Search Results (230)

This study assesses the impact of sublethal levels of four marine toxins ciguatoxin (CTX-1), maitotoxin-2 (MTX-2), saxitoxin (STX) and brevetoxin-2 (BTX-2) on murine hepatic detoxification enzymes expressed in mouse liver. CTX-1, BTX-2, and STX altered hepatic detoxification responses, but their effects were generally more limited or temporally variable than those observed with MTX-2. CTX-1 produced time-dependent changes in cytochromes P450 (CYPs)-associated activities, BTX-2 induced early CYP1A2 and CYP3A11 responses followed by later suppression, and STX reduced CYP1A2 and CYP3A11 while increasing microsomal reductase activities. Of these toxins, MTX-2 exhibited the highest toxicity, notably decreasing key proteins such as CYPs, including CYP1A2 and CYP3A11. CYP enzymes are vital for metabolizing important endogenous and various xenobiotic substances, including therapeutic drugs. After MTX-2 exposure, both CYP1A2 and CYP3A11 levels dropped significantly at 12 h (p < 0.0001 for CYP3A11, p < 0.0001 for CYP1A2). Histopathological analysis revealed liver damage; however, albumin mRNA levels remained stable post-MTX-2 treatment, indicating that hepatotoxicity was not the sole cause of CYP3A11 reduction. Immunohistochemical analysis displayed uniform CYP3A11 distribution across liver regions after MTX-2 treatment. This suggests that MTX-2 exposure could augment the toxicity of drugs like Aldactone, Erythromycin, and Cyclosporine that utilize this metabolic pathway in humans. This is the first type of research performed of this nature, which could add to our understanding of marine toxin metabolism. These findings provide additional toxicological insights into the effects of four marine toxins on detoxification enzymes, with particular interest in MTX-2 toxicity, and potential implications for the treatment of fish poisoning, including ciguatera fish poisoning. Full article

Cyanobacterial and harmful algal blooms are common components of the waters in and around Southwest Florida and are sufficiently frequent to be of concern with respect to adverse effects on short- and long-term human and animal health. Currently, the contribution of airborne exposure to cyanobacterial toxins is not as advanced as for the other known human exposure routes such as consumption of contaminated water or fish. An airborne monitoring device named Airborne Detection for Algae Monitoring (ADAM) was developed to collect air samples in the proximity of algal and/or cyanobacterial blooms to examine the relationship between naturally occurring toxins in the air and those in proximal Floridian waters. Twenty-one air and water samplings were performed between July and December 2021 using a filter and impinger system for airborne components, along with a water sample from the same location. Samples were assessed for microcystins, anatoxin-a, cylindrospermopsin, saxitoxin and BMAA and isomers. Furthermore, as Karenia brevis is common in this area, brevetoxins were also assessed in air samples. Although very few large-scale cyanobacterial bloom events were observed at sampling locations, cyanobacterial and algal toxins were present in the majority of samples at low concentration. Data obtained from ADAM indicate that people may be chronically exposed to low concentrations of cyanobacterial toxins and that further assessment is required to help protect human health. Full article

The accurate determination of chlorophyll a (Chl a) is often limited by the instability of conventional fluorescence height algorithms in algal bloom waters characterized by diverse phytoplankton morphology and red-shifted reflectance peaks. In this study, we propose the Normalized Dynamic Fluorescence Height (NDFH) as a novel hyperspectral algorithm for assessing sun-induced chlorophyll fluorescence. Using in situ bio-optical data and multi-source satellite observations (Advanced Hyperspectral Imager (AHSI) onboard ZY-1E, Hyperspectral Imager for the Coastal Ocean (HICO) onboard ISS, and Ocean Color Instrument (OCI) onboard PACE), NDFH was evaluated and compared with existing algorithms, such as the normalized Fluorescence Line Height, Cyanobacterial Index (CI), and Maximum Algal Line Height (MALH). The results show that NDFH has robust exponential correlations with Chl a concentrations in inland waters and can reliably detect fluorescence peak band shifts in bloom-dominated environments. Case studies in Lake Taihu confirm that NDFH is more effective at delineating bloom extent and Chl a distributions than conventional methods, particularly in algae-dominated waters. These findings show the potential of NDFH for operational monitoring of eutrophication and harmful algal blooms across diverse aquatic environments. Full article

In recent years, there has been growing interest in applying vision–language models (VLMs) to quantitative remote sensing. This study evaluates whether three commercial VLMs (GPT-4o, GPT-5.5, and Claude Sonnet 4.6) can detect and classify the severity of harmful algal blooms (HABs) from Sentinel-2 satellite imagery of western Lake Erie and compares them against classical machine learning classifiers (Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost)) trained on both a three-band red, green, blue (RGB) composite representation of the imagery and a 10-band multi-spectral reflectance representation. Forty bloom events identified from the National Oceanic and Atmospheric Administration (NOAA) Harmful Algal Bloom Operational Forecast System (HAB-OFS) severity assessments were assembled into the evaluation dataset, spanning seven bloom seasons (2019–2025). For binary bloom detection, the VLMs did not match the classical RGB classifiers; their F1 scores (0.69–0.75) fell below the best RGB classifier (Random Forest, 0.76) and below a trivial always-present baseline (F1 = 0.77), and they carried false positive rates of 73–93% on bloom-absent images, against 27–40% for the RGB classifiers. The VLMs reached high recall by labeling most scenes as bloom-positive, which makes them operationally unreliable in this configuration. For severity classification, the VLMs assigned 60–70% of their predictions to the “moderate” category regardless of actual conditions and identified at most one of the two severe blooms, whereas the classical classifiers tracked the ground-truth distribution and delivered two to nearly three times the exact-match accuracy (0.44–0.59 vs. 0.20–0.225). The strongest method across all metrics was the multi-spectral SVM (F1 = 0.833, false positive rate 27%, accuracy 0.795). Switching the same SVM from RGB to multi-spectral features raised accuracy from 0.675 to 0.795, a 12-percentage-point gain that measures the spectral information carried by red-edge and shortwave infrared bands that are accessible through multi-spectral sensors but unavailable to standard VLM vision encoders. Feature-importance analysis showed that the multi-spectral classifiers ranked chlorophyll-specific indices, the Normalized Difference Chlorophyll Index (NDCI) and the Floating Algae Index (FAI), among their top predictors, the same signatures used in established operational algorithms, while the RGB classifiers relied on red-channel variability and green-dominant pixel fractions because RGB inputs cannot compute those indices. Two compounded limitations therefore constrain off-the-shelf VLMs for aquatic remote sensing: the limited spectral information available through standard RGB channels and a mismatch between the land-dominated training distributions of these models and aquatic optical conditions. Domain-specific classifiers operating on multi-spectral data remain the more suitable tools for continued development of HAB monitoring and water-quality retrieval. Full article

Background: Oil spills have dramatically increased, causing significant damage and pollution to marine ecosystems. The entry of petroleum hydrocarbons into the ocean may lead to the occurrence of harmful algal blooms (HABs). The amino acid changes in harmful algae after oil spills remain unclear. Methods: In order to study the effect of oil spills on the amino acid mechanism of typical causative species, the composition and relative abundance of amino acids in Heterosigma akashiwo were investigated under different water accommodated fractions (WAFs) of 180# fuel oil. Results: Random forest prediction of polycyclic aromatic hydrocarbon toxicity to microalgae identified pyrene, benzo[k]fluoranthene, and fluoranthene as significant contributors. A total of 16 species of amino acids were detected in Heterosigma akashiwo, among which alanine, proline, aspartic acid, cysteine, lysine, and histidine were the predominant ones. As the concentration of the WAF increased, alanine abundance decreased significantly, indicating that the WAF disrupted the metabolic balance of alanine, with the degree of interference being positively correlated with exposure concentration. With the increase in culture time, the abundance of cysteine increased at 1%, 3%, and 5% WAFs, whereas the cysteine increased and then decreased at 7% and 10% WAFs. The abundance of aspartic acid and lysine showed no obvious pattern with culture time under WAF stress. Significant increases in the abundance of proline and histidine were observed in the WAF treatments. Conclusions: This study investigated the impact of oil spill pressure on the amino acid content of harmful algae, providing a scientific basis for understanding the potential impact of oil spills on the occurrence of HABs. Full article

Zeolites are naturally abundant, low-cost aluminosilicate minerals commonly found in sedimentary rock. The surface chemistry of zeolite can be modified via cationic surfactant loading, termed a surfactant-modified zeolite (SMZ), that can be used as an antimicrobial technology in water treatment processes. This raises the possibility that SMZs could be utilised to treat blooms of harmful algae and cyanobacteria; however, there is a lack of understanding of the toxicity of SMZs to non-target microorganisms, including non-problematic algae and cyanobacteria. To address this knowledge gap, this research investigates whether hexadecyltrimethylammonium-bromide (HDTMA-Br) SMZs are toxic to the cyanobacterium Synechococcus elongatus and the microalgae Chlorella vulgaris, Nannochloropsis oculata and Duniallela salina. The cells were exposed to natural zeolite, HDTMA-Br surfactant and HDTMA-Br SMZ for 24 h and analysed 2- and 26 h post-exposure via flow cytometry and imaging pulse amplitude modulated fluorometry. There was an overall trend of reduced cell density in the SMZ and surfactant treatments. The SMZ treatment reduced the effective PSII quantum yield (Y(II)) but increased the quantum yield of regulated energy dissipation for C. vulgaris. When exposed to the surfactant treatment, no Y(II) signals were detected from any species. We conclude that SMZs are toxic to non-target microorganisms, with resilience dependent upon cell wall structure. Full article

Large, shallow lakes lacking rooted aquatic vegetation are susceptible to wind-induced wave action that results in increased shear stress on the lake bottom, sediment resuspension and poor water clarity. The relationship between meteorological, hydrographical and sediment characteristics, and sediment dynamics has implications for internal phosphorus cycling and bioavailability, the frequency and duration of harmful cyanobacterial blooms, lake level management and restoration potential. In this study, a multi-parameter water quality sonde was deployed at various sites at the bottom of Utah Lake to measure water quality variables. Sediment cores were collected at each of the deployment sites and analyzed for common physical and chemical properties. Several machine learning regression techniques, including polynomial, decision tree, artificial neural network, and support vector machine, were applied to predict turbidity, a measure of water clarity and surrogate for sediment dynamics, using the observed explanatory variables wind speed and direction, fetch, water depth, sediment properties, algae, and cyanobacteria. The decision tree estimators, random forest and histogram-based gradient boosting had the best model performance, explaining 86–89% of the variability in turbidity when including all the explanatory variables. The artificial neural network estimator multi-layer perceptron and the polynomial regression models also performed well (81%), whereas the support vector machine estimator exhibited poor performance. Chlorophyll and phycocyanin, components of turbidity, were amongst the most important variables to the decision tree and artificial neural network models. Wind speed and water depth were also of high importance, which conforms with mechanistic explanations of sediment mobility caused by wave action and shear stress. Carbonate content was consistently a good predictor due to the calcareous nature of Utah Lake, whereas the importance of the other sediment properties was dependent on the machine learning technique applied. This case study demonstrated the potential for machine learning models to predict water clarity and has promise for more general applications to other shallow lakes and serves as a useful tool for lake management and restoration. Full article

Aquatic environments, whether freshwater, brackish, or marine, are increasingly disrupted, in terms of frequency, extent, geographic distribution, and duration, by the massive, worldwide proliferation of harmful and/or nuisance algae, the so-called Harmful algal blooms (HABs), which are a global phenomenon that poses a major threat to human and animal health and ecosystems [...] Full article

Large-scale green tides dominated by Ulva species have recurred annually in the Southern Yellow Sea for nearly two decades, yet early detection remains challenging due to the patchy distribution of incipient floating macroalgae. This study investigated the spatiotemporal dynamics of Ulva micropropagules during the 2020 outbreak using a systematic cultivation assay. Seawater samples were collected from 23 stations across the Subei Shoal and adjacent waters in April, May, and July, and incubated under controlled laboratory conditions to enumerate Ulva germling densities. Results revealed that Ulva micropropagule abundance peaked in April, with high-density foci concentrated in the Subei Shoal region—particularly in aquaculture areas of Neopyropia J. Brodie & L.-E. Yang, 2020—confirming this zone as one of the important sources. Abundance declined progressively through May and July as macroalgae drifted northward under wind and current forcing. This method effectively identified putative source regions and reconstructed initial dispersal patterns prior to satellite-detectable macroalgal aggregation. These findings demonstrate that Ulva micropropagule monitoring provides a cost-effective, sensitive tool for early warning and Ulva source tracking, offering finer-scale propagule distribution data to inform precision management strategies for mitigating green tide impacts on coastal marine ecosystems. Future research should expand investigations into Ulva micropropagule dynamics to elucidate their mechanistic processes and ecological significance in green tide initiation and development. Full article

Marine ranching is an important strategy for restoring marine habitats and replenishing aquatic populations. However, the effects of marine ranching on phytoplankton dynamics remain unclear. In this context, this study takes the Bailong Pearl Bay National Marine Ranching Demonstration Zone as an example to evaluate the influence of marine ranching on the spatial and temporal variation in phytoplankton abundance and community structure. A total of 101 phytoplankton species, spanning 44 genera and 26 families, were documented in the Bailong Pearl Bay National Marine Ranching Demonstration Zone, with 19 of these species identified as harmful algal blooms (HABs) or potential HABs. In spring, phytoplankton abundance remained relatively uniform across sampling stations, with community structure characterized by varying combinations of co-dominant species. In summer, phytoplankton density within the demonstration zone was higher than in adjacent regions. In contrast, lower phytoplankton abundance was observed within the demonstration zone during autumn and winter, periods marked by phytoplankton blooms in surrounding areas (autumn: Chaetoceros lorenzianus, Rhizosolenia alata, and Skeletonema costatum; winter: Nitzschia pungens). Correlation analysis indicated that phytoplankton abundance was positively correlated with nitrate and negatively correlated with phosphate, suggesting nutrient availability as a key driver of phytoplankton dynamics. These findings provide baseline information on how phytoplankton communities vary spatially and seasonally in relation to a marine ranching zone and offer insights to support the management and rehabilitation of marine ecosystems. Full article

We evaluated the algicidal properties of Bacillus Ba3 fermentation broth combined with clay for harmful algae bloom (HAB) control through in situ enclosure experiments in Suao Bay, China. It was indicated by the results that the combination significantly reduced HAB abundance, turbidity and phosphorous in water without affecting zooplankton and small fish. The treatment achieved 99.8% (Phase 1) and 100% (Phase 2, with sediment) removal rates for harmful dinoflagellates, primarily Prorocentrum donghaiense and Karenia mikimotoi, while demonstrating high taxonomic selectivity, allowing beneficial diatom populations such as Chaetoceros spp. to remain resilient. This synergy is attributed to clay acting as a physical carrier that brings adsorbed algicidal metabolites into direct, prolonged contact with algal membranes. This method shows promise for prolonged dinoflagellate control and may offer an economical and environmentally sound approach to HABs. More research is needed to establish its action on a wider scale in marine environments. Full article

Harmful algal blooms have occurred more frequently in recent decades and threaten aquaculture, tourism and human health. As a promising control method, most studies on allelopathic mechanisms have focused on the physiological effects on harmful algae. This study employed a multiomics approach to investigate the allelopathic response of the dinoflagellate Scrippsiella acuminata to the allelochemical protocatechuic acid, a phenolic compound known for its inhibitory effects on algal growth. Using transcriptomic, proteomic, and metabolomic analyses, we identified significant changes in gene expression (5247 upregulated and 81 downregulated), protein expression (56 upregulated and 49 downregulated), and metabolite profiles (320 upregulated and 168 downregulated) in response to allelochemical stress. Transcriptomic data revealed an upregulation of genes associated with antioxidant systems and energy metabolism, suggesting a potential antioxidant response to protocatechuic acid exposure. Proteomic analysis highlighted the impact on photosynthesis, energy metabolism, and genetic information processing, with a particular emphasis on the modulation of lipid and carbohydrate metabolism to adapt to stress. Metabolomic profiling corroborated these findings, demonstrating shifts in lipid and amino acid metabolism indicative of an adaptive strategy for energy storage and maintenance of cellular homeostasis under allelochemical stress. Notably, alterations in photosynthesis-related proteins and metabolites indicated a direct effect of protocatechuic acid on the photosynthetic machinery, potentially impairing algal growth and energy production. In conclusion, our multiomics analysis provides a comprehensive view of the complex response of S. acuminata to allelochemical stress, revealing the intricate interplay among genetic, proteomic, and metabolic adjustments. These insights contribute to the understanding of allelopathic interactions and offer potential avenues for the development of novel strategies to manage harmful algal blooms. Full article

Taganrog Bay is part of the Azov Sea, which has significant environmental value. However, in recent years, anthropogenic activity and climate change have increasingly impacted this coastal system. These factors have led to increased sea salinity. These factors also contribute to abundant blooms of potentially toxic cyanobacteria. One additional method for preventing the abundant growth of cyanobacteria may be the introduction of green algae into the bay. The aim of this study was to conduct a computational experiment on the biological rehabilitation of Taganrog Bay using mathematical modeling methods. For this purpose, the authors developed and analyzed a mathematical model of phytoplankton populations. A software model was developed based on modern mathematical modeling methods. The input data for the software module included grid points for advective transport velocities, salinity, and temperature, as well as phytoplankton population and nutrient concentrations. The software module outputs three-dimensional distributions of green algae and cyanobacteria concentrations. A computational experiment on biological rehabilitation of the Taganrog Bay by introducing a suspension of green algae was conducted. Green algae and cyanobacteria concentrations were obtained over 15 and 30-day time intervals. The concentration and volume of introduced suspension were empirically determined to prevent harmful cyanobacteria growth without leading to eutrophication of the bay by green algae. Full article

Harmful Algal Blooms (HABs) in large river systems present significant challenges for water quality monitoring, with traditional in-situ sampling methods limited by spatial and temporal coverage. This study evaluates the effectiveness of machine learning techniques applied to Landsat spectral data for detecting and quantifying HABs in the Ohio River system, with particular focus on the unprecedented 2015 bloom event. Our methodology combines Google Earth Engine (GEE) for satellite data processing with an ensemble machine learning approach incorporating Support Vector Regression (SVR), Neural Networks (NN), and Extreme Gradient Boosting (XGB). Analysis of Landsat 7 and 8 data revealed that the 2015 HAB event had both broader spatial extent (636.5 river miles) and earlier onset (5–7 days) than detected through conventional monitoring. The ensemble model achieved a correlation coefficient of 0.85 with ground-truth measurements and demonstrated robust performance in detecting varying bloom intensities (R² = 0.82). Field validation using ORSANCO monitoring stations confirmed the model’s reliability (Nash-Sutcliffe Efficiency = 0.82). The integration of multispectral indices, particularly the Floating Algae Index (FAI) and Normalized Difference Chlorophyll Index (NDCI), enhanced detection accuracy by 23% compared to single-index approaches. The GEE-based framework enables near real-time processing and automated alert generation, making it suitable for operational deployment in water management systems. These findings demonstrate the potential for satellite-based HAB monitoring to complement existing ground-based systems and establish a foundation for improved early warning capabilities in large river systems through the integration of remote sensing and machine learning techniques. Full article

Harmful algal blooms, particularly those caused by cyanobacteria (blue-green algae) and green algae, pose an increasing risk to aquatic ecosystems and public health. This risk is intensified by climate change and nutrient pollution. This study presents a methodology for in situ monitoring and assessment of algal contamination in surface waters, combining UV/Vis and fluorescence spectroscopy with a fuzzy pattern classifier for consideration of uncertainties. The system incorporates detailed data pre-processing to minimise measurement uncertainty and uses full-spectrum feature extraction to enhance classification accuracy. To assess the methodology under both controlled and real-world conditions, a mobile submersible probe was tested alongside a laboratory setup. The results demonstrate a high degree of agreement between the two systems, showing particular sensitivity to biological signals, such as the presence of algae. The assessment method successfully identified cyanobacterial and green algal contamination, and its predictions aligned with external observations, such as official warnings and environmental changes. By explicitly accounting for measurement uncertainty and employing a comprehensive spectral analysis approach, the system offers robust and adaptable monitoring capabilities. These findings highlight the potential for scalable, field-deployable solutions for the early detection of harmful algal blooms. Full article