Magnetic Propulsion of Microswimmers with DNA-Based Flagellar Bundles.

We show that DNA-based self-assembly can serve as a general and flexible tool to construct artificial flagella of several micrometers in length and only tens of nanometers in diameter. By attaching the DNA flagella to biocompatible magnetic microparticles, we provide a proof of concept demonstration of hybrid structures that, when rotated in an external magnetic field, propel by means of a flagellar bundle, similar to self-propelling peritrichous bacteria. Our theoretical analysis predicts that flagellar bundles that possess a length-dependent bending stiffness should exhibit a superior swimming speed compared to swimmers with a single appendage. The DNA self-assembly method permits the realization of these improved flagellar bundles in good agreement with our quantitative model. DNA flagella with well-controlled shape could fundamentally increase the functionality of fully biocompatible nanorobots and extend the scope and complexity of active materials.

Inference with minimal Gibbs free energy in information field theory.

Non-linear and non-gaussian signal inference problems are difficult to tackle. Renormalization techniques permit us to construct good estimators for the posterior signal mean within information field theory (IFT), but the approximations and assumptions made are not very obvious. Here we introduce the simple concept of minimal Gibbs free energy to IFT, and show that previous renormalization results emerge naturally. They can be understood as being the gaussian approximation to the full posterior probability, which has maximal cross information with it. We derive optimized estimators for three applications, to illustrate the usage of the framework: (i) reconstruction of a log-normal signal from poissonian data with background counts and point spread function, as it is needed for gamma ray astronomy and for cosmography using photometric galaxy redshifts, (ii) inference of a gaussian signal with unknown spectrum, and (iii) inference of a poissonian log-normal signal with unknown spectrum, the combination of (i) and (ii). Finally we explain how gaussian knowledge states constructed by the minimal Gibbs free energy principle at different temperatures can be combined into a more accurate surrogate of the non-gaussian posterior.