Shannon's measure of information excludes semantics. Deacon's three-tier framework fills this gap but leaves the physical implementation cost unspecified. Landauer's principle supplies that cost: erasing one bit dissipates at least kT ln 2. Each of Deacon's three tiers presupposes physical operations that Landauer quantifies. Structural information requires distinguishable states whose reset carries a thermodynamic floor. Referential information requires non-random states whose reset cost is bounded below. Normative information requires that work saved exceed the finite-time cost of resetting memory. Read together, Deacon and Landauer are not rivals but complementary layers of analysis. Applied to anthropogenesis, this hierarchy suggests that symbolic cognition may reduce the frequency of physically costly resets, offering a separate thermodynamic axis for selection. The proposal constrains the cost of physical implementation only. It does not reduce meaning to computation.

A collection of interacting parts is not yet an agent. Cells may signal, adhere, exchange metabolites, and constrain one another without serving a common goal; indeed, cancer shows that interaction can persist precisely when organism-level agency breaks down. The key distinction considered here is alignment: whether local activity reduces error relative to a higher-level target. A multi-agent model is introduced in which units have private goals, the collective has a distinct macroscopic goal, and commitment to that goal evolves under a standard bistable public-goods dynamic. Two observables are defined: a signed cross-scale alignment Q, measuring whether local actions reduce or increase collective error, and an order parameter Ψ, measuring recovery beyond the individual-level counterfactual. Commitment supplies the dynamical bifurcation, so Q and Ψ rise with commitment in the baseline model; however, a wrong-target control shows that maximal commitment can coexist with a collapsed order parameter Ψ, while an ablation identifies Q as the non-redundant component of the identity signature. Once formed, the higher-order agent can preserve much of its functional organization through substantial substrate turnover, although the binary same-self verdict at complete turnover remains graded across runs. Persistence has a finite margin: beyond a sufficiently high replacement rate, the functional signature drifts and identity is lost. In a complementary fragmentation regime, motivated by cancer as an organizational analogy, interaction persists while Q is progressively reversed, a reversal that appears only once the defector pull rate or target distance exceeds a fraction-dependent threshold, while the collective's recovery advantage remains positive over part of that regime. Together, these results support a dynamical account of higher-order agency as aligned collective error correction, distinguished from coordination by the direction of cross-scale influence rather than by interaction alone.

In this article, we will discuss the idea that one system cannot be both the observer and the agent of motor action at the same time. In that sense, the model of sensory prediction is somewhat biased, as it would suggest that a system would be able to observe its own action from a fixed viewpoint. We will argue that on a perceptual level, efferent and afferent signals cannot coexist simultaneously, at least as far as the point of the body leading the action is concerned. Triggering motor action activates available sensorimotor memory which a system adheres to completely. This adherence constitutes a weight that we will try to evaluate. Electrochemical reactions that neurally generate it are irreversible and once triggered can only achieve their course and collapse. The ability for a system to sustain electrochemical collapse and dynamic reinitiation onto network relays will be conceptualized as a system's collapsibility. This means that triggering motor action engages a whole system beyond its initial stance. As memory-based efferent signal is electrochemically limited in duration, its phasing with ongoing action is also limited. Beyond that limit, sensory adherence has to switch to availability to peripheral multi-sensory and somatosensory inputs registered dynamically around the motor lead that is phased with triggered memory. The dephasing of sensory peripheral afference and the imperative for a system to prevent bodily harm take over the gap between efferent impulse and sensorimotor control. In-between them lies a precarious state where sensorimotor control is unstable and relatively unpredictable. Inspecting the theory of the sensorimotor paradox on the origins of mind and language, we will see how this gap could have been instrumentalized when it comes to retaining motor expression and autonomizing efferent memory impulses into mental representations. We will try to provide a first formulation of the coefficient of collapsibility from a biophysical perspective, which could serve as a basis for further elaborations.

Dr.Pujuan Deng et al. (2026) showed that the bacterial anti-phage reverse transcriptase Drt3b synthesizes strictly alternating poly(AC) DNA without any nucleic acid template. We recast this mechanism within an information-dynamics framework in which the active pocket is a two-state automaton in constraint space and the free-nucleotide pool is real space, coupled by Boltzmann-weighted stochastic selection. The framework yields a closed-form master curve for the alternation-defect rate, from which the wild-type strict alternation and the partial fidelity loss of E26A follow directly. The E26A energetic barrier is calibrated to the experimental defect rate (Δ=2.47 kT); the E26Q product-level dG fraction is calibrated to the reported 10.16% value (giving γG=ln4); R253A and the double mutant are presented as assumption-dependent predictions; and the crossover at which the defect rate falls below 5% is an analytical criterion (Δ≈3.7 kT). Order-sensitive entropy measures distinguish fidelity loss that marginal composition cannot: the marginal entropy remains 1 bit for all A/C-symmetric systems, whereas the conditional entropy rises from 0 to 1 bit. Protein-templated synthesis is thus an instance of information-field self-organization in which a fixed rule set in constraint space drives real-space data toward a unique stationary occupation distribution, a principle formally analogous to those previously validated in DNA sequencing by hybridization and RNA inverse folding.

Biological systems can maintain their function and organization even when individual molecular components change. We propose Information Persistence Theory (IPT) to describe this idea and introduce the Meaningful Functional Organization Score (MFOS) as an estimator of coordinated biological organization. MFOS was developed using single-cell RNA-sequencing data from mouse pancreatic endocrinogenesis (GSE132188). From an initial pool of 169 candidate genes, differential-expression, functional, regulatory, protein-protein interaction, and feature-prioritization analyses were used to develop a 16-gene panel. Gene expression was scaled independently for each gene and combined with equal weights to produce a dataset-relative score ranging from 0 to 1. In the pancreatic dataset, the MFOS panel showed stronger group-associated organization than matched random gene panels, and leave-one-gene-out analysis showed that the result was not substantially dependent on any single gene. The estimator was also examined in an independent human kidney dataset (GSE131685), where the historical kidney implementation retained measurable information associated with annotated cell groups and exceeded the majority-class and label-permutation accuracy baselines. These findings indicate that MFOS can capture measurable patterns of coordinated biological organization in the datasets examined. However, MFOS is not a direct measure of information persistence, and the present datasets contain no experimental persistence labels. Longitudinal, perturbation-based, and multi-omic studies will be needed to test the proposed framework more directly.

Existing theories of consciousness insufficiently explain the neurocognitive mechanisms through which neural information acquires subjective meaning. To address this gap, we proposed the Mnemonic Emergence Theory (MET) of consciousness, which posits memory not as a passive repository but as an active, system-forming, and meaning/goal-generating basis of conscious behavior. In contrast to classical views, MET postulates that the dynamic organization of memory engrams, integrated with present motivational states, contextual frameworks, and predictive capabilities, constitutes the core mechanism by which meaning/goal emerges and consciousness is established. Within this framework, meaning is not an intrinsic property of information. It arises when current sensory input is integrated into a holistic relational system encompassing the subject's long-term experience, dominant motivation, and anticipated future outcomes. We proposed a functional system model wherein the memory apparatus serves as the integrative hub, linking all other key components of goal-directed behavior, while the ultimate system-forming factor remains the achievement of adaptive outcomes. MET suggests a hierarchical evolutionary model of memory - from simple synaptic traces and conditioned associations, through bidirectional connections and predictability, to self-reflective meta-memory, oscillatory integration, and finally, to an autobiographically extended conscious self. Consciousness, under this view, represents the function and operation of memory at its highest developmental level: a recursively structured, affectively laden, and temporally integrated composition. The central tenet of MET may be summarized as follows: from the memory engram to its actualization, through affective valence and predictive goals, towards temporal and oscillatory integration (meta-memory), and culminating in phenomenal consciousness.

How protocell-like organization can arise before cells, division programs, and genetic templates remains unresolved. We developed a three-dimensional field model in which resource, lipid, hydrothermal, waste, membrane-density, membrane-thickness, and history-retaining fields interact locally in confined aqueous environments and after release into open water. No cell was initialized. Across 168 confined runs, membrane-like components emerged and expanded to domain-spanning states. Matched counterfactual simulations showed that explicit temporal delay was not required for membrane expansion or lumen formation, but delayed their development, whereas removing direct coupling from the history-retaining field to membrane fields reduced lumen formation and overlap-defined splitting. Lumen births and splits occurred predominantly after domain spanning, indicating cavity reorganization within spatially extensive membrane-rich states rather than division of localized protocells. Signed and spatially structured field states persisted across lumen reorganization but also under instantaneous updating. Shape-field helicity exceeded a shared-geometry permutation null. After release, replicated open-water simulations showed resource-dependent persistence or regeneration of detectable membrane-rich organization, including lumens, membrane-associated field state, and displacement. These results separate delay, field coupling, boundedness, state continuity, and structural persistence as distinct components of protocell-like organization.

The intestinal microbiome is a high-dimensional ecological system, but neither host physiology nor clinical measurement has access to all of that detail. Functional redundancy, ecological filtering and niche construction explain why taxonomically different communities can converge on similar functions or host phenotypes, yet they do not answer a separate decision problem: when does a predeclared microbiome representation discard distinctions that matter for intervention choice? Here I define observation-control mismatch (OCM) as a task-specific decision audit for host-microbiome systems. An action is acceptable only when its expected loss is both within a predeclared regret margin η of the best available action and below an absolute biological or clinical loss threshold τ, with any critical-harm constraints specified separately. For a finite set of experimentally constructed realizations, intervention-panel feasibility is assessed first: each tested realization must have at least one acceptable action. Conditional on that requirement, a compressed observational class is control-sufficient when the realizations share at least one acceptable action and shows OCM when each realization is individually actionable but no common acceptable action exists; a realization with no acceptable action instead indicates intervention-panel insufficiency. For population data, actionability coverage and common-action coverage are audited separately against a prespecified coverage q. All empirical conclusions are therefore conditional on the chosen X: control sufficiency means that no intervention-relevant distinction was detected beyond OE within the information represented by X, not that the underlying state S has been exhaustively resolved. This audit is complementary to heterogeneous-treatment-effect analysis and individualized treatment-rule learning: it asks whether information deliberately discarded by an existing biological or clinical representation changes what can be done, not merely whether responses differ. The intestinal boundary provides the biological motivation because microbial ecological states are projected into host-accessible consequences, whereas host physiology and interventions modify niche variables that select among microbial states. This projection-selection architecture is biological rather than a mathematical duality. OCM is not itself a universal falsifiable biological proposition; it generates task-bounded hypotheses defined by a population, representation, intervention panel, outcome, time horizon, η, τ and, for population analyses, q. Retrospective multi-intervention data can screen for mismatch, whereas prospective defined-community or gnotobiotic experiments can test finite-support intersections directly. The audit is unnecessary when a direct causal determinant or an already validated treatment rule makes within-class auditing irrelevant. Additional dynamic or host-boundary measurements are justified only when they repair a demonstrated decision blind spot.

Biological systems often exhibit dynamic structures that generate numerous repeated or similar structures driven by fundamental evolutionary, biochemical, and systematic principles. These structures hinder the modelling and analysis of such systems due to their inherent complexity. To address this challenge, we propose a workflow for modelling and analysing dynamic structures of biological systems using quantitative coloured Petri nets (QPN[C]). The workflow describes a stepwise approach towards modelling and simulating dynamic and scalable structures of biological systems using three classes of coloured Petri nets, offering either deterministic, stochastic or hybrid semantics. We illustrate our protocol using the Gray-Scott system as a typical case study showing how two species react and diffuse over time and space, capturing typical biological patterns such as spots or stripes.

This paper develops a multiscale dynamical framework for continuity-preserving brain replacement: progressive substitution of biological neural tissue by artificial substrates while preserving ongoing causal organization. The framework distinguishes three requirements: local predictive equivalence of a candidate replacement unit, preservation of biological self-maintenance and organizational relations, and regional or organism-level continuity of collective dynamics. Functional Neural Replacement Units (FNRUs) are defined by operational predictive closure at a chosen boundary and time horizon, distinct from autopoietic or Rosenian organizational closure. A dimensionless multiscale error functional covers electrophysiological, spike, synaptic, molecular, maintenance, network, and behavioral observables. A finite-horizon composition bound links global drift to the magnitude and correlation structure of local replacement residuals, showing square-root scaling for uncorrelated residuals and near-linear scaling for coherent residuals. Population-level observables, dynamic coding, functional neural noise, and active glial state are treated explicitly. Replacement is modeled as a reversible hybrid control process with online state estimation whose update loop must remain faster than relevant code reorganization. Model-based numerical analyses examine synthetic held-out single-neuron generalization, plasticity, microcircuit dynamics, and correlation-sensitive composition; a C. elegans whole-organism simulation benchmark is proposed for future work before vertebrate escalation. No biological, animal, or clinical experiments were performed; all original results are theoretical derivations or model-based numerical analyses. Nanoscale and quantum-enabled technologies are optional implementation layers; the continuity criterion is substrate-independent and does not require neural quantum coherence. The framework is a falsifiable research program, not a claim that whole-brain replacement is currently feasible or that subjective continuity has been established.

Floral merosity exhibits pronounced numerical regularity, but whether such organization preferentially follows Fibonacci-related relationships remains unresolved. We evaluated floral organ-number relationships in 113 specimens representing 113 species and 43 families, using transition-specific numerical analysis, bootstrap uncertainty and constrained Monte Carlo testing. Among 108 valid sepal-petal observations, 103 (95.37%) showed exact numerical equality, whereas correspondence with the golden ratio was not enriched relative to the constrained null expectation. Petal-stamen and stamen-carpel transitions displayed distinct numerical behaviour. These findings demonstrate strong floral regularity but provide no evidence for universal Fibonacci organization. The framework separates numerical correspondence from statistical enrichment, architecture and developmental emergence, establishing a testable basis for future three-dimensional and generative investigations.

A recurrent thesis in theoretical biology holds that the self-referential, state-determined organisation of cellular life imposes an in-principle limit on what a cell can know about its external world, an epistemic closure that extends to all cell-based observers. Miller, Slijepčević and Baluška (2026; henceforth MSB) have recently advanced a forceful version of this argument, contending that three obligatory features of cellular life (signal ambiguity, infoautopoiesis, and membrane-enforced constraint closure) jointly preclude observer-independent knowledge. We accept the biology and contest the inference. Drawing on Terrence Deacon's teleodynamic framework, we show that the conclusion turns on a strong correspondence-theoretic premise about what knowledge requires, a premise that is dispensable. Once a weaker, constraint-theoretic notion of biological reference is admitted, the three premises stop entailing closure: signal ambiguity becomes compatible with reference, infoautopoiesis becomes the precondition of reference, and the membrane becomes the substrate of a perspective rather than the wall of a cave. The autogen, a proposed minimal model coupling reciprocal catalysis with self-assembly, makes the logic concrete. Such a system is checked by its environment at every cycle of disruption and repair; the validation MSB require is therefore stronger than biological information ever receives. We observe that MSB's own broader programme (cognitive selection, cellular sentience) already presupposes a notion of cellular reference that the 2026 paper, read at full strength, would deny. The constraint-theoretic reading we propose preserves the substance of that programme while replacing its epistemic conclusion. The cell is closed but not isolated. Closure is the condition under which functional reference becomes possible. We locate this argument within the longer history of internalist-constructivist epistemologies in biology and acknowledge its limits: it does not yet generate a decisive experimental test, though it opens tractable empirical directions and clarifies what is at stake.

Information ecology does not yet possess a unified theoretical foundation; existing approaches provide diverse interpretations without a shared philosophical core. Taking Yixin Zhong's information ecology as a point of departure, this paper explores how information ecology can be transformed into a generative framework capable of serving as a general methodology for information studies. Zhong made a significant contribution by recognizing the philosophical potential of information ecology for transforming the paradigm of information science. However, his framework faces two fundamental limitations. First, his conception of "ecology" remains largely within the framework of systemic coordination among internal elements, without fully incorporating the relational generation and open-ended evolution emphasized by contemporary philosophical ecology. Second, his conception of "information" remains grounded in a representational and transformational framework, which does not sufficiently capture the generative and processual nature of information itself. This paper therefore argues that the concepts of "ecology" and "information" must be reconstructed within a unified philosophical framework. From a generative perspective, it first clarifies the ecological conditions of information systems through contemporary philosophical ecology, and then examines the generative mechanisms of information through process philosophy. On this basis, the paper develops a generative information ecology framework centered on relational generation, autopoiesis, and vectorial information, providing a philosophical foundation for understanding information as an evolving process rather than a static representation.

The structural alterations due to protein misfolding, aggregation and clumping could lead to a number of pathological diseases. They have a considerable influence on the biological function of the intrinsically disordered proteins (IDPs) as well. The development of a comprehensive theory about neurodegenerative diseases, moving beyond the aging-related paradigms, constitutes one of the tasks of molecular biophysics. In the present paper, we developed a parsimonious but highly predictive mathematical model that takes into account hormonal regulation, genetic predisposition, immune deficiency and gut-brain axis with the nonlinear interaction of these factors and proved that protein aggregation progresses through critical tipping points instead of steady-state progression. The most notable finding is the universal convergence which is modelled at midlife around the age of 47 years, where gender-based protective effect disappears and risks become the same and where gut dysbiosis emerges as a potentially modifiable factor that accelerates the clumping of proteins in the age range of 45-65 years. The results of our research change the focus of treatment from late-stage symptom management to a stage-specific preventive intervention by emphasizing early immune support, midlife gut restoration and late-stage clearance enhancement. In essence, this work suggests the gut-brain axis as a valuable tool for theoretical prevention of neurodegeneration decades before clinical symptoms emergence. Also, our mathematical model can be adapted to solve various problems related to IDPs by adjusting the lever parameters accordingly.

Walter Pitts is usually remembered as the mathematical co-author of the 1943 McCulloch-Pitts paper and as a precursor of artificial neural networks. That placement is accurate, but it is too narrow to capture the theoretical significance of his work for biological information systems. This article reconstructs Pitts' contribution as part of an unfinished program of biological computationalism: an approach in which computation becomes explanatory when formally characterizable relations are physically realized by organized biological processes. In the original McCulloch-Pitts case, the relevant realization is neural: threshold, inhibition, connectivity, recurrence, and temporal delay provide a causal architecture capable of instantiating logical relations. The article further argues that the importance of this program lies not only in the threshold neuron but in the conjunction of neural realization, temporal organization, recurrent structure, and the problem of invariance. It also distinguishes Pitts' own neural project from later extensions of McCulloch-Pitts-style computation to proteins, cellular signaling, bacteria, morphogenesis, and other non-neural systems. This broader uptake shows that Pitts' framework was not simply forgotten; rather, the conceptual unity of his biological computationalism has been comparatively underemphasized in dominant histories of cognitive science. By connecting the 1943 logical calculus, the 1947 treatment of universals, cybernetic recurrence, biological time, and subsequent non-neural developments, the article identifies both the power and the incompleteness of the Pittsian program. Its continuing relevance lies in a still-open question: under what conditions can formal computation be realized, constrained, and transformed by living organization?