Search Results (5,736)

Diplotaxis tenuifolia (Brassicaceae), valued for its culinary use and bioactive potential, has not yet been comprehensively characterized in terms of its chemical composition and biological properties. This study investigated the nutritional profile, phytochemical composition, and antioxidant activity of D. tenuifolia cultivated in Portugal. The leaves contain substantial levels of essential minerals, particularly calcium, potassium, magnesium, iron, manganese, and chromium, while heavy metal levels were below regulatory safety limits. The nutritional profile also revealed high dietary fiber content, enriched glutamic and aspartic acids in the protein fraction, and α-linolenic acid as the predominant fatty acid. Phenolic compounds were most efficiently extracted by boiling them in 80% methanol, yielding the highest total phenolic (125.41 mg gallic acid equivalents g⁻¹) and flavonoid contents (3.72 mg quercetin equivalents g⁻¹). HPLC-PDA-ESI-MSⁿ analysis enabled the detailed characterization of phenolic acids, flavonol glycosides, and glucosinolates, highlighting the first report of sulfoglucobrassicin in D. tenuifolia. Additionally, 6-methylsulfonyl-3-oxohexyl-glucosinolate, proline, pipecolic acid, glucaric acid, eicosanoic acid, 9,10,12,13-tetrahydroxy-octadecanoic acid (sativic acid) and 9,12,13-trihydroxyoctadec-10-enoic acid were described for the first time in this species. The extract exhibited also antioxidant activity, with ABTS IC₅₀ 57.54 ± 0.18 µg mL⁻¹, DPPH IC₅₀ 302.73 ± 2.36 µg mL⁻¹, and FRAP 752.71 ± 4.59 µmol eq. Fe(II) g⁻¹. These findings establish D. tenuifolia as a nutritionally rich plant and a promising source of natural antioxidants for nutraceutical and pharmaceutical applications. Full article

Periodontal pathogens, including Porphyromonas gingivalis (P. gingivalis), are implicated in respiratory inflammatory conditions, and aspirated oral bacterial components may contribute to airway inflammation. This association has prompted the exploration of innovative therapeutic strategies in addition to conventional oral hygiene practices. We evaluated the anti-inflammatory efficacy of an alkaline extract of Sasa senanensis leaves (SE) against heat-inactivated P. gingivalis-induced inflammation in respiratory tissues. In human bronchial epithelial cells (BEAS-2B), SE reduced interleukin (IL)-6 and IL-8 mRNA expression and cytokine secretion in a dose-dependent manner. Moreover, SE attenuated nuclear factor-κB (NF-κB) and mitogen-activated protein kinases (MAPKs), including p38 and c-Jun N-terminal kinase (JNK), indicating broad anti-inflammatory actions. In mice, SE administration decreased early lung cytokine levels and reduced NF-κB activity following intratracheal challenge with heat-inactivated P. gingivalis. Together, these in vitro and in vivo findings indicate that SE suppresses proinflammatory signaling triggered by P. gingivalis components and may serve as a natural adjunct to mitigate bacteria-associated airway inflammatory responses. Full article

Conventional descriptive statistical approaches in per- and polyfluoroalkyl substance (PFAS) environmental forensics often fail under small-sample, ecosystem-level complexity, challenging the optimization of sampling, monitoring, and remediation strategies. This study presents an advance from passive description to adaptive decision-support for complex PFAS contamination. By integrating Bayesian optimization (BO) via Gaussian Processes (GP) with a Generalized Linear Mixed Model (GLMM), we developed a signal-extraction framework for both understanding and action from limited data (n = 18). The BO/GP model achieved strong predictive performance (GP leave-one-out R² = 0.807), while the GLMM confirmed significant overdispersion (1.62), indicating a patchy contamination distribution. The integrated analysis suggested a dominant spatiotemporal interaction: a transient, high-intensity perfluorooctane sulfonate (PFOS) plume that peaked at a precise location during early November (the autumn recharge period). Concurrently, the GLMM identified significant intra-sample variance (p = 0.0186), suggesting likely particulate-bound (colloid/sediment) transport, and detected n-ethyl perfluorooctane sulfonamidoacetic acid (NEtFOSAA) as a critical precursor (p < 0.0001), thus providing evidence consistent with the source as historic 3M aqueous film-forming foam. This coupled approach creates a dynamic, iterative decision-support system where signal-based diagnosis informs adaptive optimization, enabling mission-specific actions from targeted remediation to monitoring design. Full article

The rapid development of Convolutional Neural Networks (CNNs) has markedly boosted the performance of object detection in remote sensing. Nevertheless, tiny objects typically account for an extremely small fraction of the total area in remote sensing images, rendering existing IoU-based or area-based evaluation metrics highly sensitive to minor pixel deviations. Meanwhile, classic detection models face inherent bottlenecks in efficiently mining discriminative features for tiny objects, leaving the task of tiny object detection in remote sensing images as an ongoing challenge in this field. To alleviate these issues, this paper proposes a tiny object detection method based on Normalized Gaussian Label Assignment and Multi-scale Hybrid Attention. Firstly, 2D Gaussian modeling is performed on the feature receptive field and the actual bounding box, using Normalized Bhattacharyya Distance for precise similarity measurement. Furthermore, a candidate sample quality ranking mechanism is constructed to select high-quality positive samples. Finally, a Multi-scale Hybrid Attention module is designed to enhance the discriminative feature extraction of tiny objects. The proposed method achieves 25.7% and 27.9% AP on the AI-TOD-v2 and VisDrone2019 datasets, respectively, significantly improving the detection capability of tiny objects in complex remote sensing scenarios. Full article

Hemp is a high-yield crop traditionally cultivated for fiber used in products such as paper, textiles, ropes, and animal bedding, and more recently for sustainable applications in biofuels, insulation, and bioplastics. Beyond fiber, hemp is rich in phytochemicals. More than 500 compounds including cannabinoids, terpenes, phenolics, phytosterols, and tocopherols are accumulated in leaves, flowers, and seeds, which are typically considered waste products in the fiber industry. These compounds exhibit antioxidant, anti-inflammatory, neuroprotective, and antimicrobial properties, which have stimulated research into their pharmaceutical potential. However, hemp phytochemicals also find applications in other industrial sectors, including agrochemistry as natural insecticides, cosmetics for skin and hair care, and food and dietary supplements due to their associated health benefits. In light of this, the present review aims to give an overview of the available literature on the most common applications of hemp tissues, hemp extract, and purified hemp phytochemicals in agrochemical, cosmetic, and food sectors. This will be helpful to critically assess the current state of knowledge in this field and contribute to the ongoing debate over the natural and sustainable applications of hemp by-products. Full article

Orchard wind-assisted spraying technology relies on auxiliary airflow to disturb the canopy and improve droplet deposition uniformity. However, there are few effective means of quantitatively assessing the dynamic response of fruit tree leaves to airflow or the changes in airflow patterns within the canopy in real time. To address this, this study proposed an online monitoring method for the aerodynamic characteristics of fruit tree leaves using strain gauge sensors. The flexible strain gauge was affixed to the midribs of leaves from peach, pear and apple trees. Leaf deformations were captured with high-speed video recording (100 fps) alongside electrical signals in controlled wind fields. Bartlett low-pass filtering and Fourier transform were used to extract frequency-domain features spanning between 0 and 50 Hz. The AdaBoost decision tree model was used to evaluate classification performance across frequency bands. The results demonstrated high accuracy in identifying wind exposure (98%) for pear leaf and classifying the three leaf types (κ = 0.98) within the 4–6 Hz band. A comparison with the frame analysis of high-speed video recordings revealed a time error of 2 s in model predictions. This study confirms that strain gauge sensors combined with machine learning could efficiently monitor fruit tree leaf responses to external airflow in real time. It provides novel insights for optimizing wind-assisted spray parameters, reconstructing internal canopy wind field distributions and achieving precise pesticide application. Full article

The escalating threat of antibiotic resistance, particularly from Staphylococcus aureus (S. aureus), has become a critical challenge in both public health and animal husbandry. The extensive use of conventional antibiotics in livestock production accelerates the emergence of resistant strains, heightening risks to food safety and human health. Although plant-derived bioactive compounds are increasingly recognized as promising alternatives to synthetic antimicrobials, the mechanisms underlying their efficacy—and the potential for synergistic action among different plant parts—remain poorly understood. In particular, the antibacterial interactions among extracts from different tissues of Cyperus esculentus L. (C. esculentus), a plant rich in flavonoids and phenolics, have yet to be systematically evaluated. Here, we investigated the antibacterial properties and mechanisms of ethanol extracts from the tubers, stems–leaves and their mixture of C. esculentus against S. aureus. Using Oxford cup diffusion assays, scanning electron microscopy (SEM), bacterial growth kinetics, and untargeted metabolomics, we assessed both phenotypic inhibition and metabolic disruption. The mixed extract exhibited the strongest antibacterial effect, producing a 26.15 mm inhibition zone—approximately 7% greater than that of single-part extracts—and induced cell wall rupture and disintegration as observed by SEM. Growth curve analyses revealed time-dependent bacterial suppression, while metabolomic profiling identified 845 differential metabolites, indicating disturbances in amino acid, lipid, and nucleotide metabolism. Flavonoids such as acacetin, diosmetin, naringenin, and silybin A were identified as principal active compounds contributing to these effects. Full article

The valorization of agri-food residues is crucial for advancing circular bioeconomy strategies and mitigating environmental impacts. Turnip greens (Brassica rapa subsp. sylvestris) are a traditional vegetable cultivated in southern Italy. While the edible portions include flower sprouts, buds, and young leaves, the more leathery leaves and stems are typically discarded. These wastes represent valuable sources of compounds with antioxidant and antimicrobial potential. This study aims to develop the extraction of phenolic compounds from turnip green residues using two techniques: silent maceration and ultrasound-assisted extraction (UAE). Ethanol was selected over methanol as a food-safe alternative solvent, with preliminary tests confirming equivalent efficiency. A Design of Experiments (DoE) approach was applied to both leaves and stems to assess the effects of solvent composition, solvent-to-matrix ratio, and extraction time on Total Phenolic Content and Trolox Equivalent Antioxidant Capacity. DoE results identified UAE as the most effective method for stems, while for leaves, the solvent-to-dry-mass ratio was the key parameter. HPLC-DAD analysis was performed to identify and quantify the phenolic acids in selected extracts. The antibacterial activity of these extracts against biofilms of six pathogenic strains was evaluated using crystal violet and MTT assays, confirming efficacy in both biofilm formation and mature stages. Full article

Projects integrating Science, Technology, Engineering, and Mathematics (STEM) are essential to interdisciplinary research. This study presents a STEM (Science, Technology, Engineering, and Mathematics) methodology with the primary objective of designing, constructing, and validating a functional cannabinoid extraction device. To inform the device’s drying parameters, the dehydration kinetics of female hemp buds or flowering buds (FHB) were first analyzed using infrared drying at 100 °C for different durations. The plants were cultivated and harvested in accordance with good agricultural practices using Dinamed CBD Autoflowering seeds. The FHB were harvested and prepared by manually separating them from the stems and leaves. Six 5 g samples were prepared, each with a slab geometry of varying surface area and thickness. Two of these samples were ground: one into a fine powder and the other into a coarse powder. Mathematical fits were obtained for each resulting curve using either an exponential decay model or the logarithmic equation $y\left(t\right)=A\left(e^{-kt}\right)+y_0$ calculate the equilibrium moisture (m_E). The Moisture Rate (MR) was calculated, and by modelling with the logarithmic equation, the constant k and the effective diffusivity (Deff) were determined with the analytical solution of Fick’s second law. The Deff values (ranging from 10⁻⁷ to 10⁻⁵) were higher than previously reported. The coarsely ground powder sample yielded the highest k and Deff values and was selected for oil extraction. The device was then designed using Quality Function Deployment (QFD), specifically the House of Quality (HoQ) matrix, to systematically translate user requirements into technical specifications. A 200 g sample of coarsely ground, dehydrated FHB was prepared for ethanol extraction. Chemical results obtained by Liquid Chromatography coupled with Photodiode Array Detection (LC-PDA) revealed the presence of THC, CBN, CBC, and CBG. The extraction device design was validated using previous results showing the presence of CBD and CBDA. The constructed device successfully extracted cannabinoids, including Δ9-THC, CBG, CBC, and CBN, from coarsely ground FHB, validating the integrated STEM approach. This work demonstrates a practical framework for developing accessible agro-technical devices through interdisciplinary collaboration. Full article

The bilberry is a low-growing plant native to northern Europe. It belongs to the genus Vaccinium. Bilberry is essential in the local diets of some countries and is used as an herbal medicine to manage several ailments. Still, it is not used for commercial farming in many countries. It has recently been known as a great source of naturally available bioactive compounds and colorants. Bilberry is a therapeutic fruit acknowledged for its rich flavonoids, anthocyanins, carotenoids, ascorbic acid, phenolic acid, tocopherols, and vitamin content. It is one of the richest sources of natural anthocyanins. The polyphenolic compounds in bilberry provide abundant antioxidant content, which are supposed to be the vital bioactive compounds accountable for various health benefits. Even though bilberry is mostly promoted for eye care or vision improvement. It is also stated to promote antioxidant defense and lower oxidative stress, having antiaging, anti-inflammatory, lipid-lowering, antimicrobial effects, lowering blood glucose and other age-related diseases, etc. Reports suggest that apart from the fruit, the leaves of bilberry are equally rich in numerous bioactive compounds of medicinal importance. This current review offers valuable insights on bilberry fruits, leaves, and extracts, providing an inclusive assessment of their bioactive compound configuration, related biological prospects, and the extraction methodology of their major compounds. This review offers a summary of the existing information on the antiaging potential of bilberry fruits and leaves, and analytically reviews the outcome of clinical trials, with special attention towards its medicinal properties. Full article

Background/Objectives: The genus Tristerix comprises at least ten species, found from southern Chile to Colombia in South America. In Chile, several species of these hemiparasitic plants are known as quitral or quintral. Quitral, mainly T. corymbosus (syn. T. tetrandus), is used in alternative medicine for its anti-inflammatory, digestive, hemostatic, hypocholesterolemic, and wound-healing properties. This study investigates the phytochemical composition and antimicrobial properties of T. corymbosus. Methods: A hydroalcoholic extract of T. corymbosus was prepared from leaves and small branches. The addition of methanol, on the extract, produced precipitation allowing us to isolate a methanol-soluble fraction, a brown powder obtained after filtration, and a tar-like residue remaining in the flask. These fractions were resuspended and tested for antimicrobial activity. Results: All fractions showed activity against Streptococcus pyogenes, but not E. coli. The brown powder exhibits the strongest potency against Gram-positive bacteria, some Gram-negative and C. albicans. HPLC-MS analysis revealed presence of lipidic compounds with surfactant properties. Conclusions: The abundant lipidic molecules present in the analyzed fraction likely account for the antimicrobial effects through affecting membrane structure of microorganisms supporting the traditional wound-healing uses of T. corymbosus in ancestral medicine. Full article

To address the challenges faced by loquat detection algorithms in orchard settings—including complex backgrounds, severe branch and leaf occlusion, and inaccurate identification of densely clustered fruits—which lead to high computational complexity, insufficient real-time performance, and limited recognition accuracy, this study proposed a lightweight detection model based on the YOLO-MCS architecture. First, to address fruit occlusion by branches and leaves, the backbone network adopts the lightweight EfficientNet-b0 architecture. Leveraging its composite model scaling feature, this significantly reduces computational costs while balancing speed and accuracy. Second, to deal with inaccurate recognition of densely clustered fruits, the C2f module is enhanced. Spatial Channel Reconstruction Convolution (SCConv) optimizes and reconstructs the bottleneck structure of the C2f module, accelerating inference while improving the model’s multi-scale feature extraction capabilities. Finally, to overcome interference from complex natural backgrounds in loquat fruit detection, this study introduces the SimAm module during the initial detection phase. Its feature recalibration strategy enhances the model’s ability to focus on target regions. According to the experimental results, the improved YOLO-MCS model outperformed the original YOLOv8 model in terms of Precision (P) and mean Average Precision (mAP) by 1.3% and 2.2%, respectively. Additionally, the model reduced GFLOPs computation by 34.1% and Params by 43.3%. Furthermore, in tests under complex weather conditions and with interference factors such as leaf occlusion, branch occlusion, and fruit mutual occlusion, the YOLO-MCS model demonstrated significant robustness, achieving mAP of 89.9% in the loquat recognition task. The exceptional performance serves as a robust technical base on the development and research of intelligent systems for harvesting loquats. Full article

The use of unconventional water sources, such as those from marine desalination plants, is challenging for agriculture due to boron concentrations exceeding 0.5 mg L⁻¹, which can impact crop yield and quality. To ensure sustainability, it is crucial to understand crop responses to high boron levels and to develop strategies to mitigate its toxic effects. This study evaluated the impact of irrigation with a nutrient solution containing 15 mg L⁻¹ of boron on tomato plants (Solanum lycopersicum L.). To modulate the physiological effects of boron toxicity, two biostimulant products based on an extract from the brown alga Laminaria digitata and other active ingredients were applied foliarly. Agronomic, nutritional, and metabolic parameters were analyzed, including total yield, number of fruits per plant, and fruit quality. Additionally, mineral analysis and metabolomic profiling of leaves and fruits were performed, focusing on amino acids, organic acids, sugars, and other metabolites. A control treatment was irrigated with a nutrient solution containing 0.25 mg L⁻¹ of boron. The results showed that a boron concentration of 15 mg L⁻¹ significantly reduced total yield by 45% and significantly decreased fruit size and firmness. Mineral and metabolomic analyses showed significant reductions in Mg and Ca concentrations, significant increases in P and Zn levels, excessive boron accumulation in leaves and fruits, and significant changes in metabolites associated with nitrogen metabolism and the Krebs cycle. Biostimulant application did not significantly improve agronomic performance, likely due to high boron accumulation in the leaves, although significant changes were detected in leaf nutritional status and metabolic profiles. Full article

With the ongoing miniaturization of smart devices, fine-grained hand gesture recognition using millimeter-wave radar has attracted increasing attention, yet practical deployment remains challenging in continuous-gesture segmentation, robust feature extraction, and reliable classification. This paper presents an end-to-end fine-grained gesture recognition framework based on frequency modulated continuous wave(FMCW) millimeter-wave radar, including gesture design, data acquisition, feature construction, and neural network-based classification. Ten gesture types are recorded (eight valid gestures and two return-to-neutral gestures); for classification, the two return-to-neutral gesture types are merged into a single invalid class, yielding a nine-class task. A sliding-window segmentation method is developed using short-time Fourier transformation(STFT)-based Doppler-time representations, and a dataset of 4050 labeled samples is collected. Multiple signal classification(MUSIC)-based super-resolution estimation is adopted to construct range–time and angle–time representations, and instance-wise normalization is applied to Doppler and range features to mitigate inter-individual variability without test leakage. For recognition, a variable-channel deep residual shrinkage network (DRSN) is employed to improve robustness to noise, supporting single-, dual-, and triple-channel feature inputs. Results under both subject-dependent evaluation with repeated random splits and subject-independent leave one subject out(LOSO) cross-validation show that DRSN architecture consistently outperforms the RefineNet-based baseline, and the triple-channel configuration achieves the best performance (98.88% accuracy). Overall, the variable-channel design enables flexible feature selection to meet diverse application requirements. Full article

At present, fruit picking mainly relies on manual operation. Taking the litchi (litchi chinensis Sonn.)-picking robot as an example, visual perception is often affected by illumination variations, low recognition accuracy, complex maturity judgment, and occlusion, which lead to inaccurate fruit localization. This study aims to establish an embodied perception mechanism based on “perception-reasoning-execution” to enhance the visual perception and decision-making capability of the robot in complex orchard environments. First, a Y-LitchiC instance segmentation method is proposed to achieve high-precision segmentation of litchi clusters. Second, a generative artificial intelligence model is introduced to intelligently assess fruit maturity and occlusion, providing auxiliary support for automatic picking. Based on the auxiliary judgments provided by the generative AI model, two types of dynamic harvesting decisions are formulated for subsequent operations. For unoccluded main fruit-bearing branches, a skeleton thinning algorithm is applied within the segmented region to extract the skeleton line, and the midpoint of the skeleton is used to perform the first type of localization and harvesting decision. In contrast, for main fruit-bearing branches occluded by leaves, threshold-based segmentation combined with maximum connected component extraction is employed to obtain the target region, followed by skeleton thinning, thereby completing the second type of dynamic picking decision. Experimental results show that the Y-LitchiC model improves the mean average precision (mAP) by 1.6% compared with the YOLOv11s-seg model, achieving higher accuracy in litchi cluster segmentation and recognition. The generative artificial intelligence model provides higher-level reasoning and decision-making capabilities for automatic picking. Overall, the proposed embodied perception mechanism and dynamic picking strategies effectively enhance the autonomous perception and decision-making of the picking robot in complex orchard environments, providing a reliable theoretical basis and technical support for accurate fruit localization and precision picking. Full article