Search Results (19,252)

Pyruvate kinase (PK) is a key enzyme in glycolysis that regulates sugar metabolism and energy production, thereby influencing fruit quality. The ‘Feizixiao’ litchi, widely cultivated in Hainan Province, faces sugar reduction during fruit ripening. This study evaluated the effects of the foliar application of calcium and magnesium (Ca+Mg) during the fruit expansion stage to alleviate this problem. Ca+Mg foliar application significantly enhanced soluble sugar content, promoted peel coloration, and reduced respiration and PK activity. Genome-wide analysis identified 19 PK genes (LcPKs) exhibiting diverse exon-intron structures and conserved motifs. Phylogenetic analysis revealed both conserved and species-specific features, while subcellular localization predicted that most LcPK proteins are likely to be localized in the cytoplasm. Synteny analysis showed closer evolutionary relationships with species in the same genus than with Arabidopsis. Cis-regulatory element analysis implicated LcPKs in light response, hormone signaling, growth, and stress adaptation. Hormonal assays at 63 and 70 DAA after treatment revealed increased abscisic acid (ABA) and ethylene levels under Ca+Mg application. These hormonal changes correlated with the downregulation of LcPK3, LcPK4, LcPK5, LcPK8, and LcPK15, as confirmed by qRT-PCR, indicating negative regulation by ABA and ethylene. This regulatory mechanism likely contributes to overcoming sugar receding in litchi pulp. These findings offer insights into the regulation of sugar metabolism and strategies for enhancing fruit quality through the management of genes and nutrients. Full article

The confined space of coal mines characterized by curved tunnels with rough surfaces and a variety of deployed production equipment induces severe signal attenuation and interruption, which significantly degrades the accuracy of conventional channel estimation algorithms applied in coal mine wireless communication systems. To address these challenges, we propose a modified Bilinear Generalized Approximate Message Passing (mBiGAMP) algorithm enhanced by intelligent reflecting surface (IRS) technology to improve channel estimation accuracy in coal mine scenarios. Due to the presence of abundant coal-carrying belt conveyors, we establish a hybrid channel model integrating both fast-varying and quasi-static components to accurately model the unique propagation environment in coal mines. Specifically, the fast-varying channel captures the varying signal paths affected by moving conveyors, while the quasi-static channel represents stable direct links. Since this hybrid structure necessitates an augmented factor graph, we introduce two additional factor nodes and variable nodes to characterize the distinct message-passing behaviors and then rigorously derive the mBiGAMP algorithm. Simulation results demonstrate that the proposed mBiGAMP algorithm achieves superior channel estimation accuracy in dynamic conveyor-affected coal mine scenarios compared with other state-of-the-art methods, showing significant improvements in both separated and cascaded channel estimation. Specifically, when the NMSE is ${10}^{-3}$, the SNR of mBiGAMP is improved by approximately 5 dB, 6 dB, and 14 dB compared with the Dual-Structure Orthogonal Matching Pursuit (DS-OMP), Parallel Factor (PARAFAC), and Least Squares (LS) algorithms, respectively. We also verify the convergence behavior of the proposed mBiGAMP algorithm across the operational signal-to-noise ratios range. Furthermore, we investigate the impact of the number of pilots on the channel estimation performance, which reveals that the proposed mBiGAMP algorithm consumes fewer number of pilots to accurately recover channel state information than other methods while preserving estimation fidelity. Full article

This study simulates an open-cavity random distributed Raman amplifier for optimal performance across a 5 THz S-band spectrum (196.2–201.1 THz; 1490.76–1527.99 nm), evaluating its capacity via a 50-channel WDM grid with 100 GHz spacing. The primary Raman pump wavelength was tuned from 1318 to 1344 nm to identify the optimal point. A Fiber Bragg Grating (FBG), placed at the end of a 60 km single-mode fiber and upshifted 88 nm from the pump, enhances efficiency by transferring energy to the amplified signal, minimizing power variation. Results yield < 2 dB gain ripple across channels using raw Raman amplification without flattening filters with minor degradation from residual channels, confirming the DRA design’s viability for high-density S-band optical communication expansion. Full article

Background/Objectives: Estrogen deficiency-related menopause is associated with various physical and psychological symptoms. Although hormone replacement therapy (HRT) effectively alleviates these symptoms, its long-term use is associated with several side effects such as an increased risk of breast cancer and cardiovascular disease. Consequently, there is a growing interest in some plant-derived phytoestrogens that are considered safer alternatives to estrogen. Recent studies on Scrophularia buergeriana confirmed their anti-inflammatory and antioxidant properties; however, their effects on menopausal health remain unclear. Therefore, the aim of this study was to investigate the estrogen-like effects of S. buergeriana root (SB-R) extract, a potential phytoestrogen. Methods: Briefly, the MCF-7 cell line, a widely used in vitro model for assessing estrogen-like activity, was treated with SB-R extract and 17β-estradiol (E2; positive control) in the presence or absence of ICI 182,780 (Fulvestrant), an estrogen receptor antagonist. An E-screen assay and flow cytometry were performed to assess the effects of the treatments on cell proliferation and the cell cycle, respectively. Additionally, Western blotting and immunofluorescence assays were performed to elucidate the potential mechanisms underlying the estrogen-like effects of SB-R. Result: Treatment with SB-R extract promoted MCF-7 cell proliferation in a manner similar to E2. However, ICI 182,780 co-treatment inhibited the SB-R extract-induced increase in MCF-7 cell proliferation. Additionally, SB-R extract promoted cell cycle progression by increasing the proportion of cells in the S and G2/M phases. Moreover, Western blotting and immunofluorescence assays showed that SB-R extract increased the expression of estrogen receptor alpha (ERα). Furthermore, SB-R treatment activated downstream signaling pathways by enhancing AKT and ERK phosphorylation and upregulated the expression of cell cycle regulators, including cyclin D1, cyclin dependent kinase 4 (CDK4), cyclin E1, and cyclin dependent kinase 2 (CDK2). Conclusions: SB-R exhibits estrogen-like activity by activating ERα-mediated AKT and ERK pathways and thereby increasing the expression of proteins involved in cell cycle regulation. This makes it a promising phytoestrogen candidate and a safer alternative to conventional hormonal therapy for alleviating menopausal symptoms. Full article

Bolted connections are extensively utilized in aerospace, civil, and mechanical systems for structural assembly. However, inevitable structural vibrations can induce bolt loosening, leading to preload reduction and potential structural failure. Early-stage preload degradation, particularly during initial loosening, is often undetectable by conventional monitoring methods due to limited sensitivity and poor noise resilience. To address these limitations, this study proposes an intelligent bolt preload monitoring framework that combines electromechanical impedance (EMI) signal analysis with a parallel deep learning architecture. A multiphysics-coupled model of flange joint connections is developed to reveal the nonlinear relationships between preload degradation and changes in EMI conductance spectra, specifically resonance peak shifts and amplitude attenuation. Based on this insight, a parallel convolutional neural network (P-CNN) is designed, employing dual branches with 1 × 3 and 1 × 7 convolutional kernels to extract local and global spectral features, respectively. The architecture integrates dilated convolution to expand frequency–domain receptive fields and an enhanced SENet-based channel attention mechanism to adaptively highlight informative frequency bands. Experimental validation on a flange-bolt platform demonstrates that the proposed P-CNN achieves 99.86% classification accuracy, outperforming traditional CNNs by 20.65%. Moreover, the model maintains over 95% accuracy with only 25% of the original training samples, confirming its robustness and data efficiency. The results demonstrate the feasibility and scalability of the proposed approach for real-time, small-sample, and noise-resilient structural health monitoring of bolted connections. Full article

Sea level is an important variable for studying water cycle and coastal hazards under global warming. Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as a relatively new technique for monitoring sea level variations, leveraging signals from GNSS constellations. However, dynamic height errors, primarily caused by non-stationary sea surfaces, compromise the precision of GNSS-IR sea level retrievals and necessitate robust correction. In this study, we propose a new method to correct the dynamic height error by integrating the commonly used tidal analysis method and the cubic spline fitting method. The proposed method is applied to the GNSS-IR sea level retrievals from multiple systems and multiple frequency bands at two coastal GNSS stations, MAYG and HKQT. At MAYG, the results show that our method significantly reduces the Root Mean Square Error (RMSE) of the GNSS-IR sea level retrievals by 42.1% (11.4 cm) to 15.7 cm, performing better than the single tidal analysis method (16.5 cm) and the cubic spline fitting method (21.4 cm). At HKQT, our method improves the accuracy by 21.5% (3.1 cm) to 10.3 cm, which is still better than that of the tidal analysis method (11.3 cm) and the cubic spline fitting method (12.4 cm). Compared to the tidal analysis method and the cubic spline fitting method, our method maintains high retrieval retention while enhancing precision. The effectiveness of our method is further validated in the two storm surge events caused by Typhoon Hato and Typhoon Mangkhut in Hong Kong. Full article

Background: The escalating global salinization poses a significant threat to agricultural productivity, necessitating a thorough understanding of plant responses to high salinity. Pea sprouts (Pisum sativum), a nutrient-rich crop increasingly cultivated in salinized regions, serve as an ideal model for such investigations due to their rapid growth cycle and documented sensitivity to ionic stress. Methods: In order to understand the response of pea sprouts in physiological regulation, redox-metabolic adjustments, and transcriptome reprogramming under salt stress, we investigated the effects of high salt concentrations on the ascorbic acid–glutathione cycle, endogenous hormone levels, metabolite profiles, and gene expression patterns in it. Results: Our findings reveal early-phase antioxidant/hormonal adjustments, mid-phase metabolic shifts, and late-phase transcriptomic reprogramming of pea sprouts under salt conditions. In addition, a biphasic response in the ascorbic acid cycle was found, with initial increases in enzyme activities followed by a decline, suggesting a temporary enhancement of antioxidant defenses. Hormonal profiling indicated a significant increase in abscisic acid (ABA) and jasmonic acid (JA), paralleled by a decrease in indole acetic acid (IAA) and dihydrozeatin (DZ), underscoring the role of hormonal regulation in stress adaptation. Metabolomic analysis uncovered salt-induced perturbations in sugars, amino acids, and organic acids, reflecting the metabolic reconfiguration necessary for osmotic adjustment and energy reallocation. Transcriptomic analysis identified 6219 differentially expressed genes (DEGs), with a focus on photosynthesis, hormone signaling, and stress-responsive pathways, providing insights into the molecular underpinnings of salt tolerance. Conclusions: This comprehensive study offers novel insights into the complex mechanisms employed by pea sprouts to combat salinity stress, contributing to the understanding of plant salt tolerance and potentially guiding the development of salt-resistant crop varieties. Full article

Sarcopenia, a progressive loss of muscle mass and strength, is a major health concern primarily affecting older adults worldwide. With no pharmaceutical cure for sarcopenia, dietary protein, probiotic supplementation, and physical exercise have gained increasing attention as lifestyle-based interventions. Dietary protein has shown promising effects in preventing the loss of skeletal muscle and physical strength by favorably influencing muscle protein synthesis in sarcopenic individuals. Probiotic supplementation has been associated with muscle regeneration, increased muscle protein synthesis among adults with sarcopenia, and improved exercise performance based on preliminary and emerging evidence. Multimodal or hybrid exercise programs have been shown to improve muscle strength, mobility, and overall physical function in individuals with sarcopenia. This paper reviews how combining protein, probiotics, and multimodal exercise may offer complementary strategies for sarcopenia management. Evidence from preclinical and mechanistic studies suggests that these interventions may support muscle health by activating shared intracellular pathways such as mTOR signaling, the suppression of FOXO3a, and the enhancement of mitochondrial biogenesis. Full article

Improving the decoding accuracy of biological signals has been a research focus for decades to advance health, automation, and robotic industries. However, challenges like inter-subject variability, data scarcity, and multifunctional variability cause low decoding accuracy, thus hindering the practical deployment of biological signal paradigms. This paper proposes a multifunctional biological signals network (Multi-BioSig-Net) that addresses the aforementioned issues by devising a novel blind few-shot learning (FSL) technique to quickly adapt to multiple target domains without needing a pre-trained model. Specifically, our proposed multimodal similarity extractor (MMSE) and self-multiple domain adaptation (SMDA) modules address data scarcity and inter-subject variability issues by exploiting and enhancing the similarity between multimodal samples and quickly adapting the target domains by adaptively adjusting the parameters’ weights and position, respectively. For multifunctional learning, we proposed inter-function discriminator (IFD) that discriminates the classes by extracting inter-class common features and then subtracts them from both classes to avoid false prediction of the proposed model due to overfitting on the common features. Furthermore, we proposed a holistic-local fusion (HLF) module that exploits contextual-detailed features to adapt the scale-varying features across multiple functions. In addition, fractal dimension estimation (FDE) was employed for the classification of left-hand motor imagery (LMI) and right-hand motor imagery (RMI), confirming that proposed method can effectively extract the discriminative features for this task. The effectiveness of our proposed algorithm was assessed quantitatively and statistically against competent state-of-the-art (SOTA) algorithms utilizing three public datasets, demonstrating that our proposed algorithm outperformed SOTA algorithms. Full article

The variability of B-type stars offers valuable insights into the interiors of stars and the processes that drive pulsation and rotation in massive stars. In this study, we present the classification of the variability of 197 B-type stars observed in various Kepler/K2 campaigns, including 73 newly classified stars from Campaigns 13–18. For these stars, we derived atmospheric and evolutionary parameters using space-based photometry and ground-based spectroscopy. We obtained spectroscopic data for 34 targets with high-resolution instruments at OPD/LNA, which were supplemented by archival LAMOST spectra. After correcting for instrumental systematics, we analyzed the light curves using Fourier transforms and wavelet decomposition to identify both periodic and stochastic signals. The identified variability types included SPB stars, $\beta$ Cephei/SPB hybrids, fast-rotating pulsators, stochastic low-frequency variables, eclipsing binaries, and rotational variables. We also revised classifications of misidentified stars using Gaia astrometry, confirming the main-sequence nature of objects once considered subdwarfs. Our results indicate that hot-star variability exists along a continuum shaped by mass, rotation, and internal mixing rather than distinct instability domains. This study enhances our understanding of B-type star variability and supports future asteroseismic modeling with missions like PLATO. Full article

Structured illumination microscopy (SIM) with π/2 phase-shift modulation traditionally relies on frequency-domain computation, which greatly limits processing efficiency. In addition, the illumination regime inherent in structured illumination techniques often results in poor visual quality of reconstructed images. To address these dual challenges, this study introduces DM-SIM-LLIE (Differential Low-Light Image Enhancement SIM), a novel framework that integrates two synergistic innovations. First, the study pioneers a spatial-domain computational paradigm for π/2 phase-shift SIM reconstruction. Through system differentiation, mathematical derivation, and algorithm simplification, an optimized spatial-domain model is established. Second, an adaptive local overexposure correction strategy is developed, combined with a zero-shot learning deep learning algorithm, RUAS, to enhance the image quality of structured light reconstructed images. Experimental validation using specimens such as fluorescent microspheres and bovine pulmonary artery endothelial cells demonstrates the advantages of this approach: compared with traditional frequency-domain methods, the reconstruction speed is accelerated by five times while maintaining equivalent lateral resolution and excellent axial resolution. The image quality of the low-light enhancement algorithm after local overexposure correction is superior to existing methods. These advances significantly increase the application potential of SIM technology in time-sensitive biomedical imaging scenarios that require high spatiotemporal resolution. Full article

Objectives: Alpinia japonica (A. japonica) is traditionally used for digestive disorders, but its hypolipidemic mechanisms remain unclear. This study investigated the lipid-lowering effects of its fruit (SJGS), rhizome (SJGJ), and leaf (SJY) extracts, exploring their bioactive constituents and organ-specific mechanisms. Methods: Sprague Dawley rats (n = 8/group) fed a high-fat diet received SJGS, SJGJ, or SJY (200 mg/kg/day) for 4 weeks. Serum lipids (TC, TG), liver enzymes (AST, ALT), and intestinal barrier markers (DAO) were measured. Gut microbiota (16S rDNA sequencing), hepatic histopathology, and ileal tight junction proteins were analyzed. Transcriptomics and qPCR assessed ileal gene expression. LC-MS identified chemical constituents, while network pharmacology predicted compound-target interactions. Results: All extracts significantly reduced serum TC (↓ 27–33%), TG (↓ 29–38%), AST/ALT (↓ 22–30%), and DAO (↓ 35–42%) versus controls (p < 0.05). They improved hepatic steatosis, enhanced intestinal barrier function, and modulated gut microbiota (↑ α-diversity, ↓ Firmicutes/Bacteroidetes ratio). Transcriptomics revealed PPAR signaling as the core pathway: SJGS/SJGJ downregulated fatty acid oxidation genes (ACSL1, ACOX1, ACADM), while SJY upregulated APOA1 (2.3-fold). LC-MS identified 33–48 compounds/part, with seven shared constituents. Network analysis prioritized three flavonoids (pinocembrin, luteolin, galangin) targeting TNF, AKT1, and PPAR pathways. Conclusions: The findings suggest A. japonica extracts ameliorate hyperlipidemia through distinct mechanisms—SJGS/SJGJ may inhibit fatty acid oxidation, while SJY potentially enhances APOA1-mediated clearance. Shared flavonoids likely contribute to these effects via PPAR signaling, supporting its traditional use. This study provides a scientific basis for the sustainable utilization of A. japonica resources. Full article

Neuromuscular robotic prostheses have emerged as a critical convergence point between biomedical engineering, machine learning, and human–machine interfaces. This work provides a narrative state-of-the-art review regarding recent developments in robotic prosthetic technology, emphasizing sensor integration, actuator architectures, signal acquisition, and algorithmic strategies for intent decoding. Special focus is given to non-invasive biosignal modalities, particularly surface electromyography (sEMG), as well as invasive approaches involving direct neural interfacing. Recent developments in AI-driven signal processing, including deep learning and hybrid models for robust classification and regression of user intent, are also examined. Furthermore, the integration of real-time adaptive control systems with surgical techniques like Targeted Muscle Reinnervation (TMR) is evaluated for its role in enhancing proprioception and functional embodiment. Finally, this review highlights the growing importance of modular, open-source frameworks and additive manufacturing in accelerating prototyping and customization. Progress in this domain will depend on continued interdisciplinary research bridging artificial intelligence, neurophysiology, materials science, and real-time embedded systems to enable the next generation of intelligent prosthetic devices. Full article

Curcumin has been extensively investigated as an anticancer agent, yet its clinical application remains constrained by low bioavailability, incomplete mechanistic understanding, and limited therapeutic optimization. In light of growing resistance to conventional chemotherapies and the demand for safer, multi-targeted agents, this review revisits curcumin with a contemporary lens. We critically evaluate the literature published since 2020, focusing on newly elucidated molecular mechanisms by which curcumin regulates tumor progression, including modulation of oncogenic signaling pathways (Wnt/β-catenin, PI3K/Akt/mTOR, JAK/STAT, and MAPK), induction of ferroptosis, and epigenetic reprogramming. A particular emphasis is placed on recent advances in nanoformulation strategies that enhance curcumin’s pharmacokinetic profile and target-specific delivery. Furthermore, the emerging paradigm of combination therapy is explored, where curcumin acts synergistically with chemotherapeutics and phytochemicals to overcome drug resistance and potentiate anticancer efficacy. This review identifies key knowledge gaps, such as inconsistent clinical translation and the underexplored interplay between nanocurcumin systems and immune modulation, outlining directions for future translational research. Full article

Viral infection is a significant cause of morbidity and mortality following allogeneic hematopoietic stem cell transplantation (Allo-HSCT), largely due to its impact on and interaction with immune reconstitution. Both innate and adaptive immunity are essential for effective viral control, yet their recovery post-transplant is often delayed or functionally impaired. Emerging evidence suggests genetic variation, particularly polymorphisms in the IL28B gene (encoding IFN-λ3), as a critical factor influencing the quality and timing of immune responses during the early post-transplant period. This review explores the role of IL28B polymorphisms in shaping antiviral immunity, in general, as well as after Allo-HSCT. IL28B variants have been implicated in modulating interferon-stimulated gene (ISG) expression, natural killer (NK) cell activity, and type I/III interferon signaling, all central components of innate immune defense against viral infections. Furthermore, IL28B polymorphisms, particularly rs12979860, have been shown in both general populations and limited HSCT cohorts to alter T cell response and interferon production, affecting reactivation and clearance of multiple viruses such as cytomegalovirus (CMV), hepatitis B virus (HBV), hepatitis C virus (HCV), Epstein–Barr virus (EBV), COVID-19, and BK polyomavirus (BKPyV) as well as Graft vs. Host disease, thereby affecting adaptive immune reconstitution and long-term viral control. Understanding how IL28B genotype alters immune dynamics in transplant recipients could enhance risk stratification for CMV and other diseases and inform personalized prophylactic or therapeutic strategies. Therefore, this review highlights IL28B as a promising biomarker and potential immunoregulatory target in the management of viral infection post-Allo-HSCT. Full article