A longitudinal study of muscle rehabilitation in the lower leg after cast removal using Magnetic Resonance Imaging and strength assessment

Acknowledgements We thank the A&E nurses and plaster technicians for identifying suitable patients, the MRI radiographers for performing the scanning, Dr Scott Semple for invaluable help in some of the pilot studies and Mr E. C. Stevenson for constructing the footrest used in the scanner. We are very grateful to the dedicated patients themselves who gave considerable amounts of time to come in for scanning, exercise and assessment during the course of this study.Peer reviewedPublisher PD

Hybrid Numerical Methods for Multiscale Simulations of Subsurface Biogeochemical Processes

Many subsurface flow and transport problems of importance today involve coupled non-linear flow, transport, and reaction in media exhibiting complex heterogeneity. In particular, problems involving biological mediation of reactions fall into this class of problems. Recent experimental research has revealed important details about the physical, chemical, and biological mechanisms involved in these processes at a variety of scales ranging from molecular to laboratory scales. However, it has not been practical or possible to translate detailed knowledge at small scales into reliable predictions of field-scale phenomena important for environmental management applications. A large assortment of numerical simulation tools have been developed, each with its own characteristic scale including molecular (e.g., molecular dynamics), microbial (e.g., cellular automata or particle individual-based models), pore (e.g., lattice-Boltzmann, pore network models, and discrete particle methods such as smoothed particle hydrodynamics) and continuum scales (e.g., traditional partial differential equations solved by finite difference or finite element methods). While many problems can be effectively addressed by one of these models at a single scale, some problems may require explicit integration of models across multiple scales. We are developing a hybrid multi-scale subsurface reactive transport modeling framework that integrates models with diverse representations of physics, chemistry and biology at different scales (sub-pore, pore and continuum). The modeling framework is being designed to take advantage of advanced computational technologies including parallel code components using the Common Component Architecture, parallel solvers, gridding, data and workflow management, and visualization. This paper describes the specific methods/codes being used at each scale, techniques used to directly and adaptively couple across model scales, and preliminary results of application to a multi-scale model of mineral precipitation at a solute mixing interface

Particle Methods for Simulation of Subsurface Multiphase Fluid Flow and Biogeological Processes

Abstract A number of particle models that are suitable for simulating multiphase fluid flow and biogeological processes have been developed during the last few decades. Here we discuss three of them: a microscopic model -molecular dynamics; a mesoscopic model -dissipative particle dynamics; and a macroscopic model -smoothed particle hydrodynamics. Particle methods are robust and versatile, and it is relatively easy to add additional physical, chemical and biological processes into particle codes. However, the computational efficiency of particle methods is low relative to continuum methods. Multiscale particle methods and hybrid (particle-particle and particle-continuum) methods are needed to improve computational efficiency and make effective use of emerging computational capabilities. These new methods are under development Introduction The computational methods used to simulate single-and multi-phase fluid flow can be divided into two general classes: continuum methods and particle methods. Hybrid particle-continuum methods have also been developed, and some models, such as smoothed particle hydrodynamics and lattice Boltzmann models, can be considered to be either continuum or particle methods. Particle models that can be used to simulate single-and multi-phase fluid dynamics include lattice gas model

Denitrative Hydroxylation of Unactivated Nitroarenes**

A one-step method for the conversion of nitroarenes into phenols under operationally simple, transition-metal-free conditions is described. This denitrative functionalization protocol provides a concise and economical alternative to conventional three-step synthetic sequences. Experimental and computational studies suggest that nitroarenes may be substituted by an electron-catalysed radical-nucleophilic substitution (SRN1) chain mechanism

Study of the Detonation Phase in the Gravitationally Confined Detonation Model of Type Ia Supernovae

We study the gravitationally confined detonation (GCD) model of Type Ia supernovae through the detonation phase and into homologous expansion. In the GCD model, a detonation is triggered by the surface flow due to single point, off-center flame ignition in carbon-oxygen white dwarfs. The simulations are unique in terms of the degree to which non-idealized physics is used to treat the reactive flow, including weak reaction rates and a time dependent treatment of material in nuclear statistical equilibrium (NSE). Careful attention is paid to accurately calculating the final composition of material which is burned to NSE and frozen out in the rapid expansion following the passage of a detonation wave over the high density core of the white dwarf; and an efficient method for nucleosynthesis post-processing is developed which obviates the need for costly network calculations along tracer particle thermodynamic trajectories. Observational diagnostics are presented for the explosion models, including abundance stratifications and integrated yields. We find that for all of the ignition conditions studied here, a self regulating process comprised of neutronization and stellar expansion results in final \iso{Ni}{56} masses of $\sim$1.1\msun. But, more energetic models result in larger total NSE and stable Fe peak yields. The total yield of intermediate mass elements is $\sim0.1$\msun and the explosion energies are all around 1.5$\times10^{51}$ ergs. The explosion models are briefly compared to the inferred properties of recent Type Ia supernova observations. The potential for surface detonation models to produce lower luminosity (lower \iso{Ni}{56} mass) supernovae is discussed.Comment: 43 pages, 4 tables, 20 figures -- submitted to Ap

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages