Search Results (324)

Since the onset of industrialization, the safety of arable land has become a pressing global concern, with soil salinization emerging as a critical threat to agricultural productivity and food security. To address this challenge, the cultivation of economically valuable salt-tolerant plants has been proposed as a viable strategy. In the study, we investigated the physiological and molecular responses of Lycium ruthenicum Murr. to varying NaCl concentrations. Results revealed a concentration-dependent dual effect: low NaCl levels significantly promoted seed germination, while high concentrations exerted strong inhibitory effects. To elucidate the mechanisms underlying these divergent responses, a combined analysis of metabolomics and transcriptomics was applied to identify key metabolic pathways and genes. Notably, salt stress enhanced photosynthetic efficiency through coordinated modulation of ribulose 5-phosphate and erythrose-4-phosphate levels, coupled with the upregulation of critical genes encoding RPIA (Ribose 5-phosphate isomerase A) and RuBisCO (Ribulose-1,5-bisphosphate carboxylase/oxygenase). Under low salt stress, L. ruthenicum maintained intact cellular membrane structures and minimized oxidative damage, thereby supporting germination and early growth. In contrast, high salinity severely disrupted PS I (Photosynthesis system I) functionality, blocking energy flow into this pathway while simultaneously inducing membrane lipid peroxidation and triggering pronounced cellular degradation. This ultimately suppressed seed germination rates and impaired root elongation. These findings suggested a mechanistic framework for understanding L. ruthenicum adaptation under salt stress and pointed out a new way for breeding salt-tolerant crops and understanding the mechanism. Full article

Waterlogging is a critical abiotic stressor that significantly impacts plant growth. Plants under waterlogging stress release metabolic signals that recruit rhizosphere microorganisms and enhance stress resistance. However, the mechanisms through which the non-adaptive species R. delavayi responds to waterlogging stress via the synergistic interaction between root metabolites and rhizosphere microbiota remain poorly elucidated. Here, we employed pot experiments to characterize the responses of the root metabolite–microbiota complex in R. delavayi during waterlogging stress and subsequent recovery. Our results revealed that waterlogging altered the root morphology, the root metabolite profile, rhizosphere microbial diversity and network complexity, and these effects persisted during recovery. A significant correlation between root metabolites and the rhizosphere microbial community structure during waterlogging stress and recovery. Importantly, some differentially accumulated metabolites had significant effects on the assembly of rhizosphere microbes. Most of the core microbes in the rhizosphere microbial community under waterlogging and post–waterlogging recovery treatment were likely beneficial bacteria. Based on these findings, we propose a model for how root metabolites and rhizosphere microbes interact to help R. delavayi cope with waterlogging and recover. Based on these findings, we propose a possible response pattern of root metabolites and rhizosphere microbiota complex in R. delavayi under waterlogging stress and recovery. This work provides new insights into the synergistic mechanisms enhancing plant waterlogging tolerance and highlights the potential of harnessing rhizosphere microbiota to improve resilience in rhododendrons. Full article

Objective: Growth retardation in adolescents caused by nutritional deficiency requires effective intervention. A novel dietary supplement containing bamboo shoot extract, amino acids, and calcium citrate (Kidtal + Ca, KDTCa) was evaluated for its growth-promoting effects. Methods: After acclimatization, sixty-three 3-week-old male Sprague-Dawley (SD) rats were randomly divided into a normal control group and model groups. Growth retardation was induced in the modeling groups through calcium-deficient feeding, followed by administration of KDTCa, bamboo shoot extract and amino acids (Kidtal), or calcium citrate (CC). After 6 weeks of intragastric administration, the mechanical properties, microstructure, and growth plate development of bone were evaluated using three-point bending, micro-CT, and H&E staining, respectively. Bone calcium/phosphorus distribution and fecal calcium apparent absorption rate were measured by ICP-MS. Results: All inter-group differences were analyzed using one-way analysis of variance and checked using the Tuckey test. KDTCa treatment dose-dependently enhanced bone development in calcium-deficient rats. Compared to the model group, H-KDTCa significantly restored naso-anal length (p < 0.05) and body weight (p < 0.01). KDTCa supplementation significantly restored calcium and phosphorus levels in blood and bone. Three-point bending experiments showed that the stiffness and bending energy were increased by 142.58% and 384.7%. In bone microarchitecture, both bone mineral density (BMD) and microstructural parameters were significantly improved. These findings were consistent with the increased long bone length (p < 0.05) and decreased serum BALP/TRACP levels (p < 0.001). Dose-dependent IGF-1 elevation (p < 0.01) potentially mediated growth plate elongation by 35.34%. Notably, KDTCa increased calcium apparent absorption by 6.1% versus calcium-only supplementation at equal intake. Conclusions: KDTCa improves bone microstructure and strength, restores bone metabolism, and enhances growth plate height via promoting IGF-1 secretion to facilitate bone development. Further studies are needed to determine whether the components and calcium in Kidtal have a synergistic effect. Full article

This study aims to address the issue of various defects on the surface of aluminum profile materials, which can significantly impact industrial production as well as the reliability and safety of products. An algorithmic model, BBW YOLO (YOLOv8-BiFPN-BiFormer-WIoU v3), based on an enhanced YOLOv8 model is proposed for aluminum profile material surface-defect detection. First, the model can effectively eliminate redundant feature information and enhance the feature-extraction process by incorporating a weighted Bidirectional Feature Pyramid Feature-fusion Network (BiFPN). Second, the model incorporates a dynamic sparse-attention mechanism (BiFormer) along with an efficient pyramidal network architecture, which enhances the precision and detection speed of the model. Meanwhile, the model optimizes the loss function using Wise-IoU v3 (WIoU v3), which effectively enhances the localization performance of surface-defect detection. The experimental results demonstrate that the precision and recall of the BBW YOLO model are improved by 5% and 2.65%, respectively, compared with the original YOLOv8 model. Notably, the BBW YOLO model achieved a real-time detection speed of 292.3 f/s. In addition, the model size of BBW YOLO is only 6.3 MB. At the same time, the floating-point operations of BBW YOLO are reduced to 8.3 G. As a result, the BBW YOLO model offers excellent defect detection performance and opens up new opportunities for its efficient development in the aluminum industry. Full article

Hybrid pumped hydro storage plants, by integrating pump stations between cascade hydropower stations, have overcome the challenges associated with site selection and construction of pure pumped hydro storage systems, thereby becoming the optimal large-scale energy storage solution for enhancing the absorption of renewable energy. However, the multi-energy conversion between pump stations, hydropower, wind power, and photovoltaic plants poses challenges to both their planning schemes and operational performance. This study proposes a multistage stochastic coordinated planning model for cascade hydropower-wind-solar-thermal-pumped hydro storage (CHWS-PHS) systems. First, a Hybrid Pumped Hydro Storage Adaptive Initial Reservoir Capacity (HPHS-AIRC) strategy is developed to enhance the system’s regulation capability by optimizing initial reservoir levels that are synchronized with renewable generation patterns. Then, Non-anticipativity Constraints (NACs) are incorporated into this model to ensure the dynamic adaptation of investment decisions under multi-timescale uncertainties, including inter-annual natural water inflow (NWI) variations and hourly fluctuations in wind and solar power. Simulation results on the IEEE 118-bus system show that the proposed MSSP model reduces total costs by 6% compared with the traditional two-stage approach (TSSP). Moreover, the HPHS-AIRC strategy improves pumped hydro utilization by 33.8%, particularly benefiting scenarios with drought conditions or operational constraints. Full article

Herein, a facile strategy was proposed to enhance the gas sensing performance of SnO₂ for H₂ by regulating its crystalline phase composition. Sn-based metal–organic framework (Sn-MOF) precursors with different morphologies were synthesized by introducing the surfactant cetyltrimethylammonium bromide (CTAB). Upon calcination, these precursors yielded either mixed-phase (orthorhombic and tetragonal, SnO₂-C) or single-phase (pure tetragonal, SnO₂-NC) SnO₂ nanoparticles. Structural characterization and gas sensing tests revealed that SnO₂-C exhibited a high response of 7.73 to 100 ppm H₂ at 280 °C, more than twice that of SnO₂-NC (3.75). Moreover, SnO₂-C demonstrated a faster response/recovery time (10/56 s), high selectivity, a ppb-level detection limit (~79 ppb), and excellent long-term stability. Notably, although the addition of CTAB reduced the specific surface area of SnO₂, the resulting lower surface area minimized oxygen exposure during calcination, facilitating the formation of a mixed-phase heterostructure. In addition, the calcination atmosphere of SnO₂-C (flowing air or Ar) was adjusted to further investigate the role of the crystal phase in gas sensing performance. The results clearly demonstrated that mixed-phase SnO₂ exhibited superior sensing performance, achieving a higher sensitivity and a faster response to H₂. These findings underscored the critical role of crystal phase engineering in the design of high-performance gas sensing materials. Full article

Aortic stenosis (AS) poses significant risks to patient survival and quality of life. The management of AS extends beyond restoring valve function to encompass lifelong disease management. While curative treatments exist, advancements in therapeutic approaches and prosthetic valve technology continue to evolve. This review synthesizes recent developments in AS treatment modalities, prosthetic valve innovations, and their clinical implications, delineating the current therapeutic landscape. Full article

Depression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with such adverse mental outcomes are often attributed to poorer health outcomes, such as higher mortality rates. The objective of this study is to use deep learning (DL) methods to predict the risk of a stroke survivor experiencing post-stroke depression and/or post-stroke anxiety, which is collectively known as post-stroke adverse mental outcomes (PSAMO). This study studied 179 patients with stroke, who were further classified into PSAMO versus no PSAMO group based on the results of validated depression and anxiety questionnaires, which are the industry’s gold standard. This study collected demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. In addition, sequential data such as daily lab results taken seven consecutive days after admission are also collected. The combination of using DL algorithms, such as multi-layer perceptron (MLP) and long short-term memory (LSTM), which can process complex patterns in the data, and the inclusion of new data types, such as sequential data, helped to improve model performance. Accurate prediction of PSAMO helps clinicians make early intervention care plans and potentially reduce the incidence of PSAMO. Full article

In this study, liquid chromatography–mass spectrometry (LC-MS) was employed to conduct untargeted metabolomics analysis on black rice (BR), milled black rice (MBR), wet germinated black rice (WBR), and high-temperature and high-pressure-treated WBR (HTP-WBR). A total of 6988 positive ions and 7099 negative ions (multiple difference ≥1.2 or ≤0.8333, p < 0.05, and variable importance in projection ≥1) were isolated, and 98 and 100 differential metabolic pathways were identified between the different samples in the positive and negative ion modes, respectively. Distinctive variations in the metabolic compositions of BR, MBR, WBR, and HTP-WBR were observed. Flavonoids, fatty acids, lipids, phenylpropanoids, polyketides, benzenoids, and organooxygen were the dominant differential metabolites. Milling removed the majority of bran-associated bioactive components such as phenolic acids, anthocyanins, micronutrients, fatty acids, antioxidants, and dietary fiber. The germination process significantly reduced the number of flavonoids, polyketides, and lipid-related metabolites, while enzymatic activation notably increased the number of organic acids and amino acids. HTP treatment synergistically enhanced the content of heat-stable flavonoids and polyketides, while simultaneously promoting fatty acid β-oxidation. These findings establish novel theoretical foundations for optimizing processing methodologies and advancing functional characterization in black rice product development. Full article

Aims: To investigate the effect of Glutathione metabolism in prostate cancer pathogenesis. Background: There is growing evidence that Glutathione metabolism plays an important role in prostate cancer, with genes encoding key enzymes in this pathway potentially serving as diagnostic or prognostic biomarkers. Objective: To explore whether there is a causal relationship between key enzymes in the Glutathione metabolism and prostate cancer, and to further investigate the molecular mechanisms and roles of the genes encoding their proteins in relation to prostate cancer. Method: Transcriptomic datasets from the Gene Expression Omnibus (GEO) database were analyzed to identify differentially expressed genes (DEGs) and enriched pathways in prostate cancer versus normal tissues. Two-sample bidirectional Mendelian randomization (MR) was employed to assess causal relationships between Glutathione metabolic enzymes (exposure) and prostate cancer risk (outcome). Immune infiltration analysis and LASSO regression were performed to construct a diagnostic model. Single-cell RNA sequencing (scRNA-seq) data were utilized to elucidate cell-type-specific expression patterns and functional associations of target genes. Result: The results of two-sample bidirectional MR showed that Glutathione S-transferase P (GSTP) in Glutathione metabolism could reduce the risk of prostate cancer. The Glutathione S-transferase Pi-1 (GSTP1) gene was lowly expressed in prostate cancer and was able to diagnose prostate cancer more accurately. Single-cell analysis showed that the high expression of GSTP1 in prostate cancer epithelial cells was closely associated with the upregulation of the P53 pathway and apoptosis. Conclusions: Our study reveals that GSTP in Glutathione metabolism reduces the risk of prostate cancer and further analyzes the genetic association and mechanism of action between GSTP1 and prostate cancer. Full article

The NAC (NAM, ATAF1/2, and CUC2) family is one of the largest plant-specific transcription factor families, playing a crucial role in adaptation to abiotic stresses. However, the NAC gene family in sweet cherry (Prunus avium L.) remains poorly understood. In this study, we identified 130 NAC genes (PaNAC) from the sweet cherry genome, which were unevenly distributed across eight chromosomes. Phylogenetic analysis classified the PaNACs into 21 distinct groups, including 2 sweet cherry-specific groups. Comparative analysis revealed significant variations in gene proportions, exon–intron structures, and motif compositions among different groups. Furthermore, cis-element analysis suggested the potential roles of PaNACs in regulating plant growth, development, hormone signaling, and stress responses. Transcriptomic data revealed tissue-specific expression patterns for several PaNAC genes. qRT-PCR further confirmed that eight selected PaNACs were responsive to various abiotic stresses in Gisela 6, a widely used hybrid rootstock in sweet cherry production that shares high sequence similarity in NAC genes with P. avium. These findings provide valuable insights for future research on the functional characteristics of the PaNAC genes in the growth, development, and responses to abiotic stress in sweet cherry. Full article

Objectives: Evaluating middle ear function is essential for interpreting screening results and prioritizing diagnostic referrals for infants with hearing impairments. Wideband Acoustic Immittance (WAI) technology offers a comprehensive approach by utilizing sound stimuli across various frequencies, providing a deeper understanding of ear physiology. However, current clinical practices often restrict WAI data analysis to peak information at specific frequencies, limiting its comprehensiveness. Design: In this study, we developed five machine learning models—feedforward neural network, convolutional neural network, kernel density estimation, random forest, and support vector machine—to extract features from wideband acoustic immittance data collected from newborns aged 2–6 months. These models were trained to predict and assess the normalcy of middle ear function in the samples. Results: The integrated machine learning models achieved an average accuracy exceeding 90% in the test set, with various classification performance metrics (accuracy, precision, recall, F1 score, MCC) surpassing 0.8. Furthermore, we developed a program based on ML models with an interactive GUI interface. The software is available for free download. Conclusions: This study showcases the capability to automatically diagnose middle ear function in infants based on WAI data. While not intended for diagnosing specific pathologies, the approach provides valuable insights to guide follow-up testing and clinical decision-making, supporting the early identification and management of auditory conditions in newborns. Full article

Background: We performed a phase I experiment in a healthy teenage population in Sui County, Henan Province, China. The trial was randomized, double-blind, and placebo-controlled. Methods: Ninety-six adolescents were randomly assigned in three groups (high-dose, medium-dose, and low-dose) to receive a dose of the vaccine or the placebo. The patients were monitored for adverse events (AEs) for up to 30 days after each dose of the vaccine and for up to 6 months after all doses of serious AEs (SAEs). All observed AEs and SAEs were reported. Microneutralization assays were used to measure geometric mean titers (GMTs) and seroconversion rates for neutralizing antibodies. IgA and IgG antibodies specific to the rotavirus were detected. Results: The rates of total AEs in these groups were 8.33%, 37.50%, 12.50%, and 4.17%, respectively. The neutralizing antibody test revealed that the teenage groups with low, medium, and high doses of the vaccine had geometric mean titers of 424.32, 504.63, and 925.45, respectively, at 28 days following complete vaccination. The GMT of serum IgG at final immunization was 6501.86, 6501.82, and 10,173.3, in the low-dose, medium-dose, and high-dose groups, respectively. The GMT of serum IgA at final immunization was 2733.64, 2233.29, and 3596.66 in the low-dose, medium-dose group, and high-dose groups, respectively. Conclusions: The majority of adverse events (AEs) were deemed Grade 1 or 2, suggesting that the vaccine’s safety profile is suitable for healthy adolescents. For the primary immunogenicity endpoints, a preliminary examination of the GMTs and the positive transfer rate of neutralizing antibodies in the different experimental groups revealed that, in adolescents aged 6–17, the high-dose group displayed significantly higher levels of neutralizing antibodies compared to the medium- and low-dose groups. Adolescents had few side effects from the new inactivated rotavirus vaccination, and it elicited an immune response. Full article

Close-to-nature forest management is a sustainable forest management approach aimed at achieving a balance between ecological and economic benefits. The cultivation of future crop trees in the later successional stages following the removal of competitive trees is crucial for promoting positive development trajectories of succession. Understanding the dynamic process of growth investment strategies in future crop trees facilitates the rational planning of management cycles and scopes, ultimately enhancing the quality of tree cultivation. This study was conducted in a Pinus massoniana secondary forest with close-to-nature forest management in Ningbo City, Zhejiang Province, using handheld mobile laser scanning technology to precisely reconstruct the structure of future crop trees. Over a period of 2–5 years following the initial implementation of close-to-nature forest management, 3D point cloud data were collected annually from both managed and reference (non-managed) plots. Using these multi-temporal data, we analyzed the dynamics of the investment strategies, structural growth components, and crown competition of future crop trees. A linear mixed-effect model was applied to compare the temporal variations in these indices between the managed and control plots. Our results revealed that the height-to-diameter ratio of the future crop trees gradually declined over time, while the crown-to-diameter ratio initially increased and then decreased in the managed plots. These trends were significantly different from those observed in the control plots. Additionally, the height growth rates of the future crop trees in the managed plots were consistently lower than those in the control plots, whereas the crown and diameter at breast height (DBH) growth rates were higher. Furthermore, the crown gap area between the future crop trees and their neighboring trees gradually diminished, and the crown overlap progressively increased. These results suggest that the investment in height growth, initially driven by crown competition, shifted toward crown and DBH growth following close-to-nature forest management. In the initial stage after the removal of competitive trees, future crop trees benefited from ample crown radial space and minimal crown competition. However, as the crown radial space became increasingly limited, the future crop trees shifted their growth investment toward DBH to enhance mechanical stability and achieve a balanced tree structure. Understanding these dynamic processes and the underlying mechanisms of growth investment strategies contributes to predicting future forest community development, improving forest productivity, maintaining structural diversity, and ensuring sustainable forest management. Full article

Accurate quantification of the citrus dropped number plays a vital role in evaluating the disaster resistance capabilities of citrus varieties and selecting superior cultivars. However, research in this critical area remains notably insufficient. To bridge this gap, we conducted in-depth experiments using a custom dataset of 1200 citrus images and proposed a lightweight YOLO-MECD model that is built upon the YOLOv11s architecture. Firstly, the EMA attention mechanism was introduced as a replacement for the traditional C2PSA attention mechanism. This modification not only enhances feature extraction capabilities and detection accuracy for citrus fruits but also achieves a significant reduction in model parameters. Secondly, we implemented a CSPPC module based on partial convolution to replace the original C3K2 module, effectively reducing both parameter count and computational complexity while maintaining mAP values. At last, the MPDIoU loss function was employed, resulting in improved bounding box detection accuracy and accelerated model convergence. Notably, our research reveals that reducing convolution operations in the backbone architecture substantially enhances small object detection capabilities and significantly decreases model parameters, proving more effective than the addition of small object detection heads. The experimental results and comparative analysis with similar network models indicate that the YOLO-MECD model has achieved significant improvements in both detection performance and computational efficiency. This model demonstrates excellent comprehensive performance in citrus object detection tasks, with a precision (P) of 84.4%, a recall rate (R) of 73.3%, and an elevated mean average precision (mAP) of 81.6%. Compared to the baseline, YOLO-MECD has improved by 0.2, 4.1, and 3.9 percentage points in detection precision, recall rate, and mAP value, respectively. Furthermore, the number of model parameters has been substantially reduced from 9,413,574 in YOLOv11s to 2,297,334 (a decrease of 75.6%), and the model size has been compressed from 18.2 MB to 4.66 MB (a reduction of 74.4%). Moreover, YOLO-MECD also demonstrates superior performance against contemporary models, with mAP improvements of 3.8%, 3.2%, and 5.5% compared to YOLOv8s, YOLOv9s, and YOLOv10s, respectively. The model’s versatility is evidenced by its excellent detection performance across various citrus fruits, including pomelos and kumquats. These achievements establish YOLO-MECD as a robust technical foundation for advancing citrus fruit detection systems and the development of smart orchards. Full article