Bundles of polytwins as meta-elastic domains in the thin polycrystalline simple multi-ferroic system PZT.

We used enhanced piezo-response force microscopy (E-PFM) to investigate both ferroelastic and ferroelectric nanodomains in thin films of the simple multi-ferroic system PbZr(0.3)Ti(0.7)O(3) (PZT). We show how the grains are organized into a new type of elastic domain bundles of the well-known periodic elastic twins. Here we present these bundle domains and discuss their stability and origin. Moreover, we show that they can arrange in such a way as to release strain in a more effective way than simple twinning. Finally, we show that these bundle domains can arrange to form the macroscopic ferroelectric domains that constitute the basis of ferroelectric-based memory devices.

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.

Learning optimal adaptation strategies in unpredictable motor tasks.

Picking up an empty milk carton that we believe to be full is a familiar example of adaptive control, because the adaptation process of estimating the carton's weight must proceed simultaneously with the control process of moving the carton to a desired location. Here we show that the motor system initially generates highly variable behavior in such unpredictable tasks but eventually converges to stereotyped patterns of adaptive responses predicted by a simple optimality principle. These results suggest that adaptation can become specifically tuned to identify task-specific parameters in an optimal manner.