Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

With the Paris Agreement coming into effect, China, as the largest CO2 emitter in the world, will be facing greater pressure to reduce its carbon emissions. This paper discusses how to solve this issue from the perspective of financial development in China. Although many studies have analyzed its impact on carbon emissions, the conclusions are contradictory. A major criticism of the existing studies is the reasonability of the selection of appropriate indicators and panel estimation techniques. Almost all studies use only one or limited indicators to represent the financial development and ignore the cross-sectional dependence. To fulfil the gaps mentioned above, a financial development index system is built, and with the framework of the STIRPAT (Stochastic impacts by regression on population, affluence, and technology) model, this paper applies an ARDL approach to investigating the long-run relationship between financial development and carbon emissions and a dynamic panel error-corrected model to capture the short-run impact. The empirical results show that financial development can improve carbon emissions, and such impact not only shows a regional difference but also reflects the features of stage differences. Additionally, based on the discussion on seven specific aspects of financial development, our findings can be helpful for policy makers to enact corresponding policies to realize the goal of reducing carbon emissions in China.

Vascular complications contribute significantly to morbidity and mortality of diabetes mellitus. The primary cause of vascular complications in diabetes mellitus is hyperglycaemia, associated with endothelial dysfunction and impaired neovascularization. Circulating endothelial progenitor cells was shown to play important roles in vascular repair and promoting neovascularization. In this review, we will demonstrate the individual effect of high glucose on endothelial progenitor cells. Endothelial progenitor cells isolated from healthy subjects exposed to high glucose conditions or endothelial progenitor cells isolated from diabetic patients exhibit reduced number of endothelial cell colony forming units, impaired abilities of differentiation, proliferation, adhesion and migration, tubulization, secretion, mobilization and homing, whereas enhanced senescence. Increased production of reactive oxygen species by the mitochondria seems to play a crucial role in high glucose-induced endothelial progenitor cells deficit. Later, we will review the agents that might be used to alleviate dysfunction of endothelial progenitor cells induced by high glucose. The conclusions are that the relationship between hyperglycaemia and endothelial progenitor cells dysfunction is only beginning to be recognized, and future studies should pay more attention to the haemodynamic environment of endothelial progenitor cells and ageing factors to discover novel treatment agents.

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Sodium dodecyl sulfate (SDS), as an anionic surfactant, can induce protein conformational changes. Recent investigations demonstrated different effects of SDS on protein amyloid aggregation. In the present study, the effect of SDS on amyloid aggregation of bovine serum albumin (BSA) was evaluated. BSA transformed to β-sheet-rich amyloid aggregates upon incubation at pH 7.4 and 65°C, as demonstrated by thioflavin T fluorescence, circular dichroism, and transmission electron microscopy. SDS at submicellar concentrations inhibited BSA amyloid aggregation with IC of 47.5 µM. The inhibitory effects of structural analogs of SDS on amyloid aggregation of BSA were determined to explore the structure-activity relationship, with results suggesting that both anionic and alkyl moieties of SDS were critical, and that an alkyl moiety with chain length ≥10 carbon atoms was essential to amyloid inhibition. We attributed the inhibitory effect of SDS on BSA amyloid aggregation to interactions between the detergent molecule and the fatty acid binding sites on BSA. The bound SDS stabilized BSA, thereby inhibiting protein transformation to amyloid aggregates. This study reports for the first time that the inhibitory effect of SDS on albumin fibrillation is closely related to its alkyl structure. Moreover, the specific binding of SDS to albumin is the main driving force in amyloid inhibition. This study not only provides fresh insight into the role of SDS in amyloid aggregation of serum albumin, but also suggests rational design of novel anti-amyloidogenic reagents based on specific-binding ligands.