Probabilistic non-linear registration with spatially adaptive regularisation

This paper introduces a novel method for inferring spatially varying regularisation in non-linear registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on the transformation parameters is parameterised as a weighted mixture of spatially localised components. Such an approach has the advantage of allowing the registration to be more flexibly driven by the data than a traditional globally defined regularisation penalty, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The strength of the prior may be reduced in areas where the data better support deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce unwanted impacts of regularisation on the inferred transformation. This is especially important for applications where the deformation field itself is of interest, such as tensor based morphometry. The proposed approach is demonstrated using synthetic images, and with application to tensor based morphometry analysis of subjects with Alzheimer’s disease and healthy controls. The results indicate that using the proposed spatially adaptive prior leads to sparser deformations, which provide better localisation of regional volume change. Additionally, the proposed regularisation model leads to more data driven and localised maps of registration uncertainty. This paper also demonstrates for the first time the use of Bayesian model comparison for selecting different types of regularisation

Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel

A major use of the 1000 Genomes Project (1000GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants. © 2014 Macmillan Publishers Limited. All rights reserved

On the mechanisms governing gas penetration into a tokamak plasma during a massive gas injection

A new 1D radial fluid code, IMAGINE, is used to simulate the penetration of gas into a tokamak plasma during a massive gas injection (MGI). The main result is that the gas is in general strongly braked as it reaches the plasma, due to mechanisms related to charge exchange and (to a smaller extent) recombination. As a result, only a fraction of the gas penetrates into the plasma. Also, a shock wave is created in the gas which propagates away from the plasma, braking and compressing the incoming gas. Simulation results are quantitatively consistent, at least in terms of orders of magnitude, with experimental data for a D 2 MGI into a JET Ohmic plasma. Simulations of MGI into the background plasma surrounding a runaway electron beam show that if the background electron density is too high, the gas may not penetrate, suggesting a possible explanation for the recent results of Reux et al in JET (2015 Nucl. Fusion 55 093013)

Relationships between varus–valgus laxity of the severely osteoarthritic knee and gait, instability, clinical performance, and function

Increased varus–valgus laxity has been reported in individuals with knee osteoarthritis (OA) compared to controls. However, the majority of previous investigations may not report truly passive joint laxity, as their tests have been performed on conscious participants who could be guarding against motion with muscle contraction during laxity evaluation. The purpose of this study was to investigate how a measure of passive knee laxity, recorded when the participant is under anesthesia, is related to varus–valgus excursion during gait, clinical measures of performance, perceived instability, and self‐reported function in participants with severe knee OA. We assessed passive varus–valgus knee laxity in 29 participants (30 knees) with severe OA, as they underwent total knee arthroplasty (TKA). Participants also completed gait analysis, clinical assessment of performance (6‐min walk (6 MW), stair climbing test (SCT), isometric knee strength), and self‐reported measures of function (perceived instability, Knee injury, and Osteoarthritis Outcome Score (KOOS) a median of 18 days before the TKA procedure. We observed that greater passive varus–valgus laxity was associated with greater varus–valgus excursion during gait (R2 = 0.34, p = 0.002). Significant associations were also observed between greater laxity and greater isometric knee extension strength (p = 0.014), farther 6 MW distance (p = 0.033) and shorter SCT time (p = 0.046). No relationship was observed between passive varus–valgus laxity and isometric knee flexion strength, perceived instability, or any KOOS subscale. The conflicting associations between laxity, frontal excursion during gait, and functional performance suggest a complex relationship between laxity and knee cartilage health, clinical performance, and self‐reported function that merits further study. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:1644–1652, 2017