Motor cortical inactivation impairs corrective submovements in mice performing a hold-still center-out reach task.

Holding still and aiming reaches to spatial targets may depend on distinct neural circuits. Using automated homecage training and a sensitive joystick, we trained freely-moving mice to contact a joystick, hold their forelimb still, and then reach to rewarded target locations. Mice learned the task by initiating forelimb sequences with clearly resolved submillimeter-scale micromovements followed by millimeter-scale reaches to learned spatial targets. Hundreds of thousands of trajectories were decomposed into millions of kinematic submovements, while photoinhibition was used to test roles of motor cortical areas. Inactivation of both caudal and rostral forelimb areas preserved the ability to produce aimed reaches, but reduced reach speed. Inactivation specifically of contralateral caudal forelimb area (CFA) additionally impaired the ability to aim corrective submovements to remembered locations following target undershoots. Our findings show that motor cortical inactivations reduce the gain of forelimb movements but that inactivation specifically of contralateral CFA impairs corrective movements important for reaching a target location.

Application of a variational autoencoder for clustering and analyzing in situ articular cartilage cellular response to mechanical stimuli.

In various biological systems, analyzing how cell behaviors are coordinated over time would enable a deeper understanding of tissue-scale response to physiologic or superphysiologic stimuli. Such data is necessary for establishing both normal tissue function and the sequence of events after injury that lead to chronic disease. However, collecting and analyzing these large datasets presents a challenge-such systems are time-consuming to process, and the overwhelming scale of data makes it difficult to parse overall behaviors. This problem calls for an analysis technique that can quickly provide an overview of the groups present in the entire system and also produce meaningful categorization of cell behaviors. Here, we demonstrate the application of an unsupervised method-the Variational Autoencoder (VAE)-to learn the features of cells in cartilage tissue after impact-induced injury and identify meaningful clusters of chondrocyte behavior. This technique quickly generated new insights into the spatial distribution of specific cell behavior phenotypes and connected specific peracute calcium signaling timeseries with long term cellular outcomes, demonstrating the value of the VAE technique.

Microscale strain concentrations in tissue-engineered osteochondral implants are dictated by local compositional thresholds and architecture.

Tissue-engineered osteochondral implants manufactured from condensed mesenchymal stem cell bodies have shown promise for treating focal cartilage defects. Notably, such manufacturing techniques have shown to successfully recapture the bulk mechanical properties of native cartilage. However, the relationships among the architectural features, local composition, and micromechanical environment within tissue-engineered cartilage from cell-based aggregates remain unclear. Understanding such relationships is crucial for identifying critical parameters that can predict in vivo performance. Therefore, this study investigated the relationship among architectural features, composition, and micromechanical behavior of tissue-engineered osteochondral implants. We utilized fast-confocal microscopy combined with a strain mapping technique to analyze the micromechanical behavior under quasi-static loading, as well as Fourier Transform Infrared Spectroscopy to analyze the local compositions. More specifically, we investigated the architectural features and compositional distributions generated from tissue maturation, along with macro- and micro-level strain distributions. Our results showed that under compression, cell-based aggregates underwent deformation followed by body movement, generating high local strain around the boundaries, where local aggrecan concentration was low and local collagen concentration was high. By analyzing the micromechanics and composition at the single aggregate length scale, we identified a strong threshold relationship between local strain and compositions. Namely at the aggrecan concentration below 0.015 arbitrary unit (A.U.) and the collagen concentration above 0.15 A.U., the constructs experienced greater than threefold increase in compressive strain. Overall, this study suggests that local compositional features are the primary driver of the local mechanical environment in tissue-engineered cartilage constructs, providing insight into potential quality control parameters for manufacturing tissue-engineered constructs.

Anterior forebrain pathway in parrots is necessary for producing learned vocalizations with individual signatures.

Parrots have enormous vocal imitation capacities and produce individually unique vocal signatures. Like songbirds, parrots have a nucleated neural song system with distinct anterior (AFP) and posterior forebrain pathways (PFP). To test if song systems of parrots and songbirds, which diverged over 50 million years ago, have a similar functional organization, we first established a neuroscience-compatible call-and-response behavioral paradigm to elicit learned contact calls in budgerigars (Melopsittacus undulatus). Using variational autoencoder-based machine learning methods, we show that contact calls within affiliated groups converge but that individuals maintain unique acoustic features, or vocal signatures, even after call convergence. Next, we transiently inactivated the outputs of AFP to test if learned vocalizations can be produced by the PFP alone. As in songbirds, AFP inactivation had an immediate effect on vocalizations, consistent with a premotor role. But in contrast to songbirds, where the isolated PFP is sufficient to produce stereotyped and acoustically normal vocalizations, isolation of the budgerigar PFP caused a degradation of call acoustic structure, stereotypy, and individual uniqueness. Thus, the contribution of AFP and the capacity of isolated PFP to produce learned vocalizations have diverged substantially between songbirds and parrots, likely driven by their distinct behavioral ecology and neural connectivity.

Bifurcation instructed design of multistate machines.

We propose a design paradigm for multistate machines where transitions from one state to another are organized by bifurcations of multiple equilibria of the energy landscape describing the collective interactions of the machine components. This design paradigm is attractive since, near bifurcations, small variations in a few control parameters can result in large changes to the system's state providing an emergent lever mechanism. Further, the topological configuration of transitions between states near such bifurcations ensures robust operation, making the machine less sensitive to fabrication errors and noise. To design such machines, we develop and implement a new efficient algorithm that searches for interactions between the machine components that give rise to energy landscapes with these bifurcation structures. We demonstrate a proof of concept for this approach by designing magnetoelastic machines whose motions are primarily guided by their magnetic energy landscapes and show that by operating near bifurcations we can achieve multiple transition pathways between states. This proof of concept demonstration illustrates the power of this approach, which could be especially useful for soft robotics and at the microscale where typical macroscale designs are difficult to implement.

Single-cell type analysis of wing premotor circuits in the ventral nerve cord of Drosophila melanogaster.

To perform most behaviors, animals must send commands from higher-order processing centers in the brain to premotor circuits that reside in ganglia distinct from the brain, such as the mammalian spinal cord or insect ventral nerve cord. How these circuits are functionally organized to generate the great diversity of animal behavior remains unclear. An important first step in unraveling the organization of premotor circuits is to identify their constituent cell types and create tools to monitor and manipulate these with high specificity to assess their function. This is possible in the tractable ventral nerve cord of the fly. To generate such a toolkit, we used a combinatorial genetic technique (split-GAL4) to create 195 sparse driver lines targeting 198 individual cell types in the ventral nerve cord. These included wing and haltere motoneurons, modulatory neurons, and interneurons. Using a combination of behavioral, developmental, and anatomical analyses, we systematically characterized the cell types targeted in our collection. Taken together, the resources and results presented here form a powerful toolkit for future investigations of neural circuits and connectivity of premotor circuits while linking them to behavioral outputs.

Gas-phase microactuation using kinetically controlled surface states of ultrathin catalytic sheets.

Biological systems convert chemical energy into mechanical work by using protein catalysts that assume kinetically controlled conformational states. Synthetic chemomechanical systems using chemical catalysis have been reported, but they are slow, require high temperatures to operate, or indirectly perform work by harnessing reaction products in liquids (e.g., heat or protons). Here, we introduce a bioinspired chemical strategy for gas-phase chemomechanical transduction that sequences the elementary steps of catalytic reactions on ultrathin (<10 nm) platinum sheets to generate surface stresses that directly drive microactuation (bending radii of 700 nm) at ambient conditions (T = 20 °C; Ptotal = 1 atm). When fueled by hydrogen gas and either oxygen or ozone gas, we show how kinetically controlled surface states of the catalyst can be exploited to achieve fast actuation (600 ms/cycle) at 20 °C. We also show that the approach can integrate photochemically controlled reactions and can be used to drive the reconfiguration of microhinges and complex origami- and kirigami-based microstructures.

Instabilities induced by mechanical loading determine the viability of chondrocytes grown on porous scaffolds.

Tissue-engineered cartilage constructs have shown promise to treat focal cartilage defects in multiple clinical studies. Notably, products in clinical use or in late-stage clinical trials often utilize porous collagen scaffolds to provide mechanical support and attachment sites for chondrocytes. Under loading, both the local mechanical responses of collagen scaffolds and the corresponding cellular outcomes are poorly understood, despite their wide use. As such, the architecture of collagen scaffolds varies significantly among tissue-engineered cartilage products, but the effects of such architectures on construct mechanics and cell viability are not well understood. This study investigated the effects of local mechanical responses of collagen scaffolds on chondrocyte viability in tissue-engineered cartilage constructs. We utilized fast confocal microscopy combined with a strain mapping technique to analyze the architecture-dependent instabilities under quasi-static loading and subsequent chondrocyte death in honeycomb and sponge scaffolds. More specifically, we compared the isotropic and the orthotropic planes for each type of collagen scaffold. Under compression, both planes exhibited elastic, buckled, and densified deformation modes. In both loading directions, cell death was minimal in regions that experienced elastic deformation mode and a trend of increase in buckled mode. More interestingly, we saw a significant increase in cell death in densified mode. Overall, this study suggests that local instabilities are directly correlated to chondrocyte death in tissue-engineered cartilage constructs, highlighting the importance of understanding the architecture-dependent local mechanical responses under loading.

Steering self-organisation through confinement.

Self-organisation is the spontaneous emergence of spatio-temporal structures and patterns from the interaction of smaller individual units. Examples are found across many scales in very different systems and scientific disciplines, from physics, materials science and robotics to biology, geophysics and astronomy. Recent research has highlighted how self-organisation can be both mediated and controlled by confinement. Confinement is an action over a system that limits its units' translational and rotational degrees of freedom, thus also influencing the system's phase space probability density; it can function as either a catalyst or inhibitor of self-organisation. Confinement can then become a means to actively steer the emergence or suppression of collective phenomena in space and time. Here, to provide a common framework and perspective for future research, we examine the role of confinement in the self-organisation of soft-matter systems and identify overarching scientific challenges that need to be addressed to harness its full scientific and technological potential in soft matter and related fields. By drawing analogies with other disciplines, this framework will accelerate a common deeper understanding of self-organisation and trigger the development of innovative strategies to steer it using confinement, with impact on, e.g., the design of smarter materials, tissue engineering for biomedicine and in guiding active matter.

Discontinuous Metric Programming in Liquid Crystalline Elastomers.

Liquid crystalline elastomers (LCEs) are shape-changing materials that exhibit large deformations in response to applied stimuli. Local control of the orientation of LCEs spatially directs the deformation of these materials to realize a spontaneous shape change in response to stimuli. Prior approaches to shape programming in LCEs utilize patterning techniques that involve the detailed inscription of spatially varying nematic fields to produce sheets. These patterned sheets deform into elaborate geometries with complex Gaussian curvatures. Here, we present an alternative approach to realize shape-morphing in LCEs where spatial patterning of the crosslink density locally regulates the material deformation magnitude on either side of a prescribed interface curve. We also present a simple mathematical model describing the behavior of these materials. Further experiments coupled with the mathematical model demonstrate the control of the sign of Gaussian curvature, which is used in combination with heat transfer effects to design LCEs that self-clean as a result of temperature-dependent actuation properties.

Non-equilibrium ordering of liquid crystalline (LC) films driven by external gradients in surfactant concentration.

HYPOTHESIS: Gradients in the concentration of amphiphiles play an important role in many non-equilibrium processes involving complex fluids. Here we explore if non-equilibrium interfacial behaviors of thermotropic (oily) liquid crystals (LCs) can amplify microscopic gradients in surfactant concentration into macroscopic optical signals. EXPERIMENTS: We use a milli-fluidic system to generate gradients in aqueous sodium dodecyl sulfate (SDS) concentration and optically quantify the dynamic ordering of micrometer-thick nematic LC films that contact the gradients. FINDINGS: We find that the reordering of the LCs is dominated by interfacial shearing by Marangoni flows, thus providing simple methods for rapid mapping of interfacial velocities from a single optical image and investigating the effects of confinement of surfactant-driven interfacial flows. Additionally, we establish that surface advection and surfactant desorption are the two key processes that regulate the interfacial flows, revealing that the dynamic response of the LC can provide rapid and potentially high throughput approaches to measurement of non-equilibrium interfacial properties of amphiphiles. We also observe flow-induced assemblies of microparticles to form at the LC interface, hinting at new non-equilibrium approaches to microparticle assembly. We conclude that dynamic states adopted by LCs in the presence of surfactant concentration gradients provide new opportunities for engineering complex fluids beyond equilibrium.

STRAINS: A big data method for classifying cellular response to stimuli at the tissue scale.

Cellular response to stimulation governs tissue scale processes ranging from growth and development to maintaining tissue health and initiating disease. To determine how cells coordinate their response to such stimuli, it is necessary to simultaneously track and measure the spatiotemporal distribution of their behaviors throughout the tissue. Here, we report on a novel SpatioTemporal Response Analysis IN Situ (STRAINS) tool that uses fluorescent micrographs, cell tracking, and machine learning to measure such behavioral distributions. STRAINS is broadly applicable to any tissue where fluorescence can be used to indicate changes in cell behavior. For illustration, we use STRAINS to simultaneously analyze the mechanotransduction response of 5000 chondrocytes-over 20 million data points-in cartilage during the 50 ms to 4 hours after the tissue was subjected to local mechanical injury, known to initiate osteoarthritis. We find that chondrocytes exhibit a range of mechanobiological responses indicating activation of distinct biochemical pathways with clear spatial patterns related to the induced local strains during impact. These results illustrate the power of this approach.

Universal scaling for disordered viscoelastic matter near the onset of rigidity.

The onset of rigidity in interacting liquids, as they undergo a transition to a disordered solid, is associated with a rearrangement of the low-frequency vibrational spectrum. In this Letter, we derive scaling forms for the singular dynamical response of disordered viscoelastic networks near both jamming and rigidity percolation. Using effective-medium theory, we extract critical exponents, invariant scaling combinations, and analytical formulas for universal scaling functions near these transitions. Our scaling forms describe the behavior in space and time near the various onsets of rigidity, for rigid and floppy phases and the crossover region, including diverging length scales and timescales at the transitions.

Neuromuscular embodiment of feedback control elements in Drosophila flight.

While insects such as Drosophila are flying, aerodynamic instabilities require that they make millisecond time scale adjustments to their wing motion to stay aloft and on course. These stabilization reflexes can be modeled as a proportional-integral (PI) controller; however, it is unclear how such control might be instantiated in insects at the level of muscles and neurons. Here, we show that the b1 and b2 motor units-prominent components of the fly's steering muscle system-modulate specific elements of the PI controller: the angular displacement (integral) and angular velocity (proportional), respectively. Moreover, these effects are observed only during the stabilization of pitch. Our results provide evidence for an organizational principle in which each muscle contributes to a specific functional role in flight control, a finding that highlights the power of using top-down behavioral modeling to guide bottom-up cellular manipulation studies.

Microscopic robots with onboard digital control.

Autonomous robots-systems where mechanical actuators are guided through a series of states by information processing units to perform a predesigned function-are expected to revolutionize everything from health care to transportation. Microscopic robots are poised for a similar revolution in fields from medicine to environmental remediation. A key hurdle to developing these microscopic robots is the integration of information systems, particularly electronics fabricated at commercial foundries, with microactuators. Here, we develop such an integration process and build microscopic robots controlled by onboard complementary metal oxide semiconductor electronics. The resulting autonomous, untethered robots are 100 to 250 micrometers in size, are powered by light, and walk at speeds greater than 10 micrometers per second. In addition, we demonstrate a microscopic robot that can respond to an optical command. This work paves the way for ubiquitous autonomous microscopic robots that perform complex functions, respond to their environments, and communicate with the outside world.

Programming interactions in magnetic handshake materials.

The ability to rapidly manufacture building blocks with specific binding interactions is a key aspect of programmable assembly. Recent developments in DNA nanotechnology and colloidal particle synthesis have significantly advanced our ability to create particle sets with programmable interactions, based on DNA or shape complementarity. The increasing miniaturization underlying magnetic storage offers a new path for engineering programmable components for self assembly, by printing magnetic dipole patterns on substrates using nanotechnology. How to efficiently design dipole patterns for programmable assembly remains an open question as the design space is combinatorially large. Here, we present design rules for programming these magnetic interactions. By optimizing the structure of the dipole pattern, we demonstrate that the number of independent building blocks scales super linearly with the number of printed domains. We test these design rules using computational simulations of self assembled blocks, and experimental realizations of the blocks at the mm scale, demonstrating that the designed blocks give high yield assembly. In addition, our design rules indicate that with current printing technology, micron sized magnetic panels could easily achieve hundreds of different building blocks.

Cilia metasurfaces for electronically programmable microfluidic manipulation.

Cilial pumping is a powerful strategy used by biological organisms to control and manipulate fluids at the microscale. However, despite numerous recent advances in optically, magnetically and electrically driven actuation, development of an engineered cilial platform with the potential for applications has remained difficult to realize1-6. Here we report on active metasurfaces of electronically actuated artificial cilia that can create arbitrary flow patterns in liquids near a surface. We first create voltage-actuated cilia that generate non-reciprocal motions to drive surface flows at tens of microns per second at actuation voltages of 1 volt. We then show that a cilia unit cell can locally create a range of elemental flow geometries. By combining these unit cells, we create an active cilia metasurface that can generate and switch between any desired surface flow pattern. Finally, we integrate the cilia with a light-powered complementary metal-oxide-semiconductor (CMOS) clock circuit to demonstrate wireless operation. As a proof of concept, we use this circuit to output voltage pulses with various phase delays to demonstrate improved pumping efficiency using metachronal waves. These powerful results, demonstrated experimentally and confirmed using theoretical computations, illustrate a pathway towards fine-scale microfluidic manipulation, with applications from microfluidic pumping to microrobotic locomotion.

Structural origins of cartilage shear mechanics.

Articular cartilage is a remarkable material able to sustain millions of loading cycles over decades of use outperforming any synthetic substitute. Crucially, how extracellular matrix constituents alter mechanical performance, particularly in shear, remains poorly understood. Here, we present experiments and theory in support of a rigidity percolation framework that quantitatively describes the structural origins of cartilage's shear properties and how they arise from the mechanical interdependence of the collagen and aggrecan networks making up its extracellular matrix. This framework explains that near the cartilage surface, where the collagen network is sparse and close to the rigidity threshold, slight changes in either collagen or aggrecan concentrations, common in early stages of cartilage disease, create a marked weakening in modulus that can lead to tissue collapse. More broadly, this framework provides a map for understanding how changes in composition throughout the tissue alter its shear properties and ultimate in vivo function.

Rigidity and fracture of biopolymer double networks.

Tunable mechanics and fracture resistance are hallmarks of biological tissues whose properties arise from extracellular matrices comprised of double networks. To elucidate the origin of these desired properties, we study the shear modulus and fracture properties of a rigidly percolating double network model comprised of a primary network of stiff fibers and a secondary network of flexible fibers. We find that when the primary network density is just above its rigidity percolation threshold, the secondary network density can be tuned to facilitate stress relaxation via non-affine deformations and provide mechanical reinforcement. In contrast, when the primary network is far above its rigidity threshold, the double network is always stiff and brittle. These results highlight an important mechanism behind the tunability and resilience of biopolymer double networks: the secondary network can dramatically alter mechanical properties from compliant and ductile to stiff and brittle only when the primary network is marginally rigid.

Re-entrant transition as a bridge of broken ergodicity in confined monolayers of hexagonal prisms and cylinders.

The entropy-driven monolayer assembly of hexagonal prisms and cylinders was studied under hard slit confinement. At the conditions investigated, the particles have two distinct and dynamically disconnected rotational states: unflipped and flipped, depending on whether their circular/hexagonal face is parallel or perpendicular to the wall plane. Importantly, these two rotational states cast distinct projection areas over the wall plane that favor either hexagonal or tetragonal packing. Monte Carlo simulations revealed a re-entrant melting transition where an intervening disordered Flipped-Unflipped (FUN) phase is sandwiched between a fourfold tetratic phase at high concentrations and a sixfold triangular solid at intermediate concentrations. The FUN phase contains a mixture of flipped and unflipped particles and is translationally and orientationally disordered. Complementary experiments were conducted with photolithographically fabricated cylindrical microparticles confined in a wedge cell. Both simulations and experiments show the formation of phases with comparable fraction of flipped particles and structure, i.e., the FUN phase, triangular solid, and tetratic phase, indicating that both approaches sample analogous basins of particle-orientation phase-space. The phase behavior of hexagonal prisms in a soft-repulsive wall model was also investigated to exemplify how tunable particle-wall interactions can provide an experimentally viable strategy to dynamically bridge the flipped and unflipped states.