of the key regulatory enzymes in the aromatic amino acid biosynthesis in Escherichia coli, and is negatively regulated by an end-product, phenyalanine. Therefore, PheA protein has been thought as useful for protein engineering to utilize mass production of essential amino acid phenylalanine. To obtain feedback resistant PheA protein against phenylalanine, we mutated by using random mutagenesis, extensively screened, and obtained $pheA^{FBR}$ gene encoding a feedback resistant PheA protein. The mutant PheA protein contains substitution of Leu to Phe at the position of 118, displaying that higher affinity (about $290{\mu}M$) for prephenate in comparison with that (about $850{\mu}M$) of wild type PheA protein. Kinetic analysis showed that the saturation curve of $PheA^{FBR}$ against phenyalanine is hyperbolic rather than that of $PheA^{WT}$, which is sigmoidal, indicating that the L118F mutant enzyme has no cooperative effects in prephenate binding in the presence of phenylalanine. In vitro enzymatic assay showed that the mutant protein exhibited increased activity by above 3.5 folds compared to the wild type enzyme. Moreover, L118F mutant protein appeared insensitive to feedback inhibition with keeping 40% of enzymatic activity even in the presence of 10 mM phenylalanine at which the activity of wild type $PheA^{WT}$ was not observed. The substitution of Leu to Phe in CMPD may induce significant conformational change for this enzyme to acquire feedback resistance to end-product of the pathway by modulating kinetic properties.