Review and new insights into the catalytic structural domains of the Fe(ll) and 2-Oxoglutarate families.

Histone lysine demethylase (KDM), AlkB homolog (ALKBH), and Ten-Eleven Translocation (TET) proteins are members of the 2-Oxoglutarate (2OG) and ferrous iron-dependent oxygenases, each of which harbors a catalytic domain centered on a double-stranded ß-helix whose topology restricts the regions directly involved in substrate binding. However, they have different catalytic functions, and the deeply structural biological reasons are not yet clear. In this review, the catalytic domain features of the three protein families are summarized from both sequence and structural perspectives. The construction of the phylogenetic tree and comparison of the structure show ten relatively conserved ß-sheets and three key regions with substantial structural differences. We summarize the relationship between three key regions of remarkable differences and the substrate compatibility of the three protein families. This review facilitates research into substrate-selective inhibition and bioengineering by providing new insights into the catalytic domains of KDM, ALKBH, and TET proteins.

Identifying the geospatial relationship of surface ozone pollution in China: Implications for key pollution control regions.

Surface ozone pollution, as a pressing environmental concern, has garnered widespread attention across China. Due to air mass transport, effective control of ozone pollution is highly dependent on collaborative efforts across neighboring regions. However, specific regions with strong internal interactions of ozone pollution are not yet well identified. Here, we introduced the Geospatial SHapley Additive exPlanation (GeoSHAP) approach, which primarily involves machine learning and geostatistical algorithms. Based on extensive atmospheric environmental monitoring data from 2017 to 2021, machine learning models were employed to train and predict ozone concentrations at the target location. The R2 values on the test sets of different scale regions all reached 0.98 in the overall condition, indicating that the core model has good accuracy and generalization ability. The results highlight key regions with high ozone geospatial relationship (OGR) index, predominantly located in the Northern District (ND), spanning the Fen-Wei Plain, the Loess Plateau, and the North China Plain, as well as within portions of the Yangtze River Delta (YRD) and the Pearl River Delta (PRD). Further investigation indicated that high geospatial relationships stem from a synergy between anthropogenic and natural factors, with anthropogenic factors serving as a pivotal element. This study revealed key regions with the most urgent need for joint control of anthropogenic sources to mitigate ozone pollution.

Insights into PM2.5 pollution of four small and medium-sized cities in Chinese representative regions: Chemical compositions, sources and health risks.

Fine particles (PM2.5) pollution is still a severe issue in some cities in China, where the chemical characteristics of PM2.5 remain unclear due to limited studies there. Herein, we focused on PM2.5 pollution in small and medium-sized cities in key urban agglomerations and conducted a comprehensive study on the PM2.5 chemical characteristics, sources, and health risks. In the autumn and winter of 2019-2020, PM2.5 samples were collected simultaneously in four small and medium-sized cities in four key regions: Dingzhou (Beijing-Tianjin-Hebei region), Weinan (Fenwei Plain region), Fukang (Northern Slope of the Tianshan Mountain region), and Bozhou (Yangtze River Delta region). The results showed that secondary inorganic ions (43.1 %-67.0 %) and organic matter (OM, 8.6 %-36.4 %) were the main components of PM2.5 in all the cities. Specifically, Fukang with the most severe PM2.5 pollution had the highest proportion of SO42- (31.2 %), while the dominant components in other cities were NO3- and OM. The Multilinear Engine 2 (ME2) analysis identified five sources of PM2.5 in these cities. Coal combustion contributed most to PM2.5 in Fukang, but secondary sources in other cities. Combined with chemical characteristics and ME2 analysis, it was preliminarily determined that the primary emission of coal combustion had an important contribution to high SO42- in Fukang. Potential source contribution function (PSCF) analysis results showed that regional transport played an important role in PM2.5 in Dingzhou, Weinan and Bozhou, while PM2.5 in Fukang was mainly affected by short-range transport from surrounding areas. Finally, the health risk assessment indicated Mn was the dominant contributor to the total non-carcinogenic risks and Cr had higher carcinogenic risks in all cities. The findings provide a scientific basis for formulating more effective abatement strategies for PM2.5 pollution.

[Comparison and Analysis of PM2.5 Forecast in Key Areas Based on the Neural Network Model and Numerical Model].

The PM2.5 forecast models of 95 cities in Beijing-Tianjin-Hebei and its surrounding cities (BTH); the Fenwei Plain (FWP); the border area of Jiangsu, Anhui, Shandong, and Henan (JASH); and the Yangtze River Delta (YRD) regions were established using BP neural network models, and the forecast was carried out for the next seven days in the autumn and winter in 2020. By comparing the forecast results of the BP neural network models, numerical model, and artificial correction, the PM2.5 forecast effects of the three methods were analyzed and evaluated. The results showed:① The performance of the short-term forecast based on the BP neural network was relatively good but was reduced in the medium and long term and systematically overestimated in four regions. The numerical model effects were lower than those of the BP neural network models. ② The accuracy rates of the PM2.5 forecast concentration by the three methods were generally low in the four regions, with an average of less than 50%, and the accuracy values in order from high to low were the BP neural network models, artificial correction, and the numerical model. The accuracy rates of IAQI levels of PM2.5 were significantly improved by the three methods, and the averages were above 65% in the first four days. The effects of the BP neural network models and artificial correction were similar, which were generally higher than those of the numerical model. ③ The numerical model had good effects in the BTH, JASH, and YRD regions, whereas it was the worst when forecasting moderately and above-polluted days in the FWP region. The BP neural network model had a good performance when forecasting short-term PM2.5 in the BTH, JASH, and FWP regions, whereas it was poor in the YRD region. In general, the performance of artificial correction was relatively good when forecasting moderate-level days and was close to the BP neural network model when forecasting heavily polluted days.