Maltose-binding protein is a potential carrier for oral immunizations

In humans and most animal species such as pigs, vaccination via the oral route is a prerequisite for induction of a protective immunity against enteropathogens. Hereto, live attenuated microorganisms can be used. However, these microorganisms often are either too attenuated to induce sufficient intestinal immunity or are still too virulent resulting in clinical signs. We previously demonstrated that it is possible to induce immunity against enteropathogens by targeting antigen towards enterocytes. Maltose-binding protein (MBP) is part of the maltose/maltodextrin system of Escherichia coli. MBP is a relatively small protein (42.5 kDa) approximately 3 × 4 × 6.5 nm in size with surface residues capable of both hydrogen bonding interactions and hydrophobic interactions. Recombinant proteins are often fused to MPB to improve their yield and to increase their solubility. In mice, these fusion proteins showed an enhanced immunogenicity following systemic immunization. More recently, this has been attributed to interaction of MBP with TLR4 on dendritic cells (DCs). TLR4 is also expressed in the enterocytes of the gut. Therefore, we examined if oral administration of MPB-FedF to 4-week-old pigs could be used to induce an immune response against F18+ verotoxigenic E. coli in pigs. Also we examined if the oral administration of MBP to pigs is able to induce an immune response. In both experiments cholera toxin was used as oral adjuvant

Parameter reduction in nonlinear state-space identification of hysteresis

Hysteresis is a highly nonlinear phenomenon, showing up in a wide variety of science and engineering problems. The identification of hysteretic systems from input-output data is a challenging task. Recent work on black-box polynomial nonlinear state-space modeling for hysteresis identification has provided promising results, but struggles with a large number of parameters due to the use of multivariate polynomials. This drawback is tackled in the current paper by applying a decoupling approach that results in a more parsimonious representation involving univariate polynomials. This work is carried out numerically on input-output data generated by a Bouc-Wen hysteretic model and follows up on earlier work of the authors. The current article discusses the polynomial decoupling approach and explores the selection of the number of univariate polynomials with the polynomial degree, as well as the connections with neural network modeling. We have found that the presented decoupling approach is able to reduce the number of parameters of the full nonlinear model up to about 50\%, while maintaining a comparable output error level.Comment: 24 pages, 8 figure

On the smoothness of nonlinear system identification

We shed new light on the \textit{smoothness} of optimization problems arising in prediction error parameter estimation of linear and nonlinear systems. We show that for regions of the parameter space where the model is not contractive, the Lipschitz constant and $\beta$-smoothness of the objective function might blow up exponentially with the simulation length, making it hard to numerically find minima within those regions or, even, to escape from them. In addition to providing theoretical understanding of this problem, this paper also proposes the use of multiple shooting as a viable solution. The proposed method minimizes the error between a prediction model and the observed values. Rather than running the prediction model over the entire dataset, multiple shooting splits the data into smaller subsets and runs the prediction model over each subset, making the simulation length a design parameter and making it possible to solve problems that would be infeasible using a standard approach. The equivalence to the original problem is obtained by including constraints in the optimization. The new method is illustrated by estimating the parameters of nonlinear systems with chaotic or unstable behavior, as well as neural networks. We also present a comparative analysis of the proposed method with multi-step-ahead prediction error minimization

Memory-element based hysteresis:Identification and compensation of a piezoelectric actuator

Hysteresis phenomena can significantly deteriorate the performance when performing servo tasks with piezoelectric actuators. The aim of this brief is to model this nonlinear hysteresis effect and use this model to develop a feedforward controller that compensates for the hysteretic behavior. Exploiting the dual-pair concept, a connection is established between hysteresis models and general memory (MEM) elements examplified by the Ramberg–Osgood model. This facilitates both a straightforward identification procedure of a hysteresis model and a feedforward controller design. Both the identification procedure and the feedforward controller are implemented on a piezoelectric actuator indicating a performance improvement by a factor 3.5

Retrieving highly structured models starting from black-box nonlinear state-space models using polynomial decoupling

Nonlinear state-space modelling is a very powerful black-box modelling approach. However powerful, the resulting models tend to be complex, described by a large number of parameters. In many cases interpretability is preferred over complexity, making too complex models unfit or undesired. In this work, the complexity of such models is reduced by retrieving a more structured, parsimonious model from the data, without exploiting physical knowledge. Essential to the method is a translation of all multivariate nonlinear functions, typically found in nonlinear state-space models, into sets of univariate nonlinear functions. The latter is computed from a tensor decomposition. It is shown that typically an excess of degrees of freedom are used in the description of the nonlinear system whereas reduced representations can be found. The method yields highly structured state-space models where the nonlinearity is contained in as little as a single univariate function, with limited loss of performance. Results are illustrated on simulations and experiments for: the forced Duffing oscillator, the forced Van der Pol oscillator, a Bouc-Wen hysteretic system, and a Li-Ion battery model.Comment: submitted to Mechanical Systems and Signal Processin

Polynomial Nonlinear State Space Identification of an Aero-Engine Structure

Most nonlinear identification problems often require prior knowledge or an initial assumption of the mathematical law (model structure) and data processing to estimate the nonlinear parameters present in a system, i.e. they require the functional form or depend on a proposition that the measured data obey a certain nonlinear function. However, obtaining prior knowledge or performing nonlinear characterisation can be difficult or impossible for certain identification problems due to the individualistic nature of practical nonlinearities. For example, joints between substructures of large aerospace design frequently feature complex physics at local regions of the structure, making a physically motivated identification in terms of nonlinear stiffness and damping impossible. As a result, black-box models which use no prior knowledge can be regarded as an effective method. This paper explores the pragmatism of a black-box approach based on Polynomial Nonlinear State Space (PNLSS) models to identify the nonlinear dynamics observed in a large aerospace component. As a first step, the Best Linear Approximation (BLA), noise and nonlinear distortion levels are estimated over different amplitudes of excitation using the Local Polynomial Method (LPM). Next, a linear state space model is estimated on the non-parametric BLA using the frequency domain subspace identification method. Nonlinear model terms are then constructed in the form of multivariate polynomials in the state variables while the parameters are estimated through a nonlinear optimisation routine. Further analyses were also conducted to determine the most suitable monomial degree and type required for the nonlinear identification procedure. Practical application is carried out on an Aero-Engine casing assembly with multiple joints, while model estimation and validation is achieved using measured sine-sweep and broadband data obtained from the experimental campaign