• Radiation Oncology Journal
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• v.37 no.3
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• pp.193-200
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• 2019

Purpose: To explore the role of salvage radiotherapy (RT) for recurrent thymoma as an alternative to surgery. Materials and Methods: Between 2007 and 2015, 47 patients who received salvage RT for recurrent thymoma at Yonsei Cancer Center were included in this study. Recurrent sites included initial tumor bed (n = 4), pleura (n = 19), lung parenchyma (n = 10), distant (n = 9), and multiple regions (n = 5). Three-dimensional conformal and intensity-modulated RT were used in 29 and 18 patients, respectively. Median prescribed dose to gross tumor was 52 Gy (range, 30 to 70 Gy), with equivalent doses in 2-Gy fractions (EQD₂). We investigated overall survival (OS), progression-free survival (PFS), and patterns of failure. Local failure after salvage RT was defined as recurrence at the target volume receiving >50% of the prescription dose. Results: Median follow-up time was 83 months (range, 8 to 299 months). Five-year OS and PFS were 70% and 22%, respectively. The overall response rate was 97.9%; complete response, 34%; partial response, 44.7%; and stable disease, 19.1%. In multivariate analysis, histologic type and salvage RT dose (≥52 Gy, EQD₂) were significantly associated with OS. The high dose group (≥52 Gy, EQD₂) had significantly better outcomes than the low dose group (5-year OS: 80% vs. 59%, p = 0.046; 5-year PFS: 30% vs. 14%, p=0.002). Treatment failure occurred in 34 patients; out-of-field failure was dominant (intra-thoracic recurrence 35.3%; extrathoracic recurrence 11.8%), while local failure rate was 5.8%. Conclusion: Salvage RT for recurrent thymoma using high doses and advanced precision techniques produced favorable outcomes, providing evidence that recurrent thymoma is radiosensitive.

• Archives of Plastic Surgery
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• v.40 no.5
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• pp.522-529
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• 2013

Background Despite advances in surgical techniques, sterile protocols, and perioperative antibiotic regimens, surgical site infections (SSIs) remain a significant problem. We investigated the relationship between wound classification (i.e., clean, clean/contaminated, contaminated, dirty) and SSI rates in plastic surgery. Methods We performed a retrospective review of a multi-institutional, surgical outcomes database for all patients undergoing plastic surgery procedures from 2006-2010. Patient demographics, wound classification, and 30-day outcomes were recorded and analyzed by multivariate logistic regression. Results A total of 15,289 plastic surgery cases were analyzed. The overall SSI rate was 3.00%, with superficial SSIs occurring at comparable rates across wound classes. There were similar rates of deep SSIs in the clean and clean/contaminated groups (0.64%), while rates reached over 2% in contaminated and dirty cases. Organ/space SSIs occurred in less than 1% of each wound classification. Contaminated and dirty cases were at an increased risk for deep SSIs (odds ratios, 2.81 and 2.74, respectively); however, wound classification did not appear to be a significant predictor of superficial or organ/space SSIs. Clean/contaminated, contaminated, and dirty cases were at increased risk for a postoperative complication, and contaminated and dirty cases also had higher odds of reoperation and 30-day mortality. Conclusions Analyzing a multi-center database, we found that wound classification was a significant predictor of overall complications, reoperation, and mortality, but not an adequate predictor of surgical site infections. When comparing infections for a given wound classification, plastic surgery had lower overall rates than the surgical population at large.

• Proceedings of the Korean Association for Survey Research Conference
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• 2002.06a
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• pp.8-34
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• 2002

This paper is concerned with evaluating the fidelity of a non-cognitive test adaptation for use in multiple languages and cultures using two differential item functioning(DIF) techniques: (a) PSIBTEST, and (b) Logistic Discriminant Function Analysis(LDFA). In particular, this study focused on how DIF research can best be extended to the problem of evaluating the equivalence of tests across cultures and languages. The Sixteen Personality Factor (16PF) questionnaire was administered in English to 844 American college students and in Korean to 538 Korean college students. This study attempted to identify the best matching criterion for the translated tests by using both a multivariate matching technique and iterative purification process. The results generally showed a small number of DIF items on each scale, except for scales A and N where about half of the items showed DIF. The choice of matching variables based on a combination of internal measures appeared to have little effect and the iterative purification method was unsuccessful. Finally, the results were discussed and methodological implications were also presented.

• Journal of Korea Port Economic Association
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• v.26 no.4
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• pp.207-218
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• 2010

The purpose of the study is to predict the shipping business during the period of 2011 using the ARIMA-type models. This include the ARIMA and Intervention-ARIMA models. The multivariate cause-effect econometric model is not employed for not assuring a higher degree of forecasting accuracy than the univariate variable model. Such a cause-effect econometric model also fails in adjusting itself for the post-sample. This article introduces the four ARIMA models and six Intervention-ARIMA models. The monthly data cover the period January 2000 through October 2010. The out-of-sample forecasting performance is compared between the ARIMA-type models and the random walk model. Forecasting performance is measured by three summary statistics: root mean squared percent error, mean absolute percent error and mean percent error. The root mean squared percent errors of all the ARIMA-type models are somewhat higher than normally expected. Furthermore, the random walk model outperforms all the ARIMA-type models. This reveals that the BDI is just a random walk phenomenon and it's meaningless to predict the BDI using various econometric techniques. The ARIMA-type models show that the shipping market is expected to be bearish in 2011. These pessimistic ex-ante forecasts are supported by the Hodrick-Prescott filtering technique.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.276-280
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• 2016

Due to the increasing interest in safety and consistent product quality over a past few decades, demand for effective quality monitoring and safe operation in the modern industry has propelled research into statistical based fault detection and diagnosis methods. This paper describes the application of Hotelling $T^2$ index based Principal Component Analysis (PCA) method for fault detection and diagnosis in industrial processes. Multivariate statistical process control techniques are now widely used for performance monitoring and fault detection. Conventional methods such as PCA are suitable only for steady state processes. These conventional projection methods causes false alarms or missing data for the systems with transient values of processes. These issues significantly compromise the reliability of the monitoring systems. In this paper, a reliable method is used to overcome false alarms occur due to varying process conditions and missing data problems in transient states. This monitoring method is implemented and validated experimentally along with matlab. Experimental results proved the credibility of this fault detection method for both the steady state and transient operations.

• Bulletin of the Korean Chemical Society
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• v.34 no.8
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• pp.2413-2418
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• 2013

Forensic and legal medicine require reliable data to indicate excessive alcohol consumption. Ethanol is oxidatively metabolized to acetate by alcohol dehydrogenase and non-oxidatively metabolized to ethyl glucuronide (EtG), ethyl sulfate (EtS), phosphatidylethanol, or fatty acid ethyl esters (FAEE). Oxidative metabolism is too rapid to provide biomarkers for the detection of ethanol ingestion. However, the non-oxidative metabolite EtG is a useful biomarker because it is stable, non-volatile, water soluble, highly sensitive, and is detected in body fluid, hair, and tissues. EtG analysis methods such as mass spectroscopy, chromatography, or enzyme-linked immunosorbent assay techniques are currently in use. We suggest that nuclear magnetic resonance (NMR) spectroscopy could be used to monitor ethanol intake. As with current conventional methods, NMR spectroscopy doesn't require complicated pretreatments or sample separation. This method has the advantages of short acquisition time, simple sample preparation, reproducibility, and accuracy. In addition, all proton-containing compounds can be detected. In this study, we performed $^1H$ NMR analyses of urine to monitor the ethanol and EtG. Urinary samples were collected over time from 5 male volunteers. We confirmed that ethanol and EtG signals could be detected with NMR spectroscopy. Ethanol signals increased immediately upon alcohol intake, but decreased sharply over time. In contrast, EtG signal increased and reached a maximum about 9 h later, after which the EtG signal decreased gradually and remained detectable after 20-25 h. Based on these results, we suggest that $^1H$ NMR spectroscopy may be used to identify ethanol non-oxidative metabolites without the need for sample pretreatment.

• Journal of the Korea Computer Graphics Society
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• v.23 no.4
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• pp.21-29
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• 2017

Previous visual analytics researches has focused on reducing the uncertainty of predicted results using a variety of interactive visual data exploration techniques. The main purpose of the interactive search technique is to reduce the quality difference of the predicted results according to the level of the decision maker by understanding the relationship between the variables and choosing the appropriate model to predict the unknown variables. However, it is difficult to create a predictive model which forecast time series data whose overall trends is unknown such as youth physical growth data. In this paper, we pro pose a novel predictive analysis technique to forecast the physical growth value in small pieces of time series data with un certain trends. This model estimates the distribution of data at a particular point in time. We also propose a visual analytics system that minimizes the possible uncertainties in predictive modeling process.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.1
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• pp.14-26
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• 2012

The only extraterrestrial life detection experiments ever conducted were the three which were components of the 1976 Viking Mission to Mars. Of these, only the Labeled Release experiment obtained a clearly positive response. In this experiment $^{14}C$ radiolabeled nutrient was added to the Mars soil samples. Active soils exhibited rapid, substantial gas release. The gas was probably $CO_2$ and, possibly, other radiocarbon-containing gases. We have applied complexity analysis to the Viking LR data. Measures of mathematical complexity permit deep analysis of data structure along continua including signal vs. noise, entropy vs.negentropy, periodicity vs. aperiodicity, order vs. disorder etc. We have employed seven complexity variables, all derived from LR data, to show that Viking LR active responses can be distinguished from controls via cluster analysis and other multivariate techniques. Furthermore, Martian LR active response data cluster with known biological time series while the control data cluster with purely physical measures. We conclude that the complexity pattern seen in active experiments strongly suggests biology while the different pattern in the control responses is more likely to be non-biological. Control responses that exhibit relatively low initial order rapidly devolve into near-random noise, while the active experiments exhibit higher initial order which decays only slowly. This suggests a robust biological response. These analyses support the interpretation that the Viking LR experiment did detect extant microbial life on Mars.

• Journal of Chest Surgery
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• v.47 no.3
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• pp.262-268
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• 2014

Background: The development of diagnostic techniques and an awareness of health examinations can bring about an early diagnosis of lung cancer. However, appropriate postoperative management and adjuvant chemotherapy remain under debate in postoperative therapeutic strategy. The present study was conducted to assess the clinicopathologic factors that influence recurrence and prognosis after complete resection of lung cancer. Methods: The present study analyzed 62 patients with lung cancer who underwent complete resection of diagnosed adenocarcinoma between 1994 and 2007. In addition to conventional factors, which include staging factor and histological evaluation, the present study also performed univariate and multivariate analyses to consider claudin, a cell adhesion molecule, as a prognostic factor by immunohistochemical staining. Results: There was no correlation between conventional factors, including lymphatic and vascular invasion, and recurrence. However, there was a significant correlation between high expression of claudin 4 and cancer recurrence. In particular, there was a correlation between high expressions of claudin 1, 4, and 5 and a reduction of disease-free survival. Conclusion: Increased expressions of claudin 4 were negative prognostic factors in adenocarcinoma of the lung and thus could be used to identify high-risk patients for adjuvant chemotherapy, even if they had early-stage lung cancer. The present findings collectively suggest that consideration of claudin as a prognostic factor in the active postoperative treatment in patients at high risk will lead to better therapeutic outcomes with fewer side effects.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.25-40
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• 2006

Synthetic aperture radar (SAR) from satellites provides the opportunity to regularly incorporate microwave information into forest classification. Radar backscatter can improve classification accuracy, and SAR interferometry could provide improved thematic information through the use of coherence. This research examined the potential of using multi-temporal JERS-l SAR (L band) backscatter information and interferometry in distinguishing forest classes of mountainous areas in the Northeastern U.S. for future forest mapping and monitoring. Raw image data from a pair of images were processed to produce coherence and backscatter data. To improve the geometric characteristics of both the coherence and the backscatter images, this study used the interferometric techniques. It was necessary to radiometrically correct radar backscatter to account for the effect of topography. This study developed a simplified method of radiometric correction for SAR imagery over the hilly terrain, and compared the forest-type discriminatory powers of the radar backscatter, the multi-temporal backscatter, the coherence, and the backscatter combined with the coherence. Statistical analysis showed that the method of radiometric correction has a substantial potential in separating forest types, and the coherence produced from an interferometric pair of images also showed a potential for distinguishing forest classes even though heavily forested conditions and long time separation of the images had limitations in the ability to get a high quality coherence. The method of combining the backscatter images from two different dates and the coherence in a multivariate approach in identifying forest types showed some potential. However, multi-temporal analysis of the backscatter was inconclusive because leaves were not the primary scatterers of a forest canopy at the L-band wavelengths. Further research in forest classification is suggested using diverse band width SAR imagery and fusing with other imagery source.