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Department of Urban and Environmental Engineering (Urban Infrastructure Engineering)Rapid urban growth has resulted in increases in the amount of impervious surfaces in cities, inducing changes in urban climates, which are generally warmer than their rural surroundings This phenomenon is known as the urban heat island (UHI) effect. For these reasons, the use of green areas has been increasing to reduce the heat island phenomenon in the city. However, many studies have analyzed the UHI intensity using land surface temperature (LST) since it is extremely difficult to accurately measure the surface air temperature for a wide range of urban spaces. Because the LST is a measurement of how hot the land is to the touch, it is different from the thermal condition in which people actually feel. Therefore, this study evaluates the cooling effect of green areas by estimating the air temperature using the Random Forest model, one of the machine learning techniques. 138 AWS temperature data were collected for Seoul city. Land surface temperature was calculated by LandSat 8 OLI image using Surface Energy Balance Algorithm (SEBAL). Also, vegetation, Urban, and Weather related variables were used for prediction. The vegetation variables are Nomalized Difference Vegetation Index (NDVI), Weighted Difference Vegetation Index (WDVI), Soil Adjusted Vegetation Index (SAVI) and Leaf Area Index (LAI). The variables related to the weather were Albedo, elevation, longitude, latitude, and Julian Day. Also, we used the 100m and 400m buffered area within the building area, the building coverage area and the average height of the buildings. In order to analyze the cooling effect of the green space, the temperature distribution was confirmed for each land use in Jung - gu and Jongno - gu in Seoul. As a result of the analysis, Random Forest prediction model showed high prediction performance( =0.69. RMSE=1.23). The temperature cooling effect of green areas was also confirmed, but the effect was insufficient.clos

Regulation of Type IV Collagen α Chains of Glomerular Epithelial Cells in Diabetic Conditions

An early feature of diabetic nephropathy is the alteration of the glomerular basement membrane (GBM), which may result in microalbuminuria, subsequent macroproteinuria, and eventual chronic renal failure. Although type IV collagen is the main component of thickened GBM in diabetic nephropathy, cellular metabolism of each α chains of type IV collagen has not been well studied. To investigate the regulation of α(IV) chains in diabetic conditions, we examined whether glucose and advanced glycosylation endproduct (AGE) regulate the metabolism of each α(IV) chains in the diabetic tissue and glomerular epithelial cells (GEpC). Glomerular collagen α3(IV) and α5(IV) chains protein were higher and more intense in immunofluorescence staining according to diabetic durations compared to controls. In vitro, mainly high glucose and partly AGE usually increased total collagen protein of GEpC by [3H]-proline incorporation assay and each α(IV) chain proteins including α1(IV), α3(IV), and α5(IV) in time-dependent and subchain-specific manners. However, the changes of each α(IV) chains mRNA expression was not well correlated to the those of each chain proteins. The present findings suggest that the metabolism of individual α(IV) chains of GBM is differentially regulated in diabetic conditions and those changes might be induced not only by transcriptional level but also by post-translational modifications

Reversal of Trimethyltin-Induced Learning and Memory Deficits by 3,5-Dicaffeoylquinic Acid

The antiamnesic effect of 3,5-dicaffeoylquinic acid (3,5-diCQA) as the main phenolic compound in Artemisia argyi H. extract on cognitive dysfunction induced by trimethyltin (TMT) (7.1 μg/kg of body weight; intraperitoneal injection) was investigated in order to assess its ameliorating function in mice. In several behavioral tests, namely, the Y-maze, passive avoidance, and Morris water maze (MWM) test, 3,5-diCQA significantly ameliorated learning and memory deficits. After the behavioral tests, brain tissues from the mice were analyzed to characterize the basis of the neuroprotective effect. Acetylcholine (ACh) levels increased, whereas the activity of acetylcholinesterase (AChE) decreased upon administration of 3,5-diCQA. In addition, 3,5-diCQA effectively protected against an increase in malondialdehyde (MDA) content, an increase in the oxidized glutathione (GSH) ratio, and a decline of total superoxide dismutase (SOD) level. 3,5-diCQA may prevent neuronal apoptosis through the protection of mitochondrial activities and the repression of apoptotic signaling molecules such as p-Akt, BAX, and p-tau (Ser 404)

Predicting Mechanical Complications After Adult Spinal Deformity Operation Using a Machine Learning Based on Modified Global Alignment and Proportion Scoring With Body Mass Index and Bone Mineral Density

Objective This study aimed to create an ideal machine learning model to predict mechanical complications in adult spinal deformity (ASD) surgery based on GAPB (modified global alignment and proportion scoring with body mass index and bone mineral density) factors. Methods Between January 2009 and December 2018, 238 consecutive patients with ASD, who received at least 4-level fusions and were followed-up for ≥2 years, were included in the study. The data were stratified into training (n=167, 70%) and test (n=71, 30%) sets and input to machine learning algorithms, including logistic regression, random forest gradient boosting system, and deep neural network. Results Body mass index, bone mineral density, the relative pelvic version score, the relative lumbar lordosis score, and the relative sagittal alignment score of the global alignment and proportion score were significantly different in the training and test sets (p<0.05) between the complication and no complication groups. In the training set, the area under receiver operating characteristics (AUROCs) for logistic regression, gradient boosting, random forest, and deep neural network were 0.871 (0.817–0.925), 0.942 (0.911–0.974), 1.000 (1.000–1.000), and 0.947 (0.915–0.980), respectively, and the accuracies were 0.784 (0.722–0.847), 0.868 (0.817–0.920), 1.000 (1.000–1.000), and 0.856 (0.803–0.909), respectively. In the test set, the AUROCs were 0.785 (0.678–0.893), 0.808 (0.702–0.914), 0.810 (0.710–0.910), and 0.730 (0.610–0.850), respectively, and the accuracies were 0.732 (0.629–0.835), 0.718 (0.614–0.823), 0.732 (0.629–0.835), and 0.620 (0.507–0.733), respectively. The random forest achieved the best predictive performance on the training and test dataset. Conclusion This study created a comprehensive model to predict mechanical complications after ASD surgery. The best prediction accuracy was 73.2% for predicting mechanical complications after ASD surgery. This information can be used to prevent mechanical complications during ASD surgery

Major Determinants of Serum Homocysteine Concentrations in a Korean Population

The objective of this study was to identify the factors that determine serum homocysteine concentrations in Korean population. In a community-based study, 871 participants completed detailed questionnaires and physical examination. We found that increased age, male sex, family history of stroke, deficiencies of serum folate and vitamin B12, and elevated serum creatinine significantly increased the risk of hyperhomocysteinemia. However, hormonal and behavioral factors (smoking, alcohol drinking, coffee consumption, and sedentary time) were not associated with the risk of hyperhomocysteinemia. The risk of hyperhomocysteinemia was steeply increased in subjects with two or more risk factors among four selected risk factors (deficiencies of serum folate and vitamin B12, elevated creatinine, and family history of stroke) compared to subjects who did not have any risk factors, especially subjects over the age of 65 yr (odds ratio [OR], 33.5; 95% confidence interval [CI], 3.71-302.0 in men; OR, 39.2; 95% CI, 7.95-193.2 in women). In conclusion, increased age, male sex, family history of stroke, deficiencies of serum folate and vitamin B12, and elevated serum creatinine are important determinants of serum homocysteine concentrations with interaction effects between these factors