21,895 research outputs found
Identification of nonlinear lateral flow immunoassay state-space models via particle filter approach
This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEIn 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.This work was supported in part by the International Science and Technology
Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant
2009I0016
A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models
This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEEIn 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.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant
2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant
2009I0016
Inference of nonlinear state-space models for sandwich-type lateral flow immunoassay using extended Kalman filtering
Copyright [2011] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected].
By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, a mathematical model for sandwichtype 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 extend 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 inspecting the effects from various design parameters in a both rapid and inexpensive way. Furthermore, by means of the established model, the dynamic changes of 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.This work was supported in part by the International Science and Technology
Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of Fujian Province of China under Grants 2009J01280 and 2009J01281
General covariant geometric momentum, gauge potential and a Dirac fermion on a two-dimensional sphere
For a particle that is constrained on an ()-dimensional ()
curved surface, the Cartesian components of its momentum in -dimensional
flat space is believed to offer a proper form of momentum for the particle on
the surface, which is called the geometric momentum as it depends on the mean
curvature. Once the momentum is made general covariance, the spin connection
part can be interpreted as a gauge potential. The present study consists in two
parts, the first is a discussion of the general framework for the general
covariant geometric momentum. The second is devoted to a study of a Dirac
fermion on a two-dimensional sphere and we show that there is the generalized
total angular momentum whose three cartesian components form the
algebra, obtained before by consideration of dynamics of the particle, and we
demonstrate that there is no curvature-induced geometric potential for the
fermion.Comment: 8 pages, no figure. Presentation improve
Testing mechanisms of compensatory fitness of dioecy in a cosexual world
Questions: All else being equal, populations of dioecious species with a 50:50 sex ratio have only half the effective reproductive population size of bisexual species of equal abundance. Consequently, there is a need to explain how dioecious and bisexual species coexist. Increased mean individual seed mass, fecundity, and population density have all been proposed as attributes of unisexual individuals or populations that may contribute to the persistence or resilience of dioecious species. To date, no studies have compared sympatric dioecious and cosexual species with respect to all three components of fitness. In this study, we sought evidence for these compensatory advantages (higher seed mass, greater seed production per unit basal area, and higher population density) in dioecious species. Location: Five 20–25 ha forest dynamic plots spanning a latitudinal gradient in China, including two temperate, two subtropical, and one tropical forest. Methods: We used a phylogenetically corrected generalized linear modelling approach to assess the phylogenetic dependence and joint evolution of sexual system, seed mass and production, and ecological abundances among 48–333 species and 32,568–136,237 individuals per forest. Results: Across all five forests, we detected no consistent advantage for dioecious relative to sympatric cosexual species with respect to mean individual seed mass, seed production or the density of stems in any size class. Conclusions: Our study suggests that seed traits may provide compensatory mechanisms in some forests, but most often the coexistence of sexual systems cannot be explained by advantages of dioecy related to seed quality and demographic parameters. Future investigations of the factors that promote coexistence may increase our understanding by expanding the search to include attributes such as lifespan and tolerance or resistance to herbivores
A multi-view approach to cDNA micro-array analysis
The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research
Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences
under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China
under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany
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