Discovering transcriptional modules by Bayesian data integration.

MOTIVATION: We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both datasets. In particular, it allows us to identify the subset of genes that share the same structure of transcriptional modules in both datasets. RESULTS: We find that by working on a gene-by-gene basis, our model is able to extract clusters with greater functional coherence than existing methods. By combining gene expression and transcription factor binding (ChIP-chip) data in this way, we are better able to determine the groups of genes that are most likely to represent underlying TMs. AVAILABILITY: If interested in the code for the work presented in this article, please contact the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Diffusion and chemical reaction in the porous structures of solid oxide fuel cells

The Rolls-Royce Integrated-Planar Solid Oxide Fuel Cell (IP-SOFC) consists of ceramic modules which have electrochemical cells printed on the outer surfaces. The cathodes are the outermost layer of each cell and are supplied with oxygen from air flowing over the outside of the module. The anodes are in direct contact with the ceramic structure and are supplied with fuel from internal gas channels. Natural gas is reformed into hydrogen for use by the fuel cells in a separate reformer module of similar design except that the fuel cells are replaced by a reforming catalyst layer. The performance of the modules is intrinsically linked to the behaviour of the gas flows within their porous structures. Because the porous layers are very thin, a one-dimensional flow model provides a good representation of the flow property variations between fuel channel and fuel cell or reforming catalyst. The multi-component convective-diffusive flows are simulated using a new theory of flow in porous material, the Cylindrical Pore Interpolation Model. The effects of the catalysed methane reforming and water-gas shift chemical reactions are also considered using appropriate kinetic models. It is found that the shift reaction, which is catalysed by the anode material, has certain beneficial effects on the fuel cell module performance. In the reformer module it was found that the flow resistance of the porous support structure makes it difficult to sustain a high methane conversion rate. Although the analysis is based on IP-SOFC geometry, the modelling approach and general conclusions are applicable to other types of SOFC.

Two is better than one: Distinct roles for familiarity and recollection in retrieving palimpsest memories

Storing a new pattern in a palimpsest memory system comes at the cost of interfering with the memory traces of previously stored items. Knowing the age of a pattern thus becomes critical for recalling it faithfully. This implies that there should be a tight coupling between estimates of age, as a form of familiarity, and the neural dynamics of recollection, something which current theories omit. Using a normative model of autoassociative memory, we show that a dual memory system, consisting of two interacting modules for familiarity and recollection, has best performance for both recollection and recognition. This finding provides a new window onto actively contentious psychological and neural aspects of recognition memory.