Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic.

Monitoring the fine spatiotemporal distribution of urban GDP is a critical research topic for assessing the impact of the COVID-19 outbreak on economic and social growth. Based on nighttime light (NTL) images and urban land use data, this study constructs a GDP machine learning and linear estimation model. Based on the linear model with better effect, the monthly GDP of 34 cities in China is estimated and the GDP spatialization is realized, and finally the GDP spatiotemporal correction is processed. This study analyzes the fine spatiotemporal distribution of GDP, reveals the spatiotemporal change trend of GDP in China's major cities during the current COVID-19 pandemic, and explores the differences in the economic impact of the COVID-19 pandemic on China's major cities. The result shows: (1) There is a significant linear association between the total value of NTL and the GDP of subindustries, with R2 models generated by the total value of NTL and the GDP of secondary and tertiary industries being 0.83 and 0.93. (2) The impact of the COVID-19 pandemic on the GDP of cities with varied degrees of development and industrial structures obviously varies across time and space. The GDP of economically developed cities such as Beijing and Shanghai are more affected by COVID-19, while the GDP of less developed cities such as Xining and Lanzhou are less affected by COVID-19. The GDP of China's major cities fell significantly in February. As the COVID-19 outbreak was gradually brought under control in March, different cities achieved different levels of GDP recovery. This study establishes a fine spatial and temporal distribution estimation model of urban GDP by industry; it accurately monitors and assesses the spatial and temporal distribution characteristics of urban GDP during the COVID-19 pandemic, reveals the impact mechanism of the COVID-19 pandemic on the economic development of major Chinese cities. Moreover, economically developed cities should pay more attention to the spread of the COVID-19 pandemic. It should do well in pandemic prevention and control in airports and stations with large traffic flow. At the same time, after the COVID-19 pandemic is brought under control, they should speed up the resumption of work and production to achieve economic recovery. This study provides scientific references for COVID-19 pandemic prevention and control measures, as well as for the formulation of urban economic development policies.

Cancers and COVID-19 Risk: A Mendelian Randomization Study.

Observational studies have shown increased COVID-19 risk among cancer patients, but the causality has not been proven yet. Mendelian randomization analysis can use the genetic variants, independently of confounders, to obtain causal estimates which are considerably less confounded. We aimed to investigate the causal associations of cancers with COVID-19 outcomes using the MR analysis. The inverse-variance weighted (IVW) method was employed as the primary analysis. Sensitivity analyses and multivariable MR analyses were conducted. Notably, IVW analysis of univariable MR revealed that overall cancer and twelve site-specific cancers had no causal association with COVID-19 severity, hospitalization or susceptibility. The corresponding p-values for the casual associations were all statistically insignificant: overall cancer (p = 0.34; p = 0.42; p = 0.69), lung cancer (p = 0.60; p = 0.37; p = 0.96), breast cancer (p = 0.43; p = 0.74; p = 0.43), endometrial cancer (p = 0.79; p = 0.24; p = 0.83), prostate cancer (p = 0.54; p = 0.17; p = 0.58), thyroid cancer (p = 0.70; p = 0.80; p = 0.28), ovarian cancer (p = 0.62; p = 0.96; p = 0.93), melanoma (p = 0.79; p = 0.45; p = 0.82), small bowel cancer (p = 0.09; p = 0.08; p = 0.19), colorectal cancer (p = 0.85; p = 0.79; p = 0.30), oropharyngeal cancer (p = 0.31; not applicable, NA; p = 0.80), lymphoma (p = 0.51; NA; p = 0.37) and cervical cancer (p = 0.25; p = 0.32; p = 0.68). Sensitivity analyses and multivariable MR analyses yielded similar results. In conclusion, cancers might have no causal effect on increasing COVID-19 risk. Further large-scale population studies are needed to validate our findings.

Ethanol Production from Colpomenia sinuosa by an Alginate Fermentation Strain Meyerozyma guilliermondii.

With the consumption of energy and the spread of COVID-19, the demand for ethanol production is increasing in the world. The industrial ethanol fermentation microbes cannot metabolize the alginate component of macro algae, which affects the ethanol yield. In this research, the ethanol production process from macro algae by an alginate fermentation yeast Meyerozyma guilliermondii, especially the pretreatment process of Colpomenia sinuosa, was studied. At the same time, the experimental design of Box-Behnken was carried out to achieve the optimum fermentation performance. The concentration of KH2PO4 (A: 2-6 g.L-1), pH (B: 4-7), reaction time (C: 60-120 h) and temperature (D: 24-34 °C) were variable input parameters. During the ethanol production process, the algae powder was firstly mixed with water at 90 °C for 0.5 h. Later the fermentation culture medium was prepared and then it was fermented by the yeast Meyerozyma guilliermondii to produce ethanol. And the optimal fermentation parameters were as follows: fermentation temperature of 28 °C, KH2PO4 dosage of 4.7 g.L-1, initial pH of 6, and fermentation time of 99 h. The ethanol yield reached 0.268 g.g-1 (ethanol to algae), close to the predicted value of model. The generation of alginate lyase during the fermentation of algae was also examined. The highest alginate lyase activity reached 46.42 U.mL-1.