Search Results (342)

Filoviruses are among the most lethal viral human pathogens known, with significant relevance to public health, yet their evolutionary history remains poorly resolved. This study applied a multiprotein molecular phylogenetic approach to investigate the evolutionary and temporal dynamics of the family Filoviridae. Amino acid sequences from the proteome and seven individual proteins (NP, VP35, VP40, GP, VP30, VP24, L) were analyzed using MEGA 12, with RelTime inference anchored on uniform calibrations, and integration of epidemiological data (cases, fatalities, case fatality). The phylogenetic reconstructions revealed robust topologies for most proteins, though selective pressures on GP, VP30 and VP40 generated more variable patterns. Temporal inferences supported the classification of filoviruses into three groups: an ancestral lineage (>1 MYA, fish- and reptile-associated), an intermediate lineage (BCE–1 MYA, bat-associated), and a contemporary lineage (CE, ebolaviruses and marburgviruses). VP30 and VP40 showed consistent associations with epidemiological outcomes in Orthoebolavirus zairense, suggesting their interplay may underlie enhanced dispersal and virulence. Contrariwise, Orthoebolavirus restonense emerged as a natural counterpoint for comparison with other potential human pathogenic filoviruses. Taken together, these findings highlight that filoviral evolution is intrinsically linked not only to viral biology but also to the ecology and history of their hosts. Full article

Water quality is an essential part of maintaining a healthy environment for fish farming. The quality of the water is related to a few of the chemical and biological characteristics of water. The conventional evaluation methods of the water quality are often time-consuming and may overlook complex interdependencies among multiple indicators. This study has proposed a robust machine learning framework for aquaculture water quality classification by integrating the Honey Badger Algorithm (HBA) with the XGBoost classifier. The framework enhances classification accuracy and incorporates explainability through SHAP and DiCE, thereby providing both predictive performance and transparency for practical water quality management. For reliability, the dataset has been randomly shuffled, and a custom 5-fold cross-validation strategy has been applied. Later, through the metaheuristic-based HBA, feature selections and hyperparameter tuning have been performed to improve and increase the prediction accuracy. The highest accuracy of 98.45% has been achieved by a particular fold, whereas the average accuracy is 98.05% across all folds, indicating the model’s stability. SHAP analysis reveals Ammonia, Nitrite, DO, Turbidity, BOD, Temperature, pH, and CO₂ as the topmost water quality indicators. Finally, the DiCE analysis has analyzed that Temperature, Turbidity, DO, BOD, CO₂, pH, Ammonia, and Nitrite are more influential parameters of water quality. Full article

Industrial safety in high-risk sectors such as mining, construction, oil and gas, petrochemicals, and offshore fishing remains a strategic global challenge due to the high incidence of occupational accidents and their human, financial, and legal consequences. Despite international standards and advancements in safety strategies, significant barriers persist in the effective implementation of a Zero Accident culture. This scoping review, conducted under PRISMA-ScR guidelines, analyzed 11 studies selected from 232 records, focusing on documented practices in both multinational corporations from developed economies and local companies in emerging markets. The methodological synthesis validated theoretical models, practical interventions, and regulatory frameworks across diverse industrial settings. The findings led to the construction of a five-pillar model that provides the structural foundation for a comprehensive safety strategy: (1) strategic safety planning, defining long-term vision, mission, and objectives with systematic risk analysis; (2) executive leadership and commitment, expressed through decision-making, resource allocation, and on-site engagement; (3) people and competencies, emphasizing continuous training, communities of practice, and the development of safe behaviors; (4) process risk management, using validated protocols, structured methodologies, and early warning systems; and (5) performance measurement and auditing, combining reactive and proactive indicators within continuous improvement cycles. The results demonstrate that only a holistic approach, one that aligns strategy, culture, and performance, can sustain a robust safety culture. While notable reductions in incident rates were observed when these pillars were applied, the current literature is dominated by theoretical contributions and model replication from developed countries, with limited empirical evaluation in emerging contexts. This study provides a comparative, practice-oriented framework to guide the implementation and refinement of safety systems in high-risk organizations. This review was registered in Open Science Framework (OSF): 10.17605/OSF.IO/XFDPR. Full article

Ensuring reliable power system control demands innovative architectural solutions. This research introduces a fault-tolerant hybrid parallel compensator architecture for load frequency control (LFC), combining a Proportional–Integral–Derivative with Filter (PIDF) compensator with a Fuzzy Fractional-Order PI-PD (Fuzzy FOPI–FOPD) module. Particle Swarm Optimization (PSO) determines optimal PID gains, while the Catch Fish Optimization Algorithm (CFOA) tunes the Fuzzy FOPI–FOPD parameters—both minimizing the Integral Time Absolute Error (ITAE) index. The parallel compensator structure guarantees continuous operation during subsystem faults, substantially boosting grid reliability. Rigorous partial failure tests confirm uncompromised performance-controlled degradation. Benchmark comparisons against contemporary controllers reveal the proposed architecture’s superiority, quantifiable through transient metric enhancements: undershoot suppression (−9.57 × 10⁻⁵ p.u. to −1.17 × 10⁻⁷ p.u.), settling time improvement (8.8000 s to 3.1511 s), and ITAE reduction (0.0007891 to 0.0000001608), verifying precision and stability gains. Resilience analyses across parameter drift and step load scenarios, simulated in MATLAB/Simulink, demonstrate superior disturbance attenuation and operational stability. These outcomes confirm the solution’s robustness, dependability, and field readiness. Overall, this study introduces a transformative LFC strategy with high practical viability for modern power networks. Full article

Piscine orthoreovirus (PRV) is a globally distributed viral pathogen that causes heart and skeletal muscle inflammation (HSMI) in Atlantic salmon (Salmo salar) and affects other salmonids, yet no commercial vaccines are currently available. Major barriers to vaccine development include the inability to propagate PRV in cell lines and the low, variable immunogenicity of its proteins, particularly the outer capsid protein σ1, which mediates viral attachment. This protein is hypothesized to be immunologically relevant due to its homology with Mammalian orthoreoviruses. Recombinant σ1 expressed in conventional systems exhibits poor antibody recognition, whereas structural modifications such as lipidation or fusion with molecular chaperones improve epitope exposure. Formalin-inactivated vaccines have shown inconsistent protection, often failing to elicit robust innate or adaptive responses, especially under cohabitation challenge. In contrast, DNA vaccines encoding σ1 and the non-structural protein μNS have demonstrated partial efficacy, likely due to enhanced intracellular expression and antigen presentation. Nonetheless, the considerable variability observed in immune responses among individual fish and viral genotypes, together with suggestions that PRV may interfere with antiviral pathways, represent additional barriers to achieving consistent vaccine efficacy. This review summarizes the current status of PRV vaccine development and discusses future directions for rational design based on optimized antigens and intracellular delivery platforms. Full article

The Larimichthys crocea represents a critically important economic marine species in China’s East Yellow Sea. However, its populations have experienced significant decline due to overexploitation. Despite implemented conservation measures—including stock enhancement, spawning ground protection, and seasonal fishing moratoria—the recovery of yellow croaker resources remains markedly slow. To address this, our study employed the Maximum Entropy (MaxEnt) model to evaluate and characterize the habitat selection patterns of Larimichthys crocea, thereby providing a theoretical foundation for scientifically informed stock enhancement and resource recovery strategies. Species occurrence data were compiled from field surveys conducted during April and November (2019–2023), supplemented with records from the GBIF database and peer-reviewed literature. Concurrent environmental variables, including primary productivity, current velocity, depth, temperature, salinity, silicate, nitrate, phosphate, and pH, were obtained from the Copernicus and NOAA databases. After rigorous screening, 136 distribution points (April) and 369 points (November) were retained for analysis. The model performance was robust, with an AUC (Area Under the Curve) value of 0.935 for April (2019–2023) and 0.905 for November (2019–2023), indicating excellent predictive accuracy (AUC > 0.9). April (2019–2023): Nitrate, salinity, phosphate, and silicate were identified as the primary environmental factors influencing habitat suitability. November (2019–2023): Silicate, salinity, nitrate, and primary productivity emerged as the dominant drivers. Spatially, Larimichthys crocea exhibited high-density distributions in offshore regions of Zhejiang and Jiangsu, particularly near the Yangtze River estuary. Populations were also associated with island-reef systems, forming continuous distributions along Zhejiang’s offshore waters. In Jiangsu, aggregations were concentrated between Nantong and Yancheng. This study delineates habitat suitability zones for Larimichthys crocea, offering a scientific basis for optimizing stock enhancement programs, designing targeted conservation measures, and establishing marine protected areas. Our findings enable policymakers to develop sustainable fisheries management strategies, ensuring the long-term viability of this ecologically and economically vital species. Full article

Seafood fraud, such as mislabeling low-cost rainbow trout as premium salmon, poses serious food safety risks and damages consumer rights. To address this growing concern, this study develops a deep learning-based, smartphone-compatible sensing system for fish meat identification and salmon freshness grading. By providing consumers with real-time, image-based verification tools, the system supports informed purchasing decisions and enhances food safety. The system adopts a two-stage design: first classifying fish meat types, then grading salmon freshness into three levels based on visual cues. An improved DenseNet121 architecture, enhanced with global average pooling, dropout layers, and a customized output layer, improves accuracy and reduces overfitting, while transfer learning with partial layer freezing enhances efficiency by reducing training time without significant accuracy loss. Experimental results show that the two-stage method outperforms the one-stage approach and several baseline models, achieving robust accuracy in both classification and grading tasks. Sensitivity analysis demonstrates resilience to blur and camera tilt, though real-world adaptability under diverse lighting and packaging conditions remains a challenge. Overall, the proposed system represents a practical, consumer-oriented tool for seafood authentication and freshness evaluation, with potential to enhance food safety and consumer protection. Full article

The conservation and protection of fish species are crucial tasks for aquaculture and marine biology. Recognizing fish in underwater environments is highly challenging due to poor lighting and the visual similarity between fish and the background. Conventional recognition methods are extremely time-consuming and often yield unsatisfactory accuracy. This paper proposes a new method called DeepFishNET+. First, an Underwater Image Enhancement module was implemented for image correction. Second, Global CNN Stream (RestNet50) and a Local Transformer Stream were implemented to generate the Feature Map and Feature Vector. Next, a feature fusion operation was performed in the Cross-Attention Feature Fusion module. Finally, Yolov8 was used for fish detection and localization. Softmax was applied for species recognition. This new approach achieved a classification precision of 98.28% and a detection precision of 92.74%. Full article

This study presents the deployment of Machine Learning (ML) models on low-cost edge devices (ESP32) for real-time water quality prediction in tilapia aquaculture. A compact monitoring and control system was developed with low-cost sensors to capture key environmental parameters under field conditions in Northern Thailand. Three ML models—Multiple Linear Regression (MLR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)—were evaluated. RFR achieved the highest accuracy (R² > 0.80), while MLR, with moderate performance (R² ≈ 0.65–0.72), was identified as the most practical choice for ESP32 deployment due to its computational efficiency and offline operability. The system integrates sensing, prediction, and actuation, enabling autonomous regulation of dissolved oxygen and pH without constant cloud connectivity. Field validation demonstrated the system’s ability to maintain DO within biologically safe ranges and stabilize pH within an hour, supporting fish health and reducing production risks. These findings underline the potential of Edge AIoT as a scalable solution for small-scale aquaculture in resource-limited contexts. Future work will expand seasonal data coverage, explore federated learning approaches, and include economic assessments to ensure long-term robustness and sustainability. Full article

This study addresses the growing need for intelligent monitoring in aquaponic systems by developing a predictive system based on artificial intelligence and environmental sensing. The goal is to improve fish welfare through the early detection of adverse water conditions. The system integrates low-cost digital sensors to continuously measure key physicochemical variables—pH, dissolved oxygen, and temperature—using these as inputs for real-time classification of fish health status. Four supervised machine learning models were evaluated: linear discriminant analysis (LDA), support vector machines (SVMs), neural networks (NNs), and random forest (RF). A dataset of 1823 instances was collected over eight months from a red tilapia aquaponic setup. The random forest model yielded the highest classification accuracy (99%), followed by NN (98%) and SVM (97%). LDA achieved 82% accuracy. Performance was validated using 5-fold cross-validation and label permutation tests to confirm model robustness. These results demonstrate that sensor-based predictive models can reliably detect early signs of fish stress or mortality, supporting the implementation of intelligent environmental monitoring and automation strategies in sustainable aquaponic production. Full article

Fish have become increasingly prominent as experimental models due to their unique capacity to bridge basic biological research with translational applications across diverse scientific disciplines. Their biological traits, such as external fertilization, high fecundity, rapid embryonic development, and optical transparency, facilitate in vivo experimentation and real-time observation, making them ideal for integrative research. Species like zebrafish (Danio rerio) and medaka (Oryzias latipes) have been extensively validated in genetics, toxicology, neuroscience, immunology, and pharmacology, offering robust platforms for modeling human diseases, screening therapeutic compounds, and evaluating environmental risks. This review explores the multidisciplinary utility of fish models, emphasizing their role in connecting molecular mechanisms to clinical and environmental outcomes. We address the main species used, highlight their methodological advantages, and discuss the regulatory and ethical frameworks guiding their use. Additionally, we examine current limitations and future directions, particularly the incorporation of high-throughput omics approaches and real-time imaging technologies. The growing scientific relevance of fish models reinforces their strategic value in advancing cross-disciplinary knowledge and fostering innovation in translational science. Full article

Karyotype analysis and the investigation of chromosomal variations in Populus are challenging due to its small and morphologically similar chromosomes. Despite its utility in chromosome identification and karyotype evolutionary research, satellite DNA (satDNA) remains underutilized in Populus. In the present study, 12 satDNAs were identified from P. trichocarpa, and the copy numbers and chromosomal distributions of each satDNA were analyzed bioinformatically in the reference genomes of P. trichocarpa, P. simonii, and P. nigra. Ten satDNA probes for fluorescence in situ hybridization (FISH) were successfully developed and validated on chromosomes of P. xiaohei (poplar hybrid P. simonii × P. nigra). By integrating bioinformatic genomic satDNA distribution patterns with experimental FISH signals, we constructed a molecular karyotype of P. xiaohei. Comparative analysis revealed errors in current poplar genome assemblies. Comparative karyotype analysis of P. xiaohei and its doubled haploid (DH) lines revealed chromosomal variations in the DH lines relative to the donor tree. The results demonstrate that the newly developed satDNA probes constitute robust cytogenetic tools for detecting structural variations in Populus, while molecular karyotyping provides new insights into the genetic mechanisms underlying chromosome variations in P. xiaohei and the DH plants derived. Full article

Stunning of farmed fish prior to slaughter is increasingly recognized as a key animal welfare priority, yet uptake remains limited in the EU aquaculture sector. While the effects of different stunning methods on fish welfare are the subject of significant recent research, the effect on aquaculture businesses remains unclear. Therefore, this study assesses the economic feasibility of implementing electrical stunning for four species where it is not currently routine: carp, trout, seabass, and seabream. Using a granular cost model across 17 country–species–system combinations, and cost data from 2018 to 2020, the impact of introducing in-water and dry electrical stunning systems under various cost pass-through and sensitivity scenarios is evaluated. Results show that while stunning increases the production costs, under realistic assumptions, 16 out of 17 segments remain profitable, with the one unprofitable segment already being unprofitable under business-as-usual conditions. Three trout systems even experience cost savings due to reduced labor requirements. Sensitivity analyses confirm the robustness of these findings across plausible increases in operating costs and financing assumptions. Even under a 0% cost pass-through, 16 segments still remain profitable. These results provide timely, policy-relevant evidence to support species-specific welfare legislation, while identifying segments that may require targeted support for compliance. Full article

Sea anchors for fishing operations are essential equipment to enhance catch efficiency and ensure operational stability at sea. However, previous studies have mainly focused on theoretical modeling or experiments under restricted conditions, which have not sufficiently reflected the complex operating environments and practical needs of real-world fisheries. To address this gap, this study derived key factors to improve the design and operation of sea anchors and quantitatively analyze the relative importance and rank of these factors. An expert panel was formed from 25 participants, including jigging vessel captains, recreational fishing boat captains, sea anchor manufacturers, and research institute workers. Using a three-round Delphi process followed by Analytic Hierarchy Process (AHP) analysis, we distilled an initial list of 52 improvement suggestions into 15 prioritized items, quantitatively ranked by relative importance based on expert consensus. The highest-ranked factor was ‘Enhancement of fabric drying performance’, followed by ‘Application of low-cost, high-efficiency materials’, ‘Improvement of recovery’, ‘Enhancement of UV resistance’, and ‘Product quality certification’. The highest-weighted metric was ‘Improvement of usability’, followed by ‘Enhanced durability’ and ‘Improvement of functionality’. The consistency ratio (CR) of the pairwise-comparison matrix was 0.0014 (AHP acceptability criterion: CR ≤ 0.1), confirming the reliability and consistency of the analysis. By reflecting real-world priorities through a robust and systematic analytical process, this study offers a foundation for evidence-based improvements in sea anchor design and operation, overcoming the limitations of earlier approaches rooted in subjective judgment or trial-and-error experience. Full article

Catsharks are benthic elasmobranchs that share spatial niches with littoral and demersal bony fishes. The genus Schroederichthys includes five species, two of which, S. chilensis and S. bivius, occur in the waters of Chile. These species are morphologically similar and are often misidentified because of their overlapping external features and color patterns. To improve species discrimination, we analyzed the egg case morphology of both species based on 36 egg cases (12 S. chilensis, 24 S. bivius) collected from gravid females captured as bycatch in artisanal fisheries between Iquique and Puerto Montt (July–December 2021). Nine morphometric variables were measured and standardized using the total egg case length. Although the egg cases were similar in general appearance, multivariate analyses revealed significant interspecific differences, with egg case height and anterior border width emerging as the most diagnostic variables. Linear discriminant analysis achieved a 100% classification accuracy within this dataset. To confirm species identity, 24 tissue samples (12 per species) were sequenced for the mitochondrial cytochrome c oxidase subunit I (COI) gene. The haplotypes corresponded to previously published sequences from Chile (S. chilensis) and Argentina (S. bivius), with reciprocal monophyly and 100% bootstrap support. While COI barcoding provided robust confirmation, the core contribution of this study lies in the identification of species-specific egg case morphometrics. Together, these findings establish a dual-track toolkit, egg case morphology for primary discrimination and COI barcodes for confirmatory validation, that can be incorporated into bycatch monitoring and biodiversity assessments, supporting the conservation of poorly known catsharks in the Southeast Pacific. Full article