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The 20S particle, which is composed of the N-ethylmaleimide-sensitive factor (NSF), soluble NSF attachment proteins (SNAPs) and the SNAP receptor (SNARE) complex, has an essential role in intracellular vesicle fusion events. Using single-particle cryo-EM and negative stain EM, we reconstructed four related three-dimensional structures: Chinese hamster NSF hexamer in the ATPγS, ADP-AlFx and ADP states, and the 20S particle. These structures reveal a parallel arrangement between the D1 and D2 domains of the hexameric NSF and characterize the nucleotide-dependent conformational changes in NSF. The structure of the 20S particle shows that it holds the SNARE complex at two interaction interfaces around the C terminus and N-terminal half of the SNARE complex, respectively. These findings provide insight into the molecular mechanism underlying disassembly of the SNARE complex by NSF.

Chemical energy conversion/storage through water splitting for hydrogen production has been recognized as the ideal solution to the transient nature of renewable energy sources. Solid polymer electrolyte (SPE) water electrolysis is one of the most practical ways to produce pure H2. Electrocatalysts are key materials in the SPE water electrolysis. At the anode side, electrode materials catalyzing the oxygen evolution reaction (OER) require specific properties. Among the reported materials, only iridium presents high activity and is more stable. In this Minireview, an application overview of single iridium metal and its oxide catalysts—binary, ternary, and multicomponent catalysts of iridium oxides and supported composite catalysts—for the OER in SPE water electrolysis is presented. Two main strategies to improve the activity of an electrocatalyst system, namely, increasing the number of active sites and the intrinsic activity of each active site, are reviewed with detailed examples. The challenges and perspectives in this field are also discussed.

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With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender system, there have always been emerging works in this field. In recent years, graph neural network (GNN) techniques have gained considerable interests which can naturally integrate node information and topological structure. Owing to the outperformance of GNN in learning on graph data, GNN methods have been widely applied in many fields. In recommender systems, the main challenge is to learn the efficient user/item embeddings from their interactions and side information if available. Since most of the information essentially has graph structure and GNNs have superiority in representation learning, the field of utilizing graph neural network in recommender systems is flourishing. This article aims to provide a comprehensive review of recent research efforts on graph neural network based recommender systems. Specifically, we provide a taxonomy of graph neural network based recommendation models and state new perspectives pertaining to the development of this field.

This paper uses a bibliometric analysis method to probe into the evolution of China's science and technology policies from 1949 to 2010, and the roles of core government agencies in policy-making. We obtained 4,707 Chinese S&T policies from GDIS, a Chinese public policy database provided by Tsinghua University. Co-word analysis and network analysis were applied in mapping the topics of S&T policies and collaboration among the agencies, while citation analysis was applied to assess the influence of S&T policies. Findings include: first, the focus of Chinese S&T policies is mainly on applied research and industrialization, rather than basic research; second, more and more government agencies are involved in making S&T policies, but collaboration efforts are not significantly increasing; last but not least, the influence of different S&T policies is determined by the administrative ranking of the policy-making agencies responsible for drafting those policies.

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