Search Results (1,781)

To improve the sampling efficiency of Markov Chain Monte Carlo in complex parameter spaces, this paper proposes an adaptive sampling method that integrates a swarm intelligence mechanism called the BeeSwarm-MH algorithm. The method combines global exploration by scout bees with local exploitation by worker bees. It employs multi-stage perturbation intensities and adaptive step-size tuning to enable efficient posterior sampling. Focusing on Bayesian inference for parameter estimation in the soliton solutions of the two-dimensional complex Ginzburg–Landau equation, we design a dedicated inference framework to systematically compare the performance of BeeSwarm-MH with the classical Metropolis–Hastings algorithm. Experimental results demonstrate that BeeSwarm-MH achieves comparable estimation accuracy while significantly reducing the required number of iterations and total computation time for convergence. Moreover, it exhibits superior global search capabilities and adaptive features, offering a practical approach for efficient Bayesian inference in complex physical models. Full article

We investigate pseudo-complex General Relativity (pcGR)—a coordinate-extended formulation of General Relativity (GR)—within the framework of Hořava-Lifshitz gravity, a regularized theory featuring anisotropic scaling. The pcGR framework bridges GR with modified gravitational theories through the introduction of a minimal length scale. Focusing on Schwarzschild black holes, we derive the Wheeler-deWitt equation, obtaining a quantized description of pcGR. Using perturbative methods and semi-classical approximations, we analyze the solutions of the equations and their physical implications. A key finding is the avoidance of the central singularity due to nonlinear interaction terms in the Hořava-Lifshitz action. Notably, extrinsic curvature (kinetic energy) contributions prove essential for singularity resolution, even in standard GR. Furthermore, the theory offers new perspectives on dark energy, proposing an alternative mechanism for its accumulation. Full article

Zearalenone (ZEN), a mycotoxin commonly found in cereal crops and foods, induces testicular damage and disrupts gut microbial composition. Curcumin (CUR), a bioactive compound derived from turmeric, is known to enhance intestinal microbial balance and exhibit anti-inflammatory properties. This study aimed to investigate the mechanism by which CUR alleviates ZEN-induced reductions in sperm quality through the modulation of the gut microbiota–testis axis. Forty-eight 6-week-old Balb/c male mice were randomly assigned to four treatment groups: control (CON), CUR (200 mg/kg body weight CUR), ZEN (40 mg/kg body weight ZEN), and ZEN + CUR (200 mg/kg CUR + 40 mg/kg ZEN). The degree of sperm damage was quantified by assessing both the survival rate and the morphological integrity of the spermatozoa. CUR was found to mitigate ZEN-induced reductions in the testosterone levels, testicular structural damage, and disrupted spermatogenesis. Exposure to ZEN markedly perturbed the gut microbiota, characterized by increased relative abundances of Prevotella and Bacteroides and a concomitant reduction in Lactobacillus. These alterations were accompanied by pronounced activation of the IL-17A–TNF-α signaling axis, as demonstrated by elevated transcriptional and translational expression of pathway-associated genes and proteins. Co-administration of CUR effectively reinstated microbial homeostasis and mitigated ZEN-induced IL-17A pathway activation. In conclusion, ZEN induces testicular inflammation and reduced sperm quality by lowering testosterone levels and disrupting gut microbial balance, which drives the testicular IL-17A signaling pathway. CUR alleviates ZEN-induced testicular inflammation and sperm quality reduction by restoring beneficial gut microbes and testosterone levels. Full article

The development of big data analytics in healthcare has created a demand for privacy-conscious and scalable machine learning algorithms that can allow the use of patient information across different healthcare organizations. In this study, the difficulties that come with traditional federated learning frameworks in healthcare sectors, such as scalability, computational effectiveness, and preserving patient privacy for numerous healthcare systems, are discussed. In this work, a new conceptual model known as Hierarchical Federated Learning (HFL) for large, integrated healthcare organizations that include several institutions is proposed. The first level of aggregation forms regional centers where local updates are first collected and then sent to the second level of aggregation to form the global update, thus reducing the message-passing traffic and improving the scalability of the HFL architecture. Furthermore, the HFL framework leveraged more robust privacy characteristics such as Local Differential Privacy (LDP), Gaussian Differential Privacy (GDP), Secure Multi-Party Computation (SMPC) and Homomorphic Encryption (HE). In addition, a Novel Aggregated Gradient Perturbation Mechanism is presented to alleviate noise in model updates and maintain privacy and utility. The performance of the proposed HFL framework is evaluated on real-life healthcare datasets and an artificial dataset created using Generative Adversarial Networks (GANs), showing that the proposed HFL framework is better than other methods. Our approach provided an accuracy of around 97% and 30% less privacy leakage compared to the existing models of FLBM-IoT and PPFLB. The proposed HFL approach can help to find the optimal balance between privacy and model performance, which is crucial for healthcare applications and scalable and secure solutions. Full article

T-2 toxin and HT-2 toxin are commonly found in agricultural products and animal feed, posing serious effects to both humans and animals. This study employed combination index (CI) modeling and metabolomics to assess the combined cytotoxic effects of T-2 and HT-2 on four porcine cell types: intestinal porcine epithelial cells (IPEC-J2), porcine Leydig cells (PLCs), porcine ear fibroblasts (PEFs), and porcine hepatocytes (PHs). Cell viability assays revealed a dose-dependent reduction in viability across all cell lines, with relative sensitivities in the order: IPEC-J2 > PLCs > PEFs > PHs. Synergistic cytotoxicity was observed at low concentrations, while antagonistic interactions emerged at higher doses. Untargeted metabolomic profiling identified consistent and significant metabolic perturbations in four different porcine cell lines under co-exposure conditions. Notably, combined treatment with T-2 and HT-2 resulted in a uniform downregulation of LysoPC (22:6), LysoPC (20:5), and LysoPC (20:4), implicating disruption of membrane phospholipid integrity. Additionally, glycerophospholipid metabolism was the most significantly affected pathway across all cell lines. Ether lipid metabolism was markedly altered in PLCs and PEFs, whereas PHs displayed a unique metabolic response characterized by dysregulation of tryptophan metabolism. This study identified markers of synergistic toxicity and common alterations in metabolic pathways across four homologous porcine cell types under the combined exposure to T-2 and HT-2 toxins. These findings enhance the current understanding of the molecular mechanisms underlying mycotoxin-induced the synergistic toxicity. Full article

Underground blasting vibrations are a critical factor influencing the stability of mine slopes. However, existing studies have yet to establish a quantitative relationship or clarify the underlying mechanisms linking blasting-induced vibrations and slope deformation. Taking the Shilu Iron Mine as a case study, this research develops a dynamic mechanical response model of slope stability that accounts for blasting loads. By integrating slope radar remote sensing data and applying the Pearson correlation coefficient, this study quantitatively evaluates—for the first time—the correlation between underground blasting activity and slope surface deformation. The results reveal that blasting vibrations are characterized by typical short-duration, high-amplitude pulse patterns, with horizontal shear stress identified as the primary trigger for slope shear failure. Both elevation and lithological conditions significantly influence the intensity of vibration responses: high-elevation areas and structurally loose rock masses exhibit greater dynamic sensitivity. A pronounced lag effect in slope deformation was observed following blasting, with cumulative displacements increasing by 10.13% and 34.06% at one and six hours post-blasting, respectively, showing a progressive intensification over time. Mechanistically, the impact of blasting on slope stability operates through three interrelated processes: abrupt perturbations in the stress environment, stress redistribution due to rock mass deformation, and the long-term accumulation of fatigue-induced damage. This integrated approach provides new insights into slope behavior under blasting disturbances and offers valuable guidance for slope stability assessment and hazard mitigation. Full article

Novel networking paradigms such as the Internet of Things (IoT) have expanded their usage and deployment to various application domains. Consequently, unseen critical security vulnerabilities such as zero-day attacks have emerged in such deployments. The design of intrusion detection systems for IoT networks is often challenged by a lack of labeled data, which complicates the development of robust defenses against adversarial attacks. As deep learning-based network intrusion detection systems, network intrusion detection systems (NIDS) have been used to counteract emerging security vulnerabilities. However, the deep learning models used in such NIDS are vulnerable to adversarial examples. Adversarial examples are specifically engineered samples tailored to a specific deep learning model; they are developed by minimal perturbation of network packet features, and are intended to cause misclassification. Such examples can bypass NIDS or enable the rejection of regular network traffic. Research in the adversarial example detection domain has yielded several prominent methods; however, most of those methods involve computationally expensive retraining steps and require access to labeled data, which are often lacking in IoT network deployments. In this paper, we propose an unsupervised method for detecting adversarial examples that performs early detection based on the intrinsic characteristics of the deep learning model. Our proposed method requires neither computationally expensive retraining nor extra hardware overhead for implementation. For the work in this paper, we first perform adversarial example generation on a deep learning model using autoencoders. After successful adversarial example generation, we perform adversarial example detection using the intrinsic characteristics of the layers in the deep learning model. A robustness analysis of our approach reveals that an attacker can easily bypass the detection mechanism by using low-magnitude log-normal Gaussian noise. Furthermore, we also test the robustness of our detection method against further compromise by the attacker. We tested our approach on the Kitsune datasets, which are state-of-the-art datasets obtained from deployed IoT network scenarios. Our experimental results show an average adversarial example generation time of 0.337 s and an average detection rate of almost 100%. The robustness analysis of our detection method reveals a reduction of almost 100% in adversarial example detection after compromise by the attacker. Full article

The gastrointestinal tract mediates the absorption of nutrients from the diet, which is increasingly contaminated with toxic metal(loid) species (TMs) and thus threatens food safety. Evidence in support of the influx of TMs into the bloodstream of the general and vulnerable populations (babies, children, pregnant women, and industrial workers) has been obtained by accurately quantifying their blood concentrations. The interpretation of these TM blood concentrations, however, is problematic, as we cannot distinguish between those that are tolerable from those that may cause the onset of environmental diseases. Since TMs that have invaded the bloodstream may perturb biochemical processes therein that will eventually cause organ damage it is crucial to better understand their bioinorganic chemistry as these processes collectively determine their organ availability. Thus, bioinorganic processes of TMs in the bloodstream represent selectivity filters which protect organs from their influx and ultimately determine the corresponding exposure-response relationships. The need to better understand selectivity filters prompted us to mechanistically disentangle them into the major bioinorganic chemistry processes. It is argued that the detoxification of TMs in the bloodstream and the biomolecular mechanisms, which mediate their uptake into target organs, represent critical knowledge gaps to revise regulatory frameworks to reduce the disease burden. Full article

Background: Acute leukemia (AL) is the most prevalent pediatric malignancy and is frequently associated with systemic iron dysregulation, often leading to iron overload. This study aimed to characterize the regulatory mechanisms of iron metabolism in children with AL, considering treatment stages and associated clinical parameters. Methods: A total of 149 children were stratified into four groups: newly diagnosed AL (n = 43), patients post-chemotherapy (n = 55), patients following hematopoietic cell transplantation (HCT; n = 32), and healthy controls (n = 19). Serum concentrations of matriptase-2 (TMPRSS6), neogenin-1 (NEO1), and soluble hemojuvelin (sHJV) were quantified using ELISA. Results: Compared to healthy children, significantly higher serum concentrations of TMPRSS6 and NEO1 were found in patients post-chemotherapy and post-HCT, while sHJV levels were markedly decreased. Higher TMPRSS6 and NEO1 levels and lower sHJV were associated with increased ferritin levels and greater numbers of transfused packed red blood cell (PRBC) units. sHJV negatively correlated with TMPRSS6, NEO1, ferritin, C-reactive protein (CRP), and PRBC transfusions. TMPRSS6 and NEO1 showed a positive correlation. Among the analyzed biomarkers, Kaplan–Meier analysis revealed no statistically significant associations with overall survival (OS) or event-free survival (EFS) within the chemotherapy and HCT subgroups. Conclusions: AL in pediatric patients is associated with profound disruptions of systemic iron homeostasis. Our investigation identified notable perturbations in TMPRSS6, NEO1, and sHJV, suggesting that these proteins could contribute mechanistically to the pathophysiological alterations underlying iron dysregulation observed in pediatric AL. Full article

Tellurite (TeO₃²⁻) is a highly soluble and toxic oxyanion that inhibits the growth of Escherichia coli at concentrations as low as ~1 µg/mL. This toxicity has been primarily attributed to the generation of reactive oxygen species (ROS) during its intracellular reduction by thiol-containing molecules and NAD(P)H-dependent enzymes. However, under anaerobic conditions, E. coli exhibits significantly increased tellurite tolerance—up to 100-fold in minimal media—suggesting the involvement of additional, ROS-independent mechanisms. In this study, we combined chemical-genomic screening, untargeted metabolomics, and targeted biochemical assays to investigate the effects of tellurite under both aerobic and anaerobic conditions. Our findings reveal that tellurite perturbs amino acid and nucleotide metabolism, leading to intracellular imbalances that impair protein synthesis. Additionally, tellurite induces notable changes in membrane lipid composition, particularly in phosphatidylethanolamine derivatives, which may influence biophysical properties of the membrane, such as fluidity or curvature. This membrane remodeling could contribute to the increased resistance observed under anaerobic conditions, although direct evidence of altered membrane fluidity remains to be established. Overall, these results demonstrate that tellurite toxicity extends beyond oxidative stress, impacting central metabolic pathways and membrane-associated functions regardless of oxygen availability. Full article

The Tree–Seed Algorithm (TSA) is a symmetry-driven metaheuristic algorithm that shows potential for complex optimization problems, but it suffers from local optimum entrapment and slow convergence. To address these limitations, we propose the ADTSA algorithm. First, ADTSA adopts a symmetry-driven dual-layer framework for seed generation, which promotes effective information exchange between subpopulations and accelerates convergence speed. In later iterations, ADTSA enhances the population’s exploitation ability through a population fusion mechanism, further improving the convergence speed. Moreover, we propose a historical optimal solution archiving and replacement mechanism, along with a t-distribution perturbation mechanism, to enhance the algorithm’s ability to escape local optima. ADTSA also strengthens population diversity and avoids local optima through convex lens symmetric reverse generation based on the optimal solution. With these mechanisms, ADTSA converges more effectively to the global optimum during the evolutionary process. Tests on the IEEE CEC 2014 benchmark functions showed that ADTSA outperformed several top-performing algorithms, such as LSHADE, JADE, LSHADE-RSP, and the latest TSA variants, and it also excelled in comparison with other optimization algorithms, including GWO, PSO, BOA, GA, and RSA, underscoring its robust performance across diverse testing scenarios. The proposed ADTSA’s applicability in solving complex constrained problems was also validated, with the results showing that ADTSA achieved the best solutions for these complex problems. Full article

In this article, we propose a novel nonlinear cross-diffusion framework to model the distribution of susceptible and infected individuals within their habitat using a reduced SIR model that incorporates saturated incidence and treatment rates. The study investigates solution boundedness through the theory of parabolic partial differential equations, thereby validating the proposed spatio-temporal model. Through the implementation of the suggested cross-diffusion mechanism, the model reveals at least one non-constant positive equilibrium state within the susceptible–infected (SI) system. This work demonstrates the potential coexistence of susceptible and infected populations through cross-diffusion and unveils Turing instability within the system. By analyzing codimension-2 Turing–Hopf bifurcation, the study identifies the Turing space within the spatial context. In addition, we explore the results for Turing–Bogdanov–Takens bifurcation. To account for seasonal disease variations, novel perturbations are introduced. Comprehensive numerical simulations illustrate diverse emerging patterns in the Turing space, including holes, strips, and their mixtures. Additionally, the study identifies non-Turing and Turing–Bogdanov–Takens patterns for specific parameter selections. Spatial series and surfaces are graphed to enhance the clarity of the pattern results. This research provides theoretical insights into the implications of cross-diffusion in epidemic modeling, particularly in contexts characterized by localized mobility, clinically evident infections, and community-driven isolation behaviors. Full article

The rapid development of network communication technology has led to an increased focus on the security of image storage and transmission in multimedia information. This paper proposes an enhanced image security communication scheme based on Fibonacci interleaved diffusion and non-degenerate chaotic system to address the inadequacy of current image encryption technology. The scheme utilizes a hash function to extract the hash characteristic values of the plaintext image, generating initial perturbation keys to drive the chaotic system to generate initial pseudo-random sequences. Subsequently, the input image is subjected to a light scrambling process at the bit level. The Q matrix generated by the Fibonacci sequence is then employed to diffuse the obtained intermediate cipher image. The final ciphertext image is then generated by random direction confusion. Throughout the encryption process, plaintext correlation mechanisms are employed. Consequently, due to the feedback loop of the plaintext, this algorithm is capable of resisting known-plaintext attacks and chosen-plaintext attacks. Theoretical analysis and empirical results demonstrate that the algorithm fulfils the cryptographic requirements of confusion, diffusion, and avalanche effects, while also exhibiting a robust password space and excellent numerical statistical properties. Consequently, the security enhancement mechanism based on Fibonacci interleaved diffusion and non-degenerate chaotic system proposed in this paper effectively enhances the algorithm’s resistance to cryptographic attacks. Full article

Craniofacial development is a complex, highly conserved process involving multiple tissue types and molecular pathways, with perturbations resulting in congenital defects that often require invasive surgical interventions to correct. Remarkably, some species, such as Xenopus laevis, can correct some craniofacial abnormalities during pre-metamorphic stages through thyroid hormone-independent mechanisms. However, the full scope of factors mediating remodeling initiation and coordination remain unclear. This study explores the differential remodeling responses of craniofacial defects by comparing the effects of two pharmacological agents, thioridazine-hydrochloride (thio) and ivermectin (IVM), on craniofacial morphology in X. laevis. Thio-exposure reliably induces a craniofacial defect that can remodel in pre-metamorphic animals, while IVM induces a permanent, non-correcting phenotype. We examined developmental changes from feeding stages to hindlimb bud stages and mapped the effects of each agent on the patterning of craniofacial tissue types including: cartilage, muscle, and nerves. Our findings reveal that thio-induced craniofacial defects exhibit significant consistent remodeling, particularly in muscle, with gene expression analysis revealing upregulation of key remodeling genes, matrix metalloproteinases 1 and 13, as well as their regulator, prolactin.2. In contrast, IVM-induced defects show no significant remodeling, highlighting the importance of specific molecular and cellular factors in pre-metamorphic craniofacial correction. Additionally, unique neuronal profiles suggest a previously underappreciated role for the nervous system in tissue remodeling. This study provides novel insights into the molecular and cellular mechanisms underlying craniofacial defect remodeling and lays the groundwork for future investigations into tissue repair in vertebrates. Full article

Relaxation behavior of water confined in reverse micelles under temperature-induced micelle clustering is undertaken using broadband dielectric spectroscopy in frequency range 1 Hz–20 GHz. All microemulsion systems with sufficiently noticeable micelle water pool (water/surfactant molar ratio W > 10) depict three relaxation processes, in low, high and microwave frequencies, anchoring with relaxation of shell (bound) water, orientation of surfactant anions at water-surfactant interface and relaxation of bulk water confined in reverse micelles. The analysis of dielectric relaxation processes in AOT-based w/o microemulsions under temperature induced clustering of reverse micelles were made according to structural information obtained in NMR and conductometry experiments. The “wait and switch” relaxation mechanism was applied for the explanation of results for water in the bound and bulk states under spatial limitation in reverse micelles. It was shown that surfactant layer predominantly influences the bound water. The properties of water close to AOT interface are determined by strong interactions between water and ionic AOT molecules, which perturb water H-bonding network. The decrease in micelle size causes a weakening of hydrogen bonds, deformation of its steric network and reduction in co-operative relaxation effects. Full article