Search Results (64)

Postural control is essential for daily function, and while stochastic resonance (SR) enhances balance in clinical populations, its efficacy in healthy young people remains underexplored. This study investigated (1) biomechanical effects of multisite plantar vibration on postural stability using center-of-pressure (CoP) parameters, and (2) short-term and sustained effects on balance performances. Phase 1 enrolled six participants to identify the optimal plantar stimulation configuration and to evaluate acute electromyographic responses under threshold-level vibration. Phase 2 evaluated long-term efficacy through an eight-week sham-controlled parallel-group randomized controlled trial. In this trial, eight participants received vibration combined with balance training, and another eight participants completed the same training protocol using sham insoles without vibration, analyzing CoP parameters (95% ellipse area, path length) and muscle activation (tibialis anterior, medial gastrocnemius, peroneus longus, extensor digitorum longus). Results showed full-site vibration reduced CoP area versus control (265.66 ± 188.6 mm² vs. 437.84 ± 190.95 mm², p < 0.05) without altering ankle muscle activation (all p > 0.05). Longitudinal analysis revealed CoP area reduction (−4.88 ± 10.42%) in the intervention group versus sham (p < 0.001), with maximum anterior displacement increasing by 25.03% during vibration (p < 0.05). Plantar white-noise vibration modulates CoP oscillations without neuromuscular activation changes, demonstrating that full-site stimulation acutely enhances postural stability while sustained intervention improves dynamic balance control. Full article

Machine learning (ML) is transforming the analysis of biomolecular data, holding significant promise for improving the efficiency and accuracy of microscopy image analysis and for studying the dynamics of molecules in live cells. As data-driven approaches continue to evolve, they may eventually replace traditional statistical methods that rely on conventional analytical methods. This review examines and critically analyses the state of the art of ML techniques as applied to various levels of data supervision in the analysis of dynamic single-molecule datasets obtained using superresolution optical microscopy. Collectively encompassed under the umbrella of “nanoscopy”, these methods currently comprise targeted techniques such as stimulated emission depletion (STED) microscopy and stochastic techniques like single-molecule localization microscopies (SMLMs), comprising photoactivated localization microscopy (PALM), DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) microscopy, and minimal fluorescence photon flux (MINFLUX) microscopy. These techniques all enable the imaging of subcellular components and molecules beyond the diffraction limit, and some are additionally capable of studying their dynamics in real time, as reviewed here, using several ML techniques that facilitate motion analysis in two or three dimensions with qualitative and quantitative characterisation in the live cell. It is expected that the growing use of learning-based approaches in biological microscopy data processing will dramatically increase throughput and accelerate progress in this rapidly developing field. Full article

Monosynaptic reflexes (MSRs) elicited by constant-intensity group I afferent stimulation exhibit marked amplitude variability, commonly attributed to stochastic presynaptic modulation and dynamic postsynaptic excitability. Here, we tested whether this variability could be attenuated using the Ott–Grebogi–Yorke (OGY) chaos–control algorithm, which stabilizes unstable periodic orbits in low-dimensional nonlinear systems. In spinalized, anesthetized cats, real-time implementation of the OGY method failed to reduce MSR amplitude variability, as quantified by the coefficient of variation, and the return map structure showed no evidence of orbit stabilization. These negative results contrast with successful applications of OGY control in physical systems, cardiac tissue, hippocampal slices, and stochastic neuronal models. We interpret this failure in the context of the intense, ongoing synaptic bombardment characteristic of dorsal horn circuitry, which likely obscures or destroys the low-dimensional geometric structure required for OGY-based control. Our findings delineate a fundamental limit to classical chaos–control algorithms in intact neural circuits and highlight the need for control strategies explicitly robust to high dimensionality and physiological noise. Full article

This paper examines a stochastic Stackelberg differential game between an insurer and a pool of homogeneous policyholders. Policyholders dynamically optimize insurance coverage and risky asset allocations to minimize the probability of wealth shortfall, while the insurer, acting as the leader, sets the premium loading to maximize the expected exponential utility of terminal surplus. Employing dynamic programming techniques, we derive closed-form equilibrium strategies for both parties. The analysis reveals that a strong positive correlation between insurance claims and financial market returns incentivizes full coverage with modest premiums, whereas a strong negative correlation may induce market collapse as insurers exit underwriting to exploit natural hedging opportunities. Furthermore, larger policyholder pools generate diversification benefits that reduce equilibrium premiums and stimulate insurance demand. Full article

Chromatin architecture is highly dynamic, undergoing nanoscale rearrangements throughout the cell cycle and in response to environmental cues. In this study, we employed high-resolution stochastic optical reconstruction microscopy (STORM) to visualize chromatin organization and cellular plasticity at the nanoscale in two osteosarcoma cell lines, U2OS and MG63. To promote a tumor-suppressive bone microenvironment, we applied three biophysical modalities, namely mechanical vibration, electrical stimulation, and optical pulses, each previously linked to altered tumor behavior by reprogramming cells and generating induced tumor-suppressing (iTS) cells. These stimuli enlarged nuclear size and disrupted nuclear envelope integrity, as revealed by increased surface roughness. Critically, all three modalities transiently scattered nucleosome clusters, indicating chromatin decondensation as a hallmark of iTS cell generation. iTS cells exhibited elevated expression of histone demethylases lysine demethylase 3A (KDM3A) and lysine demethylase 4 (KDM4), accompanied by reduced levels of trimethylated histone H3 lysine 9 (H3K9me3). Consistently, pharmacological agents—Trichostatin A as a histone deacetylase inhibitor and chaetocin as a histone methyltransferase inhibitor—induced nucleosome scattering and converted U2OS cells into iTS cells, whose conditioned media exerted tumor-suppressive effects. Our findings highlight nucleosome clustering as a key epigenetic feature responsive to both biophysical and chemical cues, underscoring its role in microscale chromatin remodeling and reprogramming of the tumor microenvironment. Full article

Aperiodic pseudo-random impulse (APPI) trains represent deterministic yet reproducible sequences that mimic the irregularity of natural processes. They allow complete control over inter-spike intervals (ISIs) and pulse widths (PWs). Such signals are increasingly relevant for low-probability-of-intercept (LPI) communications, radar testing, and biomedical applications, where controlled variability mitigates adaptation and enhances stimulation efficiency. This paper presents a modular APPI generator implemented on an Arduino Mega platform, featuring programmable statistical models for ISI (exponential distribution) and PW (uniform distribution), dual-timing mechanisms (baseline loop and Timer/ISR, clear-timer on compare (CTC)), a real-time telemetry and software interface, and a safe output chain with opto-isolation and current limitation. The generator provides both reproducibility and tunable stochastic dynamics. Experimental validation includes jitter analysis, Kolmogorov–Smirnov tests, Q–Q plots, spectral and autocorrelation analysis, and load integration using a constant-current source with compliance margins. The results demonstrate that the Timer/ISR (CTC) implementation achieves significantly reduced jitter compared to the baseline loop, while maintaining the statistical fidelity of ISI and PW distributions, broad spectral characteristics, and fast decorrelation. Experimental verification was extended across a wider parameter space (λ = 0.1–100 Hz, PW = 10 µs–100 ms, 10 repetitions per condition), confirming robustness and repeatability. Experimental validation confirmed accurate Poisson/Uniform ISI generation, sub-millisecond jitter stability in the timer-controlled mode, robustness across λ = 0.1–100 Hz and PW = 10 µs–100 ms, and preliminary compliance with isolation and leakage limits. The accompanying Python GUI provides real-time control, telemetry, and data-logging capabilities. This work establishes a reproducible, low-cost, and open-source framework for APPI generation, with direct applicability in laboratory and field environments. Full article

The rising prevalence of neuroimmune disorders such as multiple sclerosis and fibromyalgia has renewed interest in the hygiene hypothesis, which posits that reduced early-life microbial exposure deprives the immune system of formative “noise” that calibrates thresholds of tolerance. We extended this framework by introducing stochastic resonance (SR), a system phenomenon in which optimally tuned noise enhances weak-signal detection in nonlinear networks, as a potential surrogate for missing microbial variability. As electrical noise and subthreshold stimulation have been shown to modulate cortical excitability and enhance perception, microbial noise may be necessary for sustaining immune plasticity. Conversely, a lack of stimulation, whether microbial or electrical, can lead to maladaptive states characterized by dysregulated signaling and heightened vulnerability to chronic inflammation. Evidence from immunology highlights noise-aware processes, such as T-cell receptor proofreading, NF-κB pulsatility, and cytokine quorum sensing, all of which exploit stochastic fluctuations. Computational tumor–immune models similarly suggest that tuned noise can optimize immune surveillance. Clinical data from neuroscience demonstrate that subsensory electrical noise improves motor excitability and sensory perception, whereas vagus nerve stimulation modulates inflammatory pathways, underscoring translational feasibility. We propose that SR reframes noise from a biological error to a therapeutic resource capable of recalibrating dysregulated neuroimmune thresholds. This conceptual synthesis positions microbial and electrical noise as parallel modulators of tolerance and outlines testable predictions with translational potential for neuroimmune disorders. Full article

Background: Early detection of dysglycemia in young adults is important but underexplored. This study aimed to (1) predict long-term changes in fasting plasma glucose (δ-FPG) and (2) classify future prediabetes using complementary machine learning (ML) approaches. Methods: We analyzed 6247 Taiwanese men aged 18–35 years (mean follow-up 5.9 years). For δ-FPG (continuous outcome), random forest, stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and elastic net were compared with multiple linear regression using Symmetric mean absolute percentage error (SMAPE), Root mean squared error (RMSE), Relative absolute error(RAE), and Root relative squared error (RRSE) Sensitivity analyses excluded baseline FPG (FPG_base). Shapley additive explanations(SHAP) values provided interpretability, and stability was assessed across 10 repeated train–test cycles with confidence intervals. For prediabetes (binary outcome), an XGBoost classifier was trained on top predictors, with class imbalance corrected by SMOTE-Tomek. Calibration and decision-curve analysis (DCA) were also performed. Results: ML models consistently outperformed regression on all error metrics. FPG_base was the dominant predictor in full models (100% importance). Without FPG_base, key predictors included body fat, white blood cell count, age, thyroid-stimulating hormone, triglycerides, and low-density lipoprotein cholesterol. The prediabetes classifier achieved accuracy 0.788, precision 0.791, sensitivity 0.995, ROC-AUC 0.667, and PR-AUC 0.873. At a high-sensitivity threshold (0.2892), sensitivity reached 99.53% (specificity 47.46%); at a balanced threshold (0.5683), sensitivity was 88.69% and specificity was 90.61%. Calibration was acceptable (Brier 0.1754), and DCA indicated clinical utility. Conclusions: FPG_base is the strongest predictor of glycemic change, but adiposity, inflammation, thyroid status, and lipids remain informative. A dual interpretable ML framework offers clinically actionable tools for screening and risk stratification in young men. Full article

The ongoing exploration of brain–computer interfaces (BCIs) provides deeper insights into the workings of the human brain. Motor imagery (MI) tasks, such as imagining movements of the tongue, left and right hands, or feet, can be identified through the analysis of electroencephalography (EEG) signals. The development of BCI systems opens up opportunities for their application in assistive devices, neurorehabilitation, and brain stimulation and brain feedback technologies, potentially helping patients to regain the ability to eat and drink without external help, move, or even speak. In this context, the accurate recognition and deciphering of a patient’s imagined intentions is critical for the development of effective BCI systems. Therefore, to distinguish motor tasks in a manner differing from the commonly used methods in this context, we propose a fractal dimension (FD)-based approach, which effectively captures the self-similarity and complexity of EEG signals. For this purpose, all four classes provided in the BCI Competition IV 2a dataset are utilized with nine different combinations of seven FD methods: Katz, Petrosian, Higuchi, box-counting, MFDFA, DFA, and correlation dimension. The resulting features are then used to train five machine learning models: linear, Gaussian, polynomial support vector machine, regression tree, and stochastic gradient descent. As a result, the proposed method obtained top-tier results, achieving 79.2% accuracy when using the Katz vs. box-counting vs. correlation dimension FD combination (KFD vs. BCFD vs. CDFD) classified by LinearSVM, thus outperforming the state-of-the-art TWSB method (achieving 79.1% accuracy). These results demonstrate that fractal dimension features can be applied to achieve higher classification accuracy for online/offline MI-BCIs, when compared to traditional methods. The application of these findings is expected to facilitate the enhancement of motor imagery brain–computer interface systems, which is a key issue faced by neuroscientists. Full article

We provide a summary of the research conducted in our laboratory on the relationship between ocular proprioception, general proprioception, and dyslexia. Dyslexic children show a marked proprioceptive deficit which affects motor control, attention and spatial perception. The spatial disturbances are expressed by the presence of a vertical microheterophoria which has very specific characteristics. It is associated with abnormal tone of the oblique muscles and can be modified by means of very low powered prisms and/or remote sensory stimulation. When ocular proprioception is modified, sounds cause stochastic visual losses. This may interfere with the association between phonemes and graphemes, which is necessary for learning to read. The effects of a generalized nocturnal proprioceptive disorder may play a role in the abnormal brain development that has been observed in dyslexic children. Full article

Background: The cerebral microvasculature forms a dense network of interconnected blood vessels where flow is modulated partly by astrocytes. Increased neuronal activity stimulates astrocytes to release vasoactive substances at the endfeet, altering the diameters of connected vessels. Methods: Our study simulated the coupling between blood flow variations and vessel diameter changes driven by astrocytic activity in the rat somatosensory cortex. We developed a framework with three key components: coupling between the vasculature and synthesized astrocytic morphologies, a fluid dynamics model to compute flow in each vascular segment, and a stochastic process replicating the effect of astrocytic endfeet on vessel radii. Results: The model was validated against experimental flow values from the literature across cortical depths. We found that local vasodilation from astrocyte activity increased blood flow, especially in capillaries, exhibiting a layer-specific response in deeper cortical layers. Additionally, the highest blood flow variability occurred in capillaries, emphasizing their role in cerebral perfusion regulation. We discovered that astrocytic activity impacted blood flow dynamics in a localized, clustered manner, with most vascular segments influenced by two to three neighboring endfeet. Conclusions: These insights enhance our understanding of neurovascular coupling and guide future research on blood flow-related diseases. Full article

Synchronization is a prevalent phenomenon in biological systems, including social insects such as ants. Certain ant species exhibit remarkable synchronization of their activities within the nest. To elucidate the underlying mechanisms of this coordinated behavior, we propose an integro-differential equation model that captures the autocatalytic nature of ant activation. Active ants can stimulate inactive individuals, leading to a cascade of arousal. By incorporating a stochastic component to represent variability in rest periods, we explore the conditions necessary for synchronization. Our analysis reveals a critical threshold for fluctuations in rest duration. Exceeding this threshold disrupts synchronization, driving the system towards a stable equilibrium. These findings offer valuable insights into the factors governing ant activity synchronization and highlight the delicate balance between model parameters required to generate rhythmic patterns. Full article

Mutations in the ryanodine receptor (RyR2) gene have been linked to arrhythmia and possibly sudden cardiac death (SCD) during acute emotional stress, physical activities, or catecholamine perfusion. The most prevalent disorder is catecholaminergic polymorphic ventricular tachycardia (CPVT1). Four primary mechanisms have been proposed to describe CPVT1 with a RyR2 mutation: (a) gain-of-function, (b) destabilization of binding proteins, (c) store-overload-induced Ca²⁺ release (SOICR), and (d) loss of function. The goal of this study was to use computational models to understand these four mechanisms and how they might contribute to arrhythmia. To this end, we have developed a local control stochastic model of a ventricular cardiac myocyte and used it to investigate how the Ca²⁺ dynamics in the mutant RyR2 are responsible for the development of an arrhythmogenic episode under the condition of β-adrenergic (β-AR) stimulation or pauses afterward. Into the model, we have incorporated 20,000 distinct cardiac dyads consisting of stochastically gated L-type Ca²⁺ channels (LCCs) and ryanodine receptors (RyR2s) and the intervening dyadic cleft to analyze the alterations in Ca²⁺ dynamics. Recent experimental findings were incorporated into the model parameters to test these proposed mechanisms and their role in triggering arrhythmias. The model could not find any connection between SOICR and the destabilization of binding proteins as the arrhythmic mechanisms in the mutant myocyte. On the other hand, the model was able to observe loss-of-function and gain-of-function mutations resulting in EADs (Early Afterdepolarizations) and variations in action potential amplitudes and durations as the precursors to generate arrhythmia, respectively. These computational studies demonstrate how GOF and LOF mutations can lead to arrhythmia and cast doubt on the feasibility of SOICR as a mechanism of arrhythmia. Full article

Accurate modelling of occupancy patterns is critical for reliable estimation of building stock energy demand, which is a key input for the design of district energy systems. Aiming to investigate the suitability of different occupancy-modelling approaches for the design of district energy systems, the present study examines a set of standard-based schedules (from the UK National Calculation Methodology), a widely used stochastic occupancy model, and a novel energy-data-driven occupancy model. To this end, a dynamic energy model of a higher education office building developed within a stock model of London’s Bloomsbury district serves as a testbed to implement the occupancy models, explore their implications for the estimation of annual and peak heating and cooling demand, and extrapolate the findings to the computationally demanding building stock stimulations. Furthermore, the simulations were conducted in two years before and after the COVID-19 pandemic to examine the implications of hybrid working patterns after the pandemic. From the results, the energy-data-driven model demonstrated superior performance in annual heating demand estimations, with errors of ±2.5% compared to 14% and 7% for the standard-based and stochastic models. For peak heating demand, the models performed rather similarly, with the data-driven model showing 28% error compared to 29.5% for both the standard-based and stochastic models in 2019. In cooling demand estimations, the data-driven model yielded noticeably higher annual cooling demand and lower peak cooling demand estimations as compared with the standard-based and stochastic occupancy models. Given the adopted building-modelling approach, these findings can be extended to district-level investigations and inform the decision on the choice of occupancy models for building stock energy simulation. Full article

TheraBracelet is a peripheral vibrotactile stimulation applied to affected upper extremities via a wristwatch-like wearable device during daily activities and therapy to improve upper limb function. The objective of this study was to examine the feasibility of using TheraBracelet for a child with hemiplegic cerebral palsy. Methods: A nine-year-old male with cerebral palsy was provided with TheraBracelet to use during daily activities in the home and community settings for 1.5 years while receiving standard care physical/occupational therapy. Results: The child used TheraBracelet independently and consistently, except during summer vacations and elbow-to-wrist orthotic use from growth spurt-related contracture. The use of TheraBracelet did not impede or prevent participation in daily activities. No study-related adverse events were reported by the therapist, child, or parent. Future research is warranted to investigate TheraBracelet as a propitious therapeutic device with a focus on the potential impact of use to improve the affected upper limb function in daily activities in children with hemiplegic cerebral palsy. Full article