Jiang et al. identify a selective and direct small-molecule inhibitor for NLRP3 and provide solid evidence showing that NLRP3 can be targeted in vivo to combat inflammasome-driven diseases.

Recent studies often exploit Graph Convolutional Network (GCN) to model label dependencies to improve recognition accuracy for multi-label image recognition. However, constructing a graph by counting the label co-occurrence possibilities of the training data may degrade model generalizability, especially when there exist occasional cooccurrence objects in test images. Our goal is to eliminate such bias and enhance the robustness of the learnt features. To this end, we propose an Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to dynamically generate a specific graph for each image. ADD-GCN adopts a Dynamic Graph Convolutional Network (D-GCN) to model the relation of content-aware category representations that are generated by a Semantic Attention Module (SAM). Extensive experiments on public multi-label benchmarks demonstrate the effectiveness of our method, which achieves mAPs of 85.2%, 96.0%, and 95.5% on MS-COCO, VOC2007, and VOC2012, respectively, and outperforms current state-of-the-art methods with a clear margin. All codes can be found at https://github.com/Yejin0111/ADD-GCN.

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Alloy‐type anodes are one of the most promising classes of next‐generation anode materials due to their ultrahigh theoretical capacity (2–10 times that of graphite). However, current alloy‐type anodes have several limitations: huge volume expansion, high tendency to fracture and disintegrate, an unstable solid–electrolyte interphase (SEI) layer, and low Coulombic efficiency. Efforts to overcome these challenges are ongoing. This Review details recent progress in the research of batteries based on alloy‐type anodes and discusses the direction of their future development. We conclude that improvements in structural design, the introduction of a protective interface, and the selection of suitable electrolytes are the most effective ways to improve the performance of alloy‐type anodes. Furthermore, future studies should direct more attention toward analyzing their synergistic promoting effect.

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Ankyrin-G (AnkG), a highly enriched scaffold protein in the axon initial segment (AIS) of neurons, functions to maintain axonal polarity and the integrity of the AIS. At the AIS, AnkG regulates selective intracellular cargo trafficking between soma and axons via interaction with the dynein regulator protein Ndel1, but the molecular mechanism underlying this binding remains elusive. Here we report that Ndel1’s C-terminal coiled-coil region (CT-CC) binds to giant neuron-specific insertion regions present in both AnkG and AnkB with 2:1 stoichiometry. The high-resolution crystal structure of AnkB in complex with Ndel1 CT-CC revealed the detailed molecular basis governing the AnkB/Ndel1 complex formation. Mechanistically, AnkB binds with Ndel1 by forming a stable 5-helix bundle dominated by hydrophobic interactions spread across 6 distinct interaction layers. Moreover, we found that AnkG is essential for Ndel1 accumulation at the AIS. Finally, we found that cargo sorting at the AIS can be disrupted by blocking the AnkG/Ndel1 complex formation using a peptide designed based on our structural data. Collectively, the atomic structure of the AnkB/Ndel1 complex together with studies of cargo sorting through the AIS establish the mechanistic basis for AnkG/Ndel1 complex formation and for the maintenance of axonal polarity. Our study will also be valuable for future studies of the interaction between AnkB and Ndel1 perhaps at distal axonal cargo transport.

Alloy‐type anodes are one of the most promising classes of next‐generation anode materials due to their ultrahigh theoretical capacity (2–10 times that of graphite). However, current alloy‐type anodes have several limitations: huge volume expansion, high tendency to fracture and disintegrate, an unstable solid–electrolyte interphase (SEI) layer, and low Coulombic efficiency. Efforts to overcome these challenges are ongoing. This Review details recent progress in the research of batteries based on alloy‐type anodes and discusses the direction of their future development. We conclude that improvements in structural design, the introduction of a protective interface, and the selection of suitable electrolytes are the most effective ways to improve the performance of alloy‐type anodes. Furthermore, future studies should direct more attention toward analyzing their synergistic promoting effect.

High-precision retrieval of rainfall over large areas is of great importance for the research of atmospheric detection and the social life. With the rapid development of communication satellite constellations and 5G communication networks, the use of widely distributed networks of earth–space links (ESLs) and horizontal microwave links (HMLs) to retrieve rainfall over large areas has great potential for obtaining high-precision rainfall fields and complementing traditional instruments of rainfall measurement. In this paper, we carry out the research of combining multiple ESLs with HMLs to retrieve rainfall fields. Firstly, a rainfall detection network for retrieving rainfall fields is built based on the atmospheric propagation model of ESL and HML. Then, the ordinary Kriging interpolation (OK) and radial basis function (RBF) neural network are applied to the reconstruction of rainfall fields. Finally, the performance of the joint network of ESLs and HMLs to retrieve rainfall fields in the area is validated. The results show that the joint network of ESLs and HMLs based on OK algorithm and RBF neural network is capable of retrieving the distribution of rain rates in different rain cells with high accuracy, and the root mean square error (RMSE) of retrieving the rain rates of real rainfall fields is lower than 0.56 mm/h, and the correlation coefficient (CC) is higher than 0.996. In addition, the CC for retrieving stratiform rainfall and convective rainfall by the joint network of ESLs and HMLs is higher than 0.949, indicating that the characteristics of the two different types of rainfall events can be accurately monitored.