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. In the model projections, 2030-2100, the nitrogen concentrations in the studied lakes in China may decrease, for example, by 87% in the southern districts and by 19% in the northern districts. However, the phosphorus concentrations will continue to increase by an average of 25% in the Eastern Plain, Yunnan-Guizhou Plateau, and Xinjiang. Based on this differentiation, we suggest that nitrogen and phosphorus management in Chinese lakes should be carried out at the district level to help develop rational and sustainable environmental management strategies.

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. The results showed an abrupt rise in the nutrient levels in Gonghai and the enrichment of toxic metal elements from 1950 (the Anthropocene) onwards. While, at Yueliang lake, the rise on TN was from 1990 onwards. These consequences are attributable to the aggravation of anthropogenic atmospheric deposition in N, P and toxic metals, from fertilizer consumption, mining and coal combustion. The intensity of anthropogenic deposition is considerable, which leave a significant stratigraphic signal of the Anthropocene in lake 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. The as-prepared Chitosan-g-PMPC copolymers exhibit very low coefficient of friction (µ < 0.01) on Ti6 Al4 V alloy substrate in both pure water and biological fluids. The superior lubrication is attributed primarily to the hydrated feature of PMPC side chains, interface adsorption of copolymer as well as to the hydrodynamic effect. In vivo experiments confirm that Chitosan-g-PMPC can alleviate the swelling symptom of arthritis and protect the bone and cartilage from destruction. Due to their facile preparation, distinctive lubrication properties, and good biocompatibility, Chitosan-g-PMPC copolymers represent a new type of biomimetic lubricants derived from natural biopolymer for promising arthritis treatment and artificial joint lubrication.

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. High-frequency information learned by each residual block in the color image branch is input into the corresponding residual block in the depth map branch, and this kind of channel-wise feature supplement and fusion can not only help the depth map branch to alleviate blur in details like edges, but also introduce some depth artifacts to feature maps. To avoid the above introduced artifacts, the channel interaction fuses the feature maps using weights referring to the channel attention mechanism. The parallel multi-scale network architecture with channel interaction for feature guidance is the main contribution of our work and experiments show that our proposed method had a better performance in terms of accuracy compared with other methods.