Search Results (11,393)

Osteoarthritis (OA) is primarily a degenerative disease triggered by joint inflammation and oxidative stress. While Aster glehni is an edible and traditionally medicinal herb, the beneficial effect of A. glehni on OA progression remains unknown. This study aimed to investigate the effect of A. glehni extract (AGE) and its primary biological compound—3,5-dicaffeoylquinic acid (3,5-DCQA)—on inflammation and oxidative stress in chondrocytes. AGE effectively inhibited the expression of interleukin (IL)-6, cyclooxygenase (COX)-2, matrix metalloproteinase (MMP)-1, and MMP-13 in chondrocytes stimulated by IL-1β for 24 h. In contrast, 3,5-DCQA did not inhibit IL-6, COX-2, and MMP expressions under the same conditions. However, when chondrocytes were stimulated by IL-1β for a short duration (6 h), 3,5-DCQA suppressed IL-6, COX-2, and MMP expressions. The inhibition of IL-6, COX-2, and MMP expressions by AGE was associated with the p38 kinase and nuclear factor-κB signaling pathways, but not ERK and JNK signaling pathways. Furthermore, AGE prevented cell apoptosis and reduced intracellular reactive oxygen species levels in chondrocytes induced by hydrogen peroxide (H₂ O₂). AGE restored the decreased superoxide dismutase 1 and catalase mRNA expressions caused by H₂ O₂. Collectively, AGE may protect against cartilage deterioration by inhibiting inflammation and oxidative stress, making it a promising therapeutic agent for alleviating OA. Full article

The Mongolian wild ass (Equus hemionus hemionus) is a flagship species of the desert-steppe ecosystem in Asia, and understanding its strategies for coping with cold environments is vital for both revealing its survival mechanisms and informing conservation efforts. In this study, we employed metagenomic sequencing to characterize the composition and functional potential of the gut microbiota, and applied DNA metabarcoding of the chloroplast trnL (UAA) g–h fragment to analyze dietary composition, aiming to reveal seasonal variations and the interplay between dietary plant composition and gut microbial communities. In the cold season, Bacteroidota and Euryarchaeota were significantly enriched, suggesting enhanced fiber degradation and energy extraction from low-quality forage. Moreover, genera such as Bacteroides and Alistipes were also significantly enriched and associated with short-chain fatty acid (SCFA) metabolism, bile acid tolerance, and immune modulation. In the cold season, higher Simpson index values and tighter principal coordinates analysis (PCoA) clustering indicated a more diverse and stable microbiota under harsh environmental conditions, which may represent an important microecological strategy for the host to cope with extreme environments. Functional predictions based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) further indicated upregulation of metabolic and signaling pathways, including ABC transporters, two-component systems, and quorum sensing, suggesting multi-level microbial responses to low temperatures and nutritional stress. trnL-based plant composition analysis indicated seasonal shifts, with Tamaricaceae detected more in the warm season and Poaceae, Chenopodiaceae, and Amaryllidaceae detected more in the cold season. Correlation analyses revealed that dominant microbial phyla were associated with the degradation of fiber, polysaccharides, and plant secondary metabolites, which may help maintain host energy and metabolic homeostasis. Despite the limited sample size and cross-sectional design, our findings highlight that gut microbial composition and structure may be important for host adaptation to cold environments and may also serve as a useful reference for future studies on the adaptive mechanisms and conservation strategies of endangered herbivores, including the Mongolian wild ass. Full article

Yield components are the most important breeding objectives, directly determining maize high-yield breeding. It is well known that these traits are controlled by a large number of quantitative trait loci (QTL). Therefore, deeply understanding the genetic basis of yield components and identifying key regulatory candidate genes can lay the foundation for maize marker-assisted selection (MAS) breeding. In this study, our aim was to identify the key genomic regions that regulate maize yield component formation through bioinformatic methods. Herein, 554 original QTLs related to 11 yield components, including ear length (EL), hundred-kernel weight (HKW), ear weight (EW), cob weight (CW), ear diameter (ED), cob diameter (CD), kernel row number (KRN), kernel number per row (KNR), kernel length (KL), grain weight per plant (GW), and kernel width (KW) in maize, were collected from the MaizeGDB, national center for biotechnology information (NCBI), and China national knowledge infrastructure (CNKI) databases. The consensus map was then constructed with a total length of 7154.30 cM. Approximately 80.32% of original QTLs were successfully projected on the consensus map, and they were unevenly distributed on the 10 chromosomes (Chr.). Moreover, 44 meta-QTLs (MQTLs) were identified by the meta-analysis. Among them, 39 MQTLs controlled two or more yield components, except for the MQTL4 in Chr. 1, which was associated with HKW; MQTL11 in Chr. 2, which was responsible for EL; MQTL19 in Chr. 3, which was related to KRN; MQTL26 in Chr. 5, which was involved in HKW; and MQTL36 in Chr. 7, which regulated EL. These findings were consistent with the Pearson correlation results, indicating that these traits exhibited co-linked heredity phenomena. Meanwhile, 159 candidate genes were found in all of the above MQTLs intervals, of which, 29 genes encoded E3 ubiquitin protein ligase, which was related with kernel size and weight. Other genes were involved in multiple metabolic processes, including plant hormones signaling transduction, plant growth and development, sucrose–starch synthesis and metabolism, and reproductive growth. Overall, the results will provide reliable genetic resources for high-yield molecular breeding in maize. Full article

Pediatric tumors such as neuroblastoma are characterized by a genome-wide ‘transcriptional burden’, surmising the involvement of multiple alterations of gene expression. Search for master regulators of transcription whose inactivation is lethal for tumor cells identified the non-POU domain-containing octamer-binding protein (NONO), a member of the Drosophila Behavior/Human Splicing family known for the ability to form complexes with macromolecules. NONO emerges as an essential mechanism in normal neurogenesis as well as in tumor biology. In particular, NONO interactions with RNAs, largely with long non-coding MYCN transcripts, have been attributed to the aggressiveness of neuroblastoma. Broadening its significance beyond MYCN regulation, NONO guards a subset of transcription factors that comprise a core regulatory circuit, a self-sustained loop that maintains transcription. As a component of protein–protein complexes, NONO has been implicated in the control of cell cycle progression, double-strand DNA repair, and, generally, in cell survival. Altogether, the pro-oncogenic roles of NONO justify the need for its inactivation as a therapeutic strategy. However, considering NONO as a therapeutic target, its druggability is a challenge. Recent advances in the inactivation of NONO and downstream signaling with small molecular weight compounds make promising the development of pharmacological antagonists of NONO pathway(s) for neuroblastoma treatment. Full article

Based on the characteristics of offshore pile foundation engineering, this study proposes a novel interpretation method for pile settlement time history signals in Rapid Load Testing (RLT). The approach utilizes Variational Mode Decomposition (VMD) to decompose and reconstruct the originally acquired acceleration signals, effectively eliminating high-frequency noise and significantly enhancing signal quality. After obtaining a purified acceleration signal, the study further refines the velocity signal based on the velocity characteristics at the beginning and end of the loading process, aiming to mitigate the influence of initial and boundary conditions on the velocity data. This process yields a highly accurate displacement time history curve. To validate the superiority of VMD in acceleration signal processing, a signal model test was conducted. Comparative experimental results demonstrate that the displacement time history curve derived from VMD-processed signals not only exhibits smaller relative errors and higher precision but also shows significant waveform improvements compared to curves obtained through direct integration of filtered signals. The research indicates that for marine pile foundations, using VMD to decompose and reconstruct the signals, and applying the continuous mean square error theory to identify the critical components of noise and effective signals has significant advantages in the processing of displacement signals using RLT. Compared with traditional analysis methods, the study successfully achieved the effective removal of high-frequency noise in the signal by applying the VMD technique to the decomposition and reconstruction of acceleration signals, significantly improving the quality of the signal. The assumption of zero pile head velocity before and after loading enables accurate determination of the actual pile head displacement Full article

Adipose tissue plays a crucial role in the tumor microenvironment (TME), where its secreted extracellular vesicles (EVs) are involved in the complex signaling between tumor cells and surrounding stromal components. This study aims to unravel the mechanisms through which adipocyte-derived EVs influence breast cancer (BC) progression. Human mesenchymal stem cells (hMSCs) were differentiated into adipocytes following a 21-day induction protocol that led to significant accumulation of lipid droplets within the cells. EVs were isolated from the conditioned medium of both hMSC-derived adipocytes and BC cells. Particle size distribution, morphology, and uptake into the recipient cell were investigated via nanoparticle tracking analysis, transmission electron microscopy, and fluorescence microscopy, respectively. Our results show that BC-derived EVs notably impaired cell viability and modulated the expression of key genes involved in apoptosis resistance within stromal cells. On the other hand, stromal-derived EVs significantly altered tumor cell behavior, indicating a dynamic, bidirectional exchange of bioactive signals. These findings underscore the pivotal role of EV-mediated communication in the tumor-stroma interplay, suggesting that adipocyte-cancer cell EV crosstalk contributes to the remodeling of the TME, potentially facilitating tumor progression. Full article

Pachysandra terminalis (P. terminalis), a plant belonging to the Buxaceae family, is known as a great source of aminosteroid alkaloids. In a previous communication, we reported on the isolation of a variety of aminosteroids from P. terminalis, which presented interesting activity against the protozoan pathogens, Trypanosoma brucei rhodesiense and Plasmodium falciparum. In the present study, variations in the alkaloid profile of P. terminalis related to seasonal changes as well as differences between plant organs (leaves and twigs) and between plant populations were investigated to prioritize candidates for targeted isolation in further studies. For this purpose, sample material of P. terminalis was collected from the two nearby populations in monthly intervals over one year. The ethanolic (75%) extracts were analyzed using UHPLC/+ESI-QqTOF-MS/MS, and the resulting data converted to variables encoding the intensity of MS signals in particular m/z and retention time (t_R) intervals over the chromatographic runs. The very large and complex data matrix of these <t_R:m/z> variables was evaluated using multivariate data analysis, especially principal component analysis (PCA) and volcano plot analysis of t-test data. The results of these analyses, for the first time, allowed a holistic analysis of variation in the alkaloid profiles in P. terminalis organs over the vegetation period. The evaluation of the PCA scores and loadings plots of principal components 1 through 3, as well as of volcano plots, highlighted 25 different compounds, mostly identified as aminosteroid alkaloids, that were most relevant for the differences between leaves and twigs and between the two populations and mainly determined the changes in their chemical profiles over the vegetation period. Full article

Aluminum alloys under long-term service or repetitive stress are prone to small fatigue cracks (FCs) with arbitrary orientations, necessitating eddy current probes with focused magnetic fields and directional selectivity for reliable detection. This study presents a flexible printed circuit board (FPCB) probe with a double-layer planar excitation coil and a double-layer differential receiving coil. The excitation coil employs a reverse-wound design to enhance magnetic field directionality and focusing, while the differential receiving coil improves sensitivity and suppresses common-mode noise. The probe is optimized by adjusting the excitation coil overlap and the excitation–receiving coil angles to maximize eddy current concentration and detection signals. Finite element simulations and experiments confirm the system’s effectiveness in detecting surface cracks of varying sizes and orientations. To further characterize these defects, two time-domain features are extracted: the peak-to-peak value ($\Delta$P), reflecting amplitude variations associated with defect size and orientation, and the signal width ($\Delta$W), primarily correlated with defect angle. However, substantial overlap in their value ranges for defects with different parameters means that these features alone cannot identify which specific parameter has changed, making prior defect classification using a Transformer-based approach necessary for accurate quantitative analysis. The proposed method demonstrates reliable performance and clear interpretability for defect evaluation in aluminum components. Full article

Magnetocardiography (MCG) offers a noninvasive method for early screening and precise localization of cardiovascular diseases by measuring picotesla-level weak magnetic fields induced by cardiac electrical activity. However, in unshielded magnetic environments, geomagnetic disturbances, power-frequency electromagnetic interference, and physiological/motion artifacts can significantly overwhelm effective magnetocardiographic components. To address this challenge, this paper systematically constructs an integrated denoising framework, termed “AOA-VMD-WT”. In this approach, the Arithmetic Optimization Algorithm (AOA) adaptively optimizes the key parameters (decomposition level K and penalty factor α) of Variational Mode Decomposition (VMD). The decomposed components are then regularized based on their modal center frequencies: components with frequencies ≥50 Hz are directly suppressed; those with frequencies <50 Hz undergo wavelet threshold (WT) denoising; and those with frequencies <0.5 Hz undergo baseline correction. The purified signal is subsequently reconstructed. For quantitative evaluation, we designed performance indicators including QRS amplitude retention rate, high/low frequency suppression amount, and spectral entropy. Further comparisons are made with baseline methods such as FIR and wavelet soft/hard thresholds. Experimental results on multiple sets of measured MCG data demonstrate that the proposed method achieves an average improvement of approximately 8–15 dB in high-frequency suppression, 2–8 dB in low-frequency suppression, and a decrease in spectral entropy ranging from 0.1 to 0.6 without compromising QRS amplitude. Additionally, the parameter optimization exhibits high stability. These findings suggest that the proposed framework provides engineerable algorithmic support for stable MCG measurement in ordinary clinic scenarios. Full article

Arc fault detection in photovoltaic systems is crucial, since it may cause incidents like fires and explosions. So far, most existing methods rely on an arc’s local features and do not characterize arc faults globally, which may lead to detection failure in noisy environments. In this paper, a fundamentally different method is proposed that relies on an arc’s global features instead of local ones. The core idea of the method is that the physical mechanisms of the arc fault signals and the normal signals are so different that they are thought to be generated by two independent sources. Based on this insight, independent component analysis (ICA) is introduced to decompose the photovoltaic system’s DC currents. By using ICA, the DC current signals with an arc fault can be decomposed into two independent signals, while the normal signals without an arc fault cannot be decomposed into two such independent signals. This indicates that arc faults can be detected by using the concept of “independence”. Then, the dynamic time warping algorithm was used to determine the independence level of the ICA outputs so as to realize end-to-end arc fault detection. Experimental results showed that our method has better performance than traditional methods in terms of detection accuracy and robustness against environmental disturbances. Full article

Brassinosteroids (BRs) are essential regulators of plant development and stress responses, but the distinct contributions of BR biosynthesis and signaling to hormonal crosstalk remain poorly defined. Here, we investigated the effects of the BR biosynthesis inhibitor brassinazole (BRZ) and the BR-insensitive mutant bri1-6 on endogenous phytohormone profiles in Arabidopsis thaliana. Using multivariate analysis and targeted hormone quantification, we show that BRZ treatment and BRI1 disruption alter hormone balance through partially overlapping but mechanistically distinct pathways. Principal component analysis (PCA) and hierarchical clustering revealed that BRZ and the bri1-6 mutation do not phenocopy each other and that BRZ still alters hormone profiles even in the bri1-6 mutant, suggesting potential BRI1-independent effects. Both BRZ treatment and the bri1-6 mutation tend to influence cytokinins and auxin conjugates divergently. On the contrary, their effects on stress-related hormones converge: BRZ decreases salicylic acid (SA), jasmonic acid (JA), and abscisic acid (ABA) in the WT leaves; similarly, bri1-6 mutants show reduced SA, JA, and ABA. These results indicate that BR biosynthesis and BRI1-mediated perception may contribute independently to hormonal reprogramming, with BRZ eliciting additional effects, possibly via metabolic feedback, compensatory signaling, or off-target action. Hormone correlation analyses revealed conserved co-regulation clusters that reflect underlying regulatory modules. Altogether, our findings provide evidence for a partial uncoupling of BR levels and BR signaling and illustrate how BR pathways intersect with broader hormone networks to coordinate growth and stress responses. Full article

As a key target area in China’s new round of strategic mineral exploration initiatives, Tibet possesses favorable metallogenic conditions shaped by its unique geological evolution and tectonic setting. In this paper, the Saga region of Tibet is the research object, and Level-2A Sentinel-2 imagery is utilized. By applying mixed pixel decomposition, interfering endmembers were identified, and spectral unmixing and reconstruction were performed, effectively avoiding the drawback of traditional methods that tend to remove mineral alteration signals and masking interference. Combined with band ratio analysis and principal component analysis (PCA), various types of remote sensing alteration anomalies in the region were extracted. Furthermore, the fractal box-counting method was employed to quantify the fractal dimensions of the different alteration anomalies, thereby delineating their spatial distribution and fractal structural characteristics. Based on these results, two prospective mineralization zones were identified. The results indicate the following: (1) In areas of Tibet with low vegetation cover, applying spectral mixture analysis (SMA) effectively removes substantial background interference, thereby enabling the extraction of subtle remote sensing alteration anomalies. (2) The fractal dimensions of various remote sensing alteration anomalies were calculated using the fractal box-counting method over a spatial scale range of 0.765 to 6.123 km. These values quantitatively characterize the spatial fractal properties of the anomalies, and the differences in fractal dimensions among alteration types reflect the spatiotemporal heterogeneity of the mineralization system. (3) The high-potential mineralization zones identified in the composite contour map of fractal dimensions of alteration anomalies show strong spatial agreement with known mineralization sites. Additionally, two new prospective mineralization zones were delineated in their periphery, providing theoretical support and exploration targets for future prospecting in the study area. Full article

Background/Objectives: The spinal cord is a critical component of the central nervous system that transmits neural signals between the brain and the body’s peripheral regions through its nerve roots. Despite being partially protected by the vertebral column, the spinal cord remains highly vulnerable to trauma, tumors, infections, and degenerative or inflammatory disorders. These conditions can disrupt neural conduction, resulting in severe functional impairments, such as paralysis, motor deficits, and sensory loss. Therefore, accurate and comprehensive spinal cord segmentation is essential for characterizing its structural features and evaluating neural integrity. Methods: In this study, we propose a fully automated method for segmentation of the cervical spinal cord in sagittal magnetic resonance (MR) images. This method facilitates rapid clinical evaluation and supports early diagnosis. Our approach uses a Swin-Unet architecture, which integrates vision transformer blocks into the U-Net framework. This enables the model to capture both local anatomical details and global contextual information. This design improves the delineation of the thin, curved, low-contrast cervical cord, resulting in more precise and robust segmentation. Results: In experimental studies, the proposed Swin-Unet model (SWU1), which uses transformer blocks in the encoder layer, achieved Dice Similarity Coefficient (DSC) and Hausdorff Distance 95 (HD95) scores of 0.9526 and 1.0707 mm, respectively, for cervical spinal cord segmentation. These results confirm that the model can consistently deliver precise, pixel-level delineations that are structurally accurate, which supports its reliability for clinical assessment. Conclusions: The attention-enhanced Swin-Unet architecture demonstrated high accuracy in segmenting thin and complex anatomical structures, such as the cervical spinal cord. Its ability to generalize with limited data highlights its potential for integration into clinical workflows to support diagnosis, monitoring, and treatment planning. Full article

Plant-based extracts and elicitors (signaling molecules that activate the fruit’s innate defense responses) have emerged as promising and sustainable alternatives to synthetic chemicals for preserving postharvest fruit quality and extending shelf life. This review provides a comprehensive analysis, uniquely complemented by a bibliometric assessment of the research landscape from 2005 to 2025, to identify key trends and effective solutions. This review systematically examined the efficacy of various natural compounds including essential oils (complex volatile compounds with potent antimicrobial activity such as lemongrass and thyme), phenolic-rich botanical extracts like neem and aloe vera, and plant-derived elicitors such as methyl jasmonate and salicylic acid. Their preservative mechanisms are multifaceted, involving direct antimicrobial activity by disrupting microbial membranes, potent antioxidant effects that scavenge free radicals, and the induction of a fruit’s innate defense systems, enhancing the activity of enzymes like superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD). Applications of edible coatings of chitosan or aloe vera gel, nano-emulsions, and pre- or postharvest treatments effectively reduce decay by Botrytis cinerea and Penicillium spp.), delay ripening by suppressing ethylene production, minimize water loss, and alleviate chilling injury. Despite their potential, challenges such as sensory changes, batch-to-batch variability, regulatory hurdles, and scaling production costs limit widespread commercialization. Future prospects hinge on innovative technologies like nano-encapsulation to improve stability and mask flavors, hurdle technology combining treatments synergistically, and optimizing elicitor application protocols. This review demonstrates the potential of continued research and advanced formulation to create plant-based preservatives, that can become integral components of an eco-friendly postharvest management strategy, effectively reducing losses and meeting consumer demands for safe, high-quality fruit. Full article

Emotion recognition using electroencephalogram (EEG) signals has gained significant attention due to its potential applications in human–computer interaction (HCI), brain computer interfaces (BCIs), mental health monitoring, etc. Although deep learning (DL) techniques have shown impressive performance in this domain, they often require large datasets and high computational resources and offer limited interpretability, limiting their practical deployment. To address these issues, this paper presents a novel frequency-driven ensemble framework for electroencephalogram-based emotion recognition (FREQ-EER), an ensemble of lightweight machine learning (ML) classifiers with a frequency-based data augmentation strategy tailored for effective emotion recognition in low-data EEG scenarios. Our work focuses on the targeted analysis of specific EEG frequency bands and brain regions, enabling a deeper understanding of how distinct neural components contribute to the emotional states. To validate the robustness of the proposed FREQ-EER, the widely recognized DEAP (database for emotion analysis using physiological signals) dataset, SEED (SJTU emotion EEG dataset), and GAMEEMO (database for an emotion recognition system based on EEG signals and various computer games) were considered for the experiment. On the DEAP dataset, classification accuracies of up to 96% for specific emotion classes were achieved, while on the SEED and GAMEEMO, it maintained 97.04% and 98.6% overall accuracies, respectively, with nearly perfect AUC values confirming the frameworks efficiency, interpretability, and generalizability. Full article