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We performed molecular dynamics simulations of granular beds driven by a model hydrodynamic shear flow to elucidate general grain-scale mechanisms that determine the onset and cessation of sediment transport. By varying the Shields number (the nondimensional shear stress at the top of the bed) and particle Reynolds number (the ratio of particle inertia to viscous damping), we explore how variations of the fluid flow rate, particle inertia, and fluid viscosity affect the onset and cessation of bed motion. For low to moderate particle Reynolds numbers, a critical boundary separates mobile and static states. Transition times between these states diverge as this boundary is approached both from above and below. At high particle Reynolds number, inertial effects become dominant, and particle motion can be sustained well below flow rates at which mobilization of a static bed occurs. We also find that the onset of bed motion (for both low and high particle Reynolds numbers) is described by Weibullian weakest-link statistics and thus is crucially dependent on the packing structure of the granular bed, even deep beneath the surface. [ABSTRACT FROM AUTHOR]

Chromatin is a polymer complex of DNA and proteins that regulates gene expression. The three-dimensional (3D) structure and organization of chromatin controls DNA transcription and replication. High-throughput chromatin conformation capture techniques generate Hi-C maps that can provide insight into the 3D structure of chromatin. Hi-C maps can be represented as a symmetric matrix Aij , where each element represents the average contact probability or number of contacts between chromatin loci i and j. Previous studies have detected topologically associating domains (TADs), or self-interacting regions in Aij within which the contact probability is greater than that outside the region. Many algorithms have been developed to identify TADs within Hi-C maps. However, most TAD identification algorithms are unable to identify nested or overlapping TADs and for a given Hi-C map there is significant variation in the location and number of TADs identified by different methods. We develop a novel method to identify TADs, KerTAD, using a kernel-based technique from computer vision and image processing that is able to accurately identify nested and overlapping TADs. We benchmark this method against state-of-the-art TAD identification methods on both synthetic and experimental data sets. We find that the new method consistently has higher true positive rates (TPR) and lower false discovery rates (FDR) than all tested methods for both synthetic and manually annotated experimental Hi-C maps. The TPR for KerTAD is also largely insensitive to increasing noise and sparsity, in contrast to the other methods. We also find that KerTAD is consistent in the number and size of TADs identified across replicate experimental Hi-C maps for several organisms. Thus, KerTAD will improve automated TAD identification and enable researchers to better correlate changes in TADs to biological phenomena, such as enhancer-promoter interactions and disease states. Author summary: Chromatin, which encodes the genetic information for cells, must fold into the cell nucleus that is many times smaller in size. The folded 3D structure of chromatin in the nucleus enables gene expression and proper cell function. With the advent of advanced chromatin conformation capture techniques, we can identify topologically associating domains (TADs), which are regions of the genome that prefer to interact within themselves rather than with neighboring regions. Numerous methods have been developed to automatically detect TADs in Hi-C maps, however, they frequently disagree on the location and number of TADs. We develop a new algorithm, KerTAD, to identify TADs using techniques from image processing and computer vision. We find that our method is more accurate on both synthetic and manually-annotated experimental Hi-C maps than all tested methods. Our method also performs well in the presence of noise and sparsity, which are frequently encountered in experimental Hi-C maps. KerTAD will enable future studies to elucidate the role of TADs in gene regulation and disease formation. [ABSTRACT FROM AUTHOR]

The article presents a research of Marc Miskin and Heinrich Jaeger that explores on the diverse varieties of grain shapes and computer algorithms that emulate evolution in the U.S. They discovered that composite grains are formed by two to five equal sizes of spherical particles connected in non-branched shapes. Moreover, they found that the grains are very large to encounter thermal fluctuations.

Structural relaxation during isothermal annealing, quantified by enthalpy recovery of Zr 44 Ti 11 Cu 10 Ni 10 Be 25 towards its metastable equilibrium and correlation to embrittlement, quantified through fracture toughness, K Q , is studied. Enthalpy relaxation over time obeys the Kohlrausch-William-Watts (KWW) stretch exponent with β = 0.74 and τ = 11,000 s. Such β and τ are used to fit experimental K Q ( t ) with KWW, resulting in R 2 = 0.79. This finding combined with a controlled characterization of the glasses' K Q versus temperature, fictive temperature, and their combination, revealed that embrittlement in metallic glasses is predominantly controlled by structural rearrangements, whereas volume changes from thermal expansion have negligible influence. [ABSTRACT FROM AUTHOR]

Micro- and nanoresonators have important applications including sensing, navigation, and biochemical detection. Their performance is quantified using the quality factor Q, which gives the ratio of the energy stored to the energy dissipated per cycle. Metallic glasses are a promising material class for micro- and nanoscale resonators since they are amorphous and can be fabricated precisely into complex shapes on these length scales. To understand the intrinsic dissipation mechanisms that ultimately limit large Q-values in metallic glasses, we perform molecular dynamics simulations to model metallic glass resonators subjected to bending vibrations at low temperatures. We calculate the power spectrum of the kinetic energy, redistribution of energy from the fundamental mode of vibration, and Q vs the kinetic energy per atom K of the excitation. In the harmonic and anharmonic response regimes where there are no atomic rearrangements, we find that Q → ∞ over the time periods we consider (since we do not consider coupling to the environment). We identify a characteristic Kr above which atomic rearrangements occur, and there is significant energy leakage from the fundamental mode to higher frequencies, causing finite Q. Thus, Kr is a critical parameter determining resonator performance. We show that Kr decreases as a power-law, Kr ∼ N−k, with increasing system size N, where k ≈ 1.3. We estimate the critical strain ⟨γr⟩∼ 10−8 for micrometer-sized resonators below which atomic rearrangements do not occur in the millikelvin temperature range, and thus, large Q-values can be obtained when they are operated below γr. We also find that Kr for amorphous resonators is comparable to that for resonators with crystalline order. [ABSTRACT FROM AUTHOR]

Hands-On Research in Complex Systems Schools provide an example of how graduate students and young faculty working in resource-constrained environments can apply key mindsets and methods of tabletop experiments to problems at the frontiers of science. Each day during the Schools' two-week program, participants work in small groups with experienced tabletop scientists in interactive laboratories on topics drawn from diverse disciplines in science and technology. Using modern low-cost tools, participants run experiments and perform associated data analysis together with mathematical and computational modeling. Participants also engage in other scientific professional activities; in particular, they learn best practices for communicating their results visually, orally, and in writing. In this way, the Hands-On Schools foster the development of scientific leaders in low- and middle-income countries. [ABSTRACT FROM AUTHOR]

We perform numerical simulations of repulsive, frictionless athermal disks in two and three spatial dimensions undergoing cyclic quasistatic simple shear to investigate particle-scale reversible motion. We identify three classes of steady-state dynamics as a function of packing fraction ɸ and maximum strain amplitude per cycle ymax- Point-reversible states, where particles do not collide and exactly retrace their intracycle trajectories, occur at low ɸ and ymax. Particles in loop-reversible states undergo numerous collisions and execute complex trajectories but return to their initial positions at the end of each cycle. For sufficiently large

Amorphous packings of frictionless, spherical particles are isostatic at jamming onset, with the number of constraints (contacts) equal to the number of degrees of freedom. Their structural and mechanical properties are controlled by the interparticle contact network. In contrast, amorphous packings of frictional particles are typically hyperstatic at jamming onset. We perform extensive numerical simulations in two dimensions of the geometrical asperity (GA) model for static friction to further investigate the role of isostaticity. In the GA model, interparticle forces are obtained by summing up purely repulsive central forces between periodically spaced circular asperities on contacting grains. We compare the packing fraction, contact number, mobilization distribution, and vibrational density of states (in the harmonic approximation) using the GA model to those generated using the Cundall-Strack approach. We find that static packings of frictional disks obtained from the GA model are mechanically stable and isostatic when we consider interactions between asperities on contacting particles. The crossover in the structural and mechanical properties of static packings from frictionless to frictional behavior as a function of the static friction coefficient coincides with a change in the type of interparticle contacts and the disappearance of a peak in the density of vibrational modes for the GA model. These results emphasize that mesoscale features of the model for static friction play an important role in determining the properties of granular packings. [ABSTRACT FROM AUTHOR]

When a liquid is cooled well below its melting temperature at a rate that exceeds the critical cooling rate Rc, the crystalline state is bypassed and a metastable, amorphous glassy state forms instead. Rc (or the corresponding critical casting thickness dc) characterizes the glass-forming ability (GFA) of each material. While silica is an excellent glass-former with small Rc < 10-2 K/s, pure metals and most alloys are typically poor glass-formers with large Rc > 1010 K/s. Only in the past thirty years have bulk metallic glasses (BMGs) been identified with Rc approaching that for silica. Recent simulations have shown that simple, hard-sphere models are able to identify the atomic size ratio and number fraction regime where BMGs exist with critical cooling rates more than 13 orders of magnitude smaller than those for pure metals. However, there are a number of other features of interatomic potentials beyond hard-core interactions. How do these other features affect the glass-forming ability of BMGs? In this manuscript, we perform molecular dynamics simulations to determine how variations in the softness and non-additivity of the repulsive core and form of the interatomic pair potential at intermediate distances affect the GFA of binary alloys. These variations in the interatomic pair potential allow us to introduce geometric frustration and change the crystal phases that compete with glass formation. We also investigate the effect of tuning the strength of the many-body interactions from zero to the full embedded atom model on the GFA for pure metals. We then employ the full embedded atom model for binary BMGs and show that hard-core interactions play the dominant role in setting the GFA of alloys, while other features of the interatomic potential only change the GFA by one to two orders of magnitude. Despite their perturbative effect, understanding the detailed form of the intermetallic potential is important for designing BMGs with cm or greater casting thickness. [ABSTRACT FROM AUTHOR]

The likelihood that an undercooled liquid vitrifies or crystallizes depends on the cooling rate R. The critical cooling rate Rc, below which the liquid crystallizes upon cooling, characterizes the glass-forming ability (GFA) of the system. While pure metals are typically poor glass formers with Rc > 1012 K/s, specific multi-component alloys can form bulk metallic glasses (BMGs) even at cooling rates below R ~ 1 K/s. Conventional wisdom asserts that metal alloys with three or more components are better glass formers (with smaller Rc) than binary alloys. However, there is currently no theoretical framework that provides quantitative predictions for Rc for multi-component alloys. In this manuscript, we perform simulations of ternary hard-sphere systems, which have been shown to be accurate models for the glass-forming ability of BMGs, to understand the roles of geometric frustration and demixing in determining Rc. Specifically, we compress ternary hard sphere mixtures into jammed packings and measure the critical compression rate, below which the system crystallizes, as a function of the diameter ratios sB/sA and sC/sA and number fractions xA, xB, and xC. We find two distinct regimes for the GFA in parameter space for ternary hard spheres. When the diameter ratios are close to 1, such that the largest (A) and smallest (C) species are well-mixed, the GFA of ternary systems is no better than that of the optimal binary glass former. However, when sC/sA ≲ 0.8 is below the demixing threshold for binary systems, adding a third component B with sC < sB < sA increases the GFA of the system by preventing demixing of A and C. Analysis of the available data from experimental studies indicates that most ternary BMGs are below the binary demixing threshold with sC/sA < 0.8. [ABSTRACT FROM AUTHOR]

The likelihood that an undercooled liquid vitrifies or crystallizes depends on the cooling rate R. The critical cooling rate Rc, below which the liquid crystallizes upon cooling, characterizes the glass-forming ability (GFA) of the system. While pure metals are typically poor glass formers with Rc >1012 K/s, specific multi-component alloys can form bulk metallic glasses (BMGs) even at cooling rates below R ~ 1 K/s. Conventional wisdom asserts that metal alloys with three or more components are better glass formers (with smaller Rc) than binary alloys. However, there is currently no theoretical framework that provides quantitative predictions Rc for for multi-component alloys. In this manuscript, we perform simulations of ternary hard-sphere systems, which have been shown to be accurate models for the glass-forming ability of BMGs, to understand the roles of geometric frustration and demixing in determining Rc. Specifically, we compress ternary hard sphere mixtures into jammed packings and measure the critical compression rate, below which the system crystallizes, as a function of the diameter ratios σB/σA and σC/σA and number fractions xA, xB, and xC . We find two distinct regimes for the GFA in parameter space for ternary hard spheres. When the diameter ratios are close to 1, such that the largest (A) and smallest (C) species are well-mixed, the GFA of ternary systems is no better than that of the optimal binary glass former. However, when σC/σA ≲ 0.8 is below the demixing threshold for binary systems, adding a third component B with σC<σB<σA increases the GFA of the system by preventing demixing of A and C. Analysis of the available data from experimental studies indicates that most ternary BMGs are below the binary demixing threshold with σC/σA < 0.8. [ABSTRACT FROM AUTHOR]

Bulk metallic glasses (BMGs) are amorphous alloys with desirable mechanical properties and processing capabilities. To date, the design of new BMGs has largely employed empirical rules and trial-and-error experimental approaches. Ab initio computational methods are currently prohibitively slow to be practically used in searching the vast space of possible atomic combinations for bulk glass formers. Here, we perform molecular dynamics simulations of a coarse-grained, anisotropic potential, which mimics interatomic covalent bonding, to measure the critical cooling rates for metal-metalloid alloys as a function of the atomic size ratio σS/σL and number fraction xS of the metalloid species. We show that the regime in the space of σS/σL and xS where well-mixed, optimal glass formers occur for patchy and LJ particle mixtures, coincides with that for experimentally observed metal-metalloid glass formers. Thus, our simple computational model provides the capability to perform combinatorial searches to identify novel glass-forming alloys. [ABSTRACT FROM AUTHOR]

The mechanical response of packings of purely repulsive, spherical particles to athermal, quasistatic simple shear near jamming onset is highly nonlinear. Previous studies have shown that, at small pressure p, the ensemble-averaged static shear modulus ⟨G-G0⟩ scales with pα, where α≈1, but above a characteristic pressure p**, ⟨G-G0⟩∼pβ, where β≈0.5. However, we find that the shear modulus Gi for an individual packing typically decreases linearly with p along a geometrical family where the contact network does not change. We resolve this discrepancy by showing that, while the shear modulus does decrease linearly within geometrical families, ⟨G⟩ also depends on a contribution from discontinuous jumps in ⟨G⟩ that occur at the transitions between geometrical families. For p>p**, geometrical-family and rearrangement contributions to ⟨G⟩ are of opposite signs and remain comparable for all system sizes. ⟨G⟩ can be described by a scaling function that smoothly transitions between two power-law exponents α and β. We also demonstrate the phenomenon of compression unjamming, where a jammed packing unjams via isotropic compression. [ABSTRACT FROM AUTHOR]

Bulk metallic glasses (BMGs) are produced by rapidly thermally quenching supercooled liquid metal alloys below the glass transition temperature at rates much faster than the critical cooling rate Rc below which crystallization occurs. The glass-forming ability of BMGs increases with decreasing Rc, and thus good glass-formers possess small values of Rc. We perform molecular dynamics simulations of binary Lennard-Jones (LJ) mixtures to quantify how key parameters, such as the stoichiometry, particle size difference, attraction strength, and heat of mixing, influence the glass-formability of model BMGs. For binary LJ mixtures, we find that the best glass-forming mixtures possess atomic size ratios (small to large) less than 0.92 and stoichiometries near 50:50 by number. In addition, weaker attractive interactions between the smaller atoms facilitate glass formation, whereas negative heats of mixing (in the experimentally relevant regime) do not change Rc significantly. These results are tempered by the fact that the slowest cooling rates achieved in our simulations correspond to ∼1011 K/s, which is several orders of magnitude higher than Rc for typical BMGs. Despite this, our studies represent a first step in the development of computational methods for quantitatively predicting glass-formability. [ABSTRACT FROM AUTHOR]

We show that the yielding transition in granular media displays second-order critical-point scaling behavior. We carry out discrete element simulations in the low-inertial-number limit for frictionless, purely repulsive spherical grains undergoing simple shear at fixed nondimensional shear stress Σ in two and three spatial dimensions. To find a mechanically stable (MS) packing that can support the applied Σ, isotropically prepared states with size L must undergo a total strain γms (Σ,L). The number density of MS packings (∝ γ-1ms) vanishes for Σ > Σc ≈ 0.11 according to a critical scaling form with a length scale ξ ∝ | Σ - Σc|-ν, where ν ≈ 1.7 - 1.8. Above the yield stress (Σ > Σc), no MS packings that can support Σ exist in the large-system limit L/ξ ≫ 1. MS packings generated via shear possess anisotropic force and contact networks, suggesting that Σc is associated with an upper limit in the degree to which these networks can be deformed away from those for isotropic packings. [ABSTRACT FROM AUTHOR]

We perform computational studies of static packings of a variety of nonspherical particles including circulo-lines, circulo-polygons, ellipses, asymmetric dimers, dumbbells, and others to determine which shapes form packings with fewer contacts than degrees of freedom (hypostatic packings) and which have equal numbers of contacts and degrees of freedom (isostatic packings), and to understand why hypostatic packings of nonspherical particles can be mechanically stable despite having fewer contacts than that predicted from naive constraint counting. To generate highly accurate force- and torque-balanced packings of circulo-lines and cir-polygons, we developed an interparticle potential that gives continuous forces and torques as a function of the particle coordinates. We show that the packing fraction and coordination number at jamming onset obey a masterlike form for all of the nonspherical particle packings we studied when plotted versus the particle asphericity A, which is proportional to the ratio of the squared perimeter to the area of the particle. Further, the eigenvalue spectra of the dynamical matrix for packings of different particle shapes collapse when plotted at the same A. For hypostatic packings of nonspherical particles, we verify that the number of "quartic" modes along which the potential energy increases as the fourth power of the perturbation amplitude matches the number of missing contacts relative to the isostatic value. We show that the fourth derivatives of the total potential energy in the directions of the quartic modes remain nonzero as the pressure of the packings is decreased to zero. In addition, we calculate the principal curvatures of the inequality constraints for each contact in circulo-line packings and identify specific types of contacts with inequality constraints that possess convex curvature. These contacts can constrain multiple degrees of freedom and allow hypostatic packings of nonspherical particles to be mechanically stable. [ABSTRACT FROM AUTHOR]

We focus on the response of mechanically stable (MS) packings of frictionless, bidisperse disks to thermal fluctuations, with the aim of quantifying how nonlinearities affect system properties at finite temperature. In contrast, numerous prior studies characterized the structural and mechanical properties of MS packings of frictionless spherical particles at zero temperature. Packings of disks with purely repulsive contact interactions possess two main types of nonlinearities, one from the form of the interaction potential (e.g., either linear or Hertzian spring interactions) and one from the breaking (or forming) of interparticle contacts. To identify the temperature regime at which the contact-breaking nonlinearities begin to contribute, we first calculated the minimum temperatures Tcb required to break a single contact in the MS packing for both single- and multiple-eigenmode perturbations of the T=0 MS packing. We find that the temperature required to break a single contact for equal velocity-amplitude perturbations involving all eigenmodes approaches the minimum value obtained for a perturbation in the direction connecting disk pairs with the smallest overlap. We then studied deviations in the constant volume specific heat CV and deviations of the average disk positions Δr from their T=0 values in the temperature regime TCV100 for linear spring interactions is independent of system size. This result emphasizes that contact-breaking nonlinearities are dominant over form nonlinearities in the low-temperature range Tcb

Complex materials science problems such as glass formation must consider large system sizes that are many orders of magnitude too large to be solved by first-principles calculations. The successful application of machine learning (ML) in various other fields suggests that ML could be useful to address complex problems in materials science. To test its efficacy, we attempt to predict bulk metallic glass formation using ML. Surprisingly, we find that a recently developed ML model based on 201 alloy features constructed using simple combinations of 31 elemental features is indistinguishable from models that are based on unphysical features. The 201ML-model performs better than the unphysical model only when significant separation of training and testing data is achieved. However, it performs significantly worse than a human-learning based three-feature model. The limited performance of the 201ML-model originates from the inability to accurately represent alloy features through elemental features, showing that physical insights about mixing behavior are required to develop predictable ML models. [Display omitted] [ABSTRACT FROM AUTHOR]

Clinical diagnosis of acute infectious diseases during the early stages of infection is critical to administering the appropriate treatment to improve the disease outcome. We present a data driven analysis of the human cellular response to respiratory viruses including influenza, respiratory syncytia virus, and human rhinovirus, and compared this with the response to the bacterial endotoxin, Lipopolysaccharides (LPS). Using an anomaly detection framework we identified pathways that clearly distinguish between asymptomatic and symptomatic patients infected with the four different respiratory viruses and that accurately diagnosed patients exposed to a bacterial infection. Connectivity pathway analysis comparing the viral and bacterial diagnostic signatures identified host cellular pathways that were unique to patients exposed to LPS endotoxin indicating this type of analysis could be used to identify host biomarkers that can differentiate clinical etiologies of acute infection. We applied the Multivariate State Estimation Technique (MSET) on two human influenza (H1N1 and H3N2) gene expression data sets to define host networks perturbed in the asymptomatic phase of infection. Our analysis identified pathways in the respiratory virus diagnostic signature as prognostic biomarkers that triggered prior to clinical presentation of acute symptoms. These early warning pathways correctly predicted that almost half of the subjects would become symptomatic in less than forty hours post-infection and that three of the 18 subjects would become symptomatic after only 8 hours. These results provide a proof-of-concept for utility of anomaly detection algorithms to classify host pathway signatures that can identify presymptomatic signatures of acute diseases and differentiate between etiologies of infection. On a global scale, acute respiratory infections cause a significant proportion of human co-morbidities and account for 4.25 million deaths annually. The development of clinical diagnostic tools to distinguish between acute viral and bacterial respiratory infections is critical to improve patient care and limit the overuse of antibiotics in the medical community. The identification of prognostic respiratory virus biomarkers provides an early warning system that is capable of predicting which subjects will become symptomatic to expand our medical diagnostic capabilities and treatment options for acute infectious diseases. The host response to acute infection may be viewed as a deterministic signaling network responsible for maintaining the health of the host organism. We identify pathway signatures that reflect the very earliest perturbations in the host response to acute infection. These pathways provide a monitor the health state of the host using anomaly detection to quantify and predict health outcomes to pathogens. [ABSTRACT FROM AUTHOR]

Clinicians need to predict patient outcomes with high accuracy as early as possible after disease inception. In this manuscript, we show that patient-to-patient variability sets a fundamental limit on outcome prediction accuracy for a general class of mathematical models for the immune response to infection. However, accuracy can be increased at the expense of delayed prognosis. We investigate several systems of ordinary differential equations (ODEs) that model the host immune response to a pathogen load. Advantages of systems of ODEs for investigating the immune response to infection include the ability to collect data on large numbers of ‘virtual patients’, each with a given set of model parameters, and obtain many time points during the course of the infection. We implement patient-to-patient variability v in the ODE models by randomly selecting the model parameters from distributions with coefficients of variation v that are centered on physiological values. We use logistic regression with one-versus-all classification to predict the discrete steady-state outcomes of the system. We find that the prediction algorithm achieves near 100% accuracy for v = 0, and the accuracy decreases with increasing v for all ODE models studied. The fact that multiple steady-state outcomes can be obtained for a given initial condition, i.e. the basins of attraction overlap in the space of initial conditions, limits the prediction accuracy for v > 0. Increasing the elapsed time of the variables used to train and test the classifier, increases the prediction accuracy, while adding explicit external noise to the ODE models decreases the prediction accuracy. Our results quantify the competition between early prognosis and high prediction accuracy that is frequently encountered by clinicians. [ABSTRACT FROM AUTHOR]

We perform molecular dynamics (MD) simulations of the crystallization process in binary Lennard-Jones systems during heating and cooling to investigate atomic-scale crystallization kinetics in glass-forming materials. For the cooling protocol, we prepared equilibrated liquids above the liquidus temperature Tl and cooled each sample to zero temperature at rate Rc. For the heating protocol, we first cooled equilibrated liquids to zero temperature at rate Rp and then heated the samples to temperature T > Tl at rate Rh. We measured the critical heating and cooling rates Rh* and Rc*, below which the systems begin to form a substantial fraction of crystalline clusters during the heating and cooling protocols. We show that Rh* > Rc* and that the asymmetry ratio Rh*/ Rc*. includes an intrinsic contribution that increases with the glass-forming ability (GFA) of the system and a preparation-rate dependent contribution that increases strongly as Rp → Rc* from above. We also show that the predictions from classical nucleation theory (CNT) can qualitatively describe the dependence of the asymmetry ratio on the GFA and preparation rate Rp from the MD simulations and results for the asymmetry ratio measured in Zr- and Au-based bulk metallic glasses (BMG). This work emphasizes the need for and benefits of an improved understanding of crystallization processes in BMGs and other glass-forming systems. [ABSTRACT FROM AUTHOR]

We develop a theoretical description for mechanically stable frictional packings in terms of the difference between the total number of contacts required for isostatic packings of frictionless disks and the number of contacts in frictional packings, m = N0c - Nc. The saddle order m represents the number of unconstrained degrees of freedom that a static packing would possess if friction were removed. Using a novel numerical method that allows us to enumerate disk packings for each m, we show that the probability to obtain a packing with saddle order m at a given static friction coefficient μ, Pm(μ), can be expressed as a power series in μ. Using this form for Pm (μ), we quantitatively describe the dependence of the average contact number on the friction coefficient for static disk packings obtained from direct simulations of the Cundall-Strack model for all μ and N. [ABSTRACT FROM AUTHOR]

We perform molecular dynamics simulations to compress binary hard spheres into jammed packings as a function of the compression rate R, size ratio ɑ, and number fraction xS of small particles to determine the connection between the glass-forming ability (GFA) and packing efficiency in bulk metallic glasses (BMGs). We define the GFA by measuring the critical compression rate Rc, below which jammed hard-sphere packings begin to form "random crystal" structures with defects. We find that for systems with ɑ0.8 that do not demix, Rc decreases strongly with ΔϕJ, as Rc~exp(-1/ΔϕJ²), where ΔϕJ is the difference between the average packing fraction of the amorphous packings and random crystal structures at Rc. Systems with ɑ0.8 partially demix, which promotes crystallization, but we still find a strong correlation between Rc and ΔϕJ. We show that known metal-metal BMGs occur in the regions of the ɑ and xS parameter space with the lowest values of Rc for binary hard spheres. Our results emphasize that maximizing GFA in binary systems involves two competing effects: minimizing ɑ to increase packing efficiency, while maximizing ɑ to prevent demixing. [ABSTRACT FROM AUTHOR]

Severe pediatric sepsis continues to be associated with high mortality rates in children. Thus, an important area of biomedical research is to identify biomarkers that can classify sepsis severity and outcomes. The complex and heterogeneous nature of sepsis makes the prospect of the classification of sepsis severity using a single biomarker less likely. Instead, we employ machine learning techniques to validate the use of a multiple biomarkers scoring system to determine the severity of sepsis in critically ill children. The study was based on clinical data and plasma samples provided by a tertiary care center's Pediatric Intensive Care Unit (PICU) from a group of 45 patients with varying sepsis severity at the time of admission. Canonical Correlation Analysis with the Forward Selection and Random Forests methods identified a particular set of biomarkers that included Angiopoietin-1 (Ang-1), Angiopoietin-2 (Ang-2), and Bicarbonate (HCO) as having the strongest correlations with sepsis severity. The robustness and effectiveness of these biomarkers for classifying sepsis severity were validated by constructing a linear Support Vector Machine diagnostic classifier. We also show that the concentrations of Ang-1, Ang-2, and HCO enable predictions of the time dependence of sepsis severity in children. [ABSTRACT FROM AUTHOR]

We propose a "phase diagram" for particulate systems with purely repulsive contact forces, such as granular media and colloids. We characterize two classes of behavior as a function of the input kinetic energy per degree of freedom T0 and packing fraction deviation from jamming onset ΔΦ = Φ -- ΦJ using simulations of frictionless disks. Isocoordinated solids (ICS) exist above jamming; they possess an average contact number equal to the isostatic value ziso. ICS display "strict" harmonic response, where the density of vibrational modes from the Fourier transform of the velocity autocorrelation function is a set of sharp peaks at eigenfrequencies ωkd of the dynamical matrix. In contrast, hypocoordinated solids (HCS) occur above and below jamming and possess fluctuating networks of interparticle contacts but do not undergo cage-breaking particle rearrangements. The density of vibrational frequencies for the HCS is not a collection of sharp peaks at ωkd, but it does possess a common form over a range of ΔΦ and T0. [ABSTRACT FROM AUTHOR]

Opticalpumping of spin polarization can produce almost completespin order but its application is restricted to select atomic gasesand condensed matter systems. Here, we theoretically investigate anovel route to nuclear spin hyperpolarization in arbitrary fluidsin which target molecules are exposed to polarized paramagnetic centerslocated near the surface of a host material. We find that adsorbednuclear spins relax to positive or negative polarization dependingon the average paramagnetic center depth and nanoscale surface topology.For the particular case of optically pumped nitrogen-vacancy centersin diamond, we calculate strong nuclear spin polarization at moderatemagnetic fields provided the crystal surface is engineered with surfaceroughness in the few-nanometer range. The equilibrium nuclear spintemperature depends only weakly on the correlation time describingthe molecular adsorption dynamics and is robust in the presence ofother, unpolarized paramagnetic centers. These features could be exploitedto polarize flowing liquids or gases, as we illustrate numericallyfor the model case of a fluid brought in contact with an opticallypumped diamond nanostructure. [ABSTRACT FROM AUTHOR]

Background We introduce Iterative Feature Removal (IFR) as an unbiased approach for selecting features with diagnostic capacity from large data sets. The algorithm is based on recently developed tools in machine learning that are driven by sparse feature selection goals. When applied to genomic data, our method is designed to identify genes that can provide deeper insight into complex interactions while remaining directly connected to diagnostic utility. We contrast this approach with the search for a minimal best set of discriminative genes, which can provide only an incomplete picture of the biological complexity. Results Microarray data sets typically contain far more features (genes) than samples. For this type of data, we demonstrate that there are many equivalently-predictive subsets of genes. We iteratively train a classifier using features identified via a sparse support vector machine. At each iteration, we remove all the features that were previously selected. We found that we could iterate many times before a sustained drop in accuracy occurs, with each iteration removing approximately 30 genes from consideration. The classification accuracy on test data remains essentially flat even as hundreds of top-genes are removed. Our method identifies sets of genes that are highly predictive, even when comprised of genes that individually are not. Through automated and manual analysis of the selected genes, we demonstrate that the selected features expose relevant pathways that other approaches would have missed. Conclusions Our results challenge the paradigm of using feature selection techniques to design parsimonious classifiers from microarray and similar high-dimensional, small-sample-size data sets. The fact that there are many subsets of genes that work equally well to classify the data provides a strong counter-result to the notion that there is a small number of "top genes" that should be used to build classifiers. In our results, the best classifiers were formed using genes with limited univariate power, thus illustrating that deeper mining of features using multivariate techniques is important. [ABSTRACT FROM AUTHOR]

We address the identification of optimal biomarkers for the rapid diagnosis of neonatal sepsis. We employ both canonical correlation analysis (CCA) and sparse support vector machine (SSVM) classifiers to select the best subset of biomarkers from a large hematological data set collected from infants with suspected sepsis from Yale-New Haven Hospital's Neonatal Intensive Care Unit (NICU). CCA is used to select sets of biomarkers of increasing size that are most highly correlated with infection. The effectiveness of these biomarkers is then validated by constructing a sparse support vector machine diagnostic classifier. We find that the following set of five biomarkers capture the essential diagnostic information (in order of importance): Bands, Platelets, neutrophil CD64, White Blood Cells, and Segs. Further, the diagnostic performance of the optimal set of biomarkers is significantly higher than that of isolated individual biomarkers. These results suggest an enhanced sepsis scoring system for neonatal sepsis that includes these five biomarkers. We demonstrate the robustness of our analysis by comparing CCA with the Forward Selection method and SSVM with LASSO Logistic Regression. [ABSTRACT FROM AUTHOR]

Experimental and computational model systems composed of frictionless particles in a fixed geometry have a finite number of distinct mechanically stable (MS) packings. The frequency of occurrence for each MS packing is highly variable and depends strongly on preparation protocol. Despite intense work, it is extremely difficult to predict a priori the MS packing probabilities. We describe a novel computational method for calculating the volume and other geometrical properties of the "basin of attraction" for each MS packing. The basin of attraction for an MS packing contains all initial conditions in configuration space that map to that MS packing using a given preparation protocol. We find that the basin is a highly complex structure. For a compressive-quench-from-zero-density protocol, we show the existence of a small core volume of the basin around each MS packing for which all points map to that MS packing. However, in contrast to previous studies for supercooled liquids, glasses, and over-compressed jammed systems, we find that the MS packing probabilities are very weakly correlated with this core volume. Instead, MS packing probabilities obtained from compression protocols that use initially dilute configurations and do not allow particle overlaps (i.e., those relevant to granular media) are determined by complex geometric features of the basin of attraction that are distant from the MS packing. In particular, we find that the shape of the average basin profile function S(l), which gives the probability for a point on a hyperspherical shell a distance / from a given MS packing to map back to that packing, can be described by a T distribution with a peak that increases as the system size increases and as the quench rate decreases. We find a simple model which predicts S(l) for the extreme cases of very slow and fast quench rates. [ABSTRACT FROM AUTHOR]

Abstract: The unique ability of magnetic resonance imaging (MRI) to provide spatial and temporal information from optically opaque systems, in three dimensions, make it an ideal tool to study the internal motion of rapid granular flows. This paper will focus on the use of ultra-fast velocity compensated MRI measurements to study particle velocity and density distributions in a granular gas, produced by vibrating vertically a bed of mustard seeds at 40 Hz. Specifically, a velocity compensated, double spin-echo, triggered, one-dimensional MRI profiling pulse sequence was developed. This gives an MRI temporal resolution of approximately 2 ms and also minimises MRI velocity artefacts. 12 phase measurements per vibration cycle were used. The data can be used to extract values of the mustard seed average velocity and velocity propagators (probability distributions functions) as a function of the phase of the vibration cycle and vertical height within the cell. The data show strong transient effects during the impact phase of the vibration. A detailed discussion of the temporal passage of the individual phase resolved, height resolved velocity distributions, along with seed velocity propagators at a fix height from the vibrating base is presented. [Copyright &y& Elsevier]

Many experiments over the past half century have shown that, for a range of protocols, granular materials compact under pressure and repeated small disturbances. A recent experiment on cyclically sheared spherical grains showed significant compaction via homogeneous crystallization (Rietz et al., 2018). Here we present numerical simulations of frictionless, purely repulsive spheres undergoing cyclic simple shear via Newtonian dynamics with linear viscous drag at fixed vertical load. We show that for sufficiently small strain amplitudes, cyclic shear gives rise to homogeneous crystallization at a volume fraction ϕ=0.646±0.001. This result indicates that neither friction nor gravity is essential for homogeneous crystallization in driven granular media. Understanding how crystal formation is initiated within a homogeneous disordered state gives key insights into the old open problem of glass formation in fluids. [ABSTRACT FROM AUTHOR]

Embryonic organizers establish gradients of diffusible signaling molecules to pattern the surrounding cells. Here, we elucidate an additional mechanism of embryonic organizers that is a secondary consequence of morphogen signaling. Using pharmacological and localized transgenic perturbations, 4D imaging of the zebrafish embryo, systematic analysis of cell motion, and computational modeling, we find that the vertebrate tail organizer orchestrates morphogenesis over distances beyond the range of morphogen signaling. The organizer regulates the rate and coherence of cell motion in the elongating embryo using mechanical information that is transmitted via relay between neighboring cells. This mechanism is similar to a pressure front in granular media and other jammed systems, but in the embryo the mechanical information emerges from self-propelled cell movement and not force transfer between cells. The propagation likely relies upon local biochemical signaling that affects cell contractility, cell adhesion, and/or cell polarity but is independent of transcription and translation. • Positive feedback between Bmp signaling and eve1 functions in the tail organizer • Perturbation of organizer signaling has long-range effects on cell motion • These long-range effects are beyond the range organizer signaling • These long-range effects are mediated by mechanical information Das, Jülich, and Schwendinger-Schreck et al. find that the zebrafish tail organizer orchestrates morphogenesis over distances beyond the range of its secreted cell-signaling proteins. The organizer regulates cell migration in the elongating embryo using mechanical information that propagates via relay between neighboring cells. [ABSTRACT FROM AUTHOR]

We introduce the deformable particle (DP) model for cells, foams, emulsions, and other soft particulate materials, which adds to the benefits and eliminates deficiencies of existing models. The DP model combines the ability to model individual soft particles with the shape-energy function of the vertex model, and adds arbitrary particle deformations. We focus on 2D deformable polygons with a shape-energy function that is minimized for area a0 and perimeter p0 and repulsive interparticle forces. We study the onset of jamming versus particle asphericity, A=p²0/4πa0, and find that the packing fraction grows with A until reaching A*=1.16 of the underlying Voronoi cells at confluence. We find that DP packings above and below A* are solidlike, which helps explain the solid-to-fluid transition at A* in the vertex model as a transition from tension- to compression-dominated regimes. [ABSTRACT FROM AUTHOR]

We perform computational studies of repulsive, frictionless disks to investigate the development of stress anisotropy in mechanically stable (MS) packings at jamming onset. We focus on two protocols for generating MS packings at jamming onset: (1) isotropic compression and (2) applied simple or pure shear strain γ at fixed packing fraction ϕ. MS packings of frictionless disks occur as geometric families (i.e., quasiparabolic segments with positive curvature) in the ϕ-γ plane. MS packings from protocol 1 populate parabolic segments with both signs of the slope, dϕ/dγ>0 and dϕ/dγ<0. In contrast, MS packings from protocol 2 populate segments with dϕ/dγ<0 only. For both simple and pure shear, we derive a relationship between the stress anisotropy and local dilatancy dϕ/dγ obeyed by MS packings along geometrical families. We show that for MS packings prepared using isotropic compression, the stress anisotropy distribution is Gaussian centered at zero with a standard deviation that decreases with increasing system size. For shear jammed MS packings, the stress anisotropy distribution is a convolution of Weibull distributions that depend on strain, which has a nonzero average and standard deviation in the large-system limit. We also develop a framework to calculate the stress anisotropy distribution for packings generated via protocol 2 in terms of the stress anisotropy distribution for packings generated via protocol 1. [ABSTRACT FROM AUTHOR]

Amorphous materials such as metallic, polymeric, and colloidal glasses exhibit complex preparation-dependent mechanical response to applied shear. In particular, glassy solids yield, with a mechanical response that transitions from elastic to plastic, with increasing shear strain. We perform numerical simulations to investigate the mechanical response of binary Lennard-Jones glasses undergoing athermal, quasistatic pure shear as a function of the cooling rate R used to prepare them. The ensemble-averaged stress versus strain curve 〈σ(γ)〉 resembles the spatial average in the large size limit, which appears smooth and displays a putative elastic regime at small strains, a yielding-related peak in stress at intermediate strain, and a plastic flow regime at large strains. In contrast, for each glass configuration in the ensemble, the stress-strain curve σ(γ) consists of many short nearly linear segments that are punctuated by particle-rearrangement-induced rapid stress drops. To explain the nonlinearity of 〈σ(γ)〉, we quantify the shape of the small stress-strain segments and the frequency and size of the stress drops in each glass configuration. We decompose the stress loss [i.e., the deviation in the slope of 〈σ(γ)〉 from that at 〈σ(0)〉] into the loss from particle rearrangements and the loss from softening [i.e., the reduction of the slopes of the linear segments in σ(γ)], and then compare the two contributions as a function of R and γ. For the current studies, the rearrangement-induced stress loss is larger than the softening-induced stress loss, however, softening stress losses increase with decreasing cooling rate. We also characterize the structure of the potential energy landscape along the strain direction for glasses prepared with different R, and observe a dramatic change of the properties of the landscape near the yielding transition. We then show that the rearrangement-induced energy loss per strain can serve as an order parameter for the yielding transition, which sharpens for slow cooling rates and in large systems. [ABSTRACT FROM AUTHOR]

Amorphous solids, such as metallic, polymeric, and colloidal glasses, display complex spatiotemporal response to applied deformations. In contrast to crystalline solids, during loading, amorphous solids exhibit a smooth crossover from elastic response to plastic flow. In this study, we investigate the mechanical response of binary Lennard-Jones glasses to athermal, quasistatic pure shear as a function of the cooling rate used to prepare them. We find several key results concerning the connection between strain-induced particle rearrangements and mechanical response. We show that the energy loss per strain dUloss/dγ caused by particle rearrangements for more rapidly cooled glasses is larger than that for slowly cooled glasses. We also find that the cumulative energy loss Uloss can be used to predict the ductility of glasses even in the putative linear regime of stress versus strain. Uloss increases (and the ratio of shear to bulk moduli decreases) with increasing cooling rate, indicating enhanced ductility. In addition, we characterized the degree of reversibility of particle motion during a single shear cycle. We find that irreversible particle motion occurs even in the linear regime of stress versus strain. However, slowly cooled glasses, which undergo smaller rearrangements, are more reversible during a single shear cycle than rapidly cooled glasses. Thus, we show that more ductile glasses are also less reversible. [ABSTRACT FROM AUTHOR]

We propose a theoretical framework for predicting the protocol dependence of the jamming transition for frictionless spherical particles that interact via repulsive contact forces. We study isostatic jammed disk packings obtained via two protocols: isotropic compression and simple shear. We show that for frictionless systems, all jammed packings can be obtained via either protocol. However, the probability to obtain a particular jammed packing depends on the packing-generation protocol. We predict the average shear strain required to jam initially unjammed isotropically compressed packings from the density of jammed packings, shape of their basins of attraction, and path traversed in configuration space. We compare our predictions to simulations of shear strain-induced jamming and find quantitative agreement. We also show that the packing fraction range, over which shear strain-induced jamming occurs, tends to zero in the large system limit for frictionless packings with overdamped dynamics. [ABSTRACT FROM AUTHOR]