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

Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record

Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem

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

Reference genome and comparative genome analysis for the WHO reference strain for Mycobacterium bovis BCG Danish, the present tuberculosis vaccine

Background: Mycobacterium bovis bacillus Calmette-Guerin (M. bovis BCG) is the only vaccine available against tuberculosis (TB). In an effort to standardize the vaccine production, three substrains, i.e. BCG Danish 1331, Tokyo 172-1 and Russia BCG-1 were established as the WHO reference strains. Both for BCG Tokyo 172-1 as Russia BCG-1, reference genomes exist, not for BCG Danish. In this study, we set out to determine the completely assembled genome sequence for BCG Danish and to establish a workflow for genome characterization of engineering-derived vaccine candidate strains.ResultsBy combining second (Illumina) and third (PacBio) generation sequencing in an integrated genome analysis workflow for BCG, we could construct the completely assembled genome sequence of BCG Danish 1331 (07/270) (and an engineered derivative that is studied as an improved vaccine candidate, a SapM KO), including the resolution of the analytically challenging long duplication regions. We report the presence of a DU1-like duplication in BCG Danish 1331, while this tandem duplication was previously thought to be exclusively restricted to BCG Pasteur. Furthermore, comparative genome analyses of publicly available data for BCG substrains showed the absence of a DU1 in certain BCG Pasteur substrains and the presence of a DU1-like duplication in some BCG China substrains. By integrating publicly available data, we provide an update to the genome features of the commonly used BCG strains. Conclusions: We demonstrate how this analysis workflow enables the resolution of genome duplications and of the genome of engineered derivatives of the BCG Danish vaccine strain. The BCG Danish WHO reference genome will serve as a reference for future engineered strains and the established workflow can be used to enhance BCG vaccine standardization

Kernel-based identification using Lebesgue-sampled data

Sampling in control applications is increasingly done non-equidistantly in time. This includes applications in motion control, networked control, resource-aware control, and event-based control. Some of these applications, like the ones where displacement is tracked using incremental encoders, are driven by signals that are only measured when their values cross fixed thresholds in the amplitude domain. This paper introduces a non-parametric estimator of the impulse response and transfer function of continuous-time systems based on such amplitude-equidistant sampling strategy, known as Lebesgue sampling. To this end, kernel methods are developed to formulate an algorithm that adequately takes into account the bounded output uncertainty between the event timestamps, which ultimately leads to more accurate models and more efficient output sampling compared to the equidistantly-sampled kernel-based approach. The efficacy of our proposed method is demonstrated through a mass–spring damper example with encoder measurements and extensive Monte Carlo simulation studies on system benchmarks.</p