Search Results (2,232)

The global rise in antimicrobial resistance poses a major threat to public health, with multidrug-resistant bacterial infections expected to surpass cancer in mortality by 2050. As traditional antibiotic pipelines stagnate, novel therapeutic alternatives are critically needed. Antimicrobial peptides (AMPs), particularly those derived from marine organisms, have emerged as promising antimicrobial candidates due to their broad-spectrum activity, structural diversity, and distinctive mechanisms of action. Unlike conventional antibiotics, AMPs can disrupt microbial membranes, inhibit biofilm formation, and even modulate immune responses, making them highly effective against resistant bacteria. This review highlights the potential of marine AMPs as next-generation therapeutics, emphasizing their efficacy against multidrug-resistant pathogens and biofilm-associated infections. Furthermore, marine AMPs show promise in combating persister cells and disrupting quorum sensing pathways, offering new strategies for tackling chronic infections. Despite their potential, challenges such as production scalability and limited clinical validation remain; nevertheless, the use of new technologies and bioinformatic tools is accelerating the discovery and optimization of these peptides, paving the way for bypassing these challenges. This review consolidates current findings on marine AMPs, advocating for their continued exploration as viable tools in the fight against antimicrobial resistance. Full article

Understanding population connectivity and genetic structure is crucial for the effective management of exploited marine species. This study investigates the population genetics of the common European squid (Loligo vulgaris) across the Mediterranean Sea, focusing on samples from the Aegean Sea and comparative sequences from Western Mediterranean, Eastern Mediterranean, and Atlantic coasts. A total of 67 COI mitochondrial sequences were analyzed, identifying 12 haplotypes and 27 polymorphic sites. Population-level genetic diversity, pairwise F_ST values, and haplotype network analyses revealed pronounced genetic differentiation in the eastern Mediterranean contrasting with the genetic homogeneity observed among Western populations. The low haplotype diversity observed in the Greek populations of L. vulgaris may be influenced by a combination of ecological and historical factors, as the Aegean region is recognized as a hotspot of endemism and historical population fragmentation. The results indicate the existence of at least two genetically differentiated clusters within the Mediterranean basin. This study advances current knowledge of the genetic structure of Loligo vulgaris by providing novel genetic data on populations from the eastern Mediterranean, offering valuable insights for future conservation and management strategies for the species. Full article

Underwater image enhancement is crucial for fields like marine exploration, underwater photography, and environmental monitoring, as underwater images often suffer from reduced visibility, color distortion, and contrast loss due to light absorption and scattering. Despite recent progress, existing methods struggle to generalize across diverse underwater conditions, such as varying turbidity levels and lighting. This paper proposes a novel hybrid UNet–Transformer architecture based on MaxViT blocks, which effectively combines local feature extraction with global contextual modeling to address challenges including low contrast, color distortion, and detail degradation. Extensive experiments on two benchmark datasets, UIEB and EUVP, demonstrate the superior performance of our method. On UIEB, our model achieves a PSNR of 22.91, SSIM of 0.9020, and CCF of 37.93—surpassing prior methods such as URSCT-SESR and PhISH-Net. On EUVP, it attains a PSNR of 26.12 and PCQI of 1.1203, outperforming several state-of-the-art baselines in both visual fidelity and perceptual quality. These results validate the effectiveness and robustness of our approach under complex underwater degradation, offering a reliable solution for real-world underwater image enhancement tasks. Full article

Although marine caves are among the most species-diverse habitats in the Mediterranean Sea, most available studies have focused on their sessile fauna. This study provides the first quantitative assessment of motile fauna in 27 marine caves across four geographical subareas of the Aegean and Ionian Seas, using a rapid assessment visual census protocol, applied through 3 min time transects in each ecological cave zone. Multivariate analysis revealed that the motile community structure of the cave entrance was differentiated from that of the semidark and dark zones. Deeper caves were distinct from shallower ones while caves of the east Aegean differed from those around Crete Island. A total of 163 taxa were recorded, 27 of which are reported herein for the first time in marine caves of the eastern Mediterranean Sea, while three species (two native and one introduced) are recorded in Greek waters for the first time, enriching our knowledge on the permanent and occasional cave residents. Seventeen species were introduced, comprising more than half of the total fish abundance in the southeasternmost cave. Our limited knowledge of the motile fauna of Mediterranean marine caves coupled with the continued spread of introduced species highlights the urgent need for monitoring and conservation actions, especially within marine protected areas. Full article

The classification of submarine topography and geomorphology is essential for marine resource exploitation and ocean engineering, with wide-ranging implications in marine geology, disaster assessment, resource exploration, and autonomous underwater navigation. Submarine landscapes are highly complex and diverse. Traditional visual interpretation methods are not only inefficient and subjective but also lack the precision required for high-accuracy classification. While many machine learning and deep learning models have achieved promising results in image classification, limited work has been performed on integrating backscatter and bathymetric data for multi-source processing. Existing approaches often suffer from high computational costs and excessive hyperparameter demands. In this study, we propose a novel approach that integrates pruning-enhanced ConDenseNet with label smoothing regularization to reduce misclassification, strengthen the cross-entropy loss function, and significantly lower model complexity. Our method improves classification accuracy by 2% to 10%, reduces the number of hyperparameters by 50% to 96%, and cuts computation time by 50% to 85.5% compared to state-of-the-art models, including AlexNet, VGG, ResNet, and Vision Transformer. These results demonstrate the effectiveness and efficiency of our model for multi-source submarine topography classification. Full article

Deep learning with unmanned aerial vehicles (UAVs) is transforming maritime search and rescue (SAR) by enabling rapid object identification in challenging marine environments. This study benchmarks the performance of YOLO models for maritime SAR under diverse weather conditions using the SeaDronesSee and AFO datasets. The results show that while YOLOv7 achieved the highest mAP@50, it struggled with detecting small objects. In contrast, YOLOv10 and YOLOv11 deliver faster inference speeds but compromise slightly on precision. The key challenges discussed include environmental variability, sensor limitations, and scarce annotated data, which can be addressed by such techniques as attention modules and multimodal data fusion. Overall, the research results provide practical guidance for deploying efficient deep learning models in SAR, emphasizing specialized datasets and lightweight architectures for edge devices. Full article

Oil spill simulation models are essential for predicting the oil spill behavior and movement in marine environments. In this study, we comprehensively reviewed a large and diverse body of peer-reviewed literature obtained from Scopus and Web of Science. Our initial analysis phase focused on examining trends in scientific publications, utilizing the complete dataset derived after systematic screening and database integration. In the second phase, we applied elements of a systematic review to identify and evaluate the most influential contributions in the scientific field of oil spill simulations. Our analysis revealed a steady and accelerating growth of research activity over the past five decades, with a particularly notable expansion in the last two. The field has also experienced a marked increase in collaborative practices, including a rise in international co-authorship and multi-authored contributions, reflecting a more global and interdisciplinary research landscape. We cataloged the key modeling frameworks that have shaped the field from established systems such as OSCAR, OIL-MAP/SIMAP, and GNOME to emerging hybrid and Lagrangian approaches. Hydrodynamic models were consistently central, often integrated with biogeochemical, wave, atmospheric, and oil-spill-specific modules. Environmental variables such as wind, ocean currents, and temperature were frequently used to drive model behavior. Geographically, research has concentrated on ecologically and economically sensitive coastal and marine regions. We conclude that future progress will rely on the real-time integration of high-resolution environmental data streams, the development of machine-learning-based surrogate models to accelerate computations, and the incorporation of advanced biodegradation and weathering mechanisms supported by experimental data. These advancements are expected to enhance the accuracy, responsiveness, and operational value of oil spill modeling tools, supporting environmental monitoring and emergency response. Full article

An increasing global population, rising energy demands, and the shift toward a circular bioeconomy are driving the need for more resource-efficient waste management. The increase in the world population—now exceeding 8 billion as of 2024—results in an increased need for alternative proteins, both human and feed grade proteins, as well as for biopolymers and bioenergy. As such, agricultural, forest, and marine waste biomass represent a valuable feedstock for production of food and feed ingredients, biopolymers, and bioenergy. However, the lack of integrated and efficient valorization strategies for these diverse biomass sources remains a major challenge. This literature review aims to give a systematic approach on the recent research status of agricultural, forest, and marine waste biomass valorization, focusing on cascade processing (a sequential combination of processes such as pretreatment, extraction, and conversion methods). Potential products will be identified that create the most economic value over multiple lifetimes, to maximize resource efficiency. It highlights the challenges associated with cascade processing of waste biomass and proposes technological synergies for waste biomass valorization. Moreover, this review will provide a comprehensive understanding of the potential of waste biomass valorization in the context of sustainable and circular bioeconomy. Full article

Hydrogen production is increasingly vital for global decarbonization but remains a water- and energy-intensive process, especially in arid regions. Despite growing attention to its climate benefits, limited research has addressed the environmental impacts of water sourcing. This study employs a life cycle assessment (LCA) approach to evaluate three water supply strategies for hydrogen production: (1) seawater desalination without brine treatment (BT), (2) desalination with partial BT, and (3) freshwater purification. Scenarios are modeled for the United Arab Emirates (UAE), Australia, and Spain, representing diverse electricity mixes and water stress conditions. Both electrolysis and steam methane reforming (SMR) are evaluated as hydrogen production methods. Results show that desalination scenarios contribute substantially to human health and ecosystem impacts due to high energy use and brine discharge. Although partial BT aims to reduce direct marine discharge impacts, its substantial energy demand can offset these benefits by increasing other environmental burdens, such as marine eutrophication, especially in regions reliant on carbon-intensive electricity grids. Freshwater scenarios offer lower environmental impact overall but raise water availability concerns. Across all regions, feedwater for SMR shows nearly 50% lower impacts than for electrolysis. This study focuses solely on the environmental impacts associated with water sourcing and treatment for hydrogen production, excluding the downstream impacts of the hydrogen generation process itself. This study highlights the trade-offs between water sourcing, brine treatment, and freshwater purification for hydrogen production, offering insights for optimizing sustainable hydrogen systems in water-stressed regions. Full article

Tropical sandy beaches are dynamic ecosystems where microbial communities play crucial roles in biogeochemical processes and tracking human impact. Despite their importance, these habitats remain underexplored. Here, using amplicon-based sequencing of bacterial (V3-V4 16S rRNA) and fungal (ITS2) markers, we first describe microbial communities inhabiting supralittoral–intertidal sediments of two contrasting sandy beaches in the Tadjoura Gulf (Djibouti Republic): Sagallou-Kalaf (SK, rural, siliceous sand) and Siesta Plage (SP, urban, calcareous sand). Sand samples were collected at low tide along 10 m transects perpendicular to the shoreline. Bacterial communities differed significantly between sites and along the sea-to-land gradient, suggesting an influence from both anthropogenic activity and sediment granulometry. SK was dominated by Escherichia-Shigella, Staphylococcus, and Bifidobacterium, associated with human and agricultural sources. SP showed higher richness, with enriched marine-associated genera such as Hoeflea, Xanthomarina, and Marinobacter, also linked to hydrocarbon degradation. Fungal diversity was less variable, but showed significant shifts along transects. SK communities were dominated by Kluyveromyces and Candida, while SP hosted a broader fungal assemblage, including Pichia, Rhodotorula, and Aureobasidium. The higher richness at SP suggests that calcium-rich sands, possibly due to their buffering capacity and greater moisture retention, offer more favorable conditions for microbial colonization. Full article

This study provides the first comprehensive characterization of benthic macrofaunal communities in the Loukkos estuary, highlighting their spatial and seasonal variability and the environmental factors shaping their structure. A total of 47 species were identified across 12 site–season combinations, dominated by molluscs (47%), polychaetes (23%), and crustaceans (21%). Species richness varied considerably along the estuarine gradient, ranging from fewer than five species in the upstream sector to up to 30 species downstream. Overall, higher diversity was observed in the downstream areas and during the dry season. Macrofaunal density also exhibited substantial variability, ranging from 95 ind.m⁻² to 14,852 ind.m⁻², with a mean density of 2535 ± 4058 ind.m⁻². Multivariate analyses identified four distinct benthic assemblages structured primarily by spatial factors (ANOSIM R = 0.86, p = 0.002), with negligible seasonal effect (R = −0.03, p = 0.6). Assemblages ranged from marine-influenced communities at the estuary mouth dominated by Cerastoderma edule, through rich and diverse seagrass-associated communities in the lower estuary dominated by Bittium reticulatum, and moderately enriched mid-estuary communities characterized by Scrobicularia plana and Hediste diversicolor, to species-poor upstream communities dominated by the tolerant species H. diversicolor. Canonical analysis showed that salinity and vegetation explain nearly 40% of the variation in benthic assemblages, highlighting the key role of Zostera seagrass beds as structuring habitats. Moreover, upstream anthropogenic pressures alter environmental conditions, reducing benthic diversity and favoring tolerant species. Full article

The growth and sustainability of today’s global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models mainly proposed in recent literature (2020–2025), including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in the maritime security domain. Full article

The gut microbiota plays an essential role in the host’s metabolism. Its composition and structure depend on biological and environmental factors. This work was designed to identify the composition and structure of the wild and captive red grouper (Epinephelus morio) microbiota and make predictions regarding its metabolic functions. Our hypothesis stated that wild and captive individuals would share the most abundant taxonomic groups, forming a core microbiota, and individuals in captivity might have exclusive taxonomic groups. Metagenomic DNA was extracted from the intestinal contents of wild and captive individuals. The 16S rRNA gene was amplified and sequenced using Illumina pair-end technology. QIIME2 pipeline was used for sequence analysis and alpha and beta diversity assessment. PICRUSt was used to infer metabolic functions. Twenty-nine phyla were identified; the most abundant were Pseudomonadota, Bacillota, Fusobacteriota, and Actinomycetota. The dominant genera were Photobacterium, Vibrio, Cetobacterium, and Escherichia-Shigella. The metabolic prediction analysis suggested that the Epinephelus morio gut microbiota is related to food digestion, the immune system, antioxidant enzymes, antibiotic resistance, and vitamin B12 transport. We concluded that the microbiota of E. morio established in captivity is sensitive to environmental changes such as water pollution, which can cause a decrease in diversity. Full article

The planktonic pteropod Limacina helicina is increasingly studied as a bioindicator of climate-driven changes in polar marine ecosystems. Although broadly distributed across the Arctic Basin and the North Pacific, its population structure and dispersal pathways remain poorly understood, especially in the Siberian Arctic. We analyzed mitochondrial COI sequences from populations sampled in the Barents, Kara, Laptev, East Siberian, and White Seas, as well as adjacent Pacific regions. Three major haplogroups (H1, H2, H3) were identified with distinct spatial patterns. H1 is widespread, occurring across the Pacific and most Arctic seas except the White Sea. H2 is confined to the western Arctic shelves (Barents–Kara–Laptev), and H3 is unique to the White Sea. We found a pronounced genetic discontinuity corresponding to hydrographic barriers, particularly the strong freshwater inflow from the Lena River, which restricts eastward dispersal of H2 from the Laptev to the East Siberian Sea. These patterns suggest postglacial expansions from geographically separated populations that survived the Last Glacial Maximum in isolated marine regions. The White Sea population is highly isolated and genetically distinct. Our results highlight how both glacial history and modern oceanography shape Arctic plankton diversity and define biogeographic boundaries in a rapidly changing climate. Full article

Marine-derived fungi have proven to be a rich source of structurally diverse terpenoids with significant pharmacological potential. This systematic review of 119 studies (2020–2024) identifies 512 novel terpenoids, accounting for 87% of the total discoveries to 2020, from five major classes (monoterpenes, sesquiterpenes, diterpenes, sesterterpenes, and triterpenes) isolated from 104 fungal strains across 33 genera. Sesquiterpenoids and diterpenoids constitute the predominant chemical classes, with Trichoderma, Aspergillus, Eutypella, and Penicillium being the most productive genera. These fungi were primarily sourced from distinct marine niches, including deep sea sediments, algal associations, mangrove ecosystems, and invertebrate symbioses. Notably, 57% of the 266 tested compounds exhibited diverse biological activities, encompassing anti-inflammatory, antibacterial, antimicroalgal, antifungal, cytotoxic effects, etc. The chemical diversity and biological activities of these marine fungal terpenoids underscore their value as promising lead compounds for pharmaceutical development. Full article