• Journal of Ecology and Environment
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• v.41 no.8
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• pp.223-234
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• 2017

Background: Spatial structure of plants in a population reflects complex interactions of ecological and evolutionary processes. For dioecious plants, differences in reproduction cost between sexes and sizes might affect their spatial distribution. Abiotic heterogeneity may also affect adaptation activities, and result in a unique spatial structure of the population. Thus, we examined sex- and size-related spatial distributions of old-growth forest of dioecious tree Torreya nucifera in extremely heterogeneous Gotjawal terrain of Jeju Island, South Korea. Methods: We generated a database of location, sex, and size (DBH) of T. nucifera trees for each quadrat ($160{\times}300m$) in each of the three sites previously defined (quadrat A, B, C in Site I, II, and III, respectively). T. nucifera trees were categorized into eight groups based on sex (males vs. females), size (small vs. large trees), and sex by size (small vs. large males, and small vs. large females) for spatial point pattern analysis. Univariate and bivariate spatial analyses were conducted. Results: Univariate spatial analysis showed that spatial patterns of T. nucifera trees differed among the three quadrats. In quadrat A, individual trees showed random distribution at all scales regardless of sex and size groups. When assessing univariate patterns for sex by size groups in quadrat B, small males and small females were distributed randomly at all scales whereas large males and large females were clumped. All groups in quadrat C were clustered at short distances but the pattern changed as distance was increased. Bivariate spatial analyses testing the association between sex and size groups showed that spatial segregation occurred only in quadrat C. Males and females were spatially independent at all scales. However, after controlling for size, males and females were spatially separated. Conclusions: Diverse spatial patterns of T. nucifera trees across the three sites within the Torreya Forest imply that adaptive explanations are not sufficient for understanding spatial structure in this old-growth forest. If so, the role of Gotjawal terrain in terms of creating extremely diverse microhabitats and subsequently stochastic processes of survival and mortality of trees, both of which ultimately determine spatial patterns, needs to be further examined.

• Journal of Korean Neurosurgical Society
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• v.67 no.2
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• pp.177-185
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• 2024

Objective : Delayed cerebral ischemia (DCI) is a major cause of disability in patients who survive aneurysmal subarachnoid hemorrhage (aSAH). Systemic inflammatory markers, such as peripheral leukocyte count and systemic immune-inflammatory index (SII) score, have been considered predictors of DCI in previous studies. This study aims to investigate which systemic biomarkers are significant predictors of DCI. Methods : We conducted a retrospective, observational, single-center study of 170 patients with SAH admitted between May 2018 and March 2022. We analyzed the patients' clinical and laboratory parameters within 1 hour and 3-4 and 5-7 days after admission. The DCI and non-DCI groups were compared. Variables showing statistical significance in the univariate logistic analysis (p<0.05) were entered into a multivariate regression model. Results : Hunt-Hess grade "4-5" at admission, modified Fisher scale grade "3-4" at admission, hydrocephalus, intraventricular hemorrhage, and infection showed statistical significance (p<0.05) on a univariate logistic regression. Lymphocyte and monocyte count at admission, SII scores and C-reactive protein levels on days 3-4, and leukocyte and neutrophil counts on days 5-7 exhibited statistical significance on the univariate logistic regression. Multivariate logistic regression analysis revealed that monocyte count at admission (odds ratio [OR], 1.64; 95% confidence interval [CI], 1.04-2.65; p=0.036) and SII score at days 3-4 (OR, 1.55; 95% CI, 1.02-2.47; p=0.049) were independent predictors of DCI. Conclusion : Monocyte count at admission and SII score 3-4 days after rupture are independent predictors of clinical deterioration caused by DCI after aSAH. Peripheral monocytosis may be the primer for the innate immune reaction, and the SII score at days 3-4 can promptly represent the propagated systemic immune reaction toward DCI.

• Culinary science and hospitality research
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• v.5 no.1
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• pp.89-101
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• 1999

This study attempts to identify the most accurate quantitative forecasting technique for measuring the future level of demand for food ＆ beverage in super deluxe hotel in Seoul, which will subsequently lead to determining the optimal level of purchasing food ＆ beverage. This study, in detail, examines the food purchasing system of H hotel, reviews three rigorous univariate time series models and identify the most accurate forecasting technique. The monthly data ranging from January 1990 to December 1997 (96 observations) were used for the empirical analysis and the 1998 data were left for the comparison with the ex post forecast results. In order to measure the accuracy, MAPE, MAD and RMSE were used as criteria. In this study, Box-Jenkins model was turned out to be the most accurate technique for forecasting hotel food ＆ beverage demand among selected models generating 3.8％ forecast error in average.

• Health Policy and Management
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• v.9 no.4
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• pp.41-64
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• 1999

The major concern for this research is to discuss and to offer some solutions to bring the effectiveness of existing notifiable diseases reporting system over the physicians' attitudes of reporting, the actual condition of performance and the reasons of inertia in notifiable diseases reporting through examining the physicians of medical institutions in nationwide such as pediatrics, internal medicine and family medicine. The actual conditions of notifiable communicable diseases(NCD) reporting was surveyed by mail objectifying an internal medicine, pediatrics and family medicine in nationwide on the basis of stratified random sampling method divided into the classification of medical institutions and areas. As a result of survey. the rate of respondents showed 145 persons from physicians, 105 persons from hospitals. 120 persons from general hospitals, and 51 persons from tertiary hospitals. The total number of respondents were 421 and was rated 59.0 %. The analysis of collected survey went through a descriptive analysis primarily to grasp physicians' attitudes on the notifiable communicable diseases reporting, and then upon the dependent variables. Following are major findings obtained form the data analysis. 1. The results of a descriptive analysis on physicians' attitudes towards reporting NCD were as follows: First, the respondents who didn't know that yellow fever is reporting NCD were 11.0% of clinic, 10.5% of hospital. 5.0% of general hospital. 11.8% of tertiary hospital. and in case of hepatitis B, were 26.9% of clinic, 35.2% of hospital. 35.0% of general hospital. 23.5% of tertiary hospital. Second, The rate of physicians' knowledge on penalties of not reporting the NCD by their medical institution were 35.2% of clinic, 45.7% of hospital. 36.7% of general hospital. 62.7% of tertiary hospital. Third, among the no-reporting physicians in whole, the major reason of not reporting NCD were uncertainty of diagnosis(78.9%), no need to report(46.4%), no adequate actions from PHC(29.1%), no knowledge of the cases being notifiable ones in the order of their frequencies(30.4%), meddling from PHC(29.1%), concerning of patient's privacy(26.3%). 2. To analyze the characteristics related to the physicians' behaviors to report NCD, univariate and multiple logistic regression analyses were applied to the variables related to physician, 4 medical facility, PHC, and reporting system. The result were as follows: First, the result of the univariate analysis on physicians' attitude to report NCD and characteristics related to reporting in odds ratio was in the case of hospital. 3.4 times higher positive responses on physicians' attitude to report NCD came up as compared to the clinic. Second, the result of the univariate analysis on physicians' action of reporting NCD and characteristics related to reporting by the classification of medical institutions showed that the odds ratio of hospital was 2.3 times, the odds ratio of general hospital was 2.0 times, the odds ratio of tertiary was 6.8 times significantly higher than clinic. And the medical institution with significantly higher positive attitudes rate by multiple logistic regression analysis was hospital that rated 2.5 times significantly higher than clinic. Also in the PHC related characteristics of reporting, the rate of action in reporting NCD was significantly higher in medical institution that were endowed with the good condition of reporting. In multiple logistic regression analysis, the medical institution that has a good conditions of reporting showed a significantly higher positive rate on the action of reporting than the others.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.11
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• pp.4691-4698
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• 2015

Background: The tumor suppressor phosphatase and tensin homolog (PTEN) is an important negative regulator of cell-survival signaling. However, available results for the prognostic value of PTEN expression in patients with cancer remain controversial. Therefore, a meta-analysis of published studies investigating this issue was performed. Materials and Methods: A literature search via PubMed and EMBASE databases was conducted. Statistical analysis was performed by using the STATA 12.0 (STATA Corp., College, TX). Data from eligible studies were extracted and included into the meta-analysis using a random effects model. Results: A total of 3,810 patients from 27 studies were included in the meta-analysis, 22 investigating the relationship between PTEN expression and overall survival (OS) using univariate analysis, and nine with multivariate analysis. The pooled hazard ratio (HR) for OS was 1.64 (95% confidence interval (CI): 1.32-2.05) by univariate analysis and 1.56 (95% CI: 1.20-2.03) by multivariate analysis. In addition, eight papers including two disease-free-survival analyses (DFSs), four relapse-free-survival analyses (RFSs), three progression-free-survival analyses (PFSs) and one metastasis-free-survival analysis (MFS) reported the effect of PTEN on survival. The results showed that loss of PTEN expression was significant correlated with poor prognosis, with a combined HR of 1.74 (95% CI: 1.24-2.44). Furthermore, in the stratified analysis by the year of publication, ethnicity, cancer type, method, cut-off value, median follow-up time and neoadjuvant therapy in which the study was conducted, we found that the ethnicity, cancer type, method, median follow-up time and neoadjuvant therapy are associated with prognosis. Conclusions: Our study shows that negative or loss of expression of PTEN is associated with worse prognosis in patients with cancer. However, adequately designed prospective studies need to be performed for confirmation.

• Water for future
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• v.28 no.1
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• pp.81-90
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• 1995

This study attempts to develop a stochastic system model for extension and prediction of monthly runoff series in river basins where the observed runoff data are insufficient although there are long-term hydrometeorological records. For this purpose, univariate models of a seasonal ARIMA type are derived from the time series analysis of monthly runoff, monthly precipitation and monthly evaporation data with trend and periodicity. Also, a causual model of multiple input-single output relationship that take monthly precipitation and monthly evaporation as input variables-monthly runoff as output variable is built by the cross-correlation analysis of each series. The performance of the univariate model and the multiple input-output model were examined through comparisons between the historical and the generated monthly runoff series. The results reveals that the multiple input-output model leads to the improved accuracy and wide range of applicability when extension and prediction of monthly runoff series is required.

• Journal of the Korean Society of Physical Medicine
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• v.11 no.3
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• pp.73-80
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• 2016

PURPOSE: The purpose of this study was to investigate the quality of life related to fall experiences in chronic stroke patients. METHODS: This cross-sectional study included 117 patients with stroke from 3 hospitals in D metropolitan city. General characteristics, including fall experiences and quality of life, were assessed through a face-to-face interviews conducted in a quiet place using a questionnaire. Measurement of quality of life in stroke patients was conducted using the Korean Stroke Specific Quality of Life Scale (SS-QOL). To identify the SS-QOL items related to fall experiences, the items of the SS-QOL were considered as independent variables, and the variables that were significantly different according to fall experiences were identified using a univariate analysis. A binary logistic regression was then performed using fall experiences as the independent variable. RESULTS: According to the univariate analysis, self help activities, social role, and upper extremity function were significantly lower in the fall group than that in the non-fall group (p<.05). The findings of the binary logistic regression confirmed that social roles and upper extremity function were the SS-QOL items that were related to fall experience in chronic stroke patients. CONCLUSION: These findings suggest that social roles and upper extremity function may be risk factors for fall experience in patients with chronic stroke.

• Journal of Korean Neurosurgical Society
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• v.61 no.5
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• pp.645-652
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• 2018

Objective : This study aimed to investigate the rates, types, and risk factors of surgical site infection (SSI) following intracranial neurosurgical procedures evaluated by a Korean SSI surveillance system. Methods : This was a prospective observational study of patients who underwent neurosurgical procedures at 29 hospitals in South Korea from January 2017 to June 2017. The procedures included craniectomy, craniotomy, cranioplasty, burr hole, and ventriculoperitoneal shunt. Univariate and multivariate logistic regression analyses were performed. Results : Of the 1576 cases included, 30 showed infection, for an overall SSI rate of 1.9%. Organ/space infection was the most common, found in 21 out of the 30 cases (70%). Staphylococcus aureus was the most common (41%) of all bacteria, and Serratia marcescens (12%) was the most common among gram-negative bacteria. In univariate analyses, the p-values for age, preoperative hospital stay duration, and over T-hour were <0.2. In a multivariate analysis of these variables, only preoperative hospital stay was significantly associated with the incidence of SSI (p<0.001), whereas age and over T-hour showed a tendency to increase the risk of SSI (p=0.09 and 0.06). Conclusion : Surveillance systems play important roles in the accurate analysis of SSI. The incidence of SSI after neurosurgical procedures assessed by a national surveillance system was 1.9%. Future studies will provide clinically useful results for SSI when data are accumulated.

• Korean Journal of Artificial Intelligence
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• v.6 no.2
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• pp.11-15
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• 2018

Breast cancer is one of the leading causes of cancer related death among women. So prediction of overall survival status is important into decided in adjuvant treatment. Deep belief network is a kind of artificial intelligence (AI). We intended to construct prediction model by deep belief network using associated clinicopathologic factors. 103881 cases were found in the Korean Breast Cancer Registry. After preprocessing of data, a total of 15733 cases were enrolled in this study. The median follow-up period was 82.4 months. In univariate analysis for overall survival (OS), the patients with advanced AJCC stage showed relatively high HR (HR=1.216 95% CI: 0.011-289.331, p=0.001). Based on results of univariate and multivariate analysis, input variables for learning model included 17 variables associated with overall survival rate. output was presented in one of two states: event or cencored. Individual sensitivity of training set and test set for predicting overall survival status were 89.6% and 91.2% respectively. And specificity of that were 49.4% and 48.9% respectively. So the accuracy of our study for predicting overall survival status was 82.78%. Prediction model based on Deep belief network appears to be effective in predicting overall survival status and, in particular, is expected to be applicable to decide on adjuvant treatment after surgical treatment.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.281-301
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• 2004

In this paper we consider the change point problem in a sequence of univariate normal observations. We want to know whether there is any change point or not. In case a change point exists, we will identify its change type. Namely, it can be a mean change, a variance change, or both the mean and variance change. The intrinsic Bayes factors of Berger and Pericchi (1996, 1998) are used to find the type of optimal change model. The Gibbs sampling including the Metropolis-Hastings algorithm is used to estimate all the parameters in the change model. These methods are checked via simulation and applied to the winter average temperature data in Seoul.