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PM2.5 pollution over Hangzhou Bay area, China has received continuous attention. In this study, PM2.5 samples were collected simultaneously in six typical cities in Zhejiang Province from 15 October 2019 to 15 January 2020 (autumn and winter) and from 1 June to 31 August 2020 (summer), and major water-soluble ions were analyzed. Average concentrations of NO3− and SO42− in the six cities were 3.93–15.64 μg/m3 and 4.61–7.58 μg/m3 in autumn and winter, with mass fractions of NO3− and SO42− in PM2.5 up to 19.6–34.2% and 13.6–26.3%, respectively, while in summer, they were 1.23–2.64 μg/m3 and 2.22–4.14 μg/m3, with mass fractions of 7.0–15.0% and 14.7~25.1%. Both NO3− and SO42− were mostly from gas-to-particle transformation of precursors. High relative humidity in the six cities was suggested to significantly promote the formation of NO3− and SO42−, particularly in autumn and winter, while enhanced atmospheric oxidation favored the formation of SO42− in summer. However, the formation of NO3− was inhibited under a high temperature of >15 °C. The concentrations of SO42− and NO3 were mostly correlated with each other among the six cities. Potential source contribution function analysis indicated that both SO42− and NO3− were mostly from local pollution of Hangzhou Bay area in Zhejiang Province and also transported from Shanghai and the southern region of Jiangsu Province. This study contributed to the understanding of regional characteristics of SO42− and NO3− in Hangzhou Bay area and suggested that joint prevention and control efforts should be strengthened to reduce regional PM2.5 pollution. [ABSTRACT FROM AUTHOR]

In this study, the interactions of six different flavonoids (IVa~c and Va~c) with bovine serum proteins (BSA) were investigated by fluorescence spectroscopy, thermodynamic and molecular dynamics simulations. The fluorescence behaviors of IVa~c and Va~c were quantitatively related to BSA, and the results calculated using the Stern-Volmer equation showed that IVa~c and Va~c had significant fluores-cence quenching with BSA, both of which were static quenching. The binding ability of monofluoroflavone with BSA was best when its fluorine atom was at para position, while the binding ability of monofluoroflavonol with BSA was best when its fluorine atom was at meta position. The binding ability of monofluoroflavonol with BSA was stronger than that of monofluoroflavone when hydroxyl group was attached to the C(3) of monofluoroflavone IVa~c. The calculated thermodynamic parameters of IVa~c and Va~Va were ΔG<0, ΔH<0 and ΔS<0, indicating that the interaction of IVa~c and Va~Vc with BSA was a spontaneous exothermic process, which mainly relies on hydrogen bonding and van der Waals force. [ABSTRACT FROM AUTHOR]

Choerospondias axillaries (CA) is an important fast-growing afforestation tree species in southern China, and its fruit has medicinal and edible value. High performance liquid chromatography was used to determine the composition and content of sugar and acid in CA fruits from different provenances, and cluster analysis was conducted on different provenances. The results showed that the total sugar content of CA fruit ranged from 49.31 to 139.41 mg/g, with sucrose accounting for the highest proportion of total sugar, followed by glucose, and fructose was the lowest. The total acid content of CA fruit ranged from 47.97 to 82.81 mg/g, with citric acid accounting for 67.09% of the total acid, followed by ascorbic acid, quinic acid, tartaric acid, and malic acid. Cluster analysis was conducted on 20 CA fruits, which were divided into 4 categories. It was recommended to develop N19 fruit had the highest content of sucrose and glucose, and the highest sweetness value, sugar-acid ratio and sweet-acid ratio. It can be suggested to be developed as a high-sugar fresh food source. N02 fruit with high sugar and high acid content can be used as a raw material for fruit cake processing. This result provides an important reference for the quality evaluation and rational development and utilization of CA.

Introduction: The association between elevated lactate levels and the development of disseminated intravascular coagulation (DIC) in patients with severe trauma remains unclear. Hence, this study aimed to explore the association between lactate and the development of DIC in patients with severe trauma. Methods: This prospective cohort study was conducted on consecutive patients with severe trauma who were hospitalized in the intensive care unit from January 2020 to January 2023. The primary outcome measured was the occurrence of DIC in patients in the emergency department or posthospitalization. Logistic regression analysis evaluating the risk values for lactate and DIC, the receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) examinations studying the predictive efficiency of lactate for DIC. The Kaplan–Meier survival curve was used to assess patient survival. Sensitivity robustness analysis included modified Poisson regression, E-value, subgroup analysis, and numerical variable transformation analysis. Results: Logistic regression analysis corrected for confounding factors showed that lactate was a risk factor for DIC in patients with severe trauma (adjusted odds ratio [OR]: 1.374, 95% confidence interval [CI]: 1.206–1.566). Lactate predicted DIC risk with a 0.8513 area under the ROC curve (95% CI: 0.7827–0.9199), 4.8 cutoff value, 0.8333 sensitivity, and 0.8014 specificity. DCA showed the correlation between lactate and DIC. The mortality rate of patients with a high risk of DIC was significantly higher than that of patients with a low risk (log-rank test, P < 0.001). The modified Poisson regression showed that lactate was a risk factor for DIC (risk ratio: 1.188, 95% CI: 1.140–1.237). E-value was 1.645, and the lower limit of 95% CI was 1.495. The logistic regression analysis after subgroup analysis and transformation of numerical variables showed that lactate remained a risk factor for DIC. Conclusions: Elevated lactate is closely associated with the occurrence of DIC in patients with severe trauma. Lactate seems to be a good predictive factor for DIC manifestation in patients with severe trauma.

Energy finance is the product of the close combination of the energy industry and the financial industry, and the two affect each other. The energy crisis may lead to a financial crisis, and the financial crisis may also lead to a energy crisis. Early risk warning for the energy financial crisis can effectively mitigate and reduce risks. This article used the GABP (Genetic Algorithm Back Propagation) algorithm model to systematically analyze and predict the risks of energy financial crises. After establishing indicators for energy finance risk warning, this article collected relevant data from 150 energy companies and 210 financial companies, and compared them with the GABP algorithm model and manual analysis model. The error value of the model is determined by the numerical expansion in the positive and negative directions based on zero scale values. The closer the zero scale value is, the smaller the error; the farther it is from the zero scale value, the greater the error. The results show that the average accuracy of the GABP model for energy finance risk warning is 85.2%, and the minimum error value is −0.23. The average accuracy of using manual analysis models for energy finance risk warning is 75.8%, with a minimum error value of 1.89. The GABP algorithm has advantages in constructing energy finance risk warning models.

Information dissemination and the associated change of individual behavior can significantly slow the spread of an epidemic. However, major social events which attract public attention will disturb information spread and affect epidemic transmission in ways that have not been readily quantified. We investigate the interplay between disease spreading and disease-related information dissemination in a two-layer network. We employ the SIR-UAU model with a time dependent coefficient to denote information dissemination. We found that major social events are equivalent to perturbations of information dissemination in certain time intervals and will consequently weaken the effect of information dissemination, and increase prevalence of infection. Our simulation results agree well with the trends observed from real-world data sets. We found that two specific major events explain the trend of the coronavirus epidemic in the US: the online propaganda and international agenda setting of Donald Trump early in 2020 and the 2020 US Presidential Election.