Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31

We report a promising oxadiazole-containing phenylene vinylene ether-linkage copolymer, which can emit nearly white light from a single-layer light-emitting diode. The emission spectrum is composed of a red component originating from the new excited dimer in addition to the blue-green component from an individual lumophore and excimer. This excited dimer is formed under a strong electric field inside the diode and cannot be produced by photoexcitation, which is different from the excimer or exciplex that is often found both in photoluminescence and electroluminescence, and it is termed the “electromer.”

This study examines the relationship between social media site Facebook and TV ratings drawing from audience factors of integration model of audience behavior. Based on context of Taiwan television network programs, this study collected measures for Facebook likes, shares, comments, posts for three genres of television shows and their Nielsen ratings over a period of eleven weeks, resulting in the size of sample more than 130 observations. This study applied multiple regression models and determined that the key social media measures correlate with TV ratings. In essence, TV shows with higher number of posts and engagement are likely to relate to higher ratings, special in drama shows. Subsequently, this study constructed the TV prediction models with measures for Facebook via SVR. The results suggested that prediction models are a good forecasting of which MAPE was between 10% -20%, even less than 10%. This implies that TV network should be motivated to invest in social media and engage their audience and analysts can use social media as a mechanism of exante forecasting.

The purpose of this study is to understand the relationship among leisure physical activity, sedentary lifestyle, physical fitness, and happiness in healthy elderly adults aged over 65 years old in Taiwan. Data were recruited from the National Physical Fitness Survey in Taiwan, which was proposed in the Project on the Establishment of Physical Fitness Testing Stations by the Sports Administration of the Ministry of Education. Participants were recruited from fitness testing stations set up in 22 counties and cities from October 2015 to May 2016. A total of 20,111 healthy older adults aged 65–102 years were recruited as research participants. The fitness testing procedure was described to all participants, who were provided with a standardized structured questionnaire. Participants’ data included sex, city or county of residence, living status (living together with others or living alone), education level, and income. Physical fitness testing was conducted in accordance with The Fitness Guide for Older Adults published by the Sports Administration of the Ministry of Education. The testing involved cardiorespiratory endurance, muscle strength, muscle endurance, flexibility, balance, and body composition. The t-test was used to evaluate the differences between continuous and grade variables under the two classification variables of sex, city or county of residence, and living status. We used the MARS (multivariate adaptive regression splines) model to analyze the effects of physical fitness variables and leisure physical activity variables on happiness. Among healthy elderly adults, sex, age, living status, body mass index, and leisure physical activity habits proved to be related to happiness. Aerobic endurance (2-min step test), muscular strength and endurance (30-s arm curl and 30-s chair stand tests), flexibility (back stretch and chair sit-and-reach tests), and balance ability (8-foot up-and-go tests and one-leg stance with eyes open tests) were found to be related to happiness. The results of this study indicate that increased physical activity and intensity, as well as physical fitness performance, are associated with improved happiness.

No abstract

Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possible.

BackgroundThe high prevalence of diabetes is associated with body mass index (BMI), and diabetes can cause many complications, such as hip fractures. This study investigated the effects of BMI and diabetes on the risk of hip fractures and related factors.MethodsWe retrospectively reviewed data from 22,048 subjects aged ≧ 40 years from the National Health Interview Survey in Taiwan (NHIST) in 2001, 2005, and 2009. We linked the NHIST data for individual participants with the National Health Insurance Research Database (NHIRD), which includes the incidence of hip fracture from 2000 to 2013. We defined five categories for BMI: low BMI (BMI < 18.5), normal BMI (18.5 ≦ BMI < 24), overweight (24 ≦ BMI < 27), mild obesity (27 ≦ BMI < 30), and moderate obesity (BMI ≧ 30). The Cox proportional hazards model was used to analyze the effects of BMI and diabetes on risk of hip fracture.ResultsThe Cox proportional hazards model shows that hip fracture risk in participants with diabetes was 1.64 times that of non-diabetes patients (95% confidence interval [CI]:1.30–2.15). Participants with low BMIs showed a higher hip fracture risk (HR: 1.75) than those with normal BMI. Among the five BMI groups, compared with non-diabetes patients, only diabetes patients with a normal BMI showed a significantly higher risk on hip fracture (HR: 2.13, 95% CI: 1.48–3.06). In participants with diabetes, compared with those with normal BMI, those with overweight or obesity showed significantly lower hip fracture risks (HR: 0.49 or 0.42). The hip fracture risk in participants who expend ≧ 500 kcal/week in exercise was 0.67 times lower than in those who did not exercise.ConclusionsDiabetes and low BMI separately are important risk factors for hip fracture. There was an interaction between diabetes and BMI in the relationship with hip fracture (p = 0.001). The addition of energy expenditure through exercise could effectively decrease hip fracture risk, regardless of whether the participants have diabetes or not. The results of this study could be used as a reference for health promotion measures for people with diabetes.

A benzodiazepine binding assay directed separation led to the identification of 3 flavones baicalein (1), oroxylin A (2), and skullcapflavone II (3) from the water extract of Scutellaria baicalensis root. Compounds 1, 2, and 3 interacted with the benzodiazepine binding site of GABAA receptors with a Ki value of 13.1, 14.6 and 0.36 micromol/L, respectively.