Search Results (5,068)

The degeneration of germplasm is a key factor limiting the yield and quality of Gastrodia elata Blume. Sexual reproduction is a primary method to address this degeneration, while the number of flowers and capsules is directly related to sexual reproduction. However, the genetic mechanisms underlying the high flower/fruit-bearing traits in G. elata remain unclear. We first compared the quantitative and qualitative traits during the flowering to fruiting period of G. elata, including bolting height, flowering quantity, flowering time, fruiting quantity, capsule spacing, seed quality, etc. The natural materials were selected by multi-capsule and few-capsule for transcriptome analysis to screen the differentially expressed genes (DEGs); the candidate gene GePIF4 was suspected to regulate the formation of multiple flowers and fruits. It was confirmed that GePIF4 has multiple biological functions in the overexpression of transgenic lines, including increasing numbers of vegetative propagation corms (VPCs) and promoting the growth of G. elata. Through comparative transcriptomic analysis of EV and OE-GePIF4 transgenic lines, the transcriptional regulatory network of GePIF4 was identified, and transient expression of GePIF4 was demonstrated to significantly promote gastrodin accumulation. The dual-LUC assay and in vitro yeast one hybrid results showed that GePIF4 could directly bind to GeRAX2 to regulate multi-capsule formation, and GePIF4 could directly bind to GeC4H1 to promote gastrodin accumulation. Therefore, we elucidate the role of GePIF4 in multi-capsule formation and secondary metabolite accumulation, thereby laying the groundwork for the genetic improvement of G. elata germplasm resources. Full article

Background: This study delves into the potential use of real-time UV imaging of the dissolution process combined with convolutional neural networks (CNNs) to develop multidimensional models representing the relation between in vitro and in vivo performance of drugs. Method: We utilised the capabilities of the SDi2 apparatus (Pion) to capture multidimensional dissolution data for two distinct Glucophage tablets: immediate-release 500 mg tablets and extended-release 750 mg tablets. The dissolution process was studied in various media, including a compendial pH 1.2 HCl solution, reverse osmosis water, and pH 6.8 phosphate buffer. Result: Moreover, results were captured at different wavelengths (255 nm and 520 nm) to provide a comprehensive view of the process. Our investigation focuses on two primary approaches: (1) analysing numerical data extracted from SDi2 images via a surface characterisation tool, using traditional machine learning techniques, including Scikit-learn, Tensorflow, and AutoML, and (2) utilising raw SDi2 images to train CNNs for direct prediction of in vivo metformin plasma concentrations. Conclusions: This dual approach allows us to assess the impact of data extraction on model performance and explore the potential of CNNs to capture complex dissolution patterns directly from images, potentially revealing hidden information not captured by traditional numerical data extraction methods. Full article

The Southwest Alpine Canyon Area (SACA) is a typical ecologically sensitive location in China; therefore, constructing and optimizing an ecological network for this area is essential to ensure the regional ecological security of its fragile ecosystems. This study employed the InVEST model to quantitatively assess the habitat quality of the SACA for the years 2000, 2010, and 2020. The ecological sources were determined based on the results of a habitat quality assessment and a Morphological Spatial Pattern Analysis (MSPA). Finally, ecological corridors, ecological pinch points, and ecological barrier points were identified using circuit theory. The results indicated that the SACA’s habitat quality was relatively good, but experienced slight degradation from 0.87 in 2000 to 0.84 in 2020. Anthropogenic activities have been identified as the primary contributor to habitat quality decline in the region. Geographically, the habitat quality is significantly poorer in the southeast and northwest of the SACA. A total of 319 ecological sources were identified, predominantly located in the southwest and northeast of the SACA, comprising 43.27% of the total area. Furthermore, 94 ecological corridors were delineated, covering an area of 74,015.61 km² and extending over 182.80 km in length in total. A total of 38 ecological pinch points and 39 ecological barrier points were distinguished, with a noticeable concentration in regions undergoing ecological degradation. Overall, while the ecological network structure in the SACA is complex and highly interconnected, it faces challenges relating to material cycling and ecological network circulation. Future ecological restoration and protection efforts should focus on areas along the border between the ecological maintenance area in southeastern Tibet (Region I) and the water conservation area in eastern Tibet–western Sichuan (Region II). Additionally, the establishment of ecological protection belts around potential ecological corridors is proposed to enhance ecosystem connectivity. These findings could provide a robust scientific foundation for territorial spatial planning, ecological preservation, and restoration in the SACA. Full article

Diabetes mellitus (DM) is a multifactorial metabolic disorder characterized by chronic hyperglycemia and systemic metabolic dysregulation. Although ginsenosides, the primary bioactive components of Panax ginseng Meyer, exhibit regulatory effects on glucose and lipid metabolism, their precise mechanisms and key targets in DM remain incompletely understood. Unlike previous studies focusing solely on crude extracts or individual ginsenosides, this study integrates network pharmacology, molecular docking, and molecular dynamics (MD) simulations to systematically elucidate the multi-target mechanisms of ginsenosides, with experimental validation using the ginsenoside derivative AD-1. Network pharmacology identified 134 potential targets, with protein–protein interaction (PPI) analysis revealing 25 core targets (such as NFKB1, HDAC1, ESR1, and EP300). Molecular docking and MD simulations showed that ginsenosides have stable binding conformations with these targets and exhibit excellent dynamic stability. Notably, in vivo experiments using AD-1 in streptozotocin-induced type 1 diabetic mice confirmed its therapeutic efficacy, significantly downregulating key diabetic markers (e.g., NFKB1 and HDAC1) in pancreatic tissues—a finding unreported in prior studies. This study not only revealed the multitarget pharmacological mechanism of ginsenosides but also highlighted the therapeutic potential of AD-1. These findings provide a foundation for further mechanistic studies and suggest new strategies for the application of novel ginsenoside derivatives in diabetes therapy. Full article

Soil fungi serve as key mediators of belowground ecological processes; however, the altitudinal distribution patterns and their driving mechanisms of soil fungal communities in alpine shrubland ecosystems remain poorly understood. In this study, soil samples were collected from Potentilla fruticosa shrubs at different altitudes, and their physical and chemical properties were determined. Illumina MiSeq sequencing technology was used to study the characteristics of soil fungal communities at different altitudes (3400, 3700, 4000, and 4300 m), and the driving factors affecting the composition of soil fungal communities were found through variance analysis and redundancy analysis. With the increase in altitude, species diversity decreased while total phosphorus and available phosphorus increased. Compared with 3400 m, the diversity index (Sobs, Chao1, and ACE index) of the soil fungal community at 4000 m is the highest, and that at 4300 m is the lowest. NMDS analysis showed that there were significant differences among soil fungal community structures at different altitudes. Redundancy analysis (RDA) indicated that available potassium, available phosphorus, and the Shannon–Wiener diversity index were the primary factors influencing the variation in soil fungal communities along the elevation gradient. Furthermore, the impact of soil physical and chemical properties on soil fungal communities was found to be more pronounced than that of plant characteristics. Network analysis shows that the network complexity is the highest at 4300 m above sea level. These studies provide a new perspective and basis for understanding the distribution pattern of soil fungi in the rhizosphere Potentilla fruticosa in the eastern Qinghai–Tibet Plateau. Full article

Accurate detection of steel surface defects is crucial for ensuring safety and efficiency in steel production. In this study, we propose a multi-scale edge-enhanced structured composite detection Transformer (MESC-DETR) based on the RT-DETR framework for steel surface defect detection. Three primary improvements are introduced, as follows: (1) a Composite-ConvNeXtV2 backbone network architecture is developed, which integrates ConvNeXtV2 networks through a dense higher-level composition (DHLC) method to enhance multi-scale feature extraction capabilities; (2) an edge enhancement module (EEM) is proposed, incorporating a scale sequence feature fusion (SSFF) structure to design an edge-enhanced feature fusion (EEFF) architecture, thereby improving multi-scale defect detection and edge information perception; (3) a novel Focal-MPDIoU loss function is formulated by optimizing focal loss and MPDIoU, which further enhances model convergence speed and localization accuracy. Experimental results demonstrate that on GC10-DET and NEU-DET datasets, the proposed algorithm achieves 7.2% and 3.7% improvements in mean average precision (mAP) at IoU = 0.50, along with 2.9% and 1.5% mAP enhancements under IoU = 0.50:0.95. These findings indicate that MESC-DETR exhibits superior performance in steel surface defect detection, holding significant implications for steel manufacturing processes. Full article

Benthic diatoms are key primary producers in aquatic ecosystems and sensitive bioindicators for water quality monitoring; for example, the Yellow River Basin exhibits high diatom species diversity. However, traditional microscopic identification of such species remains inefficient and inaccurate. To enable automated identification, we established a benthic diatom dataset containing 3157 SEM images of 32 genera/species from the Yellow River Basin and developed a novel identification method. Specifically, the knowledge extraction module distinguishes foreground features from background noise by guiding spatial attention to focus on mutually exclusive regions within the image. This mechanism allows the network to focus more on foreground features that are useful for the classification task while significantly reducing the interference of background noise. Furthermore, a dual knowledge guidance module is designed to enhance the discriminative representation of fine-grained diatom images. This module strengthens multi-region foreground features through grouped channel attention, supplemented with contextual information through convolution-refined background features assigned low weights. Finally, the proposed method integrates multi-granularity learning, knowledge distillation, and multi-scale training strategies, further improving the classification accuracy. The experimental results demonstrate that the proposed network outperforms comparative methods on both the self-built diatom dataset and a public diatom dataset. Ablation studies and visualization further validate the efficacy of each module. Full article

The segmentation of jujube tree branches and the estimation of primary branch inclination angles (IAs) are crucial for achieving intelligent pruning. This study presents a primary branch IA estimation algorithm for jujube trees based on an improved PointNet++ network. Firstly, terrestrial laser scanners (TLSs) are used to acquire jujube tree point clouds, followed by preprocessing to construct a point cloud dataset containing open center shape (OCS) and main trunk shape (MTS) jujube trees. Subsequently, the Chebyshev graph convolution module (CGCM) is integrated into PointNet++ to enhance its feature extraction capability, and the DBSCAN algorithm is optimized to perform instance segmentation of primary branch point clouds. Finally, the generalized rotational symmetry axis (ROSA) algorithm is used to extract the primary branch skeleton, from which the IAs are estimated using weighted principal component analysis (PCA) with dynamic window adjustment. The experimental results show that compared to PointNet++, the improved network achieved increases of 1.3, 1.47, and 3.33% in accuracy (Acc), class average accuracy (CAA), and mean intersection over union (mIoU), respectively. The correlation coefficients between the primary branch IAs and their estimated values for OCS and MTS jujube trees were 0.958 and 0.935, with root mean square errors of 2.38° and 4.94°, respectively. In summary, the proposed method achieves accurate jujube tree primary branch segmentation and IA measurement, providing a foundation for intelligent pruning. Full article

To elucidate the molecular mechanisms that underlie jujube (Ziziphus jujuba) flavor synthesis, we integrated transcriptomic and metabolomic analyses on the ‘Lingwuchangzao’ cultivar across seven developmental stages. Our multi-omics approach detected 750 metabolites, categorized into 11 primary and 35 secondary classes, with K-means clustering revealing significant stage-specific variations in sugars, alcohols, and organic acids. KEGG enrichment analysis identified differentially expressed genes (DEGs) in key metabolic pathways, including carbohydrate metabolism and plant hormone signal transduction, showing dynamic changes during development. Weighted gene co-expression network analysis (WGCNA) further pinpointed gene networks related to starch/sucrose and carbon metabolism, and eight novel genes linked to starch and fatty acid metabolism. Notably, the white ripening stage (BS) emerged as the critical phase for flavor compound accumulation, offering new molecular insights and targets for quality improvement. Full article

Xyloglucan endotransglucosylases/hydrolases (XTHs) are key enzymes involved in cell wall remodeling by modifying xyloglucan–cellulose networks, thereby influencing plant growth, development, and secondary cell wall formation. While the roles of XTHs have been extensively studied in primary and secondary growth, their functions in the formation and thickening of lignified nut shells remain largely unknown. Pecan (Carya illinoinensis), an economically important nut crop, develops a hard, lignified shell that protects the seed during fruit maturation. In this study, we performed a comprehensive genome-wide characterization of the XTH gene family in pecan and identified 38 XTH genes, which were categorized into four distinct phylogenetic groups. Structural analyses of the deduced proteins revealed conserved catalytic residues alongside divergent loop regions, suggesting functional diversification. Expression profiling across various tissues and among pecan cultivars with contrasting shell phenotypes indicated that specific XTH genes may play critical roles in shell structure formation. Moreover, gene regulatory networks in thin- and thick-shelled pecans provided new insights into the molecular mechanisms underlying shell development and thickness regulation. These findings lay a foundation for future genetic improvement strategies targeting nut shell traits in woody perennials. Full article

Wetlands and meadows are two terrestrial ecosystems that are strikingly distinct in terms of hydrological conditions and biogeochemical characteristics. Wetlands generally feature saturated soils, high accumulation of organic matter, and hypoxic environments. They support unique microbial communities and play crucial roles as carbon sinks and nutrient retainers. In contrast, meadows are characterized by lower water supply, enhanced aeration, and accelerated turnover of organic matter. The transition from wetlands to meadows under global climate change and human activities has triggered severe ecological consequences in the Sanjiangyuan region, yet the mechanisms driving microbial network stability remain unclear. This study integrates microbial sequencing, soil physicochemical analyses, and structural equation modeling (SEM) to reveal systematic changes in microbial communities during wetland degradation. Key findings indicate: (1) critical soil parameter shifts (moisture: 48.5%→19.3%; SOM: −43.6%; salinity: +170%); (2) functional microbial restructuring with drought-tolerant Actinobacteria (+62.8%) and Ascomycota (+48.3%) replacing wetland specialists (Nitrospirota: −43.2%, Basidiomycota: −28.6%); (3) fundamental network reorganization from sparse wetland connections to hypercomplex meadow networks (bacterial nodes +344%, fungal edges +139.2%); (4) SEM identifies moisture (λ = 0.82), organic matter (λ = 0.68), and salinity (λ = −0.53) as primary drivers. Particularly, the collapse of methane-oxidizing archaea (−100%) and emergence of pathogenic fungi (+28.6%) highlight functional thresholds in degradation processes. These findings provide microbial regulation targets for wetland restoration, emphasizing hydrologic management and organic carbon conservation as priority interventions. Future research should assess whether similar microbial and network transitions occur in degraded wetlands across other alpine and temperate regions, to validate the broader applicability of these ecological thresholds. Restoration efforts should prioritize re-saturating soils, reducing salinity, and enhancing organic matter retention to stabilize microbial networks and restore essential ecosystem functions. Full article

The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential “biomarker”. Conversational artificial intelligence is at the core of voice-powered technologies, enabling intelligent interactions between machines. Due to its richness and availability, voice can be leveraged for predictive analytics and enhanced healthcare insights. Utilizing this idea, we reviewed artificial intelligence (AI) models that have executed vocal analysis and their outcomes. Recordings undergo extraction of useful vocal features to be analyzed by neural networks and machine learning models. Studies reveal machine learning models to be superior to spectral analysis in dynamically combining the huge amount of data of vocal features. Clinical applications of a vocal biomarker exist in neurological diseases such as Parkinson’s, Alzheimer’s, psychological disorders, DM, CHF, CAD, aspiration, GERD, and pulmonary diseases, including COVID-19. The primary ethical challenge when incorporating voice as a diagnostic tool is that of privacy and security. To eliminate this, encryption methods exist to convert patient-identifiable vocal data into a more secure, private nature. Advancements in AI have expanded the capabilities and future potential of voice as a digital health solution. Full article

Background/Objectives: Donation after circulatory death (DCD) has emerged to expand the heart-donor pool, but many DCD donors have risk factors such as cocaine or methamphetamine use. Stimulant use can cause coronary vasospasm and premature coronary artery disease, leading to routine donor coronary angiography (left heart catheterization, LHC) for coronary screening. However, performing LHC in DCD donors is challenging. We examined whether omitting LHC in stimulant-exposed DCD donors affects outcomes. Methods: A retrospective analysis was performed using the United Network for Organ Sharing (UNOS) database (2019–2024) to identify adult heart transplant recipients from DCD donors with documented cocaine or amphetamine use. Donors were stratified by whether antemortem LHC was performed. The primary outcome was 1-year recipient survival; secondary outcomes included graft failure and acute rejection. Kaplan–Meier survival curves and Cox regression analyses were performed. Results: A total of 485 DCD heart transplant recipients were identified; 135 (28%) donors underwent LHC and 350 (72%) did not. Recipient characteristics were similar between groups. No significant differences in 30-day (6% vs. 3%; p = 0.11), 90-day (6% vs. 3%; p = 0.21), or 1-year survival (7% vs. 6%; p = 0.48) were observed between the LHC and non-LHC cohorts. Graft failure and complication rates were also similar. However, among stimulant-exposed DCD donors with diabetes, an absence of LHC was associated with higher recipient mortality (HR 5.86, 95% CI: 1.57–21.87; p = 0.008). Conclusions: Routine donor coronary angiography may be unnecessary for stimulant-exposed DCD donors without additional risk factors. Omitting LHC did not compromise transplant outcomes. A selective LHC approach for high-risk DCD donors (e.g., diabetic donors) could safely expand the donor pool. Full article

Fiber differential protection (FDP) is the primary protection scheme in power systems. However, with the increasing proportion of photovoltaic (PV) grids connected in the power system, the controllability and weak power supply characteristics of photovoltaic power stations change the amplitude and phase angle characteristics of fault currents, which makes the sensitivity of fiber differential protection decline and even increases the risk of failure to operate. In view of this phenomenon, combined with the digital and intelligent development of the new energy power system, this study integrates deep learning with relay protection to propose a protection algorithm based on a two-dimensional spatial current trajectory image and deep learning. In this algorithm, the PV side current and the system side current are, respectively, mapped to the two-dimensional space plane as X- and Y-axes to form the current trajectory image. Under different fault conditions, they have obvious differences. A data-enhanced convolutional neural network (A-CNN) based on cross-overlapping data sets is used to identify trajectory features and locate faults. After performance evaluation, the protection algorithm has the advantages of adapting to new energy access, resisting transition resistance, and robustness to current transformer (CT) saturation, and outliers. Full article

Background: The treatment and management of atrial fibrillation poses substantial complexity. A delicate balance in the trade-off between the minimising risk of stroke without increasing the risk of bleeding through anticoagulant optimisations. Natural compounds are often associated with low-toxicity effects, and their effects on atrial fibrillation have yet to be fully understood. Whilst deep learning (a subtype of machine learning that uses multiple layers of artificial neural networks) methods may be useful for drug compound interaction and discovery analysis, graphical processing units (GPUs) are expensive and often required for deep learning. Furthermore, in limited-resource settings, such as low- and middle-income countries, such technology may not be easily available. Objectives: This study aims to discover the presence of any new therapeutic candidates from a large set of natural compounds that may support the future treatment and management of atrial fibrillation anywhere using a low-cost technique. The objective is to develop a deep learning approach under a low-resource setting where suitable high-performance NVIDIA graphics processing units (GPUs) are not available and to apply to atrial fibrillation as a case study. Methods: The primary training dataset is the MINER-DTI dataset from the BIOSNAP collection. It includes 13,741 DTI pairs from DrugBank, 4510 drug compounds, and 2181 protein targets. Deep cross-modal attention modelling was developed and applied. The Database of Useful Decoys (DUD-E) was used to fine-tune the model using contrastive learning. This application and evaluation of the model were performed on the natural compound NPASS 2018 dataset as well as a dataset curated by a clinical pharmacist and a clinical scientist. Results: the new model showed good performance when compared to existing state-of-the-art approaches under low-resource settings in both the validation set (PR AUC: 0.8118 vs. 0.7154) and test set (PR AUC: 0.8134 vs. 0.7206). Tenascin-C (TNC; NPC306696) and deferoxamine (NPC262615) were identified as strong natural compound interactors of the arrhythmogenic targets ADRB1 and HCN1, respectively. A strong natural compound interactor of the bleeding-related target Factor X was also identified as sequoiaflavone (NPC194593). Conclusions: This study presented a new high-performing model under low-resource settings that identified new natural therapeutic candidates for pharmacological cardioversion and anticoagulation. Full article