• Molecules and Cells
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• v.27 no.4
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• pp.423-428
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• 2009

Superoxide dismutases (SODs) constitute the first line of cellular defense against oxidative stress in plants. SODs generally occur in three different forms with Cu/Zn, Fe, or Mn as prosthetic metals. We cloned the full-length cDNA of the Thellungiella halophila Cu/Zn-SOD gene ThCSD using degenerate RT-PCR and rapid amplification of cDNA ends (RACE). Sequence analysis indicated that the ThCSD gene (GenBank accession number EF405867) had an open reading frame of 456 bp. The deduced 152-amino acid polypeptide had a predicted molecular weight of 15.1 kDa, an estimated pI of 5.4, and a putative Cu/Zn-binding site. Recombinant ThCSD protein was expressed in Escherichia coli and assayed for SOD enzymatic activity in a native polyacrylamide gel. The SOD activity of ThCSD was inactivated by potassium cyanide and hydrogen peroxide but not by sodium azide, confirming that ThCSD is a Cu/Zn-SOD. Northern blotting demonstrated that ThCSD is expressed in roots, stems, and leaves. ThCSD mRNA levels increased by about 30-fold when plants were treated with sodium chloride (NaCl), abscisic acid (ABA), and indole-acetic acid (IAA) and by about 50-fold when treated with UVB light. These results indicate that ThCSD is involved in physiological pathways activated by a variety of environmental conditions.

• Korean Journal of Ecology and Environment
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• v.36 no.2 s.103
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• pp.108-116
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• 2003

To investigate bacteria with algal Iytic activities against Anabaena cylindrica when water blooming occurs and to study enzyme profiles of alga-Iytic bacteria, various bacterial strains were isolated from surface waters and sediments in eutrophic lakes or reservoirs in Korea. Abacterial strain AK-07 was characterized and identified as Acinetobacter johnsonii based on its16S rDNA base sequence. When AK-07 was co-cultivated with A. cylindrica, bacterial cells propagated to $8\;{\times}\;10^8$ cfu $ml^{-1}$ and Iyses algal cells. However, culture filtrates of AK-07 did not exhibit algal Iytic activities. That suggesting the enzymes on the surfaces of the bacterium might be effective algal Iytic agents to cause Iyses of cells. Acinetobacter johnsonii AK-07 exhibited high degradation activities against A. cylindrica, and formed alginase, caseinase, lipase, fucodian hydrolase, and laminarinase. Moreover, glycosidases for example ${\beta}$-galatosidase, ${\beta}$-glucosidase, ${\beta}$-glucosaminidase, and ${\beta}$-xylosidase, which hydrolyzed ${\beta}$-0-glycosidic bonds, were found in cell-free extracts of A. johnsonii AK-07. Other glycosidase such as ${\alpha}$-galctosidases, ${\alpha}$-N-Ac-galctosidases, ${\alpha}$-mannosidases, and ${\alpha}$- L-fuco-sidases, which cleavage ${\alpha}$-0-glycosidic bondsare not detected. In the results, enzyme systemsof A. johnsonii AK-07 were very complex to do-grade cell walls of cyanobacteria. The polysaccharides or peptidoglycans of A. cylindrica maybe hydrolyzed and metabolized to a range of easily utilizable monosaccharides or other low molecular weight organic substances by strain AK-07 of A. johnsonii.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.113-122
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• 2009

Since the traditional pattern mining methods only probe unspecified moving patterns that seem to satisfy users' requests among diverse patterns within the limited scopes of time and space, they are not applicable to problems involving the mining of optimal moving patterns, which contain complex time and space constraints, such as 1) searching the optimal path between two specific points, and 2) scheduling a path within the specified time. Therefore, in this paper, we illustrate some problems on mining the optimal moving patterns with complex time and space constraints from a vast set of historical data of numerous moving objects, and suggest a new moving pattern mining method that can be used to search patterns of an optimal moving path as a location-based service. The proposed method, which determines the optimal path(most frequently used path) using pattern frequency retrieved from historical data of moving objects between two specific points, can efficiently carry out pattern mining tasks using by space generalization at the minimum level on the moving object's location attribute in consideration of topological relationship between the object's location and spatial scope. Testing the efficiency of this algorithm was done by comparing the operation processing time with Dijkstra algorithm and $A^*$ algorithm which are generally used for searching the optimal path. As a result, although there were some differences according to heuristic weight on $A^*$ algorithm, it showed that the proposed method is more efficient than the other methods mentioned.

• Journal of agriculture & life science
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• v.46 no.4
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• pp.9-20
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• 2012

Eugenol is a volatile compound synthesized by eugenol synthase in various plants and belongs to phenylpropene compounds. However, characteristics of eugenol synthase in tomato has not been known. Therefore, we cloned a full length cDNA of a putative eugenol synthase from tomato 'Micro-Tom' using rapid amplification of cDNA ends (RACE) technique and named a clone SlEGS. Open reading frame of SlEGS was 921bp long and its deduced amino acid sequence was 307bp. The BLAST analysis indicated that SlEGS shared high similarity with PhEGS1 (67.1%) and CbEGS2 (69.4%). Amino acid composition of SlEGS was determined by CLC genomics workbench tool and 3D structure of SlEGS was constructed by homology modeling using Swiss-PDB viewer and validated using PROCHECK and ProSA-web tool. In addition, the physiochemical properties of SlEGS was evaluated using ExPASy's ProtParam tool. Molecular weight was 33.93kDa and isoelectric point was 5.85 showing acidic nature. Other properties such as extinction coefficient, instability index, aliphatic index, and grand average hydropathy was also analyzed.

• YAKHAK HOEJI
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• v.52 no.3
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• pp.195-200
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• 2008

Gabapentin, [1-(aminomethyl) cyclohexaneacetic acid], a structural analog of $\gamma$-aminobutyric acid (GABA), is being developed for the treatment of epilepsy. Unlike GABA, gabapentin crosses the blood-brain barrier after systemic administration. Gabapentin is an effective antiepileptic drug in patients with partial and secondarily generalized seizures who are uncontrolled with use of existing anticonvulsant drug therapy. The purpose of the present study was to evaluate the bioequivalence of two gabapentin 400 mg capsules, $Neurontin^{(R)}$ capsule 400 mg (Pfizer Inc.) and Gabatin capsule 400 mg (Korean Drug Co. Ltd), according to the guidelines of the Korea Food and Drug Administration (KFDA). The release of gabapentin from the two gabapentin formulations in vitro was tested using KP VIII Apparatus II method with various dissolution media (pH 1.2, 4.0, 6.8 buffer solution and water). Twenty six healthy male subjects, 23.58$\pm$1.50 years in age and 66.74$\pm$8.31 kg in body weight, were divided into two groups and a randomized 2$\times$2 cross-over study was employed. After one capsule containing 400 mg as gabapentin were orally administered, blood was taken at predetermined time intervals and the concentrations of gabapentin in serum were determined using HPLC with fluorescence detector. The dissolution profiles of two formulations were similar at all dissolution media. In addition, the pharmacokinetic parameters such as $AUC_t$, $C_{max}$ and $T_{max}$ were calculated and ANOVA test was utilized for the statistical analysis of the parameters using logarithmically transformed $AUC_t$, $C_{max}$ and untransformed $T_{max}$. The results showed that the differences between two formulations based on the reference drug, $Neurontin^{(R)}$ capsule 400 mg, were 2.04, -3.68 and 16.79% for $AUC_t$, $C_{max}$ and $T_{max}$, respectively. There were no sequence effects between two formulations in these parameters. The 90% confidence intervals using logarithmically transformed data were within the acceptance range of log 0.8 to log 1.25 (e.g., log 0.91$\sim$log 1.16 and log 0.87$\sim$log 1.11 for $AUC_t$ and $C_{max}$, respectively). Thus, the criteria of the KFDA bioequivalence guideline were satisfied, indicating Gabatin capsule 400 mg was bioequivalent to $Neurontin^{(R)}$ capsule 400 mg.

• YAKHAK HOEJI
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• v.52 no.3
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• pp.188-194
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• 2008

Fexofenadine, ($\pm$)-4-1-hydroxy-4-{4-(hydroxydiphenylmethyl)-1-piperidinyl}-butyl-a,a-dimethyl benzeneacetic acid, is a selective histamine $H_1$ receptor antagonist, and is clinically effective in the treatment of seasonal allergic rhinitis and chronic idiopathic urticaria as a first-line therapeutic agent. The purpose of the present study was to evaluate the bioequivalence of two fexofenadine hydrochloride tablets, $Allegra^{(R)}$ (Handok Pharmaceuticals Co., Ltd.) and Alecort (Samchundang Pharmaceutical Co., Ltd.), according to the guidelines of the Korea Food and Drug Administration (KFDA). The release of fexofenadine from the two fexofenadine hydrochloride formulations in vitro was tested using KP VIII Apparatus II method with various dissolution media. Twenty six healthy male subjects, 25.62$\pm$3.35 years in age and 70.05$\pm$11.71 kg in body weight, were divided into two groups and a randomized 2$\times$2 cross-over study was employed. After a single tablet containing 120 mg as fexofenadine hydrochloride was orally administered, blood samples were taken at predetermined time intervals and the concentrations of fexofenadine in serum were determined using HPLC with fluorescence detector. The dissolution profiles of two formulations were similar in all tested dissolution media. The harmacokinetic parameters such as $AUC_t$, $C_{max}$ and $T_{max}$ were calculated, and ANOVA test was utilized for the statistical analysis of the parameters using logarithmically transformed $AUC_t$, $C_{max}$ and untransformed $T_{max}$. The results showed that the differences between two formulations based on the reference drug, $Allegra^{(R)}$, were -1.37, 5.22 and 16.50% for $AUC_t$, $C_{max}$ and $T_{max}$, respectively. There were no sequence effects between two formulations in these parameters. The 90% confidence intervals using logarithmically transformed data were within the acceptance range of log 0.8 to log 1.25 (e.g., log 0.83$\sim$log 1.08 and log 0.81$\sim$log 1.03 for $AUC_t$ and $C_{max}$, respectively). Thus, the criteria of the KFDA bioequivalence guideline were satisfied, indicating Alecort tablet was bioequivalent to $Allegra^{(R)}$ tablet.

• Microbiology and Biotechnology Letters
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• v.48 no.4
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• pp.463-470
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• 2020

The JS18 strain was isolated from an old tree forest and produced extracellular enzymes that decolorize synthetic melanin. Phylogenetic analysis, based on the internal transcribed spacer (ITS) sequence, indicate that JS18 belongs to the Trametes velutina species. JS18 demonstrated laccase activity but no manganese peroxidase or lignin peroxidase activity. Batch culture indicated that the melanin decolorization activity of JS18 strain originated from the laccase. Syringic acid and CuSO4 induced maximum laccase production, yielding 98 U/ml laccase activity after cultivation for 7 days at 25℃. T. velutina secretes an extracellular laccase in GYP medium, and this enzyme was purified using (NH4)2SO4 precipitation, Hi-trap Q Sepharose columns and gel filtration. The molecular weight of the purified enzyme was estimated to be 67 kDa using sodium dodecyl sulfate polyacrylamide gel electrophoresis. This enzyme produced 80% of its melanin decolorization activity within the first 24 h of evaluation in the presence of 1-hydroxybenzotriazole (HBT), while only about 4% of the melanin was decolorized in the absence of the mediator. The greatest decolorization was observed at 1.5 mM/l HBT, which decolorized 81% of the melanin within the first 24 h. The optimum pH and temperature for this decolorization were found to be 5.0 and 37℃, respectively. Our results suggest the possibility of applying HBT induced T. velutina JS18 laccase-catalyzed melanin decolorization.

• Journal of Microbiology and Biotechnology
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• v.33 no.8
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• pp.1023-1029
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• 2023

Biosurfactants reduce surface and interfacial tension due to their amphiphilic properties and are an eco-friendly alternative for chemical surfactants. In this study, a new yeast strain JAF-11 that produces a biosurfactant was selected using drop collapse method, and the properties of the extracts were investigated. The nucleotide sequences of the strain were compared with closely related strains and identified based on the D1/D2 domain of the large subunit ribosomal DNA (LSU) and internal transcribed spacer (ITS) regions. Neodothiora populina CPC 39399^T, the closest species with strain JAF-11, showed a sequence similarity of 97.75% for LSU and 94.27% for ITS, respectively. The result suggests that the strain JAF-11 represents a distinct species that cannot be assigned to any existing genus or species in the family Dothideaceae. Strain JAF-11 produced a biosurfactant reducing the surface tension of water from 72 mN/m to 34.5 mN/m on the sixth day of culture and the result of measuring the critical micelle concentration (CMC) by extracting the crude biosurfactant was found to be 24 mg/l. The molecular weight 502 of the purified biosurfactant was confirmed by measuring the fast atom bombardment mass spectrum. The chemical structure was analyzed by measuring ¹H nuclear magnetic resonance (NMR), ¹³C NMR, and two-dimensional NMRs of the compound. The molecular formula was C₂₆H₄₆O₉, and it was composed of one octanoyl group and two hexanoyl groups to myo-inositol moiety. The new biosurfactant is the first report of a compound produced by a new yeast strain, JAF-11.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.37-44
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• 2024

Even though MR can reveal excellent soft-tissue contrast and functional information, CT is also required for electron density information for accurate dose calculation in Radiotherapy. For the fusion of MRI and CT images in RT treatment planning workflow, patients are normally scanned on both MRI and CT imaging modalities. Recently deep-learning-based generations of CT images from MR images became possible owing to machine learning technology. This eliminated CT scanning work. This study implemented a CycleGan deep-learning-based CT image generation from MR images. Three CT generators whose learning is based on T1- , T2- , or T1-&T2-weighted MR images were created, respectively. We found that the T1-weighted MR image-based generator can generate better than other CT generators when T1-weighted MR images are input. In contrast, a T2-weighted MR image-based generator can generate better than other CT generators do when T2-weighted MR images are input. The results say that the CT generator from MR images is just outside the practical clinics and the specific weight MR image-based machine-learning generator can generate better CT images than other sequence MR image-based generators do.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.