Supporting high mobility in millimeter wave (mmWave) systems enables a wide range of important applications such as vehicular communications and wireless virtual/augmented reality. Realizing this in practice, though, requires overcoming several challenges. First, the use of narrow beams and the sensitivity of mmWave signals to blockage greatly impact the coverage and reliability of highly-mobile links. Second, highly-mobile users in dense mmWave deployments need to frequently hand-off between base stations (BSs), which is associated with critical control and latency overhead. Further, identifying the optimal beamforming vectors in large antenna array mmWave systems requires considerable training overhead, which significantly affects the efficiency of these mobile systems. In this paper, a novel integrated machine learning and coordinated beamforming solution is developed to overcome these challenges and enable highly-mobile mmWave applications. In the proposed solution, a number of distributed yet coordinating BSs simultaneously serve a mobile user. This user ideally needs to transmit only one uplink training pilot sequence that will be jointly received at the coordinating BSs using omni or quasi-omni beam patterns. These received signals draw a defining signature not only for the user location, but also for its interaction with the surrounding environment. The developed solution then leverages a deep learning model that learns how to use these signatures to predict the beamforming vectors at the BSs. This renders a comprehensive solution that supports highly-mobile mmWave applications with reliable coverage, low latency, and negligible training overhead. Extensive simulation results, based on accurate ray-tracing, show that the proposed deep-learning coordinated beamforming strategy approaches the achievable rate of the genie-aided solution that knows the optimal beamforming vectors with no training overhead, and attains higher rates compared to traditional mmWave beamforming techniques.This work was done while the first author was with Facebook. Ahmed Alkhateeb is currently with Arizona State University

Metformin, the most prescribed antidiabetic medicine, has shown other benefits such as anti-ageing and anticancer effects1–4. For clinical doses of metformin, AMP-activated protein kinase (AMPK) has a major role in its mechanism of action4,5; however, the direct molecular target of metformin remains unknown. Here we show that clinically relevant concentrations of metformin inhibit the lysosomal proton pump v-ATPase, which is a central node for AMPK activation following glucose starvation6. We synthesize a photoactive metformin probe and identify PEN2, a subunit of γ-secretase7, as a binding partner of metformin with a dissociation constant at micromolar levels. Metformin-bound PEN2 forms a complex with ATP6AP1, a subunit of the v-ATPase8, which leads to the inhibition of v-ATPase and the activation of AMPK without effects on cellular AMP levels. Knockout of PEN2 or re-introduction of a PEN2 mutant that does not bind ATP6AP1 blunts AMPK activation. In vivo, liver-specific knockout of Pen2 abolishes metformin-mediated reduction of hepatic fat content, whereas intestine-specific knockout of Pen2 impairs its glucose-lowering effects. Furthermore, knockdown of pen-2 in Caenorhabditis elegans abrogates metformin-induced extension of lifespan. Together, these findings reveal that metformin binds PEN2 and initiates a signalling route that intersects, through ATP6AP1, the lysosomal glucose-sensing pathway for AMPK activation. This ensures that metformin exerts its therapeutic benefits in patients without substantial adverse effects.

Wang 18 19 AMPK, a master regulator of metabolic homeostasis, is activated by both 20 AMP-dependent and AMP-independent mechanisms. We investigated the 21 conditions under which these different mechanisms operate, and their biological 22 2 implications. We show that, depending on the degree of elevation of cellular 23 AMP, distinct compartmentalized pools of AMPK are activated, phosphorylating 24 different sets of targets. Low glucose activates AMPK exclusively through the 25 AMP-independent, AXIN-based pathway in lysosomes to phosphorylate targets 26

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractA Self-Diverting-Acid (SDA) using a Visco-Elastic Surfactant (VES) has been developed. The viscosity of the solution does not develop until the acid reacts with carbonate in the formation. The increases in Ca 2+ ions and pH due to the HClcarbonate reaction cause in situ gelling of the acid. The high viscosity temporarily blocks the wormholes formed in the rock matrix, allowing the acid to cover the un-acidized area. The viscosity of the gelled acid can be completely reduced by post flush of solvent or by the hydrocarbon in the formation during flow back. Unlike the polymer based gelled acid systems , the new material does not leave any residue once it has broken. Multi-Core flood testing incorporating a post acidizing Computed Tomography (CT) scans showed that the VES based Self-Diverting-Acid successfully diverted acid from high permeability section into lower permeability sections. The rock face remained clean without any trace of residue. Rheology measurements showed the consistency of the viscosity development by the gelled acid upon reacting with carbonates.