Contextuality is widely regarded as a hallmark of quantum information, yet its structural origin is often obscured by probabilistic or operational formulations. In this work, we show that non-distributive orthomodular structure need not be postulated, but arises canonically as a left adjoint from classical Boolean contexts. We introduce a gluing functor that takes pairs of Boolean algebras and identifies only their minimal and maximal elements via a categorical pushout. The resulting lattice is orthomodular but generically non-distributive. We prove that this construction is left adjoint to a forgetful functor selecting Boolean subalgebras, thereby providing a free but constrained generation of quantum-logical structure from classical contexts. Furthermore, we demonstrate that the failure of this pushout to remain Boolean is equivalent to the absence of global sections in the sheaf-theoretic framework of Abramsky and Brandenburger. This establishes a precise correspondence between contextuality as a sheaf obstruction and non-distributivity as a colimit failure. Our results offer a categorical and lattice-theoretic reconstruction of contextuality that precedes probabilistic notions and clarifies the structural necessity of quantum logic in information-theoretic settings. More broadly, the gluing construction suggests a general principle of contextual organization beyond quantum physics, providing a mathematical framework for interpreting biological systems in which globally coherent dynamics emerge from the interaction of multiple locally classical contexts.

The Caucasus region represents a unique natural laboratory for paleogenetic research due to its complex topography, long-standing role as a migratory corridor and glacial refugium, and exceptional preservation conditions for ancient DNA. This review synthesizes recent genome-wide studies to reconstruct the demographic history shaping the distinctive genetic landscape of modern Caucasus populations. The analysis reveals a deep pattern of continuity, isolation, and periodic admixture. Early genetic differentiation emerged in the Neolithic and Chalcolithic, forming distinct steppe and mountain population clusters. The Bronze Age was a pivotal period marked by large-scale gene flow from the Eurasian Steppe, particularly linked to the Yamnaya expansion, and interactions with Iranian and Anatolian-related groups. Despite these influences, many populations demonstrate remarkable genetic continuity from the Bronze Age to the present day. Significant knowledge gaps persist, particularly for the Paleolithic, Mesolithic, and Neolithic of the North Caucasus, as well as for the Late Medieval and Early Modern periods across the entire region. Addressing these gaps through targeted archaeogenomic studies is crucial for understanding the fine-scale processes that formed the hierarchical structure and high linguistic diversity of Caucasus populations, offering a powerful model for studying human adaptation, interaction, and language-genetics dynamics in a mountainous environment.

This paper develops a constraint based hypothesis from established physics and published experimental measurements. De Broglie's relation predicts that electronic quantum effects become part of the physical regime when functional structures approach nanometer dimensions. The semiconductor industry provides a test case, as reliable classical switching at few nanometer scales requires architectural intervention biological systems lack. Microtubule tryptophan networks operate at 1 to 2 nm spacing and exhibit energy migration with a 6.6 nm diffusion length that classical Förster theory cannot account for, alongside superradiant behavior across over 10[5] transition dipoles with robustness that increases with system size. Photosynthesis provides biological precedent for organizing quantum dynamics through molecular architecture and energy flow. These premises support the case that the brain operates with quantum effects at the microtubule scale. The functional question is whether these processes matter for higher order neural function. Six pharmacological and cross species constraints address this question. Anesthetics reduce microtubule energy migration by 12 to 15 percent, and anesthetic potency tracks collective tubulin oscillation changes across chemically diverse compounds with R[2] = 0.999. Volatile anesthetics suppress organized responsiveness across eukaryotic organisms without nervous systems, while microtubule stabilizing drugs delay anesthetic induced unconsciousness with a large effect size. Open energy pumped architecture addresses physiological viability, and psychedelic phenethylamines that enhance microtubule polymerization provide an opposite pharmacological test. Together, these constraints support the hypothesis that microtubule tryptophan networks and collective tubulin dynamics participate in the maintenance of higher order neural function and conscious states.

This paper offers a logical and mathematical reconciliation of four known universal but otherwise disparate laws of nature: the constructal law, the 1st and 2nd laws of thermodynamics, and special relativity are unified in a single equation proposed here as a universal framework of dynamics, applicable to any system, and capable of explaining emergent phenomena in nature as a matter of directional physical law. No laws are overturned; they are preserved and obeyed, but sublated into a single law expressible with a single equation, offering new insights into how the otherwise disparate pieces fit together to explain the universe. Among the most compelling empirical support is Chaisson's work on energy rate density, which quantitatively confirms the framework's central claims across orders of magnitude of physical and biological organization. The relationship between entropy and efficiency - long misunderstood in isolation - is shown to be one of complementarity: neither can be fully understood without the other, and together they constitute a complete and universal description of energy flow in any system.

Evolution is an intrinsic property of autopoietic systems performing natural computation, which is based on an internal formal self-description representing the arrangement of digital and analog coding structures. Autopoietic systems are capable of assigning new values to previously unproven (ambiguous) statements, which occurs, in particular, in conditions beyond the limits of adaptability. This process, defined by Marcello Barbieri as codepoiesis, occurs through the introduction of new codes or the rearrangement of existing ones, increasing the computing power of biological systems. Formally, it corresponds to Gödel numbering, representing a non-conventional algorithm that takes a sentence from a formal system and turns it into a numerical metacode, uniquely encoding each sentence in a given formal system. Ribotype, as a set of RNA molecules, serves as a codemaking component that logically preceded the information-storage (genotype) and metabolic (phenotype) components and determined the codepoietic process throughout biological evolution. In the early stages of evolution, alternative realizations of codepoiesis occurred in the appearance of distinct domains of Bacteria and Archaea. Both bacteria and archaea utilize horizontal gene transfer in evolution; in bacteria, it is constrained by DNA sequence divergence and repair mechanisms, while in archaea, it is more internally driven via the use of unique mechanisms like cell fusion, lower stringency for homology in recombination, and specialized, aggregate-based DNA repair. The interaction between bacteria and archaea in evolution led to different types of symbiotic relations and finally resulted in the appearance of eukaryotic cells, which triggered further complexification and the emergence of multicellularity. It is concluded that the codepoietic process represented the basic driving mechanism of biological evolution from the appearance of the first living organisms to the development of complex behaviour and consciousness.

Biological cognition is often described through homeostasis, prediction, exploration, or control. However, none of these principles alone specifies how a living system should act while preserving viability and enlarging its future possibilities. We tested whether living-cognition-like policy selection can be operationalized as viability-constrained expansion of controllable futures. A minimal grid-world model was implemented in which agents evaluated finite-horizon policies under internal viability dynamics, hazards, resources, movement costs, and reachable-endpoint controllability. Seven reduced policy-selection models were compared across 1000 critical environments containing viability risk, expansion opportunity, and viability-control conflict. The viability-constrained endpoint and joint viability-expansion models produced the highest joint viable-expansion scores. At the family level, viability-constrained expansion substantially outperformed models based on homeostatic preservation, avoidance of previously unvisited states, novelty seeking, random admissible action, or unrestricted controllability. Unrestricted controllability frequently violated viability, whereas homeostasis-only and uncertainty-minimizing selection preserved viability but produced limited or zero expansion. The family-level advantage remained positive across the default configuration and 47 prespecified parameter variations. These findings support viability-constrained future expansion as a candidate minimal operational principle for living-cognition-like policy selection in state-constrained agents. Under the tested environmental and computational conditions, the framework is a computational proof-of-principle and does not establish the biological or neural mechanisms of cognition.

The Orch-OR theory has been proposed as a framework for explaining consciousness based on quantum-gravitational effects in microtubules. To further support and examine this theory, a more concrete quantum circuit model is required, one that can be theoretically analyzed and experimentally tested in comparison with biological observations. In this work, we propose a hierarchical quantum circuit model that incorporates an orchestration mechanism across three structural levels: the tubulin dimer, the helical pathway, and the microtubule as a whole. The proposed model describes how a higher-level OR unit may emerge through the orchestration of lower-level OR units. We analyze the ratio of OR oscillation frequencies between the tubulin dimer and the entire microtubule and show that the predicted ratio is in close agreement with reported biological experimental data. These results suggest that microtubules may be described by a hierarchical quantum circuit model within the framework of the Orch-OR theory.

This perspective argues that biochemical reaction systems and gene regulatory systems can be interpreted not only as architectures of homeostatic control and adaptation, but also as reservoir-like dynamical substrates for natural computing. The claim is deliberately moderate: biological regulation is not denied, but some control-like functions are re-read as readouts implemented on top of richer input-driven, recurrent, dissipative, history-dependent, and often stochastic state dynamics. The distinctive contribution is not a new reservoir algorithm, nor the generic observation that dynamical systems can serve as reservoirs; it is a substrate-oriented reframing for biochemical and gene-regulatory modeling that places biochemical reaction dynamics, BST/S-systems and dBST, stochastic process descriptions, Boolean abstractions, and emerging live-cell observability within one experimentally testable perspective. To keep the argument operational rather than metaphorical, the manuscript outlines a minimal evaluation protocol using delayed-input reconstruction, information-processing capacity, nonlinear separation, and perturbation-aware prediction. The perspective is timely because recent cell-based reservoir studies, topology-aware reservoir designs, living-cell signaling studies, and live-cell visualization technologies increasingly make substrate-level signal propagation experimentally accessible rather than purely metaphorical.

At the forefront of brain-computer interface research, studies on neuronal synchronization continue to show substantial application potential. Photoreceptor neurons are crucial for maintaining normal retinal function, and computational models integrating phototubes with neuronal dynamics offer valuable tools for exploring photosensitive neural behaviors. This study constructs a bi-membrane neuron using dual capacitors, employs a flux-controlled memristor to emulate inter-membrane biological tissue flexibility, and integrates dual phototubes for optical signal acquisition, thereby establishing a novel memristive bi-membrane photosensitive neuron (MBPN) model. Through systematic analysis, we investigate the model's diverse dynamical responses under various external stimuli and noise conditions. The results show that the firing patterns of the neuron can be modulated by external signals, and coherence resonance can be induced at specific noise intensities. Moreover, magnetic noise exhibits higher efficiency and sensitivity in inducing resonance than optical noise. Experiments further reveal the synchronization regulation mechanism of the memristive bi-membrane photosensitive neuron system (MBPNS), demonstrating that its synchronization process is modulated by the coupling gain and external stimulus parameters. These findings offer theoretical guidance for neuronal modeling and provide important insights into artificial biological membrane design. This study advances understanding of dynamic properties and synchronization mechanisms in photosensitive neurons, thereby providing a useful reference for research on neural dynamics and neuromorphic systems.

The biophysics of the homoaggregation of cells (erythrocyte rouleaux) share many features of the homoaggregation of macromolecules such as α-synuclein. Consistent with a role for temperature-dependent entropic forces, evidence from both homoaggregations suggests that aggregation pressures could be determined extrinsically, rather than solely intrinsically by the concentration of a macromolecule. Critical here are concentrations of members of the local macromolecule collective and binding of water. Over evolutionary time certain self-proteins, especially the intrinsically disordered, have been under positive selection pressures (i.e., are conserved), seemingly to avoid misinteractions. For organisms with high expression of the corresponding genes, these misinteractions might have been harmful had they led to homoaggregations of the proteins. The conservation (i.e., a slowing of evolutionary rates due to functional constraints), which correlates with high expression levels (the "ER anticorrelation"), has long puzzled geneticists. Supported by recent developments, historical studies (Fåhraeus, Muller, Lauffer) from a wide range of disciplines offer promising solutions to this and other problems. The resulting knowledge integrations cast fresh light on the changes from monomeric to polymeric forms that are deemed to play fundamental roles in intracellular mechanisms for discrimination between self-macromolecules and those derived from invading microorganisms. However, these aggregates may also initiate various neurological diseases. Lifestyle changes that prevent transient increases in body temperature might diminish the diseases without unduly harming the immunological defenses that rely on heat-shock proteins.

Classical physiology elegantly explains acute homeostatic regulation via descending neuroendocrine feedback loops. However, a fundamental conceptual gap remains: how do central regulators continuously integrate spatially distributed, slowly accumulating tissue stresses-such as oxidative damage and lipotoxic drift-within a strongly mixed circulatory system? Free molecular markers face major physical limits, including rapid clearance kinetics and high-dimensional complexity, precluding them from serving as stable, long-term integrators. To bridge this gap, I propose the Systemic Homeostatic Integration-Field Theory (SHIFT). SHIFT is presented here as a testable systems-level framework rather than as an established regulatory mechanism. It hypothesizes that long-horizon homeostasis may require an "ascending observational layer" that aggregates distributed slow variables into sampleable, low-dimensional state summaries. Within this framework, the circulating lipid-based particle ensemble (LBPE) is proposed as a leading candidate substrate, rather than as an exclusive or already proven solution. By translating high-dimensional molecular and tissue stress into macroscopic ensemble phenotypes-such as size spectra, membrane material properties, surface charge, and dynamic protein-corona patterns-LBPE may function as a physiological integration medium or "memory shock-absorber". Its constituent particles may be sampled by sentinel organs and thereby contribute to the recalibration of systemic dose-response thresholds. Crucially, this theoretical framework suggests a set of experimentally testable intervention logics under the broader concept of system-state engineering. Rather than targeting single molecules, I outline how manipulating LBPE macroscopic properties could enable falsifiable therapeutic strategies, including system-state training to elevate baseline thresholds, programmable decoy sequestration to abort acute inflammatory cascades, and consensus write-back to reset long-term homeostatic set-points. Ultimately, SHIFT offers a falsifiable conceptual framework that bridges long-term physiological observation with possible future strategies for system-state engineering. Its immediate value lies in defining the physical constraints, competing explanations, and experimental outcomes required to support, modify, or refute the proposed particle-ensemble model.

This paper develops a novel fractional-order Keller-Segel cross-diffusion framework for investigating memory-dependent chemotactic aggregation, biological pattern formation, and anomalous transport in microbial populations and cellular systems. The proposed model extends the classical Keller-Segel system by incorporating a Caputo fractional time derivative and a nonlinear cross-diffusion mechanism to account for hereditary transport and density-dependent migration. A rigorous mathematical analysis is presented, including positivity and boundedness of solutions, existence of weak solutions, linear stability analysis, and explicit conditions for fractional Turing instability. The critical wave number and instability interval are also determined to characterize the onset of diffusion-driven pattern formation. Numerical simulations validate the theoretical predictions and demonstrate the emergence of spot-like, stripe-like, and mixed spot-stripe patterns. Furthermore, comparison with the corresponding integer-order model shows that fractional memory delays the onset of instability, prolongs transient dynamics, and enhances pattern persistence by slowing the temporal growth of unstable modes. These results demonstrate that fractional memory plays a fundamental role in regulating chemotactic self-organization and provide a unified mathematical framework for studying memory-dependent pattern formation in biological systems.

The aim of this paper is to test whether biological macromolecules carry information through two distinct, separable channels. The conformational channel (C-channel), governed by three-dimensional structure and the Coulomb potential, sets binding geometry, catalysis, and stability. The spectral channel (S-channel), the one-dimensional electron-ion interaction potential (EIIP) profile and its characteristic frequency fRRM, is proposed to mediate resonant molecular recognition. In quantum information biology, recognition - a protein selecting a partner among thousands of candidates - is modelled as a decision following quantum-probabilistic laws despite macroscopic physics. This requires the channels to form a bipartite system whose state space factorizes as a tensor product HC⊗HS of separable degrees of freedom. Whether the channels are coupled, independent, or complementary is the empirical prerequisite tested here. We analyse 87,389 single-nucleotide missense substitutions across 222 non-redundant, full-length proteins (51-4684 residues, <40% pairwise identity, >40 functional families), classifying each as fRRM-silent or fRRM-disruptive over seven physicochemical metrics, with within-protein clustering handled explicitly (intraclass correlation, design effect, and a protein-level cluster bootstrap). We report two principal findings. First, across the full sample fRRM is essentially uncorrelated with every conformational determinant, namely Grant ham distance, hydropathy, molecular weight, and helix and sheet propensity (all |ρ|<0.1), establishing channel independence. Second, the silent-versus-disruptive contrast reproduces: disruptive substitutions are marginally more conformationally conservative on every metric, yet each difference, though statistically significant, is negligible in magnitude (|d|<0.2), a consistent but negligible-magnitude anti-correlated tendency, not a substantive coupling. The sequence-level state is therefore separable, a product state in HC⊗HS rather than an entanglement-like or complementary one, and this holds residue by residue (aspartate is the principal spectral hotspot, tryptophan is conserved structurally yet remains spectrally neutral). We interpret these results within the quantum-like framework: the protein is a dual-channel processor whose spectral "software" (frequency-domain recognition) can be updated independently of its conformational "hardware" (spatial binding), providing the separable substrate that quantum-like molecular decision-making requires.

Biological boundaries are often modeled as instantaneous Markov blankets that separate internal and external states through sensory and active variables. However, real biological interfaces are dissipative systems with finite response times, relaxation, noise, and delayed transduction. Here, we examine whether such physical boundary dynamics can produce a non-Markovian screening relation, in which conditional separation is recovered only when boundary history is included. We constructed a minimal stochastic dissipative loop linking external, sensory, internal, and active states, with delayed sensory-to-internal coupling. Conditional mutual information was used to quantify residual internal-external dependence after conditioning on either the instantaneous sensory-active boundary or a temporally extended boundary containing finite sensory history. In the baseline linear model, instantaneous conditioning failed to fully screen internal from external states in a moderate fraction of simulations, whereas adding sensory history consistently reduced residual dependence and produced negative history-response trends. The effect was stronger in a nonlinear saturating model and was abolished when either external-to-sensory or sensory-to-internal coupling was removed. Sensitivity analyses showed that temporally extended screening depends on the balance among delay, dissipation, feedback, and noise, but is robust to changes in history-sampling stride. These results support the concept of a temporally extended dissipative blanket: a physical boundary mechanism whose statistical screening function is carried not only by its present state, but also by finite boundary history.