DNA translocation through an array of kinked nanopores

Synthetic solid-state nanopores are being intensively investigated as single-molecule sensors for detection and characterization of DNA, RNA, and proteins. This field has been inspired by the exquisite selectivity and flux demonstrated by natural biological channels and the dream of emulating these behaviors in more robust synthetic materials that are more readily integrated into practical devices. To date, the guided etching of polymer films, focused ion beam sculpting, and electron-beam lithography and tuning of silicon nitride membranes have emerged as three promising approaches to define synthetic solid-state pores with sub-nanometer resolution. These procedures have in common the formation of nominally cylindrical or conical pores aligned normal to the membrane surface. Here we report the formation of kinked\u27 silica nanopores, using evaporation induced self-assembly, and their further tuning and chemical derivatization using atomic layer deposition. Compared to \u27straight-through\u27 proteinaceous nanopores of comparable dimensions, kinked nanopores exhibit a factor of up to 5x reduction in translocation velocity, which has been identified as one of the critical issues in DNA sequencing. Additionally we demonstrate an efficient two-step approach to create a nanopore array exhibiting nearly perfect selectivity for ssDNA over dsDNA. We show that a coarse-grained drift-diffusion theory with a sawtooth like potential can reasonably describe the velocity and translocation time of DNA through the pore. By control of pore size, length, and shape, we capture the major functional behaviors of protein pores in our solid-state nanopore system.\u2

Robust stabilization of singular-impulsive-delayed systems with nonlinear perturbations

Many dynamic systems in physics, chemistry, biology, engineering, and information science have impulsive dynamical behaviors due to abrupt jumps at certain instants during the dynamical process, and these complex dynamic behaviors can be modeled by singular impulsive differential systems. This paper formulates and studies a model for singular impulsive delayed systems with uncertainty from nonlinear perturbations. Several fundamental issues such as global exponential robust stabilization of such systems are established. A simple approach to the design of a robust impulsive controller is then presented. A numerical example is given for illustration of the theoretical results. Meanwhile, some new results and refined properties associated with the M-matrices and time-delay dynamic systems are derived and discussed.published_or_final_versio

Study on antioxidant activity of Echinacea purpurea L. extracts and its impact on cell viability

This study investigates the antioxidant activity of Echinacea Purpurea L. (EP) extracts and its impact on cell viability. The polysaccharides content of EP was 159.8 ± 12.4 mg/g dry weight (DW), with extracts obtained by applying 55% ethanol at 55°C containing 11.0 ±1.0 mg gallic acid equivalent/g DW of total phenolic compound. Trolox equivalent antioxidant capacity, 0.1 mg/mL of EP extracts exhibited only 30% when compared to the ascorbic acid at the same concentration. Reducing power of extractsincreased linearly with its concentration and the concentration at 2.0 mg/mL reached about 65% of ascorbic acid at 0.3 mg/mL. The chelating capacity of ferrous iron (Fe2+) was 70% as good as that of thesynthetic metal chelater EDTA when added to 5.0 mg/mL of EP extracts. The DPPH scavenging capacity showed 85.1% at 0.5 mg/mL of extracts and with half-effective doses (ED50) was measured at 0.23mg/mL. The superoxide anions scavenging capacity of EP extracts was nearly equivalent to ascorbic acid (91.1% vs 93.0%) at the same concentration of 1.6 mg/mL and ED50 was 0.32 and 0.13 mg/mL, respectively. Microculture tetrazolium assays showed extracts had 92% cell viability at 1.6 mg/mL forchicken’s peripheral blood mononuclear cells (PBMCs) and 84% for RAW 264.7 macrophages, neither reaching the IC50 level. In summary, the EP extracts had antioxidant activity similar to that of ascorbic acid, but have no serious effect on inhibiting chicken’s PBMCs viability

The wavelet-NARMAX representation : a hybrid model structure combining polynomial models with multiresolution wavelet decompositions

A new hybrid model structure combing polynomial models with multiresolution wavelet decompositions is introduced for nonlinear system identification. Polynomial models play an important role in approximation theory, and have been extensively used in linear and nonlinear system identification. Wavelet decompositions, in which the basis functions have the property of localization in both time and frequency, outperform many other approximation schemes and offer a flexible solution for approximating arbitrary functions. Although wavelet representations can approximate even severe nonlinearities in a given signal very well, the advantage of these representations can be lost when wavelets are used to capture linear or low-order nonlinear behaviour in a signal. In order to sufficiently utilise the global property of polynomials and the local property of wavelet representations simultaneously, in this study polynomial models and wavelet decompositions are combined together in a parallel structure to represent nonlinear input-output systems. As a special form of the NARMAX model, this hybrid model structure will be referred to as the WAvelet-NARMAX model, or simply WANARMAX. Generally, such a WANARMAX representation for an input-output system might involve a large number of basis functions and therefore a great number of model terms. Experience reveals that only a small number of these model terms are significant to the system output. A new fast orthogonal least squares algorithm, called the matching pursuit orthogonal least squares (MPOLS) algorithm, is also introduced in this study to determine which terms should be included in the final model

A unified wavelet-based modelling framework for non-linear system identification: the WANARX model structure

A new unified modelling framework based on the superposition of additive submodels, functional components, and wavelet decompositions is proposed for non-linear system identification. A non-linear model, which is often represented using a multivariate non-linear function, is initially decomposed into a number of functional components via the wellknown analysis of variance (ANOVA) expression, which can be viewed as a special form of the NARX (non-linear autoregressive with exogenous inputs) model for representing dynamic input–output systems. By expanding each functional component using wavelet decompositions including the regular lattice frame decomposition, wavelet series and multiresolution wavelet decompositions, the multivariate non-linear model can then be converted into a linear-in-theparameters problem, which can be solved using least-squares type methods. An efficient model structure determination approach based upon a forward orthogonal least squares (OLS) algorithm, which involves a stepwise orthogonalization of the regressors and a forward selection of the relevant model terms based on the error reduction ratio (ERR), is employed to solve the linear-in-the-parameters problem in the present study. The new modelling structure is referred to as a wavelet-based ANOVA decomposition of the NARX model or simply WANARX model, and can be applied to represent high-order and high dimensional non-linear systems