arXiv subjects

Eiji Yamamoto

Publications and source records attributed to Eiji Yamamoto.

At least 19 recordsLinked to original sources

arXiv2026

Dimensional Crossover of Thermal Transport in Nanoconfined Liquids Driven by the Interplay of Quasi-One-Dimensional Structure and Wall Dissipation

Heat transport in nanoconfined liquids can deviate from ordinary Fourier behavior because confinement alters liquid structure and interfacial dissipation. Although such changes may lead to quasi-one-dimensional transport or overdamped sound relaxation, the conditions under which length-dependent transport persists remain unclear. Here we use molecular dynamics simulations of monatomic liquid argon confined in carbon nanotubes with systematically varied radii and lengths. We find a radius-controlled crossover: length-dependent axial thermal conductivity persists over long tube lengths in single-file and single-shell states, but is strongly truncated or nearly saturated once mixed-shell or multilayer packing develops. This crossover is accompanied by the loss of clear acoustic-like axial modes and enhanced wall--liquid friction. Thus, tube radius controls whether length-dependent heat transport persists or is truncated by coupling confined-liquid structure to wall-induced dissipation.

cond-mat.soft↗

arXiv2026

Anomalous statistics in the Langevin equation with fluctuating diffusivity: from Brownian yet non-Gaussian diffusion to anomalous diffusion and ergodicity breaking

Diffusive motion is a fundamental transport mechanism in physical and biological systems, governing dynamics across a wide range of scales -- from molecular transport to animal foraging. In many complex systems, however, diffusion deviates from classical Brownian behaviour, exhibiting striking phenomena such as Brownian yet non-Gaussian diffusion (BYNGD) and anomalous diffusion. BYNGD describes a frequently observed statistical feature characterised by the coexistence of linear mean-square displacement (MSD) and non-Gaussian displacement distributions. Anomalous diffusion, in contrast, involves a nonlinear time dependence of the MSD and often reflects mechanisms such as trapping, viscoelasticity, heterogeneity, or active processes. Both phenomena challenge the conventional framework based on constant diffusivity and Gaussian statistics. This review focuses the theoretical modelling of such behaviour via the Langevin equation with fluctuating diffusivity (LEFD) -- a flexible stochastic framework that captures essential features of diffusion in heterogeneous media. LEFD not only accounts for BYNGD but also naturally encompasses a wide range of anomalous transport phenomena, including subdiffusion, ageing, and weak ergodicity breaking. Ergodicity is discussed in terms of the correspondence between time and ensemble averages, as well as the trajectory-to-trajectory variability of time-averaged observables. The review further highlights the empirical relevance of LEFD and related models in explaining diverse experimental observations and underscores their value to uncovering the physical mechanisms governing transport in complex systems.

cond-mat.stat-mech↗

arXiv2026

Truncation by death in the sufficient cause framework

The sufficient cause framework has been used for decades to improve our understanding of both basic and more complex causal concepts in epidemiology, such as mediation and interaction. Here, we make use of this framework to provide a description of truncation by death, in which the outcome of interest is undefined for individuals who die before the time of assessment at the end of follow-up. We explain the non-causal nature of the crude estimand that compares outcomes by treatment levels conditional on observed survival by showing that it corresponds to a comparison of distinct risk status types, which are defined based on the susceptibility to sufficient causes. Further, expressions for the crude estimand and for the survivor average causal effect, a causal estimand defined under the principal stratification approach, are provided in terms of population-level joint frequencies of the background factors of sufficient causes. Finally, we also describe conditions, based on background factors of sufficient causes, under which the survivor average causal effect is null. Our description of this problem, which studies truncation by death from a new perspective, might encourage further analyses of principal stratification-based estimands using sufficient causes.

stat.ME↗

arXiv2026

Self-Viscophoresis: Autonomous Motion by Biasing Thermal Fluctuations via Self-Generated Viscosity Asymmetry

Microscale transport often relies on ubiquitous yet intrinsically random thermal fluctuations. Understanding how such fluctuations can be biased into directed motion has long been a central theme of nonequilibrium physics. Here, we introduce self-viscophoresis, a mechanism of autonomous motion based on the rectification of thermal fluctuations in a self-generated nonequilibrium viscosity field. Asymmetric colloidal particles dispersed in a thermoresponsive polymer solution induce local heating under uniform illumination, producing a spatially asymmetric viscosity profile around the particle and resulting in persistent directed motion. To elucidate the physical origin of this behavior, we develop a minimal Langevin model coupling isotropic thermal fluctuations to a dynamically updating temperature-viscosity field. The model shows that viscosity asymmetry anisotropically damps stochastic dynamics, effectively biasing thermal fluctuations into a net drift. It thus reproduces the observed directed motion without invoking deterministic propulsion terms associated with effective potentials or environmental fluid flows. Our results distinguish self-viscophoresis from conventional self-propulsion mechanisms and establish it as a general framework enabling reversible control of both the direction and dimensionality of motion.

cond-mat.soft↗

arXiv2026

Multiple binding modes of AKT on PIP$_3$-containing membranes

The PI3K/AKT signaling pathway is triggered by recruitment of AKT to cellular membranes. Although AKT is a multidomain serine/threonine kinase composed of an N-terminal pleckstrin homology (PH) domain and a C-terminal kinase domain, how these domains cooperate to regulate AKT activation on membranes remains unclear at the molecular level. Here, using molecular dynamics simulations of full-length AKT on PIP$_3$-containing lipid bilayers, we identify four distinct membrane-binding modes that differ in the orientations and membrane contacts of the PH and kinase domains. In addition to PIP$_3$ binding to the PH domain, we observe specific PIP$_3$ interactions with basic residues in the kinase domain. In the most stable mode, PIP$_3$ interacts with both the canonical and a secondary binding site in the PH domain, while the kinase domain adopts an orientation in which the activation-loop phosphorylation site is exposed to the solvent. Interestingly, the populations of these binding modes depend on the PIP$_3$ concentration in the membrane, leading to changes in the preferred orientation of AKT. These findings shed light on how lipid recognition by the PH domain and the kinase domain of AKT cooperatively shape its membrane-bound conformations.

physics.bio-ph↗

arXiv2025

TDP-43 multidomains and RNA modulate interactions and viscoelasticity in biomolecular condensates

RNA-binding proteins form biomolecular condensates with RNA through phase separation, playing crucial roles in various cellular processes. While intrinsically disordered regions (IDRs) are key drivers of phase separation, additional factors such as folded domains and RNA also influence condensate formation and physical properties. However, the molecular mechanisms underlying this regulation remain elusive. Here, using molecular dynamics simulations, we investigate how the multidomain structure of TDP-43, which consists of its IDR, RNA recognition motifs (RRMs), and N-terminal domain (NTD), interacts with RNA and affects the characteristics of phase separation. Our analysis reveals that interaction sites within the IDR undergo dynamic rearrangement, driven by key residues that depend on the specific combination of folded domains. Upon RNA binding, several intermolecular interactions of TDP-43 are replaced by TDP-43-polyA interactions, altering viscoelastic properties of the condensate. Specifically, RRMs enhance viscosity, whereas the NTD reduces it. The presence of polyA increases elasticity, making viscosity and elasticity comparable in magnitude. These findings suggest that the multidomain structure of TDP-43 and its RNA interactions orchestrate condensate organization, modulating their viscoelastic properties.

cond-mat.soft↗

arXiv2024

Thermal transport of confined water molecules in quasi-one-dimensional nanotubes

Dimensions and molecular structure play pivotal roles in the principle of heat conduction. The dimensional characteristics of solution within nanoscale systems depend on the degrees of confinement. However, the influence of such variations on heat transfer remains inadequately understood. Here, we perform quasi-one-dimensional non-equilibrium molecular dynamics simulations to calculate the thermal conductivity of water molecules confined in carbon nanotubes. The structure of water molecules is determined depending on the nanotube radius, forming a single-file, a single-layer, and a double-layer structure, corresponding to an increasing radius order. We reveal that the thermal conductivity of liquid water has a sublinear dependency on nanotube length exclusively when water molecules form a single file. Stronger confinement leads to behavioral and structural characteristics closely resembling a one-dimensional nature. Moreover, single-layer-structured water molecules exhibit enhanced thermal conductivity. We elucidate that this is due to the increase in the local water density and the absence of transitions to another layer, which typically occurs in systems with double-layer water structures within relatively large radius nanotubes.

cond-mat.soft↗

arXiv2024

Diffusion of intrinsically disordered proteins within viscoelastic membraneless droplets

In living cells, intrinsically disordered proteins (IDPs), such as FUS and DDX4, undergo phase separation, forming biomolecular condensates. Using molecular dynamics simulations, we investigate their behavior in their respective homogenous droplets. We find that the proteins exhibit transient subdiffusion due to the viscoelastic nature and confinement effects in the droplets. The conformation and the instantaneous diffusivity of the proteins significantly vary between the interior and the interface of the droplet, resulting in non-Gaussianity in the displacement distributions. This study highlights key aspects of IDP behavior in biomolecular condensates.

cond-mat.soft↗

arXiv2023

Molecular mechanism of anion permeation through aquaporin 6

Aquaporins (AQPs) are recognized as transmembrane water channels that facilitate selective water permeation through their monomeric pores. Among the AQP family, AQP6 has a unique characteristic as an anion channel, which is allosterically controlled by pH conditions and is eliminated by a single amino acid mutation. However, the molecular mechanism of anion permeation through AQP6 remains unclear. Using molecular dynamics simulations in the presence of a transmembrane voltage utilizing an ion concentration gradient, we show that chloride ions permeate through the pore corresponding to the central axis of the AQP6 homotetramer. Under low pH conditions, a subtle opening of the hydrophobic selective filter (SF), located near the extracellular part of the central pore, becomes wetted and enables anion permeation. Our simulations also indicate that a single mutation (N63G) in human AQP6, located at the central pore, significantly reduces anion conduction, consistent with experimental data. Moreover, we demonstrate the pH-sensing mechanism in which the protonation of H184 and H189 under low pH conditions allosterically triggers the gating of the SF region. These results suggest a unique pH-dependent allosteric anion permeation mechanism in AQP6 and could clarify the role of the central pore in some of the AQP tetramers.

q-bio.BM↗

arXiv2023

Heterogeneous biological membranes regulate protein partitioning via fluctuating diffusivity

Cell membranes phase separate into ordered ${\rm L_o}$ and disordered ${\rm L_d}$ domains depending on their compositions. This membrane compartmentalization is heterogeneous and regulates the localization of specific proteins related to cell signaling and trafficking. However, it is unclear how the heterogeneity of the membranes affects the diffusion and localization of proteins in ${\rm L_o}$ and ${\rm L_d}$ domains. Here, using Langevin dynamics simulations coupled with the phase-field (LDPF) method, we investigate several tens of milliseconds-scale diffusion and localization of proteins in heterogeneous biological membrane models showing phase separation into ${\rm L_o}$ and ${\rm L_d}$ domains. The diffusivity of proteins exhibits temporal fluctuations depending on the field composition. Increases in molecular concentrations and domain preference of the molecule induce subdiffusive behavior due to molecular collisions by crowding and confinement effects, respectively. Moreover, we quantitatively demonstrate that the protein partitioning into the ${\rm L_o}$ domain is determined by the difference in molecular diffusivity between domains, molecular preference of domain, and molecular concentration. These results pave the way for understanding how biological reactions caused by molecular partitioning may be controlled in heterogeneous media. Moreover, the methodology proposed here is applicable not only to biological membrane systems but also to the study of diffusion and localization phenomena of molecules in various heterogeneous systems.

cond-mat.soft↗

arXiv2023

Estimating relaxation times from a single trajectory

Complex systems such as protein conformational fluctuations and supercooled liquids exhibit a long relaxation time and are considered to posses multiple relaxation times. We analytically obtain the exact correlation function for stochastic processes with multiple relaxation times. We show that the time-averaged correlation function calculated by a trajectory whose length is shorter than the longest relaxation time exhibits an apparent aging behavior. We propose a method to extract relaxation times from a single trajectory. This method successfully extracts relaxation times of a stochastic process with multiple states when a state can be characterized by the values of the trajectory. As an application of this method, we estimate several relaxation times smaller than the longest relaxation time in conformational fluctuations of a small protein.

cond-mat.soft↗

arXiv2022

Wetting hysteresis induces effective unidirectional water transport through a fluctuating nanochannel

We propose a water pump that actively transports water molecules through nanochannels. Spatially asymmetric thermal fluctuations imposed on the channel radius cause unidirectional water flow without osmotic pressure, which can be attributed to hysteresis in the cyclic transition between the wetting/drying states. We show that the water transport depends on fluctuations, such as white, Brownian, and pink noises. Because of the high-frequency components in white noise, fast switching of open and close states inhibits channel wetting. Conversely, pink and Brownian noises generate high-pass filtered net flow. Brownian fluctuation leads to a faster water transport rate, whereas pink noise has a higher capability to overcome osmotic pressure in the opposite direction. A trade-off relationship exists between the resonant frequency of the fluctuation and the flow amplification. The proposed pump can be considered as an analogy for the reversed Carnot cycle, which is the upper limit on the energy conversion efficiency.

cond-mat.soft↗

arXiv2021

Ligand-induced protein dynamics differences correlate with protein-ligand binding affinities: An unsupervised deep learning approach

Prediction of protein-ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). The energy gap implicitly includes the effects of changes in protein dynamics induced by the binding ligand. However, the relationship between protein dynamics and binding affinity remains unclear. Here, we propose a novel method that represents protein behavioral change upon ligand binding with a simple feature that can be used to predict protein-ligand affinity. From unbiased molecular simulation data, an unsupervised deep learning method measures the differences in protein dynamics at a ligand-binding site depending on the bound ligands. A dimension-reduction method extracts a dynamic feature that is strongly correlated to the binding affinities. Moreover, the residues that play important roles in protein-ligand interactions are specified based on their contribution to the differences. These results indicate the potential for dynamics-based drug discovery.

q-bio.BM↗

arXiv2021

Universal relation between instantaneous diffusivity and radius of gyration of proteins in aqueous solution

Protein conformational fluctuations are highly complex and exhibit long-term correlations. Here, molecular dynamics simulations of small proteins demonstrate that these conformational fluctuations directly affect the protein's instantaneous diffusivity $D_I$. We find that the radius of gyration $R_g$ of the proteins exhibits $1/f$ fluctuations, that are synchronous with the fluctuations of $D_I$. Our analysis demonstrates the validity of the local Stokes-Einstein type relation $D_I\propto1/(R_g + R_0)$, where $R_0\sim0.3$ nm is assumed to be a hydration layer around the protein. From the analysis of different protein types with both strong and weak conformational fluctuations the validity of the Stokes-Einstein type relation appears to be a general property.

cond-mat.stat-mech↗

arXiv2018

Local reservoir model for choice-based learning

Decision making based on behavioral and neural observations of living systems has been extensively studied in brain science, psychology, and other disciplines. Decision-making mechanisms have also been experimentally implemented in physical processes, such as single photons and chaotic lasers. The findings of these experiments suggest that there is a certain common basis in describing decision making, regardless of its physical realizations. In this study, we propose a local reservoir model to account for choice-based learning (CBL). CBL describes decision consistency as a phenomenon where making a certain decision increases the possibility of making that same decision again later, which has been intensively investigated in neuroscience, psychology, etc. Our proposed model is inspired by the viewpoint that a decision is affected by its local environment, which is referred to as a local reservoir. If the size of the local reservoir is large enough, consecutive decision making will not be affected by previous decisions, thus showing lower degrees of decision consistency in CBL. In contrast, if the size of the local reservoir decreases, a biased distribution occurs within it, which leads to higher degrees of decision consistency in CBL. In this study, an analytical approach on local reservoirs is presented, as well as several numerical demonstrations. Furthermore, a physical architecture for CBL based on single photons is discussed, and the effects of local reservoirs is numerically demonstrated. Decision consistency in human decision-making tasks and in recruiting empirical data are evaluated based on local reservoir. In summary, the proposed local reservoir model paves a path toward establishing a foundation for computational mechanisms and the systematic analysis of decision making on different levels.

stat.ML↗

arXiv2017

Detection of Transition Times from Single-particle-tracking Trajectories

In heterogeneous environments, the diffusivity is not constant but changes with time. It is important to detect changes in the diffusivity from single-particle-tracking trajectories in experiments. Here, we devise a novel method for detecting the transition times of the diffusivity from trajectory data. A key idea of this method is the introduction of a characteristic time scale of the diffusive states, which is obtained by a fluctuation analysis of the time-averaged mean square displacements. We test our method in silico by using the Langevin equation with a fluctuating diffusivity. We show that our method can successfully detect the transition times of diffusive states and obtain the diffusion coefficient as a function of time. This method will provide a quantitative description of the fluctuating diffusivity in heterogeneous environments and can be applied to time series with transitions of states.

cond-mat.stat-mech↗

arXiv2016

Distributional Behavior of Diffusion Coefficients Obtained by Single Trajectories in Annealed Transit Time Model

Local diffusion coefficients in disordered systems such as spin glass systems and living cells are highly heterogeneous and may change over time. Such a time-dependent and spatially heterogeneous environment results in irreproducibility of single-particle-tracking measurements. Irreproducibility of time-averaged observables has been theoretically studied in the context of weak ergodicity breaking in stochastic processes. Here, we provide rigorous descriptions of equilibrium and non-equilibrium diffusion processes for the annealed transit time model, which is a heterogeneous diffusion model in living cells. We give analytical solutions for the mean square displacement (MSD) and the relative standard deviation of the time-averaged MSD for equilibrium and non-equilibrium situations. We find that the time-averaged MSD grows linearly with time and that the diffusion coefficients are intrinsically random in non-equilibrium situations. Our findings pave the way for a theoretical understanding of distributional behavior of the diffusion coefficients in disordered systems.

cond-mat.stat-mech↗

arXiv2016

Langevin equation with fluctuating diffusivity: a two-state model

Recently, anomalous subdiffusion, aging, and scatter of the diffusion coefficient have been reported in many single-particle-tracking experiments, though origins of these behaviors are still elusive. Here, as a model to describe such phenomena, we investigate a Langevin equation with diffusivity fluctuating between a fast and a slow state. We assume that the sojourn time distributions of these two states are given by power laws. It is shown that, for a non-equilibrium ensemble, the ensemble-averaged mean square displacement (MSD) shows transient subdiffusion. In contrast, the time-averaged MSD shows normal diffusion, but an effective diffusion coefficient transiently shows aging behavior. The propagator is non-Gaussian for short time, and converges to a Gaussian distribution in a long time limit; this convergence to Gaussian is extremely slow for some parameter values. For equilibrium ensembles, both ensemble-averaged and time-averaged MSDs show only normal diffusion, and thus we cannot detect any traces of the fluctuating diffusivity with these MSDs. Therefore, as an alternative approach to characterize the fluctuating diffusivity, the relative standard deviation (RSD) of the time-averaged MSD is utilized, and it is shown that the RSD exhibits slow relaxation as a signature of the long-time correlation in the fluctuating diffusivity. Furthermore, it is shown that the RSD is related to a non-Gaussian parameter of the propagator. To obtain these theoretical results, we develop a two-state renewal theory as an analytical tool.

cond-mat.stat-mech↗