Capsiate Intake with Exercise Training Additively Reduces Fat Deposition in Mice on a High-Fat Diet,but Not without Exercise Training

While exercise training (ET) is an efficient strategy to manage obesity, it is recommended with a dietary plan to maximize the antiobesity functions owing to a compensational increase in energy intake. Capsiate is a notable bioactive compound for managing obesity owing to its capacity to increase energy expenditure. We aimed to examine whether the antiobesity effects of ET can be further enhanced by capsiate intake (CI) and determine its effects on resting energy expenditure and metabolic molecules. Mice were randomly divided into four groups (n = 8 per group) and fed high-fat diet. Mild-intensity treadmill ET was conducted five times/week; capsiate (10 mg/kg) was orally administered daily. After 8 weeks, resting metabolic rate and metabolic molecules were analyzed. ET with CI additively reduced the abdominal fat rate by 18% and solely upregulated beta-3-adrenoceptors in adipose tissue (p = 0.013) but did not affect the metabolic molecules in skeletal muscles. Surprisingly, CI without ET significantly increased the abdominal fat rate (p = 0.001) and reduced energy expenditure by 9%. Therefore, capsiate could be a candidate compound for maximizing the antiobesity effects of ET by upregulating beta-3-adrenoceptors in adipose tissue, but CI without ET may not be beneficial in managing obesity.     

Korean automatic spacing using pretrained transformer encoder and analysis

Automatic spacing in Korean is used to correct spacing units in a given input sentence. The demand for automatic spacing has been increasing owing to frequent incorrect spacing in recent media, such as the Internet and mobile networks. Therefore, herein, we propose a transformer encoder that reads a sentence bidirectionally and can be pretrained using an out-of-task corpus. Notably, our model exhibited the highest character accuracy (98.42%) among the existing automatic spacing models for Korean. We experimentally validated the effectiveness of bidirectional encoding and pretraining for automatic spacing in Korean. Moreover, we conclude that pretraining is more important than fine-tuning and data size.     

Calibration of the plate thermometer for measuring heat flux using a conical heater

Journal of Mechanical Science and Technology - An experimental study was conducted on the calibration method and measurement error for a plate thermometer (PT) that can replace the heat flux gauge...     

Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images

Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation quality. Finally, we determine several skin features based on the results of wrinkle and cell segmentation. Our proposed segmentation scheme achieved a mean accuracy of 0.854, mean of intersection over union of 0.749, and mean boundary F1 score of 0.852, which achieved 1.1%, 6.7%, and 14.8% improvement over the panoptic-based semantic segmentation method, respectively.     

Phenotypic Discovery of SB1501, an Anti-obesity Agent,through Modulating Mitochondrial Activity

Obesity has become a pandemic that threatens the quality of life and discovering novel therapeutic agents that can reverse obesity and obesity-related metabolic disorders are necessary. Here, we aimed to identify new anti-obesity agents using a phenotype-based approach. We performed image-based high-content screening with a fluorogenic bioprobe (SF44), which visualizes cellular lipid droplets (LDs), to identify initial hit compounds. A structure-activity relationship study led us to yield a bioactive compound SB1501, which reduces cellular LDs in 3T3-L1 adipocytes without cytotoxicity. SB1501 induced the expression of gene products that regulate mitochondrial biogenesis and fatty acid oxidation in 3T3-L1 adipocytes. Daily treatment with SB1501 improved the metabolic states of db/db mice by reducing body fat mass, adipose tissue mass, food intake, and increasing glucose tolerance. The anti-obesity effect of SB1501 may result from perturbation of the PGC-1α–UCP1 regulatory axis in inguinal white adipose tissue and brown adipose tissue. These data suggest the therapeutic potential of SB1501 as an anti-obesity agent via modulating mitochondrial activities.     

A new smart smudge attack using CNN

International Journal of Information Security - This paper deals with a well-known problem in the area of the smudge attacks: when a user draws a pattern to unlock the pattern lock on a smartphone...     

Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting

One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time. Usually, vaccine production preparation must be done at least six months in advance, and accurate long-term influenza forecasting is essential for this. Although diverse machine learning models have been proposed for influenza forecasting, they focus on short-term forecasting, and their performance is too dependent on input variables. For a country’s long-term influenza forecasting, typical surveillance data are known to be more effective than diverse external data on the Internet. We propose a two-stage data selection scheme for worldwide surveillance data to construct a long-term forecasting model for influenza in the target country. In the first stage, using a simple forecasting model based on the country’s surveillance data, we measured the change in performance by adding surveillance data from other countries, shifted by up to 52 weeks. In the second stage, for each set of surveillance data sorted by accuracy, we incrementally added data as input if the data have a positive effect on the performance of the forecasting model in the first stage. Using the selected surveillance data, we trained a new long-term forecasting model for influenza and perform influenza forecasting for the target country. We conducted extensive experiments using six machine learning models for the three target countries to verify the effectiveness of the proposed method. We report some of the results.     

Fabrication of an electrolyte-supported protonic ceramic fuel cell with nano-sized powders of Ni-composite anode

Composite anodes of nano-sized Ni and Ba(Zr_0.85Y_0.15)O_3-δ (BZY) were fabricated by infiltrating a single precursor solution of BZY and Ni into the BZY scaffold, and decreasing the calcination temperature to 1173 K. This decrease in the fabrication temperature of the Ni-cermet anode prevents the chemical reaction between the electrolyte and nickel, thus preventing a reduction in the conductivity of the electrolyte. By optimizing the amount of Ni in the Ni-cermet and infiltrating additional catalysts such as CeO₂ and Pd, the non-ohmic ASR of the Ni-cermet anode could be optimized. This resulted in a smaller non-ohmic ASR of anode than one that was fabricated by the conventional co-sintering method. Consequently, a high power density of 790 mW/cm² at 973 K can be obtained from electrolyte-supported cells.     

Feasibility study to control fiber distribution for enhancement of composite properties via three-dimensional printing

The distribution of fibers in the composite (which takes into account both their locations and orientations) is one of the important factors that affect the mechanical properties of FRCs. However, this parameter depends on various factors during composite fabrication, and controlling the distribution of fibers in the produced material represents a significant challenge. In this study, the applicability of three-dimensional (3D) printing technique for controlling fiber distributions was evaluated. The fibers fabricated using a 3D printer were placed inside a mold to produce cementitious composites. Three-point bending tests were conducted and the results of the experiment were discussed.