Zihao Wang

Awns are important morphological markers for wheat and exert a strong physiological effect on wheat yield. The awn elongation suppressor B1 has recently been cloned through association and linkage analysis in wheat. However, the mechanism of awn inhibition centered around B1 remains to be clarified. Here, we identified an allelic variant in the coding region of B1 through analysis of re-sequencing data; this variant causes an amino acid substitution and premature termination, resulting in a long-awn phenotype. Transcriptome analysis indicated that B1 inhibited awn elongation by impeding cytokinin- and auxin-promoted cell division. Moreover, B1 directly repressed the expression of TaRAE2 and TaLks2, whose orthologs have been reported to promote awn development in rice or barley. More importantly, we found that TaTCP4 and TaTCP10 synergistically inhibited the expression of B1, and a G-to-A mutation in the B1 promoter attenuated its inhibition by TaTCP4/10. Taken together, our results reveal novel mechanisms of awn development and provide genetic resources for trait improvement in wheat.

The incorporation of new coordinate bonds and the development of universal methods for new structures have always been of major interest in metal-organic framework (MOF) research. The poor reversibility makes metal-carbon (M-C) bonds a great challenge to adopt as linkages to construct crystalline MOFs. Herein, three isoreticular microcrystalline MOFs connected by silver-carbon (Ag-C) bonds are presented for the first time and named AgC-MOFs. Their structures contain a double coordination mode (σ and π) between Ag(I) and alkynyl. The three AgC-MOFs all exhibit three-dimensional (3D) frameworks with uniform one-dimensional (1D) hexagonal channels, and the pore width could be tuned from 1.1 to 1.8 nm. The construction of crystalline MOFs using poorly reversible Ag-C coordinate bonds extends the nexuses for the MOF structure and lights up more possibilities for the systematic design of MOFs.

This manuscript comprehensively reviews the recent advancements in Cu-based atomic site catalysts in the NO 3 RR, following a sequential order with six sections: Introduction, Mechanism, Cu-based SACs, Cu-based SAAs, Cu-based DACs, and Perspectives.

Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs.In this work, we reevaluate this claim through systematic experiments, where we propose a novel group discussion framework to enrich the set of discussion mechanisms.Interestingly, our results show that a single-agent LLM with strong prompts can achieve almost the same performance as the best existing discussion approach on a wide range of reasoning tasks and backbone LLMs.We observe that the multi-agent discussion performs better than a single agent only when there is no demonstration in the prompt.Further study reveals the common interaction mechanisms of LLMs during the discussion.

Hyperspectral and synthetic aperture radar (SAR) image classification, aiming to merge multisource information to boost the precision and reliability of land cover classification, has gained increasing attention. Nevertheless, current techniques still exhibit certain limitations in extracting discriminative features and integrating heterogeneous features. In this work, a graph convolutional fusion network (GCFNet) is proposed for hyperspectral and SAR image classification. First, a spectral residual neural network is employed to extract the spectrum information. Then, a dual-branch graph convolutional network (GCN) is developed to extract the spatial information from hyperspectral and SAR images. Finally, a cross-contextual transformer fusion module is created to merge the spectral and spatial information followed by a dense layer to yield the final prediction outcome. To confirm the performance of the GCFNet, experiments on three datasets (e.g., Berlin, Augsburg, and Yellow River) demonstrate that the GCFNet significantly surpasses other representative methods.