arXiv subjects

Tatsuaki Tsuruyama

Publications and source records attributed to Tatsuaki Tsuruyama.

At least 19 recordsLinked to original sources

arXiv2026

A Comparison of Whole Slide Image Analysis and Diagnostic Field Selection in Pathology AI under Finite Resources

An important question in pathology AI is how to allocate limited analytical resources between broad coverage of a whole slide image (WSI) and detailed analysis of selected regions. We compared a minimal WSI-AI model designed for broad search with Diagnostic Field Selection AI (DFS-AI), which uses a coarse overview to select diagnostically relevant fields for detailed analysis. Three minimal models introduced localization information, contextual discrimination, and spatial structure in sequence. In Model I, WSI-AI was favored when coarse localization was uninformative or costly. When coarse observation provided useful ranking information, DFS-AI was favored over an intermediate range of resources; WSI-AI again became preferable when resources allowed nearly complete fine observation. In Model II, background heterogeneity generated distractors. In a WSI control given the same local context as DFS-AI, most of the original DFS advantage was explained by local background correction, while coarse candidate selection provided a smaller additional benefit. In Model III, larger contiguous lesions were easier for WSI-AI to discover but more difficult to characterize completely with a finite budget for fine observation. The DFS advantage for complete characterization persisted when the comparator used the same field sized unit of fine analysis and when success was defined symmetrically as 100%, 90%, or 80% lesion unit coverage. Together, the models support a framework in which the preferred strategy changes with available resources and depends on the information available for selection, its acquisition cost, lesion structure, and the diagnostic endpoint. This framework clarifies when limited pathology AI resources should be devoted to broad WSI coverage and when they should be concentrated on selected Diagnostic Fields.

q-bio.QM↗

arXiv2026

Spatial-Molecular Information Analysis Based on Histopathological Geometry: KL-Divergence Decomposition of Cell Distribution and Location-Dependent Expression States in PD-1 Immunohistochemistry

Spatial biology enables molecular expression to be analyzed together with the location of cells in tissue. However, it is often difficult to distinguish whether cells preferentially accumulate near a histopathological boundary from whether their molecular expression states vary with that location. To establish informational scientific framework, we define boundary distance as R and a binary molecular expression state as G, and compare the observed joint distribution P\left(R,G\right) with Q\left(R\right)P\left(G\right), where Q\left(R\right) is a null distribution determined by tissue geometry. The resulting Kullback-Leibler divergence decomposes into D_{R}, which quantifies deviation of the spatial distribution of cells from the geometric null, and I\left(R;G\right), which measures how strongly molecular expression state depends on boundary distance. Idealized simulations and semi-synthetic validation using geometry reconstructed from a PD-1 IHC image confirmed that these two effects can be generated separately and recovered by the corresponding terms. Plug-in estimators showed positive finite-sample bias, supporting permutation-based inference. We then applied the framework to five publicly available PD-1 lung cancer IHC images from the Human Protein Atlas. In pooled analysis, I\left(R;G\right)=0.0302 nats and was significant in a stratified within-image label-permutation test (p=0.00040). This framework provides a quantitative way to distinguish where cells are located from how their molecular expression states vary with tissue location. Larger cohorts, independent datasets, additional cancer types, and other molecular markers will be required to establish generalizability and clinical utility.

q-bio.QM↗

arXiv2026

Exchange-Symmetric Dissipation at the DNA-RNA Polymerase Interface

In the DNA-RNA polymerase complex, RNA polymerase (RNAP) selectively incorporates ribonucleoside triphosphates according to the base information encoded in DNA, elongates the RNA strand, and progresses along the template. The state of the template DNA therefore conditions RNAP binding and chemical transitions. Conversely, from the viewpoint of DNA, the binding state and coordinate position of RNAP condition the local structural response of DNA. As RNAP advances, upstream DNA reanneals, downstream DNA opens, the RNA-DNA hybrid constrains the local geometry, and local bending and twisting of the DNA are updated. We introduce an interface dissipation, Σint, that provides an exchange-symmetric measure of path irreversibility at the DNA-RNAP interface, independent of whether DNA or RNAP is assigned the role of observer or observed. Irreversibility is quantified by the path-space Kullback-Leibler (KL) divergence between the forward path measure and its time-reversed counterpart, from which the dissipation of the joint system and those of the marginal systems are defined. The non-negativity of the interface dissipation derived in this work does not rely on RNAP-specific kinetic assumptions; rather, it holds for general path processes for which the relevant KL divergences and conditional path measures are well defined. RNAP transcription is treated as a biophysical application of this general theorem to the DNA-RNAP interface.

physics.bio-ph↗

arXiv2026

Mutual-Information-Dependent Nonlinear Threshold Response Model Linked to the Free Energy Principle

Biological systems not only infer states of the external world from sensory input but also vary the expression of their responses according to the information they have acquired. Here, without altering the standard inferential and policy-evaluation schemes of the Free Energy Principle (FEP) and active inference, we propose a minimal dynamics that links the mutual information formed between an external state and an internal representation through inference to a response-expression variable distinct from policy selection. In this formulation, established information is positioned as a state signal that modulates response expression. The model introduces a piecewise nonlinear term in which response activation is driven only when mutual information exceeds an information threshold. Monte Carlo simulations using a two-state Markov environment showed that increasing observation accuracy increased the mutual information between the external state and the internal representation and, in turn, increased response activation. By contrast, in a control condition without information-response coupling, the response remained at its baseline level. The basic pattern of response activation was preserved when observation accuracy, the information-response coupling coefficient, the information threshold, and the closed-loop coefficient linking response to sensory sampling were varied. These results show that coupling information formed by FEP-consistent inference to an independent response-expression dynamics can generate a nonlinear response that depends on an information threshold.

q-bio.OT↗

arXiv2026

Thermodynamics Reconstructed from Information Theory:An Axiomatic Framework via Information-Volume Constraints and Path-Space KL Divergence

We develop an axiomatic reconstruction of thermodynamics based entirely on two primitive components: a description of what aspects of a system are observed and a reference measure that encodes the underlying descriptive convention. These ingredients define an "information volume" for each observational cell. By incorporating the logarithm of this volume as an additional constraint in a minimum-relative-entropy inference scheme, temperature, chemical potential, and pressure arise as conjugate variables of a single information-theoretic functional. This leads to a Legendre-type structure and a first-law-like relation in which pressure corresponds to information volume rather than geometric volume. For nonequilibrium dynamics, entropy production is characterized through the relative-entropy asymmetry between forward and time-reversed stochastic evolutions. A decomposition using observational entropy then separates total dissipation into system and environment contributions. Heat is defined as the part of dissipation not accounted for by the system-entropy change, yielding a representation that does not rely on local detailed balance or a specific bath model. We further show that the difference between joint and partially observed dissipation equals the average of conditional relative entropies, providing a unified interpretation of hidden dissipation and information-flow terms as projection-induced gaps.

physics.chem-ph↗

arXiv2026

Fluctuation Theorems from a Continuous-Time Markov Model of Information-Thermodynamic Capacity in Biochemical Signal Cascades

Biochemical signaling cascades transmit intracellular information while dissipating energy under nonequilibrium conditions. We model a cascade as a code string and apply information-entropy ideas to quantify an optimal transmission rate. A time-normalized entropy functional is maximized to define a capacity-like quantity governed by a conserved multiplier. To place the theory on a rigorous stochastic-thermodynamic footing, we formulate stepwise signaling as a continuous-time Markov jump process with forward and reverse competing rates. The embedded jump chain yields well-defined transition probabilities that justify time-scale-based expressions. Under local detailed balance, the log ratio of forward and reverse rates can be interpreted as entropy production per event, enabling a trajectory-level derivation of detailed and integral fluctuation theorems. We further connect the information-theoretic capacity to the mean dissipation rate and outline finite-time fluctuation structure via the scaled cumulant generating function (SCGF) and Gallavotti--Cohen symmetry, including a worked example using MAPK/ERK timescales.

q-bio.MN↗

arXiv2026

Slowdown and saturation of internal time according to the statistics of information input: a minimal model of response systems

We consider a response system that updates its internal state in accordance with information input arriving from outside. In this paper, we define as internal time the ``number of kinds'' of codes that have been observed at least once up to a given time, and analyze how the way internal time advances is determined by the statistics of information input (arrival rate and code distribution). When arrivals follow a Poisson process, the average advancing speed of internal time decreases monotonically with time, and if the number of kinds of codes is finite, it eventually approaches an upper limit and saturates. As a result, on long time scales, internal time becomes relatively shorter than physical time. For a uniform code distribution, we provide a closed form for the correspondence between internal time and physical time, and show that the physical time required to ``advance internal time by one step'' increases in later stages. As an ancillary quantity, we quantify by conditional entropy the remaining uncertainty of ``which codes have been observed'' when only internal time is known, and we give unimodality and the maximization time in the uniform case, and upper bounds, equality conditions, and expressions of the difference from the upper bound in the non-uniform case. Finally, we also present a generalization that assigns weights (description lengths) to each code so that internal time is ticked according to the amount of information in the input.

physics.comp-ph↗

arXiv2026

Coarse-Grained Kullback--Leibler Control of Diffusion-Based Generative AI

Diffusion models and score-based generative models provide a powerful framework for synthesizing high-quality images from noise. However, there is still no satisfactory theory that describes how coarse-grained quantities, such as blockwise intensity or class proportions after partitioning an image into spatial blocks, are preserved and evolve along the reverse diffusion dynamics. In previous work, the author introduced an information-theoretic Lyapunov function V for non-ergodic Markov processes on a state space partitioned into blocks, defined as the minimal Kullback-Leibler divergence to the set of stationary distributions reachable from a given initial condition, and showed that a leak-tolerant potential V-delta with a prescribed tolerance for block masses admits a closed-form expression as a scaling-and-clipping operation on block masses. In this paper, I transplant this framework to the reverse diffusion process in generative models and propose a reverse diffusion scheme that is projected by the potential V-delta (referred to as the V-delta projected reverse diffusion). I extend the monotonicity of V to time-inhomogeneous block-preserving Markov kernels and show that, under small leakage and the V-delta projection, V-delta acts as an approximate Lyapunov function. Furthermore, using a toy model consisting of block-constant images and a simplified reverse kernel, I numerically demonstrate that the proposed method keeps the block-mass error and the leak-tolerant potential within the prescribed tolerance, while achieving pixel-wise accuracy and visual quality comparable to the non-projected dynamics. This study reinterprets generative sampling as a decrease of an information potential from noise to data, and provides a design principle for reverse diffusion processes with explicit control of coarse-grained quantities.

cs.LG↗

arXiv2025

Kullback-Leibler Potential for Non-Ergodic Replication Dynamics:An Information-Theoretic Second Law

This study aims to quantify and visualize the degradation of fidelity (information degradation) that inevitably accompanies the replication of information within the framework of information thermodynamics and to propose an information-theoretic formulation of the second law based on this phenomenon. While previous research in information thermodynamics has focused on the thermodynamic costs associated with information "erasure'' or "measurement'' through concepts such as Landauer's principle and mutual information, little systematic discussion has addressed the inherently irreversible nature of "replication'' itself and the accompanying degradation of information structure. In this study, we construct a mathematical model of information replication using a discrete Markov model and Gaussian convolution, and quantify changes in information at each replication step: Shannon entropy, cross-entropy, and the Kullback--Leibler divergence (KLD). The monotonic decrease of KLD exhibits a Lyapunov-like property, which can be interpreted as a potential analogous to the free energy in the process by which a nonequilibrium system converges to a particular steady state. Furthermore, we extend this framework to the potential applicability to biological information processes such as DNA replication, showing that the free energy required for degradation and repair can be expressed in terms of KLD. This contributes to building a unified information-thermodynamic framework for operations such as replication, transmission, and repair of information.

math-ph↗

arXiv2025

Kullback-Leibler Divergence as a Measure of Irreversible Information Loss Near Black Hole Horizons

We present a unified theoretical framework that integrates information theory, thermodynamics, and general relativity to analyze the fundamental limit of decoding time-encoded signals in curved spacetime. In particular, we introduce the Kullback-Leibler divergence (KLD) as a quantitative measure of the mismatch between the transmitted and received symbol distributions induced by gravitational time dilation. Using a minimal communication model, we derive the critical radius at which information decoding becomes thermodynamically impossible due to the divergence of the KLD. We show that this radius approaches the Schwarzschild horizon in the limit where the information entropy cost becomes negligible relative to the transmission energy. This result provides a novel information-theoretic interpretation of the event horizon as a boundary of irreversible information loss governed by universal thermodynamic principles. Our framework offers new insights into the entropic and energetic constraints on communication in strong gravitational fields and may extend to general relativistic and quantum information settings.

gr-qc↗

arXiv2025

Relativistic Limits of Decoding: Critical Divergence of Kullback-Leibler Information and Free Energy

We present a statistical mechanical framework based on the Kullback-Leibler divergence (KLD) to analyze the relativistic limits of decoding time-encoded information from a moving source. By modeling the symbol durations as entropy-maximizing sequences and treating the decoding process as context-sensitive inference, we identify KLD between the sender and receiver distributions as a key indicator of contextual mismatch. We show that, under Lorentz transformations, this divergence grows with the sender's velocity and exhibits critical divergence as the velocity approaches the speed of light. Furthermore, we derive an analytic expression for the Fisher information and demonstrate that decoding sensitivity diverges similarly, indicating instability near the relativistic limit. By introducing an information-theoretic free energy based on the decoding cost, we determine a critical velocity beyond which decoding becomes thermodynamically impossible. These results reveal a phase-transition-like behavior in relativistic information transfer and provide a unified interpretation of KLD, Fisher information, and free energy as measures of decodability. The formalism developed here offers new insights into high-speed communication, relativistic signal processing, and information geometry in non-inertial frames.

math-ph↗

arXiv2025

Large Deviation Theory Approach to Fluctuation Theorems and Landauer's Principle through Heat Redefinition

Large deviation theory (LDT) provides a mathematical framework to quantify the probabilities of rare events in stochastic systems. In this study, we applied LDT to model a chemical reaction system and demonstrated that the fluctuation theorem for nonequilibrium reaction systems can be derived from the symmetry of the cumulant generating function defined through the rate function. Notably, this derivation does not depend on the assumption of local detailed balance. Furthermore, we redefined heat using this rate function based on information theory and evaluated Landauer's principle, which addresses the minimum energy cost associated with information erasure. These findings show the utility of LDT as a comprehensive framework for analyzing a wide range of nonequilibrium systems.

physics.chem-ph↗

arXiv2019

Pattern formation of skin cancers: Effects of cancer proliferation and hydrodynamic interactions

We study pattern formation of skin cancers by means of numerical simulation of a binary system consisting of cancer and healthy cells. We extend the conventional Model H for macrophase separations by considering a logistic growth of cancer cells and also a mechanical friction between dermis and epidermis. Importantly, our model exhibits a microphase separation due to the proliferation of cancer cells. By numerically solving the time evolution equations of the cancer composition and its velocity, we show that the phase separation kinetics strongly depends on the cell proliferation rate as well as on the strength of hydrodynamic interactions. A steady state diagram of cancer patterns is established in terms of these two dynamical parameters and some of the patterns correspond to clinically observed cancer patterns. Furthermore, we examine in detail the time evolution of the average composition of cancer cells and the characteristic length of the microstructures. Our results demonstrate that different sequence of cancer patterns can be obtained by changing the proliferation rate and/or hydrodynamic interactions.

cond-mat.soft↗

arXiv2017

Channel Capacity of Coding System on Tsallis Entropy and q-Statistics

The field of information science has greatly developed, and applications in various fields have emerged. In this paper, we evaluated the coding system in the theory of Tsallis entropy for transmission of messages and aimed to formulate the channel capacity by maximization of the Tsallis entropy within a given condition of code length. As a result, we obtained a simple relational expression between code length and code appearance probability and, additionally, a generalized formula of the channel capacity on the basis of Tsallis entropy statistics. This theoretical framework may contribute to data processing techniques and other applications.

q-bio.MN↗

arXiv2017

DNA insertion mutations can be predicted by a periodic probability function

It is generally difficult to predict the positions of mutations in genomic DNA at the nucleotide level. Retroviral DNA insertion is one mode of mutation, resulting in host infections that are difficult to treat. This mutation process involves the integration of retroviral DNA into the host-infected cellular genomic DNA following the interaction between host DNA and a pre-integration complex consisting of retroviral DNA and integrase. Here, we report that retroviral insertion sites around a hotspot within the Zfp521 and N-myc genes can be predicted by a periodic function that is deduced using the diffraction lattice model. In conclusion, the mutagenesis process is described by a biophysical model for DNA-DNA interactions.

physics.bio-ph↗

arXiv2017

Quantifying efficient information transduction of biochemical signaling cascades

Cells can be considered as systems that utilize changes in thermodynamic entropy as information. Therefore, they serve as useful models for investigating the relationships between entropy production and information transmission, i.e., signal transduction. Based on the hypothesis that cells apply a chemical reaction cascade for the most efficient transduction of information, we adopted a coding design that minimizes the number of bits per concentration of molecules that are employed for information transduction. As a result, the average rate of entropy production is uniform across all cycles in a cascade reaction. Thus, the entropy production rate can be a valuable measure for the quantification of intracellular signal transduction.

q-bio.MN↗

arXiv2017

Quantifying the information transduction of biochemical reaction cascades: transcription of mRNA from ligand stimulation

A cell has the ability to convert an environmental change into the expression of genetic information through a chain of intracellular signal transduction reactions. Here, we aimed to develop a method for quantifying this signal transduction. We showed that the channel capacities of individual steps in a given general model cascade were equivalent in an independent manner, and were given by the entropy production rate. Signal transduction was transmitted by fluctuation of the entropy production rate and quantified transduction was estimated by the work done in individual steps. If the individual step representing the modification to demodification of the signal molecules is considered to be a Szilard engine, the maximal work done is equivalent to the chemical potential change of the messenger that is consumed during the modification reaction. Our method was applicable to calculate the channel capacity of the MAPK cascade. In conclusion, our method is suitable for quantitative analyses of signal transduction.

q-bio.MN↗

arXiv2015

The channel capacity and information density of biochemical signaling cascades

Living cell signaling systems include multistep biochemical signaling reaction cascades (BSCs) comprising modifications of molecular signaling proteins. Substantial data on BSCs have been accumulated in the field of molecular biology and the analysis of signaling systems requires qualitative evaluation. However, quantification of the information and channel capacity of BSCs has not been focused on from the perspective of information theory. In the current study, we aimed to derive basic equations for describing the channel capacity and information density of BSCs using the fluctuation theorem. From the results, channel capacity and information density can be described using the average entropy production rate when the signaling system is minimally redundant. The channel capacity could actually be calculated for the mitogen-activated protein kinase BSC when it was minimally redundant. This quantitative method of examination is applicable to the quantitative analysis of BSCs.

q-bio.MN↗