Variational approximation for mixtures of linear mixed models

Mixtures of linear mixed models (MLMMs) are useful for clustering grouped data and can be estimated by likelihood maximization through the EM algorithm. The conventional approach to determining a suitable number of components is to compare different mixture models using penalized log-likelihood criteria such as BIC.We propose fitting MLMMs with variational methods which can perform parameter estimation and model selection simultaneously. A variational approximation is described where the variational lower bound and parameter updates are in closed form, allowing fast evaluation. A new variational greedy algorithm is developed for model selection and learning of the mixture components. This approach allows an automatic initialization of the algorithm and returns a plausible number of mixture components automatically. In cases of weak identifiability of certain model parameters, we use hierarchical centering to reparametrize the model and show empirically that there is a gain in efficiency by variational algorithms similar to that in MCMC algorithms. Related to this, we prove that the approximate rate of convergence of variational algorithms by Gaussian approximation is equal to that of the corresponding Gibbs sampler which suggests that reparametrizations can lead to improved convergence in variational algorithms as well.Comment: 36 pages, 5 figures, 2 tables, submitted to JCG

The Gravity-Loading countermeasure Skinsuit (GLCS) and its effect upon aerobic exercise performance

© 2016 IAA The Russian Pingvin suit is employed as a countermeasure to musculoskeletal atrophy in microgravity, though its 2-stage loading regime is poorly tolerated. The Gravity-Loading Countermeasure Skinsuit (GLCS) has been devised to comfortably compress the body via incrementally increasing longitudinal elastic-fibre tensions from the shoulders to the feet. We tested whether the Mk III GLCS was a feasible adjunct to sub-maximal aerobic exercise and resulting VO2Max predictions. Eight healthy subjects (5♂, 28±6 yr) performed cycle ergometry at 75% VO2Max (derived from an Astrand-Rhyming protocol) whilst wearing a GLCS and gym clothing (GYM). Ventilatory parameters, heart rate (HR), core temperature (TC), and blood lactate (BL) were recorded along with subjective perceived exertion, thermal comfort, movement discomfort and body control. Physiological and subjective responses were compared over TIME and between GYM and GLCS (ATTIRE) with 2-way repeated measures ANOVA and Wilcoxon tests respectively. Resultant VO2Max predictions were compared with paired t-tests between ATTIRE. The GLCS induced greater initial exercise ventilatory responses which stabilised by 20 min. HR and TC continued to rise from 5 min irrespective of ATTIRE, whereas BL was greater in the GLCS at 20 min. Predicted VO2Max did not differ with ATTIRE, though some observed differences in HR were noteworthy. All subjective ratings were exacerbated in the GLCS. Despite increased perception of workload and initial ventilatory augmentations, submaximal exercise performance was not impeded. Whilst predicted VO2Max did not differ, determination of actual VO2Max in the GLCS is warranted due to apparent modulation of the linear HR-VO2 relationship. The GLCS may be a feasible adjunct to exercise and potential countermeasure to unloaded-induced physiological deconditioning on Earth or in space

CoreValve vs. Sapien 3 transcatheter aortic valve replacement: a finite element analysis study

Aim: to investigate the factors implied in the development of postoperative complications in both self-expandable and balloon-expandable transcatheter heart valves by means of finite element analysis (FEA). Materials and methods: FEA was integrated into CT scans to investigate two cases of postoperative device failure for valve thrombosis after the successful implantation of a CoreValve and a Sapien 3 valve. Data were then compared with two patients who had undergone uncomplicated transcatheter heart valve replacement (TAVR) with the same types of valves. Results: Computational biomechanical modeling showed calcifications persisting after device expansion, not visible on the CT scan. These calcifications determined geometrical distortion and elliptical deformation of the valve predisposing to hemodynamic disturbances and potential thrombosis. Increased regional stress was also identified in correspondence to the areas of distortion with the associated paravalvular leak. Conclusion: the use of FEA as an adjunct to preoperative imaging might assist patient selection and procedure planning as well as help in the detection and prevention of TAVR complications