PO-185 Lifestyle intervention modify DNA methylation of adipose tissue in overweight and obese men with insomnia symptoms

Objective To study whether diet and exercise intervention affect sleep and obesity-related genes’ DNA methylation in overweight and obese men with insomnia symptoms Methods The study participants were a subgroup of a large intervention and consisted of 10 overweight or obesity men aged 34-65 years with insomnia symptoms. They participated in a 6-month progressive aerobic exercise training and individualized dietary consoling program and were randomly selected from diet (n=4), exercise (n=3) and control (n=3) groups. Body composition included fat mass and lean mass in the whole body and abdominal android region were assessed by dual-energy X-ray densitometry. The fitness level (VO2max) was determined by 2-km walk test using a standard protocol. Blood samples from venous were taken at fasted state in the morning. Total cholesterol, high density lipid cholesterol, low density lipid cholesterol, triglycerides, glucose, insulin, non-esterified fatty acid, alanine aminotransferase, aspartate aminotransferase and γ-glutamyltransferase were assessed by conventional methods. Subcutaneous adipose tissue was taken from abdominal region before and after the intervention. DNA was extracted from subcutaneous adipose tissue using a QIAamp DNeasy Tissue Kit. Whole genome-wide DNA methylation was obtained using MethylRAD-Seq. MethylRAD library preparation started from DNA digestion by FspEI, then digested products were run on agarose gel to verify digestion and DNA ligase was added to the digestion solution. After ligation products amplication, PCR was conducted by MyCycler thermal cycler (Bio-Rad). The target fragment was excised from polyacrylamide gel and DNA was diffused from the gel in nuclease-free water. For relative quantification of MethylRAD data, DNA methylation levels were determined using the normalized read depth (reads per million, RPM) for each site. For each restriction site, its methylation level was estimated by dividing the log-transformed depth of each site by the log-transformed maximum depth (representing 100% methylation; i.e. M-index ¼ log(depth site)/ log(depth max)), where depth max was summarized from the top 2% of sites (approx. 500 for the standard library) with the highest sequencing coverage. Heat map images are generated with Matlab 7.0 software and pathways are analysed by WEB-based Gene SeT AnaLysis Toolket. A statistical significance for methylated CpGs and pathways were set to p=0.001 and p=0.05, respectively. Results No significant group differences by time were found in sleep-related variables, body composition, lifestyle factors nor with measured lipid and glucose biomarkers. However, whole genome-wide DNA methylation was decreased after dietary intervention, but was increased after exercise intervention, respectively. Correspondingly, 1253 and 708 differentially methylated loci were found in diet and exercise groups by contrast to the control group. Among them, the overlap genes between diet and exercise had multiple differentially methylated CpGs, including e.g. MYT1L (4 CpGs), CAMTA1 (3 CpGs), NRXN1 (3 CpGs), RPS6KA2 (3 CpGs), SEMA4D (3 CpGs). DNA methylation in PCDH8 was negatively correlated with wake after sleep onset after exercise intervention and MYRIP associated with sleep duration showed lower methylation after the dietary intervention. Further, 13 (DIO1, GCK, GYS1, LMNA, LY86, PNMT, PPARA, PPARD, SERPINE1, TH, TMEM18, TNFRSF1B and UBL5) and 2 (SDCCAG8 and TNF) obesity-related genes’ DNA methylation profile changed in response to diet and exercise, respectively. Percentage changes of CpGs within KLHDC8A, ANKS1A, FGFRL1 and KDM3B were correlated with energy yield fat and carbohydrate, HOMA-IR and VO2max, respectively. Conclusions We found that both exercise and dietary interventions have impacts on these genes related to sleep indicating by DNA methylation in PCDH8 and MYRIP, respectively. Further diet may be more effective than aerobic exercise intervention since greater number of modified obesity-related genes observed after dietary intervention. Our results indicate that reduce insomnia symptoms may need to more focus on control obesity

The Role of Physical Activity in Sleep Quality, Physical Fitness, and Cardiovascular Risks

早稲田大学博士(スポーツ科学)早大学位記番号:新9616doctoral thesi

Incorporation of thermoelectric effect with solar power into solar panel

The worrying problems with the increase in usage of fossil fuels over the years and the exhaustion of non-renewable energy have led to the release of greenhouse gases that contributes to climate change. One of the solutions to these problems will be renewable energy. Recently, renewable energy such as wind energy, solar energy, wave energy to name a few, has become a popular alternative to replace fossil fuels. For countries like Singapore where solar irradiance is good due to its location, solar energy will be the focus. Additionally, Singapore intends to become a green country by 2035 and has a goal of reducing its carbon dioxide emission by increasing carbon taxes and the usage of solar energy. The usage of solar photovoltaic (PV) panels are one of the most common ways to convert solar energy into electrical energy. However, high temperatures are one of the biggest factors that affect the PV panels and will cause efficiency to decrease drastically. Therefore, this project aims to convert the excess heat energy into useful electrical energy to power up small demand devices such as fans, USB lights, and chargers that can charge a smartphone. This energy conversion is done by the means of a solar and thermoelectric combination system that utilises the Seebeck effect. This phenomenon allows the thermoelectric generator to convert the thermal differences between the cooler and warmer sides of the thermoelectric devices to electrical energy. With the combination of systems, the efficiency of solar PV will be increased.Bachelor of Engineering (Electrical and Electronic Engineering

Mediating Effect of Perceived Stress on the Association between Physical Activity and Sleep Quality among Chinese College Students

Background: While physical activity has been reported to positively affect stress and sleep quality, less is known about the potential relationships among them. The present study aimed to investigate the mediating effect of stress on the association between physical activity and sleep quality in Chinese college students, after controlling for age, nationality, and tobacco and alcohol use. Participants: The sample comprised 6973 college students representing three Chinese universities. Methods: Physical activity, perceived stress, and sleep quality were respectively measured using the International Physical Activity Questionnaire—Short Form (IPAQ-SF), Perceived Stress Scale—10 Items (PSS-10), and Pittsburgh Sleep Quality Index (PSQI). Results: Mediating effects of perceived stress on the association between physical activity and sleep quality were observed in males and females, with 42.4% (partial mediating effect) and 306.3% (complete mediating effect) as percentages of mediation, respectively. Conclusion: The results of this study may provide some suggestions that physical activity could improve sleep by aiding individuals in coping with stress and indicate that stress management might be an effective non-pharmaceutical therapy for sleep improvement

A Python Program Analysis Infrastructure to Facilitate Better Data Processing

<p>A powerful Python analysis infrastructure to support the three most popular kinds of analyses: static, dynamic and symbolic, just like LLVM for C/C++ and KLEE for C. This infrastructure will enable many researches across multiple CISE areas such as software engineering, programming languages, machine learning, AI and data science. With Python being the number one language in ML and data science, the infrastructure will stimulate exciting new research in improving data processing effectiveness, reliability, stability, and efficiency.</p

A Python Program Analysis Infrastructure to Facilitate Better Data Processing

<p>A powerful Python analysis infrastructure to support the three most popular kinds of analyses: static, dynamic and symbolic, just like LLVM for C/C++ and KLEE for C. This infrastructure will enable many researches across multiple CISE areas such as software engineering, programming languages, machine learning, AI and data science. With Python being the number one language in ML and data science, the infrastructure will stimulate exciting new research in improving data processing effectiveness, reliability, stability, and efficiency.</p

Geographic Exploration of the Driving Forces of the NDVI Spatial Differentiation in the Upper Yellow River Basin from 2000 to 2020

Meteorological factors and human activities are important factors affecting vegetation change. The change in the Upper Yellow River Basin&rsquo;s (UYRB&rsquo;s) ecological environment greatly impacts the ecological environment in the middle and lower reaches of the Yellow River. The purpose of this study was to evaluate remotely sensed imageries and vegetation indices as tools for accurately quantifying the driving forces of vegetation distribution. To accomplish this, we utilized the normalized difference vegetation index (NDVI) to examine the temporal and spatial variability of the vegetation distribution in the UYRB between 2000 and 2020. Based on the geographic detector method, the spatial differentiation, driving force, interaction, and suitability of the NDVI were detected. From 2000 to 2020, the estimated annual NDVI value of the UYRB was 0.515, with notable geographic variation in the distribution. The NDVI showed an obvious upward trend with a rate of 0.038 per 10 years. The vegetation coverage significantly improved. However, the vegetation coverage at the source area of the Yellow River marginally deteriorated. The primary driving factors affecting the spatial distribution of the NDVI were yearly precipitation, elevation, soil type, vegetation type, and annual average temperature, with a predictive power of 47%, 46%, 44%, 41%, and 40%, respectively. The interplay of the components had a stronger impact on the NDVI, and the interaction between the yearly precipitation and the soil type had the highest predictive power, reaching 61%. Natural factors and human activities influence NDVI change, with natural factors playing a significant role. Therefore, we should continue to implement the project of returning farmland to forest (grass), increase the efficiency of vegetation precipitation use, and promote the growth of vegetation so that ecological restoration continues to be effectively improved

Geographic Exploration of the Driving Forces of the NDVI Spatial Differentiation in the Upper Yellow River Basin from 2000 to 2020

Meteorological factors and human activities are important factors affecting vegetation change. The change in the Upper Yellow River Basin’s (UYRB’s) ecological environment greatly impacts the ecological environment in the middle and lower reaches of the Yellow River. The purpose of this study was to evaluate remotely sensed imageries and vegetation indices as tools for accurately quantifying the driving forces of vegetation distribution. To accomplish this, we utilized the normalized difference vegetation index (NDVI) to examine the temporal and spatial variability of the vegetation distribution in the UYRB between 2000 and 2020. Based on the geographic detector method, the spatial differentiation, driving force, interaction, and suitability of the NDVI were detected. From 2000 to 2020, the estimated annual NDVI value of the UYRB was 0.515, with notable geographic variation in the distribution. The NDVI showed an obvious upward trend with a rate of 0.038 per 10 years. The vegetation coverage significantly improved. However, the vegetation coverage at the source area of the Yellow River marginally deteriorated. The primary driving factors affecting the spatial distribution of the NDVI were yearly precipitation, elevation, soil type, vegetation type, and annual average temperature, with a predictive power of 47%, 46%, 44%, 41%, and 40%, respectively. The interplay of the components had a stronger impact on the NDVI, and the interaction between the yearly precipitation and the soil type had the highest predictive power, reaching 61%. Natural factors and human activities influence NDVI change, with natural factors playing a significant role. Therefore, we should continue to implement the project of returning farmland to forest (grass), increase the efficiency of vegetation precipitation use, and promote the growth of vegetation so that ecological restoration continues to be effectively improved