Structural insights into the biogenesis and biofilm formation by the Escherichia coli common pilus

Bacteria have evolved a variety of mechanisms for developing community-based biofilms. These bacterial aggregates are of clinical importance, as they are a major source of recurrent disease. Bacterial surface fibers (pili) permit adherence to biotic and abiotic substrates, often in a highly specific manner. The Escherichia coli common pilus (ECP) represents a remarkable family of extracellular fibers that are associated with both disease-causing and commensal strains. ECP plays a dual role in early-stage biofilm development and host cell recognition. Despite being the most common fimbrial structure, relatively little is known regarding its biogenesis, architecture, and function. Here we report atomic-resolution insight into the biogenesis and architecture of ECP. We also derive a structural model for entwined ECP fibers that not only illuminates interbacteria communication during biofilm formation but also provides a useful foundation for the design of novel nanofibers.     

HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ‘traditional’ set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.