Nitrogen and phosphorus trends in lake sediments of China may diverge.

The brief history of monitoring nutrient levels in Chinese lake waters limits our understanding of the causes and the long-term trends of their eutrophication and constrains effective lake management. We therefore synthesize nutrient data from lakes in China to reveal the historical changes and project their future trends to 2100 using models. Here we show that the average concentrations of nitrogen and phosphorus in lake sediments have increased by 267% and 202%, respectively since 1850.

Anthropogenic atmospheric deposition caused the nutrient and toxic metal enrichment of the enclosed lakes in North China.

Anthropogenic emissions have resulted in increases in the atmospheric fluxes of both nutrient and toxic elements. However, the long-term geochemical impacts on lake sediments of deposition activities have not been clearly clarified. We selected two small enclosed lakes in northern China-Gonghai, strongly influenced by anthropogenic activities, and Yueliang lake, relatively weakly influenced by anthropogenic activities-to reconstruct historical trends of atmospheric deposition on the geochemistry of the recent sediments.

Bioinspired Polysaccharide-Derived Zwitterionic Brush-like Copolymer as an Injectable Biolubricant for Arthritis Treatment.

Developing highly efficient and biocompatible biolubricants for arthritis treatment is extraordinarily demanded. Herein, inspired by the efficient lubrication of synovial joints, a paradigm that combines natural polysaccharide (chitosan) with zwitterionic poly[2-(methacryloyloxy) ethyl phosphorylcholine] (PMPC), to design a series of brush-like Chitosan-g-PMPC copolymers with highly efficient biological lubrication and good biocompatibility is presented. The Chitosan-g-PMPC copolymers are prepared via facile one-step graft polymerization in aqueous medium without using any toxic catalysts and organic solvents.

Color-Guided Depth Map Super-Resolution Using a Dual-Branch Multi-Scale Residual Network with Channel Interaction.

We designed an end-to-end dual-branch residual network architecture that inputs a low-resolution (LR) depth map and a corresponding high-resolution (HR) color image separately into the two branches, and outputs an HR depth map through a multi-scale, channel-wise feature extraction, interaction, and upsampling. Each branch of this network contains several residual levels at different scales, and each level comprises multiple residual groups composed of several residual blocks. A short-skip connection in every residual block and a long-skip connection in each residual group or level allow for low-frequency information to be bypassed while the main network focuses on learning high-frequency information.