Corrigendum: Rapid identification of intact bacterial resistance plasmids via optical mapping of single DNA molecules.

This corrects the article DOI: 10.1038/srep30410.

Rapid identification of intact bacterial resistance plasmids via optical mapping of single DNA molecules.

The rapid spread of antibiotic resistance - currently one of the greatest threats to human health according to WHO - is to a large extent enabled by plasmid-mediated horizontal transfer of resistance genes. Rapid identification and characterization of plasmids is thus important both for individual clinical outcomes and for epidemiological monitoring of antibiotic resistance. Toward this aim, we have developed an optical DNA mapping procedure where individual intact plasmids are elongated within nanofluidic channels and visualized through fluorescence microscopy, yielding barcodes that reflect the underlying sequence. The assay rapidly identifies plasmids through statistical comparisons with barcodes based on publicly available sequence repositories and also enables detection of structural variations. Since the assay yields holistic sequence information for individual intact plasmids, it is an ideal complement to next generation sequencing efforts which involve reassembly of sequence reads from fragmented DNA molecules. The assay should be applicable in microbiology labs around the world in applications ranging from fundamental plasmid biology to clinical epidemiology and diagnostics.

A prototype-based resonance model of rhythm categorization.

Categorization of rhythmic patterns is prevalent in musical practice, an example of this being the transcription of (possibly not strictly metrical) music into musical notation. In this article we implement a dynamical systems' model of rhythm categorization based on the resonance theory of rhythm perception developed by Large (2010). This model is used to simulate the categorical choices of participants in two experiments of Desain and Honing (2003). The model accurately replicates the experimental data. Our results support resonance theory as a viable model of rhythm perception and show that by viewing rhythm perception as a dynamical system it is possible to model central properties of rhythm categorization.