• Tunnel and Underground Space
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• v.25 no.6
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• pp.534-542
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• 2015

Round trip activity occurs discretely due to the abrasion of drill bit in the deep drilling project. Round trip has great impact on the drilling performance because it takes more time to change a drill bit as the depth goes deeper. Therefore, a reliable prediction technology of the round trip should be secured for feasibility analysis and effective management of the drilling project. Lee et al. (2013) developed the TOSA (round trip occurrence simulation algorithm) which can analyze the depth and timing of round trip occurrence at each abrasion state of bit. However, TOSA has weakness that it takes long time for simulation because the number of simulation increase exponentially as increasing the number of simulation section. This study developed the TOSA based round trip performance prediction module using genetic algorithm for simulating in a short time and verified simulation results.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.215-224
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• 2010

Most chronic diseases are complex diseases which are caused by interactions of several genes. Studies on finding SNPs and gene-gene interactions involved in the development of complex diseases can contribute to prevention and treatment of the diseases. Previous studies mostly concentrate on finding only the set of SNPs involved. In this study we suggest a way to see how these SNPs interact using boolean expressions. The proposed method consists of two stages. In the first stage we find the set of SNPs involved in the development of diseases using mutual information based on entropy. In the second stage we find the highest accuracy boolean expression that consists of the SNP set obtained in the first stage. We experimented with clinical data to demonstrate the effectiveness of the proposed method. We also compared the differences between our method and the previous results on the SNP associations studies.

• KIISE Transactions on Computing Practices
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• v.23 no.9
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• pp.557-563
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• 2017

When large-scale gene expression data are analyzed using lasso regression, the estimation of regression coefficients may be unstable due to the highly correlated expression values between associated genes. This irregularity, in which the coefficients are reduced by L1 regularization, causes difficulty in variable selection. To address this problem, we propose a regression model which exploits the repetitive bootstrapping of gene expression values prior to lasso regression. The genes selected with high frequency were used to build each regression model. Our experimental results show that several genes were consistently selected in all regression models and we verified that these genes were not false positives. We also identified that the sign distribution of the regression coefficients of the selected genes from each model was correlated to the real dependent variables.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.533-541
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• 2005

We identify the correct chromosome and locate the corresponding markers close to the QTL in the linkage analysis of a quantitative trait by using the SSVS method. We consider several markers linked to the QTL, as well as to each oyher and thus the i.b.d. values at these loci generate collinear predictors to be evaluated when using the SSVS approach. The results on considering only closely linked markers to two QTL simultaneously showed clear evidence in favor of the closest marker to the QTL considered over other markers. The results of the analysis of collinear markers with SSVS showeed high concordance to those obtained using traditional multiple regression. We conclude based on this simulation study that the SSVS is quite useful to identify linkage with multiple linked markers simultaneously for a complex quantitative trait.

• Journal of Life Science
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• v.26 no.6
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• pp.705-710
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• 2016

Colorectal cancer (CRC) is the third most common cancer and a leading cause of cancer-related death worldwide. Although several diagnostic and therapeutic tools have been available, CRC remains difficult to complete cure because of insufficient understanding of the molecular mechanisms underlying this disease progression. MicroRNAs (miRNAs) are small non-coding RNA molecules that strongly regulate gene expression via transcriptional and translational control mechanisms. Many crucial cellular pathways are frequently disrupted in cancer development process due to dysregulation of several miRNAs. Mir-31 functions as an oncogene that modulate expression of multiple cancer related genes. Thus, we aimed to demonstrate clinical relevance of miR-31 in human CRC. Quantitative RT-PCR analysis of miR-31 expression was performed in 175 CRC tissues and 16 normal colonic mucosa (NM). Next, we investigated clinical significances of miR-31 expression in various clinicopathologic features in CRC patients cohort. Mir-31 was significantly up-regulated in CRC tissues compared to NM. In CRC tissues, miR-31 expression level was significantly elevated in a stage-dependent manner, which was associated with poor survival in patients with CRC. High miR-31 levels in CRC tissues significantly correlated with poor prognosis (hazard ratio [HR]=2.4) as well as distant metastasis (odds ratio [OR]=2.3). In conclusion, we identified clinical significance of miR-31 expression in CRC. High miR-31 expression may be clinically able to use as a biomarker for CRC prognosis and predicting metastasis.

• Journal of Life Science
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• v.31 no.11
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• pp.995-1003
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• 2021

Chromatin remodeling regulates gene expression through epigenetic mechanisms. Aberrations in histone modification have been associated with depression-like behaviors in animal models. Additionally, growing evidence also indicates that epigenetic modification is associated with depression. p11 (S100A10) has been implicated in the pathophysiology of depression both in human and rodent models. In the present study, we investigated alterations in histone acetylation and methylation at the promoter of the p11 gene in the hippocampus of mice subjected to chronic unpredictable stress (CUS). C57BL/6 mice were exposed to CUS daily for 3 weeks. Depression-like behaviors were measured with the forced swimming test (FST). The levels of hippocampal p11 expression were analyzed by quantitative real-time polymerase chain reaction (PCR) and Western blotting. The levels of acetylated and methylated histone H3 at the promoter of p11 were measured by chromatin immunoprecipitation followed by real-time PCR. CUS-exposed mice displayed depression-like behaviors with prolonged immobility in FST. CUS led to significant decreases in the expression of p11 at both protein and mRNA levels. Meanwhile, there was a decrease in histone H3 acetylation (Ac-H3) and H3-K4 trimethylation (H3K4met3) and an increase in H3-K27 trimethylation (H3K27met3) at the p11 promoter. These results indicate that chronic stress causes the epigenetic suppression of p11 expression in the hippocampus.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.4
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• pp.271-277
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• 2020

There are increasing cases of monitoring the structural response of structures using multiple sensors. However, owing to cost and management problems, limited sensors are installed in the structure. Thus, few structural responses are collected, which hinders analyzing the behavior of the structure. Therefore, a technique to predict responses at a location where sensors are not installed to a reliable level using limited sensors is necessary. In this study, a numerical study is conducted to predict the seismic response of low-rise buildings using limited information. It is assumed that the available response information is only the acceleration responses of the first and top floors. Using both information, the first natural frequency of the structure can be obtained. The acceleration information on the first floor is used as the ground motion information. To minimize the error on the acceleration history response of the top floor and the first natural frequency error of the target structure, the method for predicting the mass and stiffness information of a structure using the genetic algorithm is presented. However, the constraints are not considered. To determine the range of design variables that mean the search space, the parameter prediction method based on artificial neural networks is proposed. To verify the proposed method, a five-story structure is used as an example.

• Horticultural Science & Technology
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• v.29 no.5
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• pp.461-466
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• 2011

In genetic and molecular breeding studies of plants, researchers need to design various kinds of primers based on their research purposes. So far many kinds of web- or script-based non-commercial programs for primer design are available. Because most of them do not include user interface for multipurpose usage including gene structure prediction and direct target selection on sequences, it has been a laborious work to design primers targeting on the exon or intron regions of interesting genes. Here we report a primer designing graphic user interface program, Pickprimer, that includes gene structure prediction and primer design modules by combining source codes of the Spidey and Primer3 programs. This program provides simple graphic user interface to input sequences and design primers. Genomic sequence and mRNA or coding sequence of genes can be copy and pasted or input as fasta or text files. Based on alignment of the input sequences using the Spidey module, a putative gene structure is graphically visualized along with exon-intron sequences of color codes. Primer design can be easily performed by dragging mouse on the displayed sequences or input primer targeting position with desirable values of primers. The output of designed primers with detailed information is provided by the Primer3 module. PCR evaluation of 24 selected primer sets successfully amplified single amplicons from six Brassica rapa cultivars. The Pickprimer will be a convenient tool for genetic and molecular breeding studies of plants.

• Journal of Life Science
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• v.20 no.12
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• pp.1758-1763
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• 2010

Osteoporosis is a complex systemic skeletal disease and a major public health concern worldwide. It is a heritable disorder characterized mainly by low bone density and/or low trauma osteoporotic fractures, both of which have strong genetic determination. However, the specific genetic variants determining risk for low bone density are still largely unknown. Here, we performed association analysis to elucidate the possible relationship between genetic polymorphisms in the SQSTM1 gene and low bone density. By examining a total of 7225 (men: 3622, women: 3603) subjects from the Korean population in the Korean Association REsource (KARE) study, we discovered that SQSTM1 gene polymorphisms were associated with bone density. The results of the BD-RT (bone density estimated by T-score at distal radius) showed that three SNPs (rs513235, rs3734007, and rs11249661) within the SQSTM1 gene were significantly associated with bone density. The results of the BD-TT (bone density estimated by T-score at midshaft tibia) showed that four SNPs (rs513235, rs3734007, rs2241349, and rs11249661) were significantly associated with bone density. The three SNPs (rs513235, rs3734007, and rs11249661) had common significance in both BD-RT and BD-TT. In summary, we found statistically significant SNPs in the SQSTM1 gene that are associated with bone density traits. Therefore, our findings suggest SQSTM1 gene could be related to pathogenesis of osteoporosis.

• Journal of Animal Science and Technology
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• v.51 no.1
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• pp.1-8
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• 2009

We generated 57,598 expressed sequence tags (ESTs) from 3 cDNA libraries of Hanwooo (Korean Cattle), fat, loin, liver. Liver, intermuscular fat and longissimus dorsi tissues were obtained from a 24-month-old Hanwoo steer immediately after slaughter. cDNA library was constructed according to the oligocapped method. The EST data were clustered and assembled into unique sequences, 4,759 contigs and 7,587 singletons. To carry out functional analysis, Gene Ontology annotation and identification of significant leaf nodes were performed that were detected by searching significant p-values from $2^{nd}$ level GO terms to leaf nodes using Bonferroni correction. We found that 13, 26 and 8 significant leaf nodes are unique in the transcripts according to 3 GO categories, molecular function, biological process and cellular component. Also digital gene expression profiling using the Audic's test was performed and tissue specific genes were detected in the above 3 libraries.