Search Results (8,777)

Ankylosing spondylitis (AS) displays wide inter-patient variability that is not accounted for by HLA-B27 alone, suggesting that additional immune and metabolic modifiers contribute to disease severity. Using a genetically matched design, we profiled peripheral blood mononuclear cells from two brother pairs discordant for AS severity and one healthy brother pair. Strand-specific RNA-seq was analyzed with a family-blocked DESeq2 model, while untargeted metabolites were quantified using gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS). Differential features were defined as follows: differentially expressed genes (DEGs) (|log₂FC| ≥ 1 and FDR < 0.05) and metabolites (VIP > 1, FC ≥ 1.2, and BH-adjusted p < 0.05). Pathway enrichment was performed with KEGG and Gene Ontology (GO). A total of 325 genes were differentially expressed. Type I interferon and neutrophil granule transcripts (e.g., IFI44L, ISG15, S100A8/A9) were markedly up-regulated, whereas mitochondrial β-oxidation genes (ACADM, CPT1A, ACOT12) were repressed. Metabolomics revealed 110 discriminant features, including 25 MS/MS-annotated metabolites. Primary bile acid intermediates were depleted, whereas oxidized fatty acid derivatives such as 12-Z-octadecadienal and palmitic amide accumulated. Spearman correlation identified two antagonistic modules (i) interferon/neutrophil genes linked to pro-oxidative lipids and (ii) lipid catabolism genes linked to bile acid species that persisted when severe and mild siblings were compared directly. Enrichment mapping associated these modules with viral defense, neutrophil degranulation, fatty acid β-oxidation, and bile acid biosynthesis pathways. This sibling-paired peripheral blood mononuclear cell (PBMC) dual-omics study delineates an interferon-driven lipid–bile acid axis that tracks AS severity, supporting composite PBMC-based biomarkers for future prospective validation and highlighting mitochondrial lipid clearance and bile acid homeostasis as potential therapeutic targets. Full article

Facial emotion recognition has become increasingly important in healthcare, where understanding delicate cues like pain, discomfort, or unconsciousness can support more timely and responsive care. Yet, recognizing facial expressions in real-world settings remains challenging due to varying lighting, facial occlusions, and hardware limitations in clinical environments. To address this, we propose TriViT-Lite, a lightweight yet powerful model that blends three complementary components: MobileNet, for capturing fine-grained local features efficiently; Vision Transformers (ViT), for modeling global facial patterns; and handcrafted texture descriptors, such as Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG), for added robustness. These multi-scale features are brought together through a texture-aware cross-attention fusion mechanism that helps the model focus on the most relevant facial regions dynamically. TriViT-Lite is evaluated on both benchmark datasets (FER2013, AffectNet) and a custom healthcare-oriented dataset covering seven critical emotional states, including pain and unconsciousness. It achieves a competitive accuracy of 91.8% on FER2013 and of 87.5% on the custom dataset while maintaining real-time performance (~15 FPS) on resource-constrained edge devices. Our results show that TriViT-Lite offers a practical and accurate solution for real-time emotion recognition, particularly in healthcare settings. It strikes a balance between performance, interpretability, and efficiency, making it a strong candidate for machine-learning-driven pattern recognition in patient-monitoring applications. Full article

Zero-day attacks pose a persistent threat to computing infrastructure by exploiting previously unknown software vulnerabilities that evade traditional signature-based network intrusion detection systems (NIDSs). To address this limitation, machine learning (ML) techniques offer a promising approach for enhancing anomaly detection in network traffic. This study evaluates several ML models on a labeled network traffic dataset, with a focus on zero-day attack detection. Ensemble learning methods, particularly eXtreme gradient boosting (XGBoost), achieved perfect classification, identifying all 6231 zero-day instances without false positives and maintaining efficient training and prediction times. While classical support vector machines (SVMs) performed modestly at 64% accuracy, their performance improved to 98% with the use of the borderline synthetic minority oversampling technique (SMOTE) and SMOTE + edited nearest neighbours (SMOTEENN). To explore quantum-enhanced alternatives, a quantum SVM (QSVM) is implemented using three-qubit and four-qubit quantum circuits simulated on the aer_simulator_statevector. The QSVM achieved high accuracy (99.89%) and strong F1-scores (98.95%), indicating that nonlinear quantum feature maps (QFMs) can increase sensitivity to zero-day exploit patterns. Unlike prior work that applies standard quantum kernels, this study introduces a parameterised quantum feature encoding scheme, where each classical feature is mapped using a nonlinear function tuned by a set of learnable parameters. Additionally, a sparse entanglement topology is derived from mutual information between features, ensuring a compact and data-adaptive quantum circuit that aligns with the resource constraints of noisy intermediate-scale quantum (NISQ) devices. Our contribution lies in formalising a quantum circuit design that enables scalable, expressive, and generalisable quantum architectures tailored for zero-day attack detection. This extends beyond conventional usage of QSVMs by offering a principled approach to quantum circuit construction for cybersecurity. While these findings are obtained via noiseless simulation, they provide a theoretical proof of concept for the viability of quantum ML (QML) in network security. Future work should target real quantum hardware execution and adaptive sampling techniques to assess robustness under decoherence, gate errors, and dynamic threat environments. Full article

The Tumour Microenvironment (TME) is pivotal for melanoma progression and contributes to therapy resistance. While dermal cell involvement is well established, the role of epidermal cells remains less defined. To explore the contribution of Normal Human Keratinocytes (NHKs) to melanoma biology, we investigated the modification of gene and protein expression of NHKs exposed to melanoma-conditioned medium or maintained in a co-culture system. The analysis focused on pathways related to proliferation, inflammation, Extracellular Matrix (ECM) remodelling, and cell adhesion. Due to the well-documented melanoma–fibroblast crosstalk, Normal Human Fibroblasts (NHFs) and Cancer-Associated Fibroblasts (CAFs) were used as comparative references. Keratinocyte gene expression changes under the influence of melanoma secretome only partially overlapped with those of NHFs and CAFs, indicating cell-type-specific responses. Exposure to melanoma-conditioned medium induced the upregulation of bFGF, CXCL-16, TIMP-2, and E-cadherin in NHKs, alongside downregulating TGF-β and MMP-9. Although bFGF is a recognized pro-tumorigenic factor, the modulation of CXCL-16, TIMP-2, and TGF-β may reflect a protective response. Notably, under co-culture conditions, NHKs exhibited a pronounced pro-inflammatory and ECM-remodelling phenotype, characterized by elevated production of cytokines (IL-1α, IL-1β, and IL-8) and ECM-degrading enzymes (MMP-7, 9, 12, and 13), indicative of a pro-tumoral feature. Collectively, these findings underscore an active role for NHKs in melanoma initiation and progression. Full article

In chickens, meat yield is a crucial trait in breeding programs. Identifying key molecular markers associated with increased muscle yield is essential for breeding strategies. This study applied transcriptome sequencing and machine learning methods to examine gene expression and alternative splicing (AS) events in muscle tissues of commercial broilers and local chickens. On the basis of differentially expressed genes (DEGs) and differentially spliced transcripts (DSTs) significantly related to breast muscle weight percentage (BrP), high-accuracy prediction models were developed by evaluating 10 machine learning models (e.g., eXtreme Gradient Boosting (XGBoost), Generalized Linear Model Network (Glmnet)). Feature importance was assessed using the Shapley Additive exPlanations (SHAP) method. The results revealed that 50 DEGs and 95 DSTs contributed significantly to BrP prediction. The XGBoost model achieved over 90% accuracy when using DEGs, and the Glmnet model reached 95% accuracy when using DSTs. Through Shapley evaluation, genes and AS events (e.g., ENSGALG00010012060, HINTW, and VIPR2-201) were identified as having the highest contributions to BrP prediction. Additionally, the breed effect was effectively mitigated. This study introduces new candidate genes and AS targets for the molecular breeding of poultry breast muscle traits, offering a paradigm shift from traditional gene mining approaches to artificial intelligence-driven predictive methods. Full article

Synthetic Aperture Radar (SAR) plays a pivotal role in remote-sensing target detection. However, domain shift caused by distribution discrepancies across sensors, coupled with the scarcity of target-domain samples, severely restricts the generalization and practical performance of SAR detectors. To address these challenges, this paper proposes a few-shot SAR target-detection framework tailored for cross-sensor scenarios (CS-FSDet), enabling efficient transfer of source-domain knowledge to the target domain. First, to mitigate inter-domain feature-distribution mismatch, we introduce a Multi-scale Uncertainty-aware Bayesian Distribution Alignment (MUBDA) strategy. By modeling features as Gaussian distributions with uncertainty and performing dynamic weighting based on uncertainty, MUBDA achieves fine-grained distribution-level alignment of SAR features under different resolutions. Furthermore, we design an Adaptive Cross-domain Interactive Coordinate Attention (ACICA) module that computes cross-domain spatial-attention similarity and learns interaction weights adaptively, thereby suppressing domain-specific interference and enhancing the expressiveness of domain-shared target features. Extensive experiments on two cross-sensor few-shot detection tasks, HRSID→SSDD and SSDD→HRSID, demonstrate that the proposed method consistently surpasses state-of-the-art approaches in mean Average Precision (mAP) under 1-shot to 10-shot settings. Full article

The colonization of Fusobacterium nucleatum (F. nucleatum) in the microenvironment of gastric cancer (GC) is closely associated with tumor progression, but its impact on host metabolic remodeling remains unclear. This study aims to elucidate the mechanistic link between F. nucleatum infection and metabolic changes in GC tissue. By integrating 16S rRNA microbiome sequencing and LC-MS/MS metabolomics, the differences in microbial composition and metabolic profiles between Fusobacterium sp.-positive and -negative GC tissues were systematically compared, and the correlation of differential microbes and differential metabolites was analyzed. The impact of F. nucleatum on the glutathione (GSH) metabolic pathway was validated through in vitro tissue testing and the use of the infection model of GC cell lines (such as AGS and HGC27). Integrative omics analysis showed a strong negative correlation between Fusobacterium sp. infection and antioxidant metabolite GSH levels in GCs (p < 0.001). Metabolic reprogramming features: Eleven differentially expressed metabolites were identified using LC-MS/MS metabolomics screening (p < 0.05). GSH was significantly depleted in the Fusobacterium sp.-positive group. Experimental validation: At the histological level, the abundance of F. nucleatum in GC tissues was higher than that in the paired adjacent non-cancerous (NC) tissues; at the cellular level, after F. nucleatum infection of GC cells, the intracellular GSH level decreased (p < 0.01), accompanied by a decrease in glutathione synthetase (GSS) mRNA expression and reactive oxygen species (ROS). This study is the first to demonstrate that F. nucleatum suppresses the GSH synthesis pathway, leading to the breakdown of antioxidant capacity and the formation of an oxidative stress microenvironment in GC cells. These findings provide new insights into the metabolic mechanism of F. nucleatum in promoting GC progression and suggest that targeting the F. nucleatum-GSH axis could offer a novel strategy for GC therapeutic intervention. Full article

Recurrent aphthous stomatitis (RAS) is the most common ulcerative disorder of the oral cavity, although its etiology is still unknown. The present study aimed to identify the proteomic profile associated with the RAS inflammatory process, thereby enhancing our understanding of its etiopathogenesis. We compared salivary protein profiles of RAS patients during an active episode of oral ulceration (30 patients, mean age 36.9) to those from healthy donors without a history of RAS (30 healthy subjects, mean age 37.9). Using 2D-electrophoresis and mass spectrometry (MALDI-TOF) analysis, we identified 17 proteins that were differentially expressed in the two groups. Notably, Cystatin SN (CST1) appeared to be significantly downregulated in RAS patients. These findings were validated by Western blot analysis: CST1 was detected in only 3 of the 30 RAS cases, while it was strongly expressed in all the healthy subjects. Although preliminary, our results suggest a potential role for CST1 in the etiopathogenesis of RAS. Interestingly, the relative absence of CST1 in RAS patients seems to align with some clinical and molecular features of this disease. Full article

Emotion recognition in speech is essential for enhancing human–computer interaction (HCI) systems. Despite progress in Bangla speech emotion recognition, challenges remain, including low accuracy, speaker dependency, and poor generalization across emotional expressions. Previous approaches often rely on traditional machine learning or basic deep learning models, struggling with robustness and accuracy in noisy or varied data. In this study, we propose a novel multi-stream deep learning feature fusion approach for Bangla speech emotion recognition, addressing the limitations of existing methods. Our approach begins with various data augmentation techniques applied to the training dataset, enhancing the model’s robustness and generalization. We then extract a comprehensive set of handcrafted features, including Zero-Crossing Rate (ZCR), chromagram, spectral centroid, spectral roll-off, spectral contrast, spectral flatness, Mel-Frequency Cepstral Coefficients (MFCCs), Root Mean Square (RMS) energy, and Mel-spectrogram. Although these features are used as 1D numerical vectors, some of them are computed from time–frequency representations (e.g., chromagram, Mel-spectrogram) that can themselves be depicted as images, which is conceptually close to imaging-based analysis. These features capture key characteristics of the speech signal, providing valuable insights into the emotional content. Sequentially, we utilize a multi-stream deep learning architecture to automatically learn complex, hierarchical representations of the speech signal. This architecture consists of three distinct streams: the first stream uses 1D convolutional neural networks (1D CNNs), the second integrates 1D CNN with Long Short-Term Memory (LSTM), and the third combines 1D CNNs with bidirectional LSTM (Bi-LSTM). These models capture intricate emotional nuances that handcrafted features alone may not fully represent. For each of these models, we generate predicted scores and then employ ensemble learning with a soft voting technique to produce the final prediction. This fusion of handcrafted features, deep learning-derived features, and ensemble voting enhances the accuracy and robustness of emotion identification across multiple datasets. Our method demonstrates the effectiveness of combining various learning models to improve emotion recognition in Bangla speech, providing a more comprehensive solution compared with existing methods. We utilize three primary datasets—SUBESCO, BanglaSER, and a merged version of both—as well as two external datasets, RAVDESS and EMODB, to assess the performance of our models. Our method achieves impressive results with accuracies of 92.90%, 85.20%, 90.63%, 67.71%, and 69.25% for the SUBESCO, BanglaSER, merged SUBESCO and BanglaSER, RAVDESS, and EMODB datasets, respectively. These results demonstrate the effectiveness of combining handcrafted features with deep learning-based features through ensemble learning for robust emotion recognition in Bangla speech. Full article

Pathogenic variants of MYH11, which encode smooth muscle myosin heavy chain 11, have been linked to familial thoracic aortic aneurysms and dissections (FTAAD). However, molecular pathways affected by these mutations have not been well understood. To explore downstream consequences of Myh11 disruption, we analyzed transcriptomic and proteomic profiles of aortas from male Myh11 mice with homozygous deletion of lysine 1256 (K1256) and of wild-type controls. Of 6499 proteins quantified, 1763 were differentially expressed (adjusted p < 0.05), including 942 that were downregulated and 821 that were upregulated in mutant aortas. Enrichment analysis of downregulated genes and proteins revealed a consistent reduction in extracellular matrix-related pathways. Among downregulated proteins, we identified tenascin Xb, transforming growth factor β (Tgfb) 2, and Tgfb receptor 1/2, malfunctions of which are linked to connective tissue diseases, such as Ehlers–Danlos and Loeys–Dietz syndromes. Nevertheless, unlike these syndromic diseases, mice with Myh11 pathogenic variants and patients with FTAAD do not exhibit syndromic features, likely reflecting expression of Myh11 restricted to smooth muscle. These results suggest that loss of Myh11 disrupts maintenance of extracellular matrix by SMCs, the loss of which contributes to aortic fragility without affecting other tissues. Full article

Sign language avatar systems have emerged as a promising solution to bridge communication gaps where human sign language interpreters are unavailable. However, the design of these avatars often fails to account for the diversity in how users acquire and perceive sign language. This study presents a rapid review of 17 empirical studies (2020–2025) to synthesize how linguistic and cognitive variability affects sign language perception and how these findings can guide avatar development. We extracted and synthesized key constructs, participant profiles, and capture techniques relevant to avatar fidelity. This review finds that delayed exposure to sign language is consistently linked to persistent challenges in syntactic processing, classifier use, and avatar comprehension. In contrast, early-exposed signers demonstrate more robust parsing and greater tolerance of perceptual irregularities. Key perceptual features, such as smooth transitions between signs, expressive facial cues for grammatical clarity, and consistent spatial placement of referents, emerge as critical for intelligibility, particularly for late learners. These findings highlight the importance of participatory design and user-centered validation in advancing accessible, culturally responsive human–computer interaction through next-generation avatar systems. Full article

Tumor-initiating cells (TICs) constitute a subpopulation of cancer cells with stem-like properties contributing to tumorigenesis, progression, recurrence, and therapeutic resistance. Despite their biological importance, their molecular signatures that distinguish them from non-TICs remain incompletely characterized. This study aimed to comprehensively analyze transcriptomic differences between TICs and non-TICs, identify TIC-specific gene expression patterns, and construct a machine learning-based classifier that could accurately predict TIC status. RNA sequencing data were obtained from four human cell lines representing TIC (TS10 and TS32) and non-TIC (32A and Epi). Transcriptomic profiles were analyzed via principal component, hierarchical clustering, and differential expression analysis. Gene-Ontology and Kyoto-Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted for functional interpretation. A logistic-regression model was trained on differentially expressed genes to predict TIC status. Model performance was validated using synthetic data and external projection. TICs exhibited distinct transcriptomic signatures, including enrichment of non-coding RNAs (e.g., MIR4737 and SNORD19) and selective upregulation of metabolic transporters (e.g., SLC25A1, SLC16A1, and FASN). Functional pathway analysis revealed TIC-specific activation of oxidative phosphorylation, PI3K-Akt signaling, and ribosome-related processes. The logistic-regression model achieved perfect classification (area under the curve of 1.00), and its key features indicated metabolic and translational reprogramming unique to TICs. Transcriptomic state-space embedding analysis suggested reversible transitions between TIC and non-TIC states driven by transcriptional and epigenetic regulators. This study reveals a unique transcriptomic landscape defining TICs and establishes a highly accurate machine learning-based TIC classifier. These findings enhance our understanding of TIC biology and show promising strategies for TIC-targeted diagnostics and therapeutic interventions. Full article

Metabolic rewiring is a hallmark of both hepatic regeneration and malignant transformation, complicating the identification of cancer-specific traits. This study aimed to distinguish the metabolic profiles of proliferating hepatocytes and hepatocellular carcinoma (HCC) cells through integrated analyses of mRNA and protein expression, along with functional characterization. We compared non-malignant Upcyte^® hepatocytes (HepaFH3) cultured under proliferative and confluent conditions with primary human hepatocytes, primary human hepatoma cells, and hepatoma cell lines. Proliferating HepaFH3 cells exhibited features of metabolic reprogramming, including elevated glycolysis, increased HIF1A expression, and ketone body accumulation, while maintaining low c-MYC expression and reduced BDH1 levels, distinguishing them from malignant models. In contrast, HCC cells showed upregulation of HK2, c-MYC, and BDH1, reflecting a shift toward aggressive glycolytic and ketolytic metabolism. Functional assays supported the transcript and protein expression data, demonstrating increased glucose uptake, elevated lactate secretion, and reduced glycogen storage in both proliferating and malignant cells. These findings reveal that cancer-like metabolic changes also occur during hepatic regeneration, limiting the diagnostic utility of individual metabolic markers. HepaFH3 cells thus provide a physiologically relevant in vitro model to study regeneration-associated metabolic adaptation and may offer insights that contribute to distinguishing regenerative from malignant processes. Our findings highlight the potential of integrated metabolic profiling in differentiating proliferation from tumorigenesis. Full article

The Landauer-Büttiker formalism provides a fundamental framework for mesoscopic transport, typically expressing conductance in units of the quantum of conductance, e²/h. Here, we present a theoretical study of electron transport in a quasi two-dimensional (2D) quantum wire. This system features a wide transverse confinement and a longitudinal, high-energy, narrow potential barrier. The derivation, performed within the Landauer framework, yields an analytical expression for the total conductance that is explicitly independent of Planck’s constant (h). Instead, the conductance is found to depend solely on the Fermi energy, the electron effective mass, the wire width, and the effective barrier strength. We interpret this as an emergent phenomenon where the explicit signature of the electron’s wave-like nature, commonly manifest through Planck’s constant (h) in the overall scaling of conductance, is effectively absorbed within the energy- and geometry-dependent sum of transmission probabilities. This allows the conductance to be primarily governed by the Fermi energy, representing a ‘state-counting’ quantum parameter rather than more wave-like characteristic. Full article

CD20-negative aggressive B-cell lymphomas are a rare and heterogeneous group of lymphomas representing a diagnostic challenge for pathologists and a therapeutic issue for clinicians, because the outcome of these patients is poor with the current therapeutic approaches. CD20-negative aggressive lymphomas include plasmablastic lymphoma, primary effusion lymphoma, ALK-positive large B-cell lymphoma and HHV8-positive diffuse large B-cell lymphoma. Conditions of immunosuppression and viral infections, such as Epstein–Barr virus and Human Herpes virus 8, are associated with all of these lymphomas with the exclusion of ALK-positive large B-cell lymphoma, which occurs in immunocompetent hosts and is not associated with viral infections. Common features of these aggressive tumors are high-grade histology with immunoblastic or plasmablastic differentiation, the absence or weak expression of mature B-cell markers such as CD20 and the frequent expression of plasma cell-associated markers. The aim of this review is to highlight the diagnostic challenges associated with the group of CD20-negative aggressive B-cell lymphomas, emphasizing key morphologic and molecular features, which are critical in the diagnosis of the different entities belonging to this rare group of diseases. Full article