Throwing enhances humeral shaft cortical bone properties in pre-pubertal baseball players: a 12-month longitudinal pilot study

Objectives: To explore throwing athletes as a prospective, within-subject controlled model for studying the response of the skeleton to exercise. Methods: Male pre-pubertal throwing athletes (n=12; age=10.3±0.6 yrs) had distal humerus cortical volumetric bone mineral density (Ct.vBMD), cortical bone mineral content (Ct.BMC), total area (Tt.Ar), cortical area (Ct.Ar), medullary area (Me.Ar), cortical thickness (Ct.Th) and polar moment of inertia (IP) assessed within their throwing (exercised) and nonthrowing (control) arms by peripheral quantitative computed tomography at baseline and 12 months. Throwing-to-nonthrowing arm percent differences (i.e. bilateral asymmetry) were compared over time. Results: Over 12 months, the throwing arm gained 4.3% (95% Cl=1.1% to 7.5%), 2.9% (95% Cl=0.3% to 5.4%), 3.9% (95% Cl=0.7% to 7.0%), and 8.2% (95% Cl=2.0% to 6.8%) more Ct.BMC, Ct.Ar, Tt.Ar, and IP than the nonthrowing arm, respectively (all p<0.05). There was no significant effect of throwing on Ct.vBMD, Ct.Th and Me.Ar (all p=0.18-0.82). Conclusion: Throwing induced surface-specific cortical bone adaptation at the distal humeral diaphysis that contributed to a gain in estimated strength. These longitudinal pilot data support the utility of throwing athletes as a within-subject controlled model to explore factors influencing exercise-induced bone adaptation during the critical growing years

Ambient Multi-Camera Personal Documentary

Polymnia is an automated solution for the creation of ambient multi-camera personal documentary films. This short paper introduces the system, emphasising the rule-based documentary generation engine that we have created to assemble an edited narrative from source footage. We describe how such automatically generated media can be integrated with and augment personally-authored images and videos as a contribution to an individual’s personal digital memory

Analysis of SPDEs Arising in Path Sampling Part I: The Gaussian Case

In many applications it is important to be able to sample paths of SDEs conditional on observations of various kinds. This paper studies SPDEs which solve such sampling problems. The SPDE may be viewed as an infinite dimensional analogue of the Langevin SDE used in finite dimensional sampling. Here the theory is developed for conditioned Gaussian processes for which the resulting SPDE is linear. Applications include the Kalman-Bucy filter/smoother. A companion paper studies the nonlinear case, building on the linear analysis provided here

The influence of habitat quality on the foraging strategies of the entomopathogenic nematodes Steinernema carpocapsae and Heterorhabditis megidis

Entomopathogenic nematodes (EPN) are soil-transmitted parasites and their foraging strategies are believed to range from ‘ambush’ to ‘cruise’ foragers. However, research on their behaviour has not considered the natural habitat of these nematodes. We hypothesized that EPN behaviour would be influenced by soil habitat quality and tested this hypothesis using 2 EPN species Steinernema carpocapsae (an ‘ambusher’) and Heterorhabditis megidis (a ‘cruiser’) in 2 contrasting habitats, sand and peat. As predicted from previous studies, in sand most S. carpocapsae remained at the point of application and showed no taxis towards hosts, but in peat S. carpocapsae dispersed much more and showed a highly significant taxis towards hosts. H. megidis dispersed well in both media, but only showed taxis towards hosts in sand. In outdoor mesocosms in which both species were applied, S. carpocapsae outcompeted H. megidis in terms of host finding in peat, whereas the opposite was true in sand. Our data suggest that these 2 EPN may be habitat specialists and highlight the difficulties of studying soil-transmitted parasites in non-soil media

Catalytic constructive deoxygenation of lignin-derived phenols: new C-C bond formation processes from imidazole-sulfonates and ether cleavage reactions

Funding: UK Engineering and Physical Sciences Research Council (EPSRC)As part of a programme aimed at exploiting lignin as a chemical feedstock for less oxygenated fine chemicals, several catalytic C-C bond forming reactions utilising guaiacol imidazole sulfonate are demonstrated. These include the cross-coupling of a Grignard, a non-toxic cyanide source, a benzoxazole, and nitromethane. A modified Meyers reaction is used to accomplish a second constructive deoxygenation on a benzoxazole functionalised anisole.Publisher PDFPeer reviewe

Variational data assimilation using targetted random walks

The variational approach to data assimilation is a widely used methodology for both online prediction and for reanalysis (offline hindcasting). In either of these scenarios it can be important to assess uncertainties in the assimilated state. Ideally it would be desirable to have complete information concerning the Bayesian posterior distribution for unknown state, given data. The purpose of this paper is to show that complete computational probing of this posterior distribution is now within reach in the offline situation. In this paper we will introduce an MCMC method which enables us to directly sample from the Bayesian\ud posterior distribution on the unknown functions of interest, given observations. Since we are aware that these\ud methods are currently too computationally expensive to consider using in an online filtering scenario, we frame this in the context of offline reanalysis. Using a simple random walk-type MCMC method, we are able to characterize the posterior distribution using only evaluations of the forward model of the problem, and of the model and data mismatch. No adjoint model is required for the method we use; however more sophisticated MCMC methods are available\ud which do exploit derivative information. For simplicity of exposition we consider the problem of assimilating data, either Eulerian or Lagrangian, into a low Reynolds number (Stokes flow) scenario in a two dimensional periodic geometry. We will show that in many cases it is possible to recover the initial condition and model error (which we describe as unknown forcing to the model) from data, and that with increasing amounts of informative data, the uncertainty in our estimations reduces

Strong convergence rates of probabilistic integrators for ordinary differential equations

Probabilistic integration of a continuous dynamical system is a way of systematically introducing model error, at scales no larger than errors introduced by standard numerical discretisation, in order to enable thorough exploration of possible responses of the system to inputs. It is thus a potentially useful approach in a number of applications such as forward uncertainty quantification, inverse problems, and data assimilation. We extend the convergence analysis of probabilistic integrators for deterministic ordinary differential equations, as proposed by Conrad et al.\ (\textit{Stat.\ Comput.}, 2017), to establish mean-square convergence in the uniform norm on discrete- or continuous-time solutions under relaxed regularity assumptions on the driving vector fields and their induced flows. Specifically, we show that randomised high-order integrators for globally Lipschitz flows and randomised Euler integrators for dissipative vector fields with polynomially-bounded local Lipschitz constants all have the same mean-square convergence rate as their deterministic counterparts, provided that the variance of the integration noise is not of higher order than the corresponding deterministic integrator. These and similar results are proven for probabilistic integrators where the random perturbations may be state-dependent, non-Gaussian, or non-centred random variables.Comment: 25 page

Analysis of the 3DVAR Filter for the Partially Observed Lorenz '63 Model

The problem of effectively combining data with a mathematical model constitutes a major challenge in applied mathematics. It is particular challenging for high-dimensional dynamical systems where data is received sequentially in time and the objective is to estimate the system state in an on-line fashion; this situation arises, for example, in weather forecasting. The sequential particle filter is then impractical and ad hoc filters, which employ some form of Gaussian approximation, are widely used. Prototypical of these ad hoc filters is the 3DVAR method. The goal of this paper is to analyze the 3DVAR method, using the Lorenz '63 model to exemplify the key ideas. The situation where the data is partial and noisy is studied, and both discrete time and continuous time data streams are considered. The theory demonstrates how the widely used technique of variance inflation acts to stabilize the filter, and hence leads to asymptotic accuracy

Controlled Nanoparticle Formation by Diffusion Limited Coalescence

Polymeric nanoparticles (NPs) have a great application potential in science and technology. Their functionality strongly depends on their size. We present a theory for the size of NPs formed by precipitation of polymers into a bad solvent in the presence of a stabilizing surfactant. The analytical theory is based upon diffusion-limited coalescence kinetics of the polymers. Two relevant time scales, a mixing and a coalescence time, are identified and their ratio is shown to determine the final NP diameter. The size is found to scale in a universal manner and is predominantly sensitive to the mixing time and the polymer concentration if the surfactant concentration is sufficiently high. The model predictions are in good agreement with experimental data. Hence the theory provides a solid framework for tailoring nanoparticles with a priori determined size.Comment: 4 pages, 3 figure