Generation of Reactive Oxygen Metabolites by the Varicose Vein Wall

AbstractObjectives: to evaluate the content of lipid peroxidation products (expressed by the concentration of thiobarbituric acid-reactive substances; TBARS), the content of myeloperoxidase (MPO), and the localisation of xanthine oxidase (XO) in varicose veins (vv), varicose veins with superficial thrombophlebitis and unchanged saphenous veins.Methods: varicose saphenous veins, varicose veins with superficial thrombophlebitis and normal saphenous veins obtained during varicose vein surgery on 36 patients as well as healthy saphenous veins from cadaver organ donors (control). Homogenates were prepared in which TBARS concentration and MPO content were determined. Immunohistochemical staining to detect XO was also performed.Results: the highest concentration of TBARS occurred in vv with superficial thrombophlebitis, the lowest in donor vein. The highest content of MPO was observed in vv and slightly lower – in varicose veins with thrombophlebitis. A positive reaction for XO was seen in vv wall endothelium. Specimens of vv with thrombophlebitis revealed strong, intense staining in endothelium as well as in vasa vasorum.Conclusions: varicose veins, especially those complicated with superficial thrombophlebitis revealed increased free radical generation. Its sources might be neutrophils, and in vv complicated with superficial thrombophlebitis–xanthine oxidase

Four-field finite element solver and sensitivities for quasi-Newtonian flows

International audienceA computationally efficient finite element algorithm for power law fluid is elaborated in view of extensive direct and inverse simulations. We adopt a splitting technique to simplify the nonlinear structure of the fluids equations and derive a four-field saddle point formulation for which we prove the existence of a solution. The resolution of the corresponding variational inequalities is based on an augmented Lagrangian method and a mixed finite element discretization. The resulting iterative solver reveals to be fast and robust with low memory consumption. The time-saving provided by the algorithm compared to the standard algorithms of fixed point and Newton increases with the number of degrees of freedom and the nonlinearity of the problem. It is therefore well-suited for the solution of large problems with a great number of elements and for corresponding adjoint-based computations. Bidimensional numerical experiments are performed on two realistic situations of gravity flows: an experimental viscoplastic steady wave and a continental glacier. In the present study, results emphasize that for both cases, the modeling at bottom plays a strongly dominant role. Using surface velocitiy observations, the sensitivity analysis with respect to a spatially varying power-law exponent highlights the importance of an accurate knowledge of the rheology at high shear rate. The one on the basal sliding allows to detect the presence of a short wavelength (two times the thickness) free-slip area indetectable from surface velocities

Control of distributed parameter systems modelled by parabolic variational inequalities of the obstacle type

We will investigate the numerical solution of the control problem modelled by parabolic variational inequalities. The general point of view adopted in this work has its roots in the work by R. Glowinski. The optimal control of parabolic variational inequalities is a hot topic in the control of distributed parameter system, since the classical optimality conditions such as KKT conditions do not apply and tools from non-smooth analysis have to be used. We demonstrate the simple approach to address optimal control of parabolic variational inequalities. First, we will introduce the model and describe the solution method. In Section 4 and 5, we will discuss the discretization of the model problem and then a conjugate gradient algorithm for solving the problem numerically. Finally we will present numerical results of optimal control problem related to variational inequality

Constrained optimization in seismic reflection tomography: a Gauss-Newton augmented Lagrangian approach

International audienceS U M M A R Y Seismic reflection tomography is a method for determining a subsurface velocity model from the traveltimes of seismic waves reflecting on geological interfaces. From an optimization viewpoint , the problem consists in minimizing a non-linear least-squares function measuring the mismatch between observed traveltimes and those calculated by ray tracing in this model. The introduction of a priori information on the model is crucial to reduce the under-determination. The contribution of this paper is to introduce a technique able to take into account geological a priori information in the reflection tomography problem expressed as inequality constraints in the optimization problem. This technique is based on a Gauss-Newton (GN) sequential quadratic programming approach. At each GN step, a solution to a convex quadratic optimization problem subject to linear constraints is computed thanks to an augmented Lagrangian algorithm. Our choice for this optimization method is motivated and its original aspects are described. First applications on real data sets are presented to illustrate the potential of the approach in practical use of reflection tomography

Fast and Accurate Modeling of Transient-State Gradient-Spoiled Sequences by Recurrent Neural Networks

Fast and accurate modeling of MR signal responses are typically required for various quantitative MRI applications, such as MR Fingerprinting and MR-STAT. This work uses a new EPG-Bloch model for accurate simulation of transient-state gradient-spoiled MR sequences, and proposes a Recurrent Neural Network (RNN) as a fast surrogate of the EPG-Bloch model for computing large-scale MR signals and derivatives. The computational efficiency of the RNN model is demonstrated by comparing with other existing models, showing one to three orders of acceleration comparing to the latest GPU-accelerated open-source EPG package. By using numerical and in-vivo brain data, two use cases, namely MRF dictionary generation and optimal experimental design, are also provided. Results show that the RNN surrogate model can be efficiently used for computing large-scale dictionaries of transient-states signals and derivatives within tens of seconds, resulting in several orders of magnitude acceleration with respect to state-of-the-art implementations. The practical application of transient-states quantitative techniques can therefore be substantially facilitated.Comment: Correct for typo error

A new approach to hyperbolic inverse problems

We present a modification of the BC-method in the inverse hyperbolic problems. The main novelty is the study of the restrictions of the solutions to the characteristic surfaces instead of the fixed time hyperplanes. The main result is that the time-dependent Dirichlet-to-Neumann operator prescribed on a part of the boundary uniquely determines the coefficients of the self-adjoint hyperbolic operator up to a diffeomorphism and a gauge transformation. In this paper we prove the crucial local step. The global step of the proof will be presented in the forthcoming paper.Comment: We corrected the proof of the main Lemma 2.1 by assuming that potentials A(x),V(x) are real value