Surface modification of poly(ε-caprolactone) using a dielectric barrier discharge in atmospheric pressure glow discharge mode

The role of roughening and functionalization processes involved in modifying the wettability of poly(e-caprolactone) (PCL) after treatment by an atmospheric pressure glow discharge plasma is discussed. The change in the ratio of Cdouble bond; length as m-dashO/C–O bonds is a significant factor influencing the wettability of PCL. As the contact angle decreases, the level of Cdouble bond; length as m-dashO bonds tends to rise. Surface roughness alterations are the driving force for lasting increases in wettability, while the surface functional species are shorter lived. We can approximate from ageing that the increase in wettability for PCL after plasma treatment is 55–60% due to roughening and 40–45% due to surface functionalization for the plasma device investigated.

Synthesis and SAR of novel, non-MPEP chemotype mGluR5 NAMs identified by functional HTS

This Letter describes the discovery and SAR of three novel series of mGluR5 non-competitive antagonists/negative allosteric modulators (NAMs) not based on manipulation of an MPEP/MTEP chemotype identified by a functional HTS approach. This work demonstrates fundamentally new mGluR5 NAM chemotypes with submicromolar potencies, and further examples of a mode of pharmacology 'switch' to provide PAMs with a non-MPEP scaffold. (C) 2009 Elsevier Ltd. All rights reserved.

The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique

Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source, then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are, however, considerably fewer techniques available if only a single-channel measurement is available and yet single-channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single-channel measurements. The EEMD technique is first used to decompose the single-channel signal into a multidimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second-order statistics. The new technique is tested against the currently available wavelet denoising and EEMD-ICA techniques using both electroencephalography and functional near-infrared spectroscopy data and is shown to produce significantly improved results. © 1964-2012 IEEE.