Prediction on transaction amounts of China’s CBEC with improved GM (1, 1) models based on the principle of new information priority

Benefited by e-commerce activities and information technology development, cross-border e-commerce (CBEC) has experienced rapid growth and attracted much research attention. This study takes China’s CBEC as a typical research object and intends to forecast its future development trend based on an exploration of its dynamic changing rules as a whole. The data set of transaction amounts of China’s CBEC from 2008 to 2018 was used in the modeling processes of improved grey models (GM) (1,1) proposed in this study, after which forecast results on the development of China’s CBEC from 2019 to 2020 were achieved. The experimental results reveal that, introducing the principle of new information priority to the improvement of grey models indeed works when forecasting a newly-emerging and vulnerable system like CBEC. Finally, it is predicted that China’s CBEC promises to continue to grow in the near future.

Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations.

The real-time monitoring of reductions of economic activity by containment measures and its effect on the transmission of the coronavirus (COVID-19) is a critical unanswered question. We inferred 5,642 weekly activity anomalies from the meteorology-adjusted differences in spaceborne tropospheric NO2 column concentrations after the 2020 COVID-19 outbreak relative to the baseline from 2016 to 2019. Two satellite observations reveal reincreasing economic activity associated with lifting control measures that comes together with accelerating COVID-19 cases before the winter of 2020/2021. Application of the near-real-time satellite NO2 observations produces a much better prediction of the deceleration of COVID-19 cases than applying the Oxford Government Response Tracker, the Public Health and Social Measures, or human mobility data as alternative predictors. A convergent cross-mapping suggests that economic activity reduction inferred from NO2 is a driver of case deceleration in most of the territories. This effect, however, is not linear, while further activity reductions were associated with weaker deceleration. Over the winter of 2020/2021, nearly 1 million daily COVID-19 cases could have been avoided by optimizing the timing and strength of activity reduction relative to a scenario based on the real distribution. Our study shows how satellite observations can provide surrogate data for activity reduction during the COVID-19 pandemic and monitor the effectiveness of containment to the pandemic before vaccines become widely available.

Prevalence of mental health problems and associated factors among front-line public health workers during the COVID-19 pandemic in China: an effort-reward imbalance model-informed study.

BACKGROUND: Poor mental health status and associated risk factors of public health workers have been overlooked during the COVID-19 pandemic. This study used the effort-reward imbalance model to investigate the association between work-stress characteristics (effort, over-commitment, reward) and mental health problems (anxiety and depression) among front-line public health workers during the COVID-19 pandemic in China. METHODS: A total of 4850 valid online questionnaires were collected through a self- constructed sociodemographic questionnaire, the adapted ERI questionnaire, the 9-item Patient Health Questionnaire (PHQ-9) and the 7-item General Anxiety Disorder Scale (GAD-7). Hierarchical logistic regression analysis was conducted to investigate the association between ERI factors and mental health problems (i.e., depression and anxiety), with reward treated as a potential moderator in such associations. RESULTS: The data showed that effort and over-commitment were positively associated with depression and anxiety, while reward was negatively associated with depression and anxiety. Development and job acceptance were the two dimensions of reward buffered the harmful effect of effort/over-commitment on depression and anxiety, whereas esteem was non-significant. CONCLUSIONS: This study confirmed the harmful effects of effort and over-commitment on mental health among public health workers during the COVID-19 pandemic in China. Such effects could be alleviated through an appropriate reward system, especially the development and job acceptance dimensions of such a system. These findings highlight the importance of establishing an emergency reward system, comprising reasonable work-allocation mechanism, bonuses and honorary titles, a continuous education system and better career-development opportunities.

PTP-MEG2 regulates quantal size and fusion pore opening through two distinct structural bases and substrates.

Tyrosine phosphorylation of secretion machinery proteins is a crucial regulatory mechanism for exocytosis. However, the participation of protein tyrosine phosphatases (PTPs) in different exocytosis stages has not been defined. Here we demonstrate that PTP-MEG2 controls multiple steps of catecholamine secretion. Biochemical and crystallographic analyses reveal key residues that govern the interaction between PTP-MEG2 and its substrate, a peptide containing the phosphorylated NSF-pY83 site, specify PTP-MEG2 substrate selectivity, and modulate the fusion of catecholamine-containing vesicles. Unexpectedly, delineation of PTP-MEG2 mutants along with the NSF binding interface reveals that PTP-MEG2 controls the fusion pore opening through NSF independent mechanisms. Utilizing bioinformatics search and biochemical and electrochemical screening approaches, we uncover that PTP-MEG2 regulates the opening and extension of the fusion pore by dephosphorylating the DYNAMIN2-pY125 and MUNC18-1-pY145 sites. Further structural and biochemical analyses confirmed the interaction of PTP-MEG2 with MUNC18-1-pY145 or DYNAMIN2-pY125 through a distinct structural basis compared with that of the NSF-pY83 site. Our studies thus provide mechanistic insights in complex exocytosis processes.

Daily CO2 Emission Reduction Indicates the Control of Activities to Contain COVID-19 in China.

Lockdown measures are essential to containing the spread of coronavirus disease 2019 (COVID-19), but they will slow down economic growth by reducing industrial and commercial activities. However, the benefits of activity control from containing the pandemic have not been examined and assessed. Here we use daily carbon dioxide (CO2) emission reduction in China estimated from statistical data for energy consumption and satellite data for nitrogen dioxide (NO2) measured by the Ozone Monitoring Instrument (OMI) as an indicator for reduced activities consecutive to a lockdown. We perform a correlation analysis to show that a 1% day-1 decrease in the rate of COVID-19 cases is associated with a reduction in daily CO2 emissions of 0.22% ± 0.02% using statistical data for energy consumption relative to emissions without COVID-19, or 0.20% ± 0.02% using satellite data for atmospheric column NO2. We estimate that swift action in China is effective in limiting the number of COVID-19 cases <100,000 with a reduction in CO2 emissions of up to 23% by the end of February 2020, whereas a 1-week delay would have required greater containment and a doubling of the emission reduction to meet the same goal. By analyzing the costs of health care and fatalities, we find that the benefits on public health due to reduced activities in China are 10-fold larger than the loss of gross domestic product. Our findings suggest an unprecedentedly high cost of maintaining activities and CO2 emissions during the COVID-19 pandemic and stress substantial benefits of containment in public health by taking early actions to reduce activities during the outbreak of COVID-19.