Liaoning Ocean and Fisheries Research Institute

Skin aging is a complex biological process triggered by intrinsic and extrinsic factors, causing structural and functional deterioration, and its mitigation is a priority in cosmetology and functional food science. Skin fibroblasts, which mediate skin repair, wound healing and inflammation, are closely associated with aging. Sea cucumber collagen peptides exhibit prominent anti-aging, immunomodulatory and antioxidant properties, yet their mechanisms in ameliorating skin aging remain elusive, necessitating further exploration. This study verified the anti-skin aging efficacy of sea cucumber collagen peptides in D-galactose-induced aging mice, and explored whether the mechanism involves regulating endoplasmic reticulum (ER) stress in skin fibroblasts. Aging mice were gavaged with sea cucumber collagen peptides; skin moisture, barrier function and hydroxyproline content were measured, and skin morphology was observed. Immunofluorescence and Western Blot were used to detect ER stress-related proteins. Results showed that sea cucumber collagen peptides significantly improved aging mouse skin barrier function, elevated water and collagen fiber contents, and ameliorated the status of fibroblasts and prickle cells. The underlying mechanism may involve inhibiting ER stress in skin fibroblasts and enhancing prickle cell function. These findings confirm the peptides' high bioavailability and potential as anti-aging functional food ingredients, providing insights for skin aging prevention.

Precise control of feeding rates is critically important in intensive shrimp farming for cost reduction, optimization of farming strategies, and protection of the aquatic environment. However, current assessment of residual feed in feeding trays relies predominantly on manual visual inspection, which is inefficient, highly subjective, and difficult to standardize. The residual feed particles typically exhibit characteristics such as small size, high density, irregular shapes, and mutual occlusion, posing significant challenges for automated visual detection. To address these issues, this study proposes a lightweight detection model named YOLO-Shrimp. To enhance the network’s capability in extracting features from small and dense targets, a novel attention mechanism termed EnSimAM is designed. Building upon the SimAM structure, EnSimAM incorporates local variance and edge response to achieve multi-scale feature perception. Furthermore, to improve localization accuracy for small objects, an enhanced weighted intersection over union loss function, EnWIoU, is introduced. Additionally, the lightweight RepGhost module is adopted as the backbone of the model, significantly reducing both the number of parameters and computational complexity while maintaining detection accuracy. Evaluated on a real-world aquaculture dataset containing 3461 images, YOLO-Shrimp achieves mAP@0.5 and mAP@0.5:0.95 scores of 70.01% and 28.01%, respectively, while reducing the parameter count by 19.7% and GFLOPs by 14.6% compared to the baseline model.

Ficus carica (fig tree) is an economically and ecologically important species in China. In September 2023, leaf spot was observed affecting over 23% of 1,600 fig trees (aged 3–7 years, including cultivars 'Bourjassotte Grise', 'Orphan', 'Green Ischia', and 'Banana') in a fig orchard on Songmu Island, Dalian, China (39°24'N, 121°46'E). Initial symptoms appeared as small, irregular, water-soaked brown spots on the leaves. The spots subsequently enlarged into irregular, gray to dark-brown necrotic lesions, often surrounded by a distinct chlorotic halo. In severe infections, extensive lesion development led to leaf curling, premature defoliation, and a decline in overall tree vigor. To identify the causal agent, tissue blocks (5 × 5 mm) were excised from the margins of lesions. The pieces were surface-sterilized by sequential immersion in 75% ethanol for 30 s and 2% sodium hypochlorite for 1 min, followed by three rinses in sterile distilled water. The sterilized tissues were placed onto full-strength potato dextrose agar (PDA) without antibiotics and incubated at 25°C in darkness for 5 days. Nine isolates with similar colony appearance were obtained. The identified colonies were round with clear, white edges and sparse mycelium. As the culture aged, the center initially appeared light gray but gradually darkened to black. The conidial discs featured black, heap-like protrusions. The conidia were transparent, sickle-shaped structures with rounded tips at both ends, measuring between 19.28 to 25.89 μm in length and 2.01 to 4.77 μm in width (n = 30). Meanwhile, the appressoria were oval to irregular, black, and measured between 6.30 to 13.66 μm in length and 4.81 to 9.59 μm in width (n = 30), with distinctive bristle-like appendages, preliminarily identifying the pathogen as Colletotrichum spp. For molecular identification, the internal transcribed spacer (ITS), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), chitin synthase (CHS), actin (ACT), and β-tubulin (TUB2) genes were amplified and sequenced from three isolates (DLFC2301, DLFC2302, and DLFC2303) using ITS1/ITS4 (White et al. 1990), GDF1/GDR1 (Cannon et al. 2012), CHS-79F/CHS-345R, ACT512F/ACT783R (Carbone and Kohn 1999), and BT2A/BT2B primers (Glass and Donaldson 1995), respectively. The amplicon sequences of the representative isolate DLFC2303 were deposited in GenBank under accession numbers: ITS (PQ460263), GAPDH (PQ471494), CHS (PQ471493), TUB2 (PQ471481), and ACT (PQ471491). A maximum-likelihood phylogenetic analysis of five concatenated genes in MEGA 11 identified DLFC2303 as Colletotrichum truncatum. Conidial suspension (1×10⁶ conidia/mL) of isolate DLFC2303 was inoculated onto nine healthy leaves on three separate 3-year-old potted fig trees (three leaves per tree) via needle-prick wounding. To maintain humidity, the inoculation sites were covered with sterile moist cotton and enclosed in plastic bags. For the control, nine leaves on three additional trees were treated similarly with sterile distilled water. After 10 days, lesions resembling those found on field specimens appeared, and C. truncatum was successfully reisolated from diseased leaves, fulfilling Koch's postulates. These findings confirmed the pathogen as C. truncatum. Although C. truncatum has been reported to cause leaf anthracnose of Ficus carica in Malaysia (Nur-Shakirah et al. 2023), this is the first report of fig leaf anthracnose caused by C. truncatum in China.

Accurate monitoring of polyp attachment density is critical for the efficient culture of the edible jellyfish Rhopilema esculentum , yet quantitative density–growth guidelines remain limited. This study coupled image-based deep learning with conventional morphometry to characterise density-dependent growth of polyps. Polyp calyx diameter was measured manually to establish a density–size relationship. In parallel, standardised photography combined with a U-Net segmentation model was used to obtain individual polyp counts and projected areas. Regression analyses were conducted to derive functional relationships between attachment density and polyp size metrics, and processing times were compared between manual and automated approaches. Manual measurements showed that calyx diameter followed a power-law decay with density ( Calyx diameter = 1.5752Density -0.281 , R 2 = 0.9614). Automated image analysis yielded an exponential density–polyp area model ( Polyp area = 4.3888e -0.202 Density , R 2 = 0.9909). Both models revealed a strong inverse relationship: as attachment density increased, average polyp size and relative growth efficiency declined, whereas size variability increased. The U-Net-based segmentation approach achieved processing times of under one second per image, dramatically faster than manual measurements. These results demonstrate that AI-driven image segmentation provides accurate, high-throughput estimation of polyp size and robust quantitative density–growth relationships. The approach offers a practical and efficient tool for precision monitoring and optimisation of nursery conditions in R. esculentum aquaculture.