Search Results (5,321)

Chrysanthemum has a long history of medicinal use with rich germplasm resources and extensive cultivation. Traditional chrysanthemum cultivation involves complex patterns and long flowering periods, with the ongoing expansion of planting areas complicating statistical surveys. Currently, reliable, timely, and universally applicable standardized monitoring methods for chrysanthemum cultivation areas remain underdeveloped. This research employed 16 m resolution satellite imagery spanning 2021 to 2023 alongside 2 m resolution data acquired in 2022 to quantify chrysanthemum cultivation extent across Sheyang County, Jiangsu Province, China. After evaluating multiple classifiers, Maximum Likelihood Classification was selected as the optimal method. Subsequently, time-series-based post-classification processing was implemented: initial cultivation information extraction was performed through feature comparison, supervised classification, and temporal analysis. Accuracy validation via Overall Accuracy, Kappa coefficient, Producer’s Accuracy, and User’s Accuracy identified critical issues, followed by targeted refinement of spectrally confused features to obtain precise area estimates. The chrysanthemum cultivation area in 2022 was quantified as 46,950,343 m² for 2 m resolution and 46,332,538 m² for 16 m resolution. Finally, the conversion ratio characteristics between resolutions were analyzed, yielding adjusted results of 38,466,192 m² for 2021 and 47,546,718 m² for 2023, respectively. These outcomes demonstrate strong alignment with local agricultural statistics, confirming method viability for chrysanthemum cultivation area computation. Full article

The market for plant-based alternatives to animal foods has increased rapidly in the past decade, mainly due to consumer demand. Little evidence is available regarding nutritional impacts, drivers, and barriers to using these products as substitutes for animal foods in real-life conditions. This pilot study followed 16 Danish adults (30 ± 11 years old; 11 females) for 4 weeks with substituting milk, cheese, and yogurt with plant-based analogues to dairy (PBADs) and assessed their drivers and barriers to applying the intervention with a mixed-method approach. PBADs are constantly compared to their animal counterparts, both regarding product characteristics, such as price and sensory properties, as well as cultural roles and subjective memories. The mixed methods showed dairy attachment, price, and taste were the main barriers to consuming PBAD, while changes in life and social circles were drivers (qualitative data). As for the liking of PBADs, plant-based yoghurt was the preferred intervention product (73.5/100, p < 0.05), followed by plant-based drinks (65.9/100), while plant-based cheese was the lowest rated (47.9/100, p < 0.05). As for dietary changes, a lower average intake of sugars, saturated fatty acids, cholesterol, calcium, phosphorus, and zinc was observed after the intervention. Additionally, this study describes the attachment of the study population to milk and dairy products. It shows that choosing dairy is beyond nourishment but is connected to tradition, culture, pleasure, memories, and a sense of belonging. In contrast, there is no history or attachment to PBADs. Full article

Forest ginseng (FG) is a rare medicinal and culinary plant in China, and its drying quality is heavily dependent on the drying method. This study investigated the effects of traditional hot air drying (HAD) and the self-developed negative-pressure circulating airflow-assisted desiccator drying (PCAD) method on the quality of FG using metabolomics and enzyme activity. The results revealed that the enzyme activities of dried FG were reduced considerably. PCAD preserved higher enzyme activity than HAD. Metabolomics data demonstrate that HAD promotes the formation of primary metabolites (amino acids, lipids, nucleotides, etc.), whereas PCAD promotes the formation of secondary metabolites (terpenoids, phenolic acids, etc.). A change-transformation network was built by combining the metabolites listed above and their biosynthetic pathways, and it was discovered that these biosynthetic pathways were primarily associated with the mevalonate (MVA) pathway, lipid metabolism, phenylpropane biosynthesis, and nucleotide metabolism. It is also believed that these findings are related to the chemical stimulation induced by thermal degradation and the ongoing catalysis of enzyme responses to drought stress. The facts presented above will give a scientific basis for the selection of FG drying processes, as well as helpful references for increasing the nutritional quality of processed FG. Full article

Adulteration detection in medicinal plant powders remains a critical challenge in quality control. In this study, we propose a hyperspectral imaging (HSI)-based method combined with deep learning models to quantitatively analyze adulteration levels in Anoectochilus roxburghii powder. After preprocessing the spectral data using raw, first-order, and second-order Savitzky–Golay derivatives, we systematically evaluated the performance of traditional machine learning models (Random Forest, Support Vector Regression, Partial Least Squares Regression) and deep learning architectures. While traditional models achieved reasonable accuracy (R² up to 0.885), their performance was limited by feature extraction and generalization ability. A single-channel convolutional neural network (CNN) utilizing individual spectral representations improved performance marginally (maximum R² = 0.882), but still failed to fully capture the multi-scale spectral features. To overcome this, we developed a multi-channel CNN that simultaneously integrates raw, SG-1, and SG-2 spectra, effectively leveraging complementary spectral information. This architecture achieved a significantly higher prediction accuracy (R² = 0.964, MSE = 0.005), demonstrating superior robustness and generalization. The findings highlight the potential of multi-channel deep learning models in enhancing quantitative adulteration detection and ensuring the authenticity of herbal products. Full article

Background/Objectives: Possessing red and white ecotypes, and utilized in traditional Guyanese medicine, Doliocarpus dentatus’ red ecotype is preferred locally for its purported superior therapeutic efficacy. Although therapeutic metabolites were detected in D. dentatus previously, phytohormones remain largely unexplored, until now. Cytokinins, phytohormones responsible for plant cell division, growth and differentiation, are gaining traction for their therapeutic potential in human health. This study screened and quantified endogenous cytokinins and correlated detected cytokinins with selected secondary metabolites. Methods: Liquid chromatography–mass spectrometry was used to acquire phytohormone and metabolite data. Bioinformatics tools were used to assess untargeted metabolomics datasets via statistical and pathway analyses, and chemical groupings of putative metabolites. Results: In total, 20 of the 35 phytohormones were detected and quantified in both ecotypes, with the red ecotype displaying higher free base and glucoside cytokinin concentrations and exhibited 6.2 times the total CK content when compared to the white ecotype. Pathway analysis revealed flavonoid and monoterpenoid biosynthesis in red and white ecotypes, respectively. Positive correlations between specific cytokinins and alkaloids, and between trans-Zeatin and isopentenyladenosine riboside with phenolic compounds were observed. Conclusions: These results suggest that the red ecotype’s elevated cytokinin levels coupled with flavonoid biosynthesis enrichment support its preference in Guyanese traditional medicine. Full article

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

Skin disorders are common and often chronic conditions with significant therapeutic challenges. Limitations of conventional treatments, such as adverse effects and antimicrobial resistance, have increased interest in plant-based alternatives. This article presents the phytochemical composition and pharmacological potential of several medicinal plants traditionally used in the treatment of skin diseases, including Rubus vulgaris, Plantago major, Artemisia terrae-albae, and Eryngium planum. Based on an analysis of scientific literature, the presence of bioactive compounds—including flavonoids, anthocyanins, phenolic acids, tannins, and sesquiterpenes—is summarized, along with their antioxidant, anti-inflammatory, and antimicrobial effects. Emphasis is placed on the correlation between traditional ethnomedicinal applications and pharmacological mechanisms. The findings support the potential of these species as sources for dermatological phytotherapeutics. Further research is needed to standardize active constituents, assess safety, and conduct clinical validation. Full article

Corn is one of the most consumed cereals in the Mexican diet. In this country, there are multiple varieties that exhibit nutraceutical potential due to their content of different metabolites with biological activity, such as blue corn. Another variety that has received little study is the red pigmented corn variety Chilac from Puebla, Mexico, which is being studied for its nutraceutical potential. A differential extraction using the Soxhlet method was carried out to evaluate the phenolic content, total flavonoid content, and monomeric anthocyanins, and free radical scavenging test was performed using the DPPH reagent. A proximate analysis was also conducted to identify the main macronutrients. The results of the proximate analysis were comparable to those of other traditional corn varieties, with carbohydrates being the macronutrient present in the highest amount at 77.9%. Regarding phenolic content and the presence of anthocyanins, the best extractions were obtained using alcoholic solvents; for example, ethanol for phenols, yielding 1368.420 ± 104.094 mg of gallic acid equivalents (GAE)/kg plant. In contrast, the flavonoid content was higher in the aqueous extract, with 833.984 ± 65.218 mg QE/Kg. In the case of the DPPH assay, the best result was obtained with ethyl acetate (73.81 ± 5.31%). These findings provide a foundation for expanding the use of corn varieties with nutraceutical potential, opening the possibility of studies focused on deeper characterization. Full article

The Amazon region is home to a remarkable diversity of plant species that are used in traditional medicine and cuisine. This study aimed to evaluate the functional, antioxidant, and antimicrobial properties of the leaves of Allium schoenoprasum, Brugmansia candida (white and pink), and Cyclanthemum bipartitum. Bioactive compounds (L-ascorbic acid, organic acids, carotenoids, phenolic compounds, and chlorophylls) were quantified using liquid chromatography. The ABTS and DPPH methods were used to assess the antioxidant capacity. Additionally, the antimicrobial activity against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Streptococcus mutans, Candida albicans, and Candida tropicalis was evaluated. The results revealed a high content of L-ascorbic acid (7.6 mg/100 g dry weight) and total carotenoids (509.0 mg/100 g dry weight), as well as high antioxidant capacity (4.5 mmol TE/100 g dry weight) and broad antimicrobial activity in Brugmansia candida ‘pink’. The White variety had the highest concentration of total chlorophylls (1742.8 mg/100 g DW), Cyclanthemum bipartitum had the highest total organic acid content (2814.5 mg/100 g DW), and Allium schoenoprasum had the highest concentration of total phenolic compounds (11,351.6 mg/100 g DW). These results constitute a starting point for future research, emphasizing the potential health risks that certain species may pose. Full article

With the increasing awareness of energy consumption issues, there has been a growing emphasis on energy-saving retrofits for central air-conditioning systems that constitute a significant proportion of energy consumption in buildings. Efficient energy utilization can be achieved by optimizing the modeling of the equipment within the chiller plants of central air-conditioning systems. Traditional modeling approaches have been static and have focused on modeling within narrow time frames when a certain amount of equipment operating data has accumulated, thus prioritizing the precision of the model itself while overlooking the fact that energy-saving retrofits are a long-term process. This study proposes a modeling scheme for the equipment within chiller plants throughout the energy-saving retrofit process. Based on the differences in the amount of available operating data for the equipment and the progress of retrofit implementation, the retrofit process was divided into three stages, each employing different modeling techniques and ensuring smooth transitions between the stages. The equipment within the chiller plants is categorized into two types based on the clarity of their operating characteristics, and two modeling schemes are proposed accordingly. Based on the proposed modeling scheme, chillers and chilled-water pumps were selected to represent the two types of equipment. Real operating data from actual retrofit projects was used to model the equipment and evaluate the accuracy of the model predictions. The results indicate that the models established by the proposed modeling scheme exhibit good accuracy at each stage of the retrofit, with the coefficients of variation (CV) remaining below 6.88%. Furthermore, the prediction accuracy improved as the retrofitting process progressed. The modeling scheme performs better on equipment with simpler and clearer operating characteristics, with a CV as low as 0.67% during normal operation stages. This underscores the potential application of the proposed modeling scheme throughout the energy-saving retrofit process and provides a model foundation for the subsequent optimization of the refrigeration system. Full article

Thermal Power Plants (TPPs), as widely used industrial facilities for electricity generation, represent a key task in remote sensing image interpretation. However, detecting TPPs remains a challenging task due to their complex and irregular composition. Many traditional approaches focus on detecting compact, small-scale objects, while existing composite object detection methods are mostly part-based, limiting their ability to capture the structural and textural characteristics of composite targets like TPPs. Moreover, most of them rely on single-modality data, failing to fully exploit the rich information available in remote sensing imagery. To address these limitations, we propose a novel Cross-Modal Frequency-Responsive Attention Network (CFRANet). Specifically, the Modality-Aware Fusion Block (MAFB) facilitates the integration of multi-modal features, enhancing inter-modal interactions. Additionally, the Frequency-Responsive Attention (FRA) module leverages both spatial and localized dual-channel information and utilizes Fourier-based frequency decomposition to separately capture high- and low-frequency components, thereby improving the recognition of TPPs by learning both detailed textures and structural layouts. Experiments conducted on our newly proposed AIR-MTPP dataset demonstrate that CFRANet achieves state-of-the-art performance, with a ${\mathrm{mAP}}_{50}$ of 82.41%. Full article

Bassia scoparia (Syn. Kochia scoparia (L.) Schrad.) is a medicinal plant whose fruit, Kochiae Fructus, has been extensively studied for its dermatological applications. This study focused on extracts from the whole plant B. scoparia (WPBS), excluding fruits, to address the research gap regarding the medicinal properties of non-fruit parts. The diverse skin benefits of WPBS, including its anti-photoaging, moisturizing, wound healing, anti-inflammatory, and anti-angiogenic effects, were investigated. The WPBS extract enhanced the viability of keratinocytes (HaCaT) without inducing cytotoxic effects. WPBS significantly reduced matrix metalloproteinase-1 (MMP-1) levels and increased collagen type I alpha 1 (COL1A1) levels (p < 0.01) in fibroblasts exposed to ultraviolet B (UVB) radiation, indicating strong anti-photoaging effects. WPBS upregulated skin hydration markers such as aquaporin-3 (AQP3) and hyaluronan synthase-3 (HAS3) and effectively accelerated fibroblast wound closure compared to the positive control. Furthermore, WPBS substantially downregulated the expression of inflammatory (COX-2 and IL-1β) and angiogenic markers (VEGF). Transcriptome analysis (RNA-seq) confirmed that WPBS suppressed inflammation-related and UV-induced gene expression pathways. Overall, these findings expand the therapeutic scope of B. scoparia beyond its traditional fruit use and suggest that WPBS is a promising botanical ingredient for various skin applications. Full article

Fritillaria unibracteata is a rare and endangered medicinal plant in the Liliaceae family, whose bulbs have been used in traditional Chinese traditional medicine for over 2000 years. The mevalonate (MVA) pathway is involved in the growth, development, response to environmental stress, and active ingredient production of plants; however, the functional characterization of MVA-pathway genes in the Liliaceae family remains poorly documented. In this study, an Acetyl-CoA C-acetyltransferase gene (FuAACT) was first cloned from F. unibracteata. It exhibited structural features of the thiolase family and showed the highest sequence identity with the Dioscorea cayenensis homolog. The K_m, V_max, and K_cat of the recombinant FuAACT were determined to be 3.035 ± 0.215 μM, 0.128 ± 0.0058 μmol/(min·mg), and 1.275 ± 0.0575 min⁻¹, respectively. The optimal catalytic conditions for FuAACT were ascertained to be 30 °C and pH 8.9. It was stable below 50 °C. His361 was confirmed to be a key amino acid residue to enzymatic catalysis by site-directed mutagenesis. Subsequent subcellular localization experiments demonstrated that FuAACT was localized in chloroplasts and cytoplasm. FuAACT-overexpressing transgenic Arabidopsis thaliana plants showed higher drought tolerance than wild-type plants. This phenotypic difference was corroborated by significant differences in seed germination rate, lateral root number, plant height, and leaf number (p < 0.05). Furthermore, the FuAACT transgenic plants resulted in the formation of a more developed fibrous root system. These results indicated that the FuAACT gene revealed substantial biological activity in vitro and in vivo, hopefully providing the basis for its further research and application in liliaceous ornamental and medicinal plants. Full article

The cessation of traditional mountain grazing has emerged as a principal driver of habitat degradation and the local extinction of Parnassius apollo (Linnaeus, 1758) in Serbia. While previous studies have cited multiple contributing factors, our research provides evidence that the abandonment of extensive livestock grazing has triggered vegetation succession, the disappearance of the larval host plant (Sedum album), and a reduction in microhabitat heterogeneity—conditions essential for the persistence of this stenophagous butterfly species. Through satellite-based analysis of vegetation dynamics (2015–2024), we identified clear structural differences between habitats that currently support populations and those where the species is no longer present. Occupied sites were characterized by low levels of exposed soil, moderate grass coverage, and consistently high shrub and tree density, whereas unoccupied sites exhibited dense encroachment of grasses and woody vegetation, leading to structural instability. Furthermore, MODIS-derived indices (2010–2024) revealed a consistent decline in vegetation productivity (GPP, FPAR, LAI) in succession-affected areas, alongside significant correlations between elevated land surface temperatures (LST), thermal stress (TCI), and reduced photosynthetic capacity. A wildfire event on Mount Stol in 2024 further exacerbated habitat degradation, as confirmed by remote sensing indices (BAI, NBR, NBR2), which documented extensive burn scars and post-fire vegetation loss. Collectively, these findings indicate that the decline of P. apollo is driven not only by ecological succession and climatic stressors, but also by the abandonment of land-use practices that historically maintained suitable habitat conditions. Our results underscore the necessity of restoring traditional grazing regimes and integrating ecological, climatic, and landscape management approaches to prevent further biodiversity loss in montane environments. Full article

Asphalt plant reclaimed powder is a common solid waste in road engineering. Reusing reclaimed powder as filler holds significant importance for environmental protection and resource conservation. The key factors affecting the feasibility of reclaimed powder reuse are its acidity/alkalinity and cleanliness. Traditional evaluation methods, such as the methylene blue test and plasticity index, can assess reclaimed powder properties to guide its recycling. However, these methods suffer from inefficiency, strong empirical dependence, and high variability. To address these limitations, this study proposes a rapid and precise evaluation method for reclaimed powder properties based on Fourier transform infrared spectroscopy (FTIR). To do so, five field-collected reclaimed powder samples and four artificial samples were evaluated. Scanning electron microscopy (SEM), X-ray fluorescence spectroscopy (XRF), and X-ray diffraction (XRD) were employed to characterize their microphase morphology, chemical composition, and crystal structure, respectively. Subsequently, FTIR was used to establish correlations between key acidity/alkalinity, cleanliness, and multiple characteristic peak intensities. Representative infrared characteristic peaks were selected, and a quantitative functional group index (Is) was proposed to simultaneously evaluate acidity/alkalinity and cleanliness. The results indicate that reclaimed powder primarily consists of tiny, crushed stone particles and dust, with significant variations in crystal structure and chemical composition, including calcium carbonate, silicon oxide, iron oxide, and aluminum oxide. Some samples also contained clay, which critically influenced the reclaimed powder properties. Since both filler acidity/alkalinity and cleanliness are affected by clay (silicon/carbon ratio determining acidity/alkalinity and aluminosilicate content affecting cleanliness), this study calculated four functional group indices based on FTIR absorption peaks, namely the Si-O-Si stretching vibration (1000 cm⁻¹) and the CO₃²⁻ asymmetric stretching vibration (1400 cm⁻¹). These indices were correlated with conventional testing results (XRF for acidity/alkalinity, methylene blue value, and pull-off strength for cleanliness). The results show that the Is index exhibited strong correlations (R² = 0.89 with XRF, R² = 0.80 with methylene blue value, and R² = 0.96 with pull-off strength), demonstrating its effectiveness in predicting both acidity/alkalinity and cleanliness. The developed method enhances reclaimed powder detection efficiency and facilitates high-value recycling in road engineering applications. Full article