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. Compared to a generic organization of the system components, (a) the effective physical dimension of the response to inputs decreases, (b) the response of physical degrees of freedom to random perturbations (or system "susceptibility") increases, and (c) the low-eigenvalue eigenvectors of the Hessian align with the task. Overall, these effects embody the typical scenario for learning processes in physical systems in the weak input regime, suggesting ways of discovering whether a physical network may have been trained.

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. In tandem, we construct structuro-elastoplastic (StEP) models, which reduce descriptions of the systems to a few microscopic features that control strain localization, using a machine learning-based descriptor, softness, to represent the stability of the disordered local structure. The models are based on calculated correlations of softness and rearrangements. Without additional parameters, the models exhibit semiquantitative agreement with observed stress-strain curves and softness statistics for all systems studied. Moreover, the StEP models reveal that initial structure, the near-field effect of rearrangements on local structure, and rearrangement size, respectively, are responsible for the changes in ductility observed in the three systems. Thus, StEP models provide microscopic understanding of how strain localization depends on the interplay of structure, plasticity, and elasticity.

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. Here, we report that there exists another, anomalous, type of instability caused by the breaking of a "stabilizing bond," whose removal creates an unstable vibrational mode. For commonly studied systems, such as those with harmonic finite-range interparticle interactions, such "discontinuous instabilities" are not only inevitable, they often dominate the modes of failure. Stabilizing bonds are a subset of all the bonds in the system and are prevalent in disordered solids generally. Although they do not trigger discontinuous instabilities in systems with vanishing stiffness at the interaction cutoff, they are, even in those cases, local indicators of incipient mechanical failure. They therefore provide an accurate structural predictor of instabilities not only of the discontinuous type but of the fold type as well.

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

Dorsal closure is a process that occurs during embryogenesis of Drosophila melanogaster . 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. The standard 2-dimensional vertex model, which successfully describes tissue sheet mechanics in multiple contexts, would in this case predict that the tissue should fluidize via cell neighbor changes. Surprisingly, however, the amnioserosa remains an elastic solid with no such events. We here present a minimal extension to the vertex model that explains how the amnioserosa can achieve this unexpected behavior. We show that continuous shrinkage of the preferred cell perimeter and cell perimeter polydispersity lead to the retention of the solid state of the amnioserosa. Our model accurately captures measured cell shape and orientation changes and predicts non-monotonic junction tension that we confirm with laser ablation experiments. Significance Statement: During embryogenesis, cells in tissues can undergo significant shape changes. Many epithelial tissues fluidize, i.e. cells exchange neighbors, when the average cell aspect ratio increases above a threshold value, consistent with the standard vertex model. During dorsal closure in Drosophila melanogaster , however, the amnioserosa tissue remains solid even as the average cell aspect ratio increases well above threshold. We introduce perimeter polydispersity and allow the preferred cell perimeters, usually held fixed in vertex models, to decrease linearly with time as seen experimentally. With these extensions to the standard vertex model, we capture experimental observations quantitatively. Our results demonstrate that vertex models can describe the behavior of the amnioserosa in dorsal closure by allowing normally fixed parameters to vary with time.

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

Dorsal closure is a process that occurs during embryogenesis of Drosophila melanogaster. 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. The standard 2-dimensional vertex model, which successfully describes tissue sheet mechanics in multiple contexts, would in this case predict that the tissue should fluidize via cell neighbor changes. Surprisingly, however, the amnioserosa remains an elastic solid with no such events. We here present a minimal extension to the vertex model that explains how the amnioserosa can achieve this unexpected behavior. We show that continuous shrink-age of the preferred cell perimeter and cell perimeter polydispersity lead to the retention of the solid state of the amnioserosa. Our model accurately captures measured cell shape and orientation changes and predicts non-monotonic junction tension that we confirm with laser ablation experiments.

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. Here, we explore this question in a family of harmonic spheres that range from extremely strong to extremely fragile, using "softness," a structural order parameter identified by machine learning to be highly correlated with dynamical rearrangements. We use a support vector machine to identify softness as the same linear combination of structural quantities across the entire family of liquids studied. We then use softness to identify the factors controlling fragility.

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. Using a pressure-based quenching protocol that brings the system into deeper glassy states, signals of the Gardner crossover are detected via cage size and separation order parameters for both particle positions and orientations, offering experimental evidence of Gardner physics for a system of anisotropic quasithermal particles in a low spatial dimension.

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. Here, we use machine learning to analyze simulations of jammed packings under athermal, quasistatic shear, identifying a local structural variable, softness, that correlates with rearrangements in dimensions d = 2 to d = 5. We find that softness­and even just the local coordination number Z­is essentially equally predictive of rearrangements in all d studied. This result provides direct evidence that local structure plays an important role in higher d, suggesting a modified picture for the dimensional cross-over to mean-field theory.

Program Evaluation of SeeMe™: Understanding Frailty Together.

Background: Perley Health has implemented SeeMe™: Understanding frailty together (www.perleyhealth.ca), a comprehensive approach to care that integrates the assessment and management of frailty, with an emphasis on goals of care planning. Methods: Program evaluation over the first year of SeeMe™ used a mixed-methods approach involving quantitative data from surveys, goals of care preferences, hospital transfers, and qualitative data from interviews. Results: The SeeMe™ training is an effective way to educate nurses and physicians in long-term care about frailty. For residents with documented care preferences prior to SeeMe™, there was a 15% reduction in the number of residents who preferred to be transferred to hospital post-SeeMe™ implementation. There was no significant decrease in hospital transfers during the first year the program was introduced. Conclusion: After the roll-out of SeeMe™, nurses, physicians, and families reported high satisfaction with the program, and nurses reported an increase in knowledge and confidence. There was also a reduction in the number of residents and families selecting to transfer to hospital. This suggests that the education from SeeMe™ influenced residents and families to choose less invasive interventions in the context of frailty and quality of life goals.

Regulation of GTPase function by autophosphorylation.

A unifying feature of the RAS superfamily is a conserved GTPase cycle by which these proteins transition between active and inactive states. We demonstrate that autophosphorylation of some GTPases is an intrinsic regulatory mechanism that reduces nucleotide hydrolysis and enhances nucleotide exchange, altering the on/off switch that forms the basis for their signaling functions. Using X-ray crystallography, nuclear magnetic resonance spectroscopy, binding assays, and molecular dynamics on autophosphorylated mutants of H-RAS and K-RAS, we show that phosphoryl transfer from GTP requires dynamic movement of the switch II region and that autophosphorylation promotes nucleotide exchange by opening the active site and extracting the stabilizing Mg2+. Finally, we demonstrate that autophosphorylated K-RAS exhibits altered effector interactions, including a reduced affinity for RAF proteins in mammalian cells. Thus, autophosphorylation leads to altered active site dynamics and effector interaction properties, creating a pool of GTPases that are functionally distinct from their non-phosphorylated counterparts.

Scaling concepts in 'omics: Nuclear lamin-B scales with tumor growth and often predicts poor prognosis, unlike fibrosis.

Physicochemical principles such as stoichiometry and fractal assembly can give rise to characteristic scaling between components that potentially include coexpressed transcripts. For key structural factors within the nucleus and extracellular matrix, we discover specific gene-gene scaling exponents across many of the 32 tumor types in The Cancer Genome Atlas, and we demonstrate utility in predicting patient survival as well as scaling-informed machine learning (SIML). All tumors with adjacent tissue data show cancer-elevated proliferation genes, with some genes scaling with the nuclear filament LMNB1, including the transcription factor FOXM1 that we show directly regulates LMNB1 SIML shows that such regulated cancers cluster together with longer overall survival than dysregulated cancers, but high LMNB1 and FOXM1 in half of regulated cancers surprisingly predict poor survival, including for liver cancer. COL1A1 is also studied because it too increases in tumors, and a pan-cancer set of fibrosis genes shows substoichiometric scaling with COL1A1 but predicts patient outcome only for liver cancer-unexpectedly being prosurvival. Single-cell RNA-seq data show nontrivial scaling consistent with power laws from bulk RNA and protein analyses, and SIML segregates synthetic from contractile cancer fibroblasts. Our scaling approach thus yields fundamentals-based power laws relatable to survival, gene function, and experiments.

Elastoplasticity Mediates Dynamical Heterogeneity Below the Mode Coupling Temperature.

As liquids approach the glass transition temperature, dynamical heterogeneity emerges as a crucial universal feature of their behavior. Dynamic facilitation, where local motion triggers further motion nearby, plays a major role in this phenomenon. Here we show that long-ranged, elastically mediated facilitation appears below the mode coupling temperature, adding to the short-range component present at all temperatures. Our results suggest deep connections between the supercooled liquid and glass states, and pave the way for a deeper understanding of dynamical heterogeneity in glassy systems.

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. Despite these differences, we find that the likelihood of rearrangements can nevertheless vary by several orders of magnitude within the model tissue and extract a local structural quantity, "softness," that accurately predicts the temperature dependence of the relaxation time. We use an information-theoretic measure to quantify the extent to which softness determines impending topological rearrangements; we find that softness captures nearly all of the information about rearrangements that is obtainable from structure, and that this information is large in the solid phase of the model and decreases rapidly as state variables are varied into the fluid phase.

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. Here, we answer the question of how changes at the level of individual edges collectively create functionality at the scale of an entire network. Using persistent homology, we analyze networks tuned to perform complex tasks. We find that the responses of such networks encode a hidden topological structure composed of sectors of nearly uniform pressure. Although these sectors are not apparent in the underlying network structure, they correlate strongly with the tuned function. The connectivity of these sectors, rather than that of individual nodes, provides a quantitative relationship between structure and function in flow networks.

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. This coupling allows control of the system's response, even at large strains well into the nonlinear regime, where it can be difficult to achieve control simply by design.

Characterization of G-CSF receptor expression in medulloblastoma.

BACKGROUND: Identifying mechanisms of medulloblastoma recurrence is a key to improving patient survival, and targeting treatment-resistant subpopulations within tumors could reduce disease recurrence. Expression of the granulocyte colony-stimulating factor receptor (G-CSF-R, CD114) is a potential marker of cancer stem cells, and therefore we hypothesized that a subpopulation of medulloblastoma cells would also express CD114 and would demonstrate chemoresistance and responsiveness to G-CSF. METHODS: Prevalence of CD114-positive (CD114+) cells in medulloblastoma cell lines, patient-derived xenograft (PDX) tumors, and primary patient tumor samples were assessed by flow cytometry. Growth rates, chemoresistance, and responses to G-CSF of CD114+ and CD114-negative (CD114-) cells were characterized in vitro using continuous live cell imaging and flow cytometry. Gene expression profiles were compared between CD114+ and CD114- medulloblastoma cells using quantitative RT-PCR. RESULTS: CD114+ cells were identifiable in medulloblastoma cell lines, PDX tumors, and primary patient tumors and have slower growth rates than CD114- or mixed populations. G-CSF accelerates the growth of CD114+ cells, and CD114+ cells are more chemoresistant. The CD114+ population is enriched when G-CSF treatment follows chemotherapy. The CD114+ population also has higher expression of the CSF3R, NRP-1, TWIST1, and MYCN genes. CONCLUSIONS: Our data demonstrate that a subpopulation of CD114+ medulloblastoma cells exists in cell lines and tumors, which may evade traditional chemotherapy and respond to exogenous G-CSF. These properties invite further investigation into the role of G-CSF in medulloblastoma therapy and methods to specifically target these cells.

Mechanisms of Efficacy of the FGFR1-3 Inhibitor AZD4547 in Pediatric Solid Tumor Models.

Children with aggressive pediatric solid tumors have poor outcomes and novel treatments are needed. Pediatric solid tumors demonstrate aberrant expression and activity of the fibroblast growth factor receptor (FGFR) family, suggesting FGFR inhibitors may be effective therapeutic agents. AZD4547 is a multikinase inhibitor of the FGFR1-3 kinases, and we hypothesized that AZD4547 would be effective in pediatric solid tumor preclinical models. We evaluated the effects of AZD4547 on neuroblastoma, rhabdomyosarcoma, and Ewing sarcoma cells alone and in combination with STAT3 inhibition. Continuous live cell imaging was used to measure induction of apoptosis and effects on migration. Receptor inhibition and intracellular signaling were examined by western blotting. AZD4547 treatment resulted in decreased cell confluence, increased apoptosis and reduced cell migration in all tested cell lines. AZD4547 treatment led to decreased phosphorylation of signaling proteins involved in cell survival and apoptotic pathways and increased phosphorylation of STAT3, and treatment of cell lines with AZD4547 combined with STAT3 inhibition demonstrated increased efficacy. Sensitivity to AZD4547 appears to be mediated by effects on the Ras/MAPK and JAK/STAT pathways, and AZD4547 represents a potential novel therapeutic agent for children with solid tumors.