• Journal of Microbiology and Biotechnology
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• v.24 no.8
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• pp.1044-1050
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• 2014

Since there is no consensus about the most reliable assays to detect invasive aspergillosis from samples obtained by minimally invasive or noninvasive methods, we compared the efficacy of an enzyme-linked immunosorbent assay (ELISA) for galactomannan (GM) detection and quantitative real-time PCR assay (qRT-PCR) for the diagnosis of invasive pulmonary aspergillosis. Neutropenic, male Sprague-Dawley rats (specific pathogen free; 8 weeks old; weight, $200{\pm}20g$) were immunosuppressed with cyclophosphamide and infected with Aspergillus fumigatus intratracheally. Tissue and whole blood samples were harvested on days 1, 3, 5, and 7 post-infection and examined with GM ELISA and qRT-PCR. The A. fumigatus DNA detection sequence was detected in the following number of samples from 12 immunosuppressed, infected rats examined on the scheduled days: day 1 (0/12), day 3 (0/12), day 5 (6/12), and day 7 (8/12) post-infection. The sensitivity and specificity of the qRT-PCR assay was 29.2% and 100%, respectively. Receiver operating characteristic curve (ROC) analysis indicated a Ct (cycle threshold) cut-off value of 15.35, and the area under the curve (AUC) was 0.627. The GM assay detected antigen in sera obtained on day 1 (5/12), day 3 (9/12), day 5 (12/12), and day 7 (12/12) post-infection, and thus had a sensitivity of 79.2% and a specificity of 100%. The ROC of the GM assay indicated that the optimal Ct cut-off value was 1.40 (AUC, 0.919). The GM assay was more sensitive than the qRT-PCR assay in diagnosing invasive pulmonary aspergillosis in rats.

• Tuberculosis and Respiratory Diseases
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• v.57 no.2
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• pp.125-131
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• 2004

Tuberculosis (TB) remains an enormous global health problem, and a new vaccine against TB more potent than the current inadequate BCG vaccine is urgently needed. We constructed three recombinant Mycobacterium bovis BCG (rBCG) strains over-expressing antigen (Ag) 85A, Ag85B, or both of M. tuberculosis using their own promoter and secretory sequence, or hsp60 promoter. SDS-PAGE analysis of rBCG proteins showed overexpression of Ag85A and Ag85B proteins in higher level than of those in their parental strain of BCG. In addition, rBCG(rBCG/B.FA) over-expressing Ag85A and Ag85B induced strong IFN-${\gamma}$ production in splenocytes. However, there was no significant difference in protective efficacy between rBCG and their parental BCG strain. In this study, therefore, rBCG over-expressing Ag85A, Ag85B, or both failed to show enhanced protection against M. tuberculosis infection in a mouse model.

• Journal of Microbiology and Biotechnology
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• v.18 no.12
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• pp.1975-1983
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• 2008

The neuropeptide ${\alpha}$-melanocyte-stimulating hormone (${\alpha}$-MSH) has anti-inflammatory property by down regulating the expressions of proinflammatory cytokines. Because ${\alpha}$-MSH elicits the anti-inflammatory effect in various inflammatory disease models, we examined the therapeutic effect of oral administration of recombinant Lactobacillus casei, which secretes ${\alpha}$-MSH (L. casei-${\alpha}$-MSH), on dextran sulfate sodium (DSS)-induced colitis in Balb/c mice. Thus, we constructed the ${\alpha}$-MSH-secreting Lactobacillus casei by the basic plasmid, pLUAT-ss, which was composed of a PldhUTLS promoter and ${\alpha}$-amylase signal sequence from Streptococcus bovis strain. Acute colitis was induced by oral administration of 5% DSS in drinking water for 7 days. To investigate the effect of L. casei-${\alpha}$-MSH on the colitis, L. casei or L. casei-${\alpha}$-MSH was orally administered for 7 days and their effects on body weight, mortality rate, cytokine production, and tissue myeloperoxidase (MPO) activity were observed. Administration of L. casei-${\alpha}$-MSH reduced the symptom of acute colitis as assessed by body weight loss (DSS alone: $14.45{\pm}0.2\;g$; L. casei-${\alpha}$-MSH: $18.2{\pm}0.12\;g$), colitis score (DSS alone: $3.6{\pm}0.4$; L. casei-${\alpha}$-MSH: $1.4{\pm}0.6$), MPO activity (DSS alone: $42.7{\pm}4.5\;U/g$; L. casei-${\alpha}$-MSH: $10.25{\pm}0.5\;U/g$), survival rate, and histological damage compared with the DSS alone mice. L. casei-${\alpha}$-MSH-administered entire colon showed reduced in vitro production of proinflammatory cytokines and $NF-{\kappa}B$ activation. The ${\alpha}$-MSH-secreting recombinant L. casei showed significant anti-inflammatory effects in the murine model of acute colitis and suggests a potential therapeutic role for this agent in clinical inflammatory bowel diseases.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.2
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• pp.117-124
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• 2019

Parallel sparse solvers are essential for solving large-scale finite element models. This paper introduces the combination of iterative and direct solver that can be applied efficiently to problems that require continuous solution for a subtly changing sequence of systems of equations. The iterative-direct sparse solver combination technique, proposed and implemented in the parallel sparse solver package, PARDISO, means that iterative sparse solver is applied for the newly updated linear system, but it uses the direct sparse solver's factorization of previous system matrix as a preconditioner. If the solution does not converge until the preset iterations, the solution will be sought by the direct sparse solver, and the last factorization results will be used as a preconditioner for subsequent updated system of equations. In this study, an improved method that sets the maximum number of iterations dynamically at the first Krylov iteration step is proposed and verified thereby enhancing calculation efficiency by the frequency domain analysis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.2
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• pp.74-82
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• 2015

In this study, a BMW plant process control system model which produces BMW is suggested and the BMW plant process controller with the following functions is developed. The first function is to operate the electronic overload relays to stop the blower for a certain period of time and to re-operate it again when the blower is overloaded. The second function is to close the motor operated valve automatically in case of power failure to prevent the circulation from the guided tank to the compost throwing tank and to block leak from the compost throwing tank due to the failure of ball valve. The third function is to transfer produced BMW from the concentration tank to 4 storage tanks for automatic managing of the BMW output. A device to measure the signal of the BMW plant process controller and a test equipment are developed. The designed BMW plant process controller is checked to see if it operates correctly according to the design specifications. The sequence control method based on BMW plant process controller is developed at a low cost in this study, so it is expected to bring improvements in the stability and the efficiency of system and to cause reductions in the operation and the management costs in the future.

• Journal of Distribution Science
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• v.12 no.10
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• pp.75-84
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• 2014

Purpose - This article aims to examine whether the stock issuance of firms in the retail industry follows Myers' (1984) pecking order theory, which is based on information asymmetry. According to the pecking order model, firms have a sequence of financing decisions, of which the first choice is to use retained earnings, the second one is to get into safe debt, the next involves risky debt, and the last involves finance with outside equity. Since the 2000s, the polarization of the LEs (Large enterprises) and SMEs (Small and Medium Enterprises) arose in the retail industry. The LEs exhibited an improvement in growth and profitability, whereas SMEs had a tendency to degenerate. This study contributes to corroborating the features of financing decisions in the retail industry distinguished from the other industries. Research design, data, and methodology - This study considers the stocks listed on the KOSPI and KOSDAQ markets from 1991 to 2013, and is more concentrated on the stocks in the retail industry. The data were collected from the financial information company, WISEfn. The empirical analysis is conducted by employing two measures of net equity issues (and), which were introduced in Fama and French (2005), and can be calculated from firms' accounting information. All variables are generated as the aggregate value of the numerator divided by aggregate assets, which, in effect, treats the entire sample as a single firm. Substantially, the financing decisions of the firms were analyzed by examining how often and under what circumstances firms issue and repurchase equity. Then, this study compares the features of the retail industry with those of the other industries. Results - The proportion of sample firms that show annual net stock issues reaching the level of the year's average was 54.33% for the 1990s, and fell to 39.93% per year for the 2000s. In detail, the fraction of the small firms actually increases from 45.08% to 51.04%, whereas that of large firms shows a dramatic decline from 58.94% to 24.76%. Considering the fact that the large firms' rapid increase in growth after the 2000s may lead to an increase in equity issues, this result is rather surprising. Meanwhile, net stock repurchases of assets are considerably disproportionate between the large (-50.11%) and the small firms (-15.66%) for the 2000s. Conclusions - Stock issuance of retail firms is not in line with the traditional seasoned equity offering based on information asymmetry. The net stock issuance of the small firms in the retail industry can be interpreted as part of an effort to reorganize business and solicit new investment to resolve degenerating business performance. For large firms, on the other hand, the net repurchase can be regarded as part of an effort to rearrange business for efficiency and amplifying synergy across business sections through spin-off. These results can help the government establish a support policy on retail industry according to size.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.325-330
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• 2005

We are generally interested in the analysis, detection and prediction of structural motifs in proteins, in order to infer compatibility of amino acid sequence to structure in proteins of known three-dimensional structure available in the Protein Data Bank. In this context, we are analyzing some of the well-characterized structural motifs in proteins. We have analyzed simple structural motifs, such as, ${\beta}$-turns and ${\gamma}$-turns by evaluating the statistically significant type-dependent amino acid positional preferences in enlarged representative protein datasets and revised the amino acid preferences. In doing so, we identified a number of ‘unexpected’ isolated ${\beta}$-turns with a proline amino acid residue at the (i+2) position. We extended our study to the identification of multiple turns, continuous turns and to peptides that correspond to the combinations of individual ${\beta}$ and ${\gamma}$-turns in proteins and examined the hydrogen-bond interactions likely to stabilize these peptides. This led us to develop a database of structural motifs in proteins (DSMP) that would primarily allow us to make queries based on the various fields in the database for some well-characterized structural motifs, such as, helices, ${\beta}$-strands, turns, ${\beta}$-hairpins, ${\beta}$-${\alpha}$-${\beta}$, ${\psi}$-loops, ${\beta}$-sheets, disulphide bridges. We have recently implemented this information for all entries in the current PDB in a relational database called ODSMP using Oracle9i that is easy to update and maintain and added few additional structural motifs. We have also developed another relational database corresponding to amino acid sequences and their associated secondary structure for representative proteins in the PDB called PSSARD. This database allows flexible queries to be made on the compatibility of amino acid sequences in the PDB to ‘user-defined’ super-secondary structure conformation and vice-versa. Currently, we have extended this database to include nearly 23,000 protein crystal structures available in the PDB. Further, we have analyzed the ‘structural plasticity’ associated with the ${\beta}$-propeller structural motif We have developed a method to automatically detect ${\beta}$-propellers from the PDB codes. We evaluated the accuracy and consistency of predicting ${\beta}$ and ${\gamma}$-turns in proteins using the residue-coupled model. I will discuss results of our work and describe databases and software applications that have been developed.

• Journal of The Korean Association For Science Education
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• v.33 no.7
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• pp.1385-1402
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• 2013

This study analyzes pre-service teachers' PCK dealing with visualization of the contents related to boiling point elevation and teaching methods in mock-lessons. As a result of analyzing pre-service teachers' knowledge based on PCK factors, most of the pre-service teachers accentuated on understanding boiling point elevation conceptually, whereas some of the others inclined to make students understand boiling point elevation in a scientific way, let the kids use numerical formulas to describe the concept, and motivate them to learn through the examples in real life. The pre-service teachers represented majority of the important facts of boiling point elevation as the knowledge required to understand things conceptually. However, they did not focus on improving the scientific thinking and inquiring levels of the students. Also, the pre-service teachers tended to teach at the level and order of the textbook. In some other cases, they considered the vocabularies and materials in the textbook (which could have been highlighted in the editing sequence) as the main topic to learn, or regarded the goal as giving students the ability to solve exercises in the textbook. It turned out that the pre-service teachers had a low level of knowledge of their students. It is recommended that they should make use of the materials given (such as data related to the misconception of students) during the training session. The knowledge of teaching and evaluating students was described superficially by the pre-service teachers; they merely mentioned the applications of models, such as the cyclic model and discovery learning, rather than thinking of a method related to the goals, or listed general assessment methods.

• Geophysics and Geophysical Exploration
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• v.17 no.3
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• pp.129-136
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• 2014

Electromagnetic (EM) methods are generally divided into frequency-domain EM (FDEM) and time-domain EM (TDEM) methods, depending on the source waveform. The FDEM and TDEM fields are mathematically related by the Fourier transformation, and the TDEM field can thus be obtained as the Fourier transformation of FDEM data. For modeling in time-domain, we can use fast frequency-domain modeling codes and then convert the results to the time domain with a suitable numerical method. Thus, frequency-to-time transformations are of interest to EM methods, which is generally attained through fast Fourier transform. However, faster frequency-to-time transformation is required for the 3D inversion of TDEM data or for the processing of vast air-borne TDEM data. The diffusion expansion method (DEM) is one of smart frequency-to-time transformation methods. In DEM, the EM field is expanded into a sequence of diffusion functions with a known frequency dependence, but with unknown diffusion-times that must be chosen based on the data to be transformed. Especially, accuracy of DEM is sensitive to the diffusion-time. In this study, we developed a method to determine the optimum range of diffusion-time values, minimizing the RMS error of the frequency-domain data approximated by the diffusion expansion. We confirmed that this method produces accurate results over a wider time range for a homogeneous half-space and two-layered model.