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ObjectiveIn recent years, there has been a steady increase in the occurrence and fatality rates of shrimp diseases, causing substantial impacts in shrimp aquaculture. These diseases are marked by their swift onset, high infectivity, complex control requirements, and elevated mortality rates. With the continuous growth of shrimp factory farming, traditional manual detection approaches are no longer able to keep pace with the current requirements. Hence, there is an urgent necessity for an automated solution to identify shrimp diseases. The main goal of this research is to create a cost-effective inspection method using computer vision that achieves a harmonious balance between cost efficiency and detection accuracy. The improved YOLOv8 (You Only Look Once) network and multiple features were employed to detect shrimp diseases.MethodsTo address the issue of surface foam interference, the improved YOLOv8 network was applied to detect and extract surface shrimps as the primary focus of the image. This target detection approach accurately recognizes objects of interest in the image, determining their category and location, with extraction results surpassing those of threshold segmentation. Taking into account the cost limitations of platform computing power in practical production settings, the network was optimized by reducing parameters and computations, thereby improving detection speed and deployment efficiency. Additionally, the Farnberck optical flow method and gray level co-occurrence matrix (GLCM) were employed to capture the movement and image texture features of shrimp video clips. A dataset was created using these extracted multiple feature parameters, and a Support Vector Machine (SVM) classifier was trained to categorize the multiple feature parameters in video clips, facilitating the detection of shrimp health.Results and DiscussionsThe improved YOLOv8 in this study effectively enhanced detection accuracy without increasing the number of parameters and flops. According to the results of the ablation experiment, replacing the backbone network with FasterNet lightweight backbone network significantly reduces the number of parameters and computation, albeit at the cost of decreased accuracy. However, after integrating the efficient multi-scale attention (EMA) on the neck, the mAP0.5 increased by 0.3% compared to YOLOv8s, while mAP0.95 only decreased by 2.1%. Furthermore, the parameter count decreased by 45%, and FLOPs decreased by 42%. The improved YOLOv8 exhibits remarkable performance, ranking second only to YOLOv7 in terms of mAP0.5 and mAP0.95, with respective reductions of 0.4% and 0.6%. Additionally, it possesses a significantly reduced parameter count and FLOPS compared to YOLOv7, matching those of YOLOv5. Despite the YOLOv7-Tiny and YOLOv8-VanillaNet models boasting lower parameters and Flops, their accuracy lags behind that of the improved YOLOv8. The mAP0.5 and mAP0.95 of YOLOv7-Tiny and YOLOv8-VanillaNet are 22.4%, 36.2%, 2.3%, and 4.7% lower than that of the improved YOLOv8, respectively. Using a support vector machine (SVM) trained on a comprehensive dataset incorporating multiple feature, the classifier achieved an impressive accuracy rate of 97.625%. The 150 normal fragments and the 150 diseased fragments were randomly selected as test samples. The classifier exhibited a detection accuracy of 89% on this dataset of the 300 samples. This result indicates that the combination of features extracted using the Farnberck optical flow method and GLCM can effectively capture the distinguishing dynamics of movement speed and direction between infected and healthy shrimp. In this research, the majority of errors stem from the incorrect recognition of diseased segments as normal segments, accounting for 88.2% of the total error. These errors can be categorized into three main types: 1) The first type occurs when floating foam obstructs the water surface, resulting in a small number of shrimp being extracted from the image. 2) The second type is attributed to changes in water movement. In this study, nanotubes were used for oxygenation, leading to the generation of sprays on the water surface, which affected the movement of shrimp. 3) The third type of error is linked to video quality. When the video's pixel count is low, the difference in optical flow between diseased shrimp and normal shrimp becomes relatively small. Therefore, it is advisable to adjust the collection area based on the actual production environment and enhance video quality.ConclusionsThe multiple features introduced in this study effectively capture the movement of shrimp, and can be employed for disease detection. The improved YOLOv8 is particularly well-suited for platforms with limited computational resources and is feasible for deployment in actual production settings. However, the experiment was conducted in a factory farming environment, limiting the applicability of the method to other farming environments. Overall, this method only requires consumer-grade cameras as image acquisition equipment and has lower requirements on the detection platform, and can provide a theoretical basis and methodological support for the future application of aquatic disease detection methods.

To improve the quality of natural seawater to meet the needs of aquaculture production, a large-scale subsurface-flow constructed wetland (HSFCW) was constructed and operated stably for 2 years to understand the reasons for its purification effect on natural seawater. The results of the study showed that the system could maintain a high purification effect on natural seawater during aquaculture; the average removal rates of COD, TSS, TN, NH4+-N, and DON (organic nitrogen) were 22.29%, 49.33%, 36.94%, 10.88%, and 44.08%, respectively. Additionally, the HSFCW could effectively remove harmful algae such as Cyanobacteria; the removal rate of dominant algae species in Cyanobacteria was 90.33–97.93%. The pyrosequencing of 16S ribosomal DNA revealed that Proteobacteria, Nitrospirae, and Chloroflexi were the main and key bacterial phyla in the system. Members of these key gates are regarded as playing important roles in resisting water purification. The study results suggest that the subsurface-flow wetland system can effectively improve seawater quality and reduce the density of harmful algae cells. [ABSTRACT FROM AUTHOR]

Introduction: The low yield of bioflocculants has been a bottleneck problem that limits their industrial applications. Understanding the metabolic mechanism of bacteria that produce bioflocculants, could provide valuable insights and strategies to directly regulate their yield in future., Methods: To investigate the change of metabolites in the process of bioflocculant production by a biomass-degrading bacterium, Pseudomonas boreopolis GO2, an untargeted metabolome analysis was performed., Results: The results showed that metabolites significantly differed during the fermentation process when corn stover was used as the sole carbon source. The differential metabolites were divided into four co-expression modules based on the weighted gene co-expression network analysis. Among them, a module (yellow module) was closely related to the flocculating efficiency, and the metabolites in this module were mainly involved in carbohydrate, lipid and amino acid metabolism. The top 30 metabolites with the highest degree in the yellow module were identified as hub metabolites for bioflocculants' production. Finally, 10 hub metabolites were selected to perform the additional experiments, and the addition of L-rhamnose, tyramine, tryptophan, and glutaric acid alone all could significantly improve the flocculating efficiency of GO2 strain., Conclusion: These results indicated that the hub metabolites were key for bioflocculant production in GO2 strain, and could help guide the improvement of high-efficiency and low-cost bioflocculant production., (The Author(s). Published by S. Karger AG, Basel.)

Although the effects of age on the reproductive performance of various invertebrates, including white shrimp Litopenaeus vannamei are increasingly well documented, the mechanisms manifesting these impacts remain poorly understood. To ascertain the mechanisms of age on reproductive performance, the sperm quality, intracellular contents of Ca 2+ , insemination and hatching rates, and status of sperm apoptosis in terms of the expression of key regulatory genes were investigated in 11 and 16 month old male L. vannamei. The aged male individuals (16 months) had lesser reproductive performance in terms of fertilization and hatching rates. In addition, fewer and less viable sperm were detected in aged shrimp, which may be due to the altered expression of apoptosis-related genes. Furthermore, the aged males had lesser intracellular contents of Ca 2+ in the sperm which may decrease the capacity of these gametes to undergo a complete acrosome reaction. In general, due to the decrease in intracellular contents of Ca 2+ and alterations in the process of apoptosis, aged L. vannamei have relatively lesser quality sperm and may, therefore, have lesser reproductive performance., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objective: Purpose of this work was to explore the distribution of LuxS/AI-2 quorum sensing system in Edwardsiella, and analyze expression characteristics and biological function of the key gene luxS accompanying the growth of Edwardsiella., Methods: The full-length of AI-2/LuxS of Edwardsiella tarda was cloned by PCR based on the sequence on NCBI, then characteristics and conservative structure of this protein-coding gene were analyzed using web database and bioinformatics tools. The anti-rabbits serum was prepared after this protein was purified through prokaryotic expression. The expression level of luxS gene was analyzed during different growth stages using Western blot and further the distribution of luxS gene in Edwardsiella tarda was studied by this technique. To explore whether the specific LuxS is AI-2 dependent we used the method of antibody neutralization to analyze the effect of the anti-rabbits serum on the growth of Edwardsiella tarda. RESULTS] The luxS gene was obtained by PCR, its length was 516 bp, and the sequence was highly conserved in Edwardsiella tarda. Results of Western blot analysis showed that LuxS expression level was the lowest in the lag phase and began increasing when entered index phase. It reached the peak in the late index phase and decreased in decline phase. Moreover, Antibody neutralization results showed that, it can elongate the growth plateau phase, but it has no significant effect on bacterial growth., Conclusion: The key gene of luxS was highly conserved, and LuxS/AI-2 was widely distributed among Edwardsiella tarda. The expression level of luxS gene was different during every growth period, expression of LuxS protein reached the highest level in the late index phase.

Antibacterial peptides are a family of host-defense peptides most of which are gene-encoded and produced by living organisms of all types. Antibacterial peptides are small molecular proteins with broad antimicrobial spectrum against bacteria, viruses, fungi and sometimes even as anticancer peptide. SMAP-29, a cathelicidin-like peptide derived from sheep myeloid, line alpha-helical Structure, exerts a powerful broad antimicrobial activity against different pathogens including Gram-positive and Gram-negative bacteria, fungi, viruses, parasites, spirochaetes, chlamydia and antiendotoxin activity, and particular antibacterial mechanism, rapidly to permeabilize membranes of susceptible organisms. This paper summarizes the lately research progress of SMAP-29 and Variants including the characteristics of structure, structure-activity relationships, mode of action, diverse biological functions, gene recombinant and expression. We put emphasis on the necessity of molecular design, and primary and secondary structure-based modification, to provides a strong foundation for further drug development and design of SMAP-29.

Objective: We isolated bacterial strain named M-1 from ill Sciaenops ocellatus with ulceration, and further analyzed the phylogenetics of the bacterial strain., Methods: We characterized the strain on the basis of its morphology, physiology, and sequence comparison of 16SrRNA and HSP60 genes., Results: Strain M-1 was identified as Vibrio splendidus. 16SrRNA gene of strain M-1 and Vibrio splendidus (AJ874361) had nearest kinship, and the analysis of HSP60 gene sequence indicated that strain M-1 and Vibrio (EU653883, AY837440) located in the same phylogenetic tree., Conclusion: The results suggested that strain M-1 was a member of Vibrio splendidus.