Abstract: This paper presents a comprehensive experimental and simulation study on the stick–slip characteristics of pneumatic cylinders operating at low velocities. A pneumatic servo experimental system is constructed to systematically investigate stick–slip motion by measuring piston position, piston velocity, pressures in the two-cylinder chambers, and friction force. Extensive experiments are conducted on three pneumatic cylinders of different types and sizes to examine the influences of airflow rate, air source pressure, external load, and initial piston position on stick–slip behavior. Based on experimental observations, a complete mathematical model of the pneumatic servo system is developed. Unlike conventional approaches that simulate stick–slip motion using friction models driven solely by piston velocity, the proposed system-level model explicitly describes the entire dynamic process from valve control inputs to airflow, pressure evolution in the cylinder chambers, piston motion, and friction force. In addition, a new dynamic friction model is proposed by improving the revised LuGre friction model through the incorporation of a dwell-time-dependent static friction force, which is experimentally observed to play a critical role in governing stick–slip motion. Simulation studies are performed using both the proposed friction model and the revised LuGre friction model. The simulated results are systematically compared with experimental data for all tested cylinders. The results demonstrate that the proposed system model with the new friction formulation significantly improves the prediction of stick–slip characteristics, including the number of stick–slip cycles and the evolution of pressure and friction force, compared with conventional friction-model-based simulations.

Abstract: We derive an effective gravitational potential \( Φ_{halo} (r)∼-[ln⁡( r/r_*)+1]/r \) from the asymptotic behavior of dark matter halo models. At microscopic scales, the logarithmic term changes sign, producing repulsion that prevents matter from collapsing into a singularity. The corresponding logarithmically corrected Schwarzschild metric yields parameter-free, a priori predictions for the shadows of Sgr A* and M87* that agree with Event Horizon Telescope observations. Six falsifiable predictions for unobserved black holes, particularly NGC315, can discriminate this metric from the Kerr solution. On galactic scales, the same logarithmic term fits rotation curves of the Milky Way, Andromeda, and NGC2974 using only ordinary matter, and passes the Bullet Cluster lensing test. Tidal effects in the Solar System are far below current experimental limits, ensuring consistency with the equivalence principle and parameterized post-Newtonian tests. We further derive the modified field equations via coarse-grained variation (Appendix B) from the effective action of a quantum vortex background, thus providing a more complete theoretical bridge to the modified Poisson equation and metric used in the main text. This effective theoretical framework indicates that various gravitational phenomena from black holes to galaxies may share a common quantum topological origin. It provides a unified, testable alternative to the dark matter problem, and also points out a potential path for the observable detection of quantum gravity effects.

Abstract: This study presents a unified measure-theoretic formulation of the Monte Carlo Stochastic Optimization Technique (MOST), establishing a rigorous framework that encompasses both continuous and discrete optimization. Unlike conventional optimization methods that operate on pointwise evaluations, MOST is based on regional evaluation through normalized integrals, enabling robust and global exploration of the search space. We first reformulate MOST within a finite measure space, where the evaluation of a region is defined as the measure-weighted average of the objective function. This formulation naturally connects regional optimization with expectation under an induced probability measure and provides a theoretical foundation for Monte Carlo approximation. Building upon this framework, we construct a discrete version of MOST by introducing the counting measure and extend it further using weighted measures to rigorously handle odd-cardinality partitions via midpoint sharing. A central contribution of this work is the demonstration that continuous and discrete MOST are structurally identical algorithms arising from a single measure-based principle, differing only in the choice of underlying measure. This result eliminates the traditional separation between continuous and discrete optimization within the MOST framework. Theoretical analysis reveals that MOST is particularly effective when near-optimal regions possess non-negligible measure, while its performance may degrade in the presence of isolated global minima. These properties are validated through numerical experiments using benchmark functions, including the Ackley and Sphere functions, under uniform discretization. The results confirm that discrete MOST achieves accurate approximations of global optima, with errors controlled by discretization resolution and strong robustness in multimodal landscapes. Overall, this work establishes MOST as a measure-based optimization paradigm, offering a unified, theoretically grounded, and practically robust approach to global optimization across continuous and discrete domains.

Abstract:

The alkaline pectate lyase A from Paenibacillus barcinonensis, encoded by pelA (GenBank accession no. CAB40884), is an enzyme with high activity on pectin and potential application in sustainable industrial biotechnology. In this study, pelA was expressed in Saccharomyces cerevisiae by using different domains of the cell wall protein Pir4 as translational fusion partners. Given the presence of five potential N-glycosylation sites in the amino acid sequence coded by pelA, two of them in conserved regions of class III pectate lyases, the effect of glycosylation on the enzymatic activity of the recombinant enzyme was investigated by expressing the recombinant fusion proteins in both, standard and glycosylation deficient strains of S. cerevisiae. Correct targeting of the recombinant fusion proteins was confirmed by Western blot analysis using Pir-specific antibodies, whilst enzymatic activity on polygalacturonic acid was demonstrated on both plate assays and colorimetric assays. Hyper- glycosylation of the enzyme when expressed in the standard strain of S. cerevisiae did not occur, however maximum activities were over two and a half times higher when the enzyme was expressed in the glycosylation deficient strain, suggesting a better adaptation of this strain to the secretion of the functional enzyme. Notably, pectate lyase activity was approximately fourfold higher when the pelA gene was expressed in this yeast strain compared to its expression in a prokaryotic host such as Bacillus subtilis or Escherichia coli.

Abstract: Sensory-impaired children often experience barriers to motor development and psychosocial growth in recreational programs, where traditional assessments lack real-time precision and scalability. This paper introduces an edge AI phenomics framework for tracking motor proficiency encompassing kinematics like balance and coordination and psychosocial benefits such as social engagement and self-efficacy during adaptive play activities. Deployed on low-power edge devices, the system fuses RGB-D cameras, IMUs, and bioacoustics sensors into a lightweight pipeline featuring MobileNetV3 pose estimation and conformer encoders for phenotypic feature extraction. Evaluated on a dataset from 250 children across Chennai programs, it achieves 96% motor accuracy (MPJPE <10mm) and 0.85 correlation with clinical psychosocial scales, outperforming cloud baselines by 40% in latency. Results demonstrate 25-35% gains in proficiency and well-being over 8 weeks, with implications for inclusive therapies. The framework addresses deployment challenges through quantization and federated learning, advancing scalable, privacy-preserving phenomics in paediatric recreation.

Abstract:

Polyols are widely used as non-cariogenic sweeteners in foods and oral care products, yet their comparative activity against diverse oral microbes and their potential relevance to the oral–systemic axis remain incompletely defined. Here, we performed an in vitro, optical-density (OD)-based screening of four polyols—allulose, D-mannose, erythritol, and xylitol—against Streptococcus mutans, Streptococcus anginosus, Candida albicans, and Fusobacterium nucleatum. Cultures were grown with polyols at 1–20% (w/v), and OD600 was recorded at organism-specific endpoints (~24 h). Allulose, erythritol, and xylitol produced strong, concentration-dependent suppression of streptococcal growth at ≥5–10%, whereas C. albicans showed minimal changes across the tested range. F. nucleatum was highly sensitive to allulose, D-mannose, and xylitol at ≥5% (reducing OD to ≤13% of untreated control), while low concentrations of D-mannose and erythritol increased OD above control, suggesting species-specific utilization or stress responses. One-way ANOVA with Tukey’s HSD post hoc testing supported significant between-polyol differences for most concentrations in Streptococcus spp. and F. nucleatum. Collectively, these results identify polyol- and taxon-specific growth phenotypes that can inform the formulation of swallow-safe oral hygiene products and motivate follow-up work in polymicrobial biofilm models and clinical studies targeting oral inflammation and downstream systemic risk.

Abstract: Traditional social navigation systems often treat perception and motion as decoupled tasks, leading to reactive behaviors and perceptual surprise due to limited field of view. While active vision—the ability to choose where to look—offers a solution, most existing frameworks decouple sensing from execution to simplify the learning process. This article introduces a novel joint reinforcement learning (RL) framework (Active Vision for Social Navigation) that unifies locomotion and discrete gaze control within a single, end-to-end policy. Unlike existing factored approaches, our method leverages a model-based RL architecture with a latent world model to explicitly address the credit assignment problem inherent in active sensing. Experimental results in cluttered, dynamic environments demonstrate that our joint policy outperforms factored sensing-action approaches by prioritizing viewpoints specifically relevant to social safety, such as checking blind spots and tracking human trajectories. Our findings suggest that tight sensorimotor coupling is essential for reducing perceptual surprise and ensuring safe, socially aware navigation in unstructured spaces.

Abstract: Oxidative stress (OS) is elevated in patients with end-stage kidney disease undergoing maintenance dialysis and contributes to increased cardiovascular risk. While kidney dysfunction and dialysis can generate OS, the acute effects of a single dialysis session remain unclear due to variability in study design and biomarkers used. In this observational study, blood samples from 68 hemodialysis patients were collected before and after a single session. Plasma levels of the reactive oxygen species marker superoxide (O2•⁻) and OS-damage markers lipid hydroperoxides (LOOH), protein-bound malondialdehyde (PrMDA), protein-bound thiobarbituric acid reactive substances (PrTBARS), and protein carbonyls (PrCO) were measured. LOOH increased significantly by 50% post-dialysis, whereas PrMDA and PrTBARS decreased modestly by ~10%. No significant changes were observed in O2•⁻ or PrCO. Dialysis vintage correlated positively with LOOH, PrMDA, and PrTBARS, but not with O2•⁻ or PrCO. Patients undergoing low-flux hemodialysis exhibited a greater post-dialysis increase in LOOH than those treated with high-flux hemodialysis. No significant associations were found between OS markers and comorbidities or medication. The post-dialysis rise in LOOH, an early-formed and least accumulating lipid peroxidation marker, highlights its sensitivity to acute dialysis-related oxidative changes. The rising tendency of PrMDA and PrTBARS with dialysis vintage suggests cumulative OS over time.

Abstract: Background/Objectives: Endometriosis is a chronic, estrogen-dependent inflammatory disease affecting approximately 10% of reproductive-age individuals and is associated with pelvic pain, infertility, and reduced quality of life. Despite its high prevalence, diagnosis is often delayed for years and current therapies primarily focus on hormonal suppression rather than disease modification. Recent work has clarified several biological pathways involved in endometriosis, including altered estrogen signaling, immune dysregulation, and neuroangiogenesis. These insights have prompted development of new diagnostic strategies and targeted therapies. This review aims to synthesize current evidence on advances in the diagnosis and treatment of endometriosis and to highlight emerging targeted therapies that may improve patient outcomes. Methods: A narrative review was carried out using PubMed, Scopus, and Web of Science, focusing on peer-reviewed work from the last two decades on endometriosis diagnosis and treatment. Clinical trials, systematic reviews, consensus recommendations, and observational studies were included to assemble a broad picture of established care and developing strategies. Results: Advances in diagnostic approaches include improvements in imaging modalities, development of candidate biomarkers, and exploration of non-invasive diagnostic tools aimed at reducing diagnostic delay. Therapeutic innovations include oral gonadotropin-releasing hormone (GnRH) antagonists, selective progesterone receptor modulators, aromatase inhibitors, and emerging immunomodulatory and anti-inflammatory treatments targeting key molecular pathways involved in disease progression. These developments reflect a shift toward more individualized and mechanism-based treatment strategies. Conclusions: Emerging diagnostic tools and targeted therapies represent promising advances in endometriosis care. Continued research integrating molecular insights with clinical practice may facilitate earlier diagnosis, improve symptom control, and support more personalized treatment approaches for individuals affected by endometriosis.

Abstract: A PIEZO2 variant was shown recently to protect against Alzheimer’s disease in the Hispanic population. This analysis implicates the potentially critical role of Piezo2 in Alzheimer’s disease pathophysiology. Another recent research mimicked acquired Piezo1 channelopathy by PIEZO1 manipulation. This study also showed that phosphatidylinositol 4,5-bisphosphate (PIP2) administration ameliorated brain capillary endothelial Piezo1 channelopathy in a mouse model of Alzheimer’s disease. However, the initiating microdamage is suggested to be in the prefrontal cortex further upstream of pathophysiology, namely an irreversible Piezo2 channelopathy of glutamatergic terminals that should fine modulate oxytocin release along stressful ultradian events. Implication of Piezo2 in the defensive arousal response reveals an underlying body-wide Piezo2 system of which the proposed prefrontal Piezo2 channelopathy posits a critical locus. PIP2 is emerging as a potential treatment method of Piezo channelopathy in Alzheimer’s disease, however the challenge remains how it could be administered more precisely to affected brain areas.

Abstract: Background: Deep learning has become an important tool for predicting mutation-induced changes in binding free energy (ΔΔG). However, most current state-of-the-art methods rely heavily on paired wild-type (WT) and mutant (MT) complex structures during both training and inference. This dependence on post-mutation structural information substantially limits their practical utility in real-world scenarios, such as clinical diagnosis and early-stage drug screening, where mutant structures are difficult to obtain experimentally in a timely manner. Methods: To evaluate model performance in more realistic and challenging translational settings, we conducted a systematic benchmark of graph-based deep learning models under a WT-only inductive setting. We constructed a full-protein heterogeneous graph framework that incorporates long-range spatial constraints to implicitly infer mutational effects from static wild-type structures. We compared it against a sequence-based vector baseline model. Results: Through a systematic evaluation on the MdrDB dataset, we revealed a critical generalization gap. Although random splitting yielded relatively high predictive correlation due to homologous data leakage (Pearson R ≈ 0.55), model performance dropped sharply under a strict UniProt-based cross-protein split designed to simulate prediction on truly unseen targets (Pearson R ≈ 0.15). Although the absolute performance remained limited, the graph-based model showed a weak but consistent improvement over the sequence baseline, which was close to random guessing (Pearson R ≈ 0.04). Conclusions: Further analyses suggest that the performance bottleneck may partly arise from intrinsic experimental noise in the dataset (i.e., label inconsistency) and from the absence of conformational entropy (dynamic) information in static WT structures. This study indicates that random splitting can lead to substantial overestimation of model generalizability. It highlights the need to integrate physical priors and dynamic features to overcome the current limitations of drug resistance prediction when explicit mutant structures are unavailable.

Abstract: Background: Currently marketed hepatitis B vaccines are primarily recombinant protein vaccines. However, their antigen immunogenicity is relatively weak, requiring combination with effective adjuvants to enhance the immune response. The development of novel, highly effective adjuvants is a key strategy for optimizing vaccine performance. Polyinosinic-polycytidylic acid (PolyI:C), a synthetic double-stranded RNA analog, activates TLR3/RLR pathways to enhance T-cell priming and cellular immunity. However, its utility as a sole adjuvant is limited by rapid nuclease degradation and poor cytosolic delivery. Lipid nanoparticles (LNPs), a mature delivery platform, enable high encapsulation efficiency, efficient cellular uptake, and endosomal escape. Objectives: This study aimed to evaluate the adjuvant effect of LNP-encapsulated PolyI:C (LNP-PolyI:C) on the immunogenicity of hepatitis B surface antigen (HBsAg) in vivo. Methods: The colloidal stability of LNP-PolyI:C stored at 2–8°C for 9 months was monitored using dynamic light scattering (DLS) on a Zetasizer Lab instrument. Serum levels of HBsAg-specific IgG, IgG1, and IgG2a antibodies in immunized Kunming mice were measured by enzyme-linked immunosorbent assay (ELISA). The secretion of HBsAg-specific cytokines by splenocytes was analyzed using flow cytometry and enzyme-linked immunospot (ELISpot) assay. Results: The results demonstrated that the LNP-encapsulated PolyI:C adjuvant significantly increased the secretion of HBsAg-specific IFN-γ, IL-2, and TNF-α by murine splenocytes, indicating a Th1-biased and cytotoxic T lymphocyte (CTL)-mediated cellular immune response. In addition, this formulation markedly elevated serum titers of HBsAg-specific IgG, IgG1, and IgG2a. Notably, the increased IgG2a/IgG1 ratio highlights a robust enhancement of the humoral immune response. Conclusions: These findings underscore the advantages of the LNP-PolyI:C adjuvant in enhancing both humoral and cellular immunity, demonstrating its considerable potential as a novel adjuvant.

Abstract: This work investigates multi-scalar multiplication (MSM) over a fixed base for small input sizes, where classical large-scale optimizations are less effective. We propose a novel variant of the Pippenger-based bucket method that enhance performance by using additional precomputation. In particular, our approach extends the BGMW method by introducing structured precomputations of point combinations, enabling the replacement of multiple point additions with table lookups. We further generalize this idea through chunk-based precomputation, allowing flexible trade-offs between memory usage and runtime performance. Experimental results demonstrate that the proposed variants significantly outperform the Fixed Window method for small MSM instances, achieving up to 3× speedup under practical memory constraints. These results challenge the common assumption that bucket-based methods are inefficient for small MSMs.

Abstract: Traditional MRI systems rely on large liquid-helium baths to maintain superconductivity, requiring complex infrastructure, quench pipes, and ongoing helium supply management. Modern “dry” or micro-helium MRI magnets replace this approach with conduction cooling and sealed helium volumes of only a few liters or less. These systems drastically reduce helium dependence, eliminate routine refilling, simplify installation, and lower lifetime operating costs. The major practical advance comes from moving from open helium baths to sealed systems rather than from differences between small helium volumes (e.g., 0.7 vs. 7 liters). Smaller volumes mainly influence safety margins and resilience during power interruptions rather than routine clinical operation.

Abstract: Background: New Caledonia, an archipelago in the South Pacific, experienced an unprecedented con-junction of prolonged border closure during the COVID-19 pandemic (2020 to 2022) and maked influence of the El Niño/Southern Oscillation (ENSO). This context provided a unique opportunity to explore how environmental drivers, island isolation, and so-cio-demographic factors interact to shape infectious disease dynamics. This study aimed to assess the respective and combination of climatic variability, travel restrictions, and so-cio-demographic factors on the dynamics of four priority infectious diseases. Methods: We analysed retrospectively data from 2017 to 2023 on four infectious diseases: leptospi-rosis, dengue, influenza, and hepatitis A (HAV). Satellite precipitation data and the Mul-tivariate El Niño/Southern Oscillation Index (MEI) were used. Socio-demographic and economic variables were gathered. Statistical analyses employed descriptive analysis, General Additive Mixed Models to evaluate the associations between climatic events, travel restrictions, and disease circulation using the communal level as random effect and time (daily) as spline effect. Results: We analysed: 878 cases of leptospirosis, 162 of HAV, 7,377 of influenza, and 6,607 dengue cases. Influenza was associated with rainfalls before lockdown (Odds Ratio (OR) 0.7, Con-fidence interval 95%, (CI95%), (0.6 - 0.8)), disappeared during lockdown but resurged post-reopening losing its meteorological association. Dengue epidemics declined, coin-ciding with Wolbachia program and border closure, and was associated with lower MEI (OR 0.78, CI95% (0.6-1) during the 2017 to 2020 period. HAV cases were correlated with the MEI (OR: 1.8, CI95% (1-3.3)). Leptospirosis cases were associated with cumulative rainfall (OR 1.12 (1.1-1.2), lower education (OR 1.04, CI95% (1-1.1)), and decreased with water supply (OR 0.7, CI95% (0.5-0.8)). Conclusion: Our findings highlight how climate variability, mobility restrictions, and so-cio-environmental inequities differentially shape infectious disease risks in island ecosys-tems. These results reinforce the need for integrated One Health surveillance that jointly addresses environmental change, social vulnerability, and infectious disease prevention.

Abstract: Caffeine is a widely consumed psychostimulant known to affect memory, yet its dual role in impairing long-term potentiation (LTP) while enhancing cognitive performance remains unresolved. This study aimed to clarify this paradox by investigating the differential effects of caffeine on distinct forms of synaptic plasticity in the hippocampus. Using extracellular recordings in mouse hippocampal slices, we assessed long-term (LTP and E-S potentiation), short-term plasticity, and neuronal excitability under 30 μM caffeine exposure – a physiologically relevant concentration. Our findings confirm that caffeine suppresses LTP but does not inhibit E-S potentiation; instead, it enhances it. Furthermore, caffeine alters excitability in a form-dependent manner, reducing it following LTP and increasing it following E-S potentiation. We also show that caffeine blocks short-term synaptic plasticity regardless of prior LTP induction. These results suggest that E-S potentiation may serve as a caffeine-resistant mechanism for memory formation, potentially mediated by selective modulation of adenosine receptors. This study provides new insight into how caffeine influences synaptic processes underlying learning and memory.

Abstract: Background/Objectives: Among the most common hereditary neurodegenerative disorders in people are the neuronal ceroid lipofuscinoses (NCLs), a subgroup of lysosomal storage disorders. For most cases of NCL, the genes containing the causative variants have been identified. NCLs also occur in dogs, and in most instances variants responsible for the canine NCLs occur in genes orthologous to those associated with the human disorders. An adult Miniature Dachshund presented with clinical signs consistent with NCL. Studies were undertaken to determine whether the disease phenotype supported classification of the disease as an NCL and to identify potential causal DNA sequence variants. Methods: The proband underwent complete neurologic and ophthalmological examinations followed by euthanasia. Tissues were examined for NCL-like pathology. Whole genome sequence analysis (WGS) was performed. Results: The clinical signs and tissue pathology were consistent with those of NCL disease, although with some features distinct from previously described forms of canine NCL. The proband was uniquely homozygous for variants in 5 genes associated with lysosomal function, 4 of which have not previously been associated with the NCLs. Conclusion: The proband suffered from a novel NCL-like disorder. Determining whether one or a combination of more than one of the 5 potentially causal DNA sequence variants was responsible for the disease will require evaluation of additional cases.

Abstract: Introduction: Portable non-enzymatic electrochemical glucose sensors offer potential for decentralized healthcare and medical education; however, their integration into clinically meaningful teleconsultation workflows remains limited. This study presents the functional integration of a portable copper-modified electrochemical glucose sensor into a rural web- and Android-based telemedicine platform within a simulation-based medical education framework. Materials and Methods: Screen-printed carbon electrodes were electrochemically activated and modified via copper electrodeposition. Electrochemical characterization was performed using cyclic voltammetry to identify the glucose oxidation region and chronoamperometry for quantitative detection. Glucose solutions in PBS (pH 10) were measured using 70 µL samples, and the resulting signals were converted into glucose values (mg/dL) through a calibration model and incorporated into simulated gynecological teleconsultation workflows. Results: The sensor exhibited a stable amperometric response at +0.60 V, with a linear range of 3.125–50 mM (R2 = 0.9822), an area-normalized sensitivity of 0.061 µA·mM−1·cm−2, and a limit of detection of 1.39 mM. Implementation within the simulation scenario (n = 26) demonstrated 69% high/very high perceived usability and 88% high/very high educational value. Conclusion: These results support the feasibility of integrating portable electrochemical sensing into teleconsultation-based training environments and establishing a practical framework for future validation and deployment in rural telemedicine applications.

Abstract: The integration of automated learning and video analysis enables the development of intelligent systems that can operate effectively in uncertain scenarios. These systems can autonomously identify dominant motion dynamics, depending on the theoretical framework used for representation and the learning process used for pattern identification. Current literature offers a state-based approach to describe the key temporal and spatial relationships required to understand motion dynamics. An important aspect of this approach is determining when the number of positively learned rules from a given information source is sufficient to detect dominant motion in automatic surveillance scenarios. This is crucial, as it affects both the variability of movements that monitored subjects can exhibit within the camera’s field of view and the resources needed for effective implementation. This study addresses these gaps through a grammar-based sufficiency criterion, which posits that learning is complete when production rule growth stabilizes, under the assumption of system stationarity. The stability criterion evaluates whether the most probable rules are learned over time, and whenever a high-growth rule is added, it is used to update the criterion. We outline several benefits of having a formal criterion for determining when a symbolic surveillance system has a robust model that explains the observed motion dynamics. Our hypothesis is that a correct model can consistently account for the majority of motion dynamics over time in an automated learning process. The proposed approach is evaluated by modeling motion dynamics in several scenarios using the SEQUITUR algorithm as input and computing the probability of stability along the learning curve, which indicates when the model reaches a steady state of consistent learning. Experimental validation was conducted in real-world scenarios under varying acquisition conditions. The results demonstrate that the proposed method achieves robust modeling performance, with accuracy values ranging from 83.56% to 95.92%in dynamic environments.

Abstract: In this paper, we consider the properties of the following objects: plafal and geo-space (a general overview). As an application of the created theory, the proof of the equality of complexity classes P and NP will be given. The geo-plafal is a kernel (computational template) of the proof; constructive theory of serendipity approximations, Stepanets' school and the Bogolyubov principle of the decay of correlations for an infinite systems (dim=3) is a shell.