Search Results (10,401)

Molecularly imprinted polymers (MIPs) have emerged as robust, cost-effective analogues of bioreceptors, offering high selectivity and stability. When applied in sensors, one key step is the integration of MIPs with the transducer, which critically affects sensor performance. Demanding challenges come when such integration involves nanoscaling processes, meaning that the transducer is nanostructured or the MIP itself is nanosized on a bulk transducer. In both cases, the integration results in the development of nanosensors, with advantages arising from the nanoscale, such as a high MIP surface-to-volume ratio, with surface-located, easily accessible binding sites, fast binding kinetics, and, thus, a rapid sensor response. Major advantages come also from nanostructured transducers, with nanoscale geometry enabling highly sensitive signal generation processes, not allowed on their bulk counterparts. In this review, we discuss advances in imprinting technologies, focusing on techniques that, enabling the nanoscale control of MIP synthesis, are conveniently applied to directly integrate MIPs with nanosensors in a one-step process. Two main approaches are reviewed, consisting in MIP nanostructuring on bulk transducers and in the direct growth of MIPs on nanotransducers, highlighting how different strategies achieve good conformity at the nanoscale and address spatial complexity to ensure stable and accurate signal acquisition. Finally, we consider future directions in MIP-based nanosensor development. Full article

Background and Objectives: The cytokine IL-6, methyltransferase NSD2, pro-protein convertase Furin, and growth factor receptor IGF-1R are essential factors in the proliferation of cancer cells. These proteins are involved in the tumor process by generating several cell-signaling pathways. However, the interactions of these oncogenic biomarkers, Furin, IL-6, and NSD2, and their links with the inhibitor SERPINA-1 remain largely unknown. Materials and Methods: Cell proliferation is measured by colorimetric and enzymatic methods. The genetic expressions of SERPINA-1, Furin, IL-6, and NSD2 are measured by qRT-PCR, while the expression of IGF-1R on the cell surface is measured by flow cytometry. Results: The proliferation of cells overexpressing SERPINA-1 (JP7pSer+) is decreased by more than 90% compared to control cells (JP7pSer-). The kinetics of the gene expression ratios of Furin, IL-6, and NSD2 show an increase for 48 h, followed by a decrease after 72 h for the three biomarkers in JP7pSer+ cells compared to JP7pSer- cells. The expression of IGF-1R on the cell surface in both cell lines is low, with JP7pSer- cells expressing 1.33 times more IGF-1R than JP7pSer+ cells. Conclusions: These results suggest gene correlations of SERPINA-1 overexpression with decreased cell proliferation and modulation of gene expression of Furin, IL-6, and NSD2. This study should be complemented by molecular transcriptomic and proteomic experiments to better understand the interaction of SERPINA-1 with IL-6, Furin, and NSD2, and their effect on tumor progression. Full article

Most public seismic recordings, sampled at hundreds of Hz, tend to be unlabelled, i.e., not catalogued, mainly because of the sheer volume of samples and the amount of time needed by experts to confidently label detected events. This is especially challenging for very low signal-to-noise ratio microseismic events that characterise landslides during rock and soil mass displacement. Whilst numerous supervised machine learning models have been proposed to classify landslide events, they rely on a large amount of labelled datasets. Therefore, there is an urgent need to develop tools to effectively automate the data-labelling process from a small set of labelled samples. In this paper, we propose a semi-supervised method for labelling of signals recorded by seismometers that can reduce the time and expertise needed to create fully annotated datasets. The proposed Siamese network approach learns best class-exemplar anchors, leveraging learned similarity between these anchor embeddings and unlabelled signals. Classification is performed via soft-labelling and thresholding instead of hard class boundaries. Furthermore, network output explainability is used to explain misclassifications and we demonstrate the effect of anchors on performance, via ablation studies. The proposed approach classifies four landslide classes, namely earthquakes, micro-quakes, rockfall and anthropogenic noise, demonstrating good agreement with manually detected events while requiring few training data to be effective, hence reducing the time needed for labelling and updating models. Full article

Artificial intelligence (AI) has emerged as a transformative technology in the evolution of massive multiple-input multiple-output (ma-MIMO) systems, positioning them as a cornerstone for sixth-generation (6G) wireless networks. Despite their significant potential, ma-MIMO systems face critical challenges at the receiver end, particularly in signal detection under high-dimensional and noisy environments. To address these limitations, this paper proposes MIMONet, a novel deep learning (DL)-based MIMO detection framework built upon a lightweight and optimized feedforward neural network (FFNN) architecture. MIMONet is specifically designed to achieve a balance between detection performance and complexity by optimizing the neural network architecture for MIMO signal detection tasks. Through extensive simulations across multiple MIMO configurations, the proposed MIMONet detector consistently demonstrates superior bit error rate (BER) performance. It achieves a notably lower error rate compared to conventional benchmark detectors, particularly under moderate to high signal-to-noise ratio (SNR) conditions. In addition to its enhanced detection accuracy, MIMONet maintains significantly reduced computational complexity, highlighting its practical feasibility for advanced wireless communication systems. These results validate the effectiveness of the MIMONet detector in optimizing detection accuracy without imposing excessive processing burdens. Moreover, the architectural flexibility and efficiency of MIMONet lay a solid foundation for future extensions toward large-scale ma-MIMO configurations, paving the way for practical implementations in beyond-5G (B5G) and 6G communication infrastructures. Full article

Accurate parameter estimation is essential for gravitational wave data analysis. In extreme mass-ratio inspiral binary systems, orbital eccentricity is a critical parameter for parameter estimation. However, the current software for the parameter estimation of the gravitational wave often neglects the direct estimation of orbital eccentricity. To fill this gap, we have developed the MatBYIB, a MATLAB-based software (Version 1.0) package for the parameter estimation of the gravitational wave with arbitrary eccentricity. The MatBYIB employs the Analytical Kludge waveform as a computationally efficient signal generator and computes parameter uncertainties via the Fisher Information Matrix and the Markov Chain Monte Carlo. For Bayesian inference, we implement the Metropolis–Hastings algorithm to derive posterior distributions. To guarantee convergence, the Gelman–Rubin convergence criterion (the Potential Scale Reduction Factor $\widehat{R}$) is used to determine sampling adequacy, with MatBYIB dynamically increasing the sample size until $\widehat{R}<1.05$ for all parameters. Our results demonstrate strong agreement between predictions based on the Fisher Information Matrix and full MCMC sampling. This program is user-friendly and allows for the estimation of the gravitational wave parameters with arbitrary eccentricity on standard personal computers. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

Early weaning of piglets elicits weaning stress, which in turn induces oxidative stress and consequently impairs growth and development. Hydrogen-rich water (HRW), characterized by selective antioxidant properties, mitigates oxidative stress damage and serves as an ideal intervention. This study aimed to evaluate the effects of HRW on weaned piglets, specifically investigating its impact on growth performance, diarrhea incidence, antioxidant function, intestinal morphology, gut microbiota, and hepatic metabolites. The results demonstrate that HRW significantly increased the average daily feed intake and significantly reduced the diarrhea rate in weaned piglets. Analysis of serum oxidative stress indicators revealed that HRW significantly elevated the activities of total antioxidant capacity and total superoxide dismutase while significantly decreasing malondialdehyde concentration. Assessment of intestinal morphology showed that HRW significantly increased the villus height to crypt depth ratio in the duodenum, jejunum, and ileum. Microbial analysis indicated that HRW significantly increased the abundance of Prevotella in the colon. Furthermore, HRW increased the abundance of beneficial bacteria, such as Akkermansia, in the jejunum and cecum, while concurrently reducing the abundance of harmful bacteria like Escherichia. Hepatic metabolite profiling revealed that HRW significantly altered the metabolite composition in the liver of weaned piglets. Differentially abundant metabolites were enriched in oxidative stress-related KEGG pathways, including ABC transporters; pyruvate metabolism; autophagy; FoxO signaling pathway; glutathione metabolism; ferroptosis; and AMPK signaling pathways. In conclusion, HRW alleviates diarrhea and promotes growth in weaned piglets by enhancing antioxidant capacity. These findings provide a scientific foundation for the application of HRW in swine production and serve as a reference for further exploration into the mechanisms underlying HRW’s effects on animal health and productivity. Full article

This paper proposes an optimized control filter (OCF) based on the Filtered-x Least Mean Square (FxLMS) algorithm for multi-channel active noise control (ANC) systems. The proposed OCF-McFxLMS algorithm delivers three key contributions. Firstly, even in difficult noise situations such as White Gaussian, Brownian, and pink noise, it greatly reduces error, reaching nearly zero mean squared error (MSE) values across all Microphone (Mic) channels. Secondly, it improves computational efficiency by drastically reducing execution time from 58.17 s in the standard McFxLMS algorithm to just 0.0436 s under White Gaussian noise, enabling real-time noise control without compromising accuracy. Finally, the OCF-McFxLMS demonstrates robust noise attenuation, achieving signal-to-noise ratio (SNR) values of 137.41 dB under White Gaussian noise and over 100 dB for Brownian and pink noise, consistently outperforming traditional approaches. These contributions collectively establish the OCF-McFxLMS algorithm as an efficient and effective solution for real-time ANC systems, delivering superior noise reduction and computational speed performance across diverse noise environments. Full article

The large intelligent surface (LIS) concept represents an architectural advance for enhancing the performance of 6G wireless communication systems. In this work, we address the problem of jointly selecting active panels and associating terminals to outputs of such active panels in a panel-based LIS framework to maximise the minimum signal-to-interference-and-noise ratio (SINR) across all terminals. Due to the nature of the mixed-integer linear programming (MILP) formulation, we propose an alternative approach based on a genetic algorithm (GA) that efficiently explores the solution space through tailored crossover via column swapping and adaptive mutation. We compare the GA’s performance against the CPLEX solver under various configurations and time constraints. The performance results show that the GA provides competitive solutions with reduced computational complexity, showcasing its potential for scalable LIS implementations with complex resource allocation. Full article

In compressed sensing, it is believed that the L₀ norm minimization is the best way to enforce a sparse solution. However, the L₀ norm is difficult to implement in a gradient-based iterative image reconstruction algorithm. The total variation (TV) norm minimization is considered a proper substitute for the L₀ norm minimization. This paper points out that the TV norm is not powerful enough to enforce a piecewise-constant image. This paper uses the limited-angle tomography to illustrate the possibility of using the L₀ norm to encourage a piecewise-constant image. However, one of the drawbacks of the L₀ norm is that its derivative is zero almost everywhere, making a gradient-based algorithm useless. Our novel idea is to replace the zero value of the L₀ norm derivative with a zero-mean random variable. Computer simulations show that the proposed L₀ norm minimization outperforms the TV minimization. The novelty of this paper is the introduction of some randomness in the gradient of the objective function when the gradient is zero. The quantitative evaluations indicate the improvements of the proposed method in terms of the structural similarity (SSIM) and the peak signal-to-noise ratio (PSNR). Full article

Background/Objectives: Cannabinoid use is rising among patients undergoing spinal fusion, yet its influence on bone healing is poorly defined. The endocannabinoid system (ECS)—through cannabinoid receptors 1 (CB1) and 2 (CB2)—modulates skeletal metabolism. We reviewed preclinical, mechanistic and clinical evidence to clarify how individual cannabinoids affect fracture repair and spinal arthrodesis. Methods: PubMed, Web of Science and Scopus were searched from inception to 31 May 2025 with the terms “cannabinoid”, “CB1”, “CB2”, “spinal fusion”, “fracture”, “osteoblast” and “osteoclast”. Animal studies, in vitro experiments and clinical reports that reported bone outcomes were eligible. Results: CB2 signaling was uniformly osteogenic. CB2-knockout mice developed high-turnover osteoporosis, whereas CB2 agonists (HU-308, JWH-133, HU-433, JWH-015) restored trabecular volume, enhanced osteoblast activity and strengthened fracture callus. Cannabidiol (CBD), a non-psychoactive phytocannabinoid with CB2 bias, accelerated early posterolateral fusion in rats and reduced the RANKL/OPG ratio without compromising final union. In contrast, sustained or high-dose Δ⁹-tetrahydrocannabinol (THC) activation of CB1 slowed chondrocyte hypertrophy, decreased mesenchymal-stromal-cell mineralization and correlated clinically with 6–10% lower bone-mineral density and a 1.8–3.6-fold higher pseudarthrosis or revision risk. Short-course or low-dose THC appeared skeletal neutral. Responses varied with sex, age and genetic background; no prospective trials defined safe perioperative dosing thresholds. Conclusions: CB2 activation and CBD consistently favor bone repair, whereas chronic high-THC exposure poses a modifiable risk for nonunion in spine surgery. Prospective, receptor-specific trials stratified by THC/CBD ratio, patient sex and ECS genotype are needed to establish evidence-based cannabinoid use in spinal fusion. Full article

Graphene’s exceptional carrier mobility and broadband absorption make it promising for ultrafast photodetection. However, its low optical absorption limits responsivity, while the absence of a bandgap results in high dark current, constraining the signal-to-noise ratio and efficiency. Although silicon (Si) photodetectors normally offer fabrication compatibility, their performance is severely hindered by interface trap states and optical shading. To overcome these limitations, we demonstrate an epitaxial graphene/n-Si heterojunction photodiode. This device utilizes graphene epitaxially grown on germanium integrated with a transferred Si thin film, eliminating polymer residues and interface defects common in transferred graphene. As a result, the fabricated photodetector achieves an ultralow dark current of 1.2 × 10⁻⁹ A, a high responsivity of 1430 A/W, and self-powered operation at room temperature. This work provides a strategy for high-sensitivity and low-power photodetection and demonstrates the practical integration potential of graphene/Si heterostructures for advanced optoelectronics. Full article

Skin autofluorescence (SAF) detection technology represents a noninvasive, convenient, and cost-effective optical detection approach. It can be employed for the differentiation of various diseases, including metabolic diseases and dermatitis, as well as for monitoring the treatment efficacy. Distinct from diffuse reflection signals, the autofluorescence signals of biological tissues are relatively weak, making them challenging to be captured by photoelectric sensors. Moreover, the absorption and scattering properties of biological tissues lead to a substantial attenuation of the autofluorescence of biological tissues, thereby worsening the signal-to-noise ratio. This has also imposed limitations on the development and application of compact-sized autofluorescence detection systems. In this study, a compact LED light source and a CMOS sensor were utilized as the excitation and detection devices for skin tissue autofluorescence, respectively, to construct a mobile and wireless skin tissue autofluorescence detection system. This system can achieve the detection of skin tissue autofluorescence with a high signal-to-noise ratio under the drive of a simple power supply and a single-chip microcontroller. The detection time is less than 0.1 s. To enhance the stability of the system, a pressure sensor was incorporated. This pressure sensor can monitor the pressure exerted by the skin on the detection system during the testing process, thereby improving the accuracy of the detection signal. The developed system features a compact structure, user-friendliness, and a favorable signal-to-noise ratio of the detection signal, holding significant application potential in future assessments of skin aging and the risk of diabetic complications. Full article

Cucumbers (Cucumis sativus L.) are highly sensitive to cold, but grafting onto cold-tolerant rootstocks can enhance their low-temperature resilience. This study investigates the physiological and molecular mechanisms by which exogenous vitamin C (Vc) mitigates cold stress in grafted cucumber seedlings. Using cucumber ‘Chiyu 505’ as the scion and pumpkin ‘Chuangfan No.1’ as the rootstock, seedlings were grafted using the whip grafting method. In the third true leaf expansion stage, seedlings were foliar sprayed with Vc at concentrations of 50, 100, 150, and 200 mg L⁻¹. Three days after initial spraying, seedlings were subjected to cold stress (8 °C) for 3 days, with continued spraying. After that, morphological and physiological parameters were assessed. Results showed that 150 mg L⁻¹ Vc treatment was most impactive, significantly reducing the cold damage index while increasing the root-to-shoot ratio, root vitality, chlorophyll content, and activities of antioxidant enzymes (SOD, POD, CAT). Moreover, this treatment enhanced levels of soluble sugars, soluble proteins, and proline compared to control. However, 200 mg L⁻¹ treatment elevated malondialdehyde (MDA) content, indicating potential oxidative stress. For transcriptomic analysis, leaves from the 150 mg L⁻¹ Vc and CK treatments were sampled at 0, 1, 2, and 3 days of cold stress. Differential gene expression revealed that genes associated with photosynthesis (LHCA1), stress signal transduction (MYC2-1, MYC2-2, WRKY22, WRKY2), and antioxidant defense (SOD-1, SOD-2) were initially up-regulated and subsequently down-regulated, as validated by qRT-PCR. Overall, we found that the application of 150 mg L⁻¹ Vc enhanced cold tolerance in grafted cucumber seedlings by modulating gene expression networks related to photosynthesis, stress response, and the antioxidant defense system. This study provides a way for developing Vc biostimulants to enhance cold tolerance in grafted cucumbers, improving sustainable cultivation in low-temperature regions. Full article

Background: Acute mountain sickness (AMS) is a prevalent and potentially life-threatening condition caused by rapid exposure to high-altitude hypoxia, affecting pulmonary and neurological functions. Tongqiao Jiuxin Oil (TQ), a traditional Chinese medicine formula composed of aromatic and resinous ingredients such as sandalwood, agarwood, frankincense, borneol, and musk, has been widely used in the treatment of cardiovascular and cerebrovascular disorders. Clinical observations suggest its potential efficacy against AMS, yet its pharmacological mechanisms remain poorly understood. Methods: The chemical profile of TQ was characterized using UHPLC-Q-Exactive Orbitrap HRMS. Network pharmacology was applied to predict the potential targets and pathways involved in AMS. A rat model of AMS was established by exposing animals to hypobaric hypoxia (~10% oxygen), simulating an altitude of approximately 5500 m. TQ was administered at varying doses. Physiological indices, oxidative stress markers (MDA, SOD, GSH), histopathological changes, and the expression of hypoxia- and apoptosis-related proteins (HIF-1α, VEGFA, EPO, Bax, Bcl-2, Caspase-3) in lung and brain tissues were assessed. Results: A total of 774 chemical constituents were identified from TQ. Network pharmacology predicted the involvement of multiple targets and pathways. TQ significantly improved arterial oxygenation and reduced histopathological damage in both lung and brain tissues. It enhanced antioxidant activity by elevating SOD and GSH levels and reducing MDA content. Mechanistically, TQ downregulated the expression of HIF-1α, VEGFA, EPO, and pro-apoptotic markers (Bax/Bcl-2 ratio, Caspase-3), while upregulated Bcl-2, the anti-apoptotic protein expression. Conclusions: TQ exerts protective effects against AMS-induced tissue injury by improving oxygen homeostasis, alleviating oxidative stress, and modulating hypoxia-related and apoptotic signaling pathways. This study provides pharmacological evidence supporting the potential of TQ as a promising candidate for AMS intervention, as well as the modern research method for multi-component traditional Chinese medicine. Full article