SARS-CoV-2 has made >190-million infections worldwide, thus it is pivotal to understand the viral impacts on host cells. Many viruses can significantly alter host chromatin, but such roles of SARS-CoV-2 are largely unknown. Here, we characterized the three-dimensional (3D) genome architecture and epigenome landscapes in human cells after SARS-CoV-2 infection, revealing remarkable restructuring of host chromatin architecture. High-resolution Hi-C 3.0 uncovered widespread A compartmental weakening and A-B mixing, together with a global reduction of intra-TAD chromatin contacts. The cohesin complex, a central organizer of the 3D genome, was significantly depleted from intra-TAD regions, supporting that SARS-CoV-2 disrupts cohesin loop extrusion. Calibrated ChIP-Seq verified chromatin restructuring by SARS-CoV-2 that is particularly manifested by a pervasive reduction of euchromatin modifications. Built on the rewired 3D genome/epigenome maps, a modified activity-by-contact model highlights the transcriptional weakening of antiviral interferon response genes or virus sensors (e.g., DDX58) incurred by SARS-CoV-2. In contrast, pro-inflammatory genes (e.g. IL-6) high in severe infections were uniquely regulated by augmented H3K4me3 at their promoters. These findings illustrate how SARS-CoV-2 rewires host chromatin architecture to confer immunological gene deregulation, laying a foundation to characterize the long-term epigenomic impacts of this virus.

Conductive hydrogels that are highly elastic, fatigue resistant and environmentally adaptive are promising materials in the fields of wearable electronics, bioelectronics and soft robotics. However, these materials are challenging to develop, especially for use in harsh environments including organic solvents and extreme temperatures. Here we report a simple method for the fabrication of highly compressible and fatigue-resistant conductive hydrogels with reinforced-concrete-type constituents and high-tortuosity interconnected cellular architecture through a self-assembly and two-stage in situ polymerization process. The obtained composites exhibit excellent mechanical compressibility with negligible residual strain at 50% strain for >104 cyclic loadings both in air and water. Due to the structure-favoured anisotropic response to tensile deformations coupled with elastic recovery, the hydrogel is endowed with sensing dimensions which allow the direction and velocity of movement on the sensor surface to be distinguished. In addition, by interpenetrating with an oleophilic polymer network, highly elastic and adaptive organohydrogels are developed with outstanding sensing performance in a wide variety of organic solvents and cryogenic temperatures. These materials may therefore be suitable for use in flexible and wearable devices in harsh environments.

To optimize the rigidity and dynamic mechanical properties of a sawing machine and improve its processing quality and stability, a design method for the sawing machine’s gearbox was proposed. First, a lightweight design of the gearbox was realized by topology optimization. Second, the sensitivity of different design variables of the new gearbox was determined via sensitivity analysis of the objective function. Finally, multi-objective optimization was used to obtain the optimal solution for the gearbox. Considering the complexity of the internal structure of the gearbox assembly and the accuracy of the numerical calculation process, a modeling method with mass points was proposed. A comparison between the numerical calculation results and the operation mode analysis revealed that the former was accurate and can be applied to the verification of the optimized gearbox. By optimizing the vibration signals before and after, and the analysis of the end face quality of the workpiece, the results revealed that the optimized gearbox has a significantly reduced amplitude under various operating conditions. In addition, the vibration stability was improved, and the end face quality of the workpiece was significantly enhanced compared to that before optimization. This study serves as a theoretical reference for multi-body dynamics modeling and optimization of machine tools, and also outlines technical solutions for high-speed stable cutting with sawing machines.

The importance of teachers’ affective, cognitive, and motivational factors in students’ academic achievement and well-being has been widely acknowledged. These factors are of great relevance, especially in the foreign language learning context wherein interaction between teachers and students is frequent and varies in forms. Though abundant evidence points to the high prevalence and risky factors of burnout among such a group, the extant literature lacks a quantitative synthesis of the interrelationship between emotional intelligence (EI), self-efficacy (SE), and burnout among them. This study aims to fill this research gap. The current meta-analysis is based on 42 independent samples with the experiences of 5,665 teachers reviewed. Results demonstrated moderate to large meta-correlations between these three constructs in the expected directions. EI and SE are positively correlated with each other, but negatively correlated with burnout. Moderation analysis also provides exploratory insights into the effects. Implications and future directions are also discussed.

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