Orthopaedic trauma epidemiology after Hurricane Maria in the Puerto Rico Trauma Centre.

PURPOSE: Hurricane Maria is the most devastating natural phenomenon in the recent history of Puerto Rico. Due to its destructive path through the island, the Puerto Rico Trauma Center (PRTC) remained the only hospital managing orthopaedic trauma in the immediate post-disaster period. We investigated the impact of this hurricane on the orthopaedic trauma epidemiology in the PRTC. METHODS: We evaluated the admissions by the orthopaedic surgery service in terms of demographics, mechanisms of injury, and orthopaedic diagnoses for two months after the impact of Hurricane Maria (HM) on September 20, 2017. We compared our study group with the same two month period for two years prior (2015 and 2016) and after (2018 and 2019) as control periods. A p value of < 0.05 was considered statistically significant. RESULTS: We included 384 admissions from September 20 to November 20, 2017. The majority were males (63%) and had an average age of 54 years. The most-reported mechanism of injury was fall from standing height (FFSH), showing a significant increment compared with the control periods. Contrarily, motor vehicle accidents (MVA) showed a significant reduction. Among the orthopaedic diagnoses, the hip + pelvis category showed a significant decline within the study group. CONCLUSIONS: This study highlighted the impact of HM on the orthopaedic trauma epidemiology at the PRTC. Our findings provide valuable evidence to healthcare institutions to better prepare to manage the potential changes in the orthopaedic trauma epidemiology after a major atmospheric event.

Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search.

Machine learning (ML) plays a growing role in the design and discovery of chemicals, aiming to reduce the need to perform expensive experiments and simulations. ML for such applications is promising but difficult, as models must generalize to vast chemical spaces from small training sets and must have reliable uncertainty quantification metrics to identify and prioritize unexplored regions. Ab initio computational chemistry and chemical intuition alike often take advantage of differences between chemical conditions, rather than their absolute structure or state, to generate more reliable results. We have developed an analogous comparison-based approach for ML regression, called pairwise difference regression (PADRE), which is applicable to arbitrary underlying learning models and operates on pairs of input data points. During training, the model learns to predict differences between all possible pairs of input points. During prediction, the test points are paired with all training set points, giving rise to a set of predictions that can be treated as a distribution of which the mean is treated as a final prediction and the dispersion is treated as an uncertainty measure. Pairwise difference regression was shown to reliably improve the performance of the random forest algorithm across five chemical ML tasks. Additionally, the pair-derived dispersion is both well correlated with model error and performs well in active learning. We also show that this method is competitive with state-of-the-art neural network techniques. Thus, pairwise difference regression is a promising tool for candidate selection algorithms used in chemical discovery.

Exascale applications: skin in the game.

As noted in Wikipedia, skin in the game refers to having 'incurred risk by being involved in achieving a goal', where 'skin is a synecdoche for the person involved, and game is the metaphor for actions on the field of play under discussion'. For exascale applications under development in the US Department of Energy Exascale Computing Project, nothing could be more apt, with the skin being exascale applications and the game being delivering comprehensive science-based computational applications that effectively exploit exascale high-performance computing technologies to provide breakthrough modelling and simulation and data science solutions. These solutions will yield high-confidence insights and answers to the most critical problems and challenges for the USA in scientific discovery, national security, energy assurance, economic competitiveness and advanced healthcare. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'.

An entropy-maximization approach to automated training set generation for interatomic potentials.

Machine learning-based interatomic potentials are currently garnering a lot of attention as they strive to achieve the accuracy of electronic structure methods at the computational cost of empirical potentials. Given their generic functional forms, the transferability of these potentials is highly dependent on the quality of the training set, the generation of which can be highly labor-intensive. Good training sets should at once contain a very diverse set of configurations while avoiding redundancies that incur cost without providing benefits. We formalize these requirements in a local entropy-maximization framework and propose an automated sampling scheme to sample from this objective function. We show that this approach generates much more diverse training sets than unbiased sampling and is competitive with hand-crafted training sets.

Parallel algorithms for hyperdynamics and local hyperdynamics.

Hyperdynamics (HD) is a method for accelerating the timescale of standard molecular dynamics (MD). It can be used for simulations of systems with an energy potential landscape that is a collection of basins, separated by barriers, where transitions between basins are infrequent. HD enables the system to escape from a basin more quickly while enabling a statistically accurate renormalization of the simulation time, thus effectively boosting the timescale of the simulation. In the work of Kim et al. [J. Chem. Phys. 139, 144110 (2013)], a local version of HD was formulated, which exploits the intrinsic locality characteristic typical of most systems to mitigate the poor scaling properties of standard HD as the system size is increased. Here, we discuss how both HD and local HD can be formulated to run efficiently in parallel. We have implemented these ideas in the LAMMPS MD code, which means HD can be used with any interatomic potential LAMMPS supports. Together, these parallel methods allow simulations of any size to achieve the time acceleration offered by HD (which can be orders of magnitude), at a cost of 2-4× that of standard MD. As examples, we performed two simulations of a million-atom system to model the diffusion and clustering of Pt adatoms on a large patch of the Pt(100) surface for 80 µs and 160 µs.

Humoral and cellular immune response in mice induced by the classical swine fever virus E2 protein fused to the porcine CD154 antigen.

The development of subunit vaccines against classical swine fever is a desirable goal, because it allows discrimination between vaccinated and infected animals. In this study, humoral and cellular immune response elicited in inbred BALB/c mice by immunization with a recombinant classical swine fever virus (CSFV) E2 protein fused to porcine CD154 antigen (E2CD154) was assessed. This model was used as a predictor of immune response in swine. Mice were immunized with E2CD154 emulsified in Montanide ISA50V2 or dissolved in saline on days 1 and 21. Another group received E2His antigen, without CD154, in the same adjuvant. Montanide ISA50V2 or saline served as negative controls for each experimental group. Animals immunized with 12.5 and 2.5 µg/dose of E2CD154 developed the highest titers (>1:2000) of CSFV neutralizing antibodies. Moreover, CSFV specific splenocyte gamma-interferon production, measured after seven and twenty-eight days of immunization, was significantly higher in mice immunized with 12.5 µg of E2CD154. As a conclusion, E2CD154 emulsified in Montanide ISA50 V2 was able to induce a potent humoral and an early cellular immune response in inbred BALB/c mice. Therefore, this immunogen might be an appropriate candidate to elicit immune response in swine, control CSF disease and to eliminate CSFV in swine.

Cluster analysis of accelerated molecular dynamics simulations: A case study of the decahedron to icosahedron transition in Pt nanoparticles.

Modern molecular-dynamics-based techniques are extremely powerful to investigate the dynamical evolution of materials. With the increase in sophistication of the simulation techniques and the ubiquity of massively parallel computing platforms, atomistic simulations now generate very large amounts of data, which have to be carefully analyzed in order to reveal key features of the underlying trajectories, including the nature and characteristics of the relevant reaction pathways. We show that clustering algorithms, such as the Perron Cluster Cluster Analysis, can provide reduced representations that greatly facilitate the interpretation of complex trajectories. To illustrate this point, clustering tools are used to identify the key kinetic steps in complex accelerated molecular dynamics trajectories exhibiting shape fluctuations in Pt nanoclusters. This analysis provides an easily interpretable coarse representation of the reaction pathways in terms of a handful of clusters, in contrast to the raw trajectory that contains thousands of unique states and tens of thousands of transitions.

Treponema pallidum pallidum Genotypes and Macrolide Resistance Status in Syphilitic Lesions among Patients at 2 Sexually Transmitted Infection Clinics in Lima, Peru.

We report the circulating genotypes and the frequency of macrolide-resistance patterns among Treponema pallidum pallidum DNA isolated from syphilitic lesions from patients who attended 2 sexual health clinics in Lima, Peru. We implemented and used a molecular typing scheme to describe local T. pallidum pallidum strains. Among 14 specimens, subtype 14d/f was the most prevalent strain in 7 fully typed T. pallidum DNA specimens obtained from men who have sex with men and transgender women presenting with chancre-like lesions. No macrolide-resistance mutations were found in T. pallidum DNA from 10 lesions.

The effects of cation-anion clustering on defect migration in MgAl2O4.

Magnesium aluminate spinel (MgAl2O4), like many other ceramic materials, offers a range of technological applications, from nuclear reactor materials to military body armor. For many of these applications, it is critical to understand both the formation and evolution of lattice defects throughout the lifetime of the material. We use the Speculatively Parallel Temperature Accelerated Dynamics (SpecTAD) method to investigate the effects of di-vacancy and di-interstitial formation on the mobility of the component defects. From long-time trajectories of the state-to-state dynamics, we characterize the migration pathways of defect clusters, and calculate their self-diffusion constants across a range of temperatures. We find that the clustering of Al and O vacancies drastically reduces the mobility of both defects, while the clustering of Mg and O vacancies completely immobilizes them. For interstitials, we find that the clustering of Mg and O defects greatly reduces O interstitial mobility, but has only a weak effect on Mg. These findings illuminate important new details regarding defect kinetics relevant to the application of MgAl2O4 in extreme environments.

Accelerating ring-polymer molecular dynamics with parallel-replica dynamics.

Nuclear quantum effects are important for systems containing light elements, and the effects are more prominent in the low temperature regime where the dynamics also becomes sluggish. We show that parallel replica (ParRep) dynamics, an accelerated molecular dynamics approach for infrequent-event systems, can be effectively combined with ring-polymer molecular dynamics, a semiclassical trajectory approach that gives a good approximation to zero-point and tunneling effects in activated escape processes. The resulting RP-ParRep method is a powerful tool for reaching long time scales in complex infrequent-event systems where quantum dynamics are important. Two illustrative examples, symmetric Eckart barrier crossing and interstitial helium diffusion in Fe and Fe-Cr alloy, are presented to demonstrate the accuracy and long-time scale capability of this approach.

Towards an Optimal Flow: Density-of-States-Informed Replica-Exchange Simulations.

Replica exchange (RE) is one of the most popular enhanced-sampling simulations technique in use today. Despite widespread successes, RE simulations can sometimes fail to converge in practical amounts of time, e.g., when sampling around phase transitions, or when a few hard-to-find configurations dominate the statistical averages. We introduce a generalized RE scheme, density-of-states-informed RE, that addresses some of these challenges. The key feature of our approach is to inform the simulation with readily available, but commonly unused, information on the density of states of the system as the RE simulation proceeds. This enables two improvements, namely, the introduction of resampling moves that actively move the system towards equilibrium and the continual adaptation of the optimal temperature set. As a consequence of these two innovations, we show that the configuration flow in temperature space is optimized and that the overall convergence of RE simulations can be dramatically accelerated.

Competing kinetics and he bubble morphology in W.

The growth process of He bubbles in W is investigated using molecular dynamics and parallel replica dynamics for growth rates spanning 6 orders of magnitude. Fast and slow growth regimes are defined relative to typical diffusion hopping times of W interstitials around the He bubble. Slow growth rates allow the diffusion of interstitials around the bubble, favoring the biased growth of the bubble towards the surface. In contrast, at fast growth rates interstitials do not have time to diffuse around the bubble, leading to a more isotropic growth and increasing the surface damage.

Hyperdynamics boost factor achievable with an ideal bias potential.

Hyperdynamics is a powerful method to significantly extend the time scales amenable to molecular dynamics simulation of infrequent events. One outstanding challenge, however, is the development of the so-called bias potential required by the method. In this work, we design a bias potential using information about all minimum energy pathways (MEPs) out of the current state. While this approach is not suitable for use in an actual hyperdynamics simulation, because the pathways are generally not known in advance, it allows us to show that it is possible to come very close to the theoretical boost limit of hyperdynamics while maintaining high accuracy. We demonstrate this by applying this MEP-based hyperdynamics (MEP-HD) to metallic surface diffusion systems. In most cases, MEP-HD gives boost factors that are orders of magnitude larger than the best existing bias potential, indicating that further development of hyperdynamics bias potentials could have a significant payoff. Finally, we discuss potential practical uses of MEP-HD, including the possibility of developing MEP-HD into a true hyperdynamics.

Extending atomistic simulation timescale in solid/liquid systems: crystal growth from solution by a parallel-replica dynamics and continuum hybrid method.

Deposition of solid material from solution is ubiquitous in nature. However, due to the inherent complexity of such systems, this process is comparatively much less understood than deposition from a gas or vacuum. Further, the accurate atomistic modeling of such systems is computationally expensive, therefore leaving many intriguing long-timescale phenomena out of reach. We present an atomistic/continuum hybrid method for extending the simulation timescales of dynamics at solid/liquid interfaces. We demonstrate the method by simulating the deposition of Ag on Ag (001) from solution with a significant speedup over standard MD. The results reveal specific features of diffusive deposition dynamics, such as a dramatic increase in the roughness of the film.

Acceleration of Solvation Free Energy Calculation via Thermodynamic Integration Coupled with Gaussian Process Regression and Improved Gelman-Rubin Convergence Diagnostics.

The determination of the solvation free energy of ions and molecules holds profound importance across a spectrum of applications spanning chemistry, biology, energy storage, and the environment. Molecular dynamics simulations are powerful tools for computing this critical parameter. Nevertheless, the accurate and efficient calculation of the solvation free energy becomes a formidable endeavor when dealing with complex systems characterized by potent Coulombic interactions and sluggish ion dynamics and, consequently, slow transition across various metastable states. In the present study, we expose limitations stemming from the conventional calculation of the statistical inefficiency g in the thermodynamic integration method, a factor that can hinder the determination of convergence of the solvation free energy and its associated uncertainty. Instead, we propose a robust scheme based on Gelman-Rubin convergence diagnostics. We leverage this improved estimation of uncertainties to introduce an innovative accelerated thermodynamic integration method based on the Gaussian Process regression. This methodology is applied to the calculation of the solvation free energy of trivalent rare-earth elements immersed in ionic liquids, a scenario in which the aforementioned challenges render standard approaches ineffective. The proposed method proves to be effective in computing solvation free energy in situations where traditional thermodynamic integration methods fall short.

MolSieve: A Progressive Visual Analytics System for Molecular Dynamics Simulations.

Molecular Dynamics (MD) simulations are ubiquitous in cutting-edge physio-chemical research. They provide critical insights into how a physical system evolves over time given a model of interatomic interactions. Understanding a system's evolution is key to selecting the best candidates for new drugs, materials for manufacturing, and countless other practical applications. With today's technology, these simulations can encompass millions of unit transitions between discrete molecular structures, spanning up to several milliseconds of real time. Attempting to perform a brute-force analysis with data-sets of this size is not only computationally impractical, but would not shed light on the physically-relevant features of the data. Moreover, there is a need to analyze simulation ensembles in order to compare similar processes in differing environments. These problems call for an approach that is analytically transparent, computationally efficient, and flexible enough to handle the variety found in materials-based research. In order to address these problems, we introduce MolSieve, a progressive visual analytics system that enables the comparison of multiple long-duration simulations. Using MolSieve, analysts are able to quickly identify and compare regions of interest within immense simulations through its combination of control charts, data-reduction techniques, and highly informative visual components. A simple programming interface is provided which allows experts to fit MolSieve to their needs. To demonstrate the efficacy of our approach, we present two case studies of MolSieve and report on findings from domain collaborators.

Entropic stabilization of nanoscale voids in materials under tension.

While preexisting defects are known to act as nucleation sites for plastic deformation in shocked materials, the kinetics of the early stages of plastic yield are still poorly understood. We use atomistic simulation techniques to investigate the kinetics of plastic yield around small preexisting voids in copper single crystals under uniaxial tensile strain. We demonstrate that at finite temperatures, these voids are stabilized by strong entropic effects that confer them significant lifetimes even when the static mechanical instability limit is exceeded. By virtue of its entropic nature, this effect is shown to be proportionally stronger at higher temperatures. Even accounting for thermal activation, very small voids prove to be extremely inefficient nucleation sites for plasticity.

Local hyperdynamics.

We present a new formulation of the hyperdynamics method in which the biasing effect is local, making it suitable for large systems. In standard hyperdynamics, the requirement that the bias potential be zero everywhere on the dividing surface bounding the state has the consequence that as the system size increases the boost factor decays to unity, regardless of the form of the bias potential. In the new method, the bias force on each atom is obtained by differentiating a local bias energy that depends only on the coordinates of atoms within a finite range of this atom. This bias force is thus independent of the bias force in distant parts of the system, providing a method that gives a constant boost factor, independent of the system size. We demonstrate for some realistic atomistic systems that the method gives escape rates in excellent agreement with direct molecular dynamics simulations.

Improving Estimation of the Koopman Operator with Kolmogorov-Smirnov Indicator Functions.

It has become common to perform kinetic analysis using approximate Koopman operators that transform high-dimensional timeseries of observables into ranked dynamical modes. The key to the practical success of the approach is the identification of a set of observables that form a good basis on which to expand the slow relaxation modes. Good observables are, however, difficult to identify a priori and suboptimal choices can lead to significant underestimations of characteristic time scales. Leveraging the representation of slow dynamics in terms of Hidden Markov Models (HMM), we propose a simple and computationally efficient clustering procedure to infer surrogate observables that form a good basis for slow modes. We apply the approach to an analytically solvable model system as well as on three protein systems of different complexities. We consistently demonstrate that the inferred indicator functions can significantly improve the estimation of the leading eigenvalues of Koopman operators and correctly identify key states and transition time scales of stochastic systems, even when good observables are not known a priori.

Architector for high-throughput cross-periodic table 3D complex building.

Rare-earth and actinide complexes are critical for a wealth of clean-energy applications. Three-dimensional (3D) structural generation and prediction for these organometallic systems remains a challenge, limiting opportunities for computational chemical discovery. Here, we introduce Architector, a high-throughput in-silico synthesis code for s-, p-, d-, and f-block mononuclear organometallic complexes capable of capturing nearly the full diversity of the known experimental chemical space. Beyond known chemical space, Architector performs in-silico design of new complexes including any chemically accessible metal-ligand combinations. Architector leverages metal-center symmetry, interatomic force fields, and tight binding methods to build many possible 3D conformers from minimal 2D inputs including metal oxidation and spin state. Over a set of more than 6,000 x-ray diffraction (XRD)-determined complexes spanning the periodic table, we demonstrate quantitative agreement between Architector-predicted and experimentally observed structures. Further, we demonstrate out-of-the box conformer generation and energetic rankings of non-minimum energy conformers produced from Architector, which are critical for exploring potential energy surfaces and training force fields. Overall, Architector represents a transformative step towards cross-periodic table computational design of metal complex chemistry.