Search Results (413)

This paper investigates modulation recognition technology for high-order modulated signals. Addressing the issue that existing deep learning-based modulation recognition methods struggle to effectively capture the features of long sequence signals in high-order modulation, we propose a ConvMamba model that integrates convolutional neural networks (CNNs) with the Mamba2 architecture. By employing a selective state-space model, the ConvMamba effectively captures the temporal dependencies in long sequence signals. It also combines the local feature extraction capability of CNNs with a soft-thresholding denoising module, forming a hybrid structure that possesses both global modeling and noise resistance capabilities. The evaluation results on the Sig53 dataset, which contains a rich variety of high-order modulations, demonstrate that compared to traditional CNN- or Transformer-based architectures, ConvMamba achieves a better balance between computational efficiency and recognition accuracy. Compared to Transformer models with similar performance, ConvMamba reduces computational complexity by over 60%. Compared to CNN models with comparable computational resource consumption, ConvMamba significantly improves recognition accuracy. Therefore, ConvMamba shows a distinct advantage in processing high-order modulated signals with long sequences. Full article

Improving the decoding accuracy of biological signals has been a research focus for decades to advance health, automation, and robotic industries. However, challenges like inter-subject variability, data scarcity, and multifunctional variability cause low decoding accuracy, thus hindering the practical deployment of biological signal paradigms. This paper proposes a multifunctional biological signals network (Multi-BioSig-Net) that addresses the aforementioned issues by devising a novel blind few-shot learning (FSL) technique to quickly adapt to multiple target domains without needing a pre-trained model. Specifically, our proposed multimodal similarity extractor (MMSE) and self-multiple domain adaptation (SMDA) modules address data scarcity and inter-subject variability issues by exploiting and enhancing the similarity between multimodal samples and quickly adapting the target domains by adaptively adjusting the parameters’ weights and position, respectively. For multifunctional learning, we proposed inter-function discriminator (IFD) that discriminates the classes by extracting inter-class common features and then subtracts them from both classes to avoid false prediction of the proposed model due to overfitting on the common features. Furthermore, we proposed a holistic-local fusion (HLF) module that exploits contextual-detailed features to adapt the scale-varying features across multiple functions. In addition, fractal dimension estimation (FDE) was employed for the classification of left-hand motor imagery (LMI) and right-hand motor imagery (RMI), confirming that proposed method can effectively extract the discriminative features for this task. The effectiveness of our proposed algorithm was assessed quantitatively and statistically against competent state-of-the-art (SOTA) algorithms utilizing three public datasets, demonstrating that our proposed algorithm outperformed SOTA algorithms. Full article

This study aimed to assess the anti-inflammatory properties of mare milk by analyzing immune markers in mice following gavage of mare milk. Metagenomic sequencing was employed to examine variations in the composition and functional profiles of the intestinal microbiota across different experimental groups. Bacterial diversity, abundance, and functional annotations of gut microbiota were evaluated for each group. The results show that, compared to the control group, the mare milk group exhibited a significant decrease in the pro-inflammatory cytokine IL-6 levels and a significant increase in secretory immunoglobulin A (SIgA) levels (p < 0.05). The fermented mare milk group and the pasteurized fermented mare milk group demonstrated a significant downregulation of the pro-inflammatory cytokines TNF-α and IL-1β, along with a significant increase in the anti-inflammatory cytokine IL-10 levels (p < 0.05). Additionally, metagenomic analysis revealed that both the mare milk and fermented mare milk groups were able to regulate the imbalance of the intestinal microenvironment by improving the diversity of the gut microbiota and reshaping its structure. Specifically, the mare milk group enhanced gut barrier function by increasing the abundance of Bacteroides acidifaciens, while the fermented mare milk group increased the proportion of Bacillota and the relative abundance of beneficial bacterial genera such as Faecalibaculum and Bifidobacterium. KEGG pathway annotation highlighted prominent functions related to carbohydrate and amino acid metabolism, followed by coenzyme and vitamin metabolism activities. In conclusion, mare milk and its fermented products demonstrate anti-inflammatory effects, particularly in modulating immune responses and inhibiting inflammatory cascades. Additionally, the administration of mare milk enhances the composition and metabolic activity of intestinal microbiota in mice, supporting intestinal microecological balance and overall gut health, and offering valuable insights for the development of mare milk-based functional foods. Full article

The gut microbiota is recognized as one of the extrinsic factors that modulate the clinical outcomes of immune checkpoint inhibitors (ICIs), such as inhibitors targeting programmed cell death protein 1 (PD-1), in cancer patients. However, the link between intestinal barrier, which mutually interacts with the gut microbiota, and therapeutic effects has not been extensively studied so far. Therefore, the primary goal of this study was to investigate the relationship between intestinal barrier functionality and clinical outcomes of anti-PD-1 therapy in patients with advanced melanoma. Fecal samples were collected from 64 patients before and during anti-PD-1 therapy. The levels of zonulin, calprotectin, and secretory immunoglobulin A (SIgA), which reflect intestinal permeability, inflammation, and immunity, respectively, were measured in fecal samples (n = 115) using an Enzyme-Linked Immunosorbent Assay (ELISA). Moreover, the composition of the immunoglobulin (Ig)-bound (n = 108) and total stool microbiota (n = 117) was determined by the V3–V4 region of 16S rRNA gene sequencing. ELISA indicated a higher baseline concentration of fecal SIgA in patients with favorable clinical outcomes than those with unfavorable ones. Moreover, high baseline concentrations of intestinal barrier state biomarkers correlated with survival outcomes. In the cases of fecal zonulin and fecal SIgA, there was a positive correlation, while in the case of fecal calprotectin, there was a negative correlation. Furthermore, there were differences in the microbial profiles of the Ig-bound stool microbiota between patients with favorable and unfavorable clinical outcomes and their changes during treatment. Collectively, these findings indicate an association between intestinal barrier functionality and clinical outcomes of anti-PD-1 therapy in advanced melanoma patients. Full article

Accurate classification of cardiac arrhythmias remains a crucial task in biomedical signal processing. This study proposes a hybrid deep learning approach called 1D CNN-eGRU that integrates one-dimensional convolutional neural network models (1D CNN) and a gated recurrent unit (GRU) architecture with an attention mechanism for the precise classification of cardiac arrhythmias based on ECG Lead II signals. To enhance the classification of cardiac arrhythmias, we also address data imbalances in the MIT-BIH arrhythmia dataset by introducing a hybrid data balancing method that blends resampling and class-weight learning. Additionally, we apply Sig-LIME, a refined variant of LIME tailored for signal datasets, to provide comprehensive insights into model decisions. The suggested hybrid 1D CNN-eGRU approach, a fusion of 1D CNN-GRU along with an attention mechanism, is designed to acquire intricate temporal and spatial dependencies in ECG signals. It aims to distinguish between four distinct arrhythmia classes from the MIT-BIH dataset, addressing a significant challenge in medical diagnostics. Demonstrating strong performance, the proposed hybrid 1D CNN-eGRU model achieves an overall accuracy of 0.99, sensitivity of 0.93, and specificity of 0.99. Per-class evaluation shows precision ranging from 0.80 to 1.00, sensitivity from 0.83 to 0.99, and F1-scores between 0.82 and 0.99 across four arrhythmia types (normal, supraventricular, ventricular, and fusion). The model also attains an AUC of 1.00 on average, with a final test loss of 0.07. These results not only demonstrate the model’s effectiveness in arrhythmia classification but also underscore the added value of interpretability enabled through the use of the Sig-LIME technique. Full article

This study investigated the effect of laser shock peening (LSP) treatment on the fatigue performance of Q355D steel butt-welded joints. The results demonstrate that LSP sig-nificantly enhances joint fatigue resistance through gradient hardening in surface lay-ers, introduction of high-magnitude residual compressive stress fields, and micro-structural refinement. Specifically, microhardness increased across all joint zones with gradient attenuation of strengthening effects within an approximately 700 μm depth. LSP effectively suppressed residual tensile stress concentration in regions beyond 4 mm on both sides of the weld. Fatigue tests confirmed that LSP substantially extended joint fatigue life: by 113–165% in the high-stress region (250–270 MPa) and 46–63% in the medium-low-stress region (230–240 MPa). Fractographic analysis further revealed reduced fatigue striation spacing and lower microcrack density in LSP-treated speci-mens, reflecting the synergistic effect of residual compressive stress fields and micro-structural refinement in retarding crack propagation. This work substantiates LSP as an effective method for enhancing fatigue resistance in Q355D steel welded joints. Full article

Background: Esophageal cancer (EC), including esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), remains a lethal malignancy with limited molecularly tailored treatment options. Due to substantial histologic and transcriptomic differences between subtypes, therapeutic responses often vary, underscoring the need for subtype-stratified analysis and precision drug discovery. Methods: We integrated transcriptomic data from GEO and TCGA to identify differentially expressed genes (DEGs) specific to EAC, ESCC, and their shared profiles. Functional enrichment (GO, KEGG) and protein–protein interaction (PPI) network analyses were conducted to extract hub genes using DAVID, STRING, and Cytoscape. Survival associations were evaluated using TCGA-ESCA and UALCAN. Drug repurposing was performed using L1000FWD, L1000CDS2, and SigCom LINCS. Results: We identified 79, 59, and 17 hub genes in the DEG-EAC, DEG-ESCC, and DEG-EAC&ESCC datasets, respectively. In EAC, 16 novel hub genes including SCARB1, SERPINH1, and DSC2 were discovered, which had not been previously implicated in this subtype. These genes were significantly enriched in pathways related to extracellular matrix (ECM) remodeling and epithelial structure. In addition, shared hub genes across EAC and ESCC—such as COL1A1, SPARC, and MMP1—were enriched in ECM organization and cell adhesion processes, highlighting convergent tumor–stroma interactions. Drug repositioning analysis consistently prioritized MEK inhibitors, trametinib and selumetinib, as potential therapeutic candidates across all DEG datasets. Conclusions: This study presents a comprehensive, subtype-stratified transcriptomic framework for EC, identifying both unique and shared hub genes with potential functional relevance to ECM dynamics. Our findings suggest that ECM remodelers may serve as therapeutic targets, and highlight MEK inhibition as a promising, yet exploratory, repurposing strategy. While these results offer a molecular foundation for future precision oncology efforts in EC, further validation through proteomic analysis, functional studies, and clinical evaluation is warranted. Full article

Fuzzy signature (${\mathsf{SIG}}_{\mathsf{F}}$) is a type of digital signature that preserves the core functionalities of traditional signatures, while accommodating variations and non-uniformity in the signing key. This property enables the direct use of high-entropy fuzzy data, such as biometric information, as the signing key. In this paper, we define the m-existentially unforgeable under chosen message attack ($\mathfrak{m}{-\mathrm{EUF}-\mathrm{CMA}}^{\ast }$) security of fuzzy signature. Furthermore, we propose a generic construction of fuzzy signature, which is composed of a homomorphic secure sketch ($\mathsf{SS}$) with an error-recoverable property, a homomorphic average-case strong extractor ($\mathsf{Ext}$), and a homomorphic and key-shift* secure signature scheme ($\mathsf{SIG}$). By instantiating the foundational components, we present a $\mathfrak{m}{-\mathrm{EUF}-\mathrm{CMA}}^{\ast }$ secure fuzzy signature instantiation based on the Computational Diffie–Hellman (CDH) assumption over bilinear groups in the standard model. Full article

Artificial intelligence technologies, particularly machine learning and computer vision, are being increasingly utilized to preserve, restore, and create immersive virtual experiences with cultural artifacts and sites, thus aiding in conserving cultural heritage and making it accessible to a global audience. This paper examines the performance of Generative Adversarial Networks (GAN), especially Style-Based Generator Architecture (StyleGAN), as a deep learning approach for producing realistic images of Egyptian monuments. We used Sigmoid loss for Language–Image Pre-training (SigLIP) as a unique image–text alignment system to guide monument generation through semantic elements. We also studied truncation methods to regulate the generated image noise and identify the most effective parameter settings based on architectural representation versus diverse output creation. An improved discriminator design that combined noise addition with squeeze-and-excitation blocks and a modified MinibatchStdLayer produced 27.5% better Fréchet Inception Distance performance than the original discriminator models. Moreover, differential evolution for latent-space optimization reduced alignment mistakes during specific monument construction tasks by about 15%. We checked a wide range of truncation values from 0.1 to 1.0 and found that somewhere between 0.4 and 0.7 was the best range because it allowed for good accuracy while retaining many different architectural elements. Our findings indicate that specific model optimization strategies produce superior outcomes by creating better-quality and historically correct representations of diverse Egyptian monuments. Thus, the developed technology may be instrumental in generating educational and archaeological visualization assets while adding virtual tourism capabilities. Full article

Identity-based cryptography introduced by Shamir (Crypto’84) has seen many advances through the years. In the context of post-quantum identity-based schemes, most of the efficient designs are based on lattices. In this work, we propose an identity-based identification (IBI) scheme and an identity-based signature (IBS) scheme based on codes. Our design combines the hash-and-sign signature scheme, Wave, with a Stern-like signature scheme, BGKM-SIG1, instantiated over a ternary field using the large-weight Syndrome Decoding Problem (SDP). Our scheme significantly outperforms existing code-based identity-based signature constructions. Full article

Suckling is crucial for piglet intestinal development and gut health, as it improves resilience during the challenging weaning phase and promotes subsequent growth. IPS, comprising Na⁺/K⁺ ions, whey protein, and glucose, has been shown to have positive effects on animal growth and intestinal health. The objectives of this study were to assess the impact of IPS consumption on the growth performance, immunity, intestinal growth and development, and microbiota structure of suckling piglets. A total of 160 newborn piglets were randomly divided into control and IPS groups, with IPS supplementation starting from 2 to 8 days after birth and continuing until 3 days before weaning. The findings revealed that IPS boosted the body weight at 24 days by 3.6% (p < 0.05) and improved the body weight gain from 16 to 24 days by 15.7% (p < 0.05). Additionally, the jejunal villus height and villus height to crypt depth ratio in the IPS group were notably increased to 1.08 and 1.31 times (p < 0.05), respectively, compared to the control group. Furthermore, IPS elevated the plasma levels of IgA and IgM, reduced the plasma levels of blood urea nitrogen (BUN), and enhanced the content of secretory immunoglobulin A (SIgA) in the jejunal mucosa of suckling piglets. Furthermore, IPS upregulated the mRNA expression of tight junction proteins GLP-2, ZO-1, and Claudin-1 in jejunal tissue, while downregulating the regulatory genes in the Toll-like pathway, including MyD88 and TLR-4 (p < 0.05). The analysis of gut microbiota indicated that IPS altered the relative abundance of gut microbes, with an increase in beneficial bacteria like Alloprevotella and Bacteroides. In conclusion, this study demonstrates that IPS supplementation enhances weaning weight, growth performance, immune function, and intestinal development in piglets, supporting the integration of IPS supplementation in the management of pre-weaning piglets. Full article

Accurate gene expression quantification using reverse transcription quantitative PCR (RT-qPCR) requires stable reference genes (RGs) for reliable normalization. However, few studies have systematically identified RGs suitable for simultaneous high salt, alkaline, and high-temperature conditions. This study addresses this gap by evaluating the stability of eight candidate RGs in the anaerobic halophilic alkalithermophile Natranaerobius thermophilus JW/NM-WN-LF^T under combined salt, alkali, and thermal stresses. The stability of these candidate RGs was assessed using five statistical algorithms: Delta CT, geNorm, NormFinder, BestKeeper, and RefFinder. Results indicated that recA exhibited the highest expression stability across all tested conditions and proved adequate as a single RG for normalization in both RT-qPCR and droplet digital PCR (ddPCR) assays. Furthermore, recA alone or combined with other RGs (sigA, rsmH) effectively normalized the expression of seven stress-response genes (proX, opuAC, mnhE, nhaC, trkH, ducA, and pimT). This work represents the first systematic validation of RGs under polyextreme stress conditions, providing essential guidelines for future gene expression studies in extreme environments and aiding research on microbial adaptation mechanisms in halophilic, alkaliphilic, and thermophilic microorganisms. Full article

Logics of Statements in Context have been proposed as a general framework to describe and relate, in a uniform and unifying way, a broad spectrum of logics and specification formalisms, which also comprise “open formulas”. In particular, it has been shown that we can define arbitrary first-order “open formulas” in arbitrary categories. At present, there are two deficiencies. In the general case, only signatures with predicate symbols are considered and institutions of statements in context are only defined for single signatures. In this paper, we elaborate the special case of traditional many-sorted first-order logic. We show that any many-sorted first-order signature $\mathrm{\Sigma }$ with predicates and (!) operation symbols gives rise to an institution ${\mathcal{FL}}_{\mathrm{\Sigma }}$ of $\mathrm{\Sigma }$-statements in context and that any signature morphism results in a comorphism between the corresponding institutions. We prove that we obtain a functor $\mathcal{FL}:\mathsf{Sig}\to co\mathbb{I}ns$ from the category of signatures into the category of institutions and comorphisms. We construct a corresponding Grothendieck institution ${\mathcal{FL}}^{♯}$ and prove that ${\mathcal{FL}}^{♯}$ is, indeed, an extension of the traditional institution of first-order logic, which only comprises “closed formulas”. We also investigate substitutions in detail and discuss (elementary) diagrams in the sense of traditional first-order logic. Full article

Exogenous RNA application, also known as spray-induced gene silencing (SIGS), is a new approach in plant biotechnology that utilizes RNA interference (RNAi) to modify plant traits. This technique involves applying RNA solutions of double-stranded RNA (dsRNA), hairpin RNA (hpRNA), small interfering RNA (siRNA), or microRNA (miRNA) directly onto plant surfaces. This triggers RNAi-mediated silencing of specific genes within the plant or invading pathogens. While extensively studied for enhancing resistance to pathogens, the application of exogenous RNA to regulate plant endogenous genes remains less explored, creating a rich area for further research. This review summarizes and analyzes the studies reporting on the exogenously induced silencing of plant endogenes and transgenes using various RNA types. We also discuss the RNA production and delivery approaches, analyze the uptake and transport of exogenous RNAs, and the mechanism of action. The analysis revealed that SIGS/exoRNAi affects the expression of plant genes, which may contribute to crop improvement and plant gene functional studies. Full article

Glucose-6-phosphate dehydrogenase (G6PDH) is a critical enzyme in the pentose phosphate pathway, playing an essential role in plant growth, development, and adaptation to abiotic stress. In this study, we identified four members of the G6PDH gene family in the ‘Zunla-1’ genome, designating them as CaG6PDH1-CaG6PDH4. Multiple sequence alignment revealed that the four protein sequences of pepper contain three unique binding sites characteristic of G6PDH: the substrate binding site, the NADP binding site and the Rossmann fold. The phylogenetic tree, motifs, and gene structure analysis indicate that the CaG6PDH gene sequence is relatively conserved and structurally similar, with a close relationship to the sequence of Solanaceae G6PDH members. The collinearity analysis showed that there were two pairs of collinearity between the CaG6PDH genes and the AtG6PDH genes, as well as the SiG6PDH genes. Additionally, numerous cis-elements associated with stress responses, hormone regulation, development, and light responses were identified in the promoter region of the CaG6PDH gene. Furthermore, the various members of the pepper CaG6PDH gene family exhibit specific expression patterns across different tissues and demonstrate significant variations in response to abiotic stress and phytohormone treatments, particularly the CaG6PDH1 and CaG6PDH2 genes. Subcellular localization studies indicate that CaG6PDH2 is located in chloroplasts. We conducted further investigations into the role of CaG6PDH2 in response to cold stress using Virus-Induced Gene Silencing (VIGS) technology. The tissues of seedlings with silenced CaG6PDH2 exhibited significant damage and displayed a more pronounced cold damage phenotype. This observation is further supported by the accumulation of reactive oxygen species (ROS), the activity of antioxidant enzymes, and a reduction in the expression of cold-responsive genes. In conclusion, the findings of this study indicate that CaG6PDH2 plays an important role in cold stress response and may serve as a potential gene for cultivating cold-tolerant pepper varieties. Full article