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MicroRNAs (miRNAs) are widely involved in various biological processes of plants and contribute to plant resistance against various pathogens. In this study, upon sugarcane mosaic virus (SCMV) infection, the accumulation of maize (Zea mays) miR398b (ZmmiR398b) was significantly reduced in resistant inbred line Chang7‐2, while it was increased in susceptible inbred line Mo17. Degradome sequencing analysis coupled with transient co‐expression assays revealed that ZmmiR398b can target Cu/Zn‐superoxidase dismutase2 (ZmCSD2), ZmCSD4, and ZmCSD9 in vivo, of which the expression levels were all upregulated by SCMV infection in Chang7‐2 and Mo17. Moreover, overexpressing ZmmiR398b (OE398b) exhibited increased susceptibility to SCMV infection, probably by increasing reactive oxygen species (ROS) accumulation, which were consistent with ZmCSD2/4/9‐silenced maize plants. By contrast, silencing ZmmiR398b (STTM398b) through short tandem target mimic (STTM) technology enhanced maize resistance to SCMV infection and decreased ROS levels. Interestingly, copper (Cu)‐gradient hydroponic experiments demonstrated that Cu deficiency promoted SCMV infection while Cu sufficiency inhibited SCMV infection by regulating accumulations of ZmmiR398b and ZmCSD2/4/9 in maize. These results revealed that manipulating the ZmmiR398b‐ZmCSD2/4/9‐ROS module provides a prospective strategy for developing SCMV‐tolerant maize varieties. [ABSTRACT FROM AUTHOR]

This article explores the origin and evolution of the Y chromosome in kiwifruit. The sex determination of kiwifruit is genetically controlled by an XY system, and two Y-linked sex-determining genes, Shy Girl (SyGl) and Friendly Boy (FrBy), have been identified. The study examines the structural reorganization of the kiwifruit Y chromosome and identifies the sex-determining region (SDR) on chromosome 25 in A. chinensis and chromosome 12 in A. eriantha. The research also investigates the genomic diversity and evolutionary trajectories of the Y chromosome in kiwifruit. [Extracted from the article]

Network‐wide short‐term passenger flow prediction is critical for the operation and management of metro systems. However, it is challenging due to the inherent non‐stationarity, nonlinearity, and spatial–temporal dependencies within passenger flow. To tackle these challenges, this paper introduces a hybrid model called multi‐scale dynamic propagation spatial–temporal network (MSDPSTN). Specifically, the model employs multivariate empirical mode decomposition to jointly decompose the multivariate passenger flow into multi‐scale intrinsic mode functions. Then, a set of dynamic graphs is developed to reveal the passenger propagation law in metro networks. Based on the representation, a deep learning model is proposed to achieve multistep passenger flow prediction, which employs the dynamic propagation graph attention network with long short‐term memory to extract the spatial–temporal dependencies. Extensive experiments conducted on a real‐world dataset from Chengdu, China, validate the superiority of the proposed model. Compared to state‐of‐the‐art baselines, MSDPSTN reduces the mean absolute error, root mean squared error, and mean absolute percentage error by at least 3.243%, 4.451%, and 4.139%, respectively. Further quantitative analyses confirm the effectiveness of the components in MSDPSTN. This paper contributes to addressing inherent features of passenger flow to enhance prediction performance, offering critical insights for decision‐makers in implementing real‐time operational strategies. [ABSTRACT FROM AUTHOR]