Nitrogen doping of carbon nanoelectrodes for enhanced control of DNA translocation dynamics

Controlling the dynamics of DNA translocation is a central issue in the emerging nanopore-based DNA sequencing. To address the potential of heteroatom doping of carbon nanostructures to achieve this goal, herein we carry out atomistic molecular dynamics simulations for single-stranded DNAs translocating between two pristine or doped carbon nanotube (CNT) electrodes. Specifically, we consider the substitutional nitrogen doping of capped CNT (capCNT) electrodes and perform two types of molecular dynamics simulations for the entrapped and translocating single-stranded DNAs. We find that the substitutional nitrogen doping of capCNTs stabilizes the edge-on nucleobase configurations rather than the original face-on ones and slows down the DNA translocation speed by establishing hydrogen bonds between the N dopant atoms and nucleobases. Due to the enhanced interactions between DNAs and N-doped capCNTs, the duration time of nucleobases within the nanogap was extended by up to ~ 290 % and the fluctuation of the nucleobases was reduced by up to ~ 70 %. Given the possibility to be combined with extrinsic light or gate voltage modulation methods, the current work demonstrates that the substitutional nitrogen doping is a promising direction for the control of DNA translocation dynamics through a nanopore or nanogap based of carbon nanomaterials.Comment: 11 pages, 4 figure

Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework

We propose a Bayesian evidence framework to facilitate transfer learning from pre-trained deep convolutional neural networks (CNNs). Our framework is formulated on top of a least squares SVM (LS-SVM) classifier, which is simple and fast in both training and testing, and achieves competitive performance in practice. The regularization parameters in LS-SVM is estimated automatically without grid search and cross-validation by maximizing evidence, which is a useful measure to select the best performing CNN out of multiple candidates for transfer learning; the evidence is optimized efficiently by employing Aitken's delta-squared process, which accelerates convergence of fixed point update. The proposed Bayesian evidence framework also provides a good solution to identify the best ensemble of heterogeneous CNNs through a greedy algorithm. Our Bayesian evidence framework for transfer learning is tested on 12 visual recognition datasets and illustrates the state-of-the-art performance consistently in terms of prediction accuracy and modeling efficiency.Comment: Appearing in CVPR-2016 (oral presentation