Activation of protease calpain by oxidized and glycated LDL increases the degradation of endothelial nitric oxide synthase

Oxidation and glycation of low-density lipoprotein (LDL) promote vascular injury in diabetes; however, the mechanisms underlying this effect remain poorly defined. The present study was conducted to determine the effects of 'heavily oxidized' glycated LDL (HOG-LDL) on endothelial nitric oxide synthase (eNOS) function. Exposure of bovine aortic endothelial cells with HOG-LDL reduced eNOS protein levels in a concentration- and time-dependent manner, without altering eNOS mRNA levels. Reduced eNOS protein levels were accompanied by an increase in intracellular Ca(2+), augmented production of reactive oxygen species (ROS) and induction of Ca(2+)-dependent calpain activity. Neither eNOS reduction nor any of these other effects were observed in cells exposed to native LDL. Reduction of intracellular Ca(2+) levels abolished eNOS reduction by HOG-LDL, as did pharmacological or genetic through calcium channel blockers or calcium chelator BAPTA or inhibition of NAD(P)H oxidase (with apocynin) or inhibition of calpain (calpain 1-specific siRNA). Consistent with these results, HOG-LDL impaired acetylcholine-induced endothelium-dependent vasorelaxation of isolated mouse aortas, and pharmacological inhibition of calpain prevented this effect. HOG-LDL may impair endothelial function by inducing calpain-mediated eNOS degradation in a ROS- and Ca(2+)-dependent manner.

Impact of Residual Transmit RF Impairments on Training-Based MIMO Systems

Radio-frequency (RF) impairments, which intimately exist in wireless communication systems, can severely limit the performance of multiple-input-multiple-output (MIMO) systems. Although we can resort to compensation schemes to mitigate some of these impairments, a certain amount of residual impairments always persists. In this paper, we consider a training-based point-to-point MIMO system with residual transmit RF impairments (RTRI) using spatial multiplexing transmission. Specifically, we derive a new linear channel estimator for the proposed model, and show that RTRI create an estimation error floor in the high signal-to-noise ratio (SNR) regime. Moreover, we derive closed-form expressions for the signal-to-noise-plus-interference ratio (SINR) distributions, along with analytical expressions for the ergodic achievable rates of zero-forcing, maximum ratio combining, and minimum mean-squared error receivers, respectively. In addition, we optimize the ergodic achievable rates with respect to the training sequence length and demonstrate that finite dimensional systems with RTRI generally require more training at high SNRs than those with ideal hardware. Finally, we extend our analysis to large-scale MIMO configurations, and derive deterministic equivalents of the ergodic achievable rates. It is shown that, by deploying large receive antenna arrays, the extra training requirements due to RTRI can be eliminated. In fact, with a sufficiently large number of receive antennas, systems with RTRI may even need less training than systems with ideal hardware.