Industry perceptions of barriers to commercialization of regenerative medicine products in the UK.

AIMS: Regenerative medicine is an emerging field with the potential to provide widespread improvement in healthcare and patient wellbeing via the delivery of therapies that can restore, regenerate or repair damaged tissue. As an industry, it could significantly contribute to economic growth if products are successfully commercialized. However, to date, relatively few products have reached the market owing to a variety of barriers, including a lack of funding and regulatory hurdles. The present study analyzes industry perceptions of the barriers to commercialization that currently impede the success of the regenerative medicine industry in the UK. MATERIALS & METHODS: The analysis is based on 20 interviews with leading industrialists in the field. RESULTS: The study revealed that scientific research in regenerative medicine is thriving in the UK. Unfortunately, lack of access to capital, regulatory hurdles, lack of clinical evidence leading to problems with reimbursement, as well as the culture of the NHS do not provide a good environment for the commercialization of regenerative medicine products. CONCLUSION: Policy interventions, including increased translational government funding, a change in NHS and NICE organization and policies, and regulatory clarity, would likely improve the general outcomes for the regenerative medicine industry in the UK.

Closing the Energy Efficiency Gap: A study linking demographics with barriers to adopting energy efficiency measures in the home

This paper presents a study which linked demographic variables with barriers affecting the adoption of domestic energy efficiency measures in large UK cities. The aim was to better understand the 'Energy Efficiency Gap' and improve the effectiveness of future energy efficiency initiatives. The data for this study was collected from 198 general population interviews (1.5-10 min) carried out across multiple locations in Manchester and Cardiff. The demographic variables were statistically linked to the identified barriers using a modified chi-square test of association (first order Rao-Scott corrected to compensate for multiple response data), and the effect size was estimated with an odds-ratio test. The results revealed that strong associations exist between demographics and barriers, specifically for the following variables: sex; marital status; education level; type of dwelling; number of occupants in household; residence (rent/own); and location (Manchester/Cardiff). The results and recommendations were aimed at city policy makers, local councils, and members of the construction/retrofit industry who are all working to improve the energy efficiency of the domestic built environment. © 2012 Elsevier Ltd.

Bayesian learning of noisy Markov decision processes

This work addresses the problem of estimating the optimal value function in a Markov Decision Process from observed state-action pairs. We adopt a Bayesian approach to inference, which allows both the model to be estimated and predictions about actions to be made in a unified framework, providing a principled approach to mimicry of a controller on the basis of observed data. A new Markov chain Monte Carlo (MCMC) sampler is devised for simulation from theposterior distribution over the optimal value function. This step includes a parameter expansion step, which is shown to be essential for good convergence properties of the MCMC sampler. As an illustration, the method is applied to learning a human controller.

Learning the dynamics of the external world: Brain inspired learning for robotic applications

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has become exceptionally well adapted to learning to deal not only with the complex dynamics of our own limbs but also with novel dynamics in the external world. While learning of these dynamics includes learning the complex time-varying forces at the end of limbs through the updating of internal models, it must also include learning the appropriate mechanical impedance in order to stabilize both the limb and any objects contacted in the environment. This article reviews the field of human learning by examining recent experimental evidence about adaptation to novel unstable dynamics and explores how this knowledge about the brain and neuro-muscular system can expand the learning capabilities of robotics and prosthetics. © 2006.