cAMP/PKA-dependent increases in Ca Sparks, oscillations and SR Ca stores in retinal arteriolar myocytes after exposure to vasopressin.

PURPOSE: To investigate the effects of arginine vasopressin (AVP) on Ca(2+) sparks and oscillations and on sarcoplasmic reticulum (SR) Ca(2+) content in retinal arteriolar myocytes. METHODS: Fluo-4-loaded smooth muscle in intact segments of freshly isolated porcine retinal arteriole was imaged by confocal laser microscopy. SR Ca(2+) store content was assessed by recording caffeine-induced Ca(2+) transients with microfluorimetry and fura-2. RESULTS: The frequencies of Ca(2+) sparks and oscillations were increased both during exposure to, and 10 minutes after washout of AVP (10 nM). Caffeine transients were increased in amplitude 10 and 90 minutes after a 3-minute application of AVP. Both AVP-induced Ca(2+) transients and the enhancement of caffeine responses after AVP washout were inhibited by SR 49059, a V(1a) receptor blocker. Forskolin, an activator of adenylyl cyclase, also persistently enhanced caffeine transients. Rp-8-HA-cAMPS, a membrane-permeant PKA inhibitor, prevented enhancement of caffeine transients by both AVP and forskolin. Forskolin, but not AVP, produced a reversible, Rp-8-HA-cAMPS insensitive reduction in basal [Ca(2+)](i). CONCLUSIONS: AVP activates a cAMP/PKA-dependent pathway via V(1a) receptors in retinal arteriolar smooth muscle. This effect persistently increases SR Ca(2+) loading, upregulating Ca(2+) sparks and oscillations, and may favor prolonged agonist activity despite receptor desensitization.

Energy-Efficient In-Memory Data Stores on Hybrid Memory Hierarchies

Increasingly large amounts of data are stored in main memory of data center servers. However, DRAM-based memory is an important consumer of energy and is unlikely to scale in the future. Various byte-addressable non-volatile memory (NVM) technologies promise high density and near-zero static energy, however they suffer from increased latency and increased dynamic energy consumption.

This paper proposes to leverage a hybrid memory architecture, consisting of both DRAM and NVM, by novel, application-level data management policies that decide to place data on DRAM vs. NVM. We analyze modern column-oriented and key-value data stores and demonstrate the feasibility of application-level data management. Cycle-accurate simulation confirms that our methodology reduces the energy with least performance degradation as compared to the current state-of-the-art hardware or OS approaches. Moreover, we utilize our techniques to apportion DRAM and NVM memory sizes for these workloads.