Species Selection for Oyster Reefs: Growth Simulations of Three Oysters Across Multiple Habitats - Jingyi Liu; Zhenhao Yang; Haoyuan Li; Chenxing Yang; Rong Wan (Fishes, 2026)
Oysters, as sessile organisms, are highly influenced by environmental conditions affecting their growth and reproduction. Therefore, unfavorable conditions can directly compromise the effectiveness of oyster reef restoration efforts. The study applied the dynamic energy budget (DEB) theory to model the growth of the Suminoe oyster (Crassostrea ariakensis) and estimated the model’s main parameters through linear and nonlinear regression methods. Meanwhile, the parameters of the existing DEB models for the Pacific oyster (Crassostrea gigas) and the Portuguese oyster (Crassostrea angulata) were optimized to improve the model performance. Model accuracy was confirmed by comparing the simulated and observed values of shell height and wet weight for three oyster species. The validated models were subsequently applied to three distinct habitats: Qianhu Bay, the Yangtze River Estuary, and Jiaozhou Bay, to simulate the individual dynamic growth and survival processes of the three oyster species. The simulations of shell height and wet weight for the three oyster species showed strong agreement with observations, with a mean coefficient of determination (R2) of 0.98. The mean NRMSE value of the three models was 0.16, indicating low model-prediction error. The simulation of various habitat scenarios suggested that C. ariakensis exhibited faster growth in the Yangtze River Estuary and Qianhu Bay, and C. gigas in the Yangtze River Estuary. Additionally, C. angulata experienced mortality in the Yangtze River Estuary (February) and Jiaozhou Bay (January). The findings of this study can provide scientific guidance on selecting species for oyster reefs across various marine environments to effectively enhance their ecological restoration outcomes.

Short-Term Prediction of Global Sea Surface Temperature Using Deep Learning Networks - Tianliang Xu; Zhiquan Zhou; Yingchun Li; Chenxu Wang; Ying Liu; Tian Rong (Journal of Marine Science and Engineering, 2023)
The trend of global Sea Surface Temperature (SST) has attracted widespread attention in several ocean-related fields such as global warming, marine environmental protection and marine biodiversity. Sea surface temperature is influenced by climate change; with the accumulation of data from ocean remote sensing observations year by year, many scholars have started to use deep learning methods for SST prediction. In this paper, we use a dynamic region partitioning approach to process ocean big data and design a framework applied to a global SST short-term prediction system. On the architecture of a Long Short-Term Memory (LSTM) network, two deep learning multi-region SST prediction models are proposed, which extract temporal and spatial information of SST by encoding, using feature transformation and decoding to predict future multi-step states. The models are tested using OISST data and the model performance is evaluated by different metrics. The proposed MR-EDLSTM model and MR-EDConvLSTM model obtained the best results for short-term prediction, with RMSE ranging from 0.2712 °C to 0.6487 °C and prediction accuracies ranging from 97.60% to 98.81% for ten consecutive days of prediction. The results show that the proposed MR-EDLSTM model has better prediction performance in coastal areas, while the MR-EDConvLSTM model performs better in predicting the sea area near the equator. In addition, the proposed deep learning model has a smaller RMSE compared to the forecasting system based on the ocean model, indicating that the deep learning method has certain advantages in predicting global SST.

Molecular Characterization and Dietary Regulation of Glutaminase 1 (gls1) in Triploid Crucian Carp (Carassius auratus) - Yangbo Xiao; Rong Huang; Shenping Cao; Dafang Zhao; Zhuangwen Mao; Chuchu Xiao; Zhehua Xu; Xiaomei Zhou; Xinran Zhang; Yu Zhang; Jianzhou Tang; Junyan Jin; et al. (Fishes, 2022)
Kidney-type glutaminase, encoded by the gls1 gene, plays a critical role in glutamate production and improvement of meat flavor. In this study, a gls1 gene encoding 595 amino acids was cloned from triploid crucian carp (Carassius auratus) (TCC) and showed a high similarity with the gls1 gene found in Cyprinus carpio, Sinocyclocheilus rhinocerous and Puntigrus tetrazona. Comparing the abundance of gls1 in different tissues, we found its expression level in the brain and liver were significantly higher than that in heart, gut, kidney, spleen and muscle. gls1 expression in the brain reached the highest value. In addition, the expression levels of gls1 also appeared different in diurnal variation, with the highest expression seen at 9:00, while it was low at 3:00, 6:00, 15:00 and 24:00. Furthermore, dietary regulation of gls1 expression was investigated in our study. In each feeding trial, each diet was randomly assigned to triplicate tanks. Fish were fed one of the tested diets up to satiation twice daily. The results showed that gls1 expression increased in 32% protein group and decreased in 35–41% protein group. The results of different protein source experiments showed that the expression of gls1 gene in the mixed protein group (the control group) was significantly higher than that in the fish meal and soybean meal groups. Glutamate treatment revealed that appropriate concentrations (0.10 mg/mL in vivo and 2.00% in vitro) of glutamate remarkably improved the expression of gls1. Besides, diets supplemented with 0.80–1.60% lysine-glutamate dipeptide exhibited a down regulatory impact on gls1 expression. In conclusion, this study demonstrated that the expression of gls1 in TCC was increased by 32% protein diet, mixed protein source diet and diet with 2.00% glutamate concentration, while decreased by 0.80–1.60% lysine-glutamate dipeptide. The findings of this study provide a reference for the regulation of gls1 and have a potential application in the optimization of dietary formula in aquaculture.

Molecular Characterization of Hsp47 in Grass Carp (Ctenopharyngodon idella) and Its Correlation with Type I Collagen in Response to Fish Aerobic Exercise - Xiao Liang; Ying Wan; Zhiyuan Shen; Yanmei Liu; Dapeng Li; Li Li; Rong Tang; Xi Zhang (Fishes, 2021)
Heat shock protein 47 (Hsp47) is a collagen-specific molecular chaperone that is indispensable for molecular maturation of collagen. In this study, hsp47 and hsp47-like cDNAs were cloned and characterized in grass carp (Ctenopharyngodon idella). The cDNAs were 1212 and 1218 base pairs long, respectively, and included an open reading frame encoding 403 and 405 amino acids. The molecular phylogeny based on the deduced amino acid sequences indicated that the correct sequences of the hsp47 and hsp47-like cDNA were obtained and the deduced proteins clustered distinctly into teleost clades. Primary structure analysis and characterization of Hsp47 and Hsp47-like shared the basic structure and biofunctions of Hsp47 in vertebrates. The spatial pattern of gene expression revealed that hsp47 and hsp47-like were relatively ubiquitous in different tissues and highly expressed in heart and skin. The expression levels of hsp47 and hsp47-like and type I collagen mRNAs varied similarly in different tissues. Type I collagen content increased significantly with the increase of water velocity in the muscle of grass carp in response to aerobic exercise. Among the gene expression changes of hsp47, hsp47-like, col1a1 and col1a2 in muscle that occurred in response to aerobic exercise, the change of type I collagen was most strongly correlated with hsp47 expression. Additionally, col1a1 showed the highest correlation with hsp47-like and col1a2 showed the highest correlation with hsp47. These findings suggest that grass carp Hsp47 and Hsp47-like are closely related to type I collagen synthesis. This study firstly suggests fish aerobic exercise can improve type I collagen content and Hsp47 gene expression in muscle of grass carp.