The effectiveness and safety of d,l-sotalol in the ambulatory treatment of atrial fibrillation and flutter

Data on short and long term efficacy and safety of d,l sotalol in patients with atrial fibrillation or atrial flutter is limited. The aims of this study were to (1) assess the antiarrhythmic efficacy of d,l sotalol maintaining normal sinus rhythm in patients with refractory atrial fibrillation or flutter, (2) evaluate the efficacy of d,l sotalol in preventing recurrences of paroxysmal atrial fibrillation or flutter, (3) evaluate the control of ventricular rate in patients with paroxysmal or refractory atrial fibrillation or flutter unsuccessfully treated with other antiarrhythmic agents, (4) determine predictors of efficacy (5) assess the safety of d,l sotalol in this setting. Two hundred patients with chronic or paroxysmal atrial fibrillation or atrial flutter or both, who had failed one to six previous antiarrhythmic drug trials were treated with d,l sotalol 80 to 440 mg/day orally. Fifty four percent was female, age 47 +/- 16 years (range 7-79), follow up period 7 +/- 7 months (range 1 to 14 months), 79% of patients had the arrhythmia for more than one year. The atrial fibrillation in 37.5% of patients was chronic and paroxysmal in 23.5. The atrial flutter was chronic in 31% of patients and paroxysmal in 8%. Eighty two percent of patients was in functional class I (NYHA) and 82% had cardiac heart disease: left atrial (LA) size 44 +/- 10 mm, right atrial (RA) size 37 +/- 7 mm and left ventricular ejection fraction (LVEF) 58 +/- 8%. Total success was achieved in 58% of patients (atrial fibrillation 40% and 18% in atrial flutter), partial success in 38% (atrial fibrillation in 18% and 20% in atrial flutter) and 4% of patients failure. It was p < 0.07 when compared total success vs partial success among atrial fibrillation and atrial flutter groups. Patients with cardiac heart disease responded worst (p = 0.10) to the drug than those without it, specially if the heart was dilated. We concluded that d,l sotalol has moderate efficacy to convert and maintain normal sinus rhythm, as well as it acts controlling paroxysmal relapses and ventricular heart rate.

Single hadron response measurement and calorimeter jet energy scale uncertainty with the ATLAS detector at the LHC

The uncertainty on the calorimeter energy response to jets of particles is derived for the ATLAS experiment at the Large Hadron Collider (LHC). First, the calorimeter response to single isolated charged hadrons is measured and compared to the Monte Carlo simulation using proton-proton collisions at centre-of-mass energies of root s = 900 GeV and 7 TeV collected during 2009 and 2010. Then, using the decay of K-s and Lambda particles, the calorimeter response to specific types of particles (positively and negatively charged pions, protons, and anti-protons) is measured and compared to the Monte Carlo predictions. Finally, the jet energy scale uncertainty is determined by propagating the response uncertainty for single charged and neutral particles to jets. The response uncertainty is 2-5 % for central isolated hadrons and 1-3 % for the final calorimeter jet energy scale.

Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set

Stepwise uncertainty reduction (SUR) strategies aim at constructing a sequence of points for evaluating a function  f in such a way that the residual uncertainty about a quantity of interest progressively decreases to zero. Using such strategies in the framework of Gaussian process modeling has been shown to be efficient for estimating the volume of excursion of f above a fixed threshold. However, SUR strategies remain cumbersome to use in practice because of their high computational complexity, and the fact that they deliver a single point at each iteration. In this article we introduce several multipoint sampling criteria, allowing the selection of batches of points at which f can be evaluated in parallel. Such criteria are of particular interest when f is costly to evaluate and several CPUs are simultaneously available. We also manage to drastically reduce the computational cost of these strategies through the use of closed form formulas. We illustrate their performances in various numerical experiments, including a nuclear safety test case. Basic notions about kriging, auxiliary problems, complexity calculations, R code, and data are available online as supplementary materials.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

Networks of Information Exchange Under Uncertainty: The Case of Unconventional Gas Development in the United Kingdom

Modern policy-making is increasingly influenced by different types of uncertainty. Political actors are supposed to behave differently under the context of uncertainty then in “usual” decision-making processes. Actors exchange information in order to convince other actors and decision-makers, to coordinate their lobbying activities and form coalitions, and to get information and learn on the substantive issue. The literature suggests that preference similarity, social trust, perceived power and functional interdependence are particularly important drivers of information exchange. We assume that social trust as well as being connected to scientific actors is more important under uncertainty than in a setting with less uncertainty. To investigate information exchange under uncertainty analyze the case of unconventional shale gas development in the UK from 2008 till 2014. Our study will rely on statistical analyses of survey data on a diverse set of actors dealing with shale gas development and regulation in the UK.