The large-scale collective behavior of biological systems can be characterized by macroscopic transport, which arises from the nonequilibrium microscopic interactions between individual constituents. A prominent example is the formation of dynamic aggregates by motile eukaryotic cells or bacteria mediated by active contractile forces. In this work we develop the two-dimensional fluctuating hydrodynamics theory based on the microscopic dynamics of a model system of aggregation by Neisseria gonorrhoeae bacteria. The derivation of two macroscopic transport coefficients of bulk diffusivity and conductivity which determine the hydrodynamic current of cells is the central result of this work. By showing how transport coefficients depend on cell density and microscopic parameters of the system, we predict transport slowdown during the colony formation process. This study provides valuable analytical tools for quantifying hydrodynamic transport in experimental systems involving cellular aggregation occurring due to intermittent contractile dipole forces.

We investigate the behavior of self-propelled particles under cyclic stretching, inspired by the characteristic pattern dynamics observed in microtubule (MT) motility assays subjected to uniaxial cyclic substrate stretching. We develop a self-propelled particle model that incorporates the elastic energy acting on the filaments due to substrate deformation, successfully reproducing the experimentally observed MT patterns. Additionally, the general framework of the model enables systematic exploration of collective responses to various substrate deformations, offering potential applications in the manipulation of MT patterns and other active matter systems.

The absolute growth of stimulated Raman side scattering (SRSS) predicted by previous theories appeared to be surprisingly absent in the recent ignition-scale direct-drive experiments with the absence attributed to different reasons. We present evidence from simulations that the SRSS modes are naturally all convective (i.e., absolute SRSS does not exist at all) in an experimentally relevant regime where a finite-beam-width laser is incident into a nonuniform low-density plasma below a quarter of the critical density. The convective gain demonstrated by our newly proposed formula via numerical fitting monotonically increases with the beam width without saturation, which is significantly different from the prediction of previous convective SRSS theories. The findings have significant implications for the assessment of SRSS in ignition designs.

We investigate the response of a system of hard spheres to two classes of perturbations over a range of densities spanning the fluid, crystalline, and glassy regimes within a molecular dynamics framework. First, we consider the relaxation of a “thermal inhomogeneity,” in which a central region of particles is given a higher temperature than its surroundings and is then allowed to evolve under Newtonian dynamics. In this case, the hot central “core” of particles expands and collides with the cold surrounding material, creating a transient radially expanding “compression wave,” which is rapidly dissipated by particle-particle collisions and interactions with periodic images at the boundary, leading to a rapid relaxation to equilibrium. Second, we consider a rapid compression of the spheres into a disordered glassy state at high densities. Such rapidly compressed systems exhibit very slow structural relaxation times, many orders of magnitude longer than thermalization times for simple temperature inhomogeneities. We find that thermal relaxation of the velocity distribution is determined simply by the total collision rate, whereas structural relaxation requires coordinated collective motion, which is strongly suppressed at high density, although some particle rearrangement nevertheless occurs. We further find that collisions propagate significantly faster through glassy systems than through crystalline systems at the same density, which leads to very rapid relaxation of velocity perturbations, although structural relaxation remains very slow. These results extend the validity of previous observations that glassy systems exhibit a hybrid character, sharing features with both equilibrium and nonequilibrium systems. Finally, we introduce the hard-sphere causal graph, a network-based characterization of the dynamical history of a hard-sphere system, which encapsulates several useful metrics for characterizing hard-sphere systems within a single structure, and which emphasizes the role of causality in these systems.

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.