Search Results (12,914)

The ocean heat content (“OHC”)—the heat energy within the ocean integrated to a reference depth—has physical drivers spanning spatial and temporal scales, including seasonality, the El Niño/Southern Oscillation (ENSO), and others. The present article investigates changes in the OHC100 during the period 1994–2020 using GLORYS12 monthly averaged ocean reanalysis. OHC100–ENSO correlation patterns are explored to glean insights about the oceanic mechanisms that facilitate the ENSO’s global teleconnections. After extracting known seasonality and ENSO signals using the Oceanic Niño Index (ONI), the OHC100 residual is analyzed to investigate multidecadal drivers of the OHC100. Lagged ENSO–OHC100 correlations (±12 months) reveal basin-scale oscillations in the sign of ENSO influence likely attributable to Rossby waves. The OHC100 is increasing globally (in total, 2.4 × 10²² J decade⁻¹), with the greatest increases near western boundary currents (WBCs). Some regions are decreasing, notably the Atlantic main development region (MDR) for tropical cyclones (TCs). Correlations and multidecadal variability in the OHC100 tendency (OHCT) and zonal and meridional advections of the OHC100 (ZAO and MAO) support the hypothesis that upper-ocean dynamics mediate ENSO teleconnections as well as exert independent control on OHC100 variability. Local increases in the OHC100 would support the observed TC rapid intensification irrespective of the ENSO phase as the TC-supporting region expands. Full article

High-frequency vibrations in annular gap type pressure-regulating valves of wind tunnels can induce fatigue, fracture, and operational failures, posing challenges to safe and reliable operation. This study proposes a hybrid leakage fault diagnosis framework that integrates wavelet packet-based signal analysis with advanced deep learning techniques. Time-domain acceleration signals collected from multiple sensors are processed to extract maximum component energy and its variation rate, identified as sensitive and robust indicators for leakage detection. A fluid–solid coupled finite element model of the valve system further validates the reliability of these indicators under different operational scenarios. Based on this foundation, a Vision Transformer (ViT)-based model is trained on a dedicated database encompassing multiple leakage conditions and sensor arrangements. Comparative evaluation demonstrates that the ViT model outperforms conventional deep learning architectures in terms of accuracy, stability, and predictive reliability. The integrated framework enables fast, automated, and robust leakage diagnosis, providing a comprehensive solution to enhance the monitoring, maintenance, and operational safety of wind tunnel valve systems. 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

To address the computational efficiency challenges in two-dimensional (2D) direction-of-arrival (DOA) estimation, a two-stage framework integrating the Nyström approximation with subspace decomposition techniques is proposed in this paper. The methodology strategically integrates the Nyström approximation with subspace decomposition techniques to bridge the critical performance–complexity trade-off inherent in high-resolution parameter estimation scenarios. In the first stage, the Nyström method is applied to approximate the signal subspace while simultaneously enabling construction of a reduced rank covariance matrix, which effectively reduces the computational complexity compared with eigenvalue decomposition (EVD) or singular value decomposition (SVD). This innovative approach efficiently derives two distinct signal subspaces that closely approximate those obtained from the full-dimensional covariance matrix but at substantially reduced computational cost. The second stage employs a sophisticated subspace-based estimation technique that leverages the principal singular vectors associated with these approximated subspaces. This process incorporates an iterative refinement mechanism to accurately resolve the paired azimuth and elevation angles comprising the 2D DOA solution. With the use of the Nyström approximation and reduced rank framework, the entire DOA estimation process completely circumvents traditional EVD/SVD operations. This elimination constitutes the core mechanism enabling substantial computational savings without compromising estimation accuracy. Comprehensive numerical simulations rigorously demonstrate that the proposed framework maintains performance competitive with conventional high-complexity estimators while achieving significant complexity reduction. The evaluation benchmarks the method against multiple state-of-the-art DOA estimation techniques across diverse operational scenarios, confirming both its efficacy and robustness under varying signal conditions. Full article

Breast cancer (BC) is one of the most common malignancies among women worldwide. Despite advances in early detection and treatment, prognosis remains highly variable. Molecular biomarkers, such as microRNAs (miRNAs), have emerged as promising tools to refine prognostic assessment. Among them, miR-21 is consistently overexpressed in solid tumors and implicated in key oncogenic pathways. This systematic review and meta-analysis aimed to clarify the prognostic significance of miR-21 in BC and explore its molecular mechanisms through bioinformatic analyses. A systematic search of PubMed, Scopus, and Web of Science up to April 2025 identified 18 eligible observational studies. Pooled analyses showed that high miR-21 expression was significantly associated with poorer overall survival (OS) (HR = 2.37, 95% CI: 1.42–3.98) and recurrence-related outcomes (DFS/RFS) (HR = 2.10, 95% CI: 1.32–3.34). Subgroup analyses confirmed robust associations across different cut-off definitions and revealed particularly strong effects in triple-negative BC (HR = 5.69) and mixed subtypes (HR = 2.55), but no significant association in HER2-positive BC. Bioinformatic analysis identified target genes such as PTEN, BCL2, STAT3, and MYC, involved in apoptosis regulation, proliferation, NF-κB signaling, and immune modulation. These findings provide consistent evidence that miR-21 is a promising minimally invasive prognostic biomarker in BC, particularly in aggressive subtypes, and support its integration into future multimodal prognostic models. Full article

This paper presents an approach to error correction in wireless communication systems, with a focus on the Bluetooth Low Energy standard. Our method uses the redundancy provided by the cyclic redundancy check and leaves the transmitter unchanged. The approach has two components: an error-detection algorithm that validates data packets and a neural network with reject option that classifies signals received from the channel and identifies bit errors for later correction. This design localizes and corrects errors and reduces transmission failures. Extensive simulations were conducted, and the results demonstrated promising performance. The method achieved correction rates of 94–98% for single-bit errors and 54–68% for double-bit errors, which reduced the need for packet retransmissions and lowered the risk of data loss. When applied to images, the approach enhanced visual quality compared with baseline methods. In particular, we observed improvements in visual quality for signal-to-noise ratios between 9 and 11 dB. In many cases, these enhancements were sufficient to restore the integrity of corrupted images. Full article

Sepsis is a life-threatening condition driven by a dysregulated host response to infection, with high mortality and few treatment options. Decades of failed drug development underscore the urgent need for therapies with novel mechanisms of action. Vimentin, an intermediate filament protein, acts as a network hub that senses and integrates cellular signals. Its involvement in key sepsis pathologies, including infection, hyperinflammation, immunosuppression, coagulopathy and metabolic dysregulation, positions it as a potential therapeutic target. This study evaluated the efficacy of ALD-R491, a novel small-molecule vimentin modulator, in a murine model of polymicrobial sepsis induced by cecal ligation and puncture (CLP). Mice received ALD-R491 prophylactically or therapeutically, alone or with ceftriaxone. The treatment significantly reduced serum levels of key biomarkers of sepsis, including C-reactive protein (CRP), lactate (Lac), tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), and dose-dependently improved the survival of septic mice. Organ-specific analysis confirmed the effects of ALD-R491 in mitigating hyperinflammation and multi-organ injury. The treatment reduced pulmonary edema and inflammation; preserved liver tissue architecture and improved hepatic function with lowered alanine aminotransferase/aspartate aminotransferase (ALT/AST); decreased kidney tubular damage; and improved renal function with lowered creatinine/blood urea nitrogen (BUN). These preclinical findings indicate that the vimentin-targeting agent ALD-R491 represents a promising therapeutic candidate for sepsis and merits further clinical investigation. Full article

Micro- and nanoplastic particles (MNPLs) present in the environment have recently become a potential health hazard factor due to the ability to penetrate living organisms, their organs, and cells. MNPLs interact with and absorb chemicals and elements, including metals, such as iron, copper, and zinc, and transport them into the cells. The cells subsequently respond with the altered gene expression profiles. In this study, we applied freely accessible online bioinformatic tools to draw out the sets of genes modulated by the metal ions and MNPLs. We focused on the gene interactome as revealed by The Comparative Toxicogenomics Database (CTD). To achieve a deeper insight into the biological processes that are potentially modulated, the retrieved CTD lists of genes, whose expression was influenced by MNPLs and metals, were subsequently analyzed using online tools: Metascape and String database. The genes from the revealed networks were arranged into functional clusters, annotated mainly as inflammation and immune system activity, regulation of apoptosis, oxidative stress response, Wingless-related Integration Site (WNT) signaling and ferroptosis. The complexity of the interactions between the gene sets altered by MNPLs and metal ions illustrates their pleiotropic effects on living systems. Full article

Citrobacter braakii is an emerging opportunistic pathogen of escalating clinical significance in animal hosts, though its pathogenic mechanisms remain poorly characterized. This study isolated a C. braakii strain (SCGY-1L) from diseased Siniperca chuatsi and confirmed its identity through integrated morphological, physiological, and molecular analyses. Comprehensive genomic sequencing revealed a 5.75 Mb genome comprising one circular chromosome and two plasmids. A Circos plot was constructed to visualize the genomic architecture of strain SCGY-1L, revealing 5482 protein-coding genes, 25 tRNA genes, and 86 rRNA genes. Additionally, 738 virulence-associated genes and 366 antibiotic resistance determinants were annotated, elucidating multidrug-resistant phenotypes including insensitivity to erythromycin and penicillin. Pathogenicity assessment established an LD₅₀ of 1.28 × 10⁶ CFU/mL in infected hosts, with histopathological analysis showing significant hemorrhage and necrosis in target organs (liver, spleen, kidney). Host transcriptome profiling generated 41.21 Gb of high-quality clean data, identifying 2201 differentially expressed genes post-infection (1568 up-regulated; 633 down-regulated). These were significantly enriched in phagocytosis, cytokine-mediated signaling, and inflammatory regulation pathways. These molecular insights establish C. braakii’s mechanistic framework for pathogenesis and host adaptation, providing critical targets for diagnostics and therapeutics against emerging Citrobacter infections. Full article

Fish species’ biological vocalizations serve as essential acoustic signatures for passive acoustic monitoring (PAM) and ecological assessments. However, limited availability of high-quality acoustic recordings, particularly for region-specific species like the brown croaker (Miichthys miiuy), hampers data-driven bioacoustic methodology development. In this study, we present a framework for reconstructing brown croaker vocalizations by integrating fk14 wavelet synthesis, PSO-based parameter optimization (with an objective combining correlation and normalized MSE), and deep learning-based validation. Sensitivity analysis using a normalized Bartlett processor identified delay and scale (length) as the most critical parameters, defining valid ranges that maintained waveform similarity above 98%. The reconstructed signals matched measured calls in both time and frequency domains, replicating single-pulse morphology, inter-pulse interval (IPI) distributions, and energy spectral density. Validation with a ResNet-18-based Siamese network produced near-unity cosine similarity (~0.9996) between measured and reconstructed signals. Statistical analyses (95% confidence intervals; residual errors) confirmed faithful preservation of SPL values and minor, biologically plausible IPI variations. Under noisy conditions, similarity decreased as SNR dropped, indicating that environmental noise affects reconstruction fidelity. These results demonstrate that the proposed framework can reliably generate acoustically realistic and morphologically consistent fish vocalizations, even under data-limited scenarios. The methodology holds promise for dataset augmentation, PAM applications, and species-specific call simulation. Future work will extend this framework by using reconstructed signals to train generative models (e.g., GANs, WaveNet), enabling scalable synthesis and supporting real-time adaptive modeling in field monitoring. Full article

Fusobacterium nucleatum (Fn) has been increasingly recognized as a crucial mediator of colorectal cancer (CRC) biology, particularly in microsatellite-stable (MSS) tumors, where immune checkpoint inhibitors (ICIs) have shown limited efficacy. Rather than representing a passive microbial passenger, Fn actively shapes tumor behavior by adhering to epithelial cells, activating oncogenic signaling, and promoting epithelial–mesenchymal transition (EMT). At the same time, it remodels the tumor microenvironment, driving immune suppression through inhibitory receptor engagement, accumulation of myeloid-derived cells, and metabolic reprogramming of tumor-associated macrophages. These mechanisms converge to impair cytotoxic immunity and contribute to both intrinsic and acquired resistance to ICIs. Beyond immune escape, Fn interferes with conventional chemotherapy by sustaining autophagy and blocking ferroptosis, thereby linking microbial colonization to multidrug resistance. Most of these mechanisms derive from preclinical in vitro and in vivo models, where causal relationships can be inferred. In contrast, human data are mainly observational and provide correlative evidence without proving causality. No interventional clinical studies directly targeting Fn have yet been conducted. Its enrichment across the adenoma–carcinoma sequence and consistent detection in both tumor and fecal samples highlight its potential as a biomarker for early detection and patient stratification. Importantly, multidimensional stool assays that integrate microbial, genetic, and epigenetic markers are emerging as promising non-invasive tools for CRC screening. Therapeutic strategies targeting Fn are also under exploration, ranging from antibiotics and bacteriophages to multifunctional nanodrugs, dietary modulation, and natural microbiota-derived products. These approaches may not only reduce microbial burden but also restore immune competence and enhance the efficacy of immunotherapy in MSS CRC. Altogether, current evidence positions Fn at the intersection of microbial dysbiosis, tumor progression, and therapy resistance. A deeper understanding of its pathogenic role may support the integration of microbial profiling into precision oncology frameworks, paving the way for innovative diagnostic and therapeutic strategies in CRC. Full article

Short-term passenger flow prediction provides critical data-driven support for optimizing resource allocation, guiding passenger mobility, and enhancing risk response capabilities in urban rail transit systems. To further improve prediction accuracy, this study proposes a hybrid SMA-VMD-Informer-BiLSTM prediction model. Addressing the challenge of error propagation caused by non-stationary components (e.g., noise and abrupt fluctuations) in conventional passenger flow signals, the Variational Mode Decomposition (VMD) method is introduced to decompose raw flow data into multiple intrinsic mode functions (IMFs). A Slime Mould Algorithm (SMA)-based optimization mechanism is designed to adaptively tune VMD parameters, effectively mitigating mode redundancy and information loss. Furthermore, to circumvent error accumulation inherent in serial modeling frameworks, a parallel prediction architecture is developed: the Informer branch captures long-term dependencies through its ProbSparse self-attention mechanism, while the Bidirectional Long Short-Term Memory (BiLSTM) network extracts localized short-term temporal patterns. The outputs of both branches are fused via a fully connected layer, balancing global trend adherence and local fluctuation characterization. Experimental validation using historical entry flow data from Weihouzhuang Station on Xi’an Metro demonstrated the superior performance of the SMA-VMD-Informer-BiLSTM model. Compared to benchmark models (CNN-BiLSTM, CNN-BiGRU, Transformer-LSTM, ARIMA-LSTM), the proposed model achieved reductions of 7.14–53.33% in $f_{mse}$, 3.81–31.14% in $f_{rmse}$, and 8.87–38.08% in $f_{mae}$, alongside a 4.11–5.48% improvement in $R^2$. Cross-station validation across multiple Xi’an Metro hubs further confirmed robust spatial generalizability, with prediction errors bounded within $f_{mse}$: 0.0009–0.01, $f_{rmse}$: 0.0303–0.1, $f_{mae}$: 0.0196–0.0697, and $R^2$: 0.9011–0.9971. Furthermore, the model demonstrated favorable predictive performance when applied to forecasting passenger inflows at multiple stations in Nanjing and Zhengzhou, showcasing its excellent spatial transferability. By integrating multi-level, multi-scale data processing and adaptive feature extraction mechanisms, the proposed model significantly mitigates error accumulation observed in traditional approaches. These findings collectively indicate its potential as a scientific foundation for refined operational decision-making in urban rail transit management, thereby significantly promoting the sustainable development and long-term stable operation of urban rail transit systems. Full article

Oxidative stress and inflammation are deeply interconnected processes implicated in the onset and progression of numerous chronic diseases. Despite promising mechanistic insights, conventional antioxidant and anti-inflammatory therapies such as NSAIDs, corticosteroids, and dietary antioxidants have shown limited and inconsistent success in long-term clinical applications due to challenges with efficacy, safety, and bioavailability. This review explores the molecular interplay between redox imbalance and inflammatory signaling and highlights why conventional therapeutic translation has often been inconsistent. It further examines emerging strategies that aim to overcome these limitations, including mitochondrial-targeted antioxidants, Nrf2 activators, immunometabolic modulators, redox enzyme mimetics, and advanced delivery platforms such as nanoparticle-enabled delivery. Natural polyphenols, nutraceuticals, and regenerative approaches, including stem cell-derived exosomes, are also considered for their dual anti-inflammatory and antioxidant potential. By integrating recent preclinical and clinical evidence, this review underscores the need for multimodal, personalized interventions that target the redox-inflammatory axis more precisely. These advances offer renewed promise for addressing complex diseases rooted in chronic inflammation and oxidative stress. Full article

This paper contains an analysis of methods for person classification based on signals from wearable IMU sensors during sports. While this problem has been investigated in prior work, existing approaches have not addressed it within the context of few-shot or minimal-data scenarios. A few-shot scenario is especially useful as the main use case for person identification in sports systems is to be integrated into personalised biofeedback systems in sports. Such systems should provide personalised feedback that helps athletes learn faster. When introducing a new user, it is impractical to expect them to first collect many recordings. We demonstrate that the problem can be solved with over 90% accuracy in both open-set and closed-set scenarios using established methods. However, the challenge arises when applying few-shot methods, which do not require retraining the model to recognise new people. Most few-shot methods perform poorly due to feature extractors that learn dataset-specific representations, limiting their generalizability. To overcome this, we propose a combination of an unsupervised feature extractor and a prototypical network. This approach achieves 91.8% accuracy in the five-shot closed-set setting and 81.5% accuracy in the open-set setting, with a 99.6% rejection rate for unknown athletes. Full article

This study systematically analyzed the multidimensional effects of Lactobacillus fermentation on Pueraria lobata (PL) and investigated the potential mechanisms underlying its hypolipidemic activity. Results indicated that fermentation significantly increased the total acid content from 1.02 to 3.48 g·L⁻¹, representing a 2.41-fold increase. Although slight reductions were observed in total flavonoids (8.67%) and total phenolics (6.72%), the majority of bioactive components were well preserved. Other antioxidant capacities were retained at >74.71% of baseline, except hydroxyl radical scavenging. Flavor profiling showed increased sourness and astringency, accompanied by reduced bitterness, with volatile compounds such as β-pinene and trans-2-hexenyl butyrate contributing to a distinct aromatic profile. Untargeted metabolomics analysis revealed that fermentation specifically enhanced the abundance of low-concentration isoflavone aglycones, including daidzein and genistein, suggesting a compositional shift that may improve hypolipidemic efficacy. Integrated network pharmacology and computational modeling predicted that eight key components, including genistein, could stably bind to ten core targets (e.g., AKT1 and MMP9) primarily through hydrogen bonding and hydrophobic interactions, potentially regulating lipid metabolism via the PI3K-AKT, PPAR, and estrogen signaling pathways. This study reveals the role of Lactobacillus fermentation in promoting the conversion of isoflavone glycosides to aglycones in PL and constructs a multi-dimensional “components-targets-pathways-disease” network, providing both experimental evidence and a theoretical foundation for further research on the lipid-lowering mechanisms of fermented PL and the development of related functional products. Full article