To date, general-purpose object-detection methods have achieved a great deal. However, challenges such as degraded image quality, complex backgrounds, and the detection of marine organisms at different scales arise when identifying underwater organisms. To solve such problems and further improve the accuracy of relevant models, this study proposes a marine biological object-detection architecture based on an improved YOLOv5 framework. First, the backbone framework of Real-Time Models for object Detection (RTMDet) is introduced. The core module, Cross-Stage Partial Layer (CSPLayer), includes a large convolution kernel, which allows the detection network to precisely capture contextual information more comprehensively. Furthermore, a common convolution layer is added to the stem layer, to extract more valuable information from the images efficiently. Then, the BoT3 module with the multi-head self-attention (MHSA) mechanism is added into the neck module of YOLOv5, such that the detection network has a better effect in scenes with dense targets and the detection accuracy is further improved. The introduction of the BoT3 module represents a key innovation of this paper. Finally, union dataset augmentation (UDA) is performed on the training set using the Minimal Color Loss and Locally Adaptive Contrast Enhancement (MLLE) image augmentation method, and the result is used as the input to the improved YOLOv5 framework. Experiments on the underwater datasets URPC2019 and URPC2020 show that the proposed framework not only alleviates the interference of underwater image degradation, but also makes the mAP@0.5 reach 79.8% and 79.4% and improves the mAP@0.5 by 3.8% and 1.1%, respectively, when compared with the original YOLOv8 on URPC2019 and URPC2020, demonstrating that the proposed framework presents superior performance for the high-precision detection of marine organisms.

Underwater target detection is a critical task in various applications, including environmental monitoring, underwater exploration, and marine resource management. As the demand for underwater observation and exploitation continues to grow, there is a greater need for reliable and efficient methods of detecting underwater targets. However, the unique underwater environment often leads to significant degradation of the image quality, which results in reduced detection accuracy. This paper proposes an improved YOLOv5 underwater-target-detection network to enhance accuracy and reduce missed detection. First, we added the global attention mechanism (GAM) to the backbone network, which could retain the channel and spatial information to a greater extent and strengthen cross-dimensional interaction so as to improve the ability of the backbone network to extract features. Then, we introduced the fusion block based on DAMO-YOLO for the neck, which enhanced the system’s ability to extract features at different scales. Finally, we used the SIoU loss to measure the degree of matching between the target box and the regression box, which accelerated the convergence and improved the accuracy. The results obtained from experiments on the URPC2019 dataset revealed that our model achieved an mAP@0.5 score of 80.2%, representing a 1.8% and 2.3% increase in performance compared to YOLOv7 and YOLOv8, respectively, which means our method achieved state-of-the-art (SOTA) performance. Moreover, additional evaluations on the MS COCO dataset indicated that our model’s mAP@0.5:0.95 reached 51.0%, surpassing advanced methods such as ViDT and RF-Next, demonstrating the versatility of our enhanced model architecture.

Delftia tsuruhatensis has become an emerging pathogen in humans. There is scant information on the genomic characteristics of this microorganism. In this study, we determined the complete genome sequence of a clinical D. tsuruhatensis strain, TR1180, isolated from a sputum specimen of a female patient in China in 2019. Phylogenetic and average nucleotide identity analysis demonstrated that TR1180 is a member of D. tsuruhatensis. TR1180 exhibited resistance to β-lactam, aminoglycoside, tetracycline and sulphonamide antibiotics, but was susceptible to phenicols, fluoroquinolones and macrolides. Its genome is a single, circular chromosome measuring 6,711,018 bp in size. Whole-genome analysis identified 17 antibiotic resistance-related genes, which match the antimicrobial susceptibility profile of this strain, as well as 24 potential virulence factors and a number of metal resistance genes. Our data showed that Delftia possessed an open pan-genome and the genes in the core genome contributed to the pathogenicity and resistance of Delftia strains. Comparative genomics analysis of TR1180 with other publicly available genomes of Delftia showed diverse genomic features among these strains. D. tsuruhatensis TR1180 harbored a unique 38-kb genomic island flanked by a pair of 29-bp direct repeats with the insertion of a novel In4-like integron containing most of the specific antibiotic resistance genes within the genome. This study reports the findings of a fully sequenced genome from clinical D. tsuruhatensis, which provide researchers and clinicians with valuable insights into this uncommon species.

The existing object detection methods are facing significant challenges when applied to marine environments, such as underwater image degradation caused by absorption and scattering of light, and domain transfer in water bodies with different water qualities. In this letter, we propose a marine organism detection framework to improve the detection performance and the domain generalization performance. First, a double domains data augmentation is proposed. This method combines underwater image enhancement and water quality transfer to improve the domain diversity of the original dataset. Second, we utilize the self-attention operations and the convolution to improve the detection performance of the YOLOv7, fully utilizing the advantages of self-attention and convolutional computation. Meanwhile, this model uses SIoU loss to accelerate convergence speed and improve the regression. Experiments on the URPC2019 and URPC2020 datasets show that the proposed object detection method achieves a mean average precision of 82.3% and 83.6%, respectively, which is superior to all other methods used for comparison.

Summary: The clinical and environmental infections caused by AmpC β‐lactamases have been increasingly reported recently. In this study, we characterize the novel chromosome‐encoded AmpC β‐lactamase SFDC‐1 identified in Serratia fonticola strain R28, which was isolated from a rabbit raised on a farm in southern China. SFDC‐1 shared the highest amino acid identity of 79.6% with the functionally characterized AmpC β‐lactamase gene bla _YRC‐1 , although it had highly homologous functionally uncharacterized relatives in the same species from different sources, including some of the clinical significance. The cloned bla _SFDC‐1 exhibited resistance to a broad spectrum of β‐lactam antibiotics, including most cephalosporins with the highest resistance to ampicillin, cefazolin and ceftazidime, with increased MIC levels ≥128‐fold compared with the control strains. The purified SFDC‐1 showed catalytic activities against β‐lactams with the highest catalytic activity to cefazolin. The genetic context of bla _SFDC‐1 and its relatives was conserved in the chromosome, and no mobile genetic elements were found surrounding them.