Mutagenesis analysis of the zinc-finger antiviral protein

BACKGROUND: The zinc-finger antiviral protein (ZAP) specifically inhibits the replication of certain viruses, including murine leukemia virus (MLV), by preventing the accumulation of viral mRNA in the cytoplasm. ZAP directly binds to the viral mRNA through the zinc-finger motifs and recruits the RNA exosome to degrade the target RNA. RNA helicase p72 is required for the optimal function of ZAP. In an attempt to understand the structure-function relationship of ZAP, we performed alanine scanning analysis. RESULTS: A series of ZAP mutants was generated, in which three consecutive amino acids were replaced with three alanines. The mutants were analyzed for their antiviral activities against pseudotyped MLV vector. Out of the nineteen mutants analyzed, seven displayed significantly lower antiviral activities. Two mutations were in the very N-terminal domain, and five mutations were within or around the first and second zinc-finger motifs. These mutants were further analyzed for their abilities to bind to the target RNA, the exosome, and the RNA helicase p72. Mutants Nm3 and Nm63 lost the ability to bind to RNA. Mutants Nm 63 and Nm93 displayed compromised interaction with p72, while the binding of Nm133 to p72 was very modest. The interactions of all the mutants with the exosome were comparable to wild type ZAP. CONCLUSIONS: The integrity of the very N-terminal domain and the first and second zinc-finger motifs appear to be required for ZAP's antiviral activity. Analyses of the mutants for their abilities to interact with the target RNA and RNA helicase p72 confirmed our previous results. The mutants that bind normally to the target RNA, the exosome, and the RNA helicase p72 may be useful tools for further understanding the mechanism underlying ZAP's antiviral activity

Metal‐Organic Frameworks and their Applications in Hydrogen and Oxygen Evolution Reactions

The hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) play a vital role in many energy storage and conversion systems, including water splitting, rechargeable metal‐air batteries, and the unitized regenerative fuel cells. The noble‐metal catalysts based on Pt, Ir, and Au are the best electrocatalysts for the HER/OER, but they suffer from high price and scarcity problems. Therefore, it is urgently necessary to develop efficient, low‐cost, and environment‐friendly non‐noble metal electrocatalysts. Metal‐organic frameworks (MOFs) are crystalline materials with porous network structure. MOFs possess various compositions, large specific surface area, tunable pore structures, and they are easily functionalized. MOFs have been widely studied and applied in many fields, such as gas adsorption/separation, drug delivery, catalysis, magnetism, and optoelectronics. Recently, MOFs‐based electrocatalysts for HER/OER have been rapidly developed. These MOFs‐based catalysts exhibit excellent catalytic performance for HER/OER, demonstrating a promising application prospect in HER/OER. In this chapter, the concept, structure, category, and synthesis of MOFs will be first introduced briefly. Then, the applications of the MOFs‐based catalysts for HER/OER in recent years will be discussed in details. Specially, the synthesis, structure, and catalytic performance for HER/OER of the MOFs‐based catalysts will be emphatically discussed

Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction

Multimodal fusion and multitask learning are two vital topics in machine learning. Despite the fruitful progress, existing methods for both problems are still brittle to the same challenge -- it remains dilemmatic to integrate the common information across modalities (resp. tasks) meanwhile preserving the specific patterns of each modality (resp. task). Besides, while they are actually closely related to each other, multimodal fusion and multitask learning are rarely explored within the same methodological framework before. In this paper, we propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for both multimodal fusion and multitask learning. At its core, CEN dynamically exchanges channels between subnetworks of different modalities. Specifically, the channel exchanging process is self-guided by individual channel importance that is measured by the magnitude of Batch-Normalization (BN) scaling factor during training. For the application of dense image prediction, the validity of CEN is tested by four different scenarios: multimodal fusion, cycle multimodal fusion, multitask learning, and multimodal multitask learning. Extensive experiments on semantic segmentation via RGB-D data and image translation through multi-domain input verify the effectiveness of our CEN compared to current state-of-the-art methods. Detailed ablation studies have also been carried out, which provably affirm the advantage of each component we propose.Comment: 18 pages. arXiv admin note: substantial text overlap with arXiv:2011.0500

Electromagnetic design and loss calculations of a 1.12-MW high-speed permanent-magnet motor for compressor applications

Electromagnetic design of a 1.12-MW, 18 000-r/min high-speed permanent-magnet motor (HSPMM) is carried out based on the analysis of pole number, stator slot number, rotor outer diameter, air-gap length, permanent magnet material, thickness, and pole arc. The no-load and full-load performance of the HSPMM is investigated in this paper by using 2-D finite element method (FEM). In addition, the power losses in the HSPMM including core loss, winding loss, rotor eddy current loss, and air friction loss are predicted. Based on the analysis, a prototype motor is manufactured and experimentally tested to verify the machine design