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Peter V. E. McClintock

Publications and source records attributed to Peter V. E. McClintock.

At least 19 recordsLinked to original sources

A phenomenological multiscale framework for orientational interactions and viscoelasticity in migrating epithelial monolayers

Collective migration of epithelial monolayers emerges from the interplay between mechanical interactions and biochemical signalling. Here, we present a phenomenological mechanobiological framework linking cell-scale orientational interactions to tissue-scale mechanics. We distinguish reversible and irreversible head-on and glancing collisions, showing that reversible interactions store orientational mechanical energy while preserving collision geometry, whereas irreversible interactions dissipate energy and alter cell orientation. The balance between energy storage and dissipation governs collective migration, mechanical feedback, and density-dependent processes including cell jamming and live cell extrusion. These interactions regulate cell elasticity, contractility, and adhesion, thereby modifying epithelial surface tension and the effective viscoelastic response of the monolayer. We quantify these effects using orientational interaction potentials, an effective second virial coefficient, and dimensionless measures of stored and dissipated orientational energy. The relative contribution of these mechanisms increases with cell packing density, becoming dominant near the jamming transition. This framework provides a constitutive interpretation connecting collision-induced orientation dynamics with emergent epithelial rheology and suggests how density-dependent interaction regimes shape collective migration and tissue viscoelasticity.

q-bio.CB↗

Irregular curvature at focal adhesions modulates Piezo1 activity and low frequency ultrasound induced apoptosis in cancer cells

Low-frequency, low intensity ultrasound (LIUS) has emerged as a promising physical modality capable of inducing selective apoptosis of cancer cells, while sparing healthy epithelial cells and fibroblasts. Hitherto, the mechanism underlying this selectivity has been unclear, but we now propose and develop a theoretical framework linking the distinct mechanical behaviours of cancer versus healthy cells to their differential responses to LIUS. We point out that cancer cells exhibit inhomogeneous ventral stress-fiber networks, which can produce irregular focal adhesion geometry and inward membrane curvature near focal adhesions under low-intensity ultrasound (LIUS). These curvature irregularities can favor loose packing of Piezo1 channels, thereby preserving their activity. In contrast, healthy epithelial cells and fibroblasts display more homogeneous cytoskeletal organization, which can result in more regular curvature profiles adjacent to focal adhesions. This leads to curvature-driven cholesterol redistribution, resulting in altered spatial organization of Piezo1 clusters and reduced coordinated channel activity and allowing cells to remain in their active, proliferative state when exposed to LIUS. Based on theoretical modeling and previous experimental findings, we propose that differences in cytoskeletal organization and membrane curvature can contribute to distinct Piezo1 activation patterns between healthy and cancerous cells. Our analysis identifies curvature-mediated Piezo1 redistribution as a potential physical basis for LIUS selectivity and provides a mechanistic foundation for designing ultrasound-based therapies to exploit the intrinsic cytoskeletal vulnerabilities of cancer cells.

q-bio.BM↗

Marangoni-Driven Redistribution and Activity of Piezo1 Molecules in Epithelial and Cancer Cells

The activity and distribution of Piezo1 molecules, along with the maturity and strength of focal adhesions (FAs), serve as critical factors influencing cell mechanosensing. Notably, migrating epithelial cells and mesenchymal-like cancer cells exhibit significantly different behaviors regarding these elements. In cancer cells, Piezo1 molecules are distributed uniformly, while in epithelial cells, their distribution is heterogeneous. In epithelial cells, Piezo1 molecules tend to group around FAs, a phenomenon that is enhanced by actomyosin contractility. However, a reduction in contractility results in a more uniform distribution of Piezo1 molecules. The expression and activity levels of Piezo1 molecules are markedly higher in cancer cells compared to epithelial cells. The activity of Piezo1 molecules correlates with the intracellular calcium concentration. Despite the extensive experimental studies on the properties of migrating epithelial and mesenchymal-like cancer cells, the physical explanations remain lacking. The primary objective of this theoretical study is to explore: (i) the inhomogeneous distribution of Piezo1 molecules in epithelial cells in relation to the Marangoni effect, (ii) the heightened activity of Piezo1 molecules in cancer cells by specifying the driving force, and (iii) the influence of membrane-mediated interactions among Piezo1 molecules grouped near FAs in epithelial cells on their activity.

q-bio.CB↗

Visualization of Oscillatory Electron Dynamics on the Surface of Liquid Helium

We have measured signals induced in 5 Corbino electrodes by spontaneous oscillations of 2D surface electrons on liquid helium at $\sim$0.3\,K, with a perpendicular magnetic field and microwave radiation. Analysis using multi-scale, time-resolved, methods yields results consistent with magnetoplasmons modulated by slow surface gravity waves, with the latter requiring consideration of the 3rd dimension. Calculation of phase differences and phase coherences between signals from differently-positioned pairs of electrodes enables reconstruction of the electron dynamics on the helium surface.

cond-mat.other↗

Mechanism of the Resonant Enhancement of Electron Drift in Nanometre Semiconductor Superlattices Subjected to Electric and Inclined Magnetic Fields

We address the increase of electron drift velocity that arises in semiconductor superlattices (SLs) subjected to constant electric and magnetic fields. It occurs if the magnetic field possesses nonzero components both along and perpendicular to the SL axis and the Bloch oscillations along the SL axis become resonant with cyclotron rotation in the transverse plane. It is a phenomenon of considerable interest, so that it is important to understand the underlying mechanism. In an earlier Letter (Phys. Rev. Lett. 114, 166802 (2015)) we showed that, contrary to a general belief that drift enhancement occurs through chaotic diffusion along a stochastic web (SW) within semiclassical collisionless dynamics, the phenomenon actually arises through a non-chaotic mechanism. In fact, any chaos that occurs tends to reduce the drift. We now provide fuller details, elucidating the mechanism in physical terms, and extending the investigation. In particular, we: (i) demonstrate that pronounced drift enhancement can still occur even in the complete absence of an SW; (ii) show that, where an SW does exist and its characteristic slow dynamics comes into play, it suppresses the drift enhancement even before strong chaos is manifested; (iii) generalize our theory for non-small temperature, showing that heating does not affect the enhancement mechanism and accounting for some earlier numerical observations; (iv) demonstrate that certain analytic results reported previously are incorrect; (v) provide an extended critical review of the subject and closely related issues; and (vi) discuss some challenging problems for the future.

cond-mat.mes-hall↗

Diffusion Phenomena in a Mixed Phase Space

We show that, in strongly chaotic dynamical systems, the average particle velocity can be calculated analytically by consideration of Brownian dynamics in phase space, the method of images and use of the classical diffusion equation. The method is demonstrated on the simplified Fermi-Ulam accelerator model, which has a mixed phase space with chaotic seas, invariant tori and Kolmogorov-Arnold-Moser (KAM) islands. The calculated average velocities agree well with numerical simulations and with an earlier empirical theory. The procedure can readily be extended to other systems including time-dependent billiards.

cond-mat.stat-mech↗

Coupling functions: Universal insights into dynamical interaction mechanisms

The dynamical systems found in Nature are rarely isolated. Instead they interact and influence each other. The coupling functions that connect them contain detailed information about the functional mechanisms underlying the interactions and prescribe the physical rule specifying how an interaction occurs. Here, we aim to present a coherent and comprehensive review encompassing the rapid progress made recently in the analysis, understanding and applications of coupling functions. The basic concepts and characteristics of coupling functions are presented through demonstrative examples of different domains, revealing the mechanisms and emphasizing their multivariate nature. The theory of coupling functions is discussed through gradually increasing complexity from strong and weak interactions to globally-coupled systems and networks. A variety of methods that have been developed for the detection and reconstruction of coupling functions from measured data is described. These methods are based on different statistical techniques for dynamical inference. Stemming from physics, such methods are being applied in diverse areas of science and technology, including chemistry, biology, physiology, neuroscience, social sciences, mechanics and secure communications. This breadth of application illustrates the universality of coupling functions for studying the interaction mechanisms of coupled dynamical systems.

nlin.AO↗

On the extraction of instantaneous frequencies from ridges in time-frequency representations of signals

The extraction of oscillatory components and their properties from different time-frequency representations, such as windowed Fourier transform and wavelet transform, is an important topic in signal processing. The first step in this procedure is to find an appropriate ridge curve: a sequence of amplitude peak positions (ridge points), corresponding to the component of interest. This is not a trivial issue, and the optimal method for extraction is still not settled or agreed. We discuss and develop procedures that can be used for this task and compare their performance on both simulated and real data. In particular, we propose a method which, in contrast to many other approaches, is highly adaptive so that it does not need any parameter adjustment for the signal to be analysed. Being based on dynamic path optimization and fixed point iteration, the method is very fast, and its superior accuracy is also demonstrated. In addition, we investigate the advantages and drawbacks that synchrosqueezing offers in relation to curve extraction. The codes used in this work are freely available for download.

cs.CE↗

Linear and synchrosqueezed time-frequency representations revisited. Part II: Resolution, reconstruction and concentration

Having reviewed the aspects of the linear and synchrosqueezed time-frequency representations (TFRs) needed for their understanding and correct use in Part I of this review, we now consider three more subtle issues that are nonetheless of crucial importance for effective application of these methods. (i) What effect do the window/wavelet parameters have on the resultant TFR, and how can they most appropriately be chosen? (ii) What are the errors inherent in the two reconstruction methods (direct and ridge) and which of them is the better? (iii) What are the advantages and drawbacks associated with synchrosqueezing? To answer these questions, we perform a detailed numerical and theoretical study of the TFRs under consideration. We consider the relevant estimates in the presence of the complications that arise in practical applications including interference between components, amplitude modulation, frequency modulation, and noise. Taken together, the results provide an in-depth understanding of the issues in question.

math.NA↗

Linear and synchrosqueezed time-frequency representations revisited. Part I: Overview, standards of use, related issues and algorithms

Time-frequency representations (TFRs) of signals, such as the windowed Fourier transform (WFT), wavelet transform (WT) and their synchrosqueezed variants (SWFT, SWT), provide powerful analysis tools. However, there are many important issues related to the practical use of TFRs that need to be clarified. Here we present a thorough review of these TFRs, summarizing all theoretical, practical and numerical aspects of their use, reconsidering some conventions and introducing new concepts and procedures. The purposes of this work are: (i) to provide a consistent overview of the computation, properties, and use of the (S)WFT/(S)WT methods; (ii) to establish general standards related to their use, both theoretical and practical; and (iii) to provide clean and optimized algorithms and MatLab codes, appropriate for any window or wavelet.

math.NA↗

A Tutorial on Time-Evolving Dynamical Bayesian Inference

In view of the current availability and variety of measured data, there is an increasing demand for powerful signal processing tools that can cope successfully with the associated problems that often arise when data are being analysed. In practice many of the data-generating systems are not only time-variable, but also influenced by neighbouring systems and subject to random fluctuations (noise) from their environments. To encompass problems of this kind, we present a tutorial about the dynamical Bayesian inference of time-evolving coupled systems in the presence of noise. It includes the necessary theoretical description and the algorithms for its implementation. For general programming purposes, a pseudocode description is also given. Examples based on coupled phase and limit-cycle oscillators illustrate the salient features of phase dynamics inference. State domain inference is illustrated with an example of coupled chaotic oscillators. The applicability of the latter example to secure communications based on the modulation of coupling functions is outlined. MatLab codes for implementation of the method, as well as for the explicit examples, accompany the tutorial.

physics.data-an↗

Nonlinear Mode Decomposition: a new noise-robust, adaptive decomposition method

We introduce a new adaptive decomposition tool, which we refer to as Nonlinear Mode Decomposition (NMD). It decomposes a given signal into a set of physically meaningful oscillations for any waveform, simultaneously removing the noise. NMD is based on the powerful combination of time-frequency analysis techniques - which together with the adaptive choice of their parameters make it extremely noise-robust - and surrogate data tests, used to identify interdependent oscillations and to distinguish deterministic from random activity. We illustrate the application of NMD to both simulated and real signals, and demonstrate its qualitative and quantitative superiority over the other existing approaches, such as (ensemble) empirical mode decomposition, Karhunen-Loeve expansion and independent component analysis. We point out that NMD is likely to be applicable and useful in many different areas of research, such as geophysics, finance, and the life sciences. The necessary MATLAB codes for running NMD are freely available at http://www.physics.lancs.ac.uk/research/nbmphysics/diats/nmd/.

math.NA↗

Glassy states and superrelaxation in populations of coupled phase oscillators

Large networks of coupled oscillators appear in many branches of science, so that the kinds of phenomena they exhibit are not only of intrinsic interest but also of very wide importance. In 1975, Kuramoto proposed an analytically tractable model to describe such systems, which has since been successfully applied in many contexts and remains a subject of intensive research. Some related problems, however, remain unclarified for decades, such as the properties of the oscillator glass state discovered by Daido in 1992. Here we present a detailed analysis of a very general form of the Kuramoto model. In particular, we find the conditions when it can exhibit glassy behavior, which represents a kind of synchronous disorder in the present case. Furthermore, we discover a new and intriguing phenomenon that we refer to as {\it superrelaxation} where, for a class of parameter distributions, the oscillators feel no interaction at all during relaxation to incoherence, a phenomenon reminiscent of superfluidity or superconductivity. Our findings offer the possibility of creating glassy states and observing superrelaxation in real systems, thus paving the way to a cascade of applications and further research in the field.

nlin.AO↗

Dynamics of ions in the selectivity filter of the KcsA channel: Towards a coupled Brownian particle description

The statistical and dynamical properties of ions in the selectivity filter of the KcsA ion channel are considered on the basis of molecular dynamics (MD) simulations of the KcsA protein embedded in a lipid membrane surrounded by an ionic solution. A new approach to the derivation of a Brownian dynamics (BD) model of ion permeation through the filter is discussed, based on unbiased MD simulations. It is shown that depending on additional assumptions, ion's dynamics can be described either by under-damped Langevin equation with constant damping and white noise or by Langevin equation with a fractional memory kernel. A comparison of the potential of the mean force derived from unbiased MD simulations with the potential produced by the umbrella sampling method demonstrates significant differences in these potentials. The origin of these differences is an open question that requires further clarifications.

physics.bio-ph↗

Dynamical Bayesian Inference of Time-evolving Interactions: From a Pair of Coupled Oscillators to Networks of Oscillators

Living systems have time-evolving interactions that, until recently, could not be identified accurately from recorded time series in the presence of noise. Stankovski et al. (Phys. Rev. Lett. 109 024101, 2012) introduced a method based on dynamical Bayesian inference that facilitates the simultaneous detection of time-varying synchronization, directionality of influence, and coupling functions. It can distinguish unsynchronized dynamics from noise-induced phase slips. The method is based on phase dynamics, with Bayesian inference of the time- evolving parameters being achieved by shaping the prior densities to incorporate knowledge of previous samples. We now present the method in detail using numerically-generated data, data from an analog electronic circuit, and cardio-respiratory data. We also generalize the method to encompass networks of interacting oscillators and thus demonstrate its applicability to small-scale networks.

physics.data-an↗

Stationary and Traveling Wave States of the Kuramoto Model with an Arbitrary Distribution of Frequencies and Coupling Strengths

We consider the Kuramoto model of an ensemble of interacting oscillators allowing for an arbitrary distribution of frequencies and coupling strengths. We define a family of traveling wave states as stationary in a rotating frame, and derive general equations for their parameters. We suggest empirical stability conditions which, for the case of incoherence, become exact. In addition to making new theoretical predictions, we show that many earlier results follow naturally from our general framework. The results are applicable in scientific contexts ranging from physics to biology.

nlin.AO↗

Inference of Time-Evolving Coupled Dynamical Systems in the Presence of Noise

A new method is introduced for analysis of interactions between time-dependent coupled oscillators, based on the signals they generate. It distinguishes unsynchronized dynamics from noise-induced phase slips, and enables the evolution of the coupling functions and other parameters to be followed. It is based on phase dynamics, with Bayesian inference of the time-evolving parameters achieved by shaping the prior densities to incorporate knowledge of previous samples. The method is tested numerically and applied to reveal and quantify the time-varying nature of cardiorespiratory interactions.

physics.data-an↗

Asymmetry--induced effects in coupled phase oscillator ensembles: Routes to synchronization

A system of two coupled ensembles of phase oscillators can follow different routes to inter-ensemble synchronization. Following a short report of our preliminary results [Phys. Rev. E. {\bf 78}, 025201(R) (2008)], we present a more detailed study of the effects of coupling, noise and phase asymmetries in coupled phase oscillator ensembles. We identify five distinct synchronization regions, and new routes to synchronization that are characteristic of the coupling asymmetry. We show that noise asymmetry induces effects similar to that of coupling asymmetry when the latter is absent. We also find that phase asymmetry controls the probability of occurrence of particular routes to synchronization. Our results suggest that asymmetry plays a crucial role in controlling synchronization within and between oscillator ensembles, and hence that its consideration is vital for modeling real life problems.

nlin.AO↗