Search Results (4,538)

Microplastics entering the gastrointestinal environment rapidly acquire protein coronas that alter their surface chemistry and analytical detectability. We investigated the physicochemical interactions between fluorescently labeled bovine serum albumin (BSA) and polyethylene terephthalate (PET) microplastics during simulated intestinal exposure and evaluated the stability of the resulting hard corona. Using fluorescence tracking, SDS-PAGE, and FTIR spectroscopy, we showed that BSA forms a persistent corona that resists oxidative-only treatments. Only a combination of oxidation with an alkaline (KOH) or surfactant step (SDS) effectively removed the corona. None of the protocols applied affected polymer integrity. Residual protein in less effective protocols did not show changes on PET spectra in ATR FTIR. To validate the protocol under physiologically relevant complexity, we extended it to PET incubated with single digestive enzymes. FTIR spectra confirmed the removal of protein-specific signals in both systems, with no degradation of PET ester or aromatic functional groups nor signals of protein–polymer interactions. Our results highlight the robustness of protein–PET interactions in biological conditions and provide a variety of protocols for protein corona removal, suitable for diverse applications of microplastic analysis and toxicological studies. Full article

Mycotoxin contamination represents a major risk to both human and animal health. Antioxidants can mitigate some of these effects through free radical scavenging, reduction in oxidative stress, and anti-inflammatory and immunomodulatory actions. This work investigated the potential of antioxidants derived from grapeseed and sea buckthorn to mitigate the adverse effects of aflatoxin B1 (AFB1) and ochratoxin A (OTA) in weaned piglets. An unbiased Data-Independent Acquisition (DIA) proteomic approach was used to analyse the impact of OTA- and AFB1-contaminated diets on liver and kidney cytoplasmic metabolism, particularly focusing on the conjugation phase. Our results indicate that several toxic effects of these mycotoxins were partially alleviated by dietary antioxidant supplementation. Additionally, in kidneys, some of the effects are synergistically amplified, such as proteins involved in fatty acid degradation, peroxisome, PPAR signalling, translation, the TCA cycle, and excretion pathways. Inclusion of antioxidants in the animal diet can have beneficial effects. Nevertheless, caution is advised; synergistic effects can occur with potentially more serious consequences than the effects of mycotoxins alone. Full article

Coating degradation monitoring is increasingly important in offshore industries, where protective layers ensure corrosion prevention and structural integrity. In this context, coating thickness estimation provides critical information. The ultrasound pulse-echo technique is widely used for non-destructive testing (NDT), but closely spaced acoustic interfaces often produce overlapping echoes, which complicates detection and accurate isolation of each layer’s thickness. In this study, analysis of the pulse-echo signal from a coated sample has shown that the front-coating reflection affects each main backwall echo differently; by comparing two consecutive backwall echoes, we can cancel the acquisition system’s impulse response and isolate the propagation path-related information between the echoes. This work introduces an ultrasound echo-based methodology for estimating coating thickness by first obtaining the impulse response of the test medium (reflectivity sequence) through a deconvolution model, developed using two consecutive backwall echoes. This is followed by an enhanced detection of coating layer thickness in the reflectivity function using a 1D convolutional neural network (1D-CNN) trained with synthetic signals obtained from finite-difference time-domain (FDTD) simulations with k-Wave MATLAB toolbox (v1.4.0). The proposed approach estimates the front-side coating thickness in steel samples coated on both sides, with coating layers ranging from $60\mathsf{\mu }$m to $740\mathsf{\mu }$m applied over 5 mm substrates and under varying coating and steel properties. The minimum detectable thickness corresponds to approximately $\mathit{\lambda }/5$ for an 8 MHz ultrasonic transducer. On synthetic signals, where the true coating thickness and speed of sound are known, the model achieves an accuracy of approximately $8\mathsf{\mu }$m. These findings highlight the strong potential of the model for reliably monitoring relative thickness changes across a wide range of coatings in real samples. Full article

Visible Light Positioning (VLP) has emerged as a pivotal technology for industrial Internet of Things (IoT) and smart logistics, offering high accuracy, immunity to electromagnetic interference, and cost-effectiveness. However, fluctuations in signal gain caused by target motion significantly degrade the positioning accuracy of current VLP systems. Conventional approaches face intrinsic limitations: propagation-model-based techniques rely on static assumptions, fingerprint-based approaches are highly sensitive to dynamic parameter variations, and although CNN/LSTM-based models achieve high accuracy under static conditions, their inability to capture long-term temporal dependencies leads to unstable performance in dynamic scenarios. To overcome these challenges, we propose a novel dynamic VLP algorithm that incorporates a Spatio-Temporal Feature Information Network (STFI-Net) for joint localization and orientation estimation of moving targets. The proposed method integrates a two-layer convolutional block for spatial feature extraction and employs modern Temporal Convolutional Networks (TCNs) with dilated convolutions to capture multi-scale temporal dependencies in dynamic environments. Experimental results demonstrate that the STFI-Net-based system enhances positioning accuracy by over 26% compared to state-of-the-art methods while maintaining robustness in the face of complex motion patterns and environmental variations. This work introduces a novel framework for deep learning-enabled dynamic VLP systems, providing more efficient, accurate, and scalable solutions for indoor positioning. Full article

Autophagy is a conserved process that involves the degradation of damaged proteins and organelles to restore cellular homeostasis. Autophagy plays a critical role in cell differentiation, immune responses, and protection against pathogens, as well as the development and progression of allergic inflammation. Crosstalk between autophagy and signaling pathways modulates immune responses to inflammatory signals. Here, we discuss the regulatory roles of autophagy in allergic inflammation. Autophagy can promote allergic inflammation by enhancing the secretion of inflammatory mediators. Impaired autophagy resulting from the accumulation of autophagosomes can exacerbate allergic inflammation. Mast cell degranulation and activation require energy provided by mitochondrial respiration. Mast cell activation is accompanied by morphological changes and mitochondrial fragmentation. Mitochondrial fragmentation (mitophagy) induced by oxidative stress involves the degradation of defective mitochondria. Therefore, we discuss the relationship between mitophagy and allergic inflammation. Targeting autophagy and oxidative stress can be a strategy for developing anti-allergy therapeutics. In this review, we also discuss future research directions to better understand allergic diseases with respect to autophagy and develop effective anti-allergy drugs. Full article

Additives such as surfactants (Tween-20) and cryoprotectants (glycerol and trehalose) are often used in enzymatic assays to improve the quality and long-term stabilization of proteins. However, these additives can affect the enzymatic activity and the enzyme’s affinity for active compounds, such as inhibitors, and must be considered during assay design since a slight shift in enzyme behavior may compromise the reliability of the results. In this study, the effects of Tween-20, glycerol, and trehalose on hyaluronidase (Hyal) were systematically evaluated by assessing their influence both directly—through microscale thermophoresis (MST) signals of the labeled enzyme (Hyal*)—and indirectly, by monitoring the formation of the final product of the degradation of hyaluronic acid, tetrasaccharide (Tet), using capillary electrophoresis (CE/UV). Hyal was labeled for the first time with ATTO-647 NHS ester, a commercial dye compatible with MST. Efficient labeling was achieved in a phosphate-based buffer without loss of catalytic activity. Tween-20 showed no impact on MST signals nor on enzymatic performance when used between 0.005 and 0.05% (v/v). Glycerol also did not interfere with MST measurements; however, it significantly reduced catalytic activity at concentrations above 2% (v/v). Trehalose affected Hyal* fluorescence in a concentration-dependent manner and enhanced catalytic activity even at 0.02% (v/v). Full article

A key direction of the development of modern power systems is the application of a continuously increasing number of grid-forming power converters to provide various system services. One of the possible strategies for the implementation of grid-forming control is a control algorithm based on a virtual synchronous generator (VSG). However, at present, the problem of VSG operation under abnormal conditions associated with an increase in output current remains unsolved. Existing current saturation algorithms (CSAs) lead to the degradation of grid-forming properties during overcurrent limiting or reduce the possible range of current output. In this regard, this paper proposes to use the structure of modified current-controlled VSG (CC-VSG) instead of traditional voltage-controlled VSG. A current vector amplitude limiter is used to limit the output current in the CC-VSG structure. At the same time, the angle of the current reference vector continues to be regulated based on the emerging operating conditions due to the voltage feedback in the used VSG equations. The presented simulation results have shown that it was possible to achieve a wide operating range for the current phase from 0° to 180° in comparison with a traditional VSG algorithm. At the same time, the properties of the grid-forming inverter, such as power synchronization without phase-locked loop controller, voltage, and frequency control, are preserved. In addition, in order to avoid saturation of the voltage controller, it is proposed to use a simple algorithm of blocking and switching the reference signal from the setpoint to the current voltage level. Due to this structure, it was possible to prevent saturation of integrators in the control loops and to provide a guaranteed exit from the limiting mode. The results of adding this structure showed a five-second reduction in the overvoltage that occurs when it is absent. A comparison with conditional integration also showed that it prevented lock-up in the limiting mode. The results of experimental verification of the developed prototype of the inverter with CC-VSG control and CSA are also given, including a comparison with the serial model of the hybrid inverter. The results obtained showed that the developed algorithm excludes both the dead time and the load current loss when the external grid is disconnected. In addition, there is no tripping during overload, unlike a hybrid inverter. Full article

Integrated sensing and communication (ISAC) has emerged as a transformative technology for intelligent transportation systems. Index modulation (IM), recognized for its high robustness and energy efficiency (EE), has been successfully incorporated into ISAC systems. However, most existing IM-based ISAC schemes overlook the spatial multiplexing potential of millimeter-wave channels and remain confined to single-hop vehicle-to-vehicle (V2V) setups, failing to address the challenges of energy consumption and noise accumulation in real-world multi-hop V2V networks with complex road topologies. To bridge this gap, we propose a spatial scattering modulation-based ISAC (ISAC-SSM) scheme and introduce it to multi-hop V2V networks. The proposed scheme leverages the sensed positioning information to select maximum signal-to-noise ratio relay vehicles and employs a detect-amplify-and-forward (DAF) protocol to mitigate noise propagation, while utilizing sensed angle data for Doppler compensation to enhance communication reliability. At each hop, the transmitter modulates index bits on the angular-domain spatial directions of scattering clusters, achieving higher EE. We initially derive a closed-form bit error rate expression and Chernoff upper bound for the proposed DAF ISAC-SSM under multi-hop V2V networks. Both theoretical analyses and Monte Carlo simulations have been made and demonstrate the superiority of DAF ISAC-SSM over existing alternatives in terms of EE and error performance. Specifically, in a two-hop network with 12 scattering clusters, compared with DAF ISAC-conventional spatial multiplexing, DAF ISAC-maximum beamforming, and DAF ISAC-random beamforming, the proposed DAF ISAC-SSM scheme can achieve a coding gain of 1.5 dB, 2 dB, and 4 dB, respectively. Moreover, it shows robust performance with less than a 1.5 dB error degradation under 0.018 Doppler shifts, thereby verifying its superiority in practical vehicular environments. Full article

Harmonic drive is susceptible to strength failure and performance degradation failure during operation. Given the long life test cycles, limited sample size, and incomplete understanding of degradation laws, this study conducted comprehensive life test and performance degradation test research to enable future failure prediction and reliability assessment for harmonic drive. Building upon established test rigs for a HD life test and performance degradation test, a step-down accelerated life test methodology was developed. Life tests under step-down accelerated conditions were executed, with a concurrent performance degradation test throughout the life test. Key datasets acquired include vibration signal histories, degradation data for critical performance indicators such as stiffness, precision, transmission efficiency, and backlash. Test results show that the predominant strength failure in the atmospheric environment is flexspline fatigue fracture, while significant degradation occurred in stiffness, precision, and backlash across all test conditions; transmission efficiency showed gradual degradation before strength failure followed by a marked decline post-failure. Finally, the failure mechanism of strength and performance degradation is analyzed, and the mechanism consistency of the two failures is verified by Hilbert envelope spectrum analysis and degradation trajectory shape consistency, respectively. The results from this paper provide critical data support and a reference foundation for the proactive maintenance of the harmonic drive. Full article

The completeness and integrity of multimodal medical data are critical determinants of surgical success and postoperative recovery. However, because of issues such as poor sensor contact, small vibrations, and device discrepancies during signal acquisition, there are frequent missing values in patients’ medical data. This issue is especially prominent in rare or complex cases, where the inherent complexity and sparsity of multimodal data limit dataset diversity and degrade predictive model performance. As a result, clinicians’ understanding of patient conditions is restricted, and the development of robust algorithms to predict preoperative, intraoperative, and postoperative disease progression is hindered. To address these challenges, we propose Med-Diffusion, a diffusion-based generative framework designed to enhance sensor data by imputing missing multimodal clinical data, including both categorical and numerical variables. The framework integrates one-hot encoding, simulated bit encoding, and feature tokenization to improve adaptability to heterogeneous data types, utilizing conditional diffusion modeling for accurate data completion. Med-Diffusion effectively learns the underlying distributions of multimodal datasets, synthesizing plausible data for incomplete records, and it mitigates the data sparsity caused by poor sensor contact, vibrations, and device discrepancies. Extensive experiments demonstrate that Med-Diffusion accurately reconstructs missing multimodal clinical information and significantly enhances the performance of downstream predictive models. Full article

To mitigate the degradation of positioning accuracy in sixth-generation mobile communication systems under dynamic line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, this paper proposes an improved adaptive Kalman filter positioning method based on Orthogonal Time Frequency Space (OTFS)-modulated signals. Firstly, the distance can be measured by using the OTFS-modulated signals transmitted between base stations and nodes. Secondly, the distance information is converted into the distance difference information to establish the time difference of arrival (TDOA) positioning equation, which is preliminarily solved using the Chan algorithm. Thirdly, residuals are calculated based on the preliminary positioning results, dividing the complex environment into distinct regions and adaptively determining corresponding genetic factors for each region. Finally, the selected genetic parameters are substituted into the Sage–Husa adaptive Kalman filter equations to estimate positioning results. The simulation analysis demonstrates that in complex environments featuring both line-of-sight and non-line-of-sight conditions, the vehicle motion trajectories estimated using this method more closely approximate actual trajectories. Additionally, both the accuracy and stability of positioning results show significant improvement compared to traditional methods. 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

Background: Ultraviolet B (UVB) radiation accelerates skin aging by inducing oxidative stress, collagen degradation, and cellular senescence. Although Petasites japonicus is known for its antioxidant properties, its anti-photoaging potential remains underexplored. This research explored the protective properties of a hot water extract from P. japonicus leaves (KP-1) against photoaging caused by UVB exposure. Methods: Hairless mice were exposed to UVB three times per week and orally administered KP-1 for 13 weeks. Wrinkle formation, epidermal thickness, skin hydration, and collagen content were assessed. Protein expression related to MAPK/AP-1, TGF-β/Smad2/3, and p53/p21 pathways was analyzed by Western blotting. Results: KP-1 significantly reduced UVB-induced wrinkle area, epidermal and dermal thickening, and transepidermal water loss while restoring collagen density and skin hydration. KP-1 inhibited MMP-1 expression, enhanced COL1A1 levels, suppressed MAPK/AP-1 activation, and activated TGF-β/Smad2/3 signaling. It also balanced p53/p21 expression and restored cyclin D1 and CDK4 levels, thereby preventing UVB-induced senescence. Conclusions: The findings of this research revealed that KP-1 can serve as a promising natural substance for safeguarding the skin from damage and aging caused by UVB exposure. Full article

Degradation data plays a crucial role in the reliability assessment and condition monitoring of engineering systems. The stage-wise changes in degradation rates often signal turning points in system performance or potential fault risks. To address the issue of structural changes during the degradation process, this paper constructs a degradation modeling framework based on a two-stage Inverse Gaussian (IG) process and proposes a change-point detection method based on an adjusted CUSUM (cumulative sum) statistic to identify potential stage changes in the degradation path. This method does not rely on complex prior information and constructs statistics by accumulating deviations, utilizing a binary search approach to achieve accurate change-point localization. In simulation experiments, the proposed method demonstrated superior detection performance compared to the classical likelihood ratio method and modified information criterion, verified through a combination of experiments with different change-point positions and degradation rates. Finally, the method was applied to real degradation data of a hydraulic piston pump, successfully identifying two structural change points during the degradation process. Based on these change points, the degradation stages were delineated, thereby enhancing the model’s ability to characterize the true degradation path of the equipment. Full article

This paper proposes a radar mainlobe anti-jamming method based on Space-Time Coding (STC) and Blind Source Separation (BSS). Addressing the performance degradation issue of traditional BSS methods under low Signal-to-Noise Ratio (SNR) and insufficient spatial resolution, this study first establishes the airborne SAR imaging geometric model and the jamming signal mixing model. Subsequently, STC technology is introduced to construct more equivalent phase centers and increase the system’s spatial Degrees of Freedom (DOF). Leveraging the increased DOFs, a JADE-based blind source separation algorithm is then employed to separate the mixed jamming signals. The separation of these signals significantly enhances the anti-jamming capability of the radar system. Simulation results demonstrate that the proposed method effectively improves BSS performance. As compared to traditional BSS schemes, this method provides an additional jamming suppression gain of approximately 10 dB in point target scenarios and about 3 dB in distributed target scenarios, significantly enhancing the radar system’s mainlobe anti-jamming capability in complex jamming environments. This method provides a new insight into radar mainlobe anti-jamming by combining the STC scheme and BSS technology. Full article