Glasses exhibit a liquidlike structure but a solidlike rheological response with plastic deformations only occurring beyond yielding. Thus, predicting the rheological behavior from the microscopic structure is difficult, but important for materials science. Here, we consider colloidal suspensions and propose to supplement the static structural information with the local dynamics, namely, the rearrangement and breaking of the cage of neighbors. This is quantified by the mean squared nonaffine displacement and the number of particles that remain nearest neighbors for a long time, i.e., long-lived neighbors, respectively. Both quantities are followed under shear using confocal microscopy and are the basis to calculate the affine and nonaffine contributions to the elastic stress, which is complemented by the viscoelastic stress to give the total stress. During start-up of shear, the model predicts three transient regimes that result from the interplay of affine, nonaffine, and viscoelastic contributions. Our prediction quantitatively agrees with rheological data and their dependencies on volume fraction and shear rate.

The current understanding is that the non-Newtonian rheology of active matter suspensions is governed by fluid-mediated hydrodynamic interactions associated with active self-propulsion. Here we discover an additional contribution to the suspension shear stress that predicts both thickening and thinning behavior, even when there is no nematic ordering of the microswimmers with the imposed flow. A simple micromechanical model of active Brownian particles in homogeneous shear flow reveals the existence of off-diagonal shear components in the swim stress tensor, which are independent of hydrodynamic interactions and fluid disturbances. Theoretical predictions from our model are consistent with existing experimental measurements of the shear viscosity of active suspensions, but also suggest new behavior not predicted by conventional models.

Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

Molecular motors that carry cargo along biopolymer filaments within cells play a crucial role in the functioning of the cell. In particular, these motors are essential for the formation and maintenance of the cellular protrusions that play key roles in motility and specific functionalities, such as the stereocilia in hair cells. Typically, there are several species of motors, carrying different cargos, that share the same track. Furthermore, it was observed that in the mature stereocilia, the different motors occupy well-segregated bands as a function of distance from the tip. We use a totally asymmetric exclusion process model with two- and three-motor species, to study the conditions that give rise to such spatial patterns. We find that the well-segregated bands appear for motors with a strong hierarchy of attachment or detachment rates. This is a striking example of pattern formation in nonequilibrium, low-dimensional systems.

We consider a tracer particle on a lattice in the presence of immobile obstacles. Starting from equilibrium, a force pulling on the particle is switched on, driving the system to a new stationary state. We solve for the complete transient dynamics of the fluctuations of the tracer position along the direction of the force. The analytic result, exact in first order of the obstacle density and for arbitrarily strong driving, is compared to stochastic simulations. Upon strong driving, the fluctuations grow superdiffusively for intermediate times; however, they always become diffusive in the stationary state. The diffusion constant is nonanalytic for small driving and is enhanced by orders of magnitude by increasing the force.

The amplitude of fluctuation-induced patterns might be expected to be proportional to the strength of the driving noise, suggesting that such patterns would be difficult to observe in nature. Here, we show that a large class of spatially extended dynamical systems driven by intrinsic noise can exhibit giant amplification, yielding patterns whose amplitude is comparable to that of deterministic Turing instabilities. The giant amplification results from the interplay between noise and nonorthogonal eigenvectors of the linear stability matrix, yielding transients that grow with time, and which, when driven by the ever-present intrinsic noise, lead to persistent large amplitude patterns. This mechanism shows that fluctuation-induced Turing patterns are observable, and are not strongly limited by the amplitude of demographic stochasticity nor by the value of the diffusion coefficients.

Cracks filled with fluid propagation when the pressurized fluid is injected into the crack. Subsequently, when the fluid inlet is exposed to a lower pressure, the fluid flows backwards (backflow) and the crack closes due to the elastic relaxation of the solid. Here we study the dynamics of the crack closure during the backflow. We find that the crack radius remains constant and the fluid volume in the crack decreases with time in a power-law manner at late times. The balance between the viscous stresses in the fluid and elastic stresses in the fluid and the elastic stresses in the solid yields a scaling law that agrees with the experimental results for different fluid viscosities, Young’s moduli of the solid, and initial radii of the cracks. Furthermore, we visualize the time-dependent crack shapes, and the convergence to a universal dimensionless shape demonstrates the self-similarity of the crack shapes during the backflow process.

We report on the discovery of a novel class of bursting rhythms, called amplitude-modulated bursting (AMB), in a model for intracellular calcium dynamics. We find that these rhythms are robust and exist on open parameter sets. We develop a new mathematical framework with broad applicability to detect, classify, and rigorously analyze AMB. Here we illustrate this framework in the context of AMB in a model of intracellular calcium dynamics. In the process, we discover a novel family of singularities, called toral folded singularities, which are the organizing centers for the amplitude modulation and exist generically in slow-fast systems with two or more slow variables.

Cilia and flagella exhibit regular bending waves that perform mechanical work on the surrounding fluid, to propel cellular swimmers and pump fluids inside organisms. Here, we quantify a force-velocity relationship of the beating flagellum, by exposing flagellated Chlamydomonas cells to controlled microfluidic flows. A simple theory of flagellar limit-cycle oscillations, calibrated by measurements in the absence of flow, reproduces this relationship quantitatively. We derive a link between the energy efficiency of the flagellar beat and its ability to synchronize to oscillatory flows.

The translocation of polymers through nanopores with large internal cavities bounded by two narrow pores is studied via Langevin dynamics simulations. The total translocation time is found to be a nonmonotonic function of polymer length, reaching a minimum at intermediate length, with both shorter and longer polymers taking longer to translocate. The location of the minimum is shown to shift with the magnitude of the applied force, indicating that the pore can be dynamically tuned to favor different polymer lengths. A simple model balancing the effects of entropic trapping within the cavity against the driving force is shown to agree well with simulations. Beyond the nonmonotonicity, detailed analysis of translocation uncovers rich dynamics in which factors such as going to a high force regime and the emergence of a tail for long polymers dramatically change the behavior of the system. These results suggest that nanopores with internal cavities can be used for applications such as selective extraction of polymers by length and filtering of polymer solutions, extending the uses of nanopores within emerging nanofluidic technologies.

We experimentally observed considerable solubility of tryptophan (Trp) in a ${\mathrm{CuCl}}_2$ aqueous solution, which could reach 2–5 times the solubility of Trp in pure water. Theoretical studies show that the strong cation-$\pi$ interaction between ${\mathrm{Cu}}^{2+}$ and the aromatic ring in Trp modifies the electronic distribution of the aromatic ring to enhance significantly the water affinity of Trp. Similar solubility enhancement has also been observed for other divalent transition-metal cations (e.g., ${\mathrm{Zn}}^{2+}$ and ${\mathrm{Ni}}^{2+}$), another aromatic amino acid (phenylalanine), and three aromatic peptides (Trp-Phe, Phe-Phe, and Trp-Ala-Phe).