Support vector regression to estimate the permeability enhancement of potential transdermal enhancers

This is the peer reviewed version of the following article: Shah, A., Sun, Y., Adams, R. G., Davey, N., Wilkinson, S. C. and Moss, G. P. (2016), Support vector regression to estimate the permeability enhancement of potential transdermal enhancers', Journal of Pharmacy and Pharmacology, Vol. 68 (2): 170–184, which has been published in final form at doi:10.1111/jphp.12508. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. © 2016 Royal Pharmaceutical Society.Objectives Searching for chemicals that will safely enhance transdermal drug delivery is a significant challenge. This study applies support vector regression (SVR) for the first time to estimating the optimal formulation design of transdermal hydrocortisone formulations. Methods The aim of this study was to apply SVR methods with two different kernels in order to estimate the enhancement ratio of chemical enhancers of permeability. Key findings A statistically significant regression SVR model was developed. It was found that SVR with a nonlinear kernel provided the best estimate of the enhancement ratio for a chemical enhancer. Conclusions Support vector regression is a viable method to develop predictive models of biological processes, demonstrating improvements over other methods. In addition, the results of this study suggest that a global approach to modelling a biological process may not necessarily be the best method and that a ‘mixed-methods’ approach may be best in optimising predictive models.Peer reviewedFinal Accepted Versio

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 182, July 1978

This bibliography lists 165 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1978

VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera

We present the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera. Our method combines a new convolutional neural network (CNN) based pose regressor with kinematic skeleton fitting. Our novel fully-convolutional pose formulation regresses 2D and 3D joint positions jointly in real time and does not require tightly cropped input frames. A real-time kinematic skeleton fitting method uses the CNN output to yield temporally stable 3D global pose reconstructions on the basis of a coherent kinematic skeleton. This makes our approach the first monocular RGB method usable in real-time applications such as 3D character control---thus far, the only monocular methods for such applications employed specialized RGB-D cameras. Our method's accuracy is quantitatively on par with the best offline 3D monocular RGB pose estimation methods. Our results are qualitatively comparable to, and sometimes better than, results from monocular RGB-D approaches, such as the Kinect. However, we show that our approach is more broadly applicable than RGB-D solutions, i.e. it works for outdoor scenes, community videos, and low quality commodity RGB cameras.Comment: Accepted to SIGGRAPH 201