Regulatory B Cells in Experimental Mouse Models of Arthritis

Regulatory B cells (Breg) have been shown to have a role in the suppression of a wide variety of immune responses, yet they are deficient or defective in autoimmune diseases such as rheumatoid arthritis. For the study of autoimmune inflammation, experimental models of arthritis have acted as a valuable tool in understanding the development of Bregs and their role in maintaining immune homeostasis. In this chapter, we will focus on the study of transitional-2 marginal zone precursor (T2-MZP) Bregs in the context of two experimental arthritis models: antigen-induced arthritis (AIA) and collagen-induced arthritis (CIA). We will specifically focus on how to induce arthritis, as well as on methods for the isolation and functional study of Bregs both in vitro and in vivo

2D Density Control of Micro-Particles using Kernel Density Estimation

We address the problem of 2D particle density control. The particles are immersed in dielectric fluid and acted upon by manipulating an electric field. The electric field is controlled by an array of electrodes and used to bring the particle density to a desired pattern using dielectrophoretic forces. We use a lumped, 2D, capacitive-based, nonlinear model describing the motion of a particle. The spatial dependency of the capacitances is estimated using electrostatic COMSOL simulations. We formulate an optimal control problem, where the loss function is defined in terms of the error between the particle density at some final time and a target density. We use a kernel density estimator (KDE) as a proxy for the true particle density. The KDE is computed using the particle positions that are changed by varying the electrode potentials. We showcase our approach through numerical simulations, where we demonstrate how the particle positions and the electrode potentials vary when shaping the particle positions from a uniform to a Gaussian distribution

2.5 V 35 dBm IIP3 75 MHz sixth-order active RC filter

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L'arithmétique sur le tas

National audienceOn appelle un tas de bits une somme non évaluée de variable binaires, chacune pondérée par une puissance de 2. Par exemple, tous les polynômes à plusieurs variables peuvent s'exprimer comme un tas dont chaque variable est un ET logique des bits d'entrée. Cette représentation est pertinente car elle exprime le parallélisme au niveau du bit. La littérature sur les multiplieurs binaires montre comment construire des architectures efficaces qui calculent la valeurd'un tas de bits. Le présent article montre l'intérêt de revisiter un certain nombre d'opérateurs arithmétiques composés pour les exprimer comme des tas de bits

AI Enhanced Control Engineering Methods

AI and machine learning based approaches are becoming ubiquitous in almost all engineering fields. Control engineering cannot escape this trend. In this paper, we explore how AI tools can be useful in control applications. The core tool we focus on is automatic differentiation. Two immediate applications are linearization of system dynamics for local stability analysis or for state estimation using Kalman filters. We also explore other usages such as conversion of differential algebraic equations to ordinary differential equations for control design. In addition, we explore the use of machine learning models for global parameterizations of state vectors and control inputs in model predictive control applications. For each considered use case, we give examples and results

An optimization-based approach to automated design

We propose a model-based, automated, bottom-up approach for design, which is applicable to various physical domains, but in this work we focus on the electrical domain. This bottom-up approach is based on a meta-topology in which each link is described by a universal component that can be instantiated as basic components (e.g., resistors, capacitors) or combinations of basic components via discrete switches. To address the combinatorial explosion often present in mixed-integer optimization problems, we present two algorithms. In the first algorithm, we convert the discrete switches into continuous switches that are physically realizable and formulate a parameter optimization problem that learns the component and switch parameters while inducing design sparsity through an $L_1$ regularization term. The second algorithm uses a genetic-like approach with selection and mutation steps guided by ranking of requirements costs, combined with continuous optimization for generating optimal parameters. We improve the time complexity of the optimization problem in both algorithms by reconstructing the model when components become redundant and by simplifying topologies through collapsing components and removing disconnected ones. To demonstrate the efficacy of these algorithms, we apply them to the design of various electrical circuits

On Balance: Intelligence Democratization in Post-Franco Spain

The article of record as published may be found at http://dx.doi.org/10.1080/08850607.2018.146658