Serial-multiple mediation of enjoyment and intention on the relationship between creativity and physical activity

The purpose of the present study was to examine a serial-multiple mediation of physical activity (PA) enjoyment and PA intention in the relationship between creativity and PA level (i.e., moderate-to-vigorous PA). A total of 298 undergraduate and graduate students completed a selfreported questionnaire evaluating creativity, PA enjoyment, PA intention, and PA level. Data analysis was conducted using descriptive statistics, Pearson correlation coefficient, ordinary leastsquares regression analysis, and bootstrap methodology. Based on the research findings, both PA enjoyment (β = 0.06; 95% CI [0.003, 0.12]) and PA intention (β = 0.08; 95% CI [0.03, 0.13]) were found to be a mediator of the relationship between creativity and PA level, respectively. Moreover, the serial-multiple mediation of PA enjoyment and PA intention in the relationship between creativity and PA level was statistically significant (β = 0.02; 95% CI [0.01, 0.04]). These findings underscore the importance of shaping both cognitive and affective functions for PA promotion and provide additional support for a neurocognitive affect-related model in the PA domain. In order to guide best practices for PA promotion programs aimed at positively influencing cognition and affect, future PA interventions should develop evidence-based strategies that routinely evaluate cognitive as well as affective responses to PA

3D visualization technique for occluded objects in integral imaging using modified smart pixel mapping

In this paper, we propose a modified smart pixel mapping (SPM) to visualize occluded three-dimensional (3D) objects in real image fields. In integral imaging, orthoscopic real 3D images cannot be displayed because of lenslets and the converging light field from elemental images. Thus, pseudoscopic-to-orthoscopic conversion which rotates each elemental image by 180 degree, has been proposed so that the orthoscopic virtual 3D image can be displayed. However, the orthoscopic real 3D image cannot be displayed. Hence, a conventional SPM that recaptures elemental images for the orthoscopic real 3D image using virtual pinhole array has been reported. However, it has a critical limitation in that the number of pixels for each elemental image is equal to the number of elemental images. Therefore, in this paper, we propose a modified SPM that can solve this critical limitation in a conventional SPM and can also visualize the occluded objects efficiently

HEXA-018, a Novel Inducer of Autophagy, Rescues TDP-43 Toxicity in Neuronal Cells

The autophagy-lysosomal pathway is an essential cellular mechanism that degrades aggregated proteins and damaged cellular components to maintain cellular homeostasis. Here, we identified HEXA-018, a novel compound containing a catechol derivative structure, as a novel inducer of autophagy. HEXA-018 increased the LC3-I/II ratio, which indicates activation of autophagy. Consistent with this result, HEXA-018 effectively increased the numbers of autophagosomes and autolysosomes in neuronal cells. We also found that the activation of autophagy by HEXA-018 is mediated by the AMPK-ULK1 pathway in an mTOR-independent manner. We further showed that ubiquitin proteasome system impairment- or oxidative stress-induced neurotoxicity was significantly reduced by HEXA-018 treatment. Moreover, oxidative stress-induced mitochondrial dysfunction was strongly ameliorated by HEXA-018 treatment. In addition, we investigated the efficacy of HEXA-018 in models of TDP-43 proteinopathy. HEXA-018 treatment mitigated TDP-43 toxicity in cultured neuronal cell lines and Drosophila. Our data indicate that HEXA-018 could be a new drug candidate for TDP-43-associated neurodegenerative diseases.1

Extracellular vesicle encapsulated nicotinamide delivered via a trans-scleral route provides retinal ganglion cell neuroprotection

The progressive and irreversible degeneration of retinal ganglion cells (RGCs) and their axons is the major characteristic of glaucoma, a leading cause of irreversible blindness worldwide. Nicotinamide adenine dinucleotide (NAD) is a cofactor and metabolite of redox reaction critical for neuronal survival. Supplementation with nicotinamide (NAM), a precursor of NAD, can confer neuroprotective effects against glaucomatous damage caused by an age-related decline of NAD or mitochondrial dysfunction, reflecting the high metabolic activity of RGCs. However, oral supplementation of drug is relatively less efficient in terms of transmissibility to RGCs compared to direct delivery methods such as intraocular injection or delivery using subconjunctival depots. Neither method is ideal, given the risks of infection and subconjunctival scarring without novel techniques. By contrast, extracellular vesicles (EVs) have advantages as a drug delivery system with low immunogeneity and tissue interactions. We have evaluated the EV delivery of NAM as an RGC protective agent using a quantitative assessment of dendritic integrity using DiOlistics, which is confirmed to be a more sensitive measure of neuronal health in our mouse glaucoma model than the evaluation of somatic loss via the immunostaining method. NAM or NAM-loaded EVs showed a significant neuroprotective effect in the mouse retinal explant model. Furthermore, NAM-loaded EVs can penetrate the sclera once deployed in the subconjunctival space. These results confirm the feasibility of using subconjunctival injection of EVs to deliver NAM to intraocular targets

Determining the Risk Level of Heavy Rain Damage by Region in South Korea

For risk assessment, two methods, quantitative risk assessment and qualitative risk assessment, are used. In this study, we identified the regional risk level for a disaster-prevention plan for an overall area at the national level using qualitative risk assessment. To overcome the limitations of previous studies, a heavy rain damage risk index (HDRI) was proposed by clarifying the framework and using the indicator selection principle. Using historical damage data, we also carried out hierarchical cluster analysis to identify the major damage types that were not considered in previous risk-assessment studies. The result of the risk-level analysis revealed that risk levels are relatively high in some cities in South Korea where heavy rain damage occurs frequently or is severe. Five causes of damage were derived from this study—A: landslides, B: river inundation, C: poor drainage in arable areas, D: rapid water velocity, and E: inundation in urban lowlands. Finally, a prevention project was proposed considering regional risk level and damage type in this study. Our results can be used when macroscopically planning mid- to long-term disaster prevention projects

Determining the Risk Level of Heavy Rain Damage by Region in South Korea

For risk assessment, two methods, quantitative risk assessment and qualitative risk assessment, are used. In this study, we identified the regional risk level for a disaster-prevention plan for an overall area at the national level using qualitative risk assessment. To overcome the limitations of previous studies, a heavy rain damage risk index (HDRI) was proposed by clarifying the framework and using the indicator selection principle. Using historical damage data, we also carried out hierarchical cluster analysis to identify the major damage types that were not considered in previous risk-assessment studies. The result of the risk-level analysis revealed that risk levels are relatively high in some cities in South Korea where heavy rain damage occurs frequently or is severe. Five causes of damage were derived from this study—A: landslides, B: river inundation, C: poor drainage in arable areas, D: rapid water velocity, and E: inundation in urban lowlands. Finally, a prevention project was proposed considering regional risk level and damage type in this study. Our results can be used when macroscopically planning mid- to long-term disaster prevention projects

Development of a Deep Learning-Based Prediction Model for Water Consumption at the Household Level

The importance of efficient water resource supply has been acknowledged, and it is essential to predict short-term water consumption in the future. Recently, it has become possible to obtain data on water consumption at the household level through smart water meters. The pattern of these data is nonlinear due to various factors related to human activities, such as holidays and weather. However, it is difficult to accurately predict household water consumption with a nonlinear pattern with the autoregressive integrated moving average (ARIMA) model, a traditional time series prediction model. Thus, this study used a deep learning-based long short-term memory (LSTM) approach to develop a water consumption prediction model for each customer. The proposed model considers several variables to learn nonlinear water consumption patterns. We developed an ARIMA model and an LSTM model in the training dataset for customers with four different water-use types (detached houses, apartment, restaurant, and elementary school). The performances of the two models were evaluated using a test dataset that was not used for model learning. The LSTM model outperformed the ARIMA model in all households (correlation coefficient: mean 89% and root mean square error: mean 5.60 m3). Therefore, it is expected that the proposed model can predict customer-specific water consumption at the household level depending on the type of use

Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario

Global climate models (GCMs) are used to analyze future climate change. However, the observed data of a specified region may differ significantly from the model since the GCM data are simulated on a global scale. To solve this problem, previous studies have used downscaling methods such as quantile mapping (QM) to correct bias in GCM precipitation. However, this method cannot be considered when certain variables affect the observation data. Therefore, the aim of this study is to propose a novel method that uses a convolution neural network (CNN) considering teleconnection. This new method considers how the global climate phenomena affect the precipitation data of a target area. In addition, various meteorological variables related to precipitation were used as explanatory variables for the CNN model. In this study, QM and the CNN models were applied to calibrate the spatial bias of GCM data for three precipitation stations in Korea (Incheon, Seoul, and Suwon), and the results were compared. According to the results, the QM method effectively corrected the range of precipitation, but the pattern of precipitation was the same at the three stations. Meanwhile, for the CNN model, the range and pattern of precipitation were corrected better than the QM method. The quantitative evaluation selected the optimal downscaling model, and the CNN model had the best performance (correlation coefficient (CC): 69% on average, root mean squared error (RMSE): 117 mm on average). Therefore, the new method suggested in this study is expected to have high utility in forecasting climate change. Finally, as a result of forecasting for future precipitation in 2100 via the CNN model, the average annual rainfall increased by 17% on average compared to the reference data