Min Du

A Hybrid EKF and Switching PSO Algorithm for Joint State and Parameter Estimation of Lateral Flow Immunoassay Models

In this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.

Inference of Nonlinear State-Space Models for Sandwich-Type Lateral Flow Immunoassay Using Extended Kalman Filtering

In this paper, a mathematical model for sandwich-type lateral flow immunoassay is developed via short available time series. A nonlinear dynamic stochastic model is considered that consists of the biochemical reaction system equations and the observation equation. After specifying the model structure, we apply the extended Kalman filter (EKF) algorithm for identifying both the states and parameters of the nonlinear state-space model. It is shown that the EKF algorithm can accurately identify the parameters and also predict the system states in the nonlinear dynamic stochastic model through an iterative procedure by using a small number of observations. The identified mathematical model provides a powerful tool for testing the system hypotheses and also for inspecting the effects from various design parameters in both rapid and inexpensive way. Furthermore, by means of the established model, the dynamic changes in the concentration of antigens and antibodies can be predicted, thereby making it possible for us to analyze, optimize, and design the properties of lateral flow immunoassay devices.

On multistability of delayed genetic regulatory networks with multivariable regulation functions

Many genetic regulatory networks (GRNs) have the capacity to reach different stable states. This capacity is defined as multistability which is an important regulation mechanism. Multiple time delays and multivariable regulation functions are usually inevitable in such GRNs. In this paper, multistability of GRNs is analyzed by applying the control theory and mathematical tools. This study is to provide a theoretical tool to facilitate the design of synthetic gene circuit with multistability in the perspective of control theory. By transforming such GRNs into a new and uniform mathematical formulation, we put forward a general sector-like regulation function that is capable of quantifying the regulation effects in a more precise way. By resorting to up-to-date techniques, a novel Lyapunov-Krasovskii functional (LKF) is introduced for achieving delay dependence to ensure less conservatism. New conditions are then proposed to ensure the multistability of a GRN in the form of linear matrix inequalities (LMIs) that are dependent on the delays. Our multistability conditions are applicable to several frequently used regulation functions especially the multivariable ones. Two examples are employed to illustrate the applicability and usefulness of the developed theoretical results.

Image-Based Quantitative Analysis of Gold Immunochromatographic Strip via Cellular Neural Network Approach

Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time.

A novel switching local evolutionary PSO for quantitative analysis of lateral flow immunoassay

This paper presents a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for quantification analysis of the lateral flow immunoassay (LFIA), which represents the first attempt to estimate the concentration of target analyte based on the well-established state-space model. A new switching local evolutionary PSO (SLEPSO) is developed and analyzed. The velocity updating equation jumps from one mode to another based on the non-homogeneous Markov chain, where the probability transition matrix is updated by calculating the diversity and current optimal solution. Furthermore, DE mutation and crossover operations are implemented to improve local best particles searching in PSO. Compared with some well-known PSO algorithms, the experiments results show the superiority of proposed SLEPSO. Finally, the new SLEPSO is successfully exploited to quantification analysis of the LFIA system, which is essentially nonlinear and dynamic. Therefore, this can provide a new method for the area of quantitative interpretation of LFIA system.

Identification of Nonlinear Lateral Flow Immunoassay State-Space Models via Particle Filter Approach

In this paper, the particle filtering approach is used, together with the kernel smoothing method, to identify the state-space model for the lateral flow immunoassay through available but short time-series measurement. The lateral flow immunoassay model is viewed as a nonlinear dynamic stochastic model consisting of the equations for the biochemical reaction system as well as the measurement output. The renowned extended Kalman filter is chosen as the importance density of the particle filter for the purpose of modeling the nonlinear lateral flow immunoassay. By using the developed particle filter, both the states and parameters of the nonlinear state-space model can be identified simultaneously. The identified model is of fundamental significance for the development of lateral flow immunoassay quantification. It is shown that the proposed particle filtering approach works well for modeling the lateral flow immunoassay.

Time Series Modeling of Nano-Gold Immunochromatographic Assay via Expectation Maximization Algorithm

In this paper, the expectation maximization (EM) algorithm is applied to the modeling of the nano-gold immunochromatographic assay (nano-GICA) via available time series of the measured signal intensities of the test and control lines. The model for the nano-GICA is developed as the stochastic dynamic model that consists of a first-order autoregressive stochastic dynamic process and a noisy measurement. By using the EM algorithm, the model parameters, the actual signal intensities of the test and control lines, as well as the noise intensity can be identified simultaneously. Three different time series data sets concerning the target concentrations are employed to demonstrate the effectiveness of the introduced algorithm. Several indices are also proposed to evaluate the inferred models. It is shown that the model fits the data very well.

A novel neural network approach to cDNA microarray image segmentation

Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix(®) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.

Stability analysis of standard genetic regulatory networks with time-varying delays and stochastic perturbations

In this paper, the stability analysis problem is investigated for a standard class of genetic regulatory networks (GRNs) with stochastic disturbances and time-varying delays. The standard GRNs under consideration are based on the model of the recurrent neural networks, the stochastic perturbation is in the form of a scalar Brownian motion, and the time-varying delays exist in the transcription and translation processes. Specifically, we are interested in (1) establishing a standard model for the time-varying delayed GRNs and (2) establishing conditions under which the standard GRNs are exponentially mean-square stable in the presence of time delays and stochastic disturbances. By using the linear matrix inequality (LMI) technique and S-procedure, sufficient conditions are first derived for ensuring globally asymptotic and exponential stability that can be easily solved by using standard software packages. Two numerical examples are exploited to demonstrate the effectiveness of the proposed method.

A Novel Image Methodology for Interpretation of Gold Immunochromatographic Strip

Gold immunochromatographic strip assay is a rapid, simple, single-copy and on-site method. Quantitative Interpretation of the strip can provide more information than the traditional qualitative or semiquantitative strip assay. The paper aims to develop an image based assay method for quantitative determination of trace concentrations by gold immunochromatographic strip. The image of gold immunochromatographic strip is taken by CCD, and, after the proper filter and window cutting, the test line and control line is segmented by the genetic fast fuzzy c-means(FCM) clustering algorithm. In order to improve the measure property, based on Lambert-beer law, the relative reflective integral optical density(RIOD) is selected as the feature by which the interference in the test and control lines can be canceled out each other. The proposed method is applied to the quantitative detection of human chorionic gonadotropin (hCG) as a model. Firstly, the segmentation performance of the genetic fast FCM clustering algorithm is compared with threshold method and FCM clustering algorithm in terms of the peak signal-to-noise ratio (PSNR). Furthermore, the comparison of the blind experiment between the proposed method and commercial quantitative instrument swp-sc1 is carried out. This method is shown to deliver a result comparable and even superior to existing techniques.