Myosin II molecular motors slide actin filaments relatively to each other and are essential for force generation, motility, and mechanosensing in animal cells. For nonmuscle cells, evolution has resulted in three different isoforms, which have different properties concerning the motor cycle and also occur in different abundances in the cells, but their respective biological and physical roles are not fully understood. Here we use active gel theory to demonstrate the complementary roles of isoforms A and B for cell migration. We first show that our model can be derived both from coarse-graining kinetic equations and from nonequilibrium thermodynamics as the macroscopic limit of a two-component Tonks gas. We then parametrize the model and show that motile solutions exist, in which the more abundant and more dynamic isoform A is localized to the front and the stronger isoform B to the rear, in agreement with experiments. Exploring parameter space beyond the isoform parameters typical for animal cells, we also find cell oscillations in length and velocity, which might be realized for genetically engineered systems. We also describe an analytical solution for the stiff limit, which then is used to calculate a state diagram, and the effect of actin polymerization at the boundaries that leads to an imperfect pitchfork bifurcation. Our findings highlight the importance of including isoform-specific molecular details to describe whole cell behavior.

Protein condensate aging is a widespread phenomenon, yet quantitative insights into its molecular mechanisms remain rare. Here, we employ the sticker-spacer model and integrate coarse-grained lattice simulations with machine learning to investigate the liquid-to-solid transition in protein condensates. The transition arises from the intricate interplay of heterotypic interactions among stickers within proteins. By quantitatively distinguishing inhomogeneous solid-like and liquid-like structures, we confirm the formation of a solid-like shell at the condensate interface. Proteins at the interface, characterized by extended conformations and orientations, promote the formation of solid-like structures via the aggregation of aging stickers. Notably, variations in the number, location, and intensity of aging stickers significantly influence the structural and viscoelastic properties of the condensates, mediated by the balance between competition and cooperation in heterotypic interactions. Our findings provide a quantitative framework for understanding condensate aging and suggest potential strategies to regulate aging process.

The dynamics of supercooled liquids and glasses are widely believed to be similar in two and three dimensions. However, our study challenges this assumption by showing that polydispersity-driven relaxation dynamics differ drastically across dimensions. We resolve an apparent contradiction in prior work by demonstrating that standard corrections for Mermin-Wagner fluctuations obscure size-dependent relaxation in two dimensions. Furthermore, our analysis of mobile particle clusters reveals distinct growth mechanisms, with three-dimensional systems favoring sparse, small-particle-dominated clusters, while two-dimensional systems exhibit more cooperative, size-agnostic rearrangements.

We study the separation of matter and antimatter driven by the growth of the Weibel instability in a matter-antimatter plasma. The plasma under consideration comprises protons and antiprotons initially at rest, along with a relativistic stream of leptons (electrons and positrons). This stream is maintained by an external force, potentially originating from phenomena such as a photon wind. Our findings reveal the rapid onset of a Weibel-type instability, leading to a distinct separation of matter and antimatter. Results from our particle-in-cell simulations are compared with an analytical model based on the linearized magnetohydrodynamics equations.

Frontal polymerization is a fast and energy-efficient curing process of thermoset polymers involving the self-sustained propagation of a sharp front that transforms a liquid monomer or gel into a polymer. The temperature, speed, and geometry of the polymerization front are closely intertwined quantities that can affect the curing process and the mechanical properties of the polymer. This work investigates the effect of the front curvature on the front propagation speed based on a reaction-diffusion model. The proposed analytical relations between front speed and curvature are verified by finite element simulations and validated through experiments. The models show that a concave front has a higher front temperature and speed than its convex counterpart. When the curvature of a convex front reaches a critical value, the reaction can no longer be sustained, and the front is quenched. The relation between the speed of a steadily propagating front and its geometry is derived, showing good agreement with experimental observations. This study also provides analytical estimates of the thickness of the polymerization front and insight into the relationship between the front curvature and the kinetics of the exothermic reaction.

Maximum tolerated dose (MTD) and low-dose metronomic (LDM) schedules are widely used clinical strategies in cancer chemotherapy despite the fact that both approaches have inherent limitations. MTD often leads to drug resistance in tumors, whereas LDM usually results in the predominance of drug-sensitive or drug-resistant cancer cells, depending on the specific dose applied. To circumvent these unfavorable outcomes, we propose in this work a promising adaptive therapy strategy that alternates MTD and LDM schemes across the treatment cycles. By studying a three-component tumor system with replicator equations, we find that alternate administration of MTD and LDM at different lengths of the treatment cycle can significantly delay the development of drug-resistant phenotypes compared to when the two schemes are operated separately. This outcome recapitulates the weak form of the Parrondo's paradox, where appropriate combinations of two individually suboptimal strategies can produce an even more superior outcome (a more favorable therapeutic effect in our case). We also validate the feasibility of the proposed adaptive therapy strategy in spatially distributed tumor ecosystems by implementing agent-based simulations. Our findings offer potential clues to the challenges in state-of-the-art adaptive therapy methods, which often require precise regulation of drug dose and timing based on the proportions of drug-sensitive and drug-resistant cancer cells within a tumor.

We study a family of lattice-gas systems defined on semiregular grids, obtained by projecting the vertices of three different geodesic icosahedra onto a spherical surface. By using couplings up to third neighbors, we explore various interaction patterns, ranging from core-corona repulsion to square-well attraction and short-range attractive, long-range repulsive potentials. The relatively small number of sites in each grid $(\sim 100)$ enables us to compute the exact statistical properties of the systems as a function of temperature and chemical potential by Wang-Landau sampling. For each case considered, we highlight the existence of distinct low-temperature “phases”, featuring, among others, regular-polyhedral, cluster-crystal, and wormlike structures. We highlight similarities and differences between these motifs and those observed on the triangular lattice under the same conditions. Finally, we discuss the relevance of our results for the bottom-up realization of spherical templates with desired functionalities.

Stochastic fluctuations of molecular abundances are a ubiquitous feature of cellular processes and lead to significant cell-to-cell variability even in clonal populations under identical conditions. Recent theoretical work established lower bounds for stochastic fluctuations in cells for broad classes of cellular processes by analyzing the dynamics of reaction motifs that are embedded within a larger network with arbitrary interactions and dynamics. For example, a class of generalized assembly processes in which two co-regulated subunits irreversibly form a complex was shown to exhibit an unavoidable trade-off between assembly efficiency and subunit fluctuations: Regardless of rate constants and details of feedback control, subunit fluctuations were shown to diverge as the assembly efficiency approaches 100%. In contrast, other work has reported how efficient assembly processes work as stochastic noise filters or can achieve robust adaptation through integral control. While all of these results are technically correct their seemingly contradictory conclusions raise the question of how broadly applicable the previously reported efficiency-fluctuation trade-off is. Here, we show that a much broader class of assembly processes than previously considered is subject to an efficiency-fluctuation trade-off which diverges in the high efficiency regime. We find the proposed noise filtering property of efficient assembly processes corresponds to a singular limit of this class of systems. Additionally, we show that combining feedback control with distinct subunit synthesis rates is a necessary condition to overcome the generalized efficiency-fluctuation trade-off. Through numerical examples, we show that biomolecular integral controllers are one of several realizations of such control. How small a change to joint subunit control is sufficient to avoid diverging fluctuations in the high-efficiency limit remains an open question.

A key problem in deep learning and computational neuroscience is relating the geometrical properties of neural representations to task performance. Here, we consider this problem for continuous decoding tasks where neural variability may affect task precision. Using methods from statistical mechanics, we study the average-case learning curves for $\varepsilon$-insensitive support vector regression and discuss its capacity as a measure of linear decodability. Our analysis reveals a phase transition in training error at a critical load, capturing the interplay between the tolerance parameter $\varepsilon$ and neural variability. We uncover a double-descent phenomenon in the generalization error, showing that $\varepsilon$ acts as a regularizer, both suppressing and shifting these peaks. Theoretical predictions are validated both with toy models and deep neural networks, extending the theory of support vector machines to continuous tasks with inherent neural variability.

We consider a random walker performing a continuous-time random walk (CTRW) with a symmetric step lengths' distribution possessing a finite second moment and with a power-law waiting time distribution with finite or diverging first moment. The problem we pose concerns the distribution of the number of steps of the corresponding CTRW conditioned on the final position of the walker at some long time $t$. For positions within the scaling domain of the probability density function (PDF) of final displacements, the distributions of the number of steps show a considerable amount of universality, and are different in the cases when the corresponding CTRW corresponds to subdiffusion and to normal diffusion. We moreover note that the mean value of the number of steps can be obtained independently and follows from the solution of the Poisson equation whose right-hand side depends on the PDF of displacements only. This approach works not only in the scaling domain but also in the large deviation domain of the corresponding PDF, where the behavior of the mean number of steps is very sensitive to the details of the waiting time distribution beyond its power-law asymptotics.

We report a quantitative study of the onset of dawn choruses of cicadas in several natural habitats. A time-frequency analysis of the acoustical signals is used to define an order parameter for the development of collective singing. The ensemble of recordings reveals that the chorus onset times accurately track the changing sunrise times over the course of many weeks, occurring at a solar elevation of $-3.8^{\circ }\pm 0.2^{\circ }$. Despite day-to-day variations in the amplitude of fully developed choruses, the order parameter data collapse to a common sigmoidal curve when scaled by those amplitudes and shifted by the onset time, revealing a characteristic rise time of $\approx 60$ s for a chorus to reach saturation amplitude. The results are used to obtain the cumulative distribution function of singing as a function of ground illumination, from which is obtained a generalized susceptibility which exhibits a narrow peak with a half width of $\approx 12\%$. The variance of the order parameter exhibits a similar peak, suggesting that a generalized fluctuation-dissipation theorem holds for this system. A model of decision making under ramps of a control parameter is developed and can achieve a quantitative match to the data. It suggests that sharpness of the susceptibility peak reflects cooperative decision making arising from acoustic communication.

We investigate the mechanical behavior of jammed knitted fabrics, where geometric confinement leads to an initially stiff mechanical response that softens into low stiffness behavior with additional applied stress. We show that the jammed regime is distinguished by changes in yarn geometry and contact rearrangement that must occur to allow the individual stitches to stretch. These rearrangements allow for the relaxation of high residual stresses that are present within jammed fabric, altering the low-strain response. We demonstrate that fabric jamming can be induced or reduced by changing either the constituent yarn or the fabric manufacturing parameters. Analysis of experimental samples shows that changing the “stitch size” in manufacturing affects both the in situ yarn radius and the length of yarn per stitch, both of which affect jamming.

We investigate small-scale magnetic energy generation in a turbulent conducting flow using two different approaches. One is based on the Kazantsev-Kraichnan (KK) model, developed for a random velocity field with short time correlations, and the other uses the shell magnetohydrodynamic (MHD) method, which describes a turbulent energy cascade on a finite number of spectral shells. We consider only the kinematic (linear) regime and show an exponential growth of magnetic energy for sufficiently large magnetic Reynolds numbers (Rm) in both approaches. By comparing the dependencies of the growth rates at different Rm, we try to answer the question how the model assumptions—of short time correlations or of finite spectral shells—affect the turbulent dynamo possibility and the position of the Rm threshold at which this generation starts. We analyze the generation behavior at different Reynolds and magnetic Prandtl numbers, which are not explicitly included in the KK model but taken into account in the shell approach. Based on the obtained results, we try to discuss why the small-scale generation has not been observed in real MHD dynamo experiments so far.

Tailoring energy levels in quantum systems via Hamiltonian control parameters is essential for designing quantum thermodynamic devices and materials. However, conventional approaches to manipulating finite-size quantum systems, such as tuning external fields or system size, typically lead to uniform shifts across the spectrum, limiting the scope of spectral engineering. A recently introduced technique, known as the size-invariant shape transformation, overcomes this limitation by introducing a new control parameter that deforms the potential landscape without altering the system's size parameters, thereby enabling nonuniform scaling of energy levels. This new degree of freedom—referred to as the shape parameter—gives rise to quantum shape effects in the thermodynamics of confined systems, which are conceptually distinct from quantum size effects. Here, we explore the fundamental limits of nonuniform level scaling in the spectra by asking: what is the minimal quantum system in which such behavior can arise? We demonstrate that even a two-level system can exhibit the thermodynamic consequences of quantum shape effects, including spontaneous transitions into lower-entropy states, a phenomenon absent in classical thermodynamics for noninteracting systems. We identify the spectral origin of these unconventional thermodynamic behaviors as geometry-induced asymmetric level coupling, in which the ground-state energy and energy gap respond in opposite ways to changes in a shape parameter. This asymmetry naturally extends to many-level systems, where the thermally averaged energy spacing and ground-state energy evolve in opposite directions. To characterize unconventional thermodynamic behaviors, we construct thermodynamic spontaneity maps, identifying regions of energy-driven and entropy-driven spontaneous processes in ground-state energy versus energy gap space. These effects emerge under quasistatic, isothermal changes of a shape degree of freedom and illustrate how the confinement geometry alone can enable unconventional thermodynamic behaviors that are otherwise exclusive to interacting or open systems. We argue that any scaling-invariant local parameter transformation that induces asymmetric-level coupling can be used to engineer similar responses, making this a broadly applicable framework. Our results deepen the theoretical foundations of the quantum shape effect and introduce a route to spectral gap control, with potential applications in isolating computational subspaces within quantum information platforms.

We investigate the formation of helical multifilament bundles and the torque required to achieve them as a function of applied twist. Hyperelastic filaments with circular cross sections are mounted parallel in a uniform circle onto end-clamps that can move along the twist axis depending on the applied axial load. With increasing twist, the filaments describe a hyperbolic hyperboloid surface before coming into contact in a circle and then packing in a tight helical bundle in the center with increasing twist. While the bundle appears ordered for sufficiently small number of filaments, they are disordered for large enough number of filaments and applied twist. We reveal with x-ray tomography that the packing of the filaments becomes disordered following a radial instability, which leads to a decrease in bundle radius and migration of filaments relative to each other in the bundle. Nonetheless, the helical angle of the filaments in the bundle are found to be essentially constant, resulting in inclination angles which increase with distance from axis of rotation. We develop energy minimization analysis to capture the observed variations in bundle length and torque as a function of number of filaments considering the neo-Hookean nature of the filaments. We show that the bundle geometry and the applied load can be used to describe the nonlinear torque profile measured as a function of twist angle.

We study the aging of colloidal gels using light microscopy movies of depletion gels from the International Space Station. Under such microgravity conditions, we observe a slowdown in particle dynamics consistent with gel aging. Stronger attractive forces promote the formation of thicker gel strands over time. The samples are bidisperse, composed of particles with a size ratio 1.2. Larger particles experience stronger depletion forces, which lead to a large first-neighbor peak in the pair correlation function $g\left(r\right)$ due to the prevalence of large-large particle contacts. As the gel ages, small mobile particles are incorporated into the gel structure. The changes in gel structure correlate with a slow power-law decay in particle motion, observed over nearly two orders of magnitude of timescales in microgravity experiments. Additionally, through complementary ground-based experiments, we compare two-dimensional (2D) and three-dimensional (3D) images of depletion colloidal gels. While microgravity gel data are limited to 2D projections, ground-based data establish a correspondence between the 2D and 3D $g\left(r\right)$ peak heights. Our results provide new insights into how colloidal gels age in the absence of gravitational collapse.

The physics of particle acceleration in turbulent plasmas is a topic of broad interest, which is making rapid progress thanks to dedicated, large-scale numerical experiments. The first part of this paper presents an effective theory of stochastic Fermi acceleration, which subsumes all forms of nonresonant acceleration in ideal electric fields and is applicable in generic settings. It combines an exact equation connecting the energization rate to the statistics of the velocity field with a statistical model of particle transport through the structures (i.e., the regions of strong velocity gradients). In a second part, this formalism is applied to magnetohydrodynamic turbulence to obtain a comprehensive assessment of the scale-by-scale contributions to the advection and diffusion coefficients. Acceleration peaks on scales where particles can be trapped inside structures for an eddy turnaround time, or in intense structures associated with sharp bends of the magnetic field lines in large-amplitude turbulence (as reported earlier). These spatially inhomogeneous, rapid acceleration regimes pave the way for a rich phenomenology. We discuss the scalings obtained, their interpretation, and we show that the findings compare satisfactorily with existing numerical results.

We predict a phenomenon of catastrophic material failure arising suddenly within an amorphous material, with an extremely long delay time since the material was last deformed. By simulating a mesoscopic soft glassy rheology model in one dimension (1D), a mesoscopic elastoplastic model in 1D and 2D, and a continuum fluidity model in 1D, we demonstrate the basic physics to involve a dramatic ultra-delayed shear banding instability, in which strain suddenly strongly localizes within the material and the stress drops precipitously. The delay time after the long historical shear strain was applied before failure occurs increases steeply with decreasing strain amplitude, decreasing working temperature, and increasing sample annealing prior to shear. In demonstrating the same physics—which is directly testable experimentally and in particle simulations—to obtain within three different constitutive models, we suggest it may be generic across amorphous materials. The counter-intuitive prediction of catastrophic material failure long after any deformation was last applied could have important consequences for material processing and performance.

Many of the existing approaches to assess and predict the performance of players, teams, or products in competitive contests rely on the assumption that comparisons occur between pairs of such entities. There are, however, several real contests where more than two entities are part of each comparison, e.g., sports tournaments, multiplayer board and card games, and preference surveys. The Plackett-Luce (PL) model provides a principled approach to infer the ranking of entities involved in such contests characterized by multibody comparisons. Unfortunately, traditional algorithms used to compute PL rankings suffer from slow convergence, which limits the application of the PL model to relatively small-scale systems. Here, we present an alternative implementation that allows for significant speed-ups and validate its efficiency in both synthetic and real-world sets of data. Further, we perform systematic cross-validation tests concerning the ability of the PL model to predict unobserved comparisons. We find that a PL model trained on a set composed of multibody comparisons is more predictive than a PL model trained on a set of projected pairwise comparisons derived from the very same training set, emphasizing the need of properly accounting for the true multibody nature of real-world systems whenever such information is available.

The dynamical cavity method and its backtracking version provide a powerful approach to studying the properties of dynamical processes on large random graphs. This work extends these methods to hypergraphs, enabling the analysis of interactions involving more than two variables. We apply them to analyze the $k\text{−}\mathrm{XOR}\text{−}\mathrm{sat}\text{isfiability}$ problem, an important model in theoretical computer science which is closely related to the diluted $p$-spin model from statistical physics. In particular, we examine whether the quench dynamics—a deterministic, locally greedy process—can find solutions with only a few violated constraints on $d$-regular $k$-uniform hypergraphs. Our results demonstrate that the methods accurately characterize the attractors of the dynamics. It enables us to compute the energy reached by typical trajectories of the dynamical process in different parameter regimes. We show that these predictions are accurate, including cases where a classical mean-field approach fails.