Tsinghua University

About: Tsinghua University is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 129978 authors who have published 200506 publications receiving 4549561 citations. The organization is also known as: Tsinghua & THU.

Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2.

Abstract: Angiotensin-converting enzyme 2 (ACE2) is the cellular receptor for severe acute respiratory syndrome-coronavirus (SARS-CoV) and the new coronavirus (SARS-CoV-2) that is causing the serious coronavirus disease 2019 (COVID-19) epidemic. Here, we present cryo-electron microscopy structures of full-length human ACE2 in the presence of the neutral amino acid transporter B0AT1 with or without the receptor binding domain (RBD) of the surface spike glycoprotein (S protein) of SARS-CoV-2, both at an overall resolution of 2.9 angstroms, with a local resolution of 3.5 angstroms at the ACE2-RBD interface. The ACE2-B0AT1 complex is assembled as a dimer of heterodimers, with the collectrin-like domain of ACE2 mediating homodimerization. The RBD is recognized by the extracellular peptidase domain of ACE2 mainly through polar residues. These findings provide important insights into the molecular basis for coronavirus recognition and infection.

Toward Safe Lithium Metal Anode in Rechargeable Batteries: A Review.

Abstract: The lithium metal battery is strongly considered to be one of the most promising candidates for high-energy-density energy storage devices in our modern and technology-based society. However, uncontrollable lithium dendrite growth induces poor cycling efficiency and severe safety concerns, dragging lithium metal batteries out of practical applications. This review presents a comprehensive overview of the lithium metal anode and its dendritic lithium growth. First, the working principles and technical challenges of a lithium metal anode are underscored. Specific attention is paid to the mechanistic understandings and quantitative models for solid electrolyte interphase (SEI) formation, lithium dendrite nucleation, and growth. On the basis of previous theoretical understanding and analysis, recently proposed strategies to suppress dendrite growth of lithium metal anode and some other metal anodes are reviewed. A section dedicated to the potential of full-cell lithium metal batteries for practical applicatio...

A mechanistic study of the antibacterial effect of silver ions on Escherichia coli and Staphylococcus aureus.

Abstract: To investigate the mechanism of inhibition of silver ions on microorganisms, two strains of bacteria, namely Gram-negative Escherichia coli (E. coli) and Gram-positive Staphylococcus aureus (S. aureus), were treated with AgNO(3) and studied using combined electron microscopy and X-ray microanalysis. Similar morphological changes occurred in both E. coli and S. aureus cells after Ag(+) treatment. The cytoplasm membrane detached from the cell wall. A remarkable electron-light region appeared in the center of the cells, which contained condensed deoxyribonucleic acid (DNA) molecules. There are many small electron-dense granules either surrounding the cell wall or depositing inside the cells. The existence of elements of silver and sulfur in the electron-dense granules and cytoplasm detected by X-ray microanalysis suggested the antibacterial mechanism of silver: DNA lost its replication ability and the protein became inactivated after Ag(+) treatment. The slighter morphological changes of S. aureus compared with E. coli recommended a defense system of S. aureus against the inhibitory effects of Ag(+) ions.

Global contrast based salient region detection

Abstract: Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.