Fast Semantic Segmentation of Remote Sensing Images Using a Network That Integrates Global and Local Information.

Efficient processing of ultra-high-resolution images is increasingly sought after with the continuous advancement of photography and sensor technology. However, the semantic segmentation of remote sensing images lacks a satisfactory solution to optimize GPU memory utilization and the feature extraction speed. To tackle this challenge, Chen et al. introduced GLNet, a network designed to strike a better balance between GPU memory usage and segmentation accuracy when processing high-resolution images. Building upon GLNet and PFNet, our proposed method, Fast-GLNet, further enhances the feature fusion and segmentation processes. It incorporates the double feature pyramid aggregation (DFPA) module and IFS module for local and global branches, respectively, resulting in superior feature maps and optimized segmentation speed. Extensive experimentation demonstrates that Fast-GLNet achieves faster semantic segmentation while maintaining segmentation quality. Additionally, it effectively optimizes GPU memory utilization. For example, compared to GLNet, Fast-GLNet's mIoU on the Deepglobe dataset increased from 71.6% to 72.1%, and GPU memory usage decreased from 1865 MB to 1639 MB. Notably, Fast-GLNet surpasses existing general-purpose methods, offering a superior trade-off between speed and accuracy in semantic segmentation.

Pathogenetic characterization of a Micrococcus luteus strain isolated from an infant.

Purpose: To explore the clinical characteristics of Micrococcus luteus bloodstream infection in an infant and characterize the phenotype and genotype of the isolated strains, as well as seek suitable infection models for assessing virulence. Methods: Clinical data was collected from an infant patient diagnosed with M. luteus bloodstream infection. Metagenomic sequencing was performed on the isolated blood sample. The strain was isolated and underwent mass spectrometry, biochemical tests, antibiotic susceptibility assays, and whole-genome sequencing. The Galleria mellonella infection model was used to assess M. luteus virulence. Results: Patient responded poorly to cephalosporins, but recovered after Linezolid treatment. Metagenomic sequencing identified M. luteus as the predominant species in the sample, confirming infection. They were identified as M. luteus with a high confidence level of 98.99% using mass spectrometry. The strain showed positive results for Catalase, Oxidase, and Urea tests, and negative results for Mannose, Xylose, Lactose, Mannitol, Arginine, and Galactose tests, consistent with the biochemical profile of M. luteus reference standards. M. luteus susceptibility to 15 antibiotics was demonstrated and no resistance genes were detected. Predicted virulence genes, including clpB, were associated with metabolic pathways and the type VI secretion system. The infection model demonstrated dose-dependent survival rates. Conclusion: The infant likely developed a bloodstream infection with M. luteus due to compromised immunity. Although the isolated strain is sensitive to cephalosporin antibiotics and has low pathogenicity in infection models, clinical treatment with cephalosporins was ineffective. Linezolid proved to be effective, providing valuable guidance for future clinical management of such rare infections.