Multi-stage dynamic evolution of green financial system from the perspective of bilateral moral hazard.

In order to improve the regulatory efficiency of the government in the construction process of the green financial system, the multi-stage dynamic evolution game model of green financial system from the perspective of bilateral moral risk is constructed and analyzed. It is found that cost-controllable and profitable collaborative innovation are the fundamental to realize the sustainable cooperation of green innovation between financial institutions and carbon emission enterprises. The introduction of reward and punishment mechanism and transfer payment mechanism for government is conducive to promoting the willingness of financial institutions and carbon emission enterprises to cooperate in green innovation. However, with the increase of appeal willingness of carbon emission enterprises and the cost reduction of appeal, although the risk of illegal arbitrage of financial institutions can be curbed to a certain extent, there is a risk of deterioration in the collaborative relationship between financial institutions and carbon emission enterprises.

How to improve the function of government carbon tax in promoting enterprise carbon emission reduction: from the perspective of three-stage dynamic game.

Flexible strategy of carbon tax is an important chip for the government to promote the implementation of carbon emission reduction. However, low-carbon technological innovation of enterprises is bound to produce market competition problems with traditional production technology. Based on this, in order to explore the relationship between heterogeneous objectives, carbon tax pricing strategy of government and decision-making of heterogeneous enterprise in the market, and how the government can make better use of carbon tax to promote the implementation of carbon emission reduction, this paper constructs and analyzes a three-stage dynamic game model between the government and heterogeneous enterprises. It is found that higher carbon tax pricing is not necessarily conducive to promote the implementation of carbon emission reduction. When the government's primary goal is to promote low-carbon technology innovative enterprise to increase innovative investment, the government should adopt the unified pricing strategy of carbon tax and improve carbon tax pricing; when the primary goal of the government is to promote the promotion of low-carbon products, the government should adopt the differentiated pricing strategy of carbon tax and reduce the carbon tax pricing of low-carbon technology innovative enterprise. In addition, this paper also studies the impact mechanism of different government pricing strategies on heterogeneous enterprise decision-making under heterogeneous objectives of government.

Accurate Prediction of Hydration Sites of Proteins Using Energy Model With Atom Embedding.

We propose a method based on neural networks to accurately predict hydration sites in proteins. In our approach, high-quality data of protein structures are used to parametrize our neural network model, which is a differentiable score function that can evaluate an arbitrary position in 3D structures on proteins and predict the nearest water molecule that is not present. The score function is further integrated into our water placement algorithm to generate explicit hydration sites. In experiments on the OppA protein dataset used in previous studies and our selection of protein structures, our method achieves the highest model quality in terms of F1 score, compared to several previous studies.

How longer can people live by achieving the daily ambient fine particulate pollution standards in the Pearl River Delta region, China?

BACKGROUND: Previous research has reported the effects of long-term fine particulate matter (PM2.5) pollution on years of life lost (YLL), but these effects may not represent the full impact. This study aims to estimate potential benefits in life time from adhering to daily ambient PM2.5 concentration standards/guidelines. METHODS: This study evaluated the relationship between daily ambient PM2.5 level and YLL using a two-stage approach with generalized additive models and meta-analysis. Potential life expectancy gains were then estimated by presuming that daily PM2.5 levels were in compliance with the Chinese and WHO standards. In addition, the attributable fraction of YLL due to excess PM2.5 exposure was also calculated. RESULTS: During 2013-2016, 459,468 non-accidental deaths were recorded in the six cities of Pearl River Delta, China. Each 10 µg/m3 increment in four-day average (lag03) level of PM2.5 was related to an increment of 13.31 [95% confidence interval (CI): 5.74, 20.87] years of life lost. Implementation of the WHO guidelines might avoid 180,980.83 YLLs (95% CI: 78,116.07, 283,845.60), which corresponded to 0.39 (95% CI: 0.17, 0.62) years of increased life time per death. Additionally, an estimated 0.15% (95% CI: 0.06%, 0.23%) or 2.04% (95% CI: 0.88%, 3.20%) of YLLs could be attributed to PM2.5 exposures higher than the Chinese or WHO guidelines, respectively. CONCLUSIONS: This study suggests that people might live longer by controlling daily PM2.5 concentration and highlights the need to adopt stricter standards in China.

Hourly associations between ambient temperature and emergency ambulance calls in one central Chinese city: Call for an immediate emergency plan.

BACKGROUND: Most studies examining the short-term effects of temperature on health were based on the daily scale, few were at the hourly level. Revealing the relationship between unfavorable temperatures on an hourly basis and health is conducive to the development of more accurate extreme temperature early warning systems and reasonable dispatch of ambulances. METHODS: Hourly data on temperature, air pollution (including PM2.5, O3, SO2 and NO2) and emergency ambulance calls (EACs) for all-cause, cardiovascular and respiratory diseases from January 16, 2014 to December 31, 2016 were obtained from Luoyang, China. A distributed lag non-linear model (DLNM) was used to assess the association between hourly temperature and ambulance calls after adjusting for potential confounding factors. The fractions of EACs attributable to non-optimum temperatures were also estimated. RESULTS: Hourly temperature was associated with increased ambulance calls with a varying lag pattern. Extreme hot temperature (>32.1 °C) was positively associated with all-cause, cardiovascular diseases at lag 0-30 h and lag 0-9 h, while no significant effects were found for respiratory morbidity. Extreme cold temperature (<-2.5 °C) was positively associated with all-cause, cardiovascular and respiratory morbidity at lag 56-157 h, 50-145 h and 123-170 h. An overall EACs fraction of 6.84% [Backward estimate, 95% confidence interval (CI): 5.01%, 8.59%] could be attributed to non-optimum temperatures, and more contributions were caused by cold [Backward estimate: 6.06% (95% CI: 5.10%, 8.48%)] than by heat [Backward estimate: 0.79% (95% CI: 0.12%, 1.45%)]. CONCLUSIONS: Extreme hot temperature may lead to increased ambulance calls within a few hours, while extreme cold temperature may not increase ambulance calls until more than 2 days later. Effective measures, such as forming hourly temperature warning standards, optimizing ambulance services at extreme temperatures, etc., should be taken to reduce the unfavorable temperature - associated EACs burden.

Hourly associations between ambient air pollution and emergency ambulance calls in one central Chinese city: Implications for hourly air quality standards.

BACKGROUND: Most studies on the short-term health effects of air pollution have been conducted on a daily time scale, while hourly associations remain unclear. METHODS: We collected the hourly data of emergency ambulance calls (EACs), ambient air pollution, and meteorological variables from 2014 to 2016 in Luoyang, a central Chinese city in Henan Province. We used a generalized additive model to estimate the hourly effects of ambient air pollutants (PM2.5, PM10, SO2, and NO2) on EACs for all natural causes and cardiovascular and respiratory morbidity, with adjustment for potential confounding factors. We further examined the effect modification by temperature, relative humidity, wind speed, and atmospheric pressure using stratified analyses. RESULTS: In the single-pollutant models, PM2.5, PM10, SO2, and NO2 were associated with an immediate increase in all-cause morbidity at 0, 0, 12, 10 h, separately, after exposure to these pollutants (excess risks: 0.19% (95% confidence interval (CI): 0.03%, 0.35%), 0.13% (95% CI: 0.02%, 0.24%), 0.28% (95% CI: 0.01%, 0.54%) and 0.52% (95% CI: 0.06%, 0.99%), respectively). These effects remained generally stable in two-pollutant models. SO2 and NO2 were significantly associated with an immediate increase in risk of cardiovascular morbidity, but the effects on respiratory morbidity were relatively more delayed. The stratified analyses suggested that temperature could modify the association between PM2.5 and EACs, humidity and atmospheric pressure could modify the association between SO2 and EACs. CONCLUSIONS: Our study provides new evidence that higher concentrations of PM2.5, PM10, SO2, and NO2 may have transiently acute effects on all-cause morbidity and subacute effects on respiratory morbidity. SO2 and NO2 may also have immediate effects on cardiovascular morbidity. Findings of this study have important implications for the formation of hourly air quality standards.

Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction.

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. For all 13 data sets, Chemi-Net resulted in higher R2 values compared with the Cubist benchmark. The median R2 increase rate over Cubist was 26.7%. We expect that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.