Humans are very good at visually recognizing materials and inferring their properties. Without touching surfaces, we can usually tell what they would feel like, and we enjoy vivid visual intuitions about how they typically behave. This is impressive because the retinal image that the visual system receives as input is the result of complex interactions between many physical processes. Somehow the brain has to disentangle these different factors. I will present some recent work in which we show that an unsupervised neural network trained on images of surfaces spontaneously learns to disentangle reflectance, lighting and shape. However, the disentanglement is not perfect, and we find that as a result the network not only predicts the broad successes of human gloss perception, but also the specific pattern of errors that humans exhibit on an image-by-image basis. I will argue this has important implications for thinking about appearance and vision more broadly.

The emergent dynamics exhibited by collections of living organisms often shows signatures of symmetries that are broken at the single-organism level. At the same time, organism development itself encompasses a well-coordinated sequence of symmetry breaking events that successively transform a single, nearly isotropic cell into an animal with well-defined body axis and various anatomical asymmetries. Combining these key aspects of collective phenomena and embryonic development, we describe here the spontaneous formation of hydrodynamically stabilized active crystals made of hundreds of starfish embryos that gather during early development near fluid surfaces. We describe a minimal hydrodynamic theory that is fully parameterized by experimental measurements of microscopic interactions among embryos. Using this theory, we can quantitatively describe the stability, formation and rotation of crystals and rationalize the emergence of mechanical properties that carry signatures of an odd elastic material. Our work thereby quantitatively connects developmental symmetry breaking events on the single-embryo level with remarkable macroscopic material properties of a novel living chiral crystal system.

Changes in the geometry and topology of self-assembled membranes underlie diverse processes across cellular biology and engineering. Similar to lipid bilayers, monolayer colloidal membranes studied by the Sharma (IISc Bangalore) and Dogic (UCSB) Labs have in-plane fluid-like dynamics and out-of-plane bending elasticity, but their open edges and micron length scale provide a tractable system to study the equilibrium energetics and dynamic pathways of membrane assembly and reconfiguration. First, we discuss how doping colloidal membranes with short miscible rods transforms disk-shaped membranes into saddle-shaped minimal surfaces with complex edge structures. Theoretical modeling demonstrates that their formation is driven by increasing positive Gaussian modulus, which in turn is controlled by the fraction of short rods. Further coalescence of saddle-shaped surfaces leads to exotic topologically distinct structures, including shapes similar to catenoids, tri-noids, four-noids, and higher order structures. We then mathematically explore the mechanics of these catenoid-like structures subject to an external axial force and elucidate their intimate connection to two problems whose solutions date back to Euler: the shape of an area-minimizing soap film and the buckling of a slender rod under compression. A perturbation theory argument directly relates the tensions of membranes to the stability properties of minimal surfaces. We also investigate the effects of including a Gaussian curvature modulus, which, for small enough membranes, causes the axial force to diverge as the ring separation approaches its maximal value.

Our visual experience of living in a three-dimensional world is created from the information contained in the two-dimensional images projected into our eyes. The overlapping visual fields of the two eyes mean that their images are highly correlated, and that the small differences that are present represent an important cue to depth. Binocular neurons encode this information in a way that both maximises efficiency and optimises disparity tuning for the depth structures that are found in our natural environment. Neural network models provide a clear account of how these binocular neurons encode the local binocular disparity in images. These models can be expanded to multi-layer models that are sensitive to salient features of scenes, such as the orientations and discontinuities between surfaces. These deep neural network models have also shown the importance of binocular disparity for the segmentation of images into separate objects, in addition to the estimation of distance. These results demonstrate the usefulness of machine learning approaches as a tool for understanding biological vision.

Materials with complex appearances, like textiles and foodstuffs, pose challenges for conventional theories of vision. How does the brain learn to see properties of the world—like the glossiness of a surface—that cannot be measured by any other senses? Recent advances in unsupervised deep learning may help shed light on material perception. I will show how an unsupervised deep neural network trained on an artificial environment of surfaces that have different shapes, materials and lighting, spontaneously comes to encode those factors in its internal representations. Most strikingly, the model makes patterns of errors in its perception of material that follow, on an image-by-image basis, the patterns of errors made by human observers. Unsupervised deep learning may provide a coherent framework for how many perceptual dimensions form, in material perception and beyond.

Filaments (slender, microscopic elastic bodies) are prevalent in biological and industrial settings. In the biological case, the filaments are often active, in that they are driven internally by motor proteins, with the prime examples being cilia and flagella. For cilia in particular, which can appear in dense arrays, their resulting motions are coupled through the surrounding fluid, as well as through surfaces to which they are attached. In this talk, I present numerical simulations exploring the coordinated motion of active filaments and how it depends on the driving force, density of filaments, as well as the attached surface. In particular, we find that when the surface is spherical, its topology introduces local defects in coordinated motion which can then feedback and alter the global state. This is particularly true when the surface is not held fixed and is free to move in the surrounding fluid. These simulations take advantage of a computational framework we developed for fully 3D filament motion that combines unit quaternions, implicit geometric time integration, quasi-Newton methods, and fast, matrix-free methods for hydrodynamic interactions and it will also be presented.

Individual bacteria transported in viscous flows, show complex interactions with flows and bounding surfaces resulting from their complex shape as well as their activity. Understanding these transport dynamics is crucial, as they impact soil contamination, transport in biological conducts or catheters, and constitute thus a serious health threat. Here we investigate the trajectories of individual E-coli bacteria in confined geometries under flow, using microfluidic model systems in bulk flows as well as close to surfaces using a novel Langrangian 3D tracking method. Combining experimental observations and modelling we elucidate the origin of upstream swimming, lateral drift or persistent transport along corners. [1] Junot et al, EPL, 126 (2019) 44003 [2] Mathijssen et al. 10:3 (2019) Nature Comm. [3] Figueroa-Morales et al., Soft Matter, 2015,11, 6284-6293 [4] Darnige et al. Review of Scientific Instruments 88, 055106 (2017) [5] Jing et al, Science Advances, 2020; 6 : eabb2012 [6] Figueroa-Morales et al, Sci. Adv. 2020; 6 : eaay0155, 2020, 10.1126/sciadv.aay0155

While the motion and collective behavior of cells are well-studied on flat surfaces or in unconfined liquid media, in most natural settings, cells thrive in complex 3D environments. Bioprinting processes are capable of structuring cells in 3D and conventional bioprinting approaches address this challenge by embedding cells in bio-degradable polymer networks. However, heterogeneity in network structure and biodegradation often preclude quantitative studies of cell behavior in specified 3D architectures. Here, I will present a new approach to 3D bioprinting of cellular communities that utilizes jammed, granular polyelectrolyte microgels as a support medium. The self-healing nature of this medium allows the creation of highly precise cellular communities and tissue-like structures by direct injection of cells inside the 3D medium. Further, the transparent nature of this medium enables precise characterization of cellular behavior. I will describe two examples of my work using this platform to study the behavior of two different classes of cells in 3D. First, I will describe how we interrogate the growth, viability, and migration of mammalian cells—ranging from epithelial cells, cancer cells, and T cells—in the 3D pore space. Second, I will describe how we interrogate the migration of E. coli bacteria through the 3D pore space. Direct visualization enables us to reveal a new mode of motility exhibited by individual cells, in stark contrast to the paradigm of run-and-tumble motility, in which cells are intermittently and transiently trapped as they navigate the pore space; further, analysis of these dynamics enables prediction of single-cell transport over large length and time scales. Moreover, we show that concentrated populations of E. coli can collectively migrate through a porous medium—despite being strongly confined—by chemotactically “surfing” a self-generated nutrient gradient. Together, these studies highlight how the jammed microgel medium provides a powerful platform to design and interrogate complex cellular communities in 3D—with implications for tissue engineering, microtissue mechanics, studies of cellular interactions, and biophysical studies of active matter.

The main transmission mode of the COVID-19 disease is through virus-laden aerosols and droplets generated by expiratory events, such as breathing and sneezing. Patients with respiratory diseases are typically treated with oxygenation devices in hospitals, homes, and other settings where they increase the risk of spreading the disease to caregivers and first responders. Here, I will discuss a systematic study of aerosol and droplet dispersal through the air and their final deposition on surfaces. Through laser and fluorescent imaging techniques, we measure the volumetric spatial-temporal dynamics of droplet dispersal while varying rheological properties of the mucosaliva. We then demonstrate that a standard nose and mouth mask reduces the amount of mucosaliva dispersed by a factor of at least a hundred. Our ongoing collaborations with doctors and respiratory therapists from the Baystate Medical Hospital are developing new guidelines to help mitigate disease spread in a hospital setting.

Nature is replete with extraordinary materials that can grow, move, respond, and adapt. In this talk I will describe our ongoing efforts to develop advanced polymeric materials, inspired by nature. First, I will describe my group’s efforts to develop ultrastiff, ultratough materials inspired by the byssal materials of marine mussels. These adhesive contacts allow mussels to secure themselves to rocks, wood, metals and other surfaces in the harsh conditions of the intertidal zone. By developing a foundational understanding of the structure-mechanics relationships and processing of the natural system, we can design high-performance materials that are extremely strong without compromising extensibility, as well as macroporous materials with tunable toughness and strength. In the second half of the talk, I will describe new efforts to exploit light as a means of remote control and power. By leveraging the phototransduction pathways of highly-absorbing, negatively photochromic molecules, we can drive the motion of amorphous polymeric materials as well as liquid flows. These innovations enable applications in packaging, connective tissue repair, soft robotics, and optofluidics.

Granular matter can serve as a prototype for exploring the rich physics of many-body systems driven far from equilibrium. This talk will outline a new direction for granular physics with macroscopic particles, where acoustic levitation compensates the forces due to gravity and eliminates frictional interactions with supporting surfaces in order to focus on particle interactions. Levitating small particles by intense ultrasound fields in air makes it possible to manipulate and control their positions and assemble them into larger aggregates. The small air viscosity implies that the regime of underdamped dynamics can be explored, where inertial effects are important, in contrast to typical colloids in a liquid, where inertia can be neglected. Sound scattered off individual, levitated solid particles gives rise to controllable attractive forces with neighboring particles. I will discuss some of the key concepts underlying acoustic levitation, describe how detuning an acoustic cavity can introduce active fluctuations that control the assembly statistics of small levitated particles clusters, and give examples of how interactions between neighboring levitated objects can be controlled by their shape.

Cement is the main binding agent in concrete, literally gluing together rocks and sand into the most-used synthetic material on Earth. However, cement production is responsible for significant amounts of man- made greenhouse gases—in fact if the cement industry were a country, it would be the third largest emitter in the world. Alternatives to the current, environmentally harmful cement production process are not available essentially because the gaps in fundamental understanding hamper the development of smarter and more sustainable solutions. The ultimate challenge is to link the chemical composition of cement grains to the nanoscale physics of the cohesive forces that emerge when mixing cement with water. Cement nanoscale cohesion originates from the electrostatics of ions accumulated in a water-based solution between like-charged surfaces but it is not captured by existing theories because of the nature of the ions involved and the high surface charges. Surprisingly enough, this is also the case for unexplained cohesion in a range of colloidal and biological matter. About one century after the early studies of cement hydration, we have quantitatively solved this notoriously hard problem and discovered how cement cohesion develops during hydration. I will discuss how 3D numerical simulations that feature a simple but molecular description of ions and water, together with an analytical theory that goes beyond the traditional continuum approximations, helped us demonstrate that the optimized interlocking of ion-water structures determine the net cohesive forces and their evolution. These findings open the path to scientifically grounded strategies of material design for cements and have implications for a much wider range of materials and systems where ionic water-based solutions feature both strong Coulombic and confinement effects, ranging from biological membranes to soils. Construction materials are central to our society and to our life as humans on this planet, but usually far removed from fundamental science. We can now start to understand how cement physical-chemistry determines performance, durability and sustainability.

Much of the science underpinning the global response to the COVID-19 pandemic lies in the soft matter domain. Coronaviruses are composite particles with a core of nucleic acids complexed to proteins surrounded by a protein-studded lipid bilayer shell. A dominant route for transmission is via air-borne aerosols and droplets. Viral interaction with polymeric body fluids, particularly mucus, and cell membranes controls their infectivity, while their interaction with skin and artificial surfaces underpins cleaning and disinfection and the efficacy of masks and other personal protective equipment. The global response to COVID-19 has highlighted gaps in the soft matter knowledge base. I will survey these gaps, especially as pertaining to the transmission of the disease, and suggest questions that can (and need to) be tackled, both in response to COVID-19 and to better prepare for future viral pandemics.

Motility is a crucial virulence factor for many species of bacteria, but it is not fully understood how bacterial motility is practically involved in pathogenicity. This time I will give a talk on the association of motility with pathogenicity in the zoonotic spirochete bacterium Leptospira. Recently, we measured swimming force of individual leptospires using optical tweezers and found that they can generate ~30 times of the swimming force of E. coli. We also observed that leptospires increase the reversal frequency of swimming at the gel-liquid interface, resembling host dermis exposed to contaminated water (Abe et al., 2020, Sci Rep). These could be involved in percutaneous infection of the spirochete. We have shown that Leptospira not only swims in liquid but also moves over solid surfaces (Tahara et al., 2018, Sci Adv). We quantified the surface motility called “crawling” on cultured kidney tissues from various mammals, showing that pathogenic leptospires crawl over the tissue surfaces more persistently that non-pathogenic ones (Xu et al., 2020, Front Microbiol). I will discuss the spirochete motility related to pathogenicity from the biophysical viewpoint.

Recurrent neural networks (RNNs) are powerful dynamical models, widely used in machine learning (ML) for processing sequential data, and also in neuroscience, to understand the emergent properties of networks of real neurons. Prior theoretical work in understanding the properties of RNNs has focused on models with additive interactions. However, real neurons can have gating i.e. multiplicative interactions, and gating is also a central feature of the best performing RNNs in machine learning. Here, we develop a dynamical mean-field theory (DMFT) to study the consequences of gating in RNNs. We use random matrix theory to show how gating robustly produces marginal stability and line attractors – important mechanisms for biologically-relevant computations requiring long memory. The long-time behavior of the gated network is studied using its Lyapunov spectrum, and the DMFT is used to provide a novel analytical expression for the maximum Lyapunov exponent demonstrating its close relation to relaxation-time of the dynamics. Gating is also shown to give rise to a novel, discontinuous transition to chaos, where the proliferation of critical points (topological complexity) is decoupled from the appearance of chaotic dynamics (dynamical complexity), contrary to a seminal result for additive RNNs. Critical surfaces and regions of marginal stability in the parameter space are indicated in phase diagrams, thus providing a map for principled parameter choices for ML practitioners. Finally, we develop a field-theory for gradients that arise in training, by incorporating the adjoint sensitivity framework from control theory in the DMFT. This paves the way for the use of powerful field-theoretic techniques to study training/gradients in large RNNs.

This talk aims to show how new physical methods can advance biological and biomedical research. A major advance in physical chemistry in the last two decades has been the development of quantitative methods to directly observe individual molecules in solution, attached to surfaces, in the membrane of live cells or more recently inside live cells. These single-molecule fluorescence studies have now reached a stage where they can provide new insights into important biological problems. After presenting the principles of these methods, I will give some examples from our current research to probe the molecular basis of neurodegeneration. Here we have used single-molecule fluorescence to detect and analyse the low concentrations of soluble protein aggregates thought to be responsible for Alzheimer’s disease and determine the mechanisms by which they damage neurons. Lastly, I will describe how fundamental science aimed at watching single molecules incorporating nucleotides into DNA gave rise to a new rapid method to sequence DNA that is now widely used.