Search Results (128,749)

Lyme disease (LD) is classically defined as a tick-borne infection caused by Borrelia burgdorferi sensu lato (Bbsl). However, accumulating evidence indicates that, beyond microbial persistence, Bbsl infection can initiate sustained immune dysregulation and post-infectious inflammatory phenotypes in a subset of patients. This narrative review integrates open-access experimental, translational, and clinical data and discusses LD within the spectrum of infection-triggered, immune-mediated processes. We review key immunopathogenic mechanisms, including dysregulated innate immune activation, type I interferon (IFN-I) signaling, T helper 1 and T helper 17 (Th1/Th17) polarization with regulatory T-cell (Treg) insufficiency, antigen persistence (notably borrelial peptidoglycan), and pathways linking infection to autoimmunity such as molecular mimicry, epitope spreading, and human leukocyte antigen (HLA)-restricted susceptibility. These mechanisms are integrated with immune-mediated clinical manifestations affecting the central nervous system (CNS), peripheral nervous system (PNS), musculoskeletal system, heart, skin, and hematologic compartment. Finally, we discuss translational implications for diagnosis, biomarker-guided stratification, and emerging therapeutic strategies that extend beyond antimicrobial therapy, while addressing current controversies and limitations. This framework supports a mechanistic model in which Lyme disease-associated morbidity in selected patients reflects persistent immune activation and dysregulated host responses triggered by infection. Full article

Specific Emitter Identification (SEI) distinguishes individual emitters by extracting subtle features from intercepted radio frequency signals. This process relies on the design and extraction of specific features. Current methods for selecting and characterizing radio frequency fingerprints vary by individual, and the extraction process is closely coupled with environmental conditions. As a result, the generality of such identification algorithms is often limited, particularly when the application environment does not match the premise of feature design, leading to rapid degradation or even failure of individual identification performance. This paper proposes a deep clustering model based on polarization feature learning for identifying individual communication emitters. The approach involves constructing a guided network to extract datasets of polarization features from communication signals and utilizing a contrastive representation learning network to extract dual-polarization features from I/Q data samples. Subsequently, a Bayesian nonparametric (BNP) class mixture model algorithm, capable of inferring an unknown number of clusters, is employed to build a multi-level clustering network for clustering analysis of the extracted features. Under 5 dB conditions, the method described in this paper achieves an average recognition accuracy of 87.5%. Full article

Hydrodynamically triggered landslides remain a major concern in reservoir regions, where the mechanisms controlling displacement evolution are still not fully understood and the multi-scale deformation responses induced by individual hydrodynamic factors remain difficult to quantify. To address these issues, this study establishes a TSD-TET composite framework by integrating time-series signal decomposition with deep learning for multi-scale displacement prediction and the mechanism-oriented interpretation of hydrodynamically triggered landslides. The monitored displacement sequence is first decomposed into physically interpretable components, including trend, periodic, and random terms. Each component is subsequently predicted using deep temporal learning models to capture different deformation characteristics at multiple temporal scales. Meanwhile, key hydrodynamic driving factors, including rainfall, reservoir water level, and groundwater level, are decomposed within the same framework to examine their statistical associations with different displacement components. The proposed approach is applied to the Donglingxin landslide located in the Sanbanxi Hydropower Station reservoir area. Results show that the model achieves high prediction accuracy under both long-term forecasting horizons and limited-sample conditions, with a cumulative displacement coefficient of determination reaching R² = 0.945. Mechanism analysis further indicates that trend deformation is mainly controlled by geological structure and gravitational loading, periodic deformation is strongly modulated by hydrological cycles associated with reservoir water level fluctuations, and random deformation is more likely to reflect short-term disturbances and transient hydrodynamic forcing. These findings provide new insights into the deformation mechanisms of hydrodynamically triggered landslides and offer a promising technical pathway for improving displacement prediction, monitoring, and early warning of reservoir-induced landslide hazards. Full article

Accurate fault diagnosis of hydropower units is crucial for ensuring the efficient and complete utilization of hydropower resources. Existing diagnostic methods predominantly consider either single-sensor or single-scale multi-sensor fusion, failing to fully exploit the effective information within monitoring data. Furthermore, they neglect the correlation between different sensors and faults during fusion diagnosis, thereby limiting the diagnostic performance of fusion models. To address this, this paper proposes a multi-sensor data fault diagnosis method based on multi-scale fusion. First, a feature extraction model is constructed to extract shallow-level features from multi-sensor signals across multiple dimensions. Subsequently, an attention-based feature fusion network is designed to extract and fuse multi-depth features, yielding high-quality deep-fused features. Finally, an information-entropy-based decision fusion strategy is established to effectively enhance the model’s diagnostic performance. Experimental validation on the public rotating machinery fault dataset and the hydropower unit fault dataset yielded diagnostic accuracies of 96.42% and 99.28%, respectively, demonstrating the significant effectiveness and robustness of the proposed method. Full article

Although the parental recombinant protein rLj-RGD3 exhibits antitumor activity, it carries immunogenicity risks owing to its large molecular size (13.5 kDa). We generated a genetically truncated mutant, rLj-RGD4 (6.27 kDa, four RGD motifs), which inhibited B16 melanoma cell proliferation, migration, and invasion in vitro. However, the in vivo efficacy and mechanisms of action remain unclear. Here, B16 xenograft mice were treated with rLj-RGD4 (5, 10, and 20 μg/kg i.p. daily for 14 days). Tumor growth was measured, and histopathology/apoptosis was evaluated using hematoxylin and eosin (HE), Masson’s dye, Hoechst, and TUNEL staining. Apoptotic pathways (mitochondrial, death receptor, and MAPK) were analyzed via Western blotting, whereas endocytosis mechanisms were explored using inhibitors (filipin III, NaN₃, cytochalasin D), and EGFR (epidermal growth factor receptor) interactions via fluorescence co-localization and phosphoprotein assays. The results demonstrated dose-dependent tumor growth inhibition (21.60–89.26% volume reduction, 41.03–86.51% weight reduction), with histological evidence of tissue loosening, fibrosis, and apoptosis. rLj-RGD4 induced apoptosis by activating the mitochondrial (Bax/Bcl-2 upregulation), death receptor (caspase-8 activation), and MAPK (JNK/p38 phosphorylation) pathways. Internalization was blocked by NaN₃ and cytochalasin D, indicating actin-dependent macropinocytosis. Direct EGFR binding was confirmed, accompanied by reduced EGFR expression and the inhibition of FAK/AKT/Src signaling. In conclusion, rLj-RGD4 exerts potent in vivo antitumor activity via two mechanisms: induction of multi-pathway apoptosis and EGFR-targeted suppression of pro-survival signaling. RGD4 exerts its antitumor function in vivo by targeting and co-internalizing with EGFR. Full article

Tuberculosis–diabetes mellitus (TB-DM) is increasingly recognized as a syndemic in which chronic metabolic dysregulation amplifies tuberculosis severity, delays treatment response, and increases relapse and mortality. However, conventional systemic correlates soluble cytokines and bulk whole-blood transcriptomic signatures often appear broadly similar between TB and TB-DM. This highlights a key gap: clinically meaningful immune dysfunction in TB-DM likely resides in specific lung and blood cell states that are poorly resolved by bulk assays. Small extracellular vesicles (EVs) in plasma and bronchoalveolar lavage (BAL) provide a tractable “liquid biopsy” layer because their RNA and protein cargo can integrate information from infected macrophages, neutrophils, and epithelial/endothelial compartments, and may also include pathogen-derived components. Yet most EV studies remain bulk and cell-agnostic, and interpretation is constrained by heterogeneous vesicle mixtures, selective cargo packaging, and co-isolated non-vesicular contaminants, issues that are especially problematic for nucleic-acid claims without rigorous controls. In this targeted narrative review (2010–2026), we argue that single-cell and multimodal immune reference atlases, including scRNA-seq/CITE-seq, provide a needed scaffold to link EV cargo patterns to specific immune cell states, pathways, and anatomic compartments in TB-DM, enabling prioritized candidates and testable hypotheses. We outline three complementary frameworks: reference-atlas anchoring to project EV cargo modules onto atlas-defined immune states; orthogonal triangulation combining computational inference with immunoaffinity enrichment, targeted validation, and functional assays; and cautious use of “droplet-era” extracellular signals as hypothesis-generating priors for EV-producing states. Implemented in longitudinal, clinically annotated cohorts with standardized EV workflows, atlas-guided EV profiling could yield cell-of-origin–resolved biomarkers of TB-DM immunopathology and treatment response, while prioritizing mechanistically plausible targets for host-directed intervention. Full article

The present review discusses vitamin A/retinoid metabolism as a cross-organ axis in which hepatic clock-dependent retinoid handling may affect immune clock gene expression through the stimulation of retinoic acid 6–Janus kinase 2–signal transducer and activator of transcription 5 signaling, potentially promoting pro-inflammatory monocyte states. We further highlight mechanosensory signaling as a second convergent layer that integrates hemodynamic forces with tissue microenvironmental cues. Among these pathways, G protein-coupled receptor 68, a proton- and flow-sensitive G protein-coupled receptor, is discussed as a representative druggable node linking mechanical and inflammatory signaling in chronic kidney disease-associated cardiac injury. Finally, we outline potential therapeutic directions, including (i) circadian alignment/chronopharmacology, (ii) modulation of retinoid metabolism and signaling, and (iii) targeted inhibition of primary immune and mechanosensory effectors. Full article

Fluorescence is known to occur on varying surfaces across crustacea but is only cited in sporadic reports, despite being a potentially important visual signal and changing with development and within moult cycles. Here, we present the occurrence of fluorescence in some common Northeast Atlantic crustaceans and highlight differences by age and sex, to bring attention to this potentially important facet of their biology. Fluorescence was present on dactyli tips across most species, and setae on the ventral side of edible crabs and lobsters (with greater occurrence in females) and over the whole body in spider and hermit crabs. Full article

Larix olgensis, a keystone timber species in Northeast China, is increasingly threatened by Neofusicoccum laricinum-induced shoot blight, a devastating disease that compromises forest health and necessitates sustainable management strategies. Here, we demonstrate that the endophytic bacterium Bacillus amyloliquefaciens JL54 elicits multifaceted defense responses in L. olgensis, enhancing resistance to pathogen infection. Greenhouse assays revealed that JL54 pretreatment reduced disease incidence by 12.5% and achieved 43.75% control efficacy while maintaining host vigor. Histochemical analyses identified JL54-induced rapid hydrogen peroxide (H₂O₂) accumulation, extensive lignin deposition, and localized programmed cell death (PCD), indicative of a primed immune response. Transcriptomic analyses uncovered distinct temporal defense patterns: early-stage responses (0 h post-inoculation) were characterized by upregulation of cutin, suberin, and wax biosynthesis pathways, reinforcing physical barriers, whereas late-stage responses (12 h post-inoculation) were dominated by ribosome- and proteostasis-related pathways (e.g., heat shock proteins [HSPs], glutathione S-transferases [GSTs]) to mitigate cellular damage. Biochemical assays corroborated these findings, with JL54 colonization reducing membrane lipid peroxidation (27.2% decrease in malondialdehyde content) and significantly elevating the activity of key defense enzymes, including peroxidase (POD), phenylalanine ammonia-lyase (PAL), and GST. Phytohormone profiling implicated jasmonic acid (JA) as the central mediator of induced systemic resistance (ISR), with JL54-potentiated JA signaling preceding pathogen containment. Collectively, these results demonstrate that JL54 contributes to a coordinated defense strategy in L. olgensis, integrating structural reinforcement (cuticle/lignin), oxidative stress management, and JA-mediated immune priming. These insights advance the understanding of endophyte-conferred resistance in conifers and highlight JL54’s potential as a biocontrol agent for sustainable forestry. Full article

Ayu (Plecoglossus altivelis) is an East Asian amphidromous river fish, yet seasonal microbiota dynamics remain unclear. We investigated ayu in the Dajing Stream (Zhejiang Province, China) by synchronously sampling water microbiota (H), gut content microbiota (N), and gut tissue-associated microbiota (C) across four seasons. Each season, four fish were collected, and an overlapping pooling strategy (abc/abd/bcd) generated three composite replicates for C and N (n = 3 composites/season); water was collected as three field replicates (n = 3/season), yielding 36 samples (12 per niche). Using 16S rRNA amplicon sequencing and COI barcoding of stomach contents, we observed the clearest seasonal differentiation in H and seasonal variation in N consistent with diet shifts, whereas C was comparatively stable. COI signals indicated a diet dominated by aquatic insects in spring/summer, which shifted toward smaller prey (e.g., rotifers) in winter. Together, these results highlight strong niche partitioning and season-linked shifts in water and gut content communities relative to the more stable tissue-associated microbiota. These findings should be interpreted as exploratory and require validation in larger individual-level studies. Full article

Sentiment analysis of online game reviews has increasingly become important in understanding player experiences and supporting data-driven game development. However, research in this domain has continuously faced two unresolved challenges: (1) the extreme imbalance between positive and negative feedback, and (2) the inefficiency of existing feature-weighting schemes in capturing sentiment signals embedded in informal gaming discourses. Prior works demonstrated that negative feedback—though a few in number are highly influential—usually contain richer emotional content and longer textual structures; yet, prevailing classification models often perform poorly for these minorities (i.e., negative feedback). Numerous studies explored multimodal imbalance issues, class imbalance in cross-lingual ABSA (Aspect-Based Sentiment Analysis), reinforcement-learning-based architectures for imbalanced extraction tasks, and oversampling strategies like SMOTE (Synthetic Minority Over-sampling Technique) variants. Few investigations specifically addressed imbalanced sentiment classification in the contexts of online game reviews, where user-generated content exhibits unique lexical, structural, and emotional characteristics. To address these gaps, this study integrated TF-IDF (Term Frequency-Inverse Document Frequency), VADER (Valence Aware Dictionary and Sentiment Reasoner) lexicon features, and IGM (Inverse Gravity Moment) weightings with advanced oversampling methods such as ADASYN (Adaptive Synthetic Sampling Approach for Imbalanced Learning) and Borderline-SMOTE to improve the detection of minority sentiment classes. Ensemble models, including XGBoost (Extreme Gradient Boosting) and LightGBM (Light Gradient-Boosting Machine), were further employed to enhance the robustness of imbalance. Using a large-scale dataset of Steam game reviews, the proposed framework demonstrated substantial improvement in identifying negative sentiments, addressing a critical limitation in the existing computational game-analysis literature, and advancing the modeling for detecting the emotion-rich but imbalance-prone user feedback. Full article

Pseudomonas plecoglossicida causes bacterial hemorrhagic ascites in ayu (Plecoglossus altivelis), a lethal disease characterized by abdominal distension with hemorrhagic ascites, multifocal organ hemorrhages, and histopathologically evident hepatocellular necrosis and inflammatory infiltration. The lack of effective treatments exacerbates mass mortalities, posing a significant threat to aquaculture. Given the severe pathogenesis of P. plecoglossicida infection—which involves bacterial colonization, tissue necrosis, and host immune dysregulation—effective therapeutic strategies are urgently needed. Through a screen of traditional Chinese medicine monomers, we identified harmine, an indole alkaloid derived from Peganum harmala seeds, as a potent agent against this pathogen. In vivo, harmine exhibited direct bactericidal activity by disrupting membrane integrity, as evidenced by increasing membrane permeability, and inhibiting biofilm formation. In an ayu infection model, harmine significantly increased host survival, reduced tissue bacterial load, and enhanced innate immunity by augmenting monocyte/macrophage phagocytosis and bactericidal capacity while suppressing pro-inflammatory cytokine release and apoptosis. Mechanistically, the Drug Affinity Responsive Target Stability assay was used to identify the molecular target of harmine, followed by functional validation through PRDX6−knockdown experiments. Harmine exhibited direct bactericidal activity by disrupting membrane integrity and inhibiting biofilm formation. In the ayu infection model, harmine significantly increased host survival, reduced tissue bacteria1 load, and enhanced innate immunity by augmenting monocyte/macrophage system and bactericidal capacity while suppressing pro-inflammatory cytokine release and apoptosis, the latter likely through modulation of PRDX6−mediated oxidative stress and downstream caspase signaling. Mechanistically, DARTS revealed that harmine binds to peroxiredoxin 6 (PRDX6), a multifunctional enzyme possessing peroxidase, phospholipase A₂, and lysophosphatidylcholine acyltransferase activities. This binding liberates TNF receptor-associated factor 6 (TRAF6), facilitating its mitochondrial translocation and association with the ECSIT signaling integrator complex, thereby amplifying mitochondrial reactive oxygen species (mROS) production and potentiating macrophage-mediated bacterial killing. These findings establish harmine as a promising therapeutic candidate for controlling P. plecoglossicida infections and underscore the value of host-directed immunomodulation derived from natural products in aquaculture medicine. Full article

Problem: Controlling nonlinearly parameterized systems with unknown disturbances remains challenging because classical adaptive approaches rely on separation-of-variables and reparameterization techniques, leading to increased parameter dimensions, conservative stability bounds, and implementation complexity. Objective: This paper develops a data-driven predictive fuzzy adaptive control (DD-PFAC) framework that eliminates the need for separation techniques while achieving superior tracking performance and formally certified stability. Novelty: The key innovation is a two-layer architecture. Layer 1 provides direct fuzzy approximation of composite nonlinear functions (system dynamics plus disturbance bound) without parameter reparameterization, reducing parameter complexity from $O\left(qn\right)$ to $O\left(nN\right)$. Layer 2 employs Hankel matrix-based predictive optimization to adaptively tune both control gains $c_i\left(k\right)$ and adaptation rates ${\gamma }_i\left(k\right)$ online using 80–150 recent input–output samples. Methodology: A Lyapunov function augmented with a prediction-error term is used to prove uniform ultimate boundedness of all closed-loop signals. A projection-based recursive least-squares algorithm updates the gain parameters online while guaranteeing $c_i\left(k\right)\ge c_{min}>0$ at all times. Results: Comparative simulations demonstrate 31.4% reduction in integral square error, 27.8% reduction in mean absolute error, and 37.4% reduction in steady-state error versus traditional adaptive fuzzy control. A four-group ablation study confirms that adaptive gain scheduling contributes 27.7% and predictive compensation contributes 6.5% to the total MAE improvement. Robustness tests validate consistent 28–32% performance advantage across sinusoidal, pulse, step, and large-disturbance scenarios. Full article

Generalized Sinusoidal Frequency Modulation (GSFM) signals can enhance Continuous Active Sonar (CAS) performance by providing high sub-signal processing gain while achieving high target update rates. However, conventional processing methods for GSFM often exhibit high sidelobe levels arising from the waveform’s autocorrelation which degrade detection performance, especially in severe multipath environments. To address this issue, a Mismatched-Channel Filtering (MMCF) method for GSFM in CAS is proposed to focus multipath energy while suppressing sidelobe levels. Adopting the sub-pulse processing scheme, we incorporate the orthogonality of GSFM sub-signals (optimized via a genetic algorithm) and sparse channel estimates into the MMCF design for each sub-signal. The design is formulated as a Quadratically Constrained Quadratic Program (QCQP) and solved iteratively using the Alternating Direction Method of Multipliers (ADMM) for long-duration signal processing in CAS. Numerical simulations demonstrate that, compared with the matched filtering and matched channel filtering methods, the proposed MMCF method effectively suppresses sidelobe levels by approximately 20 dB and produces a Dirack-like main-lobe peak, while efficiently focusing multipath energy. The method’s effectiveness is further validated using experimental data from a lake trial. Therefore, this algorithm has distinct advantages for signal processing in multipath environments. Full article

Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that poses a substantial threat to global human health. Yam (Dioscorea opposita Thunb.) is a traditional medicinal and edible plant that has long been used in Asia, Africa, and the Caribbean. Its major bioactive components, such as dioscin and polysaccharides, have been reported to exhibit neuroprotective effects; however, the impact of dietary yam on PD progression remains to be elucidated. Therefore, we sought to evaluate its neuroprotective potential and the underlying mechanisms in 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine (MPTP)-induced PD mice. Methods: Mice received six-week dietary yam supplementation. Behavioral, histological, and neurochemical analyses were performed to assess motor function, dopaminergic neuron integrity, and dopamine levels. Gut microbiota and metabolic profiles were analyzed using 16S rRNA gene sequencing and non-targeted metabolomics. Transcriptomic sequencing and Western blot analysis of the substantia nigra pars compacta (SNc) were conducted to investigate molecular mechanisms, and integrative multi-omics analysis was applied to explore microbiota–metabolite–host interactions. Results: Yam supplementation improved motor function, preserved nigrostriatal dopaminergic neurons, and restored striatal dopamine levels in PD mice. Notably, yam was associated with the maintenance of intestinal homeostasis by strengthening barrier integrity and enriching beneficial taxa, including Ileibacterium, Lachnospiraceae NK4A136 group, and Blautia. Consistently, yam also elevated neuroprotective purines and amino acids, including inosine, xanthine, and succinic acid. At the molecular level, yam treatment modulated mitochondrial oxidative phosphorylation by increasing PGC-1α and COX7c expression, and reduced inflammasome-related neuroinflammatory signaling. Integrative modeling showed significant associations between yam-modulated genes and PD-related indices with microbiota and metabolites. Conclusion: These findings suggest that yam may represent a potential dietary strategy for alleviating PD-related neurodegeneration by modulating the microbiota–gut–brain axis. Full article