Beyond Redox Additives: Accelerating Charge Transfer in Aqueous Energy Storage with Supporting Electrolytes.

Achieving fast, reversible interfacial charge transfer remains a critical bottleneck hindering the synergistic realization of high energy and power density in aqueous electrochemical energy storage systems. Redox-active electrolytes (RAEs) offer enhanced energy capacitance but often suffer from kinetic limitations and poor reversibility due to unconstrained reactant diffusion. Here, we demonstrate that engineering the supporting electrolyte by introducing an inert salt (NaSO) into an [Fe(CN)] RAE effectively overcomes these challenges.

3'UTR of tobacco vein mottling virus regulates downstream GFP expression and changes in host gene expression.

Introduction: Tobacco vein mottling virus (TVMV) is a member of the family . The 3' untranslated region (3'UTR) of viral genomic RNA has been reported to significantly impact viral infection. Nevertheless, the role of the TVMV 3'UTR during viral infection remains unknown.

Fischer-Tropsch Synthesis of C-C Isoparaffins via CoMnO Nanocomposites with Suppressed CO.

The synthesis of a specific product via the Fischer-Tropsch synthesis remains challenging due to the uncontrollable coupling of CH on active sites. Isoparaffins, essential high-quality petroleum additives for improving octane numbers, are primarily derived from petroleum or natural gas. With petroleum reserves dwindling and the associated low selectivity, the direct conversion of syngas to isoparaffins has emerged as a promising alternative.

Graph Aggregating-Repelling Network: Do Not Trust All Neighbors in Heterophilic Graphs.

Graph neural networks (GNNs) have demonstrated exceptional performance in processing various types of graph data, such as citation networks and social networks, etc. Although many of these GNNs prove their superiority in handling homophilic graphs, they often overlook the other kind of widespread heterophilic graphs, in which adjacent nodes tend to have different classes or dissimilar features. Recent methods attempt to address heterophilic graphs from the graph spatial domain, which try to aggregate more similar nodes or prevent dissimilar nodes with negative weights.

Whole-genome analysis revealed the growth-promoting and biological control mechanism of the endophytic bacterial strain Q2H2, with strong antagonistic activity in potato plants.

Introduction: Endophytes are colonizers of healthy plants and they normally exhibit biocontrol activities, such as reducing the occurrence of plant diseases and promoting plant growth. The endophytic bacterium Q2H2 (Q2H2) was isolated from the roots of potato plants and was found to have an antagonistic effect on pathogenic fungi.

Methods: Q2H2 was identified by morphological observations, physiological and biochemical identification, and 16S rRNA gene sequence analysis.

Graph convolutional network with tree-guided anisotropic message passing.

Graph Convolutional Networks (GCNs) with naive message passing mechanisms have limited performance due to the isotropic aggregation strategy. To remedy this drawback, some recent works focus on how to design anisotropic aggregation strategies with tricks on feature mapping or structure mining. However, these models still suffer from the low ability of expressiveness and long-range modeling for the needs of high performance in practice.

A lightweight hierarchical convolution network for brain tumor segmentation.

Background: Brain tumor segmentation plays a significant role in clinical treatment and surgical planning. Recently, several deep convolutional networks have been proposed for brain tumor segmentation and have achieved impressive performance. However, most state-of-the-art models use 3D convolution networks, which require high computational costs.

Subgraph-aware graph structure revision for spatial-temporal graph modeling.

Spatial-temporal graph modeling has been widely studied in many fields, such as traffic forecasting and energy analysis, where data has time and space properties. Existing methods focus on capturing stable and dynamic spatial correlations by constructing physical and virtual graphs along with graph convolution and temporal modeling. However, existing methods tending to smooth node features may obscure the spatial-temporal patterns among nodes.

Clinical features and prognostic factors for surgical treatment of esophageal squamous cell carcinoma in elderly patients.

Purpose: This research was designed to analyze the clinical features and prognostic factors for surgical treatment of esophageal squamous cell carcinoma (ESCC) in elderly patients aged 70 years and over.

Methods: The clinical data and follow-up data of 68 ESCC patients aged 70 years and over were collected. The characteristics of surgical treatment, perioperative complications, overall survival (OS), and the factors affecting survival were analyzed.