A minimal vertex model explains how the amnioserosa avoids fluidization during dorsal closure.

Dorsal closure is a process that occurs during embryogenesis of . During dorsal closure, the amnioserosa (AS), a one-cell thick epithelial tissue that fills the dorsal opening, shrinks as the lateral epidermis sheets converge and eventually merge. During this process, both shape index and aspect ratio of amnioserosa cells increase markedly.

Machine learning without a processor: Emergent learning in a nonlinear analog network.

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic (CLLNs) offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored.

Physical effects of learning.

Interacting many-body physical systems ranging from neural networks in the brain to folding proteins to self-modifying electrical circuits can learn to perform diverse tasks. This learning, both in nature and in engineered systems, can occur through evolutionary selection or through dynamical rules that drive active learning from experience. Here, we show that learning in linear physical networks with weak input signals leaves architectural imprints on the Hessian of a physical system.

Minimal vertex model explains how the amnioserosa avoids fluidization during dorsal closure.

Dorsal closure is a process that occurs during embryogenesis of . During dorsal closure, the amnioserosa (AS), a one-cell thick epithelial tissue that fills the dorsal opening, shrinks as the lateral epidermis sheets converge and eventually merge. During this process, the aspect ratio of amnioserosa cells increases markedly.

Identifying microscopic factors that influence ductility in disordered solids.

There are empirical strategies for tuning the degree of strain localization in disordered solids, but they are system-specific and no theoretical framework explains their effectiveness or limitations. Here, we study three model disordered solids: a simulated atomic glass, an experimental granular packing, and a simulated polymer glass. We tune each system using a different strategy to exhibit two different degrees of strain localization.

Discontinuous instabilities in disordered solids.

Under a sufficiently large load, a solid material will flow via rearrangements, where particles change neighbors. Such plasticity is most easily described in the athermal, quasistatic limit of zero temperature and infinitesimal loading rate, where rearrangements occur only when the system becomes mechanically unstable. For disordered solids, the instabilities marking the onset of rearrangements have long been believed to be fold instabilities, in which an energy barrier disappears and the frequency of a normal mode of vibration vanishes continuously.

Fragility in glassy liquids: A structural approach based on machine learning.

The rapid rise of viscosity or relaxation time upon supercooling is a universal hallmark of glassy liquids. The temperature dependence of viscosity, however, is quite nonuniversal for glassy liquids and is characterized by the system's "fragility," with liquids with nearly Arrhenius temperature-dependent relaxation times referred to as strong liquids and those with super-Arrhenius behavior referred to as fragile liquids. What makes some liquids strong and others fragile is still not well understood.

Probing Gardner Physics in an Active Quasithermal Pressure-Controlled Granular System of Noncircular Particles.

To search for experimental signals of the Gardner crossover, an active quasithermal granular glass is constructed using a monolayer of air-fluidized star-shaped particles. The pressure of the system is controlled by adjusting the tension exerted on an enclosing boundary. Velocity distributions of the internal particles and the scaling of the pressure, density, effective temperature, and relaxation time are examined, demonstrating that the system has key features of a thermal system.

Transient learning degrees of freedom for introducing function in materials.

SignificanceMany protocols used in material design and training have a common theme: they introduce new degrees of freedom, often by relaxing away existing constraints, and then evolve these degrees of freedom based on a rule that leads the material to a desired state at which point these new degrees of freedom are frozen out. By creating a unifying framework for these protocols, we can now understand that some protocols work better than others because the choice of new degrees of freedom matters. For instance, introducing particle sizes as degrees of freedom to the minimization of a jammed particle packing can lead to a highly stable state, whereas particle stiffnesses do not have nearly the same impact.

Correlation of plastic events with local structure in jammed packings across spatial dimensions.

In frictionless jammed packings, existing evidence suggests a picture in which localized physics dominates in low spatial dimensions, d = 2, 3, but quickly loses relevance as d rises, replaced by spatially extended mean-field behavior. For example, quasilocalized low-energy vibrational modes and low-coordination particles associated with deviation from mean-field behavior (rattlers and bucklers) all vanish rapidly with increasing d. These results suggest that localized rearrangements, which are associated with low-energy vibrational modes, correlated with local structure, and dominant in low dimensions, should give way in higher d to extended rearrangements uncorrelated with local structure.

Hidden Topological Structure of Flow Network Functionality.

The ability to reroute and control flow is vital to the function of venation networks across a wide range of organisms. By modifying individual edges in these networks, either by adjusting edge conductances or creating and destroying edges, organisms robustly control the propagation of inputs to perform specific tasks. However, a fundamental disconnect exists between the structure and function: networks with different local architectures can perform the same functions.

Quantifying the link between local structure and cellular rearrangements using information in models of biological tissues.

Machine learning techniques have been used to quantify the relationship between local structural features and variations in local dynamical activity in disordered glass-forming materials. To date these methods have been applied to an array of standard (Arrhenius and super-Arrhenius) glass formers, where work on "soft spots" indicates a connection between the linear vibrational response of a configuration and the energy barriers to non-linear deformations. Here we study the Voronoi model, which takes its inspiration from dense epithelial monolayers and which displays anomalous, sub-Arrhenius scaling of its dynamical relaxation time with decreasing temperature.

Periodic training of creeping solids.

We consider disordered solids in which the microscopic elements can deform plastically in response to stresses on them. We show that by driving the system periodically, this plasticity can be exploited to train in desired elastic properties, both in the global moduli and in local "allosteric" interactions. Periodic driving can couple an applied "source" strain to a "target" strain over a path in the energy landscape.

Directed aging, memory, and nature's greed.

Disordered materials are often out of equilibrium and evolve very slowly in a rugged and tortuous energy landscape. This slow evolution, referred to as aging, is deemed undesirable as it often leads to material degradation. However, we show that aging also encodes a memory of the stresses imposed during preparation.

Tuning and jamming reduced to their minima.

Inspired by protein folding, we smooth out the complex cost function landscapes of two processes: the tuning of networks and the jamming of ideal spheres. In both processes, geometrical frustration plays a role-tuning pressure differences between pairs of target nodes far from the source in a flow network impedes tuning of nearby pairs more than the reverse process, while unjamming the system in one region can make it more difficult to unjam elsewhere. By modifying the cost functions to control the order in which functions are tuned or regions unjam, we smooth out local minima while leaving global minima unaffected, increasing the success rate for reaching global minima.

Machine learning characterization of structural defects in amorphous packings of dimers and ellipses.

Structural defects within amorphous packings of symmetric particles can be characterized using a machine learning approach that incorporates structure functions of radial distances and angular arrangement. This yields a scalar field, softness, that correlates with the probability that a particle is about to be rearranged. However, when particle shapes are elongated, as in the case of dimers and ellipses, we find that the standard structure functions produce imprecise softness measurements.

Inferring statistical properties of 3D cell geometry from 2D slices.

Although cell shape can reflect the mechanical and biochemical properties of the cell and its environment, quantification of 3D cell shapes within 3D tissues remains difficult, typically requiring digital reconstruction from a stack of 2D images. We investigate a simple alternative technique to extract information about the 3D shapes of cells in a tissue; this technique connects the ensemble of 3D shapes in the tissue with the distribution of 2D shapes observed in independent 2D slices. Using cell vertex model geometries, we find that the distribution of 2D shapes allows clear determination of the mean value of a 3D shape index.

Limits of multifunctionality in tunable networks.

Nature is rife with networks that are functionally optimized to propagate inputs to perform specific tasks. Whether via genetic evolution or dynamic adaptation, many networks create functionality by locally tuning interactions between nodes. Here we explore this behavior in two contexts: strain propagation in mechanical networks and pressure redistribution in flow networks.

Machine learning determination of atomic dynamics at grain boundaries.

In polycrystalline materials, grain boundaries are sites of enhanced atomic motion, but the complexity of the atomic structures within a grain boundary network makes it difficult to link the structure and atomic dynamics. Here, we use a machine learning technique to establish a connection between local structure and dynamics of these materials. Following previous work on bulk glassy materials, we define a purely structural quantity (softness) that captures the propensity of an atom to rearrange.

Two Diverging Length Scales in the Structure of Jammed Packings.

At densities higher than the jamming transition for athermal, frictionless repulsive spheres we find two distinct length scales, both of which diverge as a power law as the transition is approached. The first, ξ_{Z}, is associated with the two-point correlation function for the number of contacts on two particles as a function of the particle separation. The second, ξ_{f}, is associated with contact-number fluctuations in subsystems of different sizes.

Nuclear rupture at sites of high curvature compromises retention of DNA repair factors.

The nucleus is physically linked to the cytoskeleton, adhesions, and extracellular matrix-all of which sustain forces, but their relationships to DNA damage are obscure. We show that nuclear rupture with cytoplasmic mislocalization of multiple DNA repair factors correlates with high nuclear curvature imposed by an external probe or by cell attachment to either aligned collagen fibers or stiff matrix. Mislocalization is greatly enhanced by lamin A depletion, requires hours for nuclear reentry, and correlates with an increase in pan-nucleoplasmic foci of the DNA damage marker γH2AX.

Linking microscopic and macroscopic response in disordered solids.

The modulus of a rigid network of harmonic springs depends on the sum of the energies in each of the bonds due to an applied distortion such as compression in the case of the bulk modulus or shear in the case of the shear modulus. However, the distortion need not be global. Here we introduce a local modulus, L_{i}, associated with changing the equilibrium length of a single bond, i, in the network.