Search Results (1,005)

Natural antioxidants are essential for protecting the body against oxidative stress and exhibit a wide range of biological activities. In this context, forty extracts derived from ten submerged cultivated mushroom species were analyzed for their mycochemical composition, antioxidant capacity, and cytotoxic effects against MCF7 breast cancer cells. Qualitative and quantitative screening revealed that, among the detected classes of bioactive compounds, the extracts were predominantly enriched in flavonoids, terpenoids, and phenolic constituents. Considerable variation was observed in the levels of total phenolics, flavonoids, and ascorbic acid among different species and solvent extracts. The highest total phenolic contents were detected in ethanol and ethyl acetate extracts of G. frondosa (110.0 ± 6.4, 227.6 ± 14.2, and 160.5 ± 5.3 mg GAE/g), while the water extract of F. velutipes also exhibited elevated phenolic levels (119.2 ± 6.5 mg GAE/g). Flavonoid concentrations ranged from 102.5 ± 10.5 to 359.9 ± 2.5 mg QE/g in biomass and culture liquid extracts obtained with organic solvents. Ascorbic acid content was generally highest in ethyl acetate culture liquid extracts, suggesting solvent-dependent enrichment of antioxidant metabolites. Free radical scavenging activity increased in a concentration-dependent manner, reaching inhibition values more than 90% at 20 mg/mL in all tested mushrooms. Cytotoxicity assays demonstrated that extract type, solvent, and incubation time strongly influenced the inhibition of MCF7 cell viability. Ethyl acetate extracts from H. erinaceus, P. ostreatus, T. versicolor, and T. pubescens exhibited the strongest cytotoxic effects, reducing cell viability by up to 70% at higher concentrations. The results demonstrate that mushroom extracts, particularly ethyl acetate extracts, possess significant antioxidant and cytotoxic activities. These findings highlight their potential as promising natural sources of medicinal bioactive compounds for antioxidant and anticancer applications. Full article

Timely and accurate monitoring of surface water dynamics via remote sensing is critical, given water resources’ importance. However, accurate water body delineation based on high-resolution remotely sensed imagery is still challenging due to the complexity of water bodies’ boundaries and the diversity of their shapes and sizes, which can lead to boundary ambiguity and varying degrees of confusion with near-water vegetation in water body maps. To address this challenge, we introduce an uncertainty-aware hybrid CNN–Transformer model for delineating water bodies using remotely sensed imagery. In our designed network, a multi-scale transformer (MST) module is first designed to effectively model and refine the multi-scale global semantic dependencies of water bodies. Subsequently, an uncertainty-guided multi-scale information fusion (MSIF) module is constructed to extract water body mapping information from these multi-scale features output from the MST module and fuse them adaptively. Across different scales, the extracted features differ in their ability to distinguish water bodies from non-water bodies and in their levels of uncertainty. Consequently, during the adaptive fusion of multi-scale water body information in the MSIF module, the mapping uncertainty is quantified and suppressed to minimize its impact, thus yielding enhanced precision in water body delineation. Ultimately, a comprehensive loss function is designed for model optimization to generate the final water body map. Furthermore, to promote water body segmentation models’ development, this study also presents the HBD_Water water body sample dataset, which contains 44 multispectral, 5000 × 5000-pixel images at 2 m spatial resolution, and will be released on the LuojiaSET platform soon. Finally, to verify the proposed model and its constituent MST and MSIF modules, extensive water mapping experiments were performed on three datasets. The experimental results substantiate their effectiveness. Furthermore, comparative experiment results demonstrate that the proposed model performs better at water body extraction than advanced networks including TransUNet, DeeplabV3+, and ADCNN. Full article

This study evaluated the effects of including palm kernel oil in the diets of pasture-raised sheep on carcass characteristics, meat quality, and fatty acid profiles. A completely randomized design with four treatments was used, consisting of 0, 20, 40, and 60 g/kg of palm kernel oil in the dry matter of the supplement, with eight replicates. Thirty-two uncastrated Santa Inês sheep, with an average initial body weight of 23.2 ± 2.6 kg, were used in this study. The animals were kept on Aruana grass (Panicum maximum (syn. Megathyrsus maximum) cv. Aruana) pastures under continuous stocking for 59 days (preceded by 15 days of adaptation), with each one fed supplements (1.4% of body weight) at 8 am. At the end of the experimental period, the animals were slaughtered in a commercial slaughterhouse for carcass and meat quality evaluation. The inclusion of palm kernel oil had a decreasing linear effect on hot and cold carcass weight (p = 0.0403) (p = 0.0398), but it did not affect hot or cold carcass yields or carcass morphometric measurements, commercial cut weights, pH, or loin area (p > 0.05). However, it affected the color of the L. lumborum muscle, showing an increasing linear effect on yellow intensity (b*) (p = 0.002) and on the centesimal composition, with an increasing linear effect on ether extract content (p = 0.006). Shear force, cooking loss, and water-holding capacity were not affected (p > 0.05). Fatty acid profiles, the atherogenicity and thrombogenicity indices, and the ratio of hypocholesterolemic to hypercholesterolemic fatty acids (h:H) were also unaffected by the inclusion of palm kernel oil (p > 0.05). The inclusion of up to 60 g/kg of palm kernel oil in the diets of pasture-raised sheep had an effect on carcass weight but not yield. It also had an effect on the color and chemical composition of L. lumborum muscle, but these changes did not compromise the overall quality of the meat. Full article

In wetland ecological monitoring, accurate acquisition of water bodies is particularly crucial, especially for hydrological monitoring and eutrophication control. Water bodies can be clearly delineated by using optical remote sensors. Optical sensors can clearly delineate water boundaries and features when extracting water bodies via remote sensing. Meanwhile, synthetic aperture radar (SAR), with its unique microwave capabilities, can easily penetrate vegetation and operate regardless of weather conditions, enabling all-weather monitoring. Each sensor type exhibits distinct advantages in water body monitoring and research. This study focuses on Caohai Wetland in Guizhou Province, utilizing data from the optical satellite Zhuhai-1 (launched by China in 2017) and the radar satellite RadarSat-2 (launched by Canada) at identical resolutions during the same period. Five supervised classification methods were applied to extract water bodies using optical imagery within the wetland area, with results evaluated against SAR data. Results indicate that the optimal water body extraction methods based on optical and SAR data are Random Forest Classification and Support Vector Machine classification, respectively, achieving an overall accuracy of 0.896 and 0.940, with Kappa coefficients of 0.791 and 0.879. The water area extracted using SAR was significantly larger than that based on optical data, thereby identifying areas within Caohai Wetland that were not fully submerged in vegetation during this period. This study holds significant implications for accurate water body extraction and analysis benefited an improved monitoring and conserving the wetland environment. Full article

Multimodal fusion of synthetic aperture radar (SAR) and optical imagery is widely used in Earth observation for applications such as land-cover mapping and surface-water mapping (including post-event flood mapping under near-synchronous acquisitions) and land-use inventory. Optical images provide rich spectral and texture cues, whereas SAR offers all-weather structural information that is complementary but heterogeneous. In practice, this heterogeneity often introduces fusion conflicts in multi-class segmentation, causing critical categories such as water bodies to be under-optimized. To address this issue, this paper presents a SAR-guided class-aware knowledge distillation (SGCAD) method for multimodal semantic segmentation. First, a SAR-only HRNet is trained as a water-expert teacher to learn discriminative backscattering and boundary priors for water extraction. Second, a lightweight multimodal student model (LightMCANet) is optimized using a class-aware distillation strategy that transfers teacher knowledge only within high-confidence water regions, thereby suppressing noisy supervision and reducing interference to other classes. Third, a SAR edge guidance module (SEGM) is introduced in the decoder to enhance boundary continuity for slender structures such as water bodies and roads. Overall, SGCAD improves targeted category learning while maintaining stable performance across the remaining classes. Experiments on a self-built dataset from GF-1 optical and LuTan-1 SAR imagery demonstrate higher overall accuracy and more coherent water/road predictions than representative baselines. Future work will extend the proposed distillation scheme to additional categories and broader geographic scenes. Full article

Effluents from industries, manufacturing companies, textile looms, and floodwater contaminate the surface water reservoirs. This endangers the quality of water for use by humans. Wastewater remediation is one of the ways to recycle the dirty water and make it suitable for use. Photocatalysis is the most common method for wastewater remediation, especially using Titanium dioxide (TiO₂) nanoparticles. However, chemical synthesis and direct addition of nanoparticles may cause toxicity to the flora and fauna present in the water body. To address this limitation, we have green-synthesized TiO₂ nanoparticles using a horticulture waste, Calendula officinalis dried flower extract and entrapped them in a natural polymer, chitosan (CTS-TiO₂-CO nanocomposite). The polymer entrapment ensures biocompatibility as well as reduced aggregation of nanoparticles. The synthesized CTS-TiO₂-CO nanocomposite was characterized using UV-visible spectrophotometry, dynamic light scattering, zeta potential, Fourier Transformed Infrared Spectroscopy (FTIR), X-ray diffractometry (XRD), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDAX) analysis. The absorption peak was found at 302 nm, and the hydrodynamic diameter at 490 nm. SEM images show flower-like morphology with 326 nm average particle diameter. The non-toxic dose of the nanoparticles was estimated by MTT assay and zebrafish embryo developmental studies. More than 82% fibroblast cells were viable after treatment with 100 μg/mL of CTS-TiO₂-CO nanocomposite. 85% embryos hatched after treatment with 50 μg/mL of CTS-TiO₂-CO nanocomposite. Further, the textile dye remediation assessment was done using the dye crystal violet, exhibiting 69.19% dye degradation after 4 h of sunlight exposure. Altogether, the results demonstrate that the CTS-TiO₂-CO nanocomposite was effective in the remediation of crystal violet without causing any toxicity up to a dose of 100 μg/mL. Full article

With the increasing demand for detecting small water bodies in remote sensing imagery, particularly in Synthetic Aperture Radar (SAR) images, traditional water body extraction models often struggle to capture fine details and accurately delineate boundaries. To address these challenges, this paper introduces the integration of Frequency-Selective Deformable Convolution (FSDC) into the TransUNet architecture, optimizing water body extraction in SAR imagery. FSDC enhances feature representation in both the frequency and spatial domains. It does so through two key components: (1) The Frequency Selection Module, which employs Fourier transform to selectively enhance or suppress features across different frequency bands, thereby emphasizing the unique structure and boundaries of water bodies. (2) The Deformable Convolution Unit, which dynamically adjusts the receptive field via content-based sampling, allowing it to adapt to local variations at multiple scales and improve fine detail capture. After incorporating FSDC into the decoder of TransUNet, experimental results on the NY and C2S-MS datasets show a significant improvement in extraction accuracy, especially in detecting small water bodies. These findings underscore the effectiveness of the FSDC mechanism for small water body extraction from SAR imagery, offering a novel solution for precise water body analysis in remote sensing. Full article

Swim bladders in some teleost fish can act as gas-filled cavities that oscillate under acoustic pressure and transfer the sound energy to the inner ears. Quantifying the resonance frequency and damping of these oscillations is useful for linking swim bladder mechanics to hearing-related and behavioural questions, but many established direct-measure approaches have relied on open-water deployments and careful avoidance of boundary reflections, making experiments logistically demanding and difficult to reproduce (e.g., requiring deep-water sites, careful control of surface/boundary reflections, and complex deployment geometries). This study presents a compact laboratory methodology for estimating swim bladder resonance properties using a closed, fully water-filled, stainless-steel impedance tube. Broadband pseudorandom excitation is applied via an end-plate shaker, and the acoustic response of the system is recorded using wall-mounted hydrophones. Resonance peaks are identified using power spectral estimates of recorded signals, allowing resonance frequency and quality factor to be extracted from the peak location and −3 dB bandwidth. The approach is first established using inflated latex balloons as surrogate encapsulated gas cavities, providing a controlled benchmark for repeatability and interpretation. It is then applied to recently euthanised brown trout (Salmo trutta), where clear resonance features attributable to the swim bladder are observed and show systematic variation with body size. A coupled finite element model reproduces the principal resonance behaviour under the experimental loading and supports interpretation of the measured peaks as swim bladder resonance. The results provide a validated foundation for subsequent non-invasive measurements on live, free-swimming fish, as well as for future applications where swim bladder condition may be relevant to management or conservation. Full article

The Copernicus Sentinel-2 (S2) data are a crucial resource for urban policymakers in land-cover classification, offering a freely accessible alternative to expensive commercial data sources. While medium spatial resolution often limits the applicability of data-intensive machine learning approaches, the Geographic Object-Based Image Analysis (GEOBIA) framework could be an effective, operational alternative for urban land-cover classification using S2 data. This study applies the Geographic Object-Based Image Analysis (GEOBIA) approach to classify land cover in Hue, Vietnam, using Sentinel-2 data processed through the eCognition interface. The study’s findings emphasize the potential of GEOBIA and S2 data in enhancing decision-making processes for city authorities, ensuring better resource allocation, environmental protection, and infrastructure development. The results indicate that the method performs reliably for mesoscale and spatially continuous classes, such as vegetation and built-up surfaces, while accuracy is lower for small or spectrally heterogeneous features, particularly shallow water bodies and fragmented rice paddies, due to mixed-pixel effects inherent in 10–20 m resolution imagery. The results demonstrate an Overall Accuracy (OA) of 91%, highlighting the method’s effectiveness in extracting and classifying urban land-cover classes. This study demonstrates a replicable model for urban land monitoring that can be adapted across various geographic contexts. Furthermore, this approach fosters a more data-driven governance model, where urban expansion and land-use changes can be monitored in real time, allowing for proactive interventions. With urbanization accelerating worldwide, particularly in rapidly developing regions, such a cost-effective and accessible classification method can significantly aid in achieving long-term urban sustainability. The findings illustrate the relevance of GEOBIA as a feasible tool for supporting data-driven urban governance, enabling systematic tracking of land-use change, informed infrastructure planning, and sustainable urban management in both developed and rapidly urbanizing regions. Full article

Alcohol hangover is a significant health concern worldwide. Java water-dropwort (Oenanthe javanica) has been traditionally used in East Asia for treating hepatitis, jaundice, and alcohol hangovers. This study evaluated the effects of java water-dropwort extract formulation on alcohol metabolism and hangover symptoms. A randomized, double-blind, placebo-controlled crossover trial was conducted with 36 healthy adults aged 19–40 years. Participants received either java water-dropwort extract formulation (8.71 mL equivalent to 6.69 g raw material) or placebo 30 min before alcohol consumption (0.8 g/kg body weight, 20.1% soju). Blood alcohol and acetaldehyde concentrations were measured at multiple time points up to 15 h post consumption. Hangover symptoms were assessed using the Alcohol Hangover Severity Scale and Acute Hangover Scale. A total of 36 participants were enrolled, and 33 completed the study per protocol. Blood alcohol area under the curve (AUC) was significantly lower in the java water-dropwort group (58.626 vs. 66.194 mmol·h/L, p = 0.008). Blood acetaldehyde AUC was also significantly reduced (69.794 vs. 88.205 mg·h/dL, p = 0.031). Hangover symptom scores in the test group were significantly lower than those in the placebo group (4.030 vs. 8.606, p = 0.026). No adverse events occurred. Java water-dropwort extract effectively enhanced alcohol metabolism and improved hangover symptoms, offering potential therapeutic value for hangover management. Full article

Satellite imagery is increasingly being scrutinized through deep learning methodologies for remote sensing applications, particularly focusing on the detection of water bodies. Identification and analysis of rivers, lakes, and reservoirs through segmentation have become feasible, enabling the exploration of their statistical information. During crises such as floods and changes in river pathways, real-time detection of water bodies via remote sensing proves to be highly advantageous. Nevertheless, achieving precise segmentation of water bodies presents a notable challenge, mainly due to the necessity of high-resolution multi-channel satellite images. Existing literature predominantly relies on satellite data from multi-band satellites for water-body extraction. Conversely, the current research emphasizes the segmentation of water-body regions using relatively lower-resolution RGB images without the incorporation of extra multi-spectral channels. To tackle this challenge, a unique methodology is suggested, involving a customized U-Net model integrated with a time-frequency attention network for segmentation. To assess the comprehensive performance of the proposed model, it is evaluated against a publicly available Sentinel-2 satellite dataset, and the outcomes are compared against standard benchmark metrics. The proposed TFA-U-Net model demonstrates superior performance compared to several recent state-of-the-art water-body segmentation models. Experimental results show that the proposed model achieves a precision of 0.94, sensitivity of 0.96, Dice score of 0.93, accuracy of 0.97, and mean IoU of 0.85, indicating its effectiveness for accurate water-body segmentation using low-resolution satellite images. Full article

As a unique cultural heritage of Chinese ethnic minorities, Dong architecture provides rich historical and cultural information. Rapid and accurate extraction of ethnic building information from remote sensing images in complex terrain and high-density settlement environments is highly important for the protection of architectural heritage and the management of rural space. Huanggang Dong Village in Liping County, Guizhou Province, China, is taken as a case study. This paper develops a multifeature fusion machine learning framework for the automatic recognition of Dong ethnic architecture based on centimeter-level visible images captured by UAV. First, the vegetation index, HSI color features and texture features based on the gray level co-occurrence matrix are extracted from the UAV visible light orthophoto image. Through the random forest feature importance ranking and correlation test, six key features, namely, the VDVI, HSI-S, HSI-I, mean, variance and contrast, are selected to construct a multifeature space. This step constitutes the feature construction stage of the proposed methodology and provides the basis for subsequent classification. Second, on the basis of a support vector machine (SVM) and random forest (RF), classification models are constructed. The effects of different feature combinations and different algorithms on classification accuracy are systematically compared, and the results are evaluated in terms of overall accuracy (OA), the kappa coefficient, user accuracy (UA) and producer accuracy (PA). This second part highlights the classification phase of the methodology, which tests the feature space using different algorithms and evaluates the performance of the models. The experimental data fully show that under the condition of a single feature, the SVM model dominated by texture features performs best, with an OA of 85.33% and a kappa of 0.799; under the condition of multifeature fusion, the RF algorithm has a stronger ability to integrate multisource features. The accuracy of building category recognition based on the total feature and dimensionality reduction feature space is particularly prominent. The total feature and overall accuracy reach 89.00%, and the kappa coefficient is 0.850. The UA and PA reached 89.66% and 94.55%, respectively. Through in-depth comparative analysis, the vegetation index–color–texture multifeature fusion and machine learning classification framework based on UAV visible light images can achieve high-precision extraction of Dong architecture without relying on high-cost sensors. It can effectively alleviate the confusion between water bodies and shadows and between dark roofs and vegetation and effectively separate traditional Dong architecture from roads, vegetation and other elements. It provides a low-cost and feasible way for digital archiving, dynamic monitoring and protection management of the traditional village architectural heritage of ethnic minorities. Full article

Thin, dry skin is characterized by impaired barrier integrity, loss of dermal density, and accelerated aging driven by intrinsic and extrinsic factors. Biomimetic collagen peptides mimic native collagen sequences, stimulating fibroblasts to enhance synthesis while limiting matrix metalloproteinase-mediated degradation. This study evaluated the clinical efficacy and safety of a multi-ingredient cosmetic product for thin, dry, aging skin, formulated as a dual-purpose body and face serum lotion containing 0.1% biomimetic collagen tripeptide (Tripeptide-29) along with Niacinamide, Citrullus lanatus fruit extract, and Selaginella lepidophylla extract. In this prospective, single-center study, 47 healthy women, aged 36–65 years with Fitzpatrick skin types I–IV, applied the formula twice daily to the face and body over four weeks. Objective measurements—including elasticity, wrinkle depth and volume, hydration, trans-epidermal water loss (TEWL), and texture—were collected weekly alongside clinical grading and self-assessments. Significant improvements were observed across all parameters, with facial dryness decreasing immediately (−74.6%) and continuing to week 4 (−93.7%), hydration increasing up to 72.5%, softness improving up to 37.7%, roughness decreasing up to 37.9%, and TEWL reductions indicating strengthened barrier function. Desquamation improved by 75.5% by week 3, and no adverse effects occurred. The serum lotion demonstrated robust, well-tolerated benefits for enhancing multiple markers of thin, dry, aging skin. Full article

Nuclear energy has become a promising substitute for traditional fossil fuels (e.g., coal, oil, and natural gas) by reason of its ultra-high energy density, firm power generation, and near-zero carbon emissions. However, the shortage of uranium resources is threatening the sustainable development of nuclear power, and meanwhile the nuclear fuel front-end cycle inevitably causes radioactive uranium-bearing wastewater discharge, resulting in severe environmental pollution. Nowadays, the extraction and enrichment of uranium in seawater and uranium-containing wastewater offer a prospective avenue to secure the long-term viability of nuclear power with environmental conservation. Among numerous strategies, photocatalytic extraction of soluble hexavalent uranyl (U(VI)) over graphitic carbon nitride (g-C₃N₄), a conjugated polymer semiconductor, is increasingly attracting widespread attention due to its high solar energy utilization, environmental friendliness, high selectivity, good stability, and low cost. A comprehensive overview that pinpoints research directions for novice researchers is urgently required. Herein, the development progress of g-C₃N₄-mediated photocatalytic U(VI) extraction is briefly introduced. Subsequently, the possible mechanisms are discussed with the assistance of advanced characterization techniques, and the influential factors for catalytic efficiency are also discussed. Moreover, multiple applications of g-C₃N₄-based catalysts on photocatalytic U(VI) reduction and extraction are elaborated, especially for modularization approaches on a large scale. At length, the future challenges and prospects in photocatalytic uranium extraction from water bodies are proposed. This review aims to offer fundamental insights into designing and exploring novel g-C₃N₄-based photocatalysts for soluble U(VI) enrichment in water bodies, especially opening up new avenues for the future development of sustainable uranium extraction technologies in practice. Full article

Most polyphenols (and tannins in their composition), secondary plant metabolites with positive effects on the human body, are soluble in water, which makes them environmentally friendly and the most accessible solvent in everyday life. The aim of this study was to examine the effects of water extraction methods, hot water extraction and maceration, on the amounts of these compounds isolated from plants, compared with ultrasonic extraction, which is not readily available. Seven Ericaceae and four Rosaceae species were selected for study, whose leaves are used in folk and/or official medicine to make herbal teas. Total polyphenolics were determined by the Folin–Ciocalteu method spectrophotometrically and total tannins by calculating the difference between the total and remaining polyphenolic content after tannin precipitation. The results demonstrated that ultrasound was not the most effective method for extracting polyphenols: it yielded the highest polyphenol amounts only from two Rosaceae species, Potentilla anserina and Alchemilla vulgaris. The hot water extraction of polyphenols was more effective than maceration. Hot water was more effective in extracting polyphenols from evergreen plants. Regardless of the extraction method, most of the polyphenols were extracted with water from Arctostaphylos uva-ursi and tannins from Rhododendron tomentosum leaves. The studied Ericaceae species accumulate higher-polarity tannins than the studied Rosaceae representatives. Full article