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Recently, methods based on deep learning have been successfully applied to ship detection for synthetic aperture radar (SAR) images. Despite the development of numerous ship detection methodologies, detecting small and coastal ships remains a significant challenge due to the limited features and clutter in coastal environments. For that, a novel adaptive multi-hierarchical attention module (AMAM) is proposed to learn multi-scale features and adaptively aggregate salient features from various feature layers, even in complex environments. Specifically, we first fuse information from adjacent feature layers to enhance the detection of smaller targets, thereby achieving multi-scale feature enhancement. Then, to filter out the adverse effects of complex backgrounds, we dissect the previously fused multi-level features on the channel, individually excavate the salient regions, and adaptively amalgamate features originating from different channels. Thirdly, we present a novel adaptive multi-hierarchical attention network (AMANet) by embedding the AMAM between the backbone network and the feature pyramid network (FPN). Besides, the AMAM can be readily inserted between different frameworks to improve object detection. Lastly, extensive experiments on two large-scale SAR ship detection datasets demonstrate that our AMANet method is superior to state-of-the-art methods., Comment: 11 pages, 7 figures

UNLABELLED: Centipedes are one of the oldest venomous arthropods using toxin as their weapon to capture prey. But little attention was focused on them and only few centipede toxins were demonstrated with activity on ion channels. Therefore, more deep works are needed to understand the diversity of centipede venom. In the present study, we use peptidomics combined with cDNA library to uncover the diversity of centipede Scolopendra subspinipes mutilans L. Koch. 192 peptides were identified by LC-MS/MS and 79 precursors were deduced by cDNA library. Surprisingly, the signal peptides of centipede toxins were more complicated than any other animal toxins and even exhibited large differences in homologues. Meanwhile, a large number of variants generated by alternative cleavage sites were detected by mass spectra. Odd number of cystein (3, 5, 7) found in the mature peptides were seldom seen in peptide toxins. In additional, two novel cysteine frameworks (C-C-C-CCC, C-C-C-C-CC-CC) were identified from 16 different cysteine frameworks from centipede peptides. Only 29 precursors have clear targets, while others may provide a potential diversity function for centipede. These findings highlight the extensive diversity of centipede toxins and provide powerful tools to understand the capture and defense weapon of centipede.BIOLOGICAL SIGNIFICANCE: Peptide toxins from venomous animal have attracted increasing attentions due to their extraordinary chemical and pharmacological diversity. Centipedes are one of the most used Chinese traditional medicines, but little was known about the active components. The venom of Scolopendra subspinipes mutilans L. Koch is first deeply analyzed in this work and most of peptides were never discovered before. Interestingly, the number and arrangement of cysteine showed a larger different to known peptide toxins such spider or scorpion toxins. Moreover, only 29 peptides from this centipede venom were identified with known function. It sugge