Biology subjects

Tozzi, A.

Publications and source records attributed to Tozzi, A..

At least 19 recordsLinked to original sources

bioRxiv2025

SIMULATING TUMOR MITOCHONDRIAL ENERGETICS THROUGH ENGINEERING-STYLE ENERGY METRICS

Cancer progression is linked to alterations in cellular energetics, where malignant cells reprogram their metabolism to sustain proliferation, resist stress and adapt to nutrient limitations. Beyond intrinsic adaptations and plastic dynamic switches between glycolysis and oxidative phosphorylation, recent work has shown that tumors actively remodel their microenvironment by acquiring functional mitochondria from surrounding stromal or immune cells. This process of mitochondrial transfer enhances tumor bioenergetics while simultaneously depleting immune cells of metabolic competence, thereby reinforcing both tumor growth and immune evasion. Despite the role of these mitochondrial exchanges, quantitative frameworks to measure their energetic consequences remain underdeveloped. Conventional assays describe oxygen consumption or glycolytic flux but lack the resolution to capture mitochondrial function in terms of throughput, efficiency and stored energy. To address this limitation, we propose a simulation-based framework that translates engineering-style energy metrics into mitochondrial biology. Specifically, we calculate three parameters that may capture distinct yet complementary aspects of mitochondrial bioenergetics: power density, defined as ATP production per unit mitochondrial volume; surface power density, reflecting ATP production per unit inner membrane area; and energy density, quantifying stored chemical free energy per unit volume. By simulating tumor and immune cell populations before and after mitochondrial transfer, we generate numerical values for each parameter that demonstrate how transfer enhances tumor energetics while diminishing immune function. This provisional quantification establishes a pathway toward standardized bioenergetic biomarkers in cancer, potentially enhancing diagnostic methods and supporting the development of strategies aimed at disrupting pathogenic mitochondrial transfer while restoring metabolic competence to immune cells.

cell biology↗

bioRxiv2025

Born to Scratch: Towards Cutaneous Microtrauma in Newborns as a Driver of Immune Imprinting

Neonatal self-scratching, often considered a harmless mechanical reflex, may play a more influential role in shaping early immune system development than previously recognized. We hypothesize that superficial cutaneous microtrauma caused by healthy newborn scratching may contribute to immune imprinting through localized inflammatory responses and antigen exposure. We developed a time-resolved stochastic model to simulate antigen encounter, microbial presence and antigen capture dynamics in the context of superficial skin injury, to evaluate the likelihood of immune activation resulting from epidermal disruption. By varying parameters such as injury depth, microbial density and antigen presentation probability, we quantified their respective impacts on cumulative immune priming outcomes across simulated conditions. Simulations show that even shallow skin injuries, when combined with microbial ingress and effective antigen presentation, can exceed activation thresholds necessary for initiating early immune priming. Results indicate that a cascade of innate immune events, including keratinocyte activation, cytokine release and engagement of antigen-presenting cells, could be initiated by the minor skin abrasions commonly observed during neonatal scratching. This model supports a plausible mechanism through which seemingly minor mechanical injuries may contribute to early immune training. By treating the skin not merely as a passive barrier but as an active immunological interface, our approach recontextualizes common neonatal behaviours within a meaningful immunological framework. If validated, our hypothesis suggests that controlled microtrauma or targeted skin exposure in newborns could serve as a strategy to support immune development in a safe manner. Still, population-level modeling may allow translation from individual simulations to public health insights.

immunology↗

bioRxiv2025

Spatial Sorting of Soft and Stiff Bacteria in the Human Colon

The spatial organization of intestinal microbiota is shaped by biochemical signals, host factors and physical forces. While chemical gradients and host immunity are well studied, the influence of mechanical properties is underexplored. Drawing on soft matter physics and microbiome research, we combined atomic force microscopy data with gut-like flow simulations to investigate whether bacterial stiffness influences spatial organization and mechanical/hydrodynamic responses. Indeed, colonic bacteria differ markedly in stiffness: soft species like Bacteroides fragilis, Escherichia coli and Akkermansia muciniphila display thin peptidoglycan layers and flexible outer membranes, while stiff species like Clostridium difficile, Lactobacillus rhamnosus, Enterococcus faecalis and Faecalibacterium prausnitzii display greater rigidity linked to thick Gram-positive cell walls. Our simulations showed that mechanical compliance can drive spatial partitioning under laminar shear, with soft bacteria localizing near the mucosal surfaces and stiff bacteria aligning along central flow paths. Further, soft bacteria aligned more quickly with shear flow, migrated more efficiently toward the lumen center, dissipated more energy during collisions and detached more readily from mucosal surfaces. Soft bacteria also formed slower, more diffuse biofilms and exhibited greater displacement under peristaltic compression. This suggests that stiffness, independent of chemical signalling, may shape microbial localization, interaction, organization and persistence in gut-like environments. Stiffness may serve as non-chemical, precision tool for microbiome modulation, paving the way for targeted, mechanically informed interventions. This approach could enable applications like stiffness-guided drug delivery to specific gut regions, selective removal of stiff pathogenic bacteria using mechanical cues and mechanical pre-sorting of microbial populations for diagnostic and therapeutic purposes.

biophysics↗

bioRxiv2025

Neuronal Hyperexcitability: A Key to Unravelling Hippocampal Synaptic Dysfunctions in Lafora Disease

Lafora disease (LD) is a rare progressive disorder caused by mutations in the EPM2A or EPM2B genes, characterized by the accumulation of Lafora bodies, drug-resistant epilepsy, and cognitive decline. To investigate the early molecular mechanisms of LD, we studied electrophysiological changes in the dentate gyrus (DG) of the Epm2aR240X knock-in mouse model at various ages. Electrophysiological recordings measured neuronal membrane properties, epileptic-like activity, epileptic thresholds, and synaptic plasticity in Epm2aR240X mice at 1, 3, and 12 months. We also employed PAS diastase staining, immunofluorescence, and Western blotting to detect Lafora bodies, amyloid beta deposition, and glutamate receptor subunit expression. Epileptic-like activity began at 1 month and intensified with age. Aberrant long-term potentiation (LTP) appeared at 3 months and worsened by 12 months. Notably, cannabidiol (CBD) treatment reduced excitability and restored LTP in older mice, suggesting its potential therapeutic value. These findings indicate that network hyperexcitability is an early event in LD, highlighting a therapeutic window for interventions like CBD.

neuroscience↗

bioRxiv2025

Applications of Wedderburn's Theorem in Modelling Non-Commutative Biological and Evolutionary Systems

A wide range of biological and evolutionary processes is determined not merely by the occurrence of specific events, but by the exact order in which those events unfold. Gene regulation, developmental pathways, metabolic cascades and genetic evolution often display non-commutative behaviour, in which reversing the sequence of events results in functionally distinct outcomes. Conventional modelling approaches often fail to account for such directionality and sequence dependence, thereby limiting their capacity to capture the complexity of regulatory logic. We represent ordered sequences of biological operations--such as transcription factor binding or mutational trajectories--as elements of a non-commutative algebra designed to encode the functional logic of systems where the order of events is critically determinant. Subsequently, we apply Wedderburns Theorem to decompose the algebra into a direct sum of matrix modules, each capturing an irreducible and functionally distinct component of the underlying sequence-dependent system. We provide examples from gene expression regulation and evolutionary dynamics, focusing on scenarios where trait development is determined by the specific order of underlying molecular or mutational events. Our results demonstrate that the algebraic framework effectively maps intricate biological processes onto smaller, linear components, facilitating clearer interpretation and analysis. Simplifying non-commutative biological systems into interpretable submodules, Wedderburn-style decomposition may clarify gene regulatory logic, capture behavioural outcomes, reduce computational burden and uncover pathway redundancies and structural symmetries. Overall, by unifying diverse biological processes within a coherent algebraic structure, our method may improve the tractability of complex, order-dependent systems.

evolutionary biology↗

bioRxiv2025

A Multi-Valued Logic Model of T Cell Activation and Cytokine Response

T cell activation results from the integration of multiple concurrent signals, including antigen engagement through the T cell receptor (TCR), co-stimulatory cues and inhibitory modulation. While traditional computational models often rely on Boolean logic to describe immune decision-making, these binary representations oversimplify the nuanced, reversible and gradated nature of actual immune responses. To address this limitation, we present a multi-valued logic framework that models T cell activation using three discrete levels--low, intermediate, and high--for each regulatory input. Focusing on cytokine production as a functional readout, our model uses threshold-based logic rules to compute output levels based on combinations of TCR, co-stimulatory and inhibitory signals. We analyzed the system through both deterministic state-space exploration and stochastic asynchronous simulations, capturing both static outcomes and dynamic trajectories. Results reveal that cytokine output scales proportionally with activating inputs, while inhibitory signals modulate these responses in a graded, context-sensitive manner. The model avoids bi-stability or irreversible attractors, instead reflecting the flexible and reversible behavior observed in biological systems. Comparative analysis with experimental data from literature supports key assumptions of the model, including dose-dependent cytokine production and the enhancing effect of co-stimulation. In contrast to purely Boolean or continuous models, our multi-valued approach strikes a balance between interpretability and computational efficiency. It provides a flexible and tractable framework for representing immune logic and is well-suited for integration into larger, systems-level models of immune function.

immunology↗

bioRxiv2025

Prebiotic Resource Constraints and the Origin of Life: A Linear Logic Framework

The origin of life is a complex scientific problem demanding interdisciplinary approaches. We propose a Linear Logic (LL)-based computational framework to formally evaluate the feasibility of early biochemical pathways across competing abiogenesis scenarios. Unlike classical logic, LL explicitly tracks resource consumption and transformation. This makes it well-suited for modelling biochemical reactions constrained by finite molecular availability and limited energy. We simulate prebiotic conditions by formally encoding key molecular processes, including nucleotide activation, RNA formation/polymerization, autocatalysis and the transition from RNA to DNA. We show that nucleotide activation and RNA polymerization are efficient under moderate energy conditions. Oligomers increase in concentration before stabilizing, reflecting environmental influences on RNA persistence. Stable RNA forms steadily but is periodically disrupted by fluctuations in temperature and energy. Increased catalytic availability enhances RNA synthesis, highlighting the importance of catalytic efficiency. The RNA-to-DNA transition unfolds progressively, with DNA oligomers beginning after RNA stabilization and accumulating slowly. Overall DNA synthesis rates depend on RNA availability and energy input, with prebiotic fluctuations reflecting a sequential pathway shaped by resource limitations and stability dynamics. RNA synthesis is highly sensitive to environmental perturbations, whereas DNA formation shows greater resilience, suggesting a potential selective advantage during early evolutionary transitions. Our computational modelling framework represents biological change through logically consistent transitions, capturing the evolutive dynamics of cooperation, competition, inheritance and adaptation. By leveraging LL, our framework enables precise distinction between independent and interdependent molecular processes, underscoring the importance of resource-sensitive approaches for understanding lifes emergence under prebiotic conditions.

evolutionary biology↗

bioRxiv2025

Topological Transformations in Hand Posture: A Biomechanical Strategy for Mitigating Raynaud's Phenomenon Symptoms

Raynauds Phenomenon (RP), characterized by episodic reductions in peripheral blood flow, leads to significant discomfort and functional impairment. Existing therapeutic strategies focus on pharmacological treatments, external heat supplementation and exercise-based rehabilitation, but fail to address biomechanical contributions to vascular dysfunction. We introduce a computational approach rooted in topological transformations of hand prehension, hypothesizing that specific hand postures can generate transient geometric structures that enhance thermal and hemodynamic properties. We examine whether a flexed hand posture--where fingers are brought together to form a closed-loop toroidal shape-- may modify heat transfer patterns and blood microcirculation. Using a combination of heat diffusion equations, fluid dynamics models and topological transformations, we implement a heat transfer and blood flow simulation to examine the differential thermodynamic behavior of the open and closed hand postures. We show that the closed-hand posture may preserve significantly more heat than the open-hand posture, reducing temperature loss by an average of 1.1 {+/-} 0.3{degrees}C compared to 3.2 {+/-} 0.5{degrees}C in the open-hand condition (p < 0.01). Microvascular circulation is also enhanced, with a 53% increase in blood flow in the closed-hand configuration (p < 0.01). Therefore, our findings support the hypothesis that maintaining a closed-hand posture may help mitigate RP symptoms by preserving warmth, reducing cold-induced vasoconstriction and optimizing peripheral flow. Overall, our topologically framed approach provides quantitative evidence that postural modifications may influence peripheral vascular function through biomechanical and thermodynamic mechanisms, elucidating how shape-induced transformations may affect physiological and pathological dynamics.

biophysics↗

bioRxiv2025

Optimal Transport Model for Gas Migration in Pneumatosis Intestinalis

Optimal transport models may provide a robust framework for quantifying gas movement from the intestinal lumen into the gut layers in pneumatosis intestinalis (PI), a condition characterized by abnormal gas accumulation within the intestinal wall. We develop a mathematical approach that integrates circular gas diffusion from the lumen with the subsequent dispersion and entrapment of microbubbles in deeper layers. Our diffusion-advection approach establishes a radial concentration gradient that decreases with distance from the lumen. Numerical simulations illustrate the spatial distribution of gas and microbubbles within a representative three-millimeter-thick gut wall, demonstrating a transition from uniform diffusion to localized gas retention. Gas migration initially follows diffusion-driven transport but increasingly becomes influenced by local trapping mechanisms, leading to spatially heterogeneous gas retention. Microbubble formation is modeled as a function of gas entrapment in deeper tissue layers, integrating mechanical effects related to gas concentration and pressure dynamics. Additionally, our model accounts for critical biological and structural factors such as tissue permeability, mucosal integrity, bacterial fermentation, microbial overgrowth and structural heterogeneity, capturing their combined influence on gas retention. Overall, gas migration in PI follows an early-stage circular diffusion pattern, until it encounters structural resistance leading to deep microbubble formation. The density and depth of localized microbubble retention show a statistically significant correlation with gas pressure gradients and permeability variations, highlighting the interplay between transport dynamics and tissue features. Our quantitative representation provides a structured approach for refining diagnostic assessments of PI and improving the evaluation of disease severity based on underlying transport mechanisms.

biophysics↗

bioRxiv2025

Fractional and Geometric Neural Dynamics: Investigating Intelligence-Related Differences in EEG Symmetry and Connectivity

Understanding intelligence-related variations in electroencephalographic (EEG) activity requires advanced mathematical approaches capable of capturing geometric transformations and long-range dependencies in neural dynamics. These approaches may provide methodological advantages over conventional spectral and connectivity-based techniques by offering deeper insights into the structural and functional organization of neural networks. In this study, we integrate Clifford algebra, Noethers theorem and fractional calculus to analyze EEG signals from high- and low-IQ individuals, looking for key intelligence-related differences in cortical organization. Clifford algebra enables the representation of EEG signals as multivectors, preserving both magnitude and directional relationships across cortical regions. Noethers theorem provides a quantitative measure of symmetry properties linked to spectral features, identifying conserved functional patterns across distinct brain regions. Mittag-Leffler functions, derived from fractional calculus, characterize long-range dependencies in neural oscillations, allowing for the detection of memory effects and scale-invariant properties often overlooked by traditional methods. We found significant differences between high- and low-IQ individuals in geometric trajectories, hemispheric connectivity, spectral properties and fractional-order dynamics. High-IQ individuals exhibited increased spectral asymmetry, enhanced spectral differentiation, distinct geometric trajectories and greater fractional connectivity, particularly in frontal and central regions. In contrast, low-IQ individuals displayed more uniform hemispheric connectivity and heightened fractional activity in occipital areas. Mittag-Leffler fractional exponents further indicated that high-IQ individuals possessed more varied neural synchronization patterns. Overall, our multi-faceted approach suggests that intelligence-related neural dynamics are characterized by an asymmetric, functionally specialized and fractionally complex cortical organization. This results in significant differences in network topology, efficiency, modularity and long-range dependencies.

neuroscience↗

bioRxiv2025

Topological Analysis of Vascular Networks: A Proof-of-Concept Study in Cerebral Angiography

The application of topological methods to cerebral angiography may provide a robust mathematical framework for analyzing cerebrovascular structures at multiple scales. In this proof-of concept study, we explored the use of algebraic and differential topology to characterize structural integrity, connectivity, flow dynamics and hierarchical organization of cerebral vascular networks. Through a hierarchical approach, we examined the topology from general to local, capturing macroscopic vascular organization down to individual vessel bifurcations. By leveraging key theorems, we assessed various aspects of topological analysis, including evaluation of total features, transition from total to local features, evaluation of local features, transition from local to total features, interaction between total and local features. These steps enable the analysis of the global connectivity of the vascular network, the detection of regional clusters and the identification of critical junctions at a local scale. A computational approach was developed to extract mathematical skeletons from angiographic images, constructing graph-based representations to study connectivity and homotopy equivalence. The Fourier decomposition of the vascular structures revealed dominant periodic patterns, indicative of structural stability and redundancy in the blood supply. Moreover, Betti number computations quantified vascular loops and branches, offering insights into collateral circulation potential. Our findings demonstrate that topological invariants can serve as diagnostic biomarkers for cerebrovascular diseases, including aneurysm susceptibility and ischemic risk assessment. This interdisciplinary methodology bridges mathematical topology with medical imaging, offering a novel lens for cerebrovascular analysis. Future work will integrate persistent homology and machine learning techniques for automated vascular topology classification.

bioinformatics↗

bioRxiv2025

Local Topological Structures and Global Connectivity Patterns in a Trachelospermum jasminoides: a Pilot Study

Topological approaches to biological systems provide insights into their growth patterns, network connectivity and spatial organization. This perspective explores how biological structures self-organize, maintain stability and adapt to environmental constraints, revealing fundamental principles of efficiency, robustness, resilience and functional optimization. In this pilot study, we analysed the local and global topological properties of a Trachelospermum jasminoides bush (commonly known as star jasmine) using persistent homology, graph theory, spectral analysis and percolation theory. The spatial positions of individual flowers were extracted from an image of the bush and represented as a point cloud to capture their structural distribution and spatial relationships. Using Delaunay triangulation, a connectivity graph revealed a dominant connected component with minimal isolated structures. DBSCAN analysis identified a large number of small, localized clusters, reflecting biological and environmental influences. Most flowers connected to five to six neighbours, forming a uniform network with high clustering. Shortest path analysis showed efficient long-range connectivity, with paths avoiding sparse regions. Spectral analysis indicated smooth percolation without bottlenecks, while percolation analysis simulations revealed resilience up to 18% flower removal, after which connectivity broke down. In sum, we showed that the star jasmine bush topology balances local structural constraints with global connectivity, ensuring efficient resource distribution and structural integrity. By integrating topological data analysis with ecological modeling, we suggested a methodological approach to understanding natural growth networks. These insights can inform broader studies on biological pattern formation, network resilience and plant architecture modeling in ecology, agricultural sciences and biomimetic design.

bioinformatics↗

bioRxiv2025

Topological Analysis of Macaca mulatta's Cortical Structures Through the Lens of Poincare Duality

Poincare duality from algebraic topology describes how shapes and spaces interrelate across different dimensions, linking structures of one scale to complementary structures at another. This suggests that densely packed neurons in certain brain areas may correspond to broader connectivity patterns, balancing local processing with global communication. Applying this framework to cortical histological images of Macaca mulatta, we analysed neuronal clustering, connectivity graphs and intensity distributions to identify self-dual structural patterns and reveal the contribution of local organization to large-scale network activity. Using image processing techniques such as contrast enhancement, edge detection and graph-theoretic modeling, we examined how dense neuron clusters correspond to functionally sparse regions and vice versa. We found that high-density neuronal zones form closed-loop topological structures that correspond to homology cycles, while sparser areas function as large-scale integrators, aligning with cohomology properties. Local connectivity hubs in neuron-dense regions support regional specialization, while large-scale, sparser areas, though less connected, facilitate global communication by acting as pathways for long-range integration. Graph-theoretic analysis of connectivity patterns confirmed a reciprocal relationship between clustering coefficients and global centrality. Statistical analysis using Kolmogorov-Smirnov tests revealed conserved topological distributions across different cortical regions, supporting the hypothesis that cortical connectivity maintains structural invariance under local perturbations. These findings provide insights into the mathematical principles governing brain architecture, suggesting that topological methods can enhance our understanding of cortical function. Future research may extend these approaches to higher-dimensional embeddings, network theory in primate brains, functional neuroimaging, human disease modeling and artificial intelligence.

neuroscience↗

bioRxiv2025

Levy Flight Patterns in the Cortical Architecture of Macaca mulatta

Levy flights (LF), a concept originating in statistical physics, describe random walks in which the step lengths follow a heavy-tailed probability distribution, often a power law. Unlike Brownian motion, where step lengths are constrained within a narrow range, LF are characterized by the coexistence of many short steps interspersed with occasional long jumps. Applying advanced computational techniques, we looked for LF-like patterns in high-resolution histological images of Macaca mulatta (Rhesus macaque) cortical area 4 from BrainMaps.org. Step-length distributions, derived from pairwise distances between neuronal somata, exhibited heavy-tailed behavior consistent with power-law models across all samples. Maximum likelihood estimation of power-law exponents ( values: 0.87-1.08) strongly supported the heavy-tailed nature of these patterns, showing a better fit with power-law models compared to exponential or normal distributions. Connectivity analyses revealed a dual organizational structure within cortical layers: densely interconnected local clusters coexisting with sparse long-range connections. k-Nearest neighbors graphs demonstrated small-world network properties, with average clustering coefficients ranging from 0.622 to 0.630 across samples. This consistent structural organization aligns with LF principles, wherein local processing is optimized alongside global integration for efficiency and functionality. The implications extend to developmental biology, as the emergence of LF-like patterns likely reflects intrinsic self-organizing processes during embryonic and fetal development. This LF-like organization provides a natural framework for designing artificial networks that optimize performance in tasks requiring both localized specialization and global integration. Moreover, understanding the developmental origins of these patterns could guide strategies for neural repair and regeneration in stroke or neurodegenerative diseases.

neuroscience↗

bioRxiv2025

Nonlinear locomotion patterns in the Egyptian Locust (Anacridium aegyptium) during walking: a detailed case study

We explored the nonlinear movement patterns of Anacridium aegyptium during terrestrial locomotion, providing insights into the walking dynamics of this large grasshopper species. Using video recordings, we analysed the trajectory of an insect and quantified key metrics, including curvature, tortuosity and fractal dimension. Curvature analysis revealed irregular turning behaviors with sharp directional changes, suggesting that locomotion was not random but deliberate. Compared with simulated linear trajectories, the curvature exhibited distinct peaks, highlighting the presence of statistically significant nonlinear features in the movement patterns. Phase space reconstruction revealed repetitive patterns indicating the potential presence of a limit cycle attractor. The trajectory remained confined within a specific region of the phase space, highlighting structured dynamics rather than unbounded behaviour. Fractal dimension analysis and Lyapunov exponent were consistent with a stable and predictable system over time, rather than one governed by chaos. These findings align with the behavioral ecology of A. aegyptium, suggesting that its walking dynamics are governed by efficient spatial exploration and obstacle negotiation rather than erratic or chaotic motion. Our study underscores the value of advanced mathematical and computational methods in boosting behavioural studies of locomotion. The insights derived from our analysis enhance our understanding of insect locomotion strategies and hold potential applications in the field of biomimetic robotics, where adaptive and efficient movement is mandatory. Future research could explore the impact of environmental factors, such as substrate type and food availability, on the observed nonlinear patterns, providing deeper context to the intricate locomotion behaviour of Anacridium aegyptium.

zoology↗

bioRxiv2025

Takens' theorem to assess EEG traces: regional variations in brain dynamics

Takens theorem (TT) proves that the behaviour of a dynamical system can be effectively reconstructed within a multidimensional phase space. This offers a comprehensive framework for examining temporal dependencies, dimensional complexity and predictability of time series data. We applied TT to investigate the physiological regional differences in EEG brain dynamics of healthy subjects, focusing on three key channels: FP1 (frontal region), C3 (sensorimotor region), and O1 (occipital region). We provided a detailed reconstruction of phase spaces for each EEG channel using time-delay embedding. The reconstructed trajectories were quantified through measures of trajectory spread and average distance, offering insights into the temporal structure of brain activity that traditional linear methods struggle to capture. Variability and complexity were found to differ across the three regions, revealing notable regional variations. FP1 trajectories exhibited broader spreads, reflecting the dynamic complexity of frontal brain activity associated with higher cognitive functions. C3, involved in sensorimotor integration, displayed moderate variability, reflecting its functional role in coordinating sensory inputs and motor outputs. O1, responsible for visual processing, showed constrained and stable trajectories, consistent with repetitive and structured visual dynamics. These findings align with the functional specialization of different cortical areas, suggesting that the frontal, sensorimotor and occipital regions operate with autonomous temporal structures and nonlinear properties. This distinction may have significant implications for advancing our understanding of normal brain function and enhancing the development of brain-computer interfaces. In sum, we demonstrated the utility of TT in revealing regional variations in EEG traces, underscoring the value of nonlinear dynamics.

neuroscience↗

bioRxiv2025

An extended Stokes' theorem for spiral paths: applications to rotational flows in Trachelospermum jasminoides stems and flowers

The traditional Stokes theorem connects the macroscopic circulation along a closed boundary to the microscopic circulation across the surface it encloses. However, it proves inadequate for addressing complex geometries such as helicoidal paths, non-planar flow patterns and dynamic systems with open boundaries. We introduce an extension of Stokes theorem (EST) that provides a robust tool for interdisciplinary research in spiral/helicoidal dynamics, facilitating the evaluation of rotational forces and circulation in both natural and engineered systems with open boundaries. We apply EST to model the rotational dynamics of flower petals and the helical forces within the stems of Trachelospermum jasminoides, known as star jasmine. For the flower, we demonstrate the equivalence between the line integral along the petal boundary and the surface integral over the enclosed disk, effectively capturing the uniform rotational stress generated by tangential forces. EST enables the analysis of external factors such as wind or pollinator interactions, while providing valuable insights to deepen our understanding of floral mechanics and petal growth patterns. For the stem, linking microscopic circulatory forces to macroscopic flow patterns, we demonstrate the interaction of torsional and bending stresses caused by the helical geometry. This finding has significant implications for understanding plant growth biomechanics and structural stability as well as for quantifying nutrient and water transport within stems, where spiral dynamics play a pivotal role. In summary, EST streamlines the analysis of rotational and translational forces in systems governed by spiral and helicoidal dynamics, including physical and biological phenomena such as phyllotaxis and plant growth.

plant biology↗

bioRxiv2025

Monte Carlo simulations predict distinct real EEG patterns in individuals with high and low IQs

The neural mechanisms underlying individual differences in intelligence are a central focus in neuroscience. We investigated the effectiveness of Monte Carlo simulations in predicting real EEG patterns and uncovering potential neural differences between individuals with high and low intelligence. EEG data were collected from two groups of volunteers categorized by IQ, namely, a high-IQ group and a low-IQ group. A univariate normal distribution was fitted to each EEG channel using Maximum Likelihood Estimation, after which synthetic datasets were generated based on the estimated parameters. Statistical analyses including Root Mean Square Error (RMSE) calculations assessed the alignment between real and simulated data. We showed that Monte Carlo simulations effectively replicated the statistical properties of the EEG data from both the groups, closely matching the real central tendencies, variability and overall distribution shapes. Specific EEG channels, particularly in the frontal and temporal bilateral regions, exhibited significant differences between the two groups, pointing to potential neural markers of cognitive abilities. Further, the low-IQ group exhibited higher predictability and more consistent neural patterns, reflected by lower RMSE values and smaller standard deviations across several EEG channels. Conversely, the high-IQ group displayed greater variability and larger RMSE values, reflecting complex neural dynamics that are less predictable by Monte Carlo simulations. Our findings underscore the utility of Monte Carlo simulations as a robust tool for replicating EEG patterns, identifying cognitive differences and predicting EEG activity associated with intelligence levels. These insights can inform predictive modeling, neurocognitive research, educational strategies and clinical interventions of targeted cognitive enhancement.

neuroscience↗