Search Results (86,985)

Rice sheath rot has progressively developed into a growing threat to global rice production, particularly in intensively managed systems conducive to disease development. Therefore, accurate identification of the causal pathogen and the development of sustainable management strategies represent urgent scientific requirements. In this study, we isolated the causal organism of rice sheath rot from infected rice tissues and identified it as Fusarium verticillioides based on multi-locus sequence analysis. Eight endophytic bacterial strains were recovered from healthy rice root systems. Among the isolates, Bacillus velezensis isolate 7-A exhibited the strongest antifungal activity against F. verticillioides. This isolate demonstrated broad-spectrum antifungal activity, with inhibition rates ranging from 54.8% to 71.8%. Phylogenetic analysis based on 16S rRNA and gyrB gene sequences identified it as B. velezensis. Further characterization revealed that B. velezensis 7-A is capable of secreting proteases and synthesizing siderophores. The filtered liquid from sterile fermentation markedly inhibited the growth of mycelium in F. verticillioides and induced marked morphological abnormalities. Liquid LC-MS analysis identified multiple antifungal active substances, including camphor, ginkgolides B, salicin, cinnamic acid, hydroxygenkwanin, stearamide, β-carotene, and others. A pot experiment demonstrated that the fermentation broth of B. velezensis 7-A effectively suppressed the occurrence of rice sheath rot, achieving a relative control efficacy of 61.3%, which is comparable to that of a 10% carbendazim water-dispersible granule (WDG). Additionally, isolate 7-A enhances plant disease resistance by activating the activities of key defense enzymes. These findings provide preliminary insights into its potential application in integrated and sustainable disease management programs. Full article

The partial substitution of chemical fertilizer with organic fertilizer has been regarded as an effective strategy for enhancing crop yield and soil quality. Nevertheless, its effects on soil properties and microbes remain contentious. In this study, we examined the effects of four different fertilization strategies (including without fertilizer (CK), 100% chemical fertilizer (NPK), 30% organic fertilizer + 70% chemical fertilizer (LOM) and 60% organic fertilizer + 40% chemical fertilizer (HOM)) on soil nutrients and microbial communities through metagenomic sequencing in a Camellia oleifera field experiment. Compared to CK and NPK, HOM significantly increased SOC, TN, TP, AK and AN contents. The substitution of organic fertilizer notably increased Camellia oleifera yield, with the highest increase of 93.35% observed in HOM relative to NPK. Soil bacterial and fungal communities responded inconsistently to fertilization patterns. Bacteria predominated as the main soil microorganisms, and higher rates of organic fertilizer substitution facilitated a shift from bacterial to fungal communities. Organic fertilizer substitution significantly increased soil bacteria diversity and fungal richness, particularly in the HOM. Soil bacterial community structure was more sensitive to fertilization regimes than soil fungi. High rates of organic fertilizer substitution substantially suppressed oligotrophic and increased copiotrophic bacterial communities. Mucoromycota emerged as the dominant fungal group, with a considerable increment in HOM soils. SOC and TN were the main factors affecting Camellia oleifera yield and shaping soil bacteria and fungal diversity and composition. This study provided crucial insights into the ecological implications of organic fertilizer application and the potential of managing soil microorganisms for sustainable Camellia oleifera productivity. Full article

Hamatocaulis vernicosus is a pleurocarpous moss of conservation concern, listed in Annex II of the EU Habitats Directive due to its significant and ongoing decline across Europe. H. vernicosus is also listed as ‘Vulnerable’ on the Red List of Romanian Bryophytes. Despite its protected status, the species remains under-recorded in Romania, where many potentially suitable habitats have yet to be surveyed. The ecosystems, classified as Transition mire and quaking bog (NATURA 2000 code: 7140), are wet peatlands with oligo- to mesotrophic conditions and a pH of 5.0–7.5. H. vernicosus is recorded in 58 Romanian locations (10 confirmed by us, 5 new), spanning the Continental and Alpine bioregions. Models showed good performance (AUC 0.79–0.83; TSS 0.54–0.59), with distribution mainly shaped by mean annual temperature and temperature range, and secondarily by precipitation. The species favors cold, stable climates with high seasonal rainfall. Even though the number of localities reported for this species has increased in recent years, this does not indicate an improvement in its conservation status, but rather is an effect of recent recording efforts. To support targeted conservation planning, an ensemble species distribution model was developed in order to predict the suitable habitats of H. vernicosus across Romania. Both climate models project major range losses for the varnished hook-moss: ~30% by 2050 and ~40–60% by 2100, depending on the scenario. Losses are gradual under SSP245 but more abrupt under SSP585, with increased fragmentation, especially between the Eastern and Southern Carpathians. By integrating field observations with predictive climate change modeling, our study brings critical insights applicable to the conservation of H. vernicosus and the unique peatland ecosystems it relies on. Full article

Accurate estimation of aboveground biomass (AGB) is essential for monitoring forage availability and guiding sustainable management in high-altitude pastures, where grazing sustains livelihoods but also drives ecological degradation. Although remote sensing has advanced biomass modeling in rangelands, applications in Andean–Amazonian ecosystems remain limited, particularly using UAV-based structural and spectral data. This study evaluated the potential of UAV LiDAR and multispectral imagery to estimate fresh and dry AGB in ryegrass (Lolium multiflorum Lam.) pastures of Amazonas, Peru. Field data were collected from subplots within 13 plots across two sites (Atuen and Molinopampa) and modeled using Random Forest (RF), Support Vector Machines, and Elastic Net. AGB maps were generated at 0.2 m and 1 m resolutions. Results revealed clear site- and month-specific contrasts, with Atuen yielding higher AGB than Molinopampa, linked to differences in climate, topography, and grazing intensity. RF achieved the best accuracy, with chlorophyll-sensitive indices dominating fresh biomass estimation, while LiDAR-derived height metrics contributed more to dry biomass prediction. Predicted maps captured grazing-induced heterogeneity at fine scales, while aggregated products retained broader gradients. Overall, this study shows the feasibility of UAV-based multi-sensor integration for biomass monitoring and supports adaptive grazing strategies for sustainable management in Andean–Amazonian ecosystems. Full article

Dy@C${}_{82}$ is one of the metallofullerenes studied as dopants for improvements of stability and performance of solar cells. Calculations should help in formulating rules for selections of fullerene endohedrals for such new applications in photovoltaics. Structure, energetics, and relative equilibrium populations of two potential-energy-lowest IPR (isolated pentagon rule) isomers of Dy@C${}_{82}$ under high synthetic temperatures are calculated using the Gibbs energy based on molecular characteristics at the B3LYP/6-31G${}^{*}$∼SDD level. Dy@$C_{2v}\left(9\right)$-C${}_{82}$ and Dy@$C_s\left(6\right)$-C${}_{82}$ are calculated as 58 and 42%, respectively, of their equilibrium mixture at a synthetic temperature of 1000 K, in agreement with observations. The Dy@$C_{2v}\left(9\right)$-C${}_{82}$ species is found as lower in the potential energy by 1.77 kcal/mol compared to the Dy@$C_s\left(6\right)$-C${}_{82}$ isomer. Full article

The Special Issue “Unlocking the Potential of Agri-Food Waste for Innovative Applications and Bio-Based Materials” brings together recent advances and emerging strategies for the valorization of agri-food residues. This Editorial provides an overview of the contributions included in the Special Issue, highlighting innovative approaches that convert waste streams into valuable bio-based materials, chemicals, and products. The collected works demonstrate how hydrodynamic, chemical, biological, and catalytic processes can be integrated to achieve sustainable waste management and circular resource recovery. By summarizing the main findings and perspectives, this Editorial emphasizes the growing relevance of agri-food waste valorization within the framework of the circular bioeconomy and encourages further interdisciplinary collaboration to accelerate the transition toward sustainable production systems. Full article

The application of Artificial Intelligence (AI) in river basin pollution control shows great potential to improve governance efficiency through real-time monitoring, pollution prediction, and intelligent decision-making. However, its rapid development also brings regulatory challenges, including data privacy, algorithmic bias, responsibility definition, and cross-regional coordination. Based on the SETO loop framework (Scoping, Existing Regulation Assessment, Tool Selection, and Organizational Design), this paper systematically analyzes the regulatory needs and pathways for AI in watershed water pollution control through typical case studies from countries such as China and the United States. The study first defines the regulatory scope, focusing on protecting the ecological environment, public health, and data security. It then assesses the shortcomings of existing environmental regulations in governing AI, such as their inability to adapt to dynamic pollution sources. Subsequently, it explores suitable regulatory tools, including information disclosure requirements, algorithmic transparency standards, and hybrid regulatory models. Finally, it proposes a multi-tiered organizational scheme that integrates international norms, national legislation, and local practices to achieve flexible and effective regulation. This study demonstrates that the SETO loop provides a viable framework for balancing technological innovation with risk prevention and control. It offers a scientific basis for policymakers and calls for establishing a dynamic, layered regulatory system to address the complex challenges of AI in environmental governance. Full article

Psoriasis and allergic contact dermatitis (ACD) are the most common chronic inflammatory diseases, which are accompanied by epithelial alterations and a T cell-mediated immunopathology. In this study, we investigated the anti-ACD and anti-psoriasis effects of sea anemone Heteractis magnifica peptide HCRG21, a blocker of the TRPV1 channel, in 2,4-dinitrofluorobenzene (DNFB)- and imiquimod (IMQ)-induced mouse models, respectively. We found that topical application of 0.005–0.1% HCRG21 gels normalized hematological and immunological blood parameters in mice, significantly reduced the severity of ACD- and psoriasiform-like skin lesions, and increased the rate of tissue repair. The use of 0.005 and 0.05% HCRG21 gels decreased the production of IL-23-A and macrophage-derived chemokine (MDC) proteins in blood plasma, reduced the expression of Tnf, Il1β, Il6, Il23a, and Il17a genes, but increased the levels of the Il10 gene in scabs and/or blood of IMQ-treated mice. On the other hand, topical application of 0.05 and 0.1% HCRG21 reduced the expression of Il6 and Il23a in the DNFB-treated mice’s blood and it had no significant effects on TNF-α and IL-1β production. Thus, HCRG21 has the potential to be a treatment for psoriasis and dermatitis due to its potent anti-inflammatory properties. This effect is achieved by reducing pro-inflammatory cytokines associated with TRPV1 and normalizing immune cell levels in the bloodstream. This, in turn, leads to a decrease in clinical symptoms and an improvement in skin healing. Full article

Skin scarring, including hypertrophic scars and keloids, often results from dysregulated collagen deposition during wound healing. Tropocollagen (TC), the soluble triple-helical precursor of collagen fibers, serves as the fundamental structural unit of the extracellular matrix (ECM) and plays a pivotal role in tissue repair. This review summarizes current knowledge on collagen and TC in wound healing, scar management, and regenerative dermatology. TC self-assembles into fibrils, providing structural support, while interacting with fibroblasts and growth factors such as transforming growth factor beta (TGF-β) and vascular endothelial growth factor (VEGF) to regulate ECM remodeling, angiogenesis, and tissue regeneration. Various collagen preparations, including hydrolyzed collagen, gelatin, and native fibrillar forms, differ in molecular structure, bioavailability, and therapeutic applications. Emerging strategies, including collagen- and TC-based hydrogels, nanomaterial composites, and smart wound dressings, enhance stability, targeted delivery, and clinical efficacy. Despite promising preclinical and early clinical data, standardized preparations and robust randomized trials are needed to validate TC’s therapeutic potential and optimize its application in scar prevention and wound repair. Full article

Heavy metal (HM) contamination in agricultural soils poses a significant threat to soil health and plant productivity. This study investigates the impact of cadmium (Cd) and zinc (Zn) stress on tomato plants (Solanum lycopersicum) and explores the mitigation potential of microbial biostimulants (MBs), including arbuscular mycorrhizal fungi (AMF) and Pseudomonas fluorescens So_08 (PGPR), over a 52-day period using multi-omics approaches. Root exudate profiling revealed distinct metabolic changes under HM stress, which compromised soil–plant interactions. Cd stress reduced the secretion of phenylpropanoids (sum LogFC: −45.18), lipids (sum LogFC: −27.67), and isoprenoids (sum LogFC: −11−67), key metabolites in antioxidative defense, while also suppressing rhizosphere fungal populations. Conversely, Zn stress enhanced lipid exudation (such as sphingolipids and sterols, as sum LogFC of 8.72 and 9.99, respectively) to maintain membrane integrity and reshaped rhizobacterial communities. The MBs application mitigated HM-induced stress by enhancing specialized metabolite syntheses, including cinnamic acids, terpenoids, and flavonoids, which promoted crop resilience. MBs also reshaped microbial diversity, fostering beneficial species like Portibacter spp., Alkalitalea saponilacus under Cd stress, and stimulating rhizobacteria like Aggregatilinea spp. under Zn stress. Specifically, under Cd stress, bacterial diversity remained relatively stable, suggesting their resilience to Cd. However, fungal communities exhibited greater sensitivity, with a decline in diversity in Cd-treated soils and partial recovery when MBs were applied. Conversely, Zn stress caused decline in bacterial α-diversity, while fungal diversity was maintained, indicating that Zn acts as an ecological filter that suppresses sensitive bacterial taxa and favors Zn-tolerant fungal species. Multi-omics data integration combined with network analysis highlighted key features associated with improved nutrient availability and reduced HM toxicity under MB treatments, including metabolites and microbial taxa linked to sulfur cycling, nitrogen metabolism, and iron reduction pathways. These findings demonstrate that MBs can modulate plant metabolic responses and restore rhizosphere microbial communities under Cd and Zn stress, with PGPR showing broader metabolomic recovery effects and AMF influencing specific metabolite pathways. This study provides new insights into plant–microbe interactions in HM-contaminated environments, supporting the potential application of biostimulants for sustainable soil remediation and plant health improvement. Full article

This paper presents the synthesis of sustainable lignin nanoparticles (LNPs) and their application in methylcellulose (MC) as LNP/MC coatings for handmade papers. LNPs were produced from bulk kraft lignin via an acetone/water and sonication method, then incorporated into a 1 wt% methylcellulose (MC) matrix at concentrations of 0.4, 1, and 2 wt%. Groundwood and cotton linter papers were coated and exposed to 90 °C and 45% relative humidity (RH) for 16 days and the samples’ response to ageing at different concentrations of nanolignin was tested using a multi-analytical approach. The morphology of the LNPs was revealed with scanning electron microscopy, and most LNPs measured below a diameter of 30.8 nm. Colourimetry showed coated samples were inherently darker than uncoated samples but mostly stable in colour. pH remained near neutral for coated groundwood papers during ageing, but cotton papers were consistently acidic. Fourier transform infrared (FTIR) spectroscopy identified spectral similarities between uncoated and coated groundwood samples at approximately 1635 cm^–1 and 1725 cm^–1, attributed to carbonyl and carboxyl groups, suggesting that LNPs did not contribute to the formation of these groups during ageing. Controlled environment dynamic mechanical analysis (DMA-RH) found improved consolidation and lower elongation in most LNP/MC-treated samples. These results indicate that there may be potential for LNPs within paper conservation. Full article

Predicting the thermal properties of nanoporous materials is a major challenge that affects their applications in efficient thermal insulation and energy storage. This narrative review discusses the application of machine learning models in nanoporous materials, including covalent organic frameworks, metal–organic frameworks, aerogels, and zeolites. It discusses model advancements, with a focus on predictive accuracy and computational efficiency. This includes models such as convolutional neural networks, graph neural networks, and physics-informed neural networks. This study also addresses the limitations of these data-driven models, including data availability, challenges in maintaining physical consistency, and difficulties in generalizing across various material families. Additionally, it covers emerging approaches such as multimodal and transfer learning, which are explored for their potential to reduce computational costs. Moreover, the benefits of interpretable machine learning methods for understanding underlying physical mechanisms are introduced and highlighted. This review provides comprehensive and practical guidelines for researchers using machine learning approaches in the study and design of nanoporous materials. Full article

Conventional whole-blood flow assays for quantifying thrombus formation are typically performed at room temperature and are technically demanding, which limits their translational applicability. We engineered a novel, disposable, mountable, and single-channel microfluidic chip (MC-2S), which is based on the Maastricht chamber (MC) and designed for automated evaluation of platelet function, coagulation and fibrinolysis under physiological conditions. The MC-2S chip allows customizable choices of thrombogenic surfaces, such as collagen and tissue factor. The chip was used in combination with an adapted, 1.3 kg brightfield/fluorescence microscope, operating at physiological temperature (37 °C), and with scripts for automated multicolor analysis of image features. The integrated system enables a robust, rapid, and high-content quantification of the kinetics of thrombus formation and dissolution. In platelet-sensitive mode, MC-2S demonstrated high sensitivity to antiplatelet therapy with aspirin or cangrelor. In coagulation-sensitive mode, it detected the anticoagulant effect of rivaroxaban plus its reversal by andexanet-α. In fibrinolysis-sensitive mode, it monitored tissue-type plasminogen activator-induced thrombus dissolution, inhibited by tranexamic acid. Collectively, the MC-2S platform was found to provide a versatile, physiologically relevant tool for functional hemostasis testing, with high potential for the acute and subacute evaluation of patient blood samples in the context of bleeding disorders, thrombosis risk, and drug monitoring. Full article

Background/Objectives: Breast cancer is a leading cause of cancer-related mortality among women worldwide. Small nucleolar RNAs (snoRNAs) represent a class of non-coding RNAs with potential as novel biomarkers applicable to improve diagnostic and prognostic applications. Methods: We performed a comprehensive evaluation of the snoRNA-related gene expression by qPCR using benign and tumor tissue samples associated with invasive breast carcinomas of no special type (NST). Selected candidate snoRNAs, i.e., SCARNA2, SCARNA3, SNORD15B, SNORD94, SNORA68, and SNHG1, along with RNU2-1 snRNA, were further validated and their associations with clinicopathological parameters were examined. External datasets and plasma samples were used for additional validation. Results: SCARNA2 was identified as the most promising snoRNA biomarker candidate, showing a positive association with better progression-free survival (PFS) in our data (13.3-month survival difference between low- and high-expression groups) and with both PFS and overall survival in external RNA-seq datasets. SNORD94, SNORD15B, SCARNA3, and RNU2-1 snRNA were also indicated as putative tumor suppressors. SNORD94 was associated with better progression-free survival (PFS) in our data as well (12.4-month survival difference between low- and high expression groups). Greater downregulation in the low-expression tumor subgroup compared to benign samples further supports the prognostic potential of SCARNA2 and SNORD94. Evidence for SNHG1 and SNORA68 as putative oncogenes was less conclusive. Conclusions: Several small nucleolar RNAs were found to be dysregulated in breast cancer specimens, supporting their further evaluation as potential biomarkers. In particular, SCARNA2, SNORD94, SNORD15B, SCARNA3, and RNU2-1 snRNA merit further investigation to determine their clinical relevance and biological roles in breast cancer. Full article

This work develops a replicable method for designing the optimal renewable hydrogen production facility, applicable to any site and based on technical parameters and actual equipment costs. The solution is based on the integration of photovoltaic (PV) energy with lithium-ion battery storage systems, which maximizes electrolyzer operating hours and significantly reduces the Levelized Cost of Hydrogen (LCOH). This study shows that increasing the inclination of the photovoltaic modules reduces the need for storage, optimizing operation and extending the electrolyzer’s annual operating hours. In the Seville case study, with current costs and efficiencies, a minimum LCOH of €4.43/kg was achieved, a value well below market benchmarks, opening the door to a potentially competitive industrial business. The analysis confirms that electrolyzer efficiency—particularly specific power consumption—is the most important factor in reducing costs, while technological progress in photovoltaics, storage, and equipment promises further reductions in the coming years. Overall, the proposed methodology offers a practical and scalable tool to accelerate the economic viability of green hydrogen in a variety of contexts. Full article