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Most deep learning-based hyperspectral anomaly detection (HAD) methods focus on modeling or reconstructing the hyperspectral background to obtain residual maps from the original hyperspectral images. However, these methods typically do not pay enough attention to the spectral similarity in the complex environment, resulting in inadequate distinction between background and anomalies. Moreover, some anomalies and background are different objects, but they are sometimes recognized as the objects with the same spectrum. To address the issues mentioned above, this paper proposes a Spectrum Difference Enhanced Network (SDENet) for HAD, which employs variational mapping and Transformer to amplify spectrum differences. The proposed network is based on the encoder–decoder structure, which contains a CSWin-Transformer encoder, Variational Mapping Module (VMModule), and CSWin-Transformer decoder. First, the CSWin-Transformer encoder and decoder are designed to supplement image information by extracting deep and semantic features, where a cross-shaped window self-attention mechanism is designed to provide strong modeling capability with minimal computational cost. Second, in order to enhance the spectral difference characteristics between anomalies and background, a randomly sampling VMModule is presented for feature space transformation. Finally, all fully connected mapping operations are replaced with convolutional layers to reduce the model parameters and computational load. The effectiveness of the proposed SDENet is verified on three datasets, and experimental results show that it achieves better detection accuracy and lower model complexity compared with existing methods. [ABSTRACT FROM AUTHOR]

Object detection in remote sensing images is crucial for airport management, hazard prevention, traffic monitoring, and more. The precise ability for object localization and identification enables remote sensing imagery to provide early warnings, mitigate risks, and offer strong support for decision-making processes. While traditional deep learning-based object detection techniques have achieved significant results in single-modal environments, their detection capabilities still encounter challenges when confronted with complex environments, such as adverse weather conditions or situations where objects are obscured. To overcome the limitations of existing fusion methods in terms of complexity and insufficient information utilization, we innovatively propose a Cosine Similarity-based Image Feature Fusion (CSIFF) module and integrate it into a dual-branch YOLOv8 network, constructing a lightweight and efficient target detection network called Multi-Modality YOLO Fusion Network (MMYFNet). This network utilizes cosine similarity to divide the original features into common features and specific features, which are then refined and fused through specific modules. Experimental and analytical results show that MMYFNet performs excellently on both the VEDAI and FLIR datasets, achieving mAP values of 80% and 76.8%, respectively. Further validation through parameter sensitivity experiments, ablation studies, and visual analyses confirms the effectiveness of the CSIFF module. MMYFNet achieves high detection accuracy with fewer parameters, and the CSIFF module, as a plug-and-play module, can be integrated into other CNN-based cross-modality network models, providing a new approach for object detection in remote sensing image fusion. [ABSTRACT FROM AUTHOR]

Polyporaceae is an important fungal family that has been a source of natural products with a range of pharmaceutical activities in China. In our previous study, two polysaccharides, PCWPW and PCWPS, with significant antioxidant and antidepressant activity were obtained from Poria cocos. In this study, we evaluated their potential molecular mechanisms in the immunomodulation of macrophages. PCWPW and PCWPS were characterized by GC–MS analysis to contain 1,3-linked Glcp. ELISA assays results demonstrated that the secretion of TNF-α was significantly enhanced by PCWPW/PCWPS. RNA-seq data demonstrated that PCWPS treatment modulated the expression of immune-related genes in macrophages, which was further confirmed by RT-qPCR assays. The activation of TNF-α secretion was found to be mannose receptor (MR) dependent and suppressed by MR inhibitor pretreatment. Moreover, the amount of TNF-α cytokine secretion in PCWPW/PCWPS-induced RAW264.7 cells was decreased when pretreated with NF-κB or MAPK signaling pathway inhibitors. Collectively, our results suggested that PCWPW and PCWPS possessed immunomodulatory activity that regulates TNF-α expression through the NF-κB/MAPK signaling pathway by binding to mannose receptors. Therefore, PCWPW and PCWPS isolated from Poria cocos have potential as drug candidates for immune-related disease treatment. [ABSTRACT FROM AUTHOR]