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.

AßPP over-expressing transgenic rat model of Alzheimer's disease utilizing the Tg2576 mouse protocol.

The current study examined behavioral and histological effects of amyloid-ß (Aß) protein precursor (AßPP) overexpression in transgenic (Tg) rats created using the same gene, mutation, and promoter as the Tg2576 mouse model of Alzheimer's disease (AD). Male Tg+ rats were bred with female wild-type rats to generate litters of hemizygous Tg+ and Tg- offspring. Tg+ rats and Tg- littermates were tested for memory deficits at 4, 8, and 12 months old using a water-maze procedure. There were no significant behavioral differences between Tg+ rats and Tg- littermates at 4 months old but there were significant differences at 8 and 12 months old, and in probe trials at 8 and 12 months old, the Tg+ rats spent significantly less time and covered less distance in the platform zone. Under acquisition of a fixed-consecutive number schedule at 3 months old, Tg- littermates demonstrated a longer latency to learning the response rule than Tg+ rats; while this might seem paradoxical, it is consistent with the role of overexpression of AßPP in learning. Histological analyses revealed activated astrocytes in brains of Tg+ rats but not Tg- littermates at 6 months old, and thioflavin-S positive staining in the hippocampus and cortex of 17-month old Tg+ rats but not Tg- littermates. Quantification of Aß load in the brain at 22 months indicated high levels of Aß38, Aß40, and Aß42 in the Tg+ rats. These data suggest this model might provide a valuable resource for AD research.

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.