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.

The Trade-offs with Space Time Cube Representation of Spatiotemporal Patterns

Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Fast and correct analysis of such information is important in for instance geospatial and social visualization applications. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a dataset to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap we report on a between-subjects experiment comparing novice users error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the dataset, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users when analyzing complex spatiotemporal patterns.