• Nutrition Research and Practice
• /
• v.7 no.2
• /
• pp.96-102
• /
• 2013

Obesity, an intractable metabolic disease, currently has no medical treatment without side effects, so studies have been actively carried out to find natural compounds that have anti-obesity activity with minimum side effects. In this study, the anti-obesity effects of water extracts of seven Capsicum annuum L. varieties being Putgochu (Pca), Oyee gochu (Oca), Kwari putgochu (Kca), Green pepper (Gca), Yellow paprika (Yca), Red paprika (Rca) and Cheongyang gochu (Cca), were examined through the evaluation of lipoprotein lipase (LPL) mRNA expression level in 3T3-L1 cells (mouse pre-adipocytes). After capsaicin elimination by chloroform defatting, freeze-dried powder of Cca was treated to 3T3-L1 cells and anti-obesity effects were examined by determining the LPL mRNA level using the RT-PCR method. Of the primary fractions, only proven fractions underwent secondary and tertiary refractionating to determine anti-obesity effects. From seven different Capsicum annuum L., there was a significant decrease of the LPL mRNA expression level of 50.9% in Cca treatment compared to the control group. A significant decrease of the LPL mRNA expression level was shown in primary fractions (Fr) 5 (36.2% decrease) and 6 (30.5% decrease) of the Cca water extracts. Due to the impurities checked by UPLC chromatography, Fr 5 and 6 were refractionated to determine the LPL mRNA expression level. Treatment of Fr 6-6 (35.8% decrease) and Fr 5-6 (35.3% decrease) showed a significant decrease in the LPL mRNA expression level. When analyzed using UPLC, major compounds of Fr 6-6 and Fr 5-6 were very similar. Subsequently, we refractionated Fr 6-6 and Fr 5-6 to isolate the major peak for structure elucidation. Treatment of Fr 5-6-1 (26.6% decrease) and Fr 6-6-1 (29.7% decrease) showed a significant decrease in the LPL mRNA expression level. Consequently, the fractions may have a possibility to ameliorate obesity through the decrease of the LPL mRNA expression level.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.4
• /
• pp.857-864
• /
• 2009

This study aimed at developing the two stage and dual filtration system. It has a sand + activated carbon layer above the underdrain system and a sand layer above the middledrain system for pretreatment. When retrofitting an old filter bed or designing a new one, this technology can substitute the existing sand filter bed without requiring a new site. In order to extend the filtering duration, the upper layer of the filter bed consists of the rapid sand filtration with large particles which pre-treats and removes coarse particles and turbidity matters. The middle layer has biological activated carbon(BAC) and granular activated carbon(GAC) to eliminate dissolved organic matters, disinfection by-products precursors etc. The lower layer consists of the sand filtration for the post filtering mode. In this study, a pilot plant of two stage and dual filtration system was operated for 4 months in the S water treatment plant in Kyounggi-Do. The stability of turbidity was maintained below 1NTU. The TOC, THMFP and HAAFP were removed about 90% by two stage and dual filtration system, which is almost 2 times higher than S WTP. From analysis result of HPC along the depth of activated carbon + sand layer at 2nd stage, microorganism was mostly not detected, however, increment of HPC was shown as it becomes deeper. It indicates that growth of microorganism is occurred at activated carbon layer.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.9
• /
• pp.5716-5725
• /
• 2014

This study examined the effects of a prenatal education program of labor and delivery experience for primigravidas in mid-pregnancy. This investigation was designed as a one group pre-post quasi-experimental study, aiming to determine the effects of prenatal education on anxiety, confidence and knowledge in delivery by demonstrating knowledge and actual situations including understanding labor, breast-feeding, baby care and others, and experiencing the education program. Subjects were collected randomly from an on-line club in D city. The simulation education proceeded for 2 hours a week from April to June, 2014, and there were 6 sessions each in the first and second periods. We included 35 primigravidas who attended all 6 sessions. The data was analyzed using descriptive statistics, exploratory factor analysis and a paired t-test. According to the study results, the subjects showed a significant difference in knowledge in delivery (t=-9.07, p<001), confidence in delivery (t=-9.00, p<001) and anxiety (t=14.39, p<001) after participating in the prenatal education program. An evaluation of satisfaction revealed experience to be the most differentiating factor, and the simulation experience was identified as most effective. The desired appropriate number of class sessions was 4 times, and 88.6% of the pregnant women intended to attend the program at their next birth, indicating a high response rate.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.2
• /
• pp.631-638
• /
• 2017

In the age of the Fourth Industrial Revolution, lifestyle and industrial structures are faced with evolution from IT-based automation to the intelligent stage, demanding talents with software capabilities in various fields. Reflecting these demands, the government has enhanced basic software education for non-majors in elementary and secondary schools as well as universities. In this study, the software convergence education of Non-Majors is proposed to improve the general problem solving ability based on computational thinking and the software convergence ability in the field of their own by developing robot activity. The subjects of this study were 91 students, who were composed of various majors. The class was designed with computing thinking, convergence elements, and creative robot activity. The study was conducted for 13 weeks. To examine the effects of software convergence education through the creative robot activity, this study observed changes in the students' learning outcomes, satisfaction with creative robot activities, and perceptions of other disciplines after class based on pre-diagnosis surveys. The survey asked 12 questions including an understanding of the learning contents, overall satisfaction with multidisciplinary collaborative learning, understanding of other disciplines, and self-evaluation of problem solving ability through creative robot activities, which were compared with that before the class. They answered that their ability was improved.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.6
• /
• pp.2527-2530
• /
• 2015

Background: The cancer of the prostate risk assessment (CAPRA) score has been defined to predict prostate cancer recurrence based on the pre-clinical data, then pathological data have also been incorporated. Thus, CAPRA post-surgical (CAPRA-S) score has been developed based on six criteria (prostate specific antigen (PSA) at diagnosis, pathological Gleason score, and information on surgical margin, seminal vesicle invasion, extracapsular extension and lymph node involvement) for the prediction of post-surgical recurrences. In the present study, biochemical recurrence (BCR)-free probabilities after open retropubic radical prostatectomy (RP) were evaluated by the CAPRA-S scoring system and its three-risk level model. Materials and Methods: CAPRA-S scores (0-12) of our 240 radical prostatectomies performed between January 2000-May 2011 were calculated. Patients were distributed into CAPRA-S score groups and also into three-risk groups as low, intermediate and high. BCR-free probabilities were assessed and compared using Kaplan-Meier analysis and Cox proportional hazards regression. Ability of CAPRA-S in BCR detection was evaluated by concordance index (c-index). Results: BCR was present in 41 of total 240 patients (17.1%) and the mean follow-up time was $51.7{\pm}33.0$ months. Mean BCR-free survival time was 98.3 months (95% CI: 92.3-104.2). Of the patients in low, intermediate and high risk groups, 5.4%, 22.0% and 58.8% had BCR, respectively and the difference among the three groups was significant (P = 0.0001). C-indices of CAPRA-S score and three-risk groups for detecting BCR-free probabilities in 5-yr were 0.87 and 0.81, respectively. Conclusions: Both CAPRA-S score and its three-risk level model well predicted BCR after RP with high c-index levels in our center. Therefore, it is a clinically reliable post-operative risk stratifier and disease recurrence predictor for prostate cancer.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.6
• /
• pp.2501-2504
• /
• 2014

Background: Capecitabine is an oral fluoropyrimidine derivative which is frequently used alone or in combination regimens for the treatment of metastatic breast cancer. Although overall and progression free survivals have increased in recent years with the use of new generation drugs, predictive factors that would further improve the outcomes are needed. Previous studies have demonstrated the relation between post-treatment increase in mean corpuscular volume (MCV) and predicting therapy response as well as survival. The present study investigated the clinical impact of MCV elevation in metastatic breast cancer patients treated with capecitabine. Materials and Methods: The data of a total of 82 patients from three centers followed between June 2005 and June 2013 were retrospectively analyzed. The demographic data and hormone receptor status of the patients, as well as initial examination before and after treatment and data concerning progression were recorded. MCV ${\geq}100$ fl was considered as macrocytosis. Capecitabine was given at a dose of $2500mg/m^2$ daily for 14 days every three weeks. Pre-treatment and post-treatment MCV and other parameters of complete blood count were recorded. Post-treatment initial evaluation was performed after 2 cycles of therapy. Results: The median age of the patients was 46.5 years (range 26-72 years) and 54% were premenopausal. Performance status was ECOG 0 and 1 in 81 (99%) patients. The median number of cycles for capecitabine therapy was 5 (min-max: 2-18). The median ${\Delta}MCV$ level (post-treatment values at sixth week - baseline) was 6.4. Whilst ${\Delta}MCV$ was ${\geq}6.4$ in 42 patients, it was <6.4 in 40 patients. Clinical benefit (complete response+partial response+stable disease) was observed in 37 (88%) of 42 patients with a median ${\Delta}MCV$ ${\geq}6.4$ and in 30 (75%) of 40 patients with ${\Delta}MCV$ <6.4 with no statistically significant difference (p=0.158). No significant difference was determined between the group with ${\Delta}MCV$ ${\geq}6.4$ and the group with ${\Delta}MCV$ <6.4 in terms of progression-free survival (11 vs 12 months) (p=0.55) and overall survival (20 months vs. 24 months) (p=0.11). Conclusions: The identification of new predictive markers in metastatic breast cancer is very important. In some recent studies, increase in MCV has been suggested as a marker in tumor response. In the present study, however, no significant difference was determined between tumor response and increase in MCV. Further studies including higher numbers of patients are needed to determine whether increase in MCV is a predictive marker or not.

• Proceedings of the Korea Contents Association Conference
• /
• 2007.11a
• /
• pp.167-171
• /
• 2007

Application areas of Knowledge Discovery in Database (KDD) have been expanded into many R&D management processes including technology trends analysis, forecasting and evaluation etc. Established research field such as informetrics (or scientometrics) has recently fully utilized techniques or methods of KDD. Various systems have been developed to support works of analyzing large-scale R&D related databases such as patent DB or bibliographic DB by a few researchers or institutions. But extant systems have some problems for korean users to use. Their prices is not cheap, korean language process not available, and user's demands not reflected. To solve these problems, Korea Institute of Science and Technology Information (KISTI) developed stand-alone type information analysis system named as KnowledgeMatrix. KnowledgeMatrix system offer various functions to analyze retrieved data set from databases. Knowledge Matrix main operation unit is composed of user-defined lists and matrix generation, cluster analysis, visualization, data pre-processing. KnowledgeMatrix show better performances and offer more various functions than extant systems.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.3
• /
• pp.2100-2108
• /
• 2015

This study was conducted to examine the effects of a simulation-based learning scenario using standardized respiratory patients regarding emergency care on learning satisfaction, and self efficacy, clinical skill competency of health related department students. A quasi-experimental non-equivalent control group pre-post test design was used. A total of 50 students, 25 students for the experimental group and 25 students for the control group, The experimental group received the 2 educations sessions and 1 evaluation session with 180 minutes for each session. It was implemented in November, 2013. Data were analysed using frequency, ratio and t-test by the SPSS/Win 18.0. The experimental group who had the simulation-based learning scenario using standardized respiratory patients showed significantly higher learning satisfaction and self efficacy, clinical skill competency compared with the control group who had a traditional simulation education. Through this study, simulation-based learning scenario using standardized patients was verified to be an effective teaching method to be grow up on professional competency of health related department students. Also the simulation-based learning scenario using standardized patients should be developed in the various fields.

• The Journal of the Korea Contents Association
• /
• v.18 no.3
• /
• pp.342-353
• /
• 2018

The purpose of this study was to investigate the awareness of early childhood teachers for the development of early childhood education programs using background music. The subjects of this study were 272 early childhood teachers of kindergartens in Busan and Kyungnam provinces. The data were collected through questionnaires on the awareness of children's appreciation of fairy tales using background music. The collected data were analyzed by frequency and percentage using SPSS 18.0 program. The results of this study are as follows: First, the number of early childhood teachers who had experience of performing fairy tales using background music were less than half of them. Secondly, the purpose of appreciation of fairy tales using background music was to develop rich emotions. In order to understand the systematic knowledge and concept of background music, it was necessary to supply background music in various life styles. Third, development of a fairy tale appreciation program using background music was generally required. Third, development of a fairy tale appreciation program using background music was generally required. Fourth, a fairy tale appreciation program using background music is appropriate for the large group activity time of 10~20 minutes one or two times a week and it was necessary to have a concatenated structure of pre-activities, introduction methods, questioning techniques in development, finishing methods, evaluation methods, and extension activity methods.

• Korean Chemical Engineering Research
• /
• v.58 no.4
• /
• pp.588-595
• /
• 2020

Prediction and control of nitrogen oxides (NO_x) emission is of great interest in industry due to stricter environmental regulations. Herein, we propose an artificial intelligence (AI)-based framework for prediction of NO_x emission. The framework includes pre-processing of data for training of neural networks and evaluation of the AI-based models. In this work, Long-Short-Term Memory (LSTM), one of the recurrent neural networks, was adopted to reflect the time series characteristics of NO_x emissions. A decision tree was used to determine a time window of LSTM prior to training of the network. The neural network was trained with operational data from a heating furnace. The optimal model was obtained by optimizing hyper-parameters. The LSTM model provided a reliable prediction of NO_x emission for both training and test data, showing an accuracy of 93% or more. The application of the proposed AI-based framework will provide new opportunities for predicting the emission of various air pollutants with time series characteristics.