Search Results (4,791)

A comprehensive multiregional characterization of the spectral response of cassava leaves across different ontogenetic stages was performed. For this, ultraviolet (UV), visible (VIS) and shortwave near-infrared (UV-VIS-NIR; 200–900 nm) regions were used to identify spectral signatures and indices for their potential use as biomarkers of leaf development and physiological status of plants under induced senescence conditions. Manihot esculenta Crantz (HMC-1 variety) was used as a model. Spectral signatures were obtained from leaves at two phenological stages (4 and 6 months after planting) using UV-VIS-NIR spectroscopy by the diffuse reflectance technique. Classical and experimental spectral indices were evaluated, and their discriminatory power through different ontogenies was assessed using ANOVA/Kruskal–Wallis and post hoc tests. Senescence effects were further examined by postharvest monitoring (1–20 days), with temporal, ontogenetic, and interaction effects validated using linear mixed models (LMMs), while multivariate structure and spectral convergence were explored via principal component analysis and hierarchical clustering (PCA-HCA). Functionally Enhanced Derivative Spectroscopy (FEDS), comparative analysis, and spectral correlation mapping allowed signal’s selective enhancement and the identification of phenolic compounds, photosynthetic pigments, and structural molecular components. Results showed high ontogenetic stability of UV-associated phenolic signals (~210–220 nm), whereas the VIS region (420–600 nm) clearly differentiated young leaves. The NIR region was stable across ontogeny but highly sensitive to temporal degradation, reflecting changes in water status and internal structure. UV-VIS-NIR indices effectively differentiated young leaves and changes by stress. It is concluded that multiregional characterization of the spectral response supported by FEDS allows the extraction of robust indices with strong potential as biomarkers of leaf maturation and senescence in cassava. Full article

Background: Oxidative stress contributes significantly to premature skin aging and inflammatory dermatological conditions. While plant-derived antioxidants have demonstrated considerable promise in topical applications, Bougainvillea glabra Choisy remains underexplored in standardized pharmaceutical dosage form development despite its documented phytochemical richness. Objective: This study aimed to develop, standardize, and characterize topical cold cream formulations incorporating B. glabra ethanolic leaf extract, with HPTLC-based quantification of marker compounds, validated antioxidant assessment, and preliminary dermal safety evaluation. Methods: The ethanolic leaf extract was prepared by maceration and characterized by preliminary phytochemical screening and HPTLC fingerprinting with quantitative densitometric analysis of quercetin and pinitol. Three cold cream formulations were developed at 10% (F1), 20% (F2), and 30% (w/w) (F3) extract loading. Formulations were evaluated for organoleptic properties, pH, homogeneity, spreadability, and viscosity. Antioxidant activity was assessed using a validated methanol extraction procedure followed by DPPH radical scavenging and potassium permanganate reduction assays. Ex vivo skin permeation was evaluated using Franz diffusion cells with freshly excised goat skin. Accelerated stability was conducted at 40 ± 2 °C/75 ± 5% RH for 90 days with HPTLC-based marker retention monitoring. Primary dermal safety was assessed in Wistar albino rats (n = 6) following OECD Test Guideline 404. Results: Quantitative HPTLC confirmed quercetin (4.82 ± 0.14 mg/g dry extract) and pinitol (2.31 ± 0.09 mg/g) as marker compounds, with linearly increasing content across F1–F3. All formulations demonstrated acceptable physicochemical properties (pH 5.7–5.9, viscosity 440,000–460,000 cP, spreadability 11.8 ± 0.3 cm·g/s). F3 exhibited the highest DPPH scavenging activity (56.68 ± 1.05%) with IC₅₀ of 1.3 ± 0.1% w/v, demonstrating a 3.2-fold improvement over F1. Extraction recovery from the cream matrix was 96.4–97.1%, validating the antioxidant data. Ex vivo quercetin permeation through goat skin reached 51.3 ± 2.8 μg/cm² at 24 h for F3, following Higuchi diffusion kinetics (R² > 0.99). No dermal irritation was observed (Primary Irritation Index = 0). Accelerated stability confirmed ≥98.3% retention of both marker compounds and antioxidant activity after 90 days. Conclusions: B. glabra leaf extract was successfully incorporated into a physicochemically stable, non-irritating cold cream with demonstrated dose-dependent antioxidant efficacy and cutaneous delivery capability. The study establishes preliminary dermal safety and in vitro antioxidant efficacy warranting further controlled clinical evaluation. Full article

Fruit tree leaves are an abundant agro-waste material with promising yet underexplored biological potential. This study compared the biological activity of aqueous extracts obtained from apple (Malus domestica), apricot (Prunus armeniaca), and cherry (Prunus cerasus) leaves and their kombucha-fermented counterparts in the context of cosmetic and dermatological applications. Phytochemical composition before and after fermentation was analyzed chromatographically. Antioxidant activity was evaluated using DPPH, ABTS, and FRAP assays, while intracellular reactive oxygen species (ROS) levels in keratinocytes and fibroblasts were assessed using the H₂DCFDA probe. Cytotoxicity was determined by Alamar Blue and Neutral Red assays. Antimicrobial activity against seven bacterial strains was investigated using minimum inhibitory concentration and disc diffusion methods. Anti-inflammatory activity was evaluated in LPS-stimulated THP-1 cells by measuring TNF-α, IL-1β, and IL-6 levels using ELISA. The influence of the samples on collagenase, elastase, and hyaluronidase activity was also analyzed. Fermentation increased the content of selected phenolic compounds and enhanced antioxidant, antimicrobial, anti-inflammatory, and anti-ageing properties. Ferments more effectively reduced oxidative stress in skin cells and showed no cytotoxicity within the tested concentration range. These findings indicate that kombucha fermentation may support the valorization of fruit tree leaf agro-waste as multifunctional ingredients for skincare formulations. Full article

The efficacy of plant-derived antimicrobials in meat systems is frequently limited by interactions with proteins, lipids, and other food matrix components that reduce the bioavailability and antimicrobial activity of phytochemicals. This study evaluated the antimicrobial effectiveness of Verbascum sinaiticum (V. sinaiticum) leaf extract encapsulated using maltodextrin (MD), gum arabic (GA), and a maltodextrin–gum arabic blend (MDGA, 8:2 w/w) through freeze-drying for application in raw beef during refrigerated storage (4 °C). The encapsulation systems exhibited process yields of 42.5–54.7%, encapsulation efficiencies of 78.3–92.5%, and loading capacities of 18.5–24.3 mg GAE/g DW, with MDGA showing the highest encapsulation efficiency. The effects of encapsulation on microbial inhibition, physicochemical properties, and sensory quality were investigated over 15 days of storage. Aerobic plate counts in the control increased from 3.04 to 8.26 log CFU/g, whereas encapsulated treatments showed significantly lower final counts (p < 0.05), reaching 7.89 log CFU/g (MD), 7.96 log CFU/g (MDGA), and 7.95 log CFU/g (GA). Similarly, encapsulated treatments reduced Escherichia coli counts during storage, with maltodextrin (MD) exhibiting the greatest inhibitory effect (6.23 × 10⁵ CFU/g) compared with the control (6.93 × 10⁵ CFU/g) on day 15. However, reductions in Staphylococcus aureus, E. coli, Candida albicans, and Bacillus cereus remained below 1 log CFU/g, indicating limited antimicrobial efficacy under the tested conditions. All encapsulated treatments slowed pH increases during storage (6.20–6.34) relative to the control (6.62) on day 15 and preserved aroma quality throughout the storage period. Overall, encapsulation improved the antimicrobial performance of V. sinaiticum extract compared with the free extract, particularly in MD-based systems; however, the antimicrobial effects in beef remained modest. These findings highlight both the potential and current limitations of encapsulated plant-derived antimicrobials for meat preservation and emphasize the need for optimized delivery systems to enhance efficacy in complex food matrices. Full article

Triple-negative breast cancer (TNBC) is an aggressive subtype lacking effective targeted therapies, highlighting the need for new anticancer agents. Natural products remain a valuable source of bioactive compounds with diverse mechanisms of action. In this study, a pyrone glucoside, 7-hydroxymaltol-3-O-β-D-glucoside, was isolated from the methanolic leaf extract of Maerua angolensis and evaluated for its anticancer activity against TNBC cells. Structural elucidation was achieved using NMR and LC–MS analyses. Both the crude extract and the isolated compound exhibited dose-dependent cytotoxicity against MDA-MB-468 cells, with IC₅₀ values of 2.94 and 0.78 µg/mL, respectively, while showing reduced toxicity toward MCF10A normal cells. Mechanistic studies revealed induction of apoptosis, evidenced by activation of caspase-9 and caspase-7 and PARP cleavage. Confocal imaging further demonstrated lysosomal disruption and nuclear morphological alterations consistent with stress-associated cell death. Gene expression analysis indicated minimal involvement of the PI3K/AKT/mTOR pathway. Molecular docking showed favorable binding of the compound to AKT1, PARP-1, and caspase-7, suggesting a multi-target mode of action. ADMET analysis indicated low oral bioavailability but a favorable safety profile. These findings highlight the potential of this compound as a lead for TNBC therapy. Full article

Accurate monitoring of the leaf area index (LAI) throughout the entire growth cycle of walnut trees using UAV multispectral imagery is essential for digital orchard management. In this study, focusing on the ‘Wen 185’ walnut variety in Xinjiang, we simultaneously acquired UAV multispectral images and ground-measured LAI data during four critical growth stages: expansion, hard shell, oil conversion, and maturity. A total of 25 vegetation indices and 48 texture features derived from the gray-level co-occurrence matrix were extracted. Hybrid feature selection combining linear (Pearson correlation), nonlinear (maximum information coefficient and random forest importance), and multiple consensus strategies was employed to reduce redundancy. LAI prediction models were constructed using four algorithms: Random Forest (RF), Support Vector Machine (SVM), LASSO, and Ridge Regression (RR), with model interpretability enhanced by SHAP analysis. Results showed that the multiple consensus screening reduced feature redundancy by an average of 69.6%. SHAP identified five core features: Redge_750_Mean, NDVI, B_Mean, RENDVI, and G_Homogeneity. Importantly, predictor importance shifted significantly with phenology: texture features dominated during the expansion stage, while red-edge indices (RENDVI and Redge_750_Mean) became predominant during the hard shell and oil conversion stages, effectively mitigating the saturation problem commonly observed in traditional indices such as NDVI within the LAI range of 1.5–5.8 in this study. The hybrid feature subset combining “red-edge spectrum + spatial texture” with the Random Forest algorithm achieved superior performance across all stages, with the RPD value exceeding 2.0 during the oil conversion stage, indicating excellent estimation capability. This study demonstrates that a “quality over quantity” feature selection strategy not only reduces model complexity but also enables high-precision, dynamic LAI monitoring throughout the entire walnut growth cycle, providing a scientific basis for intelligent management of large-scale orchards in arid regions. Full article

Populus pruinosa Schrenk is a keystone species in arid riparian ecosystems, where its physiological status is critical for biodiversity and soil stabilization. In this study, spectral reflectance, leaf chlorophyll density (CHD), and leaf water content (LWC) were measured for Populus pruinosa in the Tarim River headwater region and Awati County, Xinjiang, from July to October 2023. The aim was to estimate CHD using hyperspectral data combined with machine learning and to evaluate the effect of LWC on model accuracy. Raw spectra were preprocessed using Savitzky–Golay (SG) smoothing and continuous wavelet transform (CWT). A two-step feature selection strategy comprising Random Frog and iterative retaining informative variables (IRIV) was applied to extract characteristic bands. Three machine learning models—support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost)—were developed for CHD estimation with and without LWC as an additional input. Incorporating LWC consistently improved the predictive performance of all models. Without LWC, the RF model achieved the best accuracy (training R² = 0.842, test R² = 0.830), whereas after LWC integration, XGBoost reached the optimal performance (training R² = 0.871, test R² = 0.865). SHAP analysis identified the 687 nm wavelength and its interaction with LWC as the most important predictors. These results indicate that combining spectral information with LWC effectively improves the accuracy and stability of CHD estimation for Populus pruinosa, providing a reliable non-destructive approach for assessing forest ecosystem physiological status—a key contribution to the sustainable management of arid riparian forests. Full article

High-throughput sequencing is becoming the method of choice for plant diagnostics. It allows the detection of known and novel viruses and viroids, even in co-infection, without preliminary knowledge of the target. However, this method has its own limitations when compared to real-time PCR or ELISA. Laboratories that implement this type of technologies in-house must ensure that the performance criteria meet the requirements associated with their diagnostic activity. In this study, we present a workflow for in-house plant viruses and viroid detection, based on total RNA extraction, ribodepletion, Illumina sequencing and bioinformatics analyses. Performance criteria such as analytical sensitivity, analytical specificity, selectivity, repeatability, reproducibility and robustness were evaluated on the tomato brown rugose fruit virus (RNA genome), the tomato leaf curl New Delhi virus (DNA genome), and the pepper chat fruit viroid (RNA genome). The performance levels obtained meet the requirements for virus and viroid detection in symptomatic plant samples. Full article

Background: The rapid expansion of functional ready-to-drink (RTD) beverages—formulated with prebiotic fibers, botanical extracts, and reduced sugar—has outpaced systematic characterization of their small-molecule composition. Methods: We applied dual-mode untargeted high-resolution liquid chromatography–tandem mass spectrometry (LC–MS/MS), integrating hydrophilic interaction (HILIC) and reversed-phase C18 separations, to profile five commercial RTD beverages spanning distinct formulation categories: Coca-Cola^®, Poppi^® Orange, OLIPOP^® Cream Soda, Pure Leaf^® Unsweetened Black Tea, and BeePop™ Peach + Orange Blossom Honey. Results: Across all products, 478 compounds were structurally annotated at Metabolomics Standards Initiative (MSI) Levels 1 and 2, of which 42 matched compounds with reported bioactivity in a curated literature-based reference database. Seventeen compounds—including the NAD+ precursor trigonelline and multiple B vitamins—were detected across all five products. The number and diversity of compounds with reported bioactivity varied substantially by product and correlated with botanical ingredient complexity. Conclusions: This work presents a qualitative molecular survey of the RTD beverage category using standardized, dual-mode untargeted metabolomics, providing a reference dataset for future targeted quantitation studies. Full article

This study evaluated the effects of dietary supplementation with nano- and micro-encapsulated polyphenol extract (PE) from olive leaves (OL) and olive mill wastewater (OMWW), respectively, on growth, carcass and meat quality traits in Podolian young bulls. Fifteen 12-month-old bulls were assigned to three groups: C (control); T1 (40 g/day nano-encapsulated PE from OL); and T2 (400 g/day olive leaf pellets plus 30 g/day micro-encapsulated PE from OMWW) for 40 days. Final body weight and carcass yield were unaffected, although the average daily gain was higher in T2 (p < 0.05). Meat from T2 exhibited lower moisture and higher protein content (p < 0.01) compared with the other groups. T1 showed higher α-tocopherol levels (p < 0.05). Lipid oxidation was reduced in both treated groups (p < 0.01). Monounsaturated fatty acids tended to decrease in treated groups (p = 0.057), while saturated and polyunsaturated fatty acids (PUFA) were unaffected. However, T2 showed higher total n-3 PUFA (p < 0.05), and a more favourable n-6/n-3 ratio (p < 0.01) was found in treated groups. These results highlight the potential of olive–derived polyphenols as functional feed ingredients to enhance meat quality and promote sustainable, circular livestock systems. Full article

In response to the challenges of insufficient multi-scale feature representation and limited model adaptability in soybean leaf disease recognition from field images, a semantic residual Transformer (Sem-ResFormer) model is proposed for soybean leaf disease identification. The proposed model is constructed by integrating multi-scale residual feature extraction, Transformer-based global dependency modeling, and a semantic mapping mechanism, through which effective modeling and semantic representation of multi-scale visual information in lesion regions are achieved. A multimodal large model fine-tuning strategy combined with cross-architecture hyperparameter transfer is employed. The optimal hyperparameter configuration of the Vision Transformer, obtained via Bayesian optimization, is transferred to Qwen2.5-VL, and a progressive fine-tuning strategy is adopted, whereby the adaptability of the model to task-specific data is gradually enhanced. Experiments were conducted on a constructed five-class field-image soybean leaf disease dataset containing 3852 images, with 674 labeled images used in the initial few-shot fine-tuning stage. Under an input resolution of 720 × 720, the proposed method achieved an overall accuracy (OA) of 95.33%, surpassing the OA obtained with the default parameter configuration (93.64%) and the ResNet-50-based transfer method (93.43%). In the initial few-shot stage, the OA was improved from 74.05% under zero-shot conditions to 90.66%. These results demonstrate that the proposed method effectively improves soybean leaf disease recognition accuracy and model adaptability under the constructed field-image dataset with visual variability. Full article

Tropical dry forests of mainland Southeast Asia contain considerable above-ground carbon (AGC) but present challenges for precise satellite-based AGC quantification because seasonal leaf phenology alters canopy reflectance throughout the year. To address this, we propose a phenology-informed approach that fuses multitemporal satellite imagery with airborne LiDAR. Using 17 PlanetScope images acquired between February 2024 and April 2026 over the Sakaerat Biosphere Reserve, together with UAV-LiDAR data, we extracted 128 phenological features and 12 canopy metrics at 10, 20 and 30 m. Machine learning models (Random Forest, XGBoost and LightGBM) were trained separately for dry evergreen forest (DEF) and dry dipterocarp forest (DDF). Under random five-fold cross-validation at 30 m, the best Random Forest models yielded R² = 0.681 (95% CI: 0.626–0.729) for DEF and R² = 0.661 (95% CI: 0.615–0.705) for DDF, with RMSE of 11.85 and 7.40 Mg C ha⁻¹, respectively. Because the AGC reference labels are themselves back-calculated from LiDAR canopy height, these Combined values partly reflect allometric circularity between predictors and labels and should be read as an upper bound rather than an independent accuracy; the spectral-only PlanetScope models, which are free of this circularity, give a more conservative R² = 0.342 (DEF) and 0.473 (DDF). Multitemporal phenological features and per-forest stratification jointly outperformed single-date baselines by 3.4× in DEF and 2.0× in DDF. We produced a 30 m AGC map of the reserve (total = 0.217 Tg C) and a higher resolution 3 m layer comprising ~8.7 million pixels. The results demonstrate the value of phenology-informed features and forest-type stratification for accurate AGC mapping in seasonally dry tropical forests, marking a step forward for remote sensing carbon assessment in phenologically dynamic landscapes. Full article

Subcritical water extraction (SWE) is an eco-friendly and efficient technique for isolating bioactive ingredients from natural products. To improve the extraction yield of Artemisia argyi leaf polysaccharides (AAPs), a three-stage hybrid optimization strategy combining single-factor experiments, response surface methodology (RSM), neural network (NN), and direct search algorithm (DSA) was proposed. Single-factor experiments were used to screen key parameters. A Box–Behnken design (BBD)-based RSM was applied for preliminary optimization. A {3, 5, 1} structured NN was trained using 63 datasets from RSM, and DSA was used to determine the globally optimal process parameters. The optimal conditions were obtained as follows: extraction time 17.72 min, liquid-to-solid ratio 92.83 mL/g, extraction temperature 123.35 °C, stirring speed 1800 r/min, and natural pH. Under these conditions, the experimental AAP extraction yield reached 6.99%, with a relative error of only 1.16% compared with the predicted value of 6.91%. Fourier transform infrared (FT-IR) spectroscopy confirmed that the product exhibited typical polysaccharide structural characteristics. The integrated RSM–NN–DSA framework provides a reliable and high-precision approach for optimizing SWE of plant polysaccharides, showing good potential for industrial applications. Full article

Accurate pest recognition is an important foundation for intelligent plant protection, precision pesticide application, and sustainable agricultural management. However, in real field environments, pest targets are often small in scale, severely occluded, and embedded in complex backgrounds, which limits the performance of existing supervised learning methods under low-annotation and cross-scenario conditions. To address these issues, a multimodal self-supervised pretraining framework is proposed for pest recognition, in which field pest images and environmental sensor data are integrated to construct pest representations with environmental awareness. In this framework, image features, including pest morphology, leaf texture, and damaged regions, are first extracted through a visual encoding branch, while temporal variation features of ecological factors, including temperature, humidity, illumination, soil moisture, rainfall, and wind speed, are modeled through an environmental encoding branch. On this basis, a cross-modal contrastive consistency module is designed to align visual and environmental representations, a temporal consistency self-supervised module is introduced to characterize the continuous evolutionary relationship between pest occurrence and environmental changes, and a multimodal collaborative representation fusion module is constructed to adaptively integrate information from different modalities. The experimental results show that the proposed method achieves favorable performance in the pest recognition task, with Accuracy, Precision, Recall, and F1-score reaching $94.37\%$, $93.96\%$, $93.42\%$, and $93.69\%$, respectively, outperforming ConvNeXtV2-T, ViT-B/16, Swin-T, SimCLR, MAE, and the conventional Image + Sensor fusion method. The ablation experiments further show that, after removing the cross-modal contrastive consistency module, the temporal consistency self-supervised module, and the multimodal collaborative fusion module, the F1-score decreases to $91.00\%$, $91.36\%$, and $90.49\%$, respectively, thereby demonstrating the contribution of each module. This study provides a viable multimodal self-supervised learning approach for AI-driven intelligent pest recognition, early warning, and precision control in agriculture. Full article

This study aims to evaluate the effects of two phytobiotic mixtures on performance, nutrient digestibility, histomorphology, microbiota, and antioxidant status in fattening ducks. A total of 180 day-old male mixed-type ducks were randomly assigned to three dietary groups: a control group receiving a basal diet, and two treatment groups (PM1: commercial phytobiotic formulation containing menthol, eucalyptus oil and turmeric leaf oil as key ingredients and PM2: commercial phytobiotic formulation containing garlic oil as a key ingredient) supplemented at 250 g/ton of feed. Ducks were reared for 49 days with six replicates of ten ducks. Performance parameters, including body weight (BW), body weight gain (BWG), and average feed intake (AFI), were significantly improved in phytobiotic groups (p ≤ 0.05). Apparent digestibility of dry matter, crude protein, ether extract, and starch remained unaffected (p > 0.05). Histological analysis showed no significant differences in villus height (VH) or crypt depth (CD). However, cecal microbiota culture-based analysis revealed increased total anerobic bacteria and Lactobacillus counts in PM1 and PM2 (p ≤ 0.05). Antioxidant status demonstrated reduced MDA levels and elevated total phenolic content (TPC) and total antioxidant capacity (TAC) in breast and thigh tissues of treated ducks. Overall, phytobiotic supplementation improved performance and microbiota balance, supporting the potential application of these phytobiotic formulations at the inclusion level of 250 g/ton in fattening ducks’ nutrition. Full article