Augmented nitric oxide production and up-regulation of endothelial nitric oxide synthase during cecal ligation and perforation

Nitric oxide (NO) has been pointed out as being the main mediator involved in the hypotension and tissue injury taking place during sepsis. This study aimed to investigate the cellular mechanisms implicated in the acetylcholine (ACh)-induced relaxation detected in aortic rings isolated from rats submitted to cecal ligation and perforation (CLP group), 6 h post-CLP. The mean arterial pressure was recorded, and the concentration-effect curves for ACh were constructed for endothelium-intact aortic rings in the absence (control) or after incubation with one of the following NO synthase inhibitors: L-NAME (non-selective), L-NNA (more selective for eNOS), 7-nitroindazole (more selective for nNOS), or 1400W (selective for iNOS). The NO concentration was determined by using confocal microscopy. The protein expression of the NOS isoforms was quantified by Western blot analysis. The prostacyclin concentration was indirectly analyzed on the basis of 6-keto-prostaglandin F-1 alpha (6-keto-PGF(1 alpha)) levels measured by enzyme immunoassay. There were no differences between Sham- and CLP-operated rats in terms of the relaxation induced by acetylcholine. However, the NOS inhibitors reduced this relaxation in both groups, but this effect remained more pronounced in the CLP group as compared to the Sham group. The acetylcholine-induced NO production was higher in the rat aortic endothelial cells of the CLP group than in those of the Sham group. eNOS protein expression was larger in the CLP group, but the iNOS protein was not verified in any of the groups. The basal 6-keto-PGF(1 alpha) levels were higher in the CLP group, but the acetylcholine-stimulated levels did not increase in CLP as much as they did in the Sham group. Taken together, our results show that the augmented NO production in sepsis syndrome elicited by cecal ligation and perforation is due to eNOS up-regulation and not to iNOS. (C) 2012 Elsevier Inc. All rights reserved.

Augmented Lagrangian method with nonmonotone penalty parameters for constrained optimization

At each outer iteration of standard Augmented Lagrangian methods one tries to solve a box-constrained optimization problem with some prescribed tolerance. In the continuous world, using exact arithmetic, this subproblem is always solvable. Therefore, the possibility of finishing the subproblem resolution without satisfying the theoretical stopping conditions is not contemplated in usual convergence theories. However, in practice, one might not be able to solve the subproblem up to the required precision. This may be due to different reasons. One of them is that the presence of an excessively large penalty parameter could impair the performance of the box-constraint optimization solver. In this paper a practical strategy for decreasing the penalty parameter in situations like the one mentioned above is proposed. More generally, the different decisions that may be taken when, in practice, one is not able to solve the Augmented Lagrangian subproblem will be discussed. As a result, an improved Augmented Lagrangian method is presented, which takes into account numerical difficulties in a satisfactory way, preserving suitable convergence theory. Numerical experiments are presented involving all the CUTEr collection test problems.

The boundedness of penalty parameters in an augmented Lagrangian method with constrained subproblems

Augmented Lagrangian methods are effective tools for solving large-scale nonlinear programming problems. At each outer iteration, a minimization subproblem with simple constraints, whose objective function depends on updated Lagrange multipliers and penalty parameters, is approximately solved. When the penalty parameter becomes very large, solving the subproblem becomes difficult; therefore, the effectiveness of this approach is associated with the boundedness of the penalty parameters. In this paper, it is proved that under more natural assumptions than the ones employed until now, penalty parameters are bounded. For proving the new boundedness result, the original algorithm has been slightly modified. Numerical consequences of the modifications are discussed and computational experiments are presented.