• Journal of the Korean Wood Science and Technology
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• 제44권3호
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• pp.370-380
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• 2016

제주도 지역에서 소나무재선충병 피해를 받은 곰솔 원목의 이용확대를 위하여 열처리를 수행하였다. 열처리는 처리 원목의 중심부가 소나무재선충의 사멸온도인 $56^{\circ}C$를 30분간 유지하여야 한다. 곰솔 원목의 초기함수율과 말구지름은 각각 46% ~ 141%, 180 mm ~ 500 mm의 범위이고, 기본비중과 전건비중은 각각 0.47, 0.52이었다. $105^{\circ}C$ 조건에서 함수율과 말구지름에 따라 열처리에 소요되는 시간은 7.7 h ~ 44.2 h의 범위로 측정되었다. 다양한 함수율 및 지름을 갖는 곰솔 원목의 열처리 소요시간을 예측하기 위하여 열처리 진행 중 처리목 내부의 온도분포를 유한차분법을 적용한 2차원 열전달 해석을 통하여 제시하였다. 열전달 해석을 위한 목재의 열적 특성은 함수율에 따른 열전도계수와 비열을 적용하였으며, 자연대류와 강제대류를 합한 형태의 혼합대류에 의한 혼합대류계수를 적용하였다. 실험값과 예측 값의 오차는 3 ~ 45%의 범위로 분석되었다. 곰솔 원목에서 초기함수율이 50%이고, 말구지름이 200 mm, 300 mm, 400 mm인 경우, 예측된 열처리 소요시간은 각각 10.9 h, 18.3 h, 27.0 h이었다. 초기함수율이 75%일 때, 지름에 따라 각각 13.6 h, 22.5 h, 32.8 h이고, 초기함수율이 100%일 때, 지름에 따라 각각 16.2 h, 26.5 h, 38.2 h이었다. 이러한 열처리 소요시간의 예측방법에 소나무와 잣나무 등 다른 소나무재선충병 피해목의 물리적 특성을 적용하면, 함수율과 말구지름에 따른 열처리 소요시간을 제시할 수 있을 것으로 판단된다.

• 한국산업정보학회논문지
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• 제25권6호
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• pp.33-45
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• 2020

본 논문은 최근 다양한 종류의 웨어러블 디바이스가 헬스케어 도메인에 급증하여 사용되고 있는 상황에서 최신 첨단 기술이 실제 메디컬 환경에서 개인의 질병예측이라는 관점을 바라본다. 사용자 참여형 웨어러블 디바이스를 통하여 임상 데이터와 유전자 데이터, 라이프 로그 데이터를 병합하여 데이터를 수집, 처리, 전송하는 과정을 걸쳐 딥뉴럴 네트워크의 환경에서 학습모델의 제시와 피드백 모델을 연결하는 과정을 제시한다. 이러한 첨단 의료 현장에서 일어나는 메디컬 IT의 임상시험 절차를 걸친 실제 현장의 경우 대사 증후군에 의한 특정 유전자가 질병에 미치는 영향을 측정과 더불어 임상 정보와 라이프 로그 데이터를 병합하여 서로 각기 다른 이종 데이터를 처리하면서 질병의 특이점을 확인하게 된다. 즉, 이종 데이터의 딥뉴럴 네트워크의 객관적 적합성과 확실성을 증빙하게 되고 이를 통한 실제 딥러닝 환경에서의 노이즈에 따른 성능 평가를 실시한다. 이를 통해 자동 인코더의 경우의 1,000 EPOCH당 변화하는 정확도와 예측치가 변수의 증가 값에 수차례 선형적으로 변화하는 현상을 증명하였다.

• Spatial Information Research
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• 제20권2호
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• pp.13-24
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• 2012

기후 변화로 인한 건강 피해를 예방하기 위해서는 지역별로 취약성 평가를 실시하고 적응대책을 수립해야 한다. 본 연구에서는 적응 대책 수립을 위한 기초 정보 제공을 목적으로 취약성 평가를 실시하였다. 건강 부문의 취약성 평가는 폭염, 오존, 매개질환 전염병의 세부 부문으로 나누어 이루어졌다. 이를 위해 각 부문별로 민감도, 적응능력, 노출 규준을 설정하고, 적합한 평가 지표를 선정하였다. 그리고 GIS를 이용하여 지표별 공간자료를 구축하고 처리하였다. 그 결과, 폭염에 의한 취약성은 남부 지방의 저지대가 중부지방에 비해 높았고, 오존에 의한 취약성은 대구분지 주변과 자동차수가 많은 수도권 및 대도시권에서 높게 나타났다. 지역 특이성이 높은 말라리아와 쯔쯔가무시증은 각각 군사분계선 근방, 남서 평야지대에서 취약성이 높게 나타났다. 또한, 미래에는 전반적으로 취약성이 증가하는 것으로 나타났으며, 남부에서 중부로 그리고 평지에서 낮은 산간지대로 취약 지역이 확대되는 경향을 보였다. 향후 관련 지표 자료의 확보와 지표별 가중치를 산정하고, 새로운 시나리오에 따른 미래 기상예측자료를 사용하면 좀 더 신뢰성 높은 취약성 평가가 가능할 것으로 생각된다.

• 대한예방한의학회지
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• 제19권2호
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• pp.1-12
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• 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
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• 제17권3호
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• pp.1539-1546
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• 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
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• 제13권8호
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• pp.4113-4117
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• 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
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• 제42권1호
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• pp.98-106
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• 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
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• 제12권4호
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• pp.268-275
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• 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
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• 제17권6호
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• pp.2895-2899
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• 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
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• 제23권8호
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• pp.17-25
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• 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.