Michael J. Henry

Vascular disease is a multifactorial disease that involves atherosclerotic and thrombotic factors. Genetic polymorphisms have been associated with myocardial infarction and angina pectoris. The aim of the present study was to assess the relationship between some genetic polymorphisms and myocardial infarction (MI) or vasospastic angina pectoris in a population from southern France. Genetic polymorphisms of the renin angiotensin system (the D/I polymorphism of the ACE gene and the A1166C polymorphism of the angiotensin II type 1 receptor [AT1R]) and of haemostatic factors (the -675 4G/5G polymorphism of the plasminogen-activator inhibitor 1[PAI-1] gene, and the G to T common point mutation in exon 2, codon 34 of the Factor XIII A-subunit gene) were examined. We assessed the genotype distribution in consecutive coronary artery disease (CAD) patients with MI (n = 201) and vasospastic angina pectoris (n = 43) and in 244 healthy controls comparable in age, sex, body mass index and total cholesterol level. The genotype distribution of AT1R polymorphism was significantly different between controls and patients, the prevalence of the C allele carriers being higher in patients with MI after the age of 45 than in control individuals (61 vs 45%, p <0.01), leading to an odds ratio (OR) of 2 (CI: 1.2-3.4). When looking at the group of patients with vasospastic angina the difference was even higher (76 vs 45%, p <0.01) yielding an OR of 4.3 (CI: 1.4-17.4). Genotype distributions of ACE, PAI-1 and Factor XIII polymorphisms were similar in patients and in controls. This study is in favor of a role of ATIR gene polymorphism in myocardial infarction and vasospastic angina.

Numerical ensemble forecasting is a powerful tool that drives many risk analysis efforts and decision making tasks. These ensembles are composed of individual simulations that each uniquely model a possible outcome for a common event of interest: e.g., the direction and force of a hurricane, or the path of travel and mortality rate of a pandemic. This paper presents a new visual strategy to help quantify and characterize a numerical ensemble's predictive uncertainty: i.e., the ability for ensemble constituents to accurately and consistently predict an event of interest based on ground truth observations. Our strategy employs a Bayesian framework to first construct a statistical aggregate from the ensemble. We extend the information obtained from the aggregate with a visualization strategy that characterizes predictive uncertainty at two levels: at a global level, which assesses the ensemble as a whole, as well as a local level, which examines each of the ensemble's constituents. Through this approach, modelers are able to better assess the predictive strengths and weaknesses of the ensemble as a whole, as well as individual models. We apply our method to two datasets to demonstrate its broad applicability.