Fabian Kohler | Searcharxiv

arXiv2023

A DNA turbine powered by a transmembrane potential across a nanopore

Rotary motors play key roles in energy transduction, from macroscale windmills to nanoscale turbines such as ATP synthase in cells. Despite our capabilities to construct engines at many scales, developing functional synthetic turbines at the nanoscale has remained challenging. Here, we experimentally demonstrate rationally designed nanoscale DNA-origami turbines with three chiral blades. These DNA nanoturbines are 24-27 nm in height and diameter and can utilise transmembrane electrochemical potentials across nanopores to drive DNA bundles into sustained unidirectional rotations of up to 10 revolutions/s. The rotation direction is set by the designed chirality of the turbine. All-atom molecular dynamics simulations show how hydrodynamic flows drive this turbine. At high salt concentrations, the rotation direction of turbines with the same chirality is reversed, which is explained by a change in the anisotropy of the electrophoretic mobility. Our artificial turbines operate autonomously in physiological conditions, converting energy from naturally abundant electrochemical potentials into mechanical work. The results open new possibilities for engineering active robotics at the nanoscale.

physics.bio-ph↗

arXiv2018

Deep Learning for micro-Electrocorticographic (μECoG) Data

Machine learning can extract information from neural recordings, e.g., surface EEG, ECoG and μECoG, and therefore plays an important role in many research and clinical applications. Deep learning with artificial neural networks has recently seen increasing attention as a new approach in brain signal decoding. Here, we apply a deep learning approach using convolutional neural networks to μECoG data obtained with a wireless, chronically implanted system in an ovine animal model. Regularized linear discriminant analysis (rLDA), a filter bank component spatial pattern (FBCSP) algorithm and convolutional neural networks (ConvNets) were applied to auditory evoked responses captured by μECoG. We show that compared with rLDA and FBCSP, significantly higher decoding accuracy can be obtained by ConvNets trained in an end-to-end manner, i.e., without any predefined signal features. Deep learning thus proves a promising technique for μECoG-based brain-machine interfacing applications.

eess.SP↗