IL-33 Enhances IFNγ and TNFα Production by Human MAIT Cells: A New Pro-Th1 Effect of IL-33

Mucosal-associated invariant T (MAIT) cells represent a distinct T cell population restricted by the MHC-class-I-related molecule, MR1, which recognizes microbial-derived vitamin B2 (riboflavin) metabolites. Their abundance in humans, together with their ability to promptly produce distinct cytokines including interferon γ (IFNγ) and tumor necrosis factor α (TNFα), are consistent with regulatory functions in innate as well as adaptive immunity. Here, we tested whether the alarmin interleukin 33 (IL-33), which is secreted following inflammation or cell damage, could activate human MAIT cells. We found that MAIT cells stimulated with IL-33 produced high levels of IFNγ, TNFα and Granzyme B (GrzB). The action of IL-33 required IL-12 but was independent of T cell receptor (TCR) cross-linking. MAIT cells expressed the IL-33 receptor ST2 (suppression of tumorigenicity 2) and upregulated Tbet (T-box expressed in T cells) in response to IL-12 or IL-33. Electronically sorted MAIT cells also upregulated the expression of CCL3 (Chemokine C-C motif ligand 3), CD40L (CD40 Ligand), CSF-1 (Colony Stimulating Factor 1), LTA (Lymphotoxin-alpha) and IL-2RA (IL-2 receptor alpha chain) mRNAs in response to IL-33 plus IL-12. In conclusion, IL-33 combined with IL-12 can directly target MAIT cells to induce their activation and cytokine production. This novel mechanism of IL-33 activation provides insight into the mode of action by which human MAIT cells can promote inflammatory responses in a TCR-independent manner.     

Characterization of boride coatings on a ductile cast iron

In this work, the EN-GJS-400-15 cast iron was pack-borided in a powder mixture composed of 5% B₄C, 5% NaBF₄ and 90% SiC at the three temperatures: 900, 950 and 1000°C for 2, 4 and 6 h, respectively. The pack-borided EN-GJS-400-15 cast iron was characterized by the following experimental techniques: optical microscopy, XRD analysis and Microhardness Vickers tester. The growth kinetics of boride layers was also investigated. As a consequence, the boron activation energy was found to be 212.28 kJ mol^–1 for the EN-GJS-400-15 cast iron. Based on a regression model, a useful equation was derived to estimate the boride layer thickness as a function of the boriding parameters (time and temperature). A good agreement was then obtained between the predicted values of boride layers thicknesses and those measured experimentally. In addition, an iso-thickness diagram was proposed to be used as a simple tool to select the boride layers thicknesses according to the potential applications of EN-GJS-400-15 cast iron in industry.     

Phenotypic Characterization and Genotypic Subtyping of Salmonella enterica Serovars Enteritidis and Gallinarum Isolated from Human and Poultry-Related Samples

This study aimed to assess the serotypes, antibiotic susceptibility, and molecular subtyping of Salmonella enterica isolated from food products and human patients with gastroenteritis. A total of 59 isolates were investigated, and the results revealed a predominance of S. Enteritidis with 57.63% (34/59) S. Gallinarum held second with 15.62% (5/32) of total food borne. While, isolates from humans showed 18.51% (5/27) of S. Typhimurium. High level of resistance to nalidixic acid was noted among food strains and 35.29% of human isolates were found to be multidrug resistant. Eight representative isolates were subtyping using three molecular approaches, ribotyping, Multilocus sequence typing (MLST) and Multiple-locus variable number tandem repeat analysis (MLVA). MLST showed three sequence types corresponding to two clonal complexes, (ST-78, CC-4) for S. Gallinarum, (ST-11, CC-4) for S. Enteritidis and (S-367, CC-37) for S. Cerro. While, MLVA generated six different profiles targeting nine loci for S. Enteritidis and S. Gallinarum.     

Wavelet network for recognition system of Arabic word

Focusing on the development of new technologies of information, research in the speech communication field is an activity in full expansion. Several disciplines and skills interact in order to improve performance of Human Machine Communication Systems (HMC). In order to increase the performance of these systems, various techniques, including Hidden Markov Models (HMM) and Neural Network (NN), are implemented.     

A passivity‐based simple adaptive synergetic control for a class of nonlinear systems

For a class of nonlinear systems with the representation {A(x), B(x), C} and where the system parameters and dynamics are unknown, a simple adaptive synergetic controller ensuring the asymptotic convergence of the system to a desired manifold is proposed based on the technique of simple adaptive control (SAC). It is well known that the design of the synergetic control (SC) law requires a thorough knowledge of the system parameters and dynamics. Such problem obstructs the synthesis of the SC law and the designer is prompted to pass through the estimation methods, which, in turn, poses a problem of increasing the computation time of the control algorithm. To cope with this problem, a solution is proposed by modifying the original SC law to develop an SAC‐like adaptive SC law without the need of prior knowledge of the system. The stability of the proposed adaptive controller is formally proven via the Lyapunov approach. Experimental application to a quadrotor system is given to validate the theoretical results.     

A meta-modelling framework for knowledge consistency in collaborative design

The design of complex system requires a lot of interactions between experts and then between numerous Computer Aided X software (CAX) (where X can be Design (CAD), Engineering (CAE), Manufacturing (CAM), etc.). In order to improve the consistency of the whole system design and the related data and information, knowledge crossing the expertises must be tracked and formalized regarding a shared reference. That means that instead of defining a large reference models to which each expert refers to, a light collaborative model is defined enabling to connect data from each expert model to adhoc data from other expert models, following the least commitment principle. In this topic, a new meta-model is proposed in a Model-Driven Engineering approach to manage the integration of heterogeneous experts’ knowledge models in a collaborative process. The structure of the proposed knowledge meta-model is defined taking into account the complexity of knowledge definition and the properties of its components. This meta-model is split in a meta-model of data on one hand and a Collaboration Meta-Model in the other hand, to represent the distinction between the core concepts of knowledge and additional elements serving to represent the relation between these concepts, and between concepts of heterogeneous experts’ models. The proposed meta-model is illustrated on an industrial case study to highlight the way to put it in use, and its interests to enable collaboration between experts throughout the design process.     

QRS detection based on wavelet coefficients

Electrocardiogram (ECG) signal processing and analysis provide crucial information about functional status of the heart. The QRS complex represents the most important component within the ECG signal. Its detection is the first step of all kinds of automatic feature extraction. QRS detector must be able to detect a large number of different QRS morphologies. This paper examines the use of wavelet detail coefficients for the accurate detection of different QRS morphologies in ECG. Our method is based on the power spectrum of QRS complexes in different energy levels since it differs from normal beats to abnormal ones. This property is used to discriminate between true beats (normal and abnormal) and false beats. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database (MITDB). The obtained results show a sensitivity of 99.64% and a positive predictivity of 99.82%.     

Fault diagnosis using partial syndromes: a modified Hopfield neural network approach

This paper presents a modified Hopfield neural network (HNN) for solving the system-level fault diagnosis problem which aims at identifying the set of faulty nodes. This problem has been extensively studied in the last three decades. Nevertheless, identifying the set of all faulty nodes using only partial syndromes, i.e. when some of the testing or comparison outcomes are missing prior to initiating the diagnosis phase, remains an outstanding research issue. The new HNN-based diagnosis algorithm does not require any prior learning or knowledge about the system, nor about any faulty situation, hence providing a better generalisation performance. Results from a thorough simulation study demonstrate the effectiveness of the HNN-based fault diagnosis algorithm in terms of diagnosis correctness, diagnosis latency and diagnosis scalability, for randomly generated diagnosable systems of different sizes and under various fault scenarios. We have also conducted extensive simulations using partial syndromes. Simulations showed that the HNN-based diagnosis performed efficiently, i.e. diagnosis correctness was around 99% when at most half of the test or comparison outcomes are missing, making it a viable alternative to existing diagnosis algorithms.     

FHUQI-Miner: Fast high utility quantitative itemset mining

Applied Intelligence - High utility itemset mining is a popular pattern mining task, which aims at revealing all sets of items that yield a high profit in a transaction database. Although this task...     

Synergistic verification and validation of systems and software engineering models

In this paper, we present a unified approach for the verification and validation of software and systems engineering design models expressed in UML 2.0 and SysML 1.0. The approach is based on three well-established techniques, namely formal analysis, programme analysis and software engineering (SwE) techniques. More precisely, our contribution consists of the synergistic combination of model checking, static analysis and SwE metrics that enables the automatic and efficient assessment of design models from static and dynamic perspectives. Additionally, we present the design and implementation of an automated computer-aided assessing framework integrating the proposed approach. Moreover, we discuss the related technical details and the underlying synergism. Finally, we illustrate the proposed approach by assessing a design case study that is composed of state machine and sequence diagrams.