GLNet: global-local feature network for wheat leaf disease image classification.

Addressing the issues with insufficient multi-scale feature perception and incomplete understanding of global information in traditional convolutional neural networks for image classification of wheat leaf disease, this paper proposes a global local feature network, i.e. GLNet, which adopts a unique global-local convolutional neural network architecture, realizes the comprehensive capturing of multi-scale features in an image by processing the global feature block and local feature block in parallel and integrating the information of both of them with the help of a feature fusion block.

Genome-wide identification and expression analysis of the WRKY gene family in Mikania micrantha.

Background: WRKY transcription factors (TFs) regulate plant responses to environmental stimuli and development, including flowering. Despite extensive research on different species, their role in the invasive plant Mikania micrantha remains to be explored. The aim of this study was to identify and analyze WRKY genes in M.

Longitudinal single-cell RNA sequencing reveals a heterogeneous response of plasma cells to colonic inflammation.

A comprehensive understanding of the dynamic changes in plasma cells (PCs) during inflammation remains elusive. In this study, we analyzed the distinct responses of PCs across different phases of inflammation in a dextran sodium sulfate (DSS)-induced mouse colitis model. Six-week-old male C57BL/6 mice were treated with 2.

Assessing the performance and interpretability of the CNN-LSTM-Attention model for daily streamflow forecasting in typical basins of the eastern Qinghai-Tibet Plateau.

Hydrological forecasting is of great significance to regional water resources management and reservoir operation. Climate change has increased the complexity and difficulty of hydrological forecasting. In this study, a hybrid explainable streamflow forecasting model based on CNN-LSTM-Attention was established for five typical river source regions in the eastern Qinghai-Tibet Plateau (EQTP).

Schiff Base-Mediated Dual Active Site Catalyst for Efficient N-Formylation of Amines with CO.

Using CO as the C1 source for N-formylation of amine is a crucial energy-storage pathway to address the greenhouse effect while generating high-value-added chemicals but is limited by the activation of inert molecules. Herein, a dual active site catalyst with high CO activation and dihydrogen dissociation capacity was fabricated by incorporating a Schiff base and Au nanoparticles (NPs) on silicon dioxide (SiO). The modification of the Schiff base not only provides an alkaline environment for CO absorption but also stabilizes Au NPs in a small and highly dispersed state, which regulates the electronic density of the metal for excellent H cleavage.