Search Results (613)

Alternative substrates to traditional Camellia sinensis tea are increasingly investigated to diversify kombucha and enhance its functional properties. This review synthesizes evidence (2020–2025) on how non-tea substrates influence microbial ecology, metabolite composition, and bioactivity of kombucha. A semi-systematic search of PubMed, Scopus, Web of Science, and publisher platforms identified studies on fruit, vegetable, herbal, algal, cereal, dairy, and food-industry by-product substrates reporting compositional or functional outcomes. Extracted data included substrate characteristics, fermentation conditions, SCOBY features, analytical methods, and reported antioxidant, anti-inflammatory, metabolic, probiotic, and dermatological effects. Fermentation often leads to an increase in total phenolic content and antioxidant capacity. These effects are highly dependent on fermentation conditions, particularly duration and substrate composition. In some cases, prolonged fermentation may result in phenolic degradation or transformation, leading to reduced levels of certain compounds. Fruit- and hibiscus-based systems enhanced anthocyanin-driven antioxidant and anti-inflammatory activity. Vegetable and cereal substrates supplied phenolic acids and β-glucans associated with metabolic regulation and gut health, whereas by-products and algal fermentations supported waste valorization and enrichment in chlorogenic acids, pigments, fibers, and peptides. Despite promising functionality, substantial inter-study variability and limited in vivo validation and the lack of standardized fermentation protocols constrain translational application. In addition, the inherent variability in SCOBY microbial composition represents a major source of inconsistency, as differences in microbial communities can significantly influence fermentation dynamics, metabolite profiles, and functional outcomes. Full article

Plant-based biomaterials are increasingly recognized as bio-instructive platforms capable of actively modulating immune responses rather than functioning solely as passive structural supports. In this context, the term plant-based refers to photosynthetic biomass-derived platforms, including both terrestrial plants and marine macroalgae, reflecting their shared richness in polysaccharides and secondary metabolites relevant to immune engineering and regenerative medicine. This review critically synthesizes current evidence on plant-derived polysaccharides and phytochemicals, including algal sulfated polysaccharides (fucoidan, alginate, carrageenan, and ulvan), terrestrial plant polysaccharides (e.g., Lycium barbarum and Aloe vera derivatives), polyphenols, and other secondary metabolites such as terpenoids and alkaloids, highlighting their roles as immunomodulators in biomedical contexts. Key mechanisms include macrophage polarization along an M1–M2 continuum, pattern recognition receptor engagement, redox and metabolic regulation, and crosstalk between innate and adaptive immunity, with emphasis on context-dependent signaling and structural heterogeneity. Material design parameters, including molecular weight and chemical functionalization, are critical determinants of immune responses. Advanced delivery systems, such as hydrogels, nanocomposites, phytosomes, and plant-derived extracellular vesicles (EVs), enable improved stability and spatiotemporal control. Applications in wound and musculoskeletal regeneration are discussed alongside translational challenges, including variability, reproducibility, regulatory issues, and the need for standardized characterization and immune validation. Full article

Abiotic stresses severely constrain crop productivity by disrupting cellular redox homeostasis and hormone signaling. Although individual stresses differ in origin, plant responses converge on a conserved regulatory system centered on reactive oxygen species (ROS) and phytohormone crosstalk. Controlled ROS production in chloroplasts, mitochondria and the apoplast functions as a signaling mechanism that interacts dynamically with abscisic acid, auxin, ethylene, jasmonate and cytokinin pathways through shared regulatory nodes, including nicotinamide adenine dinucleotide phosphate (NADPH) oxidases and redox-sensitive transcriptional cascades. Endogenous metabolites, including phenolics, terpenoids, carotenoids, alkaloids, polyamines, glutathione and signaling peptides, are embedded within this network and modulate its amplitude and sensitivity. In parallel, non-microbial biostimulants derived from seaweeds, higher plants, protein hydrolysates and humic substances have been widely reported to enhance crop performance under abiotic stress. However, mechanistic integration between biostimulant research and plant stress signaling remains limited. In this review, we propose that terrestrial plant- and algal-derived biostimulants act not as external substitutes for hormones or antioxidants but as modulators of endogenous ROS–hormone signaling hubs. We first synthesize the current understanding of redox–hormone integration under abiotic stress, then examine endogenous metabolites as intrinsic regulators of this network, followed by an analysis of biostimulants in relation to shared regulatory nodes. By positioning biostimulant action within the established redox–hormone network, we provide a mechanistic framework that links stress biology with agronomic application and supports rational strategies to enhance crop resilience. Full article

Environmental monitoring systems require robust uncertainty quantification for effective decision-making in complex ecological processes. Harmful algal blooms represent a critical challenge where prediction uncertainty directly impacts resource allocation and response timing, yet current remote sensing-based prediction systems provide only deterministic classifications without confidence measures. This gap between algorithmic predictions and actionable risk assessment limits operational utility for stakeholders managing water quality under varying risk tolerances. This study developed a transferable probabilistic forecasting framework integrating Sentinel-2 multispectral imagery with quantile regression and ensemble machine learning to generate continuous confidence indicators for cyanobacteria density prediction, demonstrated through its application to Lake Okeechobee, Florida. The methodology combines spectral indices extracted from Sentinel-2 data with XGBoost for quantile regression at 0.05, 0.50, and 0.95 probability levels, and LightGBM for multi-horizon temporal forecasting. Sentinel-2’s 13 spectral bands spanning visible to shortwave infrared wavelengths, combined with its 5-day revisit frequency provide a spectrally rich and temporally dense input space that is well-suited to gradient boosting methods such as XGBoost, which can exploit complex nonlinear interactions among spectral features to distinguish cyanobacterial signatures from background water constituents. LightGBM achieved mean absolute percentage errors of 2.9% for 10-day forecasts and 5.7% for 20-day forecasts, outperforming conventional regression models. The framework generates 90% prediction intervals that enable reliable risk classifications for operational bloom management. This approach bridges the gap between satellite-based algal bloom detection and actionable decision-making by quantifying predictive uncertainty, representing a shift from binary classifications to probability-based environmental monitoring systems that accommodate varying stakeholder risk tolerances in water quality management applications. Full article

Biofouling has a detrimental effect on marine infrastructure and poses a severe challenge to the global marine industry. Therefore, developing efficient and environmentally friendly anti-fouling coatings to protect those facilities has become extremely necessary nowadays. To address marine biofouling, a series of photocurable degradable waterborne boron-containing polyurethane acrylate (WPU-PTPBx) anti-fouling coatings were prepared by grafting pyridine-triphenylborane (PTPB) onto polyurethane side chains and UV curing. FTIR and ¹H NMR confirmed the successful grafting of PTPB. The WPU-PTPBx aqueous dispersions had a particle size of 30~75 nm with excellent thermal storage stability. DSC and XRD characterizations revealed the amorphous structure of the coatings, which favored biodegradation. All coatings exhibited adhesion strength over 2 MPa, meeting marine application requirements. Antibacterial and anti-algal tests showed that PTPB content positively correlated with anti-fouling performance: the coating achieved a 99.66% inhibition rate against Escherichia coli and reduced the adhesion density of Nitzschia closterium to only 36.9 cells/mm². With favorable degradability and outstanding anti-fouling performance, WPU-PTPBx coatings are promising green anti-fouling materials for marine applications. Full article

The reliability of power transmission and distribution depends on the proper functioning of power transformers, which use conventional mineral oil as an insulating fluid. The lower fire class and biodegradability of mineral oil have led to a shift towards second-generation oils from vegetable and plant crops. Ester fluids provide a better performance in combination with solid pressboard/paper insulation, increasing the lifetime of power transformers compared to those using mineral oil. Considering the need for sustainability in the near future, second-generation oils are no longer feasible, and hence, third-generation oils derived from microalgae species are suitable alternative fuels for the energy sector. The fatty acid methyl ester (FAME) content of algae is similar to that of biodiesel, making it a suitable fluid for power transformers. A detailed overview of third-generation feedstock (algae) for power transformer applications is provided, focusing on the extraction of algal oil, in conjunction with safety precautions and its fatty acid content, and a comparison with conventional vegetable and plant-based oils is presented. Various properties of algal oil (fatty acid composition, kinematic viscosity, oxidation stability, breakdown voltage, etc.) are analyzed to assess its suitability as a transformer fluid. This review article comprehensively analyzes the current research landscape surrounding the use of algal oil as an insulating fluid in transformers. It critically evaluates both the potential advantages and the unique challenges associated with this alternative to conventional mineral oil and second-generation vegetable and plant-based oils. Full article

The monitoring of harmful algal blooms (HABs) constitutes a vital component of marine environmental protection and the sustainable development of the marine economy. However, the highly dynamic nature of these small targets, compounded by the complex water color interference prevalent in the coastal waters where HABs frequently occur, has resulted in traditional remote sensing monitoring methods, particularly those relying on fixed spectral index thresholds and pixel-wise binarization, suffering from imprecise identification in turbid coastal waters where suspended sediments, cloud cover, and sun glint create spectral confusion. These methods also exhibit low automation due to manual threshold adjustment requirements and poor transferability across different spatiotemporal conditions. Consequently, these methods struggle to meet practical application requirements. This study establishes a U-net model-based remote sensing identification framework for red tides using HY-1D CZI imagery (50 m resolution, 1–3 day revisit), targeted negative sample strategies, and event-level accuracy validation methods to achieve efficient marine red tide detection. Targeted negative sample selection involves purposefully selecting spectrally ambiguous regions as negative samples, aiming to enhance recognition accuracy and sample selection efficiency. The combination of targeted sampling with deep learning enables portability to new spatiotemporal contexts by learning invariant spectral–spatial features rather than relying on scene-specific thresholds. Experimental results demonstrate that the targeted negative sample strategy reduces event-level model false negatives by 27%, false positives by 36%, and increases the F1 score by 0.3217. Using an identical sample size, the targeted sample selection strategy yields an F1 score 0.0479 higher than random sampling. To achieve equivalent recognition accuracy, an increased number of random samples would be required. Comparative experiments reveal that the proposed method enhances sample selection efficiency by 87.5%. Transferability is demonstrated through successful identification of red tide patches in Wenzhou waters on 13 April 2022, without model retraining. This demonstrates that red tide remote sensing recognition based on targeted sample selection enables efficient, precise, and automated identification without human intervention, providing a reliable technical solution for operational marine red tide monitoring. Full article

Nitrogen (N) and phosphorus (P) are essential macronutrients for plant growth and major pollutants driving aquatic eutrophication. Microalgae represent a sustainable biological platform for nutrient recovery and circular utilization from wastewater; however, the molecular mechanisms governing efficient urea assimilation and its coordination with phosphorus uptake remain inadequately characterized. This study investigated how overexpression of the high-affinity urea transporter gene DUR3 enhances nutrient scavenging capacity in the model green alga Chlamydomonas reinhardtii. The DUR3-overexpressing line exhibited concentration-dependent growth responses to urea, showing significant promotion at low-to-moderate levels but inhibition at high urea concentration or under pure-urea conditions, where DUR3-overexpressing (DUR3-OE) was more severely inhibited than the wild-type (WT). Notably, the DUR3-OE consistently increased chlorophyll content and photosynthetic efficiency (Fv/Fm) under ammonium, urea, and mixed-N regimes. Under low-urea conditions, the total P content of the DUR3-OE was 8.8% higher and total N content was 4.3% higher than in WT (p < 0.05). Except in pure-urea medium, the engineered strains exhibited significantly increased total P accumulation and superior P recovery efficiency from the culture medium. Transcriptomic analysis revealed that DUR3 overexpression reprograms a coordinated regulatory network associated with N/P metabolism, photosynthesis, and carbon transport pathways. RT-qPCR validation confirmed significant upregulation of PMA2 (plasma membrane H⁺-ATPase), phosphate transporters (PTB3, PTB7), the inorganic carbon transporter HLA3, and photosynthesis-related genes, which was associated with improved nutrient assimilation and photosynthetic performance. These findings establish DUR3 as a key genetic target for engineering microalgae with optimized N-P co-uptake capacity, providing a robust molecular framework for developing high-efficiency algal strains for wastewater bioremediation and nutrient circular economy applications. Full article

Long-read sequencing (LRS) has transformed life science research by introducing third-generation sequencing (TGS) platforms applicable across various research fields, including environmental sciences. In the past decade, LRS platforms have been utilized to extensively study algal systems by improving genomic approaches such as metabarcoding, chromosome-level genome and pangenome assemblies, as well as providing new insights into algae-associated microbiomes and host–symbiont interactions. This review aims to discuss recent advancements in LRS in algal research. To achieve this aim, a systematic review was conducted according to the PRISMA 2020 guidelines and across three electronic databases (Web of Science, Scopus, and Google Scholar), with additional citation searching for relevant studies in four key algal research areas: metabarcoding, genomics, pangenomics, and host–symbionts interactions. Following the inclusion and exclusion criteria, only 51 studies were selected for this review. Throughout the review, we summarize the challenges of short-read sequencing (SRS) and discuss how LRS platforms address these challenges in algal studies. Furthermore, we discuss the future of LRS and explore how artificial intelligence (AI) can advance research on algal biology and ecology. Full article

Microalgae-based biostimulants may help reduce inputs in protected ornamental production, yet evidence across multiple growing seasons remains limited. We conducted a three-year polytunnel trial with six Pelargonium cultivars and applied two strains from the Mosonmagyaróvár Algal Culture Collection (MACC-612, Nostoc piscinale; MACC-922, Chlorella vulgaris). Using a factorial general linear model, we detected significant treatment effects on total dry mass, root mass, plant height, and root-collar diameter (all p < 0.001). Relative to the control, dry mass increased by 19.2% with MACC-612 and 33.1% with MACC-922, while root mass increased by >22% under both treatments. Root-collar diameter was strongly associated with overall plant size (R² = 0.89). Treatment × year interactions were not significant (p > 0.05), providing no statistical evidence of season-specific treatment effects within the three-year trial. Cultivars differed mainly in response magnitude rather than direction. Overall, microalgae applications improved biomass accumulation and root-linked structural traits associated with plant vigor under this production system. Full article

Coastal and marine systems are governed by fragile water-quality dynamics, where disturbances can trigger harmful algal blooms with significant ecological and societal consequences. These pressures have intensified interest in forecasting systems that can anticipate bloom development and support environmental management. This study presents a systematic review of simulation-based and predictive environmental modeling approaches used for marine forecasting of water quality and harmful algal bloom phenomena. Following PRISMA guidelines, 11,185 records were identified, 127 articles were screened in full text for eligibility, and 40 peer-reviewed studies published between 2015 and 2025 were included and synthesized using a structured extraction framework capturing modeling paradigms, forecast targets, data inputs, spatial and temporal scope, validation practices, operational context, and reported limitations. The reviewed literature indicates the dominance of predictive and hybrid modeling approaches, with forecasting efforts primarily focused on coastal systems and short-term applications. Harmful algal blooms and chlorophyll-a emerge as dominant forecast targets, commonly supported by satellite observations, in situ measurements, and environmental forcing variables. Despite substantial methodological advances, persistent challenges related to data availability and quality, validation rigor, system integration, and operational deployment remain evident across modeling paradigms. Overall, the findings suggest that while marine forecasting models have become increasingly sophisticated, their translation into reliable and operational systems remains uneven, highlighting the need for closer alignment. Full article

Hydroponic systems are gaining prominence in sustainable agriculture, yet their nutrient-rich effluents remain an underexplored source of microbial biodiversity with potential biotechnological interest. In this study, shotgun metagenomic sequencing was employed to profile, with a high taxonomic resolution, the photosynthetic microbial community in hydroponic effluent before and after a natural algal bloom, revealing pronounced shifts in microbial composition. Notably, relative abundance increased sixfold for Chlamydomonas reinhardtii and tenfold for Bigelowiella natans. Four dominant microalgal strains (PR1–PR4) were subsequently isolated and characterized through integrative morphological and molecular taxonomy, with phylogenetic analyses based on four genetic markers (18S rRNA, ITS, rbcL and tufA) confirming that each isolate represents a distinct lineage within Chlorophyceae families, including Chlorella sp., Chlamydomonas sp., and Scenedesmus sp. Growth kinetics under three temperature regimes, typical of Greek environmental conditions from spring to autumn (15 °C, 23 °C, 32 °C), demonstrated broad ecological plasticity and rapid biomass production, highlighting strains with strong adaptive resilience. Biochemical profiling of the isolates revealed significant inter-strain differences in primary and secondary metabolite content, including proteins (up to 43% DW), lipids (up to 31% DW), carbohydrates (up to 44% DW), photosynthetic pigments, phenolics, flavonoids, and antioxidant activity. The observed metabolic diversity of autochthonous microalgal strains from hydroponic environments, combined with their high growth rates, underscores their potential for applications in bioremediation, bioenergy, and the development of value-added products within a circular bioeconomy framework. Full article

Tertiary amines such as N,N-dimethyl-cyclohexylamine (DMCHA) are recently explored as candidate solvents for the extraction and separation of lipids from algal biomass. DMCHA exhibits the interesting property of polarity switching which is based on the interaction of DMCHA with CO₂, termed CO₂ switching. Although this approach exhibits certain advantages, various issues have to be improved to address for example the duration required for process optimization, the energy demand, or the low solvent recovery. The aim of this work is the examination of amine recovery from an oil extract, utilizing strong electrolytes (SE) such as HCl for protonation and NaOH for deprotonation of amine, instead of conventional CO₂ switching. It was found that the acid based hydrophobic-to-hydrophilic switching and the alkali based hydrophilic-to-hydrophobic back-switching carried out in the order of few minutes, a considerably shorter time compared to few hours required by gas switching, resulting in addition to higher amine recovery. In addition, the combined CO₂ switching with SE-back switching using NaOH proved to be a promising approach for large-scale applications, exhibiting several advantages related to technical, economic, environmental and safety issues. Full article

Against the backdrop of the continuous advancement of high-quality development in road infrastructure and the growing demand for waste asphalt recycling, the application limitations of traditional petroleum-based asphalt rejuvenators have become increasingly prominent due to their high resource dependence, poor compatibility with aged asphalt, and high volatility. By contrast, bio-oil, characterized by wide feedstock availability, outstanding renewability, and the inherent potential to modulate the colloidal structure and properties of aged asphalt, has gradually emerged as a critical research direction in the field of asphalt rejuvenator development. This paper provides a comprehensive review on the research, development and engineering application of bio-based rejuvenators. Firstly, the main feedstock systems, including vegetable oils, lignin derivatives and algal oils, are introduced, and the core preparation technologies (e.g., pyrolysis and alkali-catalyzed transesterification) are discussed, along with the impacts of their key process parameters on the chemical composition and storage stability of the end products. Subsequently, the performance of various bio-based rejuvenators in optimizing the rheological properties, high- and low-temperature performance, as well as fatigue and cracking resistance of aged asphalt is summarized, and the underlying rejuvenation mechanisms are generalized. Finally, the prevailing technical bottlenecks, such as inconsistent quality of as-prepared products and insufficient understanding of the long-term aging mechanism, are analyzed. Future research directions including oriented molecular modification, interfacial regulation, and full life cycle assessment (LCA) are proposed, to provide a systematic reference for their large-scale engineering application. Full article

Biopolymers from renewable sources are increasingly explored to reduce the carbon footprint of materials and mitigate plastic pollution. This review synthesizes the last five years of progress across the biopolymer value chain, comparing plant, microbial/fermentation, fungal, and marine/algal resources and critically assessing greener extraction and fractionation routes (ultrasound and microwave intensification, subcritical water, supercritical CO₂ with co-solvents, ionic liquids, deep eutectic solvents including natural deep eutectic solvents, and enzymatic or bio-mediated processes). We emphasize yield-selectivity trade-offs, scalability, energy demand, and solvent recovery. Downstream, we summarize purification and performance tuning via crosslinking, derivatization, blending/plasticization, and nanocomposites, and we map advanced characterization to targeted functional properties to bridge processing choices with end-use performance. Applications are organized across food and agriculture, biomedical and pharmaceutical technologies, packaging, and cosmetics, with cross-cutting attention to safety and regulatory compliance, quality-by-design, techno-economics, and life-cycle assessment. Key bottlenecks are feedstock variability, viscosity and recyclability limitations of designer solvents, and persistent gaps in barrier and thermal properties versus petrochemical benchmarks, compounded by uneven composting and recycling infrastructure. Promising directions include low-viscosity or switchable solvents, data- and artificial intelligence (AI)-guided process optimization, engineered biopolymers, and circular end-of-life strategies that align material design with realistic recovery routes. Full article