• Journal of the Korean Wood Science and Technology
• /
• v.44 no.3
• /
• pp.370-380
• /
• 2016

The black pine logs damaged by pine wilt disease in Jeju-do were heat-treated to extend the utilization of domestic trees damaged by pine wilt disease. The heat-treatment of wood requires wood to be heated to $56^{\circ}C$ for 30 min at the core. The average moisture content and top-diameter of the black pine logs were ranged from 46% to 141% and from 180 mm to 500 mm, respectively. And the basic specific gravity and oven-dry specific gravity of the black pine logs were 0.47 and 0.52, respectively. The time required for heat-treatment at $105^{\circ}C$ temperature was ranged from 7.7 h to 44.2 h, depending on moisture content and top-diameter. The temperature distribution was used to predict the time required for heat-treatment of black pine log with various moisture contents and top-diameters using finite difference method. The thermal properties of wood including the thermal conductivity and specific heat in accordance with moisture content were calculated. Heat transfer coefficient for mixed convection in form of adding natural convection and forced convection was used for heat transfer analysis. The error between the measured and predicted values ranged from 3% to 45%. The predicted times required for heat-treatment of black pine log with 50% moisture content and 200 mm, 300 mm, and 400 mm top-diameter were 10.9 h, 18.3 h, and 27.0 h, respectively. If the initial moisture content of black pine log is 75%, heat treatment times of 13.6 h, 22.5 h, and 32.8 h were predicted in accordance with top-diameter. And if the initial moisture content of black pine log is 100%, heat treatment times of 16.2 h, 26.5 h, and 38.2 h were predicted in accordance with top-diameter. When the physical properties of logs damaged by pine wilt disease are presented, these results can be applicable to the heat-treatment of red pine and Korean pine logs as well.

• Journal of Korea Society of Industrial Information Systems
• /
• v.25 no.6
• /
• pp.33-45
• /
• 2020

This paper aims to look at the perspective that the latest cutting-edge technologies are predicting individual diseases in the actual medical environment in a situation where various types of wearable devices are rapidly increasing and used in the healthcare domain. Through the process of collecting, processing, and transmitting data by merging clinical data, genetic data, and life log data through a user-participating wearable device, it presents the process of connecting the learning model and the feedback model in the environment of the Deep Neural Network. In the case of the actual field that has undergone clinical trial procedures of medical IT occurring in such a high-tech medical field, the effect of a specific gene caused by metabolic syndrome on the disease is measured, and clinical information and life log data are merged to process different heterogeneous data. That is, it proves the objective suitability and certainty of the deep neural network of heterogeneous data, and through this, the performance evaluation according to the noise in the actual deep learning environment is performed. In the case of the automatic encoder, we proved that the accuracy and predicted value varying per 1,000 EPOCH are linearly changed several times with the increasing value of the variable.

• Spatial Information Research
• /
• v.20 no.2
• /
• pp.13-24
• /
• 2012

To prevent the damage to human health by climate change, vulnerability assessment should be conducted for establishment of adaptation strategies. In this study, vulnerability assessment was conducted to provide information about vulnerable area for making adaptation policy. vulnerability assessment for human health was divided into three categories; extreme heat, ozone, and epidemic disease. To assess vulnerability, suitable indicators were selected by three criteria; sensitivity, adaptive capacity, and exposure, spatial data of indicators were prepared and processed using GIS technique. As a result, high vulnerability to extreme heat was shown in the low land regions of southern part. And vulnerability to harmful ozone was high in the surrounding area of Dae-gu basin and metropolitan area with a number of automobiles. Vulnerability of malaria and tsutsugamushi disease have a region-specific property. They were high in the vicinity of the Dimilitarized zone and south-western plain, respectively. In general, vulnerability of human health was increased in the future time. Vulnerable area was extended from south to central regions and from plain to low mountainous regions. For assessing vulnerability with high accuracy, it is necessary to prepare more related indicators and consider weight of indicators and use climate prediction data based on the newly released scenario when assessing vulnerability.

• Journal of Society of Preventive Korean Medicine
• /
• v.19 no.2
• /
• pp.1-12
• /
• 2015

Objective : This study was a covariance structural analysis to identify korean medicine health promotion behavior by the general characteristics of the subjects and build a predictive model and theoretical framework based on Pender's health promotion model(1996) and related literature reviews. Method : A hypothetical model was consisted of 8 theoretical variables and 27 measured variables. Related variables included Individual Characteristics and Experience, Behavior-specific cognitions and affect and Behavioral outcome. The data was collected from 802 middle and old-aged people living in Seoul and Gyeong gi province through structured questionnaires by face to face interviews between February and March, 2014. SAS ver. 9.1 and AMOS 18.0 programs were used for the data analysis. Results : Difference in the verification of Korean medicine health promotion behavior by the general characteristics, Older people who are male, with higher economic status, no chronic disease or with diabetes, no smoking, no drinking, with more exercise showed significantly higher scores, but education level has no difference. 15 paths were statistically significant among 16 paths on the direct effect, 6 paths were statistically significant among 9 paths on the indirect effect in the hypothetical model. The greatest impact variable on Korean medicine health promotion behavior was perceived self-esteem. Also, the findings showed that the higher perceived social support, perceived health status, previous Korean medicine health promotion behavior, community environment, perceived benefit and the lower perceived barrier had a significant effect on Korean medicine health promotion behavior. Conclusion : This research model has an empirical validity as the variables of this study verified their effects and significances. Therefore, the understanding of Korean medicine health promotion behavior can be increased and the utilization will be higher when seeking a comprehensive health promotion plan. Also, a strategy can be utilized the strategy for Korean medicine health promotion behavior.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.3
• /
• pp.1539-1546
• /
• 2016

Specific patterns of the hereditary breast and ovarian cancer (HBOC) syndrome are related to mutations in the BRCA1 gene. One hundred unrelated breast cancer patients were interviewed to obtain clinical symptoms and signs, pedigree and familial history of HBOC syndrome related cancer. Subsequently, data were calculated using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk prediction model. Patients with high score of BOADICEA were offered genetic testing. Eleven patients with high score of BOADICEA, 2 patients with low score of BOADICEA, 2 patient's family members and 15 controls underwent BRCA1 genetic testing. Mutation screening using PCR-HRM was carried out in 22 exons (41 amplicons) of BRCA1 gene. Sanger sequencing was subjected in all samples with aberrant graph. This study identified 10 variants in the BRCA1 gene, consisting of 6 missense mutations (c.1480C>A, c.2612C>T, c.2566T>C, c.3113A>G, c.3548 A>G, c.4837 A>G), 3 synonymous mutations (c.2082 C>T, c.2311 T>C and c.4308T>C) and one intronic mutation (c.134+35 G>T). All variants tend to be polymorphisms and unclassified variants. However, no known pathogenic mutations were found.

• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.8
• /
• pp.4113-4117
• /
• 2012

Breast cancer is a prevalent heterogeneous malignant disease. Gene expression profiling by DNA microarray can classify breast tumors into five different molecular subtypes: luminal A, luminal B, HER-2, basal and normal-like which have differing prognosis. Recently it has been shown that immunohistochemistry (IHC) markers including estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (Her2), can divide tumors to main subtypes: luminal A (ER+; PR+/-; HER-2-), luminal B (ER+;PR+/-; HER-2+), basal-like (ER-;PR-;HER2-) and Her2+ (ER-; PR-; HER-2+). Some subtypes such as basal-like subtype have been characterized by poor prognosis and reduced overall survival. Due to the importance of the ER signaling pathway in mammary cell proliferation; it appears that epigenetic changes in the $ER{\alpha}$ gene as a central component of this pathway, may contribute to prognostic prediction. Thus this study aimed to clarify the correlation of different IHC-based subtypes of breast tumors with $ER{\alpha}$ methylation in Iranian breast cancer patients. For this purpose one hundred fresh breast tumors obtained by surgical resection underwent DNA extraction for assessment of their ER methylation status by methylation specific PCR (MSP). These tumors were classified into main subtypes according to IHC markers and data were collected on pathological features of the patients. $ER{\alpha}$ methylation was found in 25 of 28 (89.3%) basal tumors, 21 of 24 (87.5%) Her2+ tumors, 18 of 34 (52.9%) luminal A tumors and 7 of 14 (50%) luminal B tumors. A strong correlation was found between $ER{\alpha}$ methylation and poor prognosis tumor subtypes (basal and Her2+) in patients (P<0.001). Our findings show that $ER{\alpha}$ methylation is correlated with poor prognosis subtypes of breast tumors in Iranian patients and may play an important role in pathogenesis of the more aggressive breast tumors.

• Journal of Ginseng Research
• /
• v.42 no.1
• /
• pp.98-106
• /
• 2018

Panax ginseng has been used since ancient times based on the traditional Asian medicine theory and clinical experiences, and currently, is one of the most popular herbs in the world. To date, most of the studies concerning P. ginseng have focused on specific mechanisms of action of individual constituents. However, in spite of many studies on the molecular mechanisms of P. ginseng, it still remains unclear how multiple active ingredients of P. ginseng interact with multiple targets simultaneously, giving the multidimensional effects on various conditions and diseases. In order to decipher the systems-level mechanism of multiple ingredients of P. ginseng, a novel approach is needed beyond conventional reductive analysis. We aim to review the systems-level mechanism of P. ginseng by adopting novel analytical framework-network pharmacology. Here, we constructed a compound-target network of P. ginseng using experimentally validated and machine learning-based prediction results. The targets of the network were analyzed in terms of related biological process, pathways, and diseases. The majority of targets were found to be related with primary metabolic process, signal transduction, nitrogen compound metabolic process, blood circulation, immune system process, cell-cell signaling, biosynthetic process, and neurological system process. In pathway enrichment analysis of targets, mainly the terms related with neural activity showed significant enrichment and formed a cluster. Finally, relative degrees analysis for the target-disease association of P. ginseng revealed several categories of related diseases, including respiratory, psychiatric, and cardiovascular diseases.

• Genomics & Informatics
• /
• v.12 no.4
• /
• pp.268-275
• /
• 2014

The harshness of legionellosis differs from mild Pontiac fever to potentially fatal Legionnaire's disease. The increasing development of drug resistance against legionellosis has led to explore new novel drug targets. It has been found that phosphoglucosamine mutase, phosphomannomutase, and phosphoglyceromutase enzymes can be used as the most probable therapeutic drug targets through extensive data mining. Phosphoglucosamine mutase is involved in amino sugar and nucleotide sugar metabolism. The purpose of this study was to predict the potential target of that specific drug. For this, the 3D structure of phosphoglucosamine mutase of Legionella pneumophila (strain Paris) was determined by means of homology modeling through Phyre2 and refined by ModRefiner. Then, the designed model was evaluated with a structure validation program, for instance, PROCHECK, ERRAT, Verify3D, and QMEAN, for further structural analysis. Secondary structural features were determined through self-optimized prediction method with alignment (SOPMA) and interacting networks by STRING. Consequently, we performed molecular docking studies. The analytical result of PROCHECK showed that 95.0% of the residues are in the most favored region, 4.50% are in the additional allowed region and 0.50% are in the generously allowed region of the Ramachandran plot. Verify3D graph value indicates a score of 0.71 and 89.791, 1.11 for ERRAT and QMEAN respectively. Arg419, Thr414, Ser412, and Thr9 were found to dock the substrate for the most favorable binding of S-mercaptocysteine. However, these findings from this current study will pave the way for further extensive investigation of this enzyme in wet lab experiments and in that way assist drug design against legionellosis.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.6
• /
• pp.2895-2899
• /
• 2016

Background: Increasing breast cancer (BC) incidence rates have been reported from India; causal factors for this increased incidence are not understood and diagnosis is mostly in advanced stages. Trivandrum exhibits the highest BC incidence rates in India. This study aimed to estimate trends in incidence by age from 2005-2014, to predict rates through 2020 and to assess the stage at diagnosis of BC in Trivandrum. Materials and Methods: BC cases were obtained from the Population Based Cancer Registry, Trivandrum. Distribution of stage at diagnosis and incidence rates of BC [Age-specific (ASpR), crude (CR) and age-standardized (ASR)] are described and employed with a joinpoint regression model to estimate average annual percent changes (AAPC) and a Bayesian model to estimate predictive rates. Results: BC accounts for 31% (2681/8737) of all female cancers in Trivandrum. Thirty-five percent (944/2681) are <50 years of age and only 9% present with stage I disease. Average age increased from 53 to 56.4 years (p=0.0001), CR (per $10^5$ women) increased from 39 (ASR: 35.2) to 55.4 (ASR: 43.4), AAPC for CR was 5.0 (p=0.001) and ASR was 3.1 (p=0.001). Rates increased from 50 years. Predicted ASpR is 174 in 50-59 years, 231 in > 60 years and overall CR is 80 (ASR: 57) for 2019-20. Conclusions: BC, mostly diagnosed in advanced stages, is rising rapidly in South India with large increases likely in the future; particularly among post-menopausal women. This increase might be due to aging and/or changes in lifestyle factors. Reasons for the increased incidence and late stage diagnosis need to be studied.

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.17-25
• /
• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.