Renmin University of China

About: Renmin University of China is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: China & Population. The organization has 11325 authors who have published 15498 publications receiving 238419 citations. The organization is also known as: Renmin University & People's University of China.

Energy-Related Small Molecule Activation Reactions: Oxygen Reduction and Hydrogen and Oxygen Evolution Reactions Catalyzed by Porphyrin- and Corrole-Based Systems

Abstract: Globally increasing energy demands and environmental concerns related to the use of fossil fuels have stimulated extensive research to identify new energy systems and economies that are sustainable, clean, low cost, and environmentally benign. Hydrogen generation from solar-driven water splitting is a promising strategy to store solar energy in chemical bonds. The subsequent combustion of hydrogen in fuel cells produces electric energy, and the only exhaust is water. These two reactions compose an ideal process to provide clean and sustainable energy. In such a process, a hydrogen evolution reaction (HER), an oxygen evolution reaction (OER) during water splitting, and an oxygen reduction reaction (ORR) as a fuel cell cathodic reaction are key steps that affect the efficiency of the overall energy conversion. Catalysts play key roles in this process by improving the kinetics of these reactions. Porphyrin-based and corrole-based systems are versatile and can efficiently catalyze the ORR, OER, and HER. Becau...

The ACE2 expression in human heart indicates new potential mechanism of heart injury among patients infected with SARS-CoV-2.

Abstract: A new type of pneumonia caused by a novel coronavirus SARS-CoV-2 outbreaks recently in China and spreads into many other countries. This disease, named as COVID-19, is similar to patients infected by SARS-CoV and MERS-CoV, and nearly 20% of patients developed severe condition. Cardiac injury is a prevalent complication of severe patients, exacerbating the disease severity in coronavirus disease 2019 (COVID-19) patients. Angiotensin-converting enzyme 2 (ACE2), the key host cellular receptor of SARS-CoV-2, has been identified in multiple organs, but its cellular distribution in human heart is not illuminated clearly. This study performed the first state-of-art single cell atlas of adult human heart, and revealed that pericytes with high expression of ACE2 might act as the target cardiac cell of SARS-CoV-2. The pericytes injury due to virus infection may result in capillary endothelial cells dysfunction, inducing microvascular dysfunction. And patients with basic heart failure disease showed increased ACE2 expression at both mRNA and protein levels, meaning that if infected by the virus these patients may have higher risk of heart attack and critically ill condition. The finding of this study explains the high rate of severe cases among COVID-19 patients with basic cardiovascular disease; and these results also perhaps provide important reference to clinical treatment of cardiac injury among severe patients infected by SARS-CoV-2.

Heterogeneous Information Network Embedding for Recommendation

Abstract: Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, called HIN based recommendation . It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance.