We present an analysis of a simulated climate projection covering the period 1971-2080 over the Korean Peninsula with a regional climate model (RegCM3) using a one-way double-nested system. The mother and nested domain cover the East Asian region at 60 km grid spacing and the Korean Peninsula at 20 km grid spacing, respectively. The mean climate state as well as the frequency and intensity of daily extreme events are investigated at various temporal and spatial scales, with a focus on surface air temperature and precipitation. Our analysis is primarily centered on the comparison of two 30-year climate periods between 2021-2050 and 2051-2080, in order to assess climatic changes at different time periods in response to anthropogenic forcings under the IPCC B2 emission scenario.Regarding the interdecadal variation of projected temperature and precipitation over the whole integration period, we find that the temperature change gradually increases by 3.2 K for the 2070s, with a persistently increasing trend. The projected precipitation in the future shows generally greater amounts than in the reference run despite significant interdecadal variation. A substantial increase (decrease) of hot (frost) spells is projected along with increasing of maximum and minimum temperature. Wet spells of long periods tend to be more frequent, accompanying the increase of precipitation amounts. The temperature changes are statistically significant at the 90% confidence level in all seasons and regions, indicating robustness of the projected change while the precipitation changes show low statistical confidence, especially summer season, due to the large natural variability. Based on the spatial distribution, although the general pattern between the mother and nested domain simulations shows similarity, more detailed structure over Korea is found in the nested domain simulation. By comparison of the change between 2021-2050 and 2051-2080 periods, the climate change signal of both temperature and precipitation becomes more pronounced in the late 21 st century as the greenhouse gas concentration is increased.

Background: Besifovir dipivoxil maleate (BSV) with L-carnitine is the first-line antiviral agent for chronic hepatitis B (CHB) infection. We investigated whether BSV combined with L-carnitine improves hepatic steatosis (HS). Methods: Treatment-naïve patients with CHB who were initiated on antiviral therapy (AVT) were enrolled. The magnitude of HS was assessed using hepatic steatosis index (HSI), and HS improvement was defined as a ≥ 10% reduction in the HSI score from the baseline. Results: The mean age of the study patients was 56 years with a male predominance (n = 178, 64.7%). The mean body mass index (BMI), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and platelet count were 23.5 kg/m 2 , 49.6 IU/L, 49.0 IU/L, and 191.3 × 10 9 /L, respectively. The mean HSI and fibrosis (FIB)-4 index were 32.6 and 0.5, respectively. After 6 months of AVT, platelet count (mean, 191.3→167.0 × 10 9 /L), fasting glucose (mean, 113.1→105.9 mg/dL), AST (mean, 49.6→28.0 IU/L), ALT (mean, 49.0→33.9 IU/L), and total cholesterol (mean, 170.0→162.1 mg/dL) levels significantly decreased (all P < 0.05). In the BSV group, AST (mean, 95.2→30.2 IU/L) and ALT (mean, 81.1→31.1 IU/L) levels significantly reduced (all P < 0.05), whereas HSI and FIB-4 index were maintained (all P > 0.05). In the univariate analysis, age, BMI, diabetes, cirrhosis, fasting glucose level, and ALT were significantly associated with HS improvement (all P < 0.05). Conclusion: BSV with L-carnitine did not show any improvement of HS in patients with CHB. Further prospective randomized controlled studies are needed to validate the potential beneficial effects of BSV with L-carnitine in CHB infection.

There are nowadays strong demands for intelligent surveillance systems, which can infer or understand more complex behavior. The application of crowd density estimation methods could lead to a better understanding of crowd behavior, improved design of the built environment, and increased pedestrian safety. In this paper, we propose a new crowd density estimation method, which aims at estimating not only a moving crowd, but also a stationary crowd, using images captured from surveillance cameras situated in various public locations. The crowd density of the moving people is measured, based on the moving area during a specified time period. The moving area is defined as the area where the magnitude of the accumulated optical flow exceeds a predefined threshold. In contrast, the stationary crowd density is estimated from the coarseness of textures, under the assumption that each person can be regarded as a textural unit. A multilayer neural network is designed, to classify crowd density levels into 5 classes. Finally, the proposed method is experimented with PETS 2009 and the platform of Gangnam subway station image sequences.

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Aims: Homeostasis model assessment for insulin resistance (HOMA-IR) is a biomarker for Type 2 diabetes mellitus(T2DM). However, the role of HOMA-IR in the non-diabetic is unclear. Methods: From a prospective community-based cohort of 10,030 individuals, 4,314 individuals younger than 65 years and without diabetes were enrolled and divided into three groups by baseline HOMA-IR tertiles: high (n=1244), moderate (n=1248), and low (n=1822). The primary outcome was new onset T2DM. Secondary outcomes were chronic kidney disease (CKD) and a composite of coronary artery disease, myocardial infarction, and stroke as macrovascular events. Results:The mean age was 51 years. The prevalence of hypertension and cholesterol and HbA1c were higher in the high HOMA-IR group. New onset T2DM (6.3%) and CKD (12.2%) incidence in the high HOMA-IR group was higher than in the others. The prevalence of macrovascular events did not differ among groups. High-HOMA-IR was an independent risk factor for new onset T2DM (odds ratio 2.07 [1.32-3.25]; p =0.001) and CKD (1.42 [1.08-1.87]; p =0.01). Conclusions:High HOMA-IR was an early predictor of new onset T2DM and CKD regardless of the HbA1c in non-diabetic individuals. Further research on the specific cut off value will be needed.

Myopia is one of the risk factors for glaucoma, making accurate diagnosis of glaucoma in myopic eyes particularly important. However, diagnosis of glaucoma in myopic eyes is challenging due to the frequent associations of distorted optic disc and distorted parapapillary and macular structures. Macular vertical scan has been suggested as a useful tool to detect glaucomatous retinal nerve fiber layer loss even in highly myopic eyes. The present study was performed to develop and validate a deep learning (DL) system to detect glaucoma in myopic eyes using macular vertical optical coherence tomography (OCT) scans and compare its diagnostic power with that of circumpapillary OCT scans. The study included a training set of 1416 eyes, a validation set of 471 eyes, a test set of 471 eyes, and an external test set of 249 eyes. The ability to diagnose glaucoma in eyes with large myopic parapapillary atrophy was greater with the vertical than the circumpapillary OCT scans, with areas under the receiver operating characteristic curves of 0.976 and 0.914, respectively. These findings suggest that DL artificial intelligence based on macular vertical scans may be a promising tool for diagnosis of glaucoma in myopic eyes.