• Parasites, Hosts and Diseases
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• v.53 no.2
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• pp.147-154
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• 2015

Various constituents in clinical specimens, particularly feces, can inhibit the PCR assay and lead to false-negative results. To ensure that negative results of a diagnostic PCR assay are true, it should be properly monitored by an inhibition control. In this study, a cloning vector harboring a modified target DNA sequence (${\approx}375bp$) was constructed to be used as a competitive internal amplification control (IAC) for a conventional PCR assay that detects ${\approx}550bp$ of the Cryptosporidium oocyst wall protein (COWP) gene sequence in human feces. Modification of the native PCR target was carried out using a new approach comprising inverse PCR and restriction digestion techniques. IAC was included in the assay, with the estimated optimum concentration of 1 fg per reaction, as duplex PCR. When applied on fecal samples spiked with variable oocysts counts, ${\approx}2$ oocysts were theoretically enough for detection. When applied on 25 Cryptosporidium-positive fecal samples of various infection intensities, both targets were clearly detected with minimal competition noticed in 2-3 samples. Importantly, both the analytical and the diagnostic sensitivities of the PCR assay were not altered with integration of IAC into the reactions. When tried on 180 randomly collected fecal samples, 159 were Cryptosporidium-negatives. Although the native target DNA was absent, the IAC amplicon was obviously detected on gel of all the Cryptosporidium-negative samples. These results imply that running of the diagnostic PCR, inspired with the previously developed DNA extraction protocol and the constructed IAC, represents a useful tool for Cryptosporidium detection in human feces.

• Journal of Microbiology and Biotechnology
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• v.31 no.4
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• pp.570-583
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• 2021

Pyrococcus furiosus α-amylase can hydrolyze α-1,4 linkages in starch and related carbohydrates under hyperthermophilic condition (~ 100℃), showing great potential in a wide range of industrial applications, while its relatively low productivity from heterologous hosts has limited the industrial applications. Bacillus subtilis, a gram-positive bacterium, has been widely used in industrial production for its non-pathogenic and powerful secretory characteristics. This study was conducted to increase production of P. furiosus α-amylase in B. subtilis through three strategies. Initial experiments showed that co-expression of P. furiosus molecular chaperone peptidyl-prolyl cis-trans isomerase through genomic integration mode, using a CRISPR/Cas9 system, increased soluble amylase production. Therefore, considering that native P. furiosus α-amylase is produced within a hyperthermophilic environment and is highly thermostable, heat treatment of intact culture at 90℃ for 15 min was performed, thereby greatly increasing soluble amylase production. After optimization of the culture conditions (nitrogen source, carbon source, metal ion, temperature and pH), experiments in a 3-L fermenter yielded a soluble activity of 3,806.7 U/ml, which was 3.3- and 28.2-fold those of a control without heat treatment (1,155.1 U/ml) and an empty expression vector control (135.1 U/ml), respectively. This represents the highest P. furiosus α-amylase production reported to date and should promote innovation in the starch liquefaction process and related industrial productions. Meanwhile, heat treatment, which may promote folding of aggregated P. furiosus α-amylase into a soluble, active form through the transfer of kinetic energy, may be of general benefit when producing proteins from thermophilic archaea.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.5
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• pp.39-53
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• 2007

The purpose of this paper is the development of a compensation algorithm by which the interoperability of fingerprint recognition can be improved among various different fingerprint sensor. In order to compensate for the different characteristics of fingerprint sensor, an initial evaluation of the sensors using both the ink-stamped method and the flat artificial finger pattern method was undertaken. This paper proposes Common resolution method and Relative resolution method for compensating different resolution of fingerprint images captured by disparate sensors. Both methods can be applied to image-level and minutia-level. In order to compensate the direction of minutiae in minutia-level, Unit vector method is proposed. The EER of the proposed method was improved by average 64.8% better than before compensation. This paper will make a significant contribution to interoperability in the system integration using different sensors.

• Tribology and Lubricants
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• v.38 no.3
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• pp.73-83
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• 2022

This paper is concerned with an analysis of a surface edge crack emanated from a sharp contact edge. For a geometrical model, a square wedge is in contact with a half plane whose materials are identical, and a surface perpendicular crack initiated from the contact edge exists in the half plane. To analyze this crack problem, it is necessary to evaluate the stress field on the crack line which are induced by the contact tractions and pseudo-dislocations that simulate the crack, using the Bueckner principle. In this Part I, the stress filed in the half plane due to the contact is re-summarized using an asymptotic analysis method, which has been published before by the author. Further focus is given to the stress field in the half plane due to a pseudo-edge dislocation, which will provide a stress solution due to a crack (i.e. a continuous distribution of edge dislocations) later, using the Burgers vector. Essential result of the present work is the corrective functions which modify the stress field of an infinite domain to apply for the present one which has free surfaces, and thus the infiniteness is no longer preserved. Numerical methods and coordinate normalization are used, which was developed for an edge crack problem, using the Gauss-Jacobi integration formula. The convergence of the corrective functions are investigated here. Features of the corrective functions and their application to a crack problem will be given in Part II.

• Land and Housing Review
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• v.14 no.4
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• pp.95-102
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• 2023

Numerous investigations have explored the integration of machine leaning algorithms with high-resolution drone image for object detection in urban settings. However, a prevalent limitation in vehicle extraction studies involves the reliance on bounding boxes rather than instance segmentation. This limitation hinders the precise determination of vehicle direction and exact boundaries. Instance segmentation, while providing detailed object boundaries, necessitates labour intensive labelling for individual objects, prompting the need for research on automating unsupervised instance segmentation in vehicle extraction. In this study, a novel approach was proposed for vehicle extraction utilizing unsupervised SVM classification applied to vehicle bounding boxes in drone images. The method aims to address the challenges associated with bounding box-based approaches and provide a more accurate representation of vehicle boundaries. The study showed promising results, demonstrating an 89% accuracy in vehicle extraction. Notably, the proposed technique proved effective even when dealing with significant variations in spectral characteristics within the vehicles. This research contributes to advancing the field by offering a viable solution for automatic and unsupervised instance segmentation in the context of vehicle extraction from image.

• The Journal of Engineering Geology
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• v.34 no.1
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• pp.107-115
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• 2024

This study assesses forest fire susceptibility in Gangwon-do, South Korea, which hosts the largest forested area in the nation and constitutes ~21% of the country's forested land. With 81% of its terrain forested, Gangwon-do is particularly susceptible to wildfires, as evidenced by the fact that seven out of the ten most extensive wildfires in Korea have occurred in this region, with significant ecological and economic implications. Here, we analyze 480 historical wildfire occurrences in Gangwon-do between 2003 and 2019 using 17 predictor variables of wildfire occurrence. We utilized three machine learning algorithms—random forest, logistic regression, and support vector machine—to construct wildfire susceptibility prediction models and identify the best-performing model for Gangwon-do. Forest and soil map data were integrated as important indicators of wildfire susceptibility and enhanced the precision of the three models in identifying areas at high risk of wildfires. Of the three models examined, the random forest model showed the best predictive performance, with an area-under-the-curve value of 0.936. The findings of this study, especially the maps generated by the models, are expected to offer important guidance to local governments in formulating effective management and conservation strategies. These strategies aim to ensure the sustainable preservation of forest resources and to enhance the well-being of communities situated in areas adjacent to forests. Furthermore, the outcomes of this study are anticipated to contribute to the safeguarding of forest resources and biodiversity and to the development of comprehensive plans for forest resource protection, biodiversity conservation, and environmental management.

• Korean Journal of Microbiology
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• v.51 no.3
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• pp.256-262
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• 2015

In order to study the effect of the receptor protein (SeaR), which is isolated from Saccharopolyspora erythraea, we introduced the SeaR gene to Streptomyces virginiae as host strains. An effective transformation procedure for S. virginiae was established based on transconjugation by Escherichia coli ET12567/pUZ8002 with a ${\varphi}C31$-derived integration vector, pSET152, which contained int, oriT, attP, and $ermEp^{\ast}$ (erythromycin promotor). Therefore, the pEV615 was introduced into S. virginiae by conjugation and integrated at the attB locus in the chromosome of the recipients by the ${\varphi}C31$ integrase (int) function. Transformants of S. virginiae containing the SeaR gene were confirmed by PCR and transcriptional expression of the SeaR gene in the transformants was analyzed by RT-PCR, respectively. And, we examined the production time of virginiamycins in the culture media of both the transformants and the wild type. The production time of virginiamycins in the wild type and transformants was the same. When 100 ng/ml of synthetic $VB-C_6$ was added to the state of 6 or 8 hour cultivation of wild type and transformants, respectively, the virginiamycins production was induced, meaning that the virginiamycins production in the wild type was detected 2 h early than transformants. From these results, SeaR expression was also affected to virginiamycins production in transformants derived from S. virginiae. In this study, we showed that the SeaR protein worked as a repressor in transformants.

• Korean Journal of Microbiology
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• v.51 no.1
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• pp.39-47
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• 2015

Secondary metabolism in actinomycetes has been known to be controlled by a small molecule, ${\gamma}$-butyrolactone autoregulator, the binding of which to each corresponding receptor leads to the regulation of the transcriptional expression of the secondary metabolites. We expected that expression of an autoregulator receptor or a pleiotropic regulator in a non-host was to be gained insight of effective production of new metabolic materials. In order to study the function of the receptor protein (seaR), which is isolated from Saccharopolyspora erythraea, we introduced the seaR gene to Streptomyces coelicolor A3(2) as host strains. An effective transformation procedure for S. coelicolor A3(2) was established based on transconjugation by Escherichia coli ET12567/pUZ8002 with a ${\varphi}C31$-derived integration vector, pSET152, which contained int, oriT, attP and $ermEp^*$ (erythromycin promotor). Therefore, the pEV615 was introduced into S. coelicolor A3(2) by conjugation and integrated at the attB locus in the chromosome of the recipients by the ${\varphi}C31$ integrase (int) function. Exconjugant of S. coelicolor A3(2) containing the seaR gene was confirmed by PCR and transcriptional expression of the seaR gene in the transformant was analyzed by RT-PCR. In case of S. coelicolor A3(2), a phenotype microarray was used to analyze the phenotype of transformant compared with wild type by seaR expression. After that, in order to confirm the accuracy of the results obtained from the phenotype microarray, an antimicrobial susceptibility test was carried out. This test indicated that sensitivity of the transformant was higher than wild type in tetracycline case. These results indicated that some biosynthesis genes or resistance genes for tetracycline biosynthesis in transformant might be repressed by seaR expression. Therefore, subsequent experiments, analysis of transcriptional pattern of genes for tetracycline production or resistance, are needed to confirm whether biosynthesis genes or resistance genes for tetracycline are repressed or not.

• Radiation Oncology Journal
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• v.17 no.4
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• pp.299-306
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• 1999

Purpose : The human genetic disorder ataxia-telangiectasia (AT) is a multisystem disease characterized by extreme radiosensitivity. The recent identification of the gene mutated in AT, ATM, and the demonstration that it encodes a homologous domain of phosphatidylinositol 3-kinase (PI3-K), the catalytic subunit of an enzyme involved in transmitting signals from the cell surface to the nucleus, provide support for a role of this gene in signal transduction. Although ionizing radiation was known to induce c-fos transcription, nothing is known about how ATM or PKCI mediated signal transduction pathway modulates the c-fos gene transcription and gene expression. Here we have studied the effect of PKCI on radiation sensitivity and c-fos transcription in normal and AT cells. Materials and Methods: Normal (LM217) and AT (AT5BIVA) cells were transfected with PKCI expression plasmid and the overexpression and integration of PKCI was evaluated by northern blotting and polymerase chain reaction, respectively. 5 Gy of radiation was exposed to LM and AT cells transfected with PKCI expression plasmid and cells were harvested 48 hours after radiation and investigated apoptosis with TUNEL method. The c-fos transcription activity was studied by performing CAT assay of reporter gene after transfection of c-fos CAT plasmid into AT and LM cells. Results: Our results demonstrate for the first time a role of PKCI on the radiation sensitivity and c-fos expression in LM and AT cells. PKCI increased radiation induced apoptosis in LM cells but reduced apoptosis in AT cells. The basal c-fos transcription activity is 70 times lower in AT cells than that in LM cells. The c-fos transcription activity was repressed by overexpression of PKCI in LM cells but not in AT cells. After induction of c-fos by Ras protein, overexpression of PKCI repressed c-fos transcription in LM cells but not in AT cells Conclusion: Overexpression of PKCI increased radiation sensitivity and repressed c-fos transcription in LM cells but not in AT cells. The results may be a. reason of increased radiation sensitivity of AT cells. PKCI may be involved in an ionizing radiation induced signal transduction pathway responsible for radiation sensitivity and c-fos transcription. The data also provided evidence for novel transcriptional difference between LM and AT cells.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.