• Journal of Periodontal and Implant Science
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• v.36 no.2
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• pp.397-408
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• 2006

치주질환은 치아 지지조직의 파괴로 튿정 지어지는 감염성 질환으로서 이것은 주로 조직의 교원질 성분을 분해시키는 MMP(matrix metalloproteinase)에 의해 이루어진다. 한편, 여러 연구에서 당뇨병과 치주질환의 심도와의 관계에 대한 논의가 있어왔으며 당뇨병이 치주염을 포함한 구강 감염에 대한 감수성을 증가시키며 역으로 만성 치주염의 염증성 매개물질에 의해 인슐린 작용이 방해받을 수 있음을 보고하였다. 본 연구의 목적은 만성 치주염 환자와 제 2형 당뇨병을 동반한 만성 치주염 환자의 치은조직에서 MMP-13과 elastase의 발현 양상을 비교하여 병리 기전의 차이점을 규명하고, 두 단백질간의 상호관계를 알아보기 위한 것이다. 경북대 병원 치주과에 내원한 환자의 비당뇨 환자의 정상 치은 부위, 비당뇨 환자의 만성 치주염 부위, 제 2형 당뇨병 환자의 만성 치주염 부위에서 각각 8개의 변연 치은을 채득하여 액화 질소에 급속 동결시켰다. 모든 조직 샘플에서 동량의 단백질을 western blotting을 통해 분석하여였고, densitometer를 이용하여 정량한 후 ANOVA 분석으로 통계처리 하였다. 결과분석에서 MMP-13은 제 2형 당뇨를 가진 환자의 염증성 조직에서 가장 높게 발현되었고 전신적으로 건강한 환자의 염증성 조직과 정상 조직에서는 유사한 양상을 보였으며 그 차이는 통계적으로 유의하였다. 또한 elastas는 그룹간에 유의한 차이 없이 유사한 양상으로 발현되었고, 염증성 조직에서도 당뇨병의 유무에 관계없이 유사하게 나타났다. 한편 조직내 MMP-13과 elastase의 발현 양상간에 유의한 상관관계는 보이지 않았다. 결론적으로 MMP-13은 당뇨병을 동반한 환자의 치은 염증초직에서 유의하게 증가되어 나타났으며, 전신적으로 건강한 환자의 염증조직과 당뇨를 동반한 환자의 염증조직에서 MMP-13과 elastase의 발현양상은 큰 상관관계가 발견되지 않았다.

• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.262-271
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• 2002

This study presents a practical procedure for the Bayesian inversion of geophysical data. We have applied geostatistical techniques for the acquisition of prior model information, then the Markov Chain Monte Carlo (MCMC) method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter.

• Geotechnical Engineering
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• v.13 no.1
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• pp.19-34
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• 1997

Applications of geostatistical method to cone penetrometer data have been performed at the overconsolidated clay site. Randomlylocated 28 electronic CPT soundings (Location A) and consistently-located 38 CPT soundings(Location B) are investigated geostatistically. Variograms for Locations A and B have been developed for q, from the CPT data by using "kriging" principles, which establish the horizontal and vertical correlation distrances at this site. These vertical and horizontal correlation distances can be used to optimal sampling design, where, if one needs to compare two test results, sampling must be made within these vertical and horizontal correlation distances. Analysis of the variograms indicated that the geological formation between two locations are not very different in both vertical direction and horizontal direction.direction.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.235-241
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• 2019

The purpose of this study is to investigate the effect of the nonverbal voice component of the occupational therapist on the respondent's attitude of acceptance The study method was to randomly selected and provided 4 voice samples which were from the current female occupational therapist and adjusted its pause and tempo to participants who visited the local health center. After providing voices, the participants were asked to complete the survey including the questions about the acceptance attitude according to voice components. The study results showed that there were statistically significant differences in attitudes of "likeability", "credibility," "concentration", "expertise", "comprehension" (p<.05). Findings suggested that client's attitude of acceptance may change according to non-verbal voice components of the therapist.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.157-176
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• 2024

Integrative analysis for better understanding of complex biological systems gains more attention. Observing subjects from various perspectives and conducting integrative analysis of those multiple datasets enables a deeper understanding of the subject. In this paper, we compared two methods that simultaneously consider two datasets gathered from the same objects, canonical correlation analysis (CCA) and co-inertia analysis (CIA). Since CCA cannot handle the case when the data exhibit high-dimensionality, two strategies were considered instead: Utilization of a ridge constant (CCA-ridge) and substitution of covariance matrices of each data to identity matrix and then applying penalized singular value decomposition (CCA-PMD). To illustrate CIA and CCA, both extensions of CCA and CIA were applied to NCI60 cell line data. It is shown that both methods yield biologically meaningful and significant results by identifying important genes that enhance our comprehension of the data. Their results shows some dissimilarities arisen from the different criteria used to measure the relationship between two sets of data in each method. Additionally, CIA exhibits variations dependent on the weight matrices employed.

• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.381-393
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• 2024

This study investigates the impact of pseudonymization of personal information and its effect on the accuracy of data analysis. We quantitatively evaluated the relationship between the degree of pseudonymization and the accuracy of data analysis using logistic regression models, decision trees, and random forests. Through this, we confirmed that pseudonymizing sensitive information can realize personal information protection without significantly damaging data quality. However, we recognized limitations such as single sample data and consistent application of pseudonymization ratios. To overcome these limitations, additional research on diverse datasets is necessary to strengthen the generalizability of results. Moreover, we propose developing and applying methodologies to find optimal pseudonymization ratios for individual variables. The results from this study provide new insights into maintaining usability of data while achieving regulatory compliance and personal information protection.

• The Journal of Bigdata
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• v.9 no.1
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• pp.61-73
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• 2024

Class imbalance is one of the significant challenges in deep learning tasks, particularly pronounced in areas with limited data. This study proposes a new approach that utilizes minimal labeled data for effectively classifying tomato leaf diseases. We introduced a recursive learning method using the YOLOv8 model. By utilizing the detection predictions of images on the training data as additional training data, the number of labeled data is progressively increased. Unlike conventional data augmentation and up-down sampling techniques, this method seeks to fundamentally solve the class imbalance problem by maximizing the utility of actual data. Based on the secured labeled data, tomato leaves were extracted, and diseases were classified using the EfficientNet model. This process achieved a high accuracy of 98.92%. Notably, a 12.9% improvement compared to the baseline was observed in the detection of Late blight diseases, which has the least amount of data. This research presents a methodology that addresses data imbalance issues while offering high-precision disease classification, with the expectation of application to other crops.

• The korean journal of orthodontics
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• v.34 no.3 s.104
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• pp.261-267
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• 2004

Nonsyndromic cleft lip and/or palate (NSCLP) is one of the most common congenital deformities and its prevalence in Far East Asia, such as within Korean and Japanese populations, is relatively high. However, in the eastern part of Europe, clefts are relatively rare situations. These ethnic differences infer a genetic background of the disease. The objective of this study was to compare the frequency of single nucleotide polymorphism (SNP) in $TGF-{\beta}3$ between Korean and Romanian cleft families. Korean cleft families samples were collected from twenty-six families (n=78) and Romanian cleft families samples were collected from eighteen families (n=41). For sequencing, the blood or saliva of the subjects was sampled. A single nucleotide polymorphism was observed in the intron 5 of $TGF-{\beta}3$ (Al8141G). The frequency of each allele was significantly different between the Korean and Romanian samples. The Ah allele was present in 18 out of 78 Korean samples (23.1%) and in 27 out of 41 Romanian samples (65.9%). The AG was present in 27 (34.6%) out of 78 Koreans and in 13 (31.7%) out of 41 Romanians. The GG was found in 33 (42.3%) Koreans and in 1 (2.4%) Romanian. The difference between the groups was significant (p<0.001). In conclusion, the frequency of observed SNP was significantly different between the two countries. SNP in $TGF-{\beta}3$ in the Korean population seemed to have a higher possibility of occurrence for nonsyndromic cleft palate than the Romanian population.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2447-2453
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• 2013

In this study, the effects of weather conditions such as rainfall, discomfort index, snowfall, and sensible temperature on public transport demand in Seoul were analyzed using statistical data. The reasons were also derived from the survey. The data for the analysis were collected over the weekdays and weekends, and seasonal data of summer and winter were also gathered separately. Rainfall amount, discomfort index, and sensible temperature except snowfall amount, whose samples were insufficient, decreased the public transport demand by 2-7%. Rainfall amount and sensible temperature were statistically significant. Correlation analysis also showed that rainfall amount and sensible temperature are highly correlated with the demand. To find the reasons, the survey was conducted on citizens living in the Seoul Metropolitan Area. About 30% of the respondents wished to give up using bus when rainfall was heavy or temperature was low. On the contrary, auto and subway users increased by 10%. The results of this study could be used as the basic data when the public transportation planning or operation related policies according to the weather condition are concerned.

• Journal of Korean Society of Transportation
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• v.35 no.3
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• pp.220-233
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• 2017

Traffic volumes are fundamental data widely used in various traffic analysis, such as origin-and-destination establishment, total traveled kilometer distance calculation, congestion evaluation, and so on. The low number of links collecting the traffic-volume data in a large urban highway network has weakened the quality of the analyses in practice. This study proposes a method to estimate the traffic volume data on a highway link where no collection device is available by introducing a spatial statistic technique with (1) the traffic-volume data from TOPIS, and National Transport Information Center in the Ministry of Land, Infrastructure, and (2) the navigation data from private navigation. Two different component models were prepared for the interrupted and the uninterrupted flows respectively, due to their different traffic-flow characteristics: the piecewise constant function and the regression kriging. The comparison of the traffic volumes estimated by the proposed method against the ones counted in the field showed that the level of error includes 6.26% in MAPE and 5,410 in RMSE, and thus the prediction error is 20.3% in MAPE.