Fishes live in aquatic environments and several aquatic environmental factors have undergone recent alterations. The molecular mechanisms underlying fish responses to hypoxia and acidification stress have become a serious concern in recent years. This study revealed that hypoxia and acidification stress suppressed the growth of body length and height of the large yellow croaker (Larimichthys crocea). Subsequent transcriptome analyses of L. crocea juveniles under hypoxia, acidification, and hypoxia–acidification stress led to the identification of 5897 differentially expressed genes (DEGs) in the five groups. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that several DEGs were enriched in the ‘protein digestion and absorption’ pathway. Enrichment analysis revealed that this pathway was closely related to hypoxia and acidification stress in the five groups, and we found that genes of the collagen family may play a key role in this pathway. The zf-C2H2 transcription factor may play an important role in the hypoxia and acidification stress response, and novel genes were additionally identified. The results provide new clues for further research on the molecular mechanisms underlying hypoxia–acidification tolerance in L. crocea and provides a basic understanding of the potential combined effects of reduced pH and dissolved oxygen on Sciaenidae fishes.

Hot-air drying is a key step for Antarctic krill (Euphausia superba) onboard processing; however, few studies have explored the effects of different drying temperatures on the quality and flavor alternations of Antarctic krill. In this study, we investigated the effects of hot-air drying temperatures on the physicochemical properties and flavor of Antarctic krill. Sensory evaluation, as well as physical and chemical property tests, revealed that Antarctic krill treated with hot-air drying exhibited substantial changes in moisture status, lipid oxidation indices, and b* value. The sensory evaluation of Antarctic krill under high temperatures (120 °C and 150 °C) showed higher scores (8.45 ± 0.05 and 8.58 ± 0.22, respectively) on smells, whereas the color changes caused by high temperatures also resulted in lower overall sensory evaluation scores. The POV and TBARS values reached the highest at 26.63 ± 0.28 mg/g and 1.45 ± 0.19 mg/100 g, respectively. The b* value decreased significantly to 22.32 ± 4.56 following 150 °C treatment. Furthermore, a total of 53 volatile compounds were identified by GC-IMS, and the results showed that aldehydes, alcohols, alkanes, ketones, pyrazines, and furans were the main flavor sources of Antarctic krill. At the same time, the GC-MS results showed that the thermal process had no significant effect on the nutrient content of Antarctic krill. The findings obtained in this study provide foundational information for future research on ship-borne processing and high-value utilization of Antarctic krill.

Offshore and deep-sea aquaculture is increasingly recognized as a key pathway for expanding marine food production as nearshore resources decline and global demand for high-quality aquatic products grows. However, open-ocean farming operates under highly dynamic environmental conditions and long production cycles, which impose significant challenges on conventional experience-based management. This review synthesizes recent research on informatized management in offshore and deep-sea aquaculture and proposes a structured management framework based on five functional layers: perception, transmission, platform, decision, and execution. By systematically analyzing environmental constraints, technical bottlenecks, and management requirements, this framework integrates key technologies including the Internet of Things, unmanned surface and underwater vehicles, big data analytics, and artificial intelligence. The review further examines representative application scenarios, including environmental monitoring and early warning, intelligent feeding and nutrition management, disease prevention and control, and remote monitoring and management. Through cross-study comparison, this work highlights current limitations in system integration and long-term validation, while clarifying the technological pathways required for scalable and reliable offshore deployment. Overall, this review provides a conceptual foundation and technical reference for improving operational safety, production efficiency, and environmental sustainability in offshore and deep-sea aquaculture.

Traditional morphometric analysis of Large Yellow Croaker (Larimichthys crocea) relies heavily on manual dissection, which is time-consuming, labor-intensive, and prone to subjectivity. To address these limitations, we propose an automated quantitative approach based on deep-learning–driven instance segmentation. A dataset comprising 160 X-ray images of L. crocea was established, encompassing five anatomical categories: whole fish, air bladder, spine, eyes, and otoliths. Building upon the baseline YOLOv11-Seg model, we integrated a lightweight Convolutional Block Attention Module (CBAM) to construct an improved YOLOv11-CBAM network, thereby enhancing segmentation accuracy for complex backgrounds and fine-grained targets. Experimental results demonstrated that the modified model achieved superior performance in both mAP50 and mAP50–95 compared with the baseline, with particularly notable improvements in the segmentation of small-scale structures such as the air bladder and spine. By introducing coin-based calibration, pixel counts were converted into absolute areas and relative proportions. The measured area ratios of the air bladder, otoliths, eyes, and spine were 7.72%, 0.59%, 2.20%, and 8.48%, respectively, with standard deviations remaining within acceptable ranges, thus validating the robustness of the proposed method. Collectively, this study establishes a standardized, efficient, and non-destructive workflow for X-ray image-based morphometric analysis, providing practical applications for aquaculture management, germplasm conservation, and fundamental biological research.

This study addresses the challenges of manual measurement bias and low robustness in detecting small, occluded targets in complex marine environments during real-time onboard sorting of Portunus trituberculatus. We propose YOLO-DFAM, an enhanced YOLOv11n-based model that replaces the global average pooling in the Focal Modulation module with a spatial–channel dual-attention mechanism and incorporates the ASF-YOLO cross-scale fusion strategy to improve feature representation across varying target sizes. These enhancements significantly boost detection, achieving an mAP@50 of 98.0% and precision of 94.6%, outperforming RetinaNet-CSL and Rotated Faster R-CNN by up to 6.3% while maintaining real-time inference at 180.3 FPS with only 7.2 GFLOPs. Unlike prior static-scene approaches, our unified framework integrates attention-guided detection, scale-adaptive tracking, and lightweight weight estimation for dynamic marine conditions. A ByteTrack-based tracking module with dynamic scale calibration, EMA filtering, and optical flow compensation ensures stable multi-frame tracking. Additionally, a region-specific allometric weight estimation model (R² = 0.9856) reduces dimensional errors by 85.7% and maintains prediction errors below 4.7% using only 12 spline-interpolated calibration sets. YOLO-DFAM provides an accurate, efficient solution for intelligent onboard fishery monitoring.

The gut microbiome of rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) is increasingly recognized as a functional interface linking dietary inputs, epithelial barrier integrity, mucosal immunity, environmental stress, disease susceptibility, and antimicrobial-resistance risk in intensive aquaculture. Based on available salmonid studies and relevant evidence from broader fish and aquaculture systems, this review synthesizes current knowledge on salmonid gut microbial composition, nutritional modulation, microbiome–mucosal immune interactions, aquaculture stressors, antibiotic exposure, antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), metagenomics, multi-omics, and emerging microbiome-informed decision-support tools. Current evidence does not support a universally stable single-core microbiota in these species. Instead, community structure is shaped by developmental stage, freshwater–seawater transition, intestinal segment, digesta versus mucosa sampling, diet, temperature, stress, health status, and methodological workflow. Feed substitution and functional additives can remodel the gut microbiota, but these shifts should be interpreted alongside histology, barrier function, metabolic profiles, immune indicators, and disease-resistance phenotypes. Antibiotic exposure may reduce acute bacterial disease pressure while disturbing community structure and potentially enriching ARGs or ARG–MGE associations. Risk assessment should therefore move beyond ARG abundance toward host–ARG–MGE linkage using shotgun metagenomics, metagenome-assembled genomes, long-read sequencing, Hi-C, and externally validated multi-omics models. Machine learning and artificial intelligence approaches may support feature screening, risk stratification, and decision support, but their application in salmonid gut-health management remains at an early stage and requires external validation across sites, production stages, diets, and seasons.