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Evaporation is a critical process involved in energy and water balance at the Earth’s surface and bears significant implications for water resource management, agricultural irrigation, and drought monitoring, among others. In this study, we focused on establishing a 1 km daily surface evaporation estimation for the Yangtze River Basin from 2010 to 2019 by using a machine learning method, and then analyzed its spatiotemporal patterns. The findings showed spatial heterogeneity in the Yangtze River Basin, indicating higher evaporation rates in the southwestern and southeastern regions in contrast to the western and northern areas. Additionally, the basin exhibited a strong spatial autocorrelation, indicating the influence of one spatial unit on the others. Furthermore, most regions in the basin displayed non-significant changes in surface evaporation, with some areas in the upper reaches exhibiting significant increases and a few regions near the source of the Yangtze River experiencing significant decreases. This study contributes to a better understanding of the spatial and temporal distribution of evaporation in the Yangtze River Basin, providing valuable insights for water resource management, environmental studies, and hydrological modeling in the region.

Ginkgolide B (GB), an important active constituent of Ginkgo biloba extract, has been used in clinical applications for the treatment of dementia, cerebral insufficiency or related cognitive decline. To investigate the main pharmacokinetic characteristics of three different GB formulations in beagle dogs, a simple, specific and sensitive LC-MS/MS method was established and validated. The separation of the analytes was achieved on an Agilent Eclipse Plus C18 column (1.8 μm, 2.1 × 50 mm) with a mobile phase consisting of water and acetonitrile. The flow rate was set at 0.4 mL/min. Quantitation was performed using multiple reaction monitoring (MRM) in negative ion mode, with the transitions at m/z (Q1/Q3) 423.1/367.1 for GB and m/z 269.3/170.0 for IS. The linear calibration curve of GB was obtained over the concentration range of 2–200 ng/mL. The intra- and inter-day precisions were

Hyperlipidemia is a major component of metabolic syndrome, and regarded as one of the main risk factors causing metabolic diseases. We have developed a therapeutic drug, akebia saponin D (ASD), and determined its anti-hyperlipidemia activity and the potential mechanism(s) of action by analyzing the metabolome and intestinal microbiota. Male Sprague-Dawley rats were fed a high fat diet to induce hyperlipidemia, and then given ASD orally for 8 weeks. Lipid levels in serum were determined biochemically. Metabolites in serum, urine and feces were analyzed by UPLC-Q/TOF-MS, and the structure of the intestinal microbiota was determined by 16S rRNA sequencing. The ASD treatment significantly decreased the levels of TC, TG and LDL-c and increased the serum level of HDL-c. Metabolomics analysis indicated that the ASD treatment mainly impacted seven differential metabolites in the serum, sixteen differential metabolites in the urine and four differential metabolites in feces compared to the model group. The ASD treatment significantly changed eight bacteria at the genus level compared to the model group. In conclusion, ASD treatment can significantly alleviate HFD-induced hyperlipidemia and the hypolipidemic effect of ASD treatment is certainly associated with a systematic change in the metabolism, as well as dynamic changes in the structure of the intestinal microbiota.

The authors wish to correct the funding projects number of “Natural Science Foundation of Jiangsu Province” in the Acknowledgments section of this paper [1]: The correct funding projects number should be “No. BK20130403”, not “No. BK2013403”.[...]