Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning.

Annual gross primary productivity (AGPP) is the basis for grain production and terrestrial carbon sequestration. Mapping regional AGPP from site measurements provides methodological support for analysing AGPP spatiotemporal variations thereby ensures regional food security and mitigates climate change. Based on 641 site-year eddy covariance measuring AGPP from China, we built an AGPP mapping scheme based on its formation and selected the optimal mapping way, which was conducted through analysing the predicting performances of divergent mapping tools, variable combinations, and mapping approaches in predicting observed AGPP variations.

[Analysis of the extraction of red pellicle of walnut (Juglans regia L.) by ultraviolet-visible spectra and HPLC-ESI-MS(n)].

The extraction of the red pellicle of walnut (Juglans regia L.) was analyzed by UV-visible spectra and HPLC-ESI-MS(n) (high-performance-liquid-chromatography coupled with electrospray ionization mass spectrometry). The extraction in ethanol-HCl showed two absorption peaks at 560 and 591 nm respectively in the UV-Vis spectrum; after purified by lead acetate and thin-layer-chromatography, the extraction in ethanol-HCl showed 4 absorption peaks at 340, 370, 552 and 585 nm respectively.