Marmoset Viral Hepatic Inflammation Induced by Hepatitis C Virus Core Protein via IL-32.

Common marmosets infected with GB virus-B (GBV-B) chimeras containing hepatitis C virus (HCV) core and envelope proteins (CE1E2p7) developed more severe hepatitis than those infected with HCV envelope proteins (E1E2p7), suggesting that HCV core protein might be involved in the pathogenesis of viral hepatitis. The potential role of HCV core in hepatic inflammation was investigated. Six individual cDNA libraries of liver tissues from HCV CE1E2p7 or E1E2p7 chimera-infected marmosets (three animals per group) were constructed and sequenced. By differential expression gene analysis, 30 of 632 mRNA transcripts were correlated with the immune system process, which might be associated with hepatitis. A protein-protein interaction network was constituted by STRING database based on these 30 differentially expressed genes (DEGs), showing that IL-32 might play a central regulatory role in HCV core-related hepatitis. To investigate the effect of HCV core protein on IL-32 production, HCV core expressing and mock constructs were transfected into Huh7 cells. IL-32 mRNA and secretion protein were detected at significantly higher levels in cells expressing HCV core protein than in those without HCV core expression (P < 0.01 and P < 0.001, respectively). By KEGG enrichment analysis and using the specific signaling pathway inhibitor LY294002 for inhibition of PI3K, IL-32 expression was significantly reduced (P < 0.001). In conclusion, HCV core protein induces an increase of IL-32 expression via the PI3K pathway in hepatic cells, which played a major role in development of HCV-related severe hepatitis

Multiferroicity in plastically deformed SrTiO3_3

A major challenge in the development of quantum technologies is to induce additional types of ferroic orders into materials that exhibit other useful quantum properties. Various techniques have been applied to this end, such as elastically straining, doping, or interfacing a compound with other materials. Plastic deformation introduces permanent topological defects and large local strains into a material, which can give rise to qualitatively new functionality. Here we show via local magnetic imaging that plastic deformation induces robust magnetism in the quantum paraelectric SrTiO3, in both conducting and insulating samples. Our analysis indicates that the magnetic order is localized along dislocation walls and coexists with polar order along the walls. The magnetic signals can be switched on and off in a controllable manner with external stress, which demonstrates that plastically deformed SrTiO3 is a quantum multiferroic. These results establish plastic deformation as a versatile platform for quantum materials engineering

OpenGSL: A Comprehensive Benchmark for Graph Structure Learning

Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes. However, the inherent suboptimal nature of node connections, resulting from the complex and contingent formation process of graphs, presents significant challenges in modeling them effectively. To tackle this issue, Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models. Despite the proposal of numerous GSL methods, the progress in this field remains unclear due to inconsistent experimental protocols, including variations in datasets, data processing techniques, and splitting strategies. In this paper, we introduce OpenGSL, the first comprehensive benchmark for GSL, aimed at addressing this gap. OpenGSL enables a fair comparison among state-of-the-art GSL methods by evaluating them across various popular datasets using uniform data processing and splitting strategies. Through extensive experiments, we observe that existing GSL methods do not consistently outperform vanilla GNN counterparts. However, we do observe that the learned graph structure demonstrates a strong generalization ability across different GNN backbones, despite its high computational and space requirements. We hope that our open-sourced library will facilitate rapid and equitable evaluation and inspire further innovative research in the field of GSL. The code of the benchmark can be found in https://github.com/OpenGSL/OpenGSL.Comment: 9 pages, 4 figure

System Dynamics Modeling of Hybrid Renewable Energy Systems and Combined Heating and Power Generator

The role of energy in the present world is critical in terms of both economical development and environmental impact. Renewable energy sources are considered essential in addressing these challenges. As a result, a growing number of organisations have been adopting hybrid renewable energy system (HRES) to reduce their environmental impact and sometimes take advantage of various incentives. When a HRES is being planned, the ability to model a HRES can provide an organisation with numerous benefits including the capability of optimising sub-systems, predicting performances and carrying out sensitivity analysis. In this paper, we present a comprehensive system dynamics model of HRES and combined heating and power (CHP) generator. Data from a manufacturing company using HRES and CHP generator are used to validate the model and discuss important findings. The results illustrate that the components of a HRES can have conflicting effects on cost and environmental benefits; thus, there is a need for an organisation to make trade-off decisions. The model can be a platform to further simulate and study the composition and operating strategies of organisations that are venturing to adopt new or additional HRESs

Nonlinear Magneto-Electro-Mechanical Response of Physical Cross-Linked Magneto-Electric Polymer Gel

This work reports on a novel magnetorheological polymer gel with carbon nanotubes and carbonyl iron particles mixed into the physical cross-linked polymer gel matrix. The resulting composites show unusual nonlinear magneto-electro-mechanical responses. Because of the low matrix viscosity, effective conductive paths formed by the CNTs were mobile and high-performance sensing characteristics were observed. In particular, due to the transient and mutable physical cross-linked bonds in the polymer gel, the electromechanical behavior acted in a rate-dependent manner. External stimulus at a high rate significantly enhanced the electrical resistance response during mechanical deformation. Meanwhile, the rheological properties were regulated by the external magnetic field when magnetic particles were added. This dual enhancement mechanism further contributes to the active control of electromechanical performance. These polymer composites could be adopted as electromechanical sensitive sensors to measure impact and vibration under different frequencies. There is great potential for this magnetorheological polymer gel in the application of intelligent vibration controls