Identifying effector proteins of secretion systems in Gram-negative bacteria is crucial for deciphering their pathogenic mechanisms and guiding the development of antimicrobial strategies. Extracting evolutionary and sequence features using pre-trained protein language models (PLMs) has emerged as an effective approach to improve the performance of effector protein prediction. However, the high-dimensional features generated by PLMs contain extensive general biological information, making it difficult to focus on core features when applied directly to effector protein tasks, which in turn limits prediction performance. In this study, we propose MoCETSE, a deep learning model for predicting effector proteins in Gram-negative bacteria. Specifically, MoCETSE first extracts contextual representations of sequences using the pre-trained protein language model ESM-1b. Subsequently, it refines key functional features via a target preprocessing network to construct more expressive sequence representations. Finally, integrated with a transformer module incorporating relative positional encoding, MoCETSE explicitly models the relative spatial relationships between residues, enabling highly accurate prediction of secreted effector proteins. MoCETSE exhibits excellent and robust performance in both five-fold cross-validation and independent testing. Benchmark results demonstrate that it maintains strong competitiveness compared to existing binary and multi-class predictors. Additionally, the model can effectively perform genome-wide effector protein prediction, showing outstanding specificity and reliability. MoCETSE provides an efficient and robust computational framework for the accurate identification of bacterial effector substrates and offers key biological insights.

Flood segmentation using synthetic aperture radar (SAR) images is essential for determining the extent of inundation areas, and informing subsequent management recommendations. However, existing networks for flood segmentation using single modality SAR images often face inherent challenges, including interference from terrain shadows and water-like surfaces, leading to degraded segmentation performance. In this study, we introduced a multi-level multi-modal fusion network (FloodNet), in which an Adaptive Gated Feature Fusion Module (AGFFM) is designed to integrate multi-modal features from Sentinel-1 SAR images, Digital Elevation Model (DEM) and Joint Research Centre Global Surface Water (JRC-gsw). Furthermore, we proposed a semantic consistency constraint strategy to alleviate the blurring of water edges during the prediction process. Experiments on two publicly available flood datasets, C2S-Flood and ETCI-Flood, demonstrate the competitive performance of the proposed FloodNet compared with other state-of-the-art single- and multi-modal networks. The code is available at https://github.com/SuperPixelPioneer/Flood-Net.