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Social bees, with their specialized gut microbiota and societal transmission between individuals, provide an ideal model for studying host-gut microbiota interactions. While the functional disparities arising from strain-level diversity of gut symbionts and their effects on host health have been studied in Apis mellifera and bumblebees, studies focusing on host-specific investigations of individual strains across different honeybee hosts remain relatively unexplored. In this study, the complete genomic sequences of 17 strains of Gilliamella from A.

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Pseudomonas aeruginosa T6SS secretes an oxygen-binding hemerythrin to facilitate competitive growth under microaerobic conditions.

Microbiol Res

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State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, Shaanxi Key Laboratory of Agricultural and Environmental Microbiology, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China. Electronic address:

Pseudomonas aeruginosa is a prominent respiratory pathogen in cystic fibrosis (CF) patients, thriving in the hypoxic airway mucus. Previous studies have established the role of the oxygen-binding hemerythrin, Mhr, in enhancing P. aeruginosa's fitness under microaerobic conditions.

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RetinaRegNet: A zero-shot approach for retinal image registration.

Comput Biol Med

January 2025

Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32610, United States; Department of Medicine, University of Florida, Gainesville, FL, 32610, United States; Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, 32610, United States; Intelligent Clinical Care Center, University of Florida, Gainesville, FL, 32610, United States. Electronic address:

Retinal image registration is essential for monitoring eye diseases and planning treatments, yet it remains challenging due to large deformations, minimal overlap, and varying image quality. To address these challenges, we propose RetinaRegNet, a multi-stage image registration model with zero-shot generalizability across multiple retinal imaging modalities. RetinaRegNet begins by extracting image features using a pretrained latent diffusion model.

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VcaNet: Vision Transformer with fusion channel and spatial attention module for 3D brain tumor segmentation.

Comput Biol Med

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College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, China; Zhejiang Institute of Optoelectronics, Jinhua, 321004, China. Electronic address:

Accurate segmentation of brain tumors from MRI scans is a critical task in medical image analysis, yet it remains challenging due to the complex and variable nature of tumor shapes and sizes. Traditional convolutional neural networks (CNNs), while effective for local feature extraction, struggle to capture long-range dependencies crucial for 3D medical image analysis. To address these limitations, this paper presents VcaNet, a novel architecture that integrates a Vision Transformer (ViT) with a fusion channel and spatial attention module (CBAM), aimed at enhancing 3D brain tumor segmentation.

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Background: Diabetes mellitus (DM), a chronic metabolic disease, is characterized by long-term hyperglycemia resulting from the defect of insulin production and insulin resistance. The damage and dysfunction of pancreatic β-cells is a main link in DM development.

Methods: In this work, pancreatic β-cell line INS-1E cells were exposed to 30 mM glucose for 48 h to construct an in vitro DM model.

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