• Journal of Life Science
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• v.21 no.5
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• pp.631-646
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• 2011

Mesenchymal stem cells (MSC) are multipotent and can be isolated from diverse human tissues including bone marrow, fat, placenta, dental pulp, synovium, tonsil, and the thymus. They function as regulators of tissue homeostasis. Because of their various advantages such as plasticity, easy isolation and manipulation, chemotaxis to cancer, and immune regulatory function, MSCs have been considered to be a potent cell source for regenerative medicine, cancer treatment and other cell based therapy such as GVHD. However, relating to its supportive feature for surrounding cell and tissue, it has been frequently reported that MSCs accelerate tumor growth by modulating cancer microenvironment through promoting angiogenesis, secreting growth factors, and suppressing anti-tumorigenic immune reaction. Thus, clinical application of MSCs has been limited. To understand the underlying mechanism which modulates MSCs to function as tumor supportive cells, we co-cultured human adipose tissue derived mesenchymal stem cells (ASC) with cancer cell lines H460 and U87MG. Then, expression data of ASCs co-cultured with cancer cells and cultured alone were obtained via microarray. Comparative expression analysis was carried out using DAVID (Database for Annotation, Visualization and Integrated Discovery) and PANTHER (Protein ANalysis THrough Evolutionary Relationships) in divers aspects including biological process, molecular function, cellular component, protein class, disease, tissue expression, and signal pathway. We found that cancer cells alter the expression profile of MSCs to cancer associated fibroblast like cells by modulating its energy metabolism, stemness, cell structure components, and paracrine effect in a variety of levels. These findings will improve the clinical efficacy and safety of MSCs based cell therapy.

• The Korean Journal of Mycology
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• v.6 no.2
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• pp.1-10
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• 1978

In de novo biosynthesis of the extracellulor enzymes-proteinsaes, alpha and gluc-amylases during the synchronized differentiation of Aspergillus niger in submerged culture and surface liquid culture were investigated. Gluc-amylase was synthesized in the stage of presporulation in which phialide formation is involved. Proteinase was synthesized both in the stages of conidiophore formation and presporulation. Alpha-amylase was synthesized during presporulation and sporulation stages, the activity of enzyme lasted for seven days on surface liquid culture. It seemed that the synthesis was occured in de novo partly repressed by the catabolite, and its nature was found to be constitutive since it is produced in non-starch medium. Polyacrylamide gel electrophoresis have shown that presporulating and sporulating body produced diverse types of the proteins whereas the earlier stages of vegetative body showed simpler profiles. The uptake of C-14 uracil into RNA and C-14 glutamate into protein were shown to be vigorous in presporulating body rather than those in sporulating body. Coincidence of alpha-amylase biosynthesis in de novo and sporulation may be significant in the study of differentiation in which gene expression is involved.

• Journal of Nutrition and Health
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• v.50 no.6
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• pp.565-577
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• 2017

Purpose: There have been limited studies investigating the relationship between high-sensitivity C-reactive protein (hsCRP), metabolic diseases, and dietary factors in Korean adults. Here, we examined the association between nutrient intake and serum hsCRP among Korean adults. Methods: Using data on 2,624 healthy Korean adults (1,537 women and 1,087 men) from the 2015 Korea National Health and Nutrition Examination Survey, demographic, anthropometric, biochemical, and dietary factors were analyzed once the subjects were grouped into either sex, age, or BMI. Nutrient intake was evaluated using the dietary data obtained by one-day 24-hour recall. Based on the guidelines of the US Centers for Disease Control and Prevention and the American Heart Association, hsCRP level was classified as HCRPG (High CRP Group, hsCRP > 1 mg/L) and LCRPG (Low CRP Group, hsCRP ${\leq}1mg/L$). Proc surveyreg procedure was performed to examine the associations between nutrient intake and hsCRP after adjustment for potential confounding variables. Results: The average hsCRP level of healthy Korean adults was $0.95{\pm}0.03mg/L$ ($0.97{\pm}0.04mg/L$ in men, $0.92{\pm}0.05mg/L$ in women). Obese subjects had significantly higher hsCRP than non-obese subjects in both sexes. The hsCRP level was positively associated with current smoking, physical inactivity, BMI, waist circumference, fasting blood glucose, triglycerides, total cholesterol, LDL-cholesterol, and blood pressure and inversely associated with HDL-cholesterol. LCRPG had significantly higher intake of dietary fiber compared to HCRPG in women. High hsCRP level was associated with more dietary cholesterol intake but less omega-3 fatty acid intake among subjects aged ${\geq}50y$. HCRPG of obese subjects had higher intakes of fat and saturated fatty acid than LCRPG. Conclusion: The hsCRP level is closely associated with several lifestyle variables and nutrient intake in healthy Korean adults. Individuals with high hsCRP level show low intakes of dietary fiber and omega-3 fatty acids but high intakes of dietary fat and cholesterol. Our findings suggest that a potential anti-inflammatory role for nutrients and lifestyle in the Korean adult population.

• Korean Journal of Ichthyology
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• v.21 no.1
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• pp.7-14
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• 2009

The effect of water temperature (T) and body weight (W) on oxygen consumption of fasted starry flounder Platichthys stellatus was investigated in order to assess the metabolic response of this species at given conditions. The oxygen consumption rate (OCR) was measured under six different water temperatures (4, 7, 10, 13, 16 and $19^{\circ}C$) and at two different body weights (mean weight of fry group : 1.5 g; fingerling group : 37.4 g) at an interval of 5 minutes for 24 hours using a continuous flow-through respirometer. In each treatment three replicates were set up and a total 540 fish in fry groups and 90 fish in fingerling groups were used. The OCRs increased with increase of water temperature in both groups (p<0.001). Mean OCRs at 4, 7, 10, 13, 16 and $19^{\circ}C$ were 1386.0, 1601.7, 1741.0, 1799.2, 2239.1 and $2520.3mg\;O_2\;kg\;fish^{-1}\;h^{-1}$ in fry groups, and 83.8, 111.4, 126.3, 147.1, 187.7 and $221.3mg\;O_2\;kg\;fish^{-1}\;h^{-1}$ in fingerling groups, respectively. The OCRs decreased with increasing body weights at six different water temperatures (p<0.001). The relationship between water temperature and body weight is described by the following equation : OCR=1520.91+40.85T-49.22W ($r^2=0.95$, p<0.001). The energy loss by metabolic response increased with an increase in water temperature and a decrease in body weight (p<0.001). Mean energy loss rates by oxygen consumption at 4, 7, 10, 13, 16 and $19^{\circ}C$ were 907.9, 1046.5, 1141.6, 1177.0, 1467.3 and $1650.1kJ\;kg\;fish^{-1}\;d^{-1}$ in fry groups and 54.8, 73.0, 82.9, 96.2, 122.9 and $144.6kJ\;kg\;fish^{-1}\;d^{-1}$ in fingerling groups, respectively. The $Q_{10}$ values of fingerling groups were higher than those of fry groups at given temperature ranges. The $Q_{10}$ values at $4{\sim}7^{\circ}C$, $7{\sim}10^{\circ}C$, $10{\sim}13^{\circ}C$, $13{\sim}16^{\circ}C$ and $16{\sim}19^{\circ}C$ were 1.62, 1.32, 1.12, 2.07 and 1.48 in fry groups, and 2.59, 1.52, 1.67, 2.25 and 1.73 in fingerling groups, respectively.

• Applied Chemistry for Engineering
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• v.34 no.2
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• pp.111-120
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• 2023

The nucleation mechanism was studied using a calcium ion selective electrode (Ca ISE) to observe the formation of CaCO₃, a representative mineral in the CO2 cycle, and to analyze the effect of the Mg/Ca-ratio and temperature on the formation of pre-nucleation cluster (PNC) and CaCO₃. As a result of the experiment, a small amount of crystal was formed. Energy dispersive X-ray spectroscopy (EDS) was used for surface element analysis, and a field emission scanning-electron microscope (FE-SEM) was used for the morphology analysis of synthesized carbonates. These results showed that various shapes of crystalline CaCO₃ (calcite, aragonite, etc.) were observed for each Mg/Ca ratio and temperature. In addition, the calibration plot obtained from Ca ISE showed information on the formation process of CaCO₃. Our results showed that as magnesium ions interfered with the binding of calcium and carbonate ions and delayed the aggregation between PNCs, the nucleation and formation of CaCO₃ were delayed. On the other hand, the temperature showed an opposite trend as compared to the effect of magnesium under our experimental conditions, indicating that temperature accelerated the formation of CaCO₃. Furthermore, the morphology of CaCO₃ clearly changed according to the Mg/Ca ratio and temperature, and it was confirmed that the two factors are very important for CaCO₃ formation in that they could affect the overall process.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.