Search Results (6,294)

In coastal wetlands, Tamarix chinensis often occurs as patches intermixed with Phragmites australis, Suaeda salsa, and saline–alkaline bare soil. This mixed distribution makes tamarisk prone to omission in medium-resolution remote sensing classification, while the recall of the target species is often masked by a relatively high overall accuracy. In this study, we focused on the Yellow River Delta National Nature Reserve and developed a multi-source feature set using summer 2025 Sentinel-2, Sentinel-1, and UAV/GPS data, comprising spectral, SAR, phenological, and red-edge-oriented features. To enhance the separability between tamarisk and co-occurring herbaceous vegetation, we introduced a red-edge–vegetation-cover coupling feature (RE_cov) based on their contrasting responses in the red-edge region. Within an XGBoost framework, we evaluated the marginal contribution of this feature using feature ablation, replacement, and spatial block cross-validation. The full feature set achieved an AUC of 0.8042, a recall of 0.9340, and an overall accuracy of 0.8194 on an independent test set. Ablation and replacement experiments showed that the red-edge-oriented features contributed to both model separability and tamarisk recall, and this contribution remained evident under spatial block validation. We further converted the pixel-level extraction results into local tamarisk density grades, revealing a pattern of a few clustered cores embedded within a broad low-density background. These results suggest that target-species-oriented red-edge–vegetation-cover coupling features can improve tamarisk recall while maintaining acceptable overall accuracy, providing a spatial product to support zoned patrol and management in protected coastal wetlands. Full article

This review intends to provide an insight into the wide range of possibilities for vegetable oils to be used as solvents to extract natural ingredients for various applications with a special emphasis on antioxidants. The potential of using oils as food-grade solvents for extraction of carotenoids, crocins, curcuminoids, cannabinoids, capsaicinoids, different volatile organic compounds and other lipid-soluble phytochemicals from plant and marine sources and by-products is summarized. Most studies focus on optimizing extraction parameters and evaluating the physical and chemical characteristics of the obtained oily plant extract. On the one hand, these infused or enriched oils can be considered as plant extracts, but on the other hand, one should not ignore the fact that lipid oxidation is a problem that needs to be addressed. The characterization and analysis of the obtained oily extracts is closely related to their specific application in the food or cosmetic industry. Despite all the advantages, disadvantages related to the stability of the fortified oils are discussed as well. Full article

Background/Objectives: Plant secondary metabolites remain an invaluable source of novel antibacterial phytochemicals in the fight against antibiotic resistance. The medicinal plant Solidago gigantea Ait. (giant goldenrod) is an invasive species in Europe and represents an abundant, yet largely underexplored reservoir of such bioactive compounds. The primary aim of this study was to perform a non-targeted, effect-directed screening, detection, bioassay-guided isolation, structure elucidation, and microbiological assessment of the antibacterial constituents present in the inflorescences of S. gigantea. Methods: Thin-layer chromatography coupled with direct bioautography (TLC–DB) assay using Bacillus subtilis was utilized for the non-targeted, effect-directed analysis of antibacterial components and the evaluation of in vitro antibacterial activity. Successive preparative flash column chromatography, semi-preparative reversed-phase high-performance liquid chromatography (RP-HPLC), and thin-layer chromatography–mass spectrometry (TLC–MS) were employed for the bioassay-guided fractionation and isolation. The structures of the isolated compounds were elucidated using one- and two-dimensional nuclear magnetic resonance (NMR) spectroscopy and high-resolution tandem mass spectrometry (HRMS/MS). The presence of known antibacterial compounds was established via reversed-phase ultra-high-performance liquid chromatography coupled with high-resolution electrospray ionization tandem mass spectrometry (RP-UHPLC–HR-ESI-MS/MS). Results: Two previously undescribed cis-clerodane diterpenoids, the isomeric solidagoic acid L (1) and solidagoic acid M (2), were isolated, identified, and characterized from the ethyl acetate extract of S. gigantea inflorescences. Both compounds exhibited in vitro antibacterial activity against the Gram-positive B. subtilis, confirmed via TLC–DB. In addition, 23 known compounds with antibacterial activity, including 17 clerodane diterpenes, four hydroxylated polyunsaturated fatty acids, and two unsaturated monoacylglycerols, were detected. All of these are reported for the first time in the inflorescences of this plant species. Conclusions: With further optimization, the isolated compounds may represent promising leads for antibacterial drug development. Our findings demonstrate the potential of non-targeted, bioassay-guided approaches for the discovery of novel plant-derived bioactive natural products. Full article

Polyphenols are natural compounds with antioxidant properties that help prevent chronic diseases. Red onion (Allium cepa) peels are an underutilized source of polyphenols, offering a sustainable opportunity for the valorization of agricultural by-products. Polyphenol-rich extracts were obtained from red onion peels using subcritical water extraction and adsorption resin chromatography. Their biological effects were evaluated in Caenorhabditis elegans under oxidative stress conditions. A multi-omics approach integrating transcriptomics, metabolomics, and lipidomics was applied to assess molecular responses. The extract exhibited a high total phenolic content (>400 mg GAE/g) and strong antioxidant capacity, supporting a highly enriched polyphenolic profile. Survival assays confirmed the absence of toxicity at 100 µg/mL GAE. Transcriptomic analysis revealed 158 differentially expressed genes associated with stress response and metabolic regulation. Metabolomic profiling indicated a systematic reduction in amino acid levels alongside an increase in betaine in treated worms, suggesting adaptive metabolic reprogramming. Lipidomic analysis revealed significant lipid remodeling, characterized by decreased triglycerides, increased diacylglycerols, and changes in membrane phospholipids, indicating alterations in membrane composition and cellular responses associated with oxidative stress adaptation. Together, these results support a model in which red onion peel extracts induce coordinated transcriptional and metabolic adaptations associated with cellular resilience and stress adaptation. This study highlights the potential of agro-industrial by-products as functional bioactive ingredients and demonstrates the value of multi-omics approaches for uncovering system-level responses. Full article

Nanoemulsions are promising nanotechnology-based delivery systems that may improve the stability, bioavailability, and cellular uptake of therapeutic agents. Silver nanoparticles (AgNPs) have been reported to exhibit high antibacterial and anticancer activities via several mechanisms, such as the generation of oxidative stress and disruption of cellular membrane integrity. Breast cancer (MCF−7) and ovarian cancer (SK-OV−3) represent two highly aggressive malignancies that pose major global health challenges. Brown algae oil is a natural marine-derived product with a number of bioactive compounds, including fatty acids, sterols, and antioxidants, responsible for its numerous biological activities. Oil extracted from the brown alga Turbinaria turbinata, using hexane as an organic solvent, was formulated with silver nitrate (AgNO₃) using a surfactant-stabilized spontaneous emulsification method to prepare a silver-loaded T. turbinata oil nanoemulsion (Ag-TTO-NE). The biological performance of the system was evaluated against human cancer cell lines, including MCF−7 (breast cancer) and SK-OV−3 (ovarian cancer), in addition to pathogenic bacterial strains, and for antioxidant activity. The results demonstrated that the silver-loaded oil nanoemulsion (Ag-TTO-NE) exhibited anticancer activities against MCF−7 (breast cancer) and SK-OV−3, with IC50 values of 105.86 and 72.45 µg/mL and a Selectivity Index of 2.34 and 3.41, respectively. The silver-loaded oil nanoemulsion (Ag-TTO-NE) possessed antioxidant and antimicrobial activities against Bacillus subtilis (ATCC 6633), Staphylococcus aureus (ATCC 6538), Pseudomonas aeruginosa (ATCC90274) and Salmonella typhi (ATCC 6539). These results indicate that T. turbinata-based silver nanoemulsions deserve further exploration as multifunctional marine-derived nanoformulations. In silico ADMET analysis projected moderate to high oral absorption for most of the discovered compounds and suggested favorable pharmacokinetic properties of the individual ingredients. ADMET analysis suggested that the major compounds discovered by GC–MS have good medication-like characteristics. These computational predictions are supplemental information and are not to be taken as the pharmacokinetic behavior of the nanoemulsion itself. Overall, the present results are based on in vitro biological assays together with exploratory computational studies and constitute preliminary evidence for the subsequent exploration of this marine-derived nanoformulation. Full article

The fruits of Lycopus europaeus L. represent an unusual source of highly oxygenated aromatic abietane diterpenoids. Following our previous identification of euroabienol (1) from this plant material, a phytochemical reinvestigation of the dichloromethane fruit extract was undertaken to characterize related minor constituents. Three new aromatic abietane diterpenoids, 4-epileonubiastrin (2), 3α-acetoxyeuroabienol (3), and 11-deoxyeuroabienol (4), were isolated together with euroabienol (1). Their structures and relative configurations were established by MS, HRMS, IR, and extensive 1D and 2D NMR analyses. Manual iterative full spin analysis of selected ¹H NMR spin systems enabled refined determination of chemical shifts and coupling constants and provided additional support for conformational and configurational assignments, particularly in structurally congested parts of the molecules. To obtain a preliminary indication of biological relevance, compounds 1–4 and the semisynthetic O-methylated euroabienol derivative 5 were evaluated for acetylcholinesterase and urease inhibition. The observed effects were modest: compound 2 showed the highest AChE inhibition, reaching 31% at 50 μM, whereas compound 4 was the most active against jack bean urease, producing 40% inhibition at 100 μM. The study expands current knowledge of L. europaeus fruit diterpenoids and illustrates the value of ¹H NMR simulation as a complementary tool in the elucidation of closely related abietane natural products. Full article

Background/Objectives: Tall Gastrodia Tuber (tuber of Gastrodia elata, Tianma) is an important herb with therapeutic effects on multiple central nervous system diseases. Although some chemical compounds with their bioactivities have been reported, there are still unrevealed bioactive compounds. This study focuses on discovering new compounds with pharmacological effects in Tianma. Methods: Compounds were isolated from ethyl acetate extract of Tianma by column chromatography and semi-preparative HPLC. Their chemical structures were elucidated by spectroscopic analysis including two-dimensional NMR (DEPT90/135, COSY, HSQC, HMBC), HR-ESI-MS, and IR, compared with reported data. Meanwhile, the protective effects against glutamate-induced excitotoxicity of two phenolic glucosides and two rare phenolic nucleosides were screened by a HT-22 cell model. Results: Fourteen compounds were identified, comprising six new natural products and eight known compounds. The six new compounds were named gastrotribenzyloside A (1), gastrotribenzyloside B (2), gastrotribenzyloside C (3), gastrotetrabenzyloside D (4), gastronucleoside B (5) and gastronucleoside C (6). Compounds 1–4 were phenolic glucosides. Compounds 5 and 6 were the first discovery of phenolic nucleosides substituted by multiple benzyl groups in Tianma. A bioactive experiment indicated that the two phenolic glucosides 1 and 2 did not exhibit protective effects against glutamate-induced excitotoxicity of HT-22 cells, while both of the phenolic nucleosides 5 and 11 significantly relieved the cell viability reduction caused by glutamate. Conclusions: Six new phenolic glucosides and phenolic nucleosides are discovered in Tianma. These phenolic nucleosides are potential bioactive components exhibiting cytoprotective effects against excitotoxicity. They deserve further in vivo studies to verify their bioactivity and investigate the mechanism. Full article

In-mold electronics (IMEs) present a highly promising monolithic integration strategy for manufacturing miniaturized 3D MEMS optical tweezers, offering exceptional environmental adaptability and structural compactness. However, the precision of such optical systems is heavily constrained by the injection molding process. During the molding phase, high-pressure melt scouring and severe thermo-mechanical coupling frequently induce geometric misalignment, manifesting as node displacement, localized warpage, and residual stress accumulation in the embedded circuits. This displacement critically alters the cross-sectional area of conductive traces, leading to resistance fluctuations that can destabilize the driving current. According to American Wire Gauge (AWG) standards, ensuring the geometric fidelity of this sensor-CPU interconnect pathway is fundamental to maintaining signal integrity. To address these manufacturing bottlenecks, this study systematically investigates the process stability of IME circuits Cyclic Olefin Copolymer (COC) is strategically selected as the substrate material over Polycarbonate (PC) and Liquid Silicone Rubber (LSR) due to its ultra-high light transmittance, extremely low water absorption, and superior thermomechanical stability. Based on finite element simulation, a data-driven intelligent optimization framework is developed. Latin Hypercube Sampling (LHS) is first utilized to efficiently sample the multi-dimensional process space, comprising melt temperature, packing pressure, and packing time. To handle the non-stationary nature of process feedback signals, wavelet analysis is introduced to decouple high-frequency noise, extracting Wavelet Energy Entropy (WEE) as a highly robust dynamic metric for process stability. Subsequently, a hybrid NSGA-II-MOPSO multi-objective algorithm is deployed to cooperatively optimize the injection parameters. The simulation-based optimization results demonstrate a substantial enhancement in manufacturing precision. Under the optimal parameter configuration, the average node displacement of the embedded circuits decreases significantly from 0.034 mm to 0.014 mm, achieving a 58.82% reduction. Simultaneously, volumetric shrinkage drops from 5.755% to 4.832% (a 16.04% reduction), while residual stress is maintained well within the structural safety threshold of optical-grade polymers. By clarifying the deformation control mechanism during the manufacturing phase, this study provides a highly reliable, data-driven methodological framework for the precision mass production of micro-nano optical systems. Full article

Existing anti-aging drugs are often limited by toxicity and resistance. In contrast, natural substances derived from food resources, edible plants, and agricultural by-products offer advantages such as low toxicity and suitability for dietary intake. Utilizing these resources aligns with sustainable development goals by promoting the valorization of food waste and functional food development; however, their complex composition makes traditional discovery inefficient and resource-intensive. Machine learning (ML) provides a powerful, sustainable in silico solution. By analyzing vast datasets, computational models can rapidly screen thousands of candidates, significantly reducing the chemical waste and time associated with traditional wet-lab screening. This review focuses on the current status of food-derived anti-aging bioactives and the emerging ML-based perspectives in this field. Key natural compounds and plant extracts are discussed, highlighting their dietary origins and mechanisms. Furthermore, we explore how advanced algorithms accelerate the identification of novel bioactives. Importantly, we address current translational gaps, including the need for explainable AI, ADME (Absorption, Distribution, Metabolism, and Excretion) prediction, and the standardization of complex mixtures. Overcoming these bottlenecks is essential for the sustainable development of effective, food-based anti-aging ingredients. Full article

The replacement of n-hexane in vegetable oil extraction remains a significant challenge due to environmental and health concerns. This study integrates thermodynamic modeling and kinetic analysis to evaluate green solvents for soybean oil extraction. Solvent–triglyceride interactions were predicted using Conductor-like Screening Model for Real Solvents (COSMO-RS), employing σ-surfaces, σ-profiles, σ-potentials, activity coefficients at infinite dilution (γ∞), and relative solubility descriptors ($x_{RS}$ and $w_{RS}$). Representative triglycerides were modeled using DFT-optimized structures. Based on these predictions and sustainability criteria, cyclopentyl methyl ether (CPME), 2-methyltetrahydrofuran (2-MeTHF), tert-butyl methyl ether (TBME), and ethyl acetate were experimentally evaluated against n-hexane using accelerated solvent extraction (ASE) at 100 °C. CPME and 2-MeTHF achieved the highest extraction yields, exceeding n-hexane, while TBME showed comparable performance and ethyl acetate underperformed. Kinetic analysis using the hot-ball diffusion model revealed a two-stage mechanism: an initial solvation-controlled stage followed by a diffusion-controlled regime. COSMO-RS predictions correlated strongly with early-stage extraction behavior, whereas diffusion coefficients highlighted the influence of mass transfer properties at later stages. The proposed COSMO-RS, experimental extraction, and kinetic modeling framework, validated here for soybean oil, offers a transferable and resource-efficient platform for designing sustainable solvent-based extraction processes across diverse oilseed and natural product matrices. Full article

Fusarium oxysporum f. sp. lycopersici (Fol) is a destructive soil-borne pathogen responsible for major global losses in tomato production, within a context of increasingly constrained fungicide use in intensive farming systems. Sustainable alternatives to chemical fungicides are urgently needed, and microbial secondary metabolites represent a promising source of antifungal compounds. This study explores the antifungal potential of secondary metabolites produced by a natural isolate of Beauveria bassiana (Bb758) previously reported to exhibit strong biocontrol activity against Fol. LC-HRMS metabolomic profiling revealed a chemically diverse metabolite profile dominated by amino acid-derived compounds, peptides and alkaloids. Purification of the crude extract by column chromatography and reverse-phase HPLC yielded beauvericin and several 2,5-diketopiperazines as the main bioactive constituents. These compounds were identified by NMR and LC-HRMS analyses. The antifungal activity of beauvericin (BEA), cyclo(l-Phe-l-Pro) (CFP) and fractions was evaluated against spore germination and germ tube development of Fol. BEA exhibited dose-dependent but limited activity, showing significant inhibition only at high concentrations (100 μg mL⁻¹). In contrast, CFP showed significantly higher antifungal activity than BEA, inhibiting both germination and germ tube formation at concentrations as low as 10 μg mL⁻¹. These results identify CFP as a promising antifungal compound targeting the early infection stages of Fol and highlight fungal-derived diketopiperazines as candidate molecules for sustainable disease management strategies. Full article

Sorghum bran, an abundant milling by-product, is rich in phenolics but underused as a food ingredient. Anthocyanin-rich sorghum bran extract (SBE) was added to Taosu, a traditional Chinese shortbread cookie, at 0–2%, and its effects on quality, flavor, antioxidant stability during storage, and heat-induced contaminants were evaluated. UHPLC-QTOF-MS/MS showed that SBE was rich in flavones (apigenin, luteolin) and 3-deoxyanthocyanidin apigeninidin and had strong in vitro antioxidant capacity; these phenolics transferred dose-dependently into Taosu. SBE raised the total phenolic, flavonoid, and anthocyanin contents and the radical-scavenging capacity and imparted a natural reddish color. The instrumental taste profile was essentially unchanged, whereas HS-SPME-GC-MS revealed a dose-dependent shift in the volatile profile, with more Maillard-derived furans (e.g., furfuryl alcohol) and fewer lipid-oxidation aldehydes at the highest level. At the 2% level, acrylamide and 5-hydroxymethylfurfural (5-HMF) were reduced by 30.9% and 46.0%, respectively. After 14 days of storage, the fortified cookies retained much higher phenolic and antioxidant levels than the control, with the 2% sample still exceeding the fresh control, indicating improved retention of phenolics and antioxidant capacity during the period evaluated. Overall, sorghum bran offers a route to upcycle a low-value by-product into a clean-label, multifunctional ingredient that improves the healthfulness and processing safety of traditional baked goods. Full article

This study investigates the thermodynamic and drying performance of a greenhouse dryer integrated with a parabolic trough solar collector (PTSC) and develops interpretable operating rules using a bio-inspired explainable artificial intelligence framework. Outdoor apple-drying experiments were conducted, and system performance was evaluated in terms of energy, drying, and exergy efficiencies. The experimental results indicated that energy efficiency ranged from 17.7% to 29.2%, drying efficiency from 1.0% to 9.7%, and exergy efficiency from 5.6% to 8.4%. Measured variables, including temperature, relative humidity, product weight, and solar radiation, were used to classify the efficiencies into low, medium, and high categories using the Chaotic Rule-based Strength Pareto Evolutionary Algorithm 2 (CRb-SPEA2). As a bio-inspired evolutionary computing approach, CRb-SPEA2 employs population-based search, selection, Pareto dominance, and multi-objective optimization mechanisms inspired by natural evolutionary processes. In contrast to conventional black-box machine learning models, the proposed method extracts explicit decision rules that define physically meaningful operating ranges. The maximum recall values were 0.952, 1.000, and 0.971 for the high-energy-, drying-, and exergy-efficiency classes, respectively. The extracted rules identified solar radiation, temperature, relative humidity, and product weight as dominant factors affecting dryer performance. Full article

Groundwater-fed irrigation sustains global food production but drives chronic aquifer depletion, creating an urgent need for quantitative tools that link irrigation intensity to groundwater response. This study proposes a gray-box machine learning (ML) framework that learns the parametric coefficients of polynomial irrigation–groundwater response functions—rather than predicting state variables directly—thereby embedding physical interpretability into the ML output. Using a well-validated SWAT-GW model of a representative over-exploited piedmont plain in the North China Plain as the training data generator, gradient irrigation scenarios were constructed for 70 hydrological response units over 20 years, producing 21,000 paired records of winter-wheat irrigation intensity versus three groundwater response variables: vertical recharge, aquifer storage change, and water table depth change. Quadratic polynomials were identified as the optimal functional form through joint evaluation of fitting accuracy (R² > 0.994) and ML learnability. Ensemble boosting algorithms predicted the three quadratic coefficients, with R² ranging from 0.74 to 0.97, and retained acceptable accuracy even when input features were restricted to readily available meteorological and soil data. Four management-critical hydrogeological parameters—the precipitation infiltration coefficient (α), irrigation infiltration coefficient (β), natural recharge (R_nat), and recharge–abstraction equilibrium point (IRR_eq)—were successfully inverted from the predicted coefficients and validated against independent regional groundwater resource assessments. The SHapley Additive exPlanations and Causal Forest analyses confirmed that the learned relationships are governed by physically interpretable drivers. The framework advances groundwater machine learning from state-variable prediction toward functional-structure extraction, offering a transferable approach for deriving irrigation–groundwater response curves and sustainability thresholds in over-exploited aquifer systems. Full article

Natural coagulants have emerged as potential alternatives to synthetic chemicals in water treatment, especially for decentralized and low-resource applications. However, many previously reported Aloe vera-based coagulant preparation methods rely on drying, powder production, distilled water extraction, refrigeration, or other laboratory-dependent procedures that increase operational complexity and limit practical implementation. This study presents a simplified and rapid protocol for producing an Aloe vera-based natural coagulant using accessible materials and simplified preparation steps. The proposed methodology consists of extracting Aloe vera g13el, homogenizing 2 g of fresh gel with 50 mL of tap water using a household blender, and applying simple paper filtration to obtain the liquid coagulant. The protocol can be completed in less than 10 min without specialized laboratory infrastructure, energy-intensive processing, or laboratory-grade reagents. Coagulation performance was evaluated using synthetic turbid water with initial turbidity levels of 100, 200, and 300 NTU. Significant turbidity reduction was observed under all tested conditions, with several samples reaching residual turbidity values close to or equal to 0 NTU after 50–60 min of sedimentation. The results demonstrate the potential of the proposed protocol as a rapid, reproducible, and accessible approach for future investigation in point-of-use and decentralized water treatment applications. Full article