Strategies and Communications for Innovations

Chapter 5 Developing a Technology Intelligence Strategy to Access Knowledge of Innovation Clusters

Surface attachment of protein fibrils via covalent modification strategies.

Chemical control of surface functionality and topography is an essential requirement for many technological purposes. In particular, the covalent attachment of monomeric proteins to surfaces has been the object of intense studies in recent years, for applications as varied as electrochemistry, immuno-sensing, and the production of biocompatible coatings. Little is known, however, about the characteristics and requirements underlying surface attachment of supramolecular protein nanostructures. Amyloid fibrils formed by the self-assembly of peptide and protein molecules represent one important class of such structures. These highly organized beta-sheet-rich assemblies are a hallmark of a range of neurodegenerative disorders, including Alzheimer's disease and type II diabetes, but recent findings suggest that they have much broader significance, potentially representing the global free energy minima of the energy landscapes of proteins and having potential applications in material science. In this paper, we describe strategies for attaching amyloid fibrils formed from different proteins to gold surfaces under different solution conditions. Our methods involve the reaction of sulfur containing small molecules (cystamine and 2-iminothiolane) with the amyloid fibrils, enabling their covalent linkage to gold surfaces. We demonstrate that irreversible attachment using these approaches makes possible quantitative analysis of experiments using biosensor techniques, such as quartz crystal microbalance (QCM) assays that are revolutionizing our understanding of the mechanisms of amyloid growth and the factors that determine its kinetic behavior. Moreover, our results shed light on the nature and relative importance of covalent versus noncovalent forces acting on protein superstructures at metal surfaces.

Fixed-lag blind equalization and sequence estimation in digital communications systems using sequential importance sampling

We present methods for fixed-lag smoothing using Sequential Importance sampling (SIS) on a discrete non-linear, non-Gaussian state space system with unknown parameters. Our particular application is in the field of digital communication systems. Each input data point is taken from a finite set of symbols. We represent transmission media as a fixed filter with a finite impulse response (FIR), hence a discrete state-space system is formed. Conventional Markov chain Monte Carlo (MCMC) techniques such as the Gibbs sampler are unsuitable for this task because they can only perform processing on a batch of data. Data arrives sequentially, so it would seem sensible to process it in this way. In addition, many communication systems are interactive, so there is a maximum level of latency that can be tolerated before a symbol is decoded. We will demonstrate this method by simulation and compare its performance to existing techniques.

Innovation processes within the healthcare industry: Determining critical success factors aligned to product strategies

The impact of differing product strategies on product innovation processes pursued by healthcare firms is discussed. The critical success factors aligned to product strategies are presented. A definite split between pioneering product strategies and late entrant product strategies is also recognised.