The Effects of Pregnenolone 16α-Carbonitrile Dosing on Digoxin Pharmacokinetics and Intestinal Absorption in the Rat

The effect of Pgp induction in rats by pregnenolone 16α-carbonitrile (PCN) (3 days, 35 mg/kg/d, p.o.) on digoxin pharmacokinetics and intestinal transport has been assessed. After intravenous or oral digoxin dosing the arterial and hepatic portal vein (oral) AUC(0-24h) were significantly reduced by PCN pre-treatment. Biliary digoxin clearance increased 2-fold following PCN treatment. PCN significantly increased net digoxin secretion (2.05- and 4.5-fold respectively) in ileum and colon but not in duodenum or jejunum. This increased secretion correlated with increased Pgp protein expression in ileum and colon. Both intestinal and biliary excretion therefore contribute to altered digoxin disposition following PCN

LC-PCN: The Load Control PCN Solution

There is an increased interest of simple and scalable resource provisioning solution for Diffserv network. The Load Control PCN (LC-PCN) addresses the following issues:\ud o Admission Control for real time data flows in stateless Diffserv Domains\ud o Flow Termination: Termination of flows in case of exceptional events, such as severe congestion after re-routing.\ud Admission control in a Diffserv stateless domain is a combination of:\ud o Probing, whereby a probe packet is sent along the forwarding path in a network to determine whether a flow can be admitted based upon the current congestion state of the network\ud o Admission Control based on data marking, whereby in congestion situations the data packets are marked to notify the PCN-egress-node that a congestion occurred on a particular PCN-ingress-node to PCN-egress-node path.\ud \ud The scheme provides the capability of controlling the traffic load in the network without requiring signaling or any per-flow processing in the PCN-interior-nodes. The complexity of Load Control is kept to a minimum to make implementation simple.\u

A hybrid adaptive MCMC algorithm in function spaces

The preconditioned Crank-Nicolson (pCN) method is a Markov Chain Monte Carlo (MCMC) scheme, specifically designed to perform Bayesian inferences in function spaces. Unlike many standard MCMC algorithms, the pCN method can preserve the sampling efficiency under the mesh refinement, a property referred to as being dimension independent. In this work we consider an adaptive strategy to further improve the efficiency of pCN. In particular we develop a hybrid adaptive MCMC method: the algorithm performs an adaptive Metropolis scheme in a chosen finite dimensional subspace, and a standard pCN algorithm in the complement space of the chosen subspace. We show that the proposed algorithm satisfies certain important ergodicity conditions. Finally with numerical examples we demonstrate that the proposed method has competitive performance with existing adaptive algorithms.Comment: arXiv admin note: text overlap with arXiv:1511.0583

Phase Closure Nulling: results from the 2009 campaign

We present here a new observational technique, Phase Closure Nulling (PCN), which has the potential to obtain very high contrast detection and spectroscopy of faint companions to bright stars. PCN consists in measuring closure phases of fully resolved objects with a baseline triplet where one of the baselines crosses a null of the object visibility function. For scenes dominated by the presence of a stellar disk, the correlated flux of the star around nulls is essentially canceled out, and in these regions the signature of fainter, unresolved, scene object(s) dominates the imaginary part of the visibility in particular the closure phase. We present here the basics of the PCN method, the initial proof-of-concept observation, the envisioned science cases and report about the first observing campaign made on VLTI/AMBER and CHARA/MIRC using this technique.Comment: To be published in the proceedings of the SPIE'2010 conference on "Optical and Infrared Interferometry II