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| ai.viXra.org > Physics of Biology > 2605 |
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[3] ai.viXra.org:2605.0070 [pdf] submitted on 2026-05-29 23:35:52
Authors: Keiji Yoshimura
Comments: 8 Pages.
Sleep is widely associated with homeostatic recovery, local use-dependent regulation, and state-dependent reorganization of network interactions. This manuscript proposes the Dynamical Threshold Theory of Sleep, a mathematical and computational prototype in which a multicellular biological system is represented as a network of adaptively coupled oscillatory elements with local homeostatic load, local sleep propensity, and selectively protected interactions.In this framework, wakefulness is modeled as a noisy adaptive synchronization regime under continued perturbation, whereas sleep emerges when the joint accumulation of local load and degradation of local coherence drive the system across a collective dynamical threshold into a sleep-dominant renormalization phase. To avoid the unbounded-coupling pathology of naive adaptive phase-oscillator models, the sleep-dominant subsystem is formulated as a bounded gradient flow of a reduced free energy.Numerical proof-of-concept simulations on hierarchical modular small-world networks reproduce three central features of the proposed framework: spatially heterogeneous accumulation of homeostatic burden, rapid growth of mean sleep propensity into a sleep-dominant regime, and bounded global downscaling of mean coupling strength toward finite fixed points. Additional analyses indicate that edges with stronger pre-sleep protection are preferentially preserved during sleep, consistent with selective down-selection rather than uniform weakening.This manuscript should be read strictly as a reduced network-dynamical prototype for sleep-like recovery transitions in adaptive multicellular systems. It is not medical advice, not a clinical sleep model, not a diagnostic or treatment tool, and not a claim of validation against human or animal sleep data.
Category: Physics of Biology
[2] ai.viXra.org:2605.0040 [pdf] submitted on 2026-05-20 18:46:44
Authors: Alberto Coe
Comments: 15 Pages.
The Topological Polar Surface Area (TPSA) is a core molecular descriptor in pharmacology, whose spatial distribution across chemical spaces has historically been treated as a continuous variable. This study introduces a normative geometric framework in which TPSA is quantized as a function of the fundamental Bohr area unit L0=a_0^2=28003 Å^2. Utilizing a pure logarithmic grid model (m=0) derived from a three-dimensional spatial resonance constant A=1.1547, we evaluated three independent datasets:120 clinically approved synthetic drugs, and 50 failed or market-withdrawn compounds.The results reveal a radical statistical asymmetry: the successful drug cohort structurally couples to the discrete nodes of the lattice with extreme mathematical significance (P = 0.0002) and a compressed mean residual of 0.030. Conversely, the failed drug cohort exhibits a distribution indistinguishable from stochastic background noise (P-value 0.30). This paper elucidates the computational behavior of Monte Carlo simulations under highly dense grid topologies and formalizes a zero-parameter biophysical screening route capable of pre-clinically anticipating molecular viability.
Category: Physics of Biology
[1] ai.viXra.org:2605.0012 [pdf] submitted on 2026-05-07 19:26:11
Authors: Alberto Coe
Comments: 6 Pages.
The Topological Polar Surface Area (TPSA) is a critical descriptor in pharmacology, yet itsdistribution across natural toxins has long been considered stochastic. This study proposes anovel geometric framework where TPSA is quantized as a function of the Bohr area unit (au2080² ≈ 0.28003 Ų). By applying a harmonic resonance model based on a modified Euler’s constant �� = �� − 2/3, we analyzed 28 diverse natural toxins, ranging from Hydrocyanic Acid to Maitotoxin. The results reveal a discrete double octave grid (Nodes 23 to 39) that accounts fortoxin dimensions with a mean residual of 0.18 (in node space) and a high level of significance. A Monte Carlo simulation confirms the model's validity with a significance level of P = 0.0056. These findings suggest that the chemical space of natural toxicity is governed by a fundamental area-scaling law rooted in quantum constants, offering new predictive capabilities for molecular toxicology.
Category: Physics of Biology