The morphology of decorated amyloid fibers is controlled by the conformation and position of the displayed protein.

Self-assembled structures capable of mediating electron transfer are an attractive scientific and technological goal. Therefore, systematic variants of SH3-Cytochrome b(562) fusion proteins were designed to make amyloid fibers displaying heme-b(562) electron transfer complexes. TEM and AFM data show that fiber morphology responds systematically to placement of b(562) within the fusion proteins. UV-vis spectroscopy shows that, for the fusion proteins under test, only half the fiber-borne b(562) binds heme with high affinity. Cofactor binding also improves the AFM imaging properties and changes the fiber morphology through changes in cytochrome conformation. Systematic observations and measurements of fiber geometry suggest that longitudinal registry of subfilaments within the fiber, mediated by the interaction and conformation of the displayed proteins and their interaction with surfaces, gives rise to the observed morphologies, including defects and kinks. Of most interest is the role of small molecule modulation of fiber structure and mechanical stability. A minimum complexity model is proposed to capture and explain the fiber morphology in the light of these results. Understanding the complex interplay between these factors will enable a fiber design that supports longitudinal electron transfer.

Hierarchical electrohydrodynamic structures for surface-enhanced Raman scattering.

Surface enhanced Raman scattering (SERS) is a well-established spectroscopic technique that requires nanoscale metal structures to achieve high signal sensitivity. While most SERS substrates are manufactured by conventional lithographic methods, the development of a cost-effective approach to create nanostructured surfaces is a much sought-after goal in the SERS community. Here, a method is established to create controlled, self-organized, hierarchical nanostructures using electrohydrodynamic (HEHD) instabilities. The created structures are readily fine-tuned, which is an important requirement for optimizing SERS to obtain the highest enhancements. HEHD pattern formation enables the fabrication of multiscale 3D structured arrays as SERS-active platforms. Importantly, each of the HEHD-patterned individual structural units yield a considerable SERS enhancement. This enables each single unit to function as an isolated sensor. Each of the formed structures can be effectively tuned and tailored to provide high SERS enhancement, while arising from different HEHD morphologies. The HEHD fabrication of sub-micrometer architectures is straightforward and robust, providing an elegant route for high-throughput biological and chemical sensing.

Hierarchical electrohydrodynamic structures for surface-enhanced Raman scattering (adv. Mater. 23/2012).

On page OP 175, U. Steiner and co-workers destabilise polymer trilayer films using an electric field to generate separated micrometre-sized core-shell pillars, which are further modified by selective polymer dissolution to yield polymer core columns surrounded by a rim and micro-volcano rim structures. When coated with gold and decorated with Raman active probes, all three structure types give rise to substantial enhancement in surface-enhanced Raman scattering (SERS). Since this SERS enhancement arises from each of the isolated structures in the array, these surface patterns are an ideal platform for multiplexed SERS detection.

A subdivision-based implementation of the hierarchical b-spline finite element method

A novel technique is presented to facilitate the implementation of hierarchical b-splines and their interfacing with conventional finite element implementations. The discrete interpretation of the two-scale relation, as common in subdivision schemes, is used to establish algebraic relations between the basis functions and their coefficients on different levels of the hierarchical b-spline basis. The subdivision projection technique introduced allows us first to compute all element matrices and vectors using a fixed number of same-level basis functions. Their subsequent multiplication with subdivision matrices projects them, during the assembly stage, to the correct levels of the hierarchical b-spline basis. The proposed technique is applied to convergence studies of linear and geometrically nonlinear problems in one, two and three space dimensions. © 2012 Elsevier B.V.

A Hierarchical Model for Ordinal Matrix Factorization

This paper proposes a hierarchical probabilistic model for ordinal matrix factorization. Unlike previous approaches, we model the ordinal nature of the data and take a principled approach to incorporating priors for the hidden variables. Two algorithms are presented for inference, one based on Gibbs sampling and one based on variational Bayes. Importantly, these algorithms may be implemented in the factorization of very large matrices with missing entries. The model is evaluated on a collaborative filtering task, where users have rated a collection of movies and the system is asked to predict their ratings for other movies. The Netflix data set is used for evaluation, which consists of around 100 million ratings. Using root mean-squared error (RMSE) as an evaluation metric, results show that the suggested model outperforms alternative factorization techniques. Results also show how Gibbs sampling outperforms variational Bayes on this task, despite the large number of ratings and model parameters. Matlab implementations of the proposed algorithms are available from cogsys.imm.dtu.dk/ordinalmatrixfactorization.

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/.