Search Results (16,484)

Microorganisms, as abundant biological resources, offer significant potential in the development of selenium-enrichment technologies. Selenium-enriched microorganisms not only absorb, reduce, and accumulate selenium efficiently but also produce various selenium compounds without relying on synthetic chemical processes. In particular, nano-selenium produced by these microorganisms during cultivation has garnered attention due to its unique physicochemical properties and biological activity, making it a promising raw material for functional foods and pharmaceutical products. This paper reviews selenium-enriched microorganisms, focusing on their classification, selenium metabolism, and transformation mechanisms. It explores how selenium is absorbed, reduced, and transformed within microbial cells, analyzing the biochemical processes by which inorganic selenium is converted into organic and nano-selenium forms. Finally, the broad applications of selenium-enriched microbial products in food, medicine, and agriculture are explored, including their roles in selenium-rich foods, nano-selenium materials, and disease prevention and treatment. Full article

Glucosinolates are secondary metabolites of the Brassicales, playing a role in plant protection and as health-promoting compounds. Here, Na₂SO₄ was used to modulate the aliphatic glucosinolate content in different organs of Eruca sativa Mill. In flowers, which accumulate the highest amount of glucosinolates, Na₂SO₄ increased the concentration of glucoraphanin, in roots of glucoerucin and in apical leaves it doubled the amount of dimeric 4-mercaptobutyl glucosinolate. The biosynthetic gene Branched-Chain Aminotransferase 4 was also induced in roots at the highest salt concentration, while in leaves all tested genes biosynthetic genes were downregulated or unaffected. Cytochromes P450 83A1 monooxygenase was downregulated at the highest salt concentration in all organs. Overall, E. sativa is a reliable source of glucosinolates, which can be modulated with Na₂SO₄. Full article

This study investigates the Enzyme Biofilm Method (EBM), a biological wastewater treatment technology previously developed by the authors. EBM employs microbial-derived hydrolytic enzyme groups in the initial treatment stage to break down high-molecular-weight organic matter—such as starch, proteins, and fats—into low-molecular-weight compounds. These compounds enhance the growth of native microorganisms, promoting biofilm formation on carriers and improving treatment efficiency. Over the past decade, EBM has been practically applied in food factory wastewater facilities handling high organic loads. The enzyme groups used in EBM are derived from cultures of Bacillus mojavensis, Saccharomyces cariocanus, and Lacticaseibacillus paracasei. To clarify the system’s mechanism and ensure its practical viability, this study focused on starch—a prevalent and recalcitrant component of food wastewater—using two evaluation approaches. Verification 1: Field testing at a starch factory showed that adding enzyme groups to the equalization tank effectively reduced biological oxygen demand (BOD) through starch degradation. Verification 2: Laboratory experiments confirmed that the enzyme groups possess both amylase and maltase activities, sequentially breaking down starch into glucose. The resulting glucose supports microbial growth, facilitating biofilm formation and BOD reduction. These findings confirm EBM’s potential as a sustainable and effective solution for treating high-strength food industry wastewater. Full article

Enhancing forage protein is key to sustainable ruminant nutrition. The nutritive value of Hedysarum coronarium L. was investigated by studying different preservation systems (fresh vs. dehydrated pellet vs. hay) (Exp. 1) and morphological fractions (flowers vs. leaves vs. stems) (Exp. 2). For the fresh and pelleted systems, two cuts were used. Proximately, total polyphenols and condensed tannins were detected. In vitro fermentation characteristics were studied by incubating samples with buffered sheep rumen fluid, estimating methane production by volatile fatty acids. Fresh and pelleted sulla were more nutritionally advantageous than hay, in terms of metabolizable energy and protein-to-fiber ratio. Pelleting at the beginning of flowering proved to be a suitable forage for sheep feeding due to protein (15.1% DM), metabolizable energy (9.64 MJ/kg DM), structural carbohydrates (39.5% DM), and total polyphenols (13.5 GAE g/kg DM) content. This cut showed an in vitro fermentation rate (9.86 mL/h), organic matter degradability (55.7%), and volatile fatty acids (87.3 mmoL/g) that were higher (p < 0.05) than hay. Flowers and leaves showed higher (p < 0.05) levels of secondary metabolites than stems as well as lower methane production. These results suggest the potential influence of these compounds in reducing rumen emissions. Dehydration and pelleting resulted in an effective preservation method for maintaining nutrients in sulla forage. Full article

In the coming decades, the agricultural system will predictably rely on organic material to produce crops and maintain food security. Currently, the use of inorganic fertilizers to grow crops and vegetables, such as Swiss chard, spinach, and lettuce, is on the rise and has been proven to be detrimental to the soil in the long run. Hence, there is a growing need to use organic waste material, such as spent coffee grounds (SCGs), to grow crops. Spent coffee grounds are made of depleted coffee beans that contain important soluble compounds. This study aimed to determine the influence of different levels (0.32 g, 0.63 g, 0.92 g, and 1.20 g) of spent coffee grounds on the metabolomic profile of Swiss chard. The 1H-nuclear magnetic resonance (NMR) results showed that Swiss chard grown with different levels of SCGs contains a total of 10 metabolites, which included growth-promoting metabolites (trehalose; betaine), defense mechanism metabolites (alanine; cartinine), energy-reserve metabolites (sucrose; 1,6 Anhydro-β-D-glucose), root metabolites (thymine), stress-related metabolites (2-deoxyadenosine), caffeine metabo-lites (1,3 Dimethylurate), and body-odor metabolites (trimethylamine). Interestingly, caprate, with the abovementioned metabolites, was detected in Swiss chard grown without the application of SCGs. The findings of the current study suggest that SCGs are an ideal organic material for growing Swiss chard for its healthy metabolites. Full article

The increasing shortage of drinking water, driven by reduced rainfall and the intensification of industrial and agricultural activities, has raised justified concerns about the quantity and quality of available water resources. These sectors not only demand high water consumption but also discharge large amounts of toxic substances such as organic matter, metal ions and inorganic anions, posing risks to both public health and the environment. This study evaluated the effectiveness of clay-based nanomaterials in the treatment of contaminated industrial wastewater from the mining sector. The materials tested included montmorillonite, high-loading expandable synthetic mica, and their organically functionalized forms (MMT, Mica-Na-4, C18-MMT, and C18-Mica-4). The experimental results show that these clays had minimal impact on the pH of the water, while a notable decrease in the chemical oxygen demand (COD) was observed. Ion chromatography indicated an increase in nitrogen and sulfur compounds with higher oxidation states. Inductively coupled plasma analysis revealed a significant reduction in the calcium concentration and an increase in the sodium concentration, likely due to cation exchange mechanisms. However, the removal of copper and iron was ineffective, possibly due to competitive interactions with other cations in the solution. Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) confirmed the structural modifications and interlayer spacing changes in the clay materials upon exposure to contaminated water. These findings demonstrate the potential of clay minerals as effective and low-cost materials for the remediation of industrial wastewater. Full article

The ongoing global climate change has led to an increase in the frequency and complexity of drought events. Pinus yunnanensis, a native tree species in southwest China that possesses significant ecological and economic value, exhibits a high sensitivity to drought stress, particularly in its seedlings. This study investigates the response mechanisms of non-structural carbohydrates (NSCs, defined as the sum of soluble sugars and starch) and the stoichiometric characteristics of carbon (C), nitrogen (N), and phosphorus (P) to repeated drought conditions in Pinus yunnanensis seedlings. We established three treatment groups in a potting water control experiment involving 2-year-old Pinus yunnanensis seedlings: normal water supply (CK), a single drought (D1), and three drought–rewatering cycles (D3). The findings indicated that the frequency of drought occurrences, organ responses, and their interactions significantly influenced the non-structural carbohydrate (NSC) content and its fractions, as well as the C/N/P content and its stoichiometric ratios. Under D3 treatment, stem NSC content increased by 24.97% and 29.08% compared to CK and D1 groups (p < 0.05), respectively, while root NSC content increased by 41.35% and 49.46% versus CK and D1 (p < 0.05). The pronounced accumulation of soluble sugars and starch in stems and roots under D3 suggests a potential stress memory effect. Additionally, NSC content in the stems increased significantly by 77.88%, while the roots enhanced their resource acquisition by dynamically regulating the C/P ratio, which increased by 23.26% (p < 0.05). Needle leaf C content decreased (18.77%) but P uptake increased (8%) to maintain basal metabolism (p < 0.05). Seedling growth was N-limited (needle N/P < 14) and the degree of N limitation was exacerbated by repeated droughts. Phenotypic plasticity indices and principal component analysis revealed that needle nitrogen and phosphorus, soluble sugars in needles, stem C/N ratio (0.61), root C/N ratio (0.53), and stem C/P ratio were crucial for drought adaptation. This study elucidates the physiological mechanisms underlying the resilience of Pinus yunnanensis seedlings to recurrent droughts, as evidenced by their organ-specific strategies for allocating carbon, nitrogen, and phosphorus, alongside the dynamic regulation of nitrogen storage compounds (NSCs). These findings provide a robust theoretical foundation for implementing drought-resistant afforestation and ecological restoration initiatives targeting Pinus yunnanensis in southwestern China. Full article

Amino acids (AAs), a type of nitrogen-based organic compounds in the atmosphere, are directly and indirectly related to climate change, and as their link to allergic diseases becomes more known, the need for quantitative analysis of ultrafine dust (PM_2.5) will become increasingly necessary. When sensing water-soluble AAs using a gas chromatograph combined with a tandem mass spectrometer (GC-MS/MS), derivatization should be considered to increase the volatility and sensitivity of target analytes. In this study, two methods were used to compare and evaluate 13 AA derivatives in PM_2.5 samples: N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide with 1% tert-butyldimethylchlorosilane (MTBSTFA w/1% t-BDMCS), which is preferred for silylation, and ethyl chloroformate (ECF) with methanol (MeOH) for chloroformate derivatization. The most appropriate reaction conditions for these two derivative methods, such as temperature and time, and the analytical conditions of GC-MS/MS for the qualitative and quantitative analysis of AAs were optimized. Furthermore, the calibration curve, detection limit, and recovery of both methods for validating the quantification were determined. The two derivative methods were applied to 23 actual PM_2.5 samples to detect and quantify target AAs. The statistical significances between pairwise measurements of individual AAs detected by both methods were evaluated. This study will help in selecting and utilizing appropriate derivative methods for the quantification of individual AAs in PM_2.5 samples. Full article

The genus Trichoderma plays a pivotal role in sustainable agriculture through its multifaceted contributions to plant health and productivity. This review explores Trichoderma’s biological functions, including its roles as a biocontrol agent, plant growth promoter, and stress resilience enhancer. By producing various enzymes, secondary metabolites, and volatile organic compounds, Trichoderma effectively suppresses plant pathogens, promotes root development, and primes plant immune responses. This review details the evolutionary adaptations of Trichoderma, which has transitioned from saprotrophism to mycoparasitism and established beneficial symbiotic relationships with plants. It also highlights the ecological versatility of Trichoderma in colonizing plant roots and improving soil health, while emphasizing its role in mitigating both biotic and abiotic stressors. With increasing recognition as a biostimulant and biocontrol agent, Trichoderma has become a key player in reducing chemical inputs and advancing eco-friendly farming practices. This review addresses challenges such as strain selection, formulation stability, and regulatory hurdles and concludes by advocating for continued research to optimize Trichoderma’s applications in addressing climate change, enhancing food security, and promoting a sustainable agricultural future. Full article

Botrytis cinerea is a highly versatile pathogenic fungus, causing significant damage across a wide range of plant species. A central focus of this review is the recent advances made through proteomics, an advanced molecular tool, in understanding the mechanisms of B. cinerea infection. Recent advances in mass spectrometry-based proteomics—including LC-MS/MS, iTRAQ, MALDI-TOF, and surface shaving—have enabled the in-depth characterization of B. cinerea subproteomes such as the secretome, surfactome, phosphoproteome, and extracellular vesicles, revealing condition-specific pathogenic mechanisms. Notably, in under a decade, the proportion of predicted proteins experimentally identified has increased from 10% to 52%, reflecting the rapid progress in proteomic capabilities. We explore how proteomic studies have significantly enhanced our knowledge of the fungus secretome and the role of extracellular vesicles (EVs), which play key roles in pathogenesis, by identifying secreted proteins—such as pH-responsive elements—that may serve as biomarkers and therapeutic targets. These technologies have also uncovered fine regulatory mechanisms across multiple levels of the fungal proteome, including post-translational modifications (PTMs), the phosphomembranome, and the surfactome, providing a more integrated view of its infection strategy. Moreover, proteomic approaches have contributed to a better understanding of host–pathogen interactions, including aspects of the plant’s defensive responses. Furthermore, this review discusses how proteomic data have helped to identify metabolic pathways affected by novel, more environmentally friendly antifungal compounds. A further update on the advances achieved in the field of proteomics discovery for the organism under consideration is provided in this paper, along with a perspective on emerging tools and future developments expected to accelerate research and improve targeted intervention strategies. Full article

Tetrabromobisphenol A (TBBPA), a widely utilised brominated flame retardant, demonstrates toxicological effects in aquatic organisms. Tea polyphenols (TPs), natural compounds found in tea leaves, exhibit both antioxidant and anti-inflammatory activities. The kidney is one of the major metabolic organs in common carp and serves as a target organ for toxic substances. This study evaluated the therapeutic potential of TPs in mitigating TBBPA-induced nephrotoxicity in common carp. Common carp were exposed to 0.5 mg/L TBBPA in water and/or fed a diet supplemented with 1 g/kg TPs for 14 days. In vitro, primary renal cells were treated with 60 μM TBBPA and/or 2.5 μg/L TPs for 24 h. Methods included histopathology, TUNEL assay for apoptosis, ROS detection, and molecular analyses. Antioxidant enzymes (SOD, CAT) and inflammatory cytokines (IL-1β, IL-6, TNF-α) were quantified using ELISA kits. Results showed that TBBPA induced oxidative stress, and activated the ROS-PI3K/AKT-NF-κB pathway, thereby resulting in inflammatory responses. TBBPA upregulated apoptosis-related genes (Caspase-3, Bax, and Bcl-2) and induced apoptosis. TBBPA upregulated the expression of RIPK3/MLKL, thereby exacerbating necroptosis. TPs intervention significantly mitigated these effects by reducing ROS, suppressing NF-κB activation, and restoring antioxidant enzyme activities (SOD, CAT). Moreover, TPs attenuated apoptosis and necrosis in the carp kidney, thereby enhancing the survival ability and immunity of common carp. Full article

Cooked-rice aroma strongly affects consumer choice, yet the chemical traits distinguishing glutinous rice from normal-amylose japonica rice remain underexplored because earlier studies targeted only a few dozen volatiles using one-dimensional gas chromatography–mass spectrometry (GC-MS). In this study, four glutinous and seven normal Japanese cultivars were cooked under identical conditions, their headspace volatiles trapped with MonoTrap and qualitatively profiled by comprehensive GC × GC-TOFMS. The two-dimensional platform resolved 1924 peaks—about ten-fold previous coverage—and, together with hierarchical clustering, PCA, heatmap visualization and volcano plots, cleanly separated the starch classes (78.3% cumulative PCA variance; Euclidean distance >140). Volcano plots highlighted 277 compounds enriched in the glutinous cultivars and 295 in Koshihikari, including 270 compounds that were not previously documented in rice. Normal cultivars were dominated by ethers, aldehydes, amines and other nitrogenous volatiles associated with grainy, grassy and toasty notes. Glutinous cultivars showed abundant ketones, furans, carboxylic acids, thiols, steroids, nitro compounds, pyrroles and diverse hydrocarbons and aromatics, yielding sweeter, fruitier and floral accents. These results expand the volatile library for japonica rice, provide molecular markers for flavor-oriented breeding and demonstrate the power of GC × GC-TOFMS coupled with chemometrics for grain aroma research. Full article

Candidiasis arises from the proliferation of Candida species in the human body, especially in individuals with compromised immune systems. Efficient therapeutic management of candidiasis is often hampered by the limited availability of potent antifungal drugs and the emergence of drug-resistant strains. We have previously identified the N-[(4-sulfamoylphenyl)methyl][1,1′-biphenyl]-4-carboxamide to have fungistatic and fungicidal properties, likely due to the hydrophobic biphenyl–chemical features affecting the structural organization of Candida spp. cell membrane. Here, we designed and synthesized a novel series of twelve 5-arylfuran-2-carboxamide derivatives bearing a new hydrophobic tail as bioisosteric replacement of the diphenyl fragment. Its antifungal effectiveness against C. albicans, C. glabrata, and C. parapsilosis, including ATCC and clinically isolated strains, was assessed for all compounds. The most active compound was N-benzyl-5-(3,4-dichlorophenyl)furan-2-carboxamide (6), with fungistatic and fungicidal effects against C. glabrata and C. parapsilosis strains (MIC = 0.062–0.125 and 0.125–0.250 mg/mL, respectively). No synergistic effects were observed when combined with fluconazole. Interestingly, fluorescent microscopy analysis after staining with SYTO 9 and propidium iodide revealed that compound 6 affected the cell membrane integrity in C. albicans strain 16. Finally, carboxamide 6 exhibited a dose-dependent cytotoxicity on erythrocytes, based on assessing the LDH release. Full article

The growing need for rapid screening of adsorption energies in organic materials has driven substantial progress in developing various architectures of equivariant graph neural networks (eGNNs). This advancement has largely been enabled by the availability of extensive Density Functional Theory (DFT)-generated datasets, sufficiently large to train complex eGNN models effectively. However, certain material groups with significant industrial relevance, such as aromatic compounds, remain underrepresented in these large datasets. In this work, we aim to bridge the gap between limited, domain-specific DFT datasets and large-scale pretrained eGNNs. Our methodology involves creating a specialized dataset by segregating aromatic compounds after a targeted ensemble extraction process, then fine-tuning a pretrained model via approaches that include full retraining and systematically freezing specific network sections. We demonstrate that these approaches can yield accurate energy and force predictions with minimal domain-specific training data and computation. Additionally, we investigate the effects of augmenting training datasets with chemically related but out-of-domain groups. Our findings indicate that incorporating supplementary data that closely resembles the target domain, even if approximate, would enhance model performance on domain-specific tasks. Furthermore, we systematically freeze different sections of the pretrained models to elucidate the role each component plays during adaptation to new domains, revealing that relearning low-level representations is critical for effective domain transfer. Overall, this study contributes valuable insights and practical guidelines for efficiently adapting deep learning models for accurate adsorption energy predictions, significantly reducing reliance on extensive training datasets. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article