Over the past two decades, quantum-like modeling (QLM) has emerged as a powerful framework for describing non-classical features of cognition and decision-making. Rather than assuming physical quantum processes in the brain, QLM employs the Hilbert space formalism to model contextuality, incompatibility of mental observables, and entanglement-like correlations. In this paper, we develop a quantum-informational model of mental markers within the broader I-field (information field) approach. We propose that, under conditions of information overload and limited cognitive resources, individuals primarily respond not to detailed semantic content but to compact content labels - mental markers - carrying cognitive and affective components. We formalize mental markers as structured quantum-like states and analyze the nonclassical correlations between their cognitive and affective components using the Contextuality-Incompatibility-Entanglement triad. Special attention is given to intra-system entanglement between rational (cognitive) evaluation and emotional (affective) coloring, accounting for context-dependent judgments, order effects, and affect-driven decision shifts. Illustrative examples with psychological interpretation and experimental perspectives are provided. An Appendix briefly discusses neurobiological analogues of information overload in neural networks, highlighting structural parallels with the proposed marker-based framework; coupling to the origin and diagnostics of neurological diseases is analyzed. The paper contributes to QLM by distinguishing inter-system and intra-system entanglement and by demonstrating that cognitive - affective entanglement constitutes a fundamental structural feature of mental markers in socially mediated information environments.

Dendrogramic Holographic Theory (DHT) is a purely relational theory of information in which the primitive elements are events, and physical description is defined by an observer’s dendrogram: a hierarchical tree of binary questions that distinguishes events only through operationally accessible relations. The core postulate is an epistemic form of Leibniz’s Principle of the Identity of Indiscernibles: if two states of affairs cannot be distinguished by any admissible measurement for a given observer, they are identified for that observer. From each dendrogram we construct a views distribution over relational distances and define a one-particle wavefunction from that distribution; many distinct relational configurations can therefore map to the same distribution and the same wavefunction, so a particle is naturally an equivalence class of dendrograms sharing the same wavefunction. We then study two-particle sectors by embedding a pair of finite dendrograms into a host context consisting of one or two larger dendrograms, allowing both equal host and distinct-host comparisons and accommodating a range of size relations. When neither host can be embedded into the other, the host pair is space-like separated in a Minkowski-like parameter space, so exchange signatures arise purely from the relative organization of the embedded structures rather than from causal nesting. Our simulations show that bosonic versus fermionic exchange behaviour is not an intrinsic label of the embedded inputs, but an emergent invariant of the composite relational wiring diagram linking two, often non-relationally closed, information sets through cross-host correlations, reproducing exclusion-like and pile-up behaviour without postulating the Pauli principle. In this sense, DHT offers a unifying perspective on bosonic and fermionic fields: both arise from the same underlying relational degrees of freedom, and boson–fermion conversion corresponds to operations that break or restore relational closure, by changing host choice and embedding pattern, while leaving the one-particle state fixed—an analogue of supersymmetric unification that does not require a new particle spectrum. This suggests a unification of matter and forces at the level of relational organization without introducing new particles and while remaining compatible with a Minkowski-like spacetime encoding.

Over the past two decades, quantum-like modeling (QLM) has emerged as a powerful framework for describing non-classical features of cognition and decision-making. Rather than assuming physical quantum processes in the brain, QLM employs the Hilbert space formalism to model contextuality, incompatibility of mental observables, and entanglement-like correlations. In this paper, we develop a quantum-informational model of mental markers within the broader information field approach. We propose that, under conditions of information overload and limited cognitive resources, individuals primarily respond not to detailed semantic content but to compact content labels - mental markers - carrying cognitive and affective components. We formalize mental markers as structured quantum-like states and analyze the nonclassical correlations between their cognitive and affective components using the Contextuality-Incompatibility-Entanglement triad. Special attention is given to intra-system entanglement between rational (cognitive) evaluation and emotional (affective) coloring, accounting for context-dependent judgments, order effects, and affect-driven decision shifts. Illustrative examples with psychological interpretation and experimental perspectives are provided. Appendix B briefly discusses neurobiological analogues of information overload in neural networks, highlighting structural parallels with the proposed marker-based framework; coupling to the origin and diagnostics of neurological diseases is analyzed. The paper contributes to QLM by distinguishing inter-system and intra-system entanglement and by demonstrating that cognitive-affective entanglement constitutes a fundamental structural feature of mental markers in socially mediated information environments.

OBJECTIVE:Epilepsy diagnosis and treatment monitoring are hindered by the episodic, heterogeneous expression of seizures and by normal-appearing scalp electroencephalography (EEG) in many patients. We previously described paroxysmal slow-wave events (PSWEs), brief epochs of broadband slowing detectable on EEG in people with epilepsy. In the present study, we sought to further define the clinical significance of this biomarker. METHODS:We used intracerebral and epidural recordings in a paraoxon rat model of temporal lobe epilepsy, as well as long-term video-EEG monitoring (LTM) in patients with temporal lobe epilepsy undergoing intracerebral recordings combined with scalp EEG, or with scalp EEG alone. RESULTS:We show that PSWEs arise preferentially in temporo-frontal networks, co-occur with global slowing, and increase during both spontaneous and pharmacologically induced seizures. Epidurally recorded PSWEs were temporally coupled to deep temporal discharges and were bidirectionally modulated by γ-aminobutyric acid (GABA)ergic agents (increased with pentylenetetrazol and decreased with pentobarbital). In patients with temporal lobe epilepsy, simultaneous intracerebral recordings and scalp EEG showed that scalp PSWEs mirrored hippocampal spike-and-wave activity. PSWEs were more frequent during the preictal and ictal periods than during the interictal baseline, a finding confirmed in a retrospective analysis of 137 seizures from 18 patients recorded with scalp EEG alone. SIGNIFICANCE:These data indicate that surface PSWEs can index remote epileptiform activity and support their use as a quantitative, noninvasive biomarker for detecting EEG-silent deep foci and for pharmacodynamic evaluation.

Epilepsy diagnosis and treatment monitoring are hindered by the episodic, heterogeneous expression of seizures and by normal-appearing scalp EEG in many patients. We previously described paroxysmal slow-wave events (PSWEs)-brief epochs of broadband slowing detectable on EEG. Here, using intracerebral and epidural recordings in a paraoxon rat model of temporal lobe epilepsy, we show that PSWEs arise preferentially in temporo-frontal networks, co-occur with global slowing, and increase during both spontaneous and pharmacologically induced seizures. Epidurally recorded PSWEs were temporally coupled to deep temporal discharges and were bidirectionally modulated by GABAergic agents (increased with pentylenetetrazol and decreased with pentobarbital). In long-term video-EEG monitoring (LTM) patients with temporal lobe epilepsy, simultaneous stereo-EEG and scalp EEG showed that scalp PSWEs mirrored hippocampal spike-and-wave activity and were more often observed in the preictal and ictal periods than during interictal baseline. These data indicate that surface PSWEs can index remote epileptiform activity and support their use as a quantitative, noninvasive biomarker for detecting EEG-silent deep foci and for pharmacodynamic.

In the framework of relational information, we explore analogs of physical theories and their properties. Specifically, we investigate the causal characteristics of relational information, examining how initial knowledge impacts future relational understanding of the universe/system. To achieve this, we establish a parameter space defining relational structures called dendrograms, exhibiting causal properties akin to those of Minkowski metric. Subsequently, we propose a statistical-dynamical model on this Minkowski-like parameter space, unifying Bohmian and Many Worlds interpretations of quantum theory in the framework of relational information. Additionally, we provide an analytical proof of the non-ergodicity of the relational information framework, revealing CHSH inequality violations as an emergent phenomenon. Our focus on relational information underscores its significance across scientific disciplines, where a single measurement or observation lacks meaning without context.

The past few years have seen a surge in the application of quantum theory methodologies and quantum-like modeling in fields such as cognition, psychology, and decision-making. Despite the success of this approach in explaining various psychological phenomena — such as order, conjunction, disjunction, and response replicability effects — there remains a potential dissatisfaction due to its lack of clear connection to neurophysiological processes in the brain. Currently, it remains a phenomenological approach.In this paper, we develop a quantum-like representation of networks of communicating neurons. This representation is not based on standard quantum theory but on {\it generalized probability theory} (GPT), with a focus on the operational measurement framework. Specifically, we use a version of GPT that relies on ordered linear state spaces rather than the traditional complex Hilbert spaces. A network of communicating neurons is modeled as a weighted directed graph, which is encoded by its weight matrix. The state space of these weight matrices is embedded within the GPT framework, incorporating effect-observables and state updates within the theory of measurement instruments — a critical aspect of this model. This GPT-based approach successfully reproduces key quantum-like effects, such as order, non-repeatability, and disjunction effects (commonly associated with decision interference). Moreover, this framework supports quantum-like modeling in medical diagnostics for neurological conditions such as depression and epilepsy.While this paper focuses primarily on cognition and neuronal networks, the proposed formalism and methodology can be directly applied to a wide range of biological and social networks.

We developed the novel mathematical model for event-universe by representing events as branches of dendrograms (finite trees) expressing the hierarchic relation between events. At the ontic level we operate with infinite trees. Algebraically such mathematical structures are represented as p-adic numbers. We call this kind of event mechanics Dendrogramic Holographic theory (DHT). It can be considered as a fundamental theory generating both GR and QM. In this paper we endower DHT with Rao-Cramer's information geometry. Following Smolin's derivation of QM from the event-universe, we introduce views from one event to others and by using their probability distributions we invent stochastic geometry. The important mathematical result is that all such views' distributions can be parametrized by four real parameters that are a part of the shape complexity measure introduced by Barbour in his particle shape dynamics theory - adapted to DHT. Hence, within DHT all possible event-universes can be embedded in four-dimensional real space. Asin GR, we introduce proper time. This "proper time" depends only on the change between one distribution of an observer to the other. The linkage of time to change is highlighted in the ideology of Rovelli and Barbour's shape dynamics.

The diagnosis of psychiatric disorders is currently based on a clinical and psychiatric examination (intake). Ancillary tests are used minimally or only to exclude other disorders. Here, we demonstrate a novel mathematical approach based on the field of p-adic numbers and using electroencephalograms (EEGs) to identify and differentiate patients with schizophrenia and depression from healthy controls. This novel approach examines spatio-temporal relations of single EEG electrode signals and characterizes the topological structure of these relations in the individual patient. Our results indicate that the relational topological structures, characterized by either the personal universal dendrographic hologram (DH) signature (PUDHS) or personal block DH signature (PBDHS), form a unique range for each group of patients, with impressive correspondence to the clinical condition. This newly developed approach results in an individual patient signature calculated from the spatio-temporal relations of EEG electrodes signals and might help the clinician with a new objective tool for the diagnosis of a multitude of psychiatric disorders.

This paper is devoted to event-observational modelling in physics and more generally natural science. The basic entities of such modelling are events and where space-time is the secondary structure for the representation of events. The novelty of our approach is in using a new mathematical picture of events universe. The events observed by an observer are described by a dendrogram, a finite tree. The event dynamics are realized in the dendrogramic configuration space. In a dendrogram, all events are intercoupled via the hierarchic relational structure of the tree. This approach is called Dendrogramic Holographic Theory (DHT). We introduce the causal structure on the dendrogramic space, like the causal structure on the Minkowski space-time. In contrast to the latter, DHT-emergent causality is of a statistical nature. Each dendrogram represents an ensemble of observers with the same relational tree representation of the events they measured/collected. Technically the essence of causal modelling is in encoding dendrograms by real parameters and in this way transitioning to the real space-time. Then we proceed in the framework of information geometry corresponding to Hellinger distance and introduce a kind of light cone in the space of dendrograms. This is a step towards the development of DHT-analog special relativity.

The CRISPR-Cas system holds great promise in the treatment of diseases caused by genetic variations. As wildtype SpyCas9 is known to generate many off-target effects, its use in the clinic remains controversial due to safety concerns. Several high-fidelity Cas9 variants with greater specificity have been developed using rational design and directed evolution. However, the enhancement of specificity by these methods is limited by factors like selection pressure and library diversity. Thus, in-silico protein engineering may provide a more efficient route for enhancing specificity, although computationally testing these proteins remains challenging. We recently demonstrated the advantage of normal mode analysis to simulate and predict the enzymatic function of SpyCas9 in the presence of mismatches. Here, we report several mathematical models describing the entropy and functionality relationships in the CRISPR-Cas9 system. We demonstrate the invariant characteristics of these models across different conformational structures. Based on these invariant models, we developed ComPE, a novel computational protein engineering method to modify the protein and measure the vibrational entropy of wildtype or variant SpyCas9 in complex with its sgRNA and target DNA. Using this platform, we discovered novel high-fidelity Cas9 variants with improved specificity. We functionally validated the improved specificity of four variants, and the intact on-target activity in one of them. Lastly, we demonstrate their reduced off-target editing and non-specific gRNA-independent DNA damage, highlighting their advantages for clinical applications. The described method could be applied to a wide range of proteins, from CRISPR-Cas orthologs to distinct proteins in any field where engineered proteins can improve biological processes.### Competing Interest StatementR.R., O.S., F.B. and D.O. have filed a patent application on entropy-based computational protein engineering and high fidelity SpyCas9 variants with improved specificity. R.R. is a co-founder of and shareholder in Kanso Diagnostics. U.B.-D. receives consulting fees from Accent Therapeutics. F.B. received consulting fees from NeuroHelp. The remaining authors declare no competing interests.

Quantum mechanics (QM) is derived based on a universe composed solely of events, for example, outcomes of observables. Such an event universe is represented by a dendrogram (a finite tree) and in the limit of infinitely many events by the p-adic tree. The trees are endowed with an ultrametric expressing hierarchical relationships between events. All events are coupled through the tree structure. Such a holistic picture of event-processes was formalized within the Dendrographic Hologram Theory (DHT). The present paper is devoted to the emergence of QM from DHT. We used the generalization of the QM-emergence scheme developed by Smolin. Following this scheme, we did not quantize events but rather the differences between them and through analytic derivation arrived at Bohmian mechanics. We remark that, although Bohmian mechanics is not the main stream approach to quantum physics, it describes adequately all quantum experiments. Previously, we were able to embed the basic elements of general relativity (GR) into DHT, and now after Smolin-like quantization of DHT, we can take a step toward quantization of GR. Finally, we remark that DHT is nonlocal in the treelike geometry, but this nonlocality refers to relational nonlocality in the space of events and not Einstein's spatial nonlocality. By shifting from spatial nonlocality to relational we make Bohmian mechanics less exotic.

This paper is devoted to the event-observational modelling in physics and more generally natural science. The basic entities of such modelling are events and where space-time is the secondary structure for representation of events. The novelty of our approach is in using new mathematical picture for events universe. The events recorded by an observer are described by a dendrogram, a finite tree. The event dynamics is realized in the dendrogramic configuration space. In a dendrogram all events are intercoupled via the hierarchic relational structure of the tree. This approach is called Dendrogramic Holographic Theory (DHT). We introduce the causal structure on the dendrogramic space, like the causal structure on the Minkowski space-time. In contrast to the latter, DHT-emergent causality is of the statistical nature. Each dendrogram represents an ensemble of observers with same relational tree-representation of the events they measured/collected. Technically the essence of the causal modelling is in encoding dendrograms by real parameters and in this way transition to the real space-time. Then we proceed in the framework of information geometry corresponding to Hellinger distance and introduce a kind of light-cone in the space of dendrograms. The real parameter spaces discovered in our numerical analysis, while related to an ensemble of observers, primarily represent purely observer-subjective and observer-dependent knowledge of an observer about the universe. In that sense these spaces are inherently subjective. This is a step towards development of DHT-analog special relativity.

Toll-like receptor 3 (TLR3), plays an important role in the development of epilepsy after brain insults. Previously, TLR3 deficiency in a pilocarpine model of temporal lobe epilepsy (TLE) was shown to reduce mortality, spontaneous recurrent seizures (SRS) and neuroinflammation. We hypothesized that pharmacological inhibition of TLR3 would reduce epileptogenesis following status epilepticus. We show that Resveratrol and FC99, two TLR3 blockers, demonstrate anti-epileptogenic effects in a pilocarpine model of TLE. While both Resveratrol and FC99 were previously shown to benefit in other pathologies, neither of these blockers had been proposed for the treatment of epilepsy. Our results provide substantial evidence to the importance of TLR3 inhibition in the prevention of epilepsy and specifically highlighting FC99 as a promising novel anti-epileptic drug. We anticipate our data to be a starting point for further studies assessing the anti-epileptogenic potential of FC99 and other TLR3 blockers, paving the way for pharmacological interventions that prevent epileptogenesis.

Diversity of interpretations of quantum mechanics is often considered as a sign of foundational crisis. In this note we proceed towards unification the relational quantum mechanics of Rovelli, Bohmian mechanics, and many worlds interpretation on the basis so called _Dendrogramic Holographic Theory_ (DHT). DHT is based on the representation of observed events by dendrograms (finite trees) presenting observers subjective image of universe. Dendrograms encode the relational hierarchy between events, in applications they are generated by clustering algorithms; an algorithm with the branching index p >1 generate p-adic trees. The infinite p-adic tree represents the ontic event universe. We consider an ensemble of observers performing observations on each other and representing them by p-adic trees. In such “observers universe” we introduce a kind of Minkowski space structure, which is statistical by its nature. This model unites the observer/system discrepancy. Measurements are performed by observers on observers. Such “observers universe” is dynamically changing and is background independent since the space itself is emergent. And within this model, we unify the aforementioned interpretations.

Quantum mechanics (QM) is derived based on a universe composed solely of events, for example, outcomes of observables. Such an event universe is represented by a dendrogram (a finite tree) and in the limit of infinitely many events by the p-adic tree. The trees are endowed with an ultrametric expressing hierarchical relationships between events. All events are coupled through the tree structure. Such a holistic picture of event-processes was formalized within the Dendrographic Hologram Theory (DHT). The present paper is devoted to the emergence of QM from DHT. We used the generalization of the QM-emergence scheme developed by Smolin. Following this scheme, we did not quantize events but rather the differences between them and through analytic derivation arrived at Bohmian mechanics. Previously, we were able to embed the basic elements of general relativity (GR) into DHT, and now after Smolin-like quantization of DHT, we can take a step toward quantization of GR. Finally, we remark that DHT is nonlocal in the treelike geometry, but this nonlocality refers to relational nonlocality in the space of events and not Einstein's spatial nonlocality.

The CRISPR-Cas system has transformed the field of gene-editing and created opportunities for novel genome engineering therapeutics. The field has significantly progressed, and recently, CRISPR-Cas9 was utilized in clinical trials to target disease-causing mutations. Existing tools aim to predict the on-target efficacy and potential genome-wide off-targets by scoring a particular gRNA according to an array of gRNA design principles or machine learning algorithms based on empirical results of large numbers of gRNAs. However, such tools are unable to predict the editing outcome by variant Cas enzymes and can only assess potential off-targets related to reference genomes. Here, we employ normal mode analysis (NMA) to investigate the structure of the Cas9 protein complexed with its gRNA and target DNA and explore the function of the protein. Our results demonstrate the feasibility and validity of NMA to predict the activity and specificity of SpyCas9 in the presence of mismatches by comparison to empirical data. Furthermore, despite the absence of their exact structures, this method accurately predicts the enzymatic activity of known high-fidelity engineered Cas9 variants.

TUBB4A -associated disorder is a rare condition affecting the central nervous system. It displays a wide phenotypic spectrum, ranging from isolated late-onset torsion dystonia to a severe early-onset disease with developmental delay, neurological deficits, and atrophy of the basal ganglia and cerebellum, therefore complicating variant interpretation and phenotype prediction in patients carrying TUBB4A variants. We applied entropy-based normal mode analysis (NMA) to investigate genotype–phenotype correlations in TUBB4A -releated disease and to develop an in-silico approach to assist in variant interpretation and phenotype prediction in this disorder. Variants included in our analysis were those reported prior to the conclusion of data collection for this study in October 2019. All TUBB4A pathogenic missense variants reported in ClinVar and Pubmed, for which associated clinical information was available, and all benign/likely benign TUBB4A missense variants reported in ClinVar, were included in the analysis. Pathogenic variants were divided into five phenotypic subgroups. In-silico point mutagenesis in the wild-type modeled protein structure was performed for each variant. Wild-type and mutated structures were analyzed by coarse-grained NMA to quantify protein stability as entropy difference value (ΔG) for each variant. Pairwise ΔG differences between all variant pairs in each structural cluster were calculated and clustered into dendrograms. Our search yielded 41 TUBB4A pathogenic variants in 126 patients, divided into 11 partially overlapping structural clusters across the TUBB4A protein. ΔG-based cluster analysis of the NMA results revealed a continuum of genotype–phenotype correlation across each structural cluster, as well as in transition areas of partially overlapping structural clusters. Benign/likely benign variants were integrated into the genotype–phenotype continuum as expected and were clearly separated from pathogenic variants. We conclude that our results support the incorporation of the NMA-based approach used in this study in the interpretation of variant pathogenicity and phenotype prediction in TUBB4A -related disease. Moreover, our results suggest that NMA may be of value in variant interpretation in additional monogenic conditions.