An ultrasensitive photoelectrochemical nucleic acid biosensor

A simple and ultrasensitive procedure for non-labeling detection of nucleic acids is described in this study. It is based on the photoelectrochemical detection of target nucleic acids by forming a nucleic acid/photoreporter adduct layer on an ITO electrode. The target nucleic acids were hybridized with immobilized oligonucleotide capture probes on the ITO electrode. A subsequent binding of a photoreporter—a photoactive threading bis-intercalator consisting of two N,N′-bis(3-propyl-imidazole)-1,4,5,8-naphthalene diimides (PIND) linked by a [Formula: see text] (bpy = 2,2′-bipyridine) complex (PIND–Ru–PIND)—allowed for photoelectrochemical detection of the target nucleic acids. The extremely low dissociation rate of the adduct and the highly reversible photoelectrochemical response under visible light illumination (490 nm) make it possible to conduct nucleic acid detection, with a sensitivity enhancement of four orders of magnitude over voltammetry. These results demonstrate for the first time the potential of photoelectrochemical biosensors for PCR-free ultrasensitive detection of nucleic acids

Nonlinear dynamics of sand banks and sand waves

Sand banks and sand waves are two types of sand structures that are commonly observed on an off-shore sea bed. We describe the formation of these features using the equations of the fluid motion coupled with the mass conservation law for the sediment transport. The bottom features are a result of an instability due to tide–bottom interactions. There are at least two mechanisms responsible for the growth of sand banks and sand waves. One is linear instability, and the other is nonlinear coupling between long sand banks and short sand waves. One novel feature of this work is the suggestion that the latter is more important for the generation of sand banks. We derive nonlinear amplitude equations governing the coupled dynamics of sand waves and sand banks. Based on these equations, we estimate characteristic features for sand banks and find that the estimates are consistent with measurements

Influence of pH and type of myrosinase complex on the products obtained in the myrosinase catalysed hydrolysis of glucosinolates – a MECC study

Environmental conditions, e.g. pH and the presence of Fe2+ are well known factors that influence the product profile of the myrosinase catalysed hydrolysis of glucosinolates. Depending on the plant genera, the species and tissue of origin myrosinase isoenzymes (thioglucohydrolase EC 3.2.1.147) have different characteristics in terms of MW, subunit composition and pI. However, the influence of these parameters on the outcome of glucosinolate hydrolysis has not been traditionally studied, which hinders the full exploitation of the catalytic potential of these enzymes. In the present experiments the effect of myrosinase type on the products obtained in the hydrolysis of glucosibarin was studied by MECC using two B. carinata myrosinase preparations differing on their affinity to the Con A material, Con A 1 (first eluting fractions) and Con A 2 (last eluting fractions). At pH 3 Con A 1 isoenzymes were more active than Con A 2 isoenzymes. At pH 5 and 6.5 Con A 1 isoenzymes produced oxazolidine-2-thione to a higher extent than Con A 2 isoenzymes. The production of nitriles by Con A 1 isoenzymes was not influenced by pH and at pH 5 and 6.5 the amount of nitrile produced by Con A 1 isoenzymes was lower than that produced by Con A 2 isoenzymes. Formation of nitriles requires the presence of two redox equivalents which leads to the release of the sulphur atom from the aglucone. Isothiocyanates and nitriles differ in their bioactivity towards different targets; therefore the possibility for directing the glucosinolate hydrolysis towards the desired compound in a particular situation is of great relevance

Distributed Estimation of a Parametric Field Using Sparse Noisy Data

The problem of distributed estimation of a parametric physical field is stated as a maximum likelihood estimation problem. Sensor observations are distorted by additive white Gaussian noise. Prior to data transmission, each sensor quantizes its observation to $M$ levels. The quantized data are then communicated over parallel additive white Gaussian channels to a fusion center for a joint estimation. An iterative expectation-maximization (EM) algorithm to estimate the unknown parameter is formulated, and its linearized version is adopted for numerical analysis. The numerical examples are provided for the case of the field modeled as a Gaussian bell. The dependence of the integrated mean-square error on the number of quantization levels, the number of sensors in the network and the SNR in observation and transmission channels is analyzed.Comment: to appear at Milcom-201

Limited-Feedback-Based Channel-Aware Power Allocation for Linear Distributed Estimation

This paper investigates the problem of distributed best linear unbiased estimation (BLUE) of a random parameter at the fusion center (FC) of a wireless sensor network (WSN). In particular, the application of limited-feedback strategies for the optimal power allocation in distributed estimation is studied. In order to find the BLUE estimator of the unknown parameter, the FC combines spatially distributed, linearly processed, noisy observations of local sensors received through orthogonal channels corrupted by fading and additive Gaussian noise. Most optimal power-allocation schemes proposed in the literature require the feedback of the exact instantaneous channel state information from the FC to local sensors. This paper proposes a limited-feedback strategy in which the FC designs an optimal codebook containing the optimal power-allocation vectors, in an iterative offline process, based on the generalized Lloyd algorithm with modified distortion functions. Upon observing a realization of the channel vector, the FC finds the closest codeword to its corresponding optimal power-allocation vector and broadcasts the index of the codeword. Each sensor will then transmit its analog observations using its optimal quantized amplification gain. This approach eliminates the requirement for infinite-rate digital feedback links and is scalable, especially in large WSNs.Comment: 5 Pages, 3 Figures, 1 Algorithm, Forty Seventh Annual Asilomar Conference on Signals, Systems, and Computers (ASILOMAR 2013

Effects of Spatial Randomness on Locating a Point Source with Distributed Sensors

Most studies that consider the problem of estimating the location of a point source in wireless sensor networks assume that the source location is estimated by a set of spatially distributed sensors, whose locations are fixed. Motivated by the fact that the observation quality and performance of the localization algorithm depend on the location of the sensors, which could be randomly distributed, this paper investigates the performance of a recently proposed energy-based source-localization algorithm under the assumption that the sensors are positioned according to a uniform clustering process. Practical considerations such as the existence and size of the exclusion zones around each sensor and the source will be studied. By introducing a novel performance measure called the estimation outage, it will be shown how parameters related to the network geometry such as the distance between the source and the closest sensor to it as well as the number of sensors within a region surrounding the source affect the localization performance.Comment: 7 Pages, 5 Figures, To appear at the 2014 IEEE International Conference on Communications (ICC'14) Workshop on Advances in Network Localization and Navigation (ANLN), Invited Pape

Power Allocation for Distributed BLUE Estimation with Full and Limited Feedback of CSI

This paper investigates the problem of adaptive power allocation for distributed best linear unbiased estimation (BLUE) of a random parameter at the fusion center (FC) of a wireless sensor network (WSN). An optimal power-allocation scheme is proposed that minimizes the $L^2$-norm of the vector of local transmit powers, given a maximum variance for the BLUE estimator. This scheme results in the increased lifetime of the WSN compared to similar approaches that are based on the minimization of the sum of the local transmit powers. The limitation of the proposed optimal power-allocation scheme is that it requires the feedback of the instantaneous channel state information (CSI) from the FC to local sensors, which is not practical in most applications of large-scale WSNs. In this paper, a limited-feedback strategy is proposed that eliminates this requirement by designing an optimal codebook for the FC using the generalized Lloyd algorithm with modified distortion metrics. Each sensor amplifies its analog noisy observation using a quantized version of its optimal amplification gain, which is received by the FC and used to estimate the unknown parameter.Comment: 6 pages, 3 figures, to appear at the IEEE Military Communications Conference (MILCOM) 201