Hypoxic modulation of ca(2+) signaling in human venous and arterial endothelial cells

Our understanding of vascular endothelial cell physiology is based on studies of endothelial cells cultured from various vascular beds of different species for varying periods of time. Systematic analysis of the properties of endothelial cells from different parts of the vasculature is lacking. Here, we compare Ca(2+) homeostasis in primary cultures of endothelial cells from human internal mammary artery and saphenous vein and how this is modified by hypoxia, an inevitable consequence of bypass grafting (2.5% O(2), 24 h). Basal [Ca(2+)]( i ) and store depletion-mediated Ca(2+) entry were significantly different between the two cell types, yet agonist (ATP)-mediated mobilization from endoplasmic reticulum stores was similar. Hypoxia potentiated agonist-evoked responses in arterial, but not venous, cells but augmented store depletion-mediated Ca(2+) entry only in venous cells. Clearly, Ca(2+) signaling and its remodeling by hypoxia are strikingly different in arterial vs. venous endothelial cells. Our data have important implications for the interpretation of data obtained from endothelial cells of varying sources.

Rapid preconditioning of data for accelerating convex hull algorithms

Given a dataset of two-dimensional points in the plane with integer coordinates, the method proposed reduces a set of n points down to a set of s points s ≤ n, such that the convex hull on the set of s points is the same as the convex hull of the original set of n points. The method is O(n). It helps any convex hull algorithm run faster. The empirical analysis of a practical case shows a percentage reduction in points of over 98%, that is reflected as a faster computation with a speedup factor of at least 4.

Conditioning and preconditioning of the optimal state estimation problem

Optimal state estimation is a method that requires minimising a weighted, nonlinear, least-squares objective function in order to obtain the best estimate of the current state of a dynamical system. Often the minimisation is non-trivial due to the large scale of the problem, the relative sparsity of the observations and the nonlinearity of the objective function. To simplify the problem the solution is often found via a sequence of linearised objective functions. The condition number of the Hessian of the linearised problem is an important indicator of the convergence rate of the minimisation and the expected accuracy of the solution. In the standard formulation the convergence is slow, indicating an ill-conditioned objective function. A transformation to different variables is often used to ameliorate the conditioning of the Hessian by changing, or preconditioning, the Hessian. There is only sparse information in the literature for describing the causes of ill-conditioning of the optimal state estimation problem and explaining the effect of preconditioning on the condition number. This paper derives descriptive theoretical bounds on the condition number of both the unpreconditioned and preconditioned system in order to better understand the conditioning of the problem. We use these bounds to explain why the standard objective function is often ill-conditioned and why a standard preconditioning reduces the condition number. We also use the bounds on the preconditioned Hessian to understand the main factors that affect the conditioning of the system. We illustrate the results with simple numerical experiments.

PKC-dependent activation of p46/p54 JNKs during ischemic preconditioning in conscious rabbits.

A conscious rabbit model was used to study the effect of ischemic preconditioning (PC) on stress-activated kinases [c-Jun NH(2)-terminal kinases (JNKs) and p38 mitogen-activated protein kinase (MAPK)] in an environment free of surgical trauma and attending external stress. Ischemic PC (6 cycles of 4-min ischemia/4-min reperfusion) induced significant activation of protein kinase C (PKC)-epsilon in the particulate fraction, which was associated with activation of p46 JNK in the nuclear fraction and p54 JNK in the cytosolic fraction; all of these changes were completely abolised by the PKC inhibitor chelerythrine. Selective enhancement of PKC-epsilon activity in adult rabbit cardiac myocytes resulted in enhanced activity of p46/p54 JNKs, providing direct in vitro evidence that PKC-epsilon is coupled to both kinases. Studies in rabbits showed that the activation of p46 JNK occurred during ischemia, whereas that of p54 JNK occurred after reperfusion. A single 4-min period of ischemia induced a robust activation of the p38 MAPK cascade, which, however, was attenuated after 5 min of reperfusion and disappeared after six cycles of 4-min ischemia/reperfusion. Overexpression of PKC-epsilon in cardiac myocytes failed to increase the p38 MAPK activity. These results demonstrate that ischemic PC activates p46 and p54 JNKs via a PKC-epsilon-dependent signaling pathway and that there are important differences between p46 and p54 JNKs with respect to the subcellular compartment (cytosolic vs. nuclear) and the mechanism (ischemia vs. reperfusion) of their activation after ischemic PC.

Preconditioning 2D integer data for fast convex hull computations

In order to accelerate computing the convex hull on a set of n points, a heuristic procedure is often applied to reduce the number of points to a set of s points, s ≤ n, which also contains the same hull. We present an algorithm to precondition 2D data with integer coordinates bounded by a box of size p × q before building a 2D convex hull, with three distinct advantages. First, we prove that under the condition min(p, q) ≤ n the algorithm executes in time within O(n); second, no explicit sorting of data is required; and third, the reduced set of s points forms a simple polygonal chain and thus can be directly pipelined into an O(n) time convex hull algorithm. This paper empirically evaluates and quantifies the speed up gained by preconditioning a set of points by a method based on the proposed algorithm before using common convex hull algorithms to build the final hull. A speedup factor of at least four is consistently found from experiments on various datasets when the condition min(p, q) ≤ n holds; the smaller the ratio min(p, q)/n is in the dataset, the greater the speedup factor achieved.