Search Results (638)

Scrophulariae Radix (SR), the dried root of Scrophularia ningpoensis Hemsl (S. ningpoensis), has been extensively used as traditional Chinese medicine for thousands of years. However, since the mid-20th century, the traditional processing technology of S. ningpoensis has been interrupted. Therefore, ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry technology, together with a Global Natural Product Social Molecular Networking (GNPS) method, was applied to comprehensively analyze the characteristic changes and mutual transformation of chemical constituents in the differently processed roots of S. ningpoensis, as well as to scientifically elucidate the processing mechanism of differently processed SR. As a result, a total of 149 components were identified. Notably, with the help of the GNPS data platform and MS² fragment ions, the possible structures of four new compounds (47, 48, 50, and 73) were deduced in differently processed SR samples, in which 47, 48, and 50 are iridoid glycosides, and 73 is a phenylpropanoid glycoside. Five cyclopeptides (78, 86, 97, 99, and 104) derived from leucine (isoleucine) were identified in SR for the first time. The heatmaps analysis results indicated that leucine or isoleucine may be converted to cyclopeptides under the prolonged high-temperature conditions. Moreover, it is found that short-time steaming can effectively prevent the degradation of glycosides by inactivating enzymes. This study provides a new and efficient technical strategy for systematically identifying the chemical components, rapidly discovering the components, and preliminarily clarifying the processing mechanism of S. ningpoensis, as well as also providing a scientific basis for the improvement of the quality standards and field processing of S. ningpoensis. Full article

Neuropathic pain resulting from spinal cord injury (SCI) is a significant secondary health issue affecting around 60% of individuals with SCI. After SCI, activation of microglia, the immune cells within the central nervous system, leads to neuroinflammation by producing pro-inflammatory cytokines and affects neuropathic pain. This interplay between inflammation and pain contributes to the persistent and intense pain experienced by many individuals with SCI. MicroRNAs (miRs) have been critical regulators of neuroinflammation. Previous research in our laboratory has revealed upregulation levels of circulating miR-19a and miR-19b in individuals with SCI with neuropathic pain compared to those without pain. In this study, we treated primary microglial cultures from mice with miR-19a and miR-19b for 24 h and conducted RNA sequencing analysis. Our results showed that miR-19a and miR-19b up- and downregulate different genes according to the volcano plots and the heatmaps. miR-19a and miR-19b regulate inflammation through distinct signaling pathways. The results showed that miR-19a promotes inflammation via toll-like receptor signaling, TNF signaling, and cytokine–cytokine receptor interactions, while miR-19b increases inflammatory responses through the PI3K-Akt signaling pathway, focal adhesion, and extracellular matrix receptor interactions. The protein–protein interaction (PPI) networks used the STRING database to identify transcription factors associated with genes up- or downregulated by miR-19a and miR-19b. Key transcription factors, such as STAT1, STAT2, and KLF4 for miR-19a, and Nr4a1, Nr4a2, and Nr4a3 for miR-19b, were identified and revealed their roles in regulating neuroinflammation. This study demonstrates that miR-19a and miR-19b modulate diverse patterns of gene expression, regulate inflammation, and induce inflammatory responses in microglia. Full article

Pandan (Pandanus amaryllifolius Roxb.) has been used in the production of bakery goods either as a functional ingredient or a natural flavoring that, when roasted, exerts a fragrant rice-like aroma and an attractive green color. This study elucidated the typical aroma compounds from pandan leaves and explored the influence of thermal treatments on their aroma profiles using GC-O-MS, E-nose, and GC-IMS analyses. The effects of formulation and baking conditions on the qualities of pandan-flavored sponge cake were comprehensively evaluated through a holistic approach covering several aspects including cake batter gravity, color, texture, and sensory characteristics. The baking treatment introduced more types of pleasant aromas (9 aromas vs. 17 aromas) and increased the odor intensities of the original volatile compounds, especially for the roasted and steamed rice-like odors. The increased amount of pandan flavoring reshaped the color of the cake crumb (especially for the L* and a* spaces) and significantly decreased the hardness (3.87 N to 1.01 N), gumminess (3.81 N to 0.67 N), and chewiness (13.22 mJ to 4.56 mJ) of the sponge cake. The perceived intensities of bitterness and sweetness can be adjusted by modulating the levels of 2-phenylethanol, 2-methyl-1-butanol, hexyl alcohol, and decanal, along with the total alcohols and aldehydes, due to their significant correlations revealed by correlation heatmap analyses. Full article

This study evaluated the antioxidant and anti-glycemic properties of black lemon Chenpi (BLC) (Citrus limon (L.) Burm. f. cv. Eureka), processed using three thermal browning models—hot-air drying (HAL), high temperature and humidity, and steam-drying cycle (SCL)—and compared them to fresh lemon peel and commercial Chenpi. The moisture-assisted aging technology (MAAT) is an environmentally friendly process for inducing browning reactions in the lemon peel, enhancing its functional properties. Our results demonstrated significant increases in sucrose, total flavonoid content, and antioxidant capacities (2,2-diphenylpicrylhydrazyl: 12.86 Trolox/g dry weight; ferric reducing antioxidant power: 14.92 mg Trolox/g dry weight) with the MAAT-HAL model. The MAAT-SCL model significantly improved the browning degree, fructose, total polyphenol content, narirutin, and 5-hydroxymethylfurfural synthesis (p < 0.05). Additionally, aged lemon peel exhibited potential α-glucosidase inhibitory activity (28.28%), suggesting its role in blood sugar regulation after meals. The multivariate analysis (principal component and heatmap analyses) indicated that BLC processed using the MAAT-SCL model exhibited similarities to commercial Chenpi, indicating its potential for functional food development. Our results indicate that MAAT-SCL can enhance the economic value of lemon by-products, offering a sustainable and functional alternative to traditional Chenpi. Full article

Road surface quality is essential for driver comfort and safety, making it crucial to monitor pavement conditions and detect defects in real time. However, the diversity of defects and the complexity of ambient conditions make it challenging to develop an effective and robust classification and detection algorithm. In this study, we adopted a semi-supervised learning approach to train ResNet-18 for image feature retrieval and then classification and detection of pavement defects. The resulting feature embedding vectors from image patches were retrieved, concatenated, and randomly sampled to model a multivariate normal distribution based on the only one-class training pavement image dataset. The calibration pavement image dataset was used to determine the defect score threshold based on the receiver operating characteristic curve, with the Mahalanobis distance employed as a metric to evaluate differences between normal and defect pavement images. Finally, a heatmap derived from the defect score map for the testing dataset was overlaid on the original pavement images to provide insight into the network’s decisions and guide measures to improve its performance. The results demonstrate that the model’s classification accuracy improved from 0.868 to 0.887 using the expanded and augmented pavement image data based on the analysis of heatmaps. Full article

Lake microbiota play a crucial role in geochemical cycles, influencing both energy flow and material production. However, the distribution patterns of bacterial communities in lake sediments remain largely unclear. In this study, we used 16S rRNA high-throughput sequencing technology to investigate the bacterial structure and diversity in sediments across different locations (six independent lakes) within Lianhuan Lake and analyzed their relationship with environmental factors. Our findings revealed that both the alpha and beta diversity of sediment bacterial communities varied significantly among the six independent lakes. Furthermore, changes between lakes had a significant impact on the relative abundance of bacterial phyla, such as Pseudomonadota and Chloroflexota. The relative abundance of Pseudomonadota was highest in Habuta Lake and lowest in Xihulu Lake, while Chloroflexota abundance was lowest in Habuta Lake and highest in Tiehala Lake. At the genus level, the relative abundance of Luteitalea was highest in Xihulu Lake compared to the other five lakes, whereas the relative abundances of Clostridium, Thiobacillus, and Ilumatobacter were highest in Habuta Lake. Mantel tests and heatmaps revealed that the relative abundance of Pseudomonadota was significantly negatively correlated with pH, while the abundance of Chloroflexota was significantly positively correlated with total phosphorus and total nitrogen in water, and negatively correlated with electrical conductivity. In conclusion, this study significantly enhances our understanding of bacterial communities in the different lakes within the Lianhuan Lake watershed. Full article

Flowering cherry is a renowned ornamental woody plant valued for its landscape applications and economic benefits in gardens. However, waterlogging during the rainy season in some areas causes death and heavy losses. Fortunately, we have found that the flowering cherry ‘Gotenba zakura’ (Prunus incisa Thunberg) is capable of generating aerial roots when subjected to heavy rains and prolonged floods. In this study, we conducted an associated analysis to explore the core regulating network of the aerial root growth mechanism in flowering cherry ‘Gotenba zakura’ by combining phenotypic observations, physiological assays, and transcriptome comparisons across five distinct stages. Through the analysis of the heatmap of DEGs (Differentially Expressed Genes) and the gene co-expression network (GCN), we identified genes that may play critical roles under waterlogging stress. The gene network indicates that aerial roots enhance waterlogging tolerance through ROS degradation, endogenous hormone induction, and energy production. This discovery provides a solid foundation for understanding the waterlogging tolerance of flowering cherry and offers molecular evidence for selecting promising rootstocks for breeding, aimed at improving waterlogging tolerance through grafting. Full article

In agriculture, one of the most crucial elements for sustained plant production is light. Artificial lighting can meet the specific light requirements of various plants. However, it is a challenge to find optimal lighting schemes that can facilitate a balance of plant growth and nutritional qualities. In this study, we experimented with the light intensity required for plant growth and nutrient elements. We designed three light intensity treatments, 180 μmol m⁻² s⁻¹ (L1), 210 μmol m⁻² s⁻¹ (L2), and 240 μmol m⁻² s⁻¹ (L3), to investigate the effect of light intensity on lettuce growth and quality. It can be clearly seen from the radar charts that L2 significantly affected the plant height, fresh weight, dry weight, and leaf area. L3 mainly affected the canopy diameter and root shoot ratio. The effect of L1 on lettuce phenotype was not significant compared with that of the others. The total soluble sugar, vitamin C, nitrate, and free amino acid in lettuce showed more significant increases under the L2 treatment than under the other treatments. In addition, the transpiration rate and stomatal conductance were opposite to each other. The comprehensive evaluation of the membership function value method and heatmap analysis showed that lettuce had the highest membership function value in L2 light intensity conditions, indicating that the lettuce grown under this light intensity could obtain higher yield and better quality. This study provides a new insight into finding the best environmental factors to balance plant nutrition and growth. Full article

Wheat breeding programs are currently focusing on using non-destructive and cost-effective hyperspectral sensing tools to expeditiously and accurately phenotype large collections of genotypes. This approach is expected to accelerate the development of the abiotic stress tolerance of genotypes in breeding programs. This study aimed to assess salt tolerance in wheat genotypes using non-destructive canopy spectral reflectance measurements as an alternative to direct laborious and time-consuming phenological selection criteria. Eight wheat genotypes and sixteen F₈ RILs were tested under 150 mM NaCl in real field conditions for two years. Fourteen spectral reflectance indices (SRIs) were calculated from the spectral data, including vegetation SRIs and water SRIs. The effectiveness of these indices in assessing salt tolerance was compared with four morpho-physiological traits using genetic parameters, SSR markers, the Mantel test, hierarchical clustering heatmaps, stepwise multiple linear regression, and principal component analysis (PCA). The results showed significant differences (p ≤ 0.001) among RILs/cultivars for both traits and SRIs. The heritability, genetic gain, and genotypic and phenotypic coefficients of variability for most SRIs were comparable to those of measured traits. The SRIs effectively differentiated between salt-tolerant and sensitive genotypes and exhibited strong correlations with SSR markers (R² = 0.56–0.89), similar to the measured traits and allelic data of 34 SSRs. A strong correlation (r = 0.27, p < 0.0001) was found between the similarity coefficients of SRIs and SSR data, which was higher than that between measured traits and SSR data (r = 0.20, p < 0.0003) based on the Mantel test. The PCA indicated that all vegetation SRIs and most water SRIs were grouped with measured traits in a positive direction and effectively identified the salt-tolerant RILs/cultivars. The PLSR models, which were based on all SRIs, accurately and robustly estimated the various morpho-physiological traits compared to using individual SRIs. The study suggests that various SRIs can be integrated with PLSR in wheat breeding programs as a cost-effective and non-destructive tool for phenotyping and screening large wheat populations for salt tolerance in a short time frame. This approach can replace the need for traditional morpho-physiological traits and accelerate the development of salt-tolerant wheat genotypes. Full article

The task of localizing distinct anatomical structures in medical image data is an essential prerequisite for several medical applications, such as treatment planning in orthodontics, bone-age estimation, or initialization of segmentation methods in automated image analysis tools. Currently, Anatomical Landmark Localization (ALL) is mainly solved by deep-learning methods, which cannot guarantee robust ALL predictions; there may always be outlier predictions that are far from their ground truth locations due to out-of-distribution inputs. However, these localization outliers are detrimental to the performance of subsequent medical applications that rely on ALL results. The current ALL literature relies heavily on implicit anatomical constraints built into the loss function and network architecture to reduce the risk of anatomically infeasible predictions. However, we argue that in medical imaging, where images are generally acquired in a controlled environment, we should use stronger explicit anatomical constraints to reduce the number of outliers as much as possible. Therefore, we propose the end-to-end trainable Global Anatomical Feasibility Filter and Analysis (GAFFA) method, which uses prior anatomical knowledge estimated from data to explicitly enforce anatomical constraints. GAFFA refines the initial localization results of a U-Net by approximately solving a Markov Random Field (MRF) with a single iteration of the sum-product algorithm in a differentiable manner. Our experiments demonstrate that GAFFA outperforms all other landmark refinement methods investigated in our framework. Moreover, we show that GAFFA is more robust to large outliers than state-of-the-art methods on the studied X-ray hand dataset. We further motivate this claim by visualizing the anatomical constraints used in GAFFA as spatial energy heatmaps, which allowed us to find an annotation error in the hand dataset not previously discussed in the literature. Full article

Public space is a crucial forum for public interaction and diverse activities among urban residents. Understanding how people interact with and perceive these spaces is essential for public placemaking. With billions of users engaging in social media expression and generating millions of data points every second, Social Media Big Data (SMBD) offers an invaluable lens for evaluating public spaces over time, surpassing traditional methods like surveys and questionnaires. This research introduces a comprehensive analytical framework that integrates SMBD with placemaking practices, specifically applied to the city of Xiamen, China. The result shows the social sentiment, vibrancy heatmaps, leisure activities, visitor behaviors, and preferred visual elements of Xiamen, offering urban designers valuable insights into the dynamic nature of citizen experiences. The findings underscore the potential of SMBD to inform and enhance public space design, providing a holistic approach to creating more inclusive, vibrant, and functional urban environments. Full article

Background: Age-related macular degeneration (AMD) is a complex eye disorder with an environmental and genetic origin, affecting millions worldwide. The study aims to explore the association between retinal morphology and the polygenic risk score (PRS) for AMD using fundus images and deep learning techniques. Methods: The study used and pre-processed 23,654 fundus images from 332 subjects (235 patients with AMD and 97 controls), ultimately selecting 558 high-quality images for analysis. The fine-tuned DenseNet121 deep learning model was employed to estimate PRS from single fundus images. After training, deep features were extracted, fused, and used in machine learning regression models to estimate PRS for each subject. The Grad-CAM technique was applied to examine the relationship between areas of increased model activity and the retina’s morphological features specific to AMD. Results: Using the hybrid approach improved the results obtained by DenseNet121 in 5-fold cross-validation. The final evaluation metrics for all predictions from the best model from each fold are MAE = 0.74, MSE = 0.85, RMSE = 0.92, R² = 0.18, MAPE = 2.41. Grad-CAM heatmap evaluation showed that the model decisions rely on lesion area, focusing mostly on the presence of drusen. The proposed approach was also shown to be sensitive to artifacts present in the image. Conclusions: The findings indicate an association between fundus images and AMD PRS, suggesting that deep learning models may effectively estimate genetic risk for AMD from retinal images, potentially aiding in early detection and personalized treatment strategies. Full article

Rosa roxburghii (R. roxburghii), native to the southwest provinces of China, is a fruit crop of important economic value in Guizhou Province. However, the changes in fruit quality and flavor during R. roxburghii fruit ripening have remained unknown. Here, this study investigated the changes of seven active components and volatile organic compounds (VOCs) during the ripening of the R. roxburghii fruit at five different ripening stages including 45, 65, 75, 90, and 105 days after anthesis. The results indicated that during the ripening process, the levels of total acid, vitamin C, and soluble sugar significantly increased (p < 0.05), while the levels of total flavonoids, superoxide dismutase (SOD), and soluble tannin significantly decreased (p < 0.05). Additionally, the content of total phenol exhibited a trend of first decreasing significantly and then increasing significantly (p < 0.05). A total of 145 VOCs were detected by HS-SPME-GC-MS at five mature stages, primarily consisting of aldehydes, alcohols, esters, and alkenes. As R. roxburghii matured, both the diversity and total quantity of VOCs in the fruit increased, with a notable rise in the contents of acids, ketones, and alkenes. By calculating the ROAV values of these VOCs, 53 key substances were identified, which included aromas such as fruit, citrus, green, caramel, grass, flower, sweet, soap, wood, and fat notes. The aromas of citrus, caramel, sweet, and wood were predominantly concentrated in the later stages of R. roxburghii fruit ripening. Cluster heatmap analysis revealed distinct distribution patterns of VOCs across five different maturity stages, serving as characteristic chemical fingerprints for each stage. Notably, stages IV and V were primarily characterized by a dominance of alkenes. OPLS-DA analysis categorized the ripening process of R. roxburghii fruit into three segments: the first segment encompassed the initial three stages (I, II, and III), the second segment corresponded to the fourth stage (IV), and the third segment pertained to the fifth stage (V). Following the variable importance in projection (VIP) > 1 criterion, a total of 30 key differential VOCs were identified across the five stages, predominantly comprising ester compounds, which significantly influenced the aroma profiles of R. roxburghii fruit. By integrating the VIP > 1 and ROAV > 1 criteria, 21 differential VOCs were further identified as key contributors to the aroma changes in R. roxburghii fruit during the ripening process. This study provided data on the changes in quality and aroma of R. roxburghii fruit during ripening and laid the foundation for the investigation of the mechanism of compound accumulation during ripening. Full article

The rapidly growing global energy demand, environmental concerns, and the urgent need to reduce carbon footprints have made sustainable household energy consumption a critical priority. This study aims to analyze household energy data to predict the electricity self-sufficiency rate of households and extract meaningful insights that can enhance it. For this purpose, we use LightGBM (Light Gradient Boosting Machine)-, SHAP (SHapley Additive exPlanations)-, and correlation-heatmap-based approaches to analyze 12 months of energy and questionnaire survey data collected from over 200 smart houses in Kitakyushu, Japan. First, we use LightGBM to predict the ESSR of households and identify the key features that impact the prediction model. By using LightGBM, we demonstrated that the key features are the housing type, average monthly electricity bill, presence of floor heating system, average monthly gas bill, electricity tariff plan, electrical capacity, number of TVs, cooking equipment used, number of washing and drying machines, and the frequency of viewing home energy management systems (HEMSs). Furthermore, we adopted the LightGBM classifier with ℓ1 regularization to extract the most significant features and established a statistical correlation between these features and the electricity self-sufficiency rate. This LightGBM-based model can also predict the electricity self-sufficiency rate of households that did not participate in the questionnaire survey. The LightGBM-based model offers a global view of feature importance but lacks detailed explanations for individual predictions. For this purpose, we used SHAP analysis to identify the impact-wise order of key features that influence the electricity self-sufficiency rate (ESSR) and evaluated the contribution of each feature to the model’s predictions. A heatmap is also used to analyze the correlation among household variables and the ESSR. To evaluate the performance of the classification model, we used a confusion matrix showing a good F1 score (Weighted Avg) of 0.90. The findings discussed in this article offer valuable insights for energy policymakers to achieve the objective of developing energy-self-sufficient houses. Full article

Entomological radar is one of the most effective tools for monitoring insect migration, capable of detecting migratory insects concentrated in layers and facilitating the analysis of insect migration behavior. However, traditional entomological radar, with its low resolution, can only provide a rough observation of layer concentrations. The advent of High-Resolution Phased Array Radar (HPAR) has transformed this situation. With its high range resolution and high data update rate, HPAR can generate detailed concentration spatiotemporal distribution heatmaps. This technology facilitates the detection of changes in insect concentrations across different time periods and altitudes, thereby enabling the observation of large-scale take-off, landing, and layering phenomena. However, the lack of effective techniques for extracting insect concentration data of different phenomena from these heatmaps significantly limits detailed analyses of insect migration patterns. This paper is the first to apply instance segmentation technology to the extraction of insect data, proposing a method for segmenting and extracting insect concentration data from spatiotemporal distribution heatmaps at different phenomena. To address the characteristics of concentrations in spatiotemporal distributions, we developed the Heatmap Feature Fusion Network (HFF-Net). In HFF-Net, we incorporate the Global Context (GC) module to enhance feature extraction of concentration distributions, utilize the Atrous Spatial Pyramid Pooling with Depthwise Separable Convolution (SASPP) module to extend the receptive field for understanding various spatiotemporal distributions of concentrations, and refine segmentation masks with the Deformable Convolution Mask Fusion (DCMF) module to enhance segmentation detail. Experimental results show that our proposed network can effectively segment concentrations of different phenomena from heatmaps, providing technical support for detailed and systematic studies of insect migration behavior. Full article