Search Results (1,099)

Viruses use a range of sophisticated strategies to evade detection by cytotoxic T-lymphocytes (CTLs) within host cells. Beyond elaborating dedicated viral proteins that disrupt the MHC class I antigen-presentation machinery, some viruses possess intrinsic, cis-acting genome-encoded elements that interfere with antigen processing and display. These protein features, including G-quadruplex motifs, repetitive peptide sequences, and rare-codon usage, counterintuitively limit production of proteins critical to virus survival, particularly during latency. By slowing viral protein synthesis, these features reduce antigen production and proteosomal degradation, ultimately limiting the generation of peptides for MHC I presentation. These built-in evasion tactics enable viruses to remain “invisible” to CTLs during latency. While these primary sequence intrinsic immune evasion (PSI) mechanisms are well-described in select herpesviruses, emerging evidence suggests that they may also play a critical role in RNA viruses. How these proteins are made, rather than what they functionally target, determines their immune evasion properties. Understanding PSI mechanisms could rationally inform the design of engineered viral antigens with altered or removed evasion elements to restore antigen CTL priming and activation. Such vaccine strategies have the potential to enhance immune recognition, improve clearance of chronically infected cells, and contribute to the treatment of persistent viral infections and virus-associated cancers. Full article

Retrieval-Augmented Generation (RAG) systems have emerged as a critical approach for enhancing large language models with external knowledge, yet the field lacks systematic theoretical analysis for understanding their fundamental characteristics and optimization principles. A novel information-theoretic approach for analyzing and optimizing RAG systems is introduced in this paper by modeling them as cascading information channel systems where each component (query encoding, retrieval, context integration, and generation) functions as a distinct information-theoretic channel with measurable capacity. Following established practices in information theory research, theoretical insights are evaluated through systematic experimentation on controlled synthetic datasets that enable precise manipulation of schema entropy and isolation of information flow dynamics. Through this controlled experimental approach, the following key theoretical insights are supported: (1) RAG performance is bounded by the minimum capacity across constituent channels, (2) the retrieval channel represents the primary information bottleneck, (3) errors propagate through channel-dependent mechanisms with specific interaction patterns, and (4) retrieval capacity is fundamentally limited by the minimum of embedding dimension and schema entropy. Both quantitative metrics for evaluating RAG systems and practical design principles for optimization are provided by the proposed approach. Retrieval improvements yield 58–85% performance gains and generation improvements yield 58–110% gains, substantially higher than context integration improvements (∼9%) and query encoding modifications, as shown by experimental results on controlled synthetic environments, supporting the theoretical approach. A systematic theoretical analysis for understanding RAG system dynamics is provided by this work, with real-world validation and practical implementation refinements representing natural next phases for this research. Full article

Enhancing quality traits is a primary objective in silage maize breeding programs. The use of genome-wide association studies (GWAS) for quality traits, in combination with the integration of genetic resources, presents an opportunity to identify crucial genomic regions and candidate genes influencing silage maize quality. In this study, a GWAS was conducted on 580 inbred lines of silage maize, and a meta-analysis was performed on 477 quantitative trait loci (QTLs) from 34 studies. The analysis identified 27 significant single nucleotide polymorphisms (SNPs) and 87 consensus QTLs (cQTLs), with 7 cQTLs associated with multiple quality traits. By integrating the SNPs identified through association mapping, one SNP was found to overlap with the cQTL interval related to crude protein, neutral detergent fiber, and starch content. Furthermore, enrichment analysis predicted 300 and 5669 candidate genes through GWAS and meta-analysis, respectively, highlighting pathways such as cellular metabolism, the biosynthesis of secondary metabolites, ribosome function, carbon metabolism, protein processing in the endoplasmic reticulum, and amino acid biosynthesis. The examination of 13 candidate genes from three co-located regions revealed Zm00001d050977 as a cytochrome P450 family gene, while the other 2 genes primarily encode proteins involved in stress responses and other biological pathways. In conclusion, this research presents a methodology combining GWAS and meta-analysis to identify genomic regions and potential genes influencing quality traits in silage maize. These findings serve as a foundation for the identification of significant QTLs and candidate genes crucial for improving silage maize quality. Full article

Powdery mildew caused by Sphaerotheca fusca poses a significant threat to cucumber (Cucumis sativus L.) production worldwide, underscoring the need for sustainable disease management strategies. This study investigates the potential of L-lysine, abundantly produced by Bacillus subtilis M 320 (BSM320), to prime systemic acquired resistance (SAR) pathways in cucumber plants. Liquid chromatography–mass spectrometry analysis identified L-lysine as the primary bioactive metabolite in the BSM320 culture filtrate. Foliar application of purified L-lysine significantly reduced powdery mildew symptoms, lowering disease severity by up to 92% at concentrations ≥ 2500 mg/L. However, in vitro spore germination assays indicated that L-lysine did not exhibit direct antifungal activity, indicating that its protective effect is likely mediated through the activation of plant immune responses. Quantitative reverse transcription PCR revealed marked upregulation of key defense-related genes encoding pathogenesis-related proteins 1 and 3, lipoxygenase 1 and 23, WRKY transcription factor 20, and L-type lectin receptor kinase 6.1 within 24 h of treatment. Concurrently, salicylic acid (SA) levels increased threefold in lysine-treated plants, confirming the induction of an SA-dependent SAR pathway. These findings highlight L-lysine as a sustainable, residue-free priming agent capable of enhancing broad-spectrum plant immunity, offering a promising approach for amino acid-based crop protection. Full article

Migraine is a neurovascular paroxysmal disorder characterized by neurogenic inflammation and has a remarkable impact on the quality of life. The Food and Drug Administration (FDA) approved onabotulinumtoxin A in 2010 for the prophylactic treatment of chronic migraine. Today, in its 4th decade, it is approved in 100 countries for 15 main indications. Its mechanism of action, based on the inhibition of neurotransmitter release from primary sensory neurons, is very complex: it affords antinociception, but it also has an analgesic effect on neuropathic pain conditions and reduces the need for rescue medications. Genetic variants have been investigated for their potential role in the pathogenesis and clinical expression of migraine and of the response to treatments. These studies primarily involved genes associated with vascular regulation and cardiovascular pathology, including those encoding angiotensin-converting enzyme (ACE) and methylenetetrahydrofolate reductase (MTHFR). However, epigenetics and, particularly, genetic and epigenetic modifications are still poorly studied in terms of understanding the mechanisms implicated in susceptibility to migraine, aura, chronification and response to symptomatic and preventive treatments. In particular, the aim of the present study is to gather evidence on the genetic variants and epigenetic modifications affecting the pathway of the calcitonin gene-related peptide (CGRP), the target of onabotulinumtoxin A and of all the novel monoclonal antibodies. Full article

The capitula of Chrysanthemum indicum Linné or C. morifolium Ramatuelle (Kikuka in Japanese) are included in several formulae of Kampo medicines (traditional Japanese medicines), such as Chotosan, which is used for headache and dizziness. Luteolin, the principal constituent of C. indicum, has antioxidant and anti-inflammatory activities. However, the effects of other flavonoids on this crude drug have not yet been thoroughly investigated. To evaluate and compare anti-inflammatory effects, we used primary cultured rat hepatocytes, which produce proinflammatory mediators, such as nitric oxide (NO) and proinflammatory cytokines, in response to interleukin (IL)-1β. Eight derivatives of 5,7-dihydroxyflavone were purified and identified in the ethyl acetate-soluble fraction of a C. indicum capitulum extract: luteolin (Compound 1), apigenin (2), diosmetin (3), 5,7-dihydroxy-3′,4′,5′-trimethoxyflavone (4), acacetin (5), eupatilin (6), jaceosidin (7), and 6-methoxytricin (8). Luteolin is the most abundant compound in this fraction. All compounds significantly suppressed NO production in hepatocytes, with apigenin and acacetin showing the greatest efficacy. The comparison of the IC₅₀ values of the inhibition of NO production suggests that substitutions by hydroxyl and methoxy groups at the C-3′ and C-4′ positions of 5,7-dihydroxyflavone may be at least essential for the suppression of NO production. In hepatocytes, acacetin and luteolin decreased the levels of mRNAs encoding inducible nitric oxide synthase (iNOS), proinflammatory cytokines, including tumor necrosis factor, IL-6, and type 1 IL-1 receptor, which regulates inflammatory responses. Based on the comparison of the IC₅₀ values and the content, luteolin, jaceosidin, and diosmetin may be responsible for the anti-inflammatory effects of C. indicum capitula. Full article

MicroRNA (miR)-200c enhances osteogenesis, modulates inflammation, and participates in dentin development. This study was to investigate the beneficial potential of miR-200c in vital pulp therapy (VPT) by mitigating pulpitis and promoting dentin regeneration. We explored the miR-200c variations in inflamed pulp tissues from patients with symptomatic irreversible pulpitis and primary human dental pulp-derived cells (DPCs) challenged with P.g. lipopolysaccharide (Pg-LPS). We further assessed the functions of overexpression of miR-200c on odontogenic differentiation, pulpal inflammation, and dentin regeneration in vitro and in vivo. Our findings revealed a noteworthy downregulation of miR-200c expression in inflamed pulp tissues and primary human DPCs. Through the overexpression of miR-200c via transfecting plasmid DNA (pDNA), we observed a substantial downregulation of proinflammatory cytokines interleukin (IL)-6 and IL-8 in human DPCs. Furthermore, this overexpression significantly enhanced the transcript and protein levels of odontogenic differentiation markers, including Runt-related transcription factor (Runx)2, osteocalcin (OCN), dentin matrix protein (DMP)1, and dentin sialophosphoprotein (DSPP). In a rat model of pulpitis induced by Pg-LPS, we demonstrated notable benefits by local application of pDNA encoding miR-200c delivered by CaCO₃-based nanoparticles to reduce pulpal inflammation and promote dentin formation. These results underscore the significant impact of locally applied miR-200c in modulating pulpal inflammation and facilitating dentin repair, showcasing its ability to improve VPT outcomes. Full article

The paper introduces EVOCA—Explainable Verification Of Claims by Graph Alignment—a hybrid approach that combines NLP (Natural Language Processing) techniques with the structural advantages of knowledge graphs to manage and reduce the amount of evidence required to evaluate statements. The approach leverages the explicit and interpretable structure of semantic graphs, which naturally represent the semantic structure of a sentence—or a set of sentences—and explicitly encodes the relationships among different concepts, thereby facilitating the extraction and manipulation of relevant information. The primary objective of the proposed tool is to condense the evidence into a short sentence that preserves only the salient and relevant information of the target claim. This process eliminates superfluous and redundant information, which could impact the performance of the subsequent verification task and provide useful information to explain the outcome. To achieve this, the proposed tool called EVOCA—Explainable Verification Of Claims by Graph Alignment—generates a sub-graph in AMR (Abstract Meaning Representation), representing the tokens of the claim–evidence pair that exhibit high semantic similarity. The structured representation offered by the AMR graph not only aids in identifying the most relevant information but also improves the interpretability of the results. The resulting sub-graph is converted back into natural language with the SPRING AMR tool, producing a concise but meaning-rich “sub-evidence” sentence. The output can be processed by lightweight language models to determine whether the evidence supports, contradicts, or is neutral about the claim. The approach is tested on the 4297 sentence pairs of the Climate-BERT-fact-checking dataset, and the promising results are discussed. Full article

Medulloblastoma (MB) is the most common malignant brain tumor in children and typically arises in the cerebellum, likely due to disruptions in neuronal precursor development. The primary inhibitory neurotransmitter in the central nervous system (CNS), γ-aminobutyric acid (GABA), exerts its effects through GABA_A, GABA_B, and GABA_C receptors. GABA receptor activity regulates the development and function of cerebellar neurons, including glutamatergic cerebellar granule cells (CGCs). Beyond the nervous system, GABA is also a common metabolite in non-neuronal cell types. An increasing body of evidence indicates that GABA can influence cell proliferation, differentiation, and migration in several types of adult solid tumors, including brain cancers. GABA and GABA_A receptor agonists can impair the viability and survival of MB cells, primarily acting on GABA_A receptors containing the α5 subunit. A marked expression of the gene encoding the α5 subunit is found across all MB tumor molecular subgroups, particularly Group 3 MB, which has a poor prognosis. Importantly, high levels of the γ-aminobutyric acid type A receptor subunit α5 (GABRA5) gene are associated with shorter patient overall survival in Group 3 and Group 4 MB. In contrast, high γ-aminobutyric acid type A receptor subunit β1 (GABRB1) gene expression is related to longer survival in all MB subgroups. The GABAergic system may, therefore, regulate MB cell function and tumor progression and influence patient prognosis, and is worthy of further investigation as a biomarker and therapeutic target in MB. Full article

T-cell receptors (TCRs) exhibit degeneracy, enabling individual TCRs to recognize multiple altered peptide ligands (APLs) derived from a single cognate antigen. This characteristic has been involved in the pathogenesis of autoimmune diseases through cross-reactivity between microbial and self-antigens. Cytotoxic T lymphocytes (CTLs), which recognize peptide–MHC class I complexes via TCRs, play a critical role in the immune response against viral infections. However, the extent to which TCR degeneracy within a population of virus-specific CTLs contributes to effective viral control remains poorly understood. In this study, we investigated the magnitude and functional relevance of TCR degeneracy in CTLs targeting an immunodominant epitope of human T-cell leukemia virus type 1 (HTLV-1) in patients with HTLV-1-associated myelopathy (HAM). Using peripheral blood mononuclear cells (PBMCs) from these patients, we quantified TCR degeneracy at the population level by comparing CTL responses to a panel of APLs with responses to the cognate epitope. Our findings demonstrated that increased TCR degeneracy, particularly at the primary TCR contact residue at position 5 of the antigen, was inversely correlated with HTLV-1 proviral load (p = 0.038, R = −0.40), despite similar functional avidity across patient-derived CTLs. Viral sequencing further revealed that CTLs with high TCR degeneracy exerted stronger selective pressure on the virus, as indicated by a higher frequency of nonsynonymous substitutions within the epitope-encoding region in patients with highly degenerate TCR repertoires. Moreover, TCR degeneracy was positively correlated with the recognition rate of epitope variants (p = 0.018, R = 0.76), suggesting that CTLs with high TCR degeneracy exhibited enhanced recognition of naturally occurring epitope variants compared to those with low TCR degeneracy. Taken together, these results suggest that virus-specific CTLs with high TCR degeneracy possess superior antiviral capacity, characterized by broadened epitope recognition and more effective suppression of HTLV-1 infection. To our knowledge, this is the first study to systematically quantify TCR degeneracy in HTLV-1-specific CTLs and evaluate its contribution to viral control in HAM patients. These findings establish TCR degeneracy as a critical determinant of antiviral efficacy and provide a novel immunological insight into the mechanisms of viral suppression in chronic HTLV-1 infection. Full article

Semantic segmentation of remote sensing imagery is a pivotal task for intelligent interpretation, with critical applications in urban monitoring, resource management, and disaster assessment. Recent advancements in deep learning have significantly improved RS image segmentation, particularly through the use of convolutional neural networks, which demonstrate remarkable proficiency in local feature extraction. However, due to the inherent locality of convolutional operations, prevailing methodologies frequently encounter challenges in capturing long-range dependencies, thereby constraining their comprehensive semantic comprehension. Moreover, the preprocessing of high-resolution remote sensing images by dividing them into sub-images disrupts spatial continuity, further complicating the balance between local feature extraction and global context modeling. To address these limitations, we propose DMA-Net, a Dynamic Morphology-Aware Segmentation Network built on an encoder–decoder architecture. The proposed framework incorporates three primary parts: a Multi-Axis Vision Transformer (MaxViT) encoder achieves a balance between local feature extraction and global context modeling through multi-axis self-attention mechanisms; a Hierarchy Attention Decoder (HA-Decoder) enhanced with Hierarchy Convolutional Groups (HCG) for precise recovery of fine-grained spatial details; and a Channel and Spatial Attention Bridge (CSA-Bridge) to mitigate the encoder–decoder semantic gap while amplifying discriminative feature representations. Extensive experimentation has been conducted to demonstrate the state-of-the-art performance of DMA-Net, which has been shown to achieve 87.31% mIoU on Potsdam, 83.23% on Vaihingen, and 54.23% on LoveDA, thereby surpassing existing methods. Full article

Caffeic acid-O-methyltransferase (COMT) is a key enzyme in lignin synthesis and secondary metabolism in plants, and it participates in the regulation of plant growth and development as well as plants’ stress response. To further investigate the function of COMT in grapevine, a total of 124 COMT family genes were identified from three Vitis species in this study, namely Pinot noir (Vitis vinifera L.), Vitis amurensis, and Vitis riparia. The amino acid sequence encoded by these genes ranged from 55 to 1422 aa, and their molecular mass ranged from 6640.82 to 77,034.43 Da. Subcellular localization prediction inferred that they were mainly located in the plasma membrane and cytoplasm. The prediction of secondary structures showed that α-helix and irregular coiled-coil were primary structural elements. These genes were unevenly distributed across 10 different chromosomes, respectively. Phylogenetic tree analysis of the amino acid sequences of VvCOMT, VaCOMT, VrCOMT, and AtCOMT proteins showed that they were closely related and were divided into four subgroups. The motif distribution was similar among the cluster genes, and the gene sequence was notably conserved. The 124 members of the COMT gene family possessed a variable number of exons, ranging from 2 to 13. The promoter region of all of these COMTs genes contained multiple cis-acting elements related to hormones (e.g., ABA, IAA, MeJA, GA, and SA), growth and development (e.g., endosperm, circadian, meristem, light response), and various stress responses (e.g., drought, low temperature, wounding, anaerobic, defense, and stress). The intraspecies collinearity analysis suggested that there were one pair, three pairs, and six pairs of collinear genes in Va, Pinot noir, and Vr, respectively, and that tandem duplication contributed more to the expansion of these gene family members. In addition, interspecific collinearity revealed that the VvCOMTs had the strongest homology with the VaCOMTs, followed by the VrCOMTs, and the weakest homology with the AtCOMTs. The expression patterns of different tissues and organs at different developmental stages indicated that the VvCOMT genes had obvious tissue expression specificity. The majority of VvCOMT genes were only expressed at higher levels in certain tissues. Furthermore, we screened 13 VvCOMT genes to conduct qRT-PCR verification according to the transcriptome data of VvCOMTs under abiotic stresses (NaCl, PEG, and cold). The results confirmed that these genes were involved in the responses to NaCl, PEG, and cold stress. This study lays a foundation for the exploration of the function of the COMT genes, and is of great importance for the genetic improvement of abiotic stress resistance in grapes. Full article

Anthracyclines have been clinically well established in cancer chemotherapy for decades. The main limitations of this drug class are the development of resistance and severe side effects. In the present investigation, we analyzed 30 anthracyclines in a panel of 59 cell lines of the National Cancer Institute, USA. The log₁₀IC₅₀ values varied from −10.49 M (3′-deamino-3′-(4″-(3″-cyano)morpholinyl)-doxorubicin, 1) to −4.93 M (N,N-dibenzyldaunorubicin hydrochloride, 30). Multidrug-resistant NCI-ADR-Res ovarian cancer cells revealed a high degree of resistance to established anthracyclines (between 18-fold to idarubicin (4) and 166-fold to doxorubicin (13) compared to parental, drug-sensitive OVCAR8 cells). The resistant cells displayed only low degrees of resistance (1- to 5-fold) to four other anthracyclines (7, 18, 28, 30) and were even hypersensitive (collaterally sensitive) to two compounds (1, 26). Live cell time-lapse microscopy proved the cross-resistance of the three chosen anthracyclines (4, 7, 9) on sensitive CCRF/CEM and multidrug-resistant CEM/ADR5000 cells. Structure–activity relationships showed that the presence of tertiary amino functions is helpful in avoiding resistance, while primary amines rather increased resistance development. An α-aminonitrile function as in compound 1 was favorable. Investigating the mRNA expression of 49 ATP-binding cassette (ABC) transporter genes showed that ABCB1/MDR1 encoding P-glycoprotein was the most important one for acquired and inherent resistance to anthracyclines. Molecular docking demonstrated that all anthracyclines bound to the same binding domain at the inner efflux channel side of P-glycoprotein with high binding affinities. Kaplan–Meier statistics of RNA sequencing data of more than 8000 tumor biopsies of TCGA database revealed that out of 23 tumor entities high ABCB1 expression was significantly correlated with worse survival times for acute myeloid leukemia, multiple myeloma, and hepatocellular carcinoma patients. This indicates that ABCB1 may serve as a prognostic marker in anthracycline-based chemotherapy regimens in these tumor types and a target for the development of novel anthracycline derivatives. Full article

Food pattern recognition plays a crucial role in modern healthcare by enabling automated dietary monitoring and personalised nutritional interventions, particularly for vulnerable populations with complex dietary needs. Current food recognition systems struggle to balance high accuracy with interpretability and computational efficiency when analysing complex meal compositions in real-world settings. We developed a novel self-explaining neural architecture that integrates specialised attention mechanisms with temporal modules within a streamlined framework. Our methodology employs hierarchical feature extraction through successive convolution operations, multi-head attention mechanisms for pattern classification, and bidirectional LSTM networks for temporal analysis. Architecture incorporates self-explaining components utilising attention-based mechanisms and interpretable concept encoders to maintain transparency. We evaluated our model on the FOOD101 dataset using 5-fold cross-validation, ablation studies, and comprehensive computational efficiency assessments. Training employed multi-objective optimisation with adaptive learning rates and specialised loss functions designed for dietary pattern recognition. Experiments demonstrate our model’s superior performance, achieving 94.1% accuracy with only 29.3 ms inference latency and 3.8 GB memory usage, representing a 63.3% parameter reduction compared to baseline transformers. The system maintains detection rates above 84% in complex multi-item recognition scenarios, whilst feature attribution analysis achieved scores of 0.89 for primary components. Cross-validation confirmed consistent performance with accuracy ranging from 92.8% to 93.5% across all folds. This research advances automated dietary analysis by providing an efficient, interpretable solution for food recognition with direct applications in nutritional monitoring and personalised healthcare, particularly benefiting vulnerable populations who require transparent and trustworthy dietary guidance. Full article

Rotator cuff tears are a leading cause of shoulder pain and dysfunction, yet the molecular mechanisms that link tendon injury to inflammation and nociceptive signaling remain poorly understood. Netrin-1, a classical axon guidance cue signaling through dependence receptors UNC5B and Neogenin-1, has been implicated in both neuronal plasticity and inflammatory processes, but its role in tendon pathology has not been explored. A rat supraspinatus tear model was employed to assess, in vivo, the expression of genes encoding netrin-1 (Ntn1) and its receptors (Unc5b and Neo1) at 0, 7, 14, 28, and 56 days post-injury (n = 10 per time point). Primary rat tenocytes isolated from rotator cuff tissue were treated in vitro with recombinant netrin-1, and transcriptional changes in genes encoding TNF-α (Tnfa), IL-6 (Il6), MMP-1 (Mmp1), and MMP-3 (Mmp3) were quantified by qRT-PCR. Separately, human iPSC-derived sensory neurons were exposed to netrin-1, and dose- and time-dependent effects on neurite outgrowth were measured at 4 and 14 days in culture. In injured tendons, Ntn1 mRNA increased significantly at day 14 (p = 0.010) and 28 (p = 0.042), Unc5b at day 7 (p = 0.002) and 14 (p < 0.001), and Neo1 at day 14 (p < 0.001) versus intact controls. Tenocyte exposure to 500 ng/mL netrin-1 induced transient upregulation of Tnfa (3 h, p = 0.023; 6 h, p = 0.009) and Il6 (3 h–24 h, all p < 0.013), as well as Mmp3 (3–24 h, p < 0.043) and Mmp1 (6 h–24 h, p < 0.024); no induction was observed at 50 ng/mL. In sensory neurons, 50 ng/mL of netrin-1 enhanced neurite extension at day 4 (p = 0.006) but not at 500 ng/mL or at day 14 for either dose. Netrin-1 and its receptors are upregulated in a rat rotator cuff tear model, and netrin-1 elicits distinct pro-inflammatory and matrix-remodeling responses in tenocytes while promoting early neurite growth in sensory neurons. These findings suggest netrin-1 as a key modulator of tendon inflammation, matrix turnover, and peripheral nerve plasticity following injury. Full article