Considerable specific cross-sectional and review studies have linked exposure to green spaces to improving public health, but there is no bibliometric review attempting to systemically and retrospectively analyze these existing articles. Here we aim to uncover global research status, trends, and future prospects in green spaces and health (G-H) research then propose a framework for the underlying mechanisms and pathways that link green space to public health. We obtained 18 961 G-H research publications from the core Web of Science collection from 1901 to 2019, analyzing the characteristics of publication outputs, key scientific disciplines, and differences in performance between countries and institutions. Besides, content analysis was conducted to analyze the keywords, including keyword co-occurrence analysis and keyword clustering analysis. We found: (1) a steady quantitative increase in publications, active journals, and involved countries and institutions since the 1990s; (2) a significant increase and changes in G-H related interdisciplinary categories, with environment-related disciplines becoming the mainstream; (3) research focus and trends that were identified based on the analysis of high-frequency co-occurring keywords; (4) three main knowledge domains, namely, green spaces and physical health, mental health, and ecosystem health, that were identified and visualized based on keyword clustering analysis; (5) a framework of underlying mechanisms and pathways linking green space to public health that is proposed based on visualization of the three main knowledge domains. We suggest that the main challenge of G-H research is to further clarify in-depth the underlying mechanisms and pathways from multiple perspectives, including multiple nations, disciplines, and study designs. The lack of co-occurring keywords and clustering information related to social well-being suggests that research related to 'social health' is lacking. Based on a clear understanding of the quantity, quality, and characteristics of green space for public health, a health-based environmental plan should be proposed in the future.

SUMMARY Increasing antibiotic resistance in human pathogens necessitates the development of new approaches against infections. Targeting virulence regulation at the transcriptional level represents a promising strategy yet to be explored. A global transcriptional regulator MgrA in Staphylococcus aureus was identified previously as a key virulence determinant. We have performed a fluorescence anisotropy (FA)-based high-throughput screen which identified 5, 5-methylenedisalicylic acid (MDSA) that blocks the DNA binding of MgrA. MDSA represses the expression of α-toxin that is up-regulated by MgrA and activates the transcription of protein A, a gene down-regulated by MgrA. MDSA alters bacterial antibiotic susceptibilities via an MgrA-dependent pathway. A mouse model of infection indicated that MDSA could attenuate S. aureus virulence. This work is a rare demonstration of utilizing small molecules to block protein-DNA interaction, thus tuning important biological regulation at the transcriptional level.

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Abstract. Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60 % of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 ∘C, the mean absolute error (MAE) varies from 1.23 to 1.37 ∘C and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1 K (R>0.71, P<0.05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. The data are available through Zenodo at https://doi.org/10.5281/zenodo.3528024 (Zhao et al., 2019).