Polydispersity modifies relaxation mechanisms in glassy liquids

State-of-the-art techniques for simulating deeply supercooled liquids require a high degree of size polydispersity to be effective. While these techniques have enabled great insight into the microscopic dynamics near the glass transition, the effect of the artificially introduced polydispersity on the dynamics has remained largely unstudied. Here we show that a particle's size not only has a strong correlation with its mobility, but we also observe that, as the mode-coupling temperature is crossed and the system becomes more deeply supercooled, a dynamic separation between small mobile and larger quiescent particles emerges at timescales corresponding to cage escape. Our results suggest that the cage escape of this population of mobile particles facilitates the later structural relaxation of the quiescent particles. This indicates that it is of vital importance to account for particle size effects when generalizing results to other glass-forming systems

Competing Relaxation Channels in Continuously Polydisperse Fluids: A Mode-Coupling Study

Systems with a high degree of size polydispersity are becoming standard in the computational study of deeply supercooled liquids. In this work we perform a systematic analysis of continuously polydisperse fluids as a function of the degree of polydispersity within the framework of the Mode-Coupling Theory of the glass transition (MCT). Our results show that a high degree of polydispersity tends to stabilize the liquid phase against vitrification, the magnitude of which depends on the shape of the polydispersity distribution. Further, we report on a separation between the localization lengths of the smallest and largest particles. A diameter-resolved analysis of the intermediate scattering functions reveals that this separation significantly stretches the relaxation patterns, which we quantitatively study by an analysis of the dynamical exponents predicted by the theory. Our observations have strong implications for our understanding of the nature of dynamical heterogeneities and localization lengths in continuously polydisperse systems. These results suggest that the dynamics of the smallest particles is of central importance to understand structural relaxation of continuously size polydisperse fluids, already in the mildly supercooled regime where MCT is usually applicable.Comment: 12 pages, 10 figure

A deep learning approach to the measurement of long-lived memory kernels from Generalised Langevin Dynamics

Memory effects are ubiquitous in a wide variety of complex physical phenomena, ranging from glassy dynamics and metamaterials to climate models. The Generalised Langevin Equation (GLE) provides a rigorous way to describe memory effects via the so-called memory kernel in an integro-differential equation. However, the memory kernel is often unknown, and accurately predicting or measuring it via e.g. a numerical inverse Laplace transform remains a herculean task. Here we describe a novel method using deep neural networks (DNNs) to measure memory kernels from dynamical data. As proof-of-principle, we focus on the notoriously long-lived memory effects of glassy systems, which have proved a major challenge to existing methods. Specifically, we learn a training set generated with the Mode-Coupling Theory (MCT) of hard spheres. Our DNNs are remarkably robust against noise, in contrast to conventional techniques which require ensemble averaging over many independent trajectories. Finally, we demonstrate that a network trained on data generated from analytic theory (hard-sphere MCT) generalises well to data from simulations of a different system (Brownian Weeks-Chandler-Andersen particles). We provide a general pipeline, KernelLearner, for training networks to extract memory kernels from any non-Markovian system described by a GLE. The success of our DNN method applied to glassy systems suggests deep learning can play an important role in the study of dynamical systems that exhibit memory effects

Emergent structural correlations in dense liquids

The complete quantitative description of the structure of dense and supercooled liquids remains a notoriously difficult problem in statistical physics. Most studies to date focus solely on two-body structural correlations, and only a handful of papers have sought to consider additional three-body correlations. Here, we go beyond the state of the art by extracting many-body static structure factors from molecular dynamics simulations and by deriving accurate approximations up to the six-body structure factor via density functional theory. We find that supercooling manifestly increases four-body correlations, akin to the two- and three-body case. However, at small wave numbers, we observe that the four-point structure of a liquid drastically changes upon supercooling, both qualitatively and quantitatively, which is not the case in two-point structural correlations. This indicates that theories of the structure or dynamics of dense liquids should incorporate many-body correlations beyond the two-particle level to fully capture their intricate behavior.</p

Unveiling the anatomy of mode-coupling theory

The mode-coupling theory of the glass transition (MCT) has been at the forefront of fundamental glass research for decades, yet the theory's underlying approximations remain obscure. Here we quantify and critically assess the effect of each MCT approximation separately. Using Brownian dynamics simulations, we compute the memory kernel predicted by MCT after each approximation in its derivation, and compare it with the exact one. We find that some often-criticized approximations are in fact very accurate, while the opposite is true for others, providing new guiding cues for further theory development

Many-body correlations are non-negligible in both fragile and strong glassformers

It is widely believed that the emergence of slow glassy dynamics is encoded in a material's microstructure. First-principles theory [mode-coupling theory (MCT)] is able to predict the dramatic slowdown of the dynamics from only static two-point correlations as input, yet it cannot capture all of the observed dynamical behavior. Here we go beyond two-point spatial correlation functions by extending MCT systematically to include higher-order static and dynamic correlations. We demonstrate that only adding the static triplet direct correlations already qualitatively changes the predicted glass-transition diagram of binary hard spheres and silica. Moreover, we find a non-trivial competition between static triplet correlations that work to stabilize the glass state, and dynamic higher-order correlations which destabilize it for both materials. We conclude that the conventionally neglected static triplet direct correlations as well as higher-order dynamic correlations are in fact non-negligible in both fragile and strong glassformers.Comment: 2 figures, accepted for publication in Physical Review Letter

The interaction of Thrombospondins with extracellular matrix proteins

The thrombospondins (TSPs) are a family of five matricellular proteins that appear to function as adapter molecules to guide extracellular matrix synthesis and tissue remodeling in a variety of normal and disease settings. Various TSPs have been shown to bind to fibronectin, laminin, matrilins, collagens and other extracellular matrix (ECM) proteins. The importance of TSP-1 in this context is underscored by the fact that it is rapidly deposited at the sites of tissue damage by platelets. An association of TSPs with collagens has been known for over 25 years. The observation that the disruption of the TSP-2 gene in mice leads to collagen fibril abnormalities provided important in vivo evidence that these interactions are physiologically important. Recent biochemical studies have shown that TSP-5 promotes collagen fibril assembly and structural studies suggest that TSPs may interact with collagens through a highly conserved potential metal ion dependent adhesion site (MIDAS). These interactions are critical for normal tissue homeostasis, tumor progression and the etiology of skeletal dysplasias

Biomechanical spinal growth modulation and progressive adolescent scoliosis – a test of the 'vicious cycle' pathogenetic hypothesis: Summary of an electronic focus group debate of the IBSE

There is no generally accepted scientific theory for the causes of adolescent idiopathic scoliosis (AIS). As part of its mission to widen understanding of scoliosis etiology, the International Federated Body on Scoliosis Etiology (IBSE) introduced the electronic focus group (EFG) as a means of increasing debate on knowledge of important topics. This has been designated as an on-line Delphi discussion. The text for this debate was written by Dr Ian A Stokes. It evaluates the hypothesis that in progressive scoliosis vertebral body wedging during adolescent growth results from asymmetric muscular loading in a "vicious cycle" (vicious cycle hypothesis of pathogenesis) by affecting vertebral body growth plates (endplate physes). A frontal plane mathematical simulation tested whether the calculated loading asymmetry created by muscles in a scoliotic spine could explain the observed rate of scoliosis increase by measuring the vertebral growth modulation by altered compression. The model deals only with vertebral (not disc) wedging. It assumes that a pre-existing scoliosis curve initiates the mechanically-modulated alteration of vertebral body growth that in turn causes worsening of the scoliosis, while everything else is anatomically and physiologically 'normal' The results provide quantitative data consistent with the vicious cycle hypothesis. Dr Stokes' biomechanical research engenders controversy. A new speculative concept is proposed of vertebral symphyseal dysplasia with implications for Dr Stokes' research and the etiology of AIS. What is not controversial is the need to test this hypothesis using additional factors in his current model and in three-dimensional quantitative models that incorporate intervertebral discs and simulate thoracic as well as lumbar scoliosis. The growth modulation process in the vertebral body can be viewed as one type of the biologic phenomenon of mechanotransduction. In certain connective tissues this involves the effects of mechanical strain on chondrocytic metabolism a possible target for novel therapeutic intervention

Can mental health diagnoses in administrative data be used for research? A systematic review of the accuracy of routinely collected diagnoses

BACKGROUND: There is increasing availability of data derived from diagnoses made routinely in mental health care, and interest in using these for research. Such data will be subject to both diagnostic (clinical) error and administrative error, and so it is necessary to evaluate its accuracy against a reference-standard. Our aim was to review studies where this had been done to guide the use of other available data. METHODS: We searched PubMed and EMBASE for studies comparing routinely collected mental health diagnosis data to a reference standard. We produced diagnostic category-specific positive predictive values (PPV) and Cohen’s kappa for each study. RESULTS: We found 39 eligible studies. Studies were heterogeneous in design, with a wide range of outcomes. Administrative error was small compared to diagnostic error. PPV was related to base rate of the respective condition, with overall median of 76 %. Kappa results on average showed a moderate agreement between source data and reference standard for most diagnostic categories (median kappa = 0.45–0.55); anxiety disorders and schizoaffective disorder showed poorer agreement. There was no significant benefit in accuracy for diagnoses made in inpatients. CONCLUSIONS: The current evidence partly answered our questions. There was wide variation in the quality of source data, with a risk of publication bias. For some diagnoses, especially psychotic categories, administrative data were generally predictive of true diagnosis. For others, such as anxiety disorders, the data were less satisfactory. We discuss the implications of our findings, and the need for researchers to validate routine diagnostic data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12888-016-0963-x) contains supplementary material, which is available to authorized users