Genetic studies of diseases

Pancreatitis is usually inflammation of the pancreas without infection. Our understanding of pancreatitis has been built on autopsy studies, surgical biopsies and surrogate markers of inflammation and fibroses, including abdominal imaging techniques and pancreatic functional studies. However, the discovery that a number of different environmental factors and various genetic abnormalities are seen in patients with similar appearing pancreatitis phenotypes teaches us that end-stage pathology is not the disorder. Understanding complex associations and interactions requires that the components and their interactions be organized, stratified and functionally defined. Systems biology, in the broad sense, provides the approach and tools to define the complex mechanisms driving pathology. As the mathematics behind these pathways and mechanisms are defined and calibrated, the potential pathology of patients with early signs of disease can be predicted, and a number of patient-specific targets for intervention can be defined.     

Physiology: does gut hormone PYY3-36 decrease food intake in rodents?

Batterham et al. report that the gut peptide hormone PYY3-36 decreases food intake and body-weight gain in rodents, a discovery that has been heralded as potentially offering a new therapy for obesity. However, we have been unable to replicate their results. Although the reasons for this discrepancy remain undetermined, an effective anti-obesity drug ultimately must produce its effects across a range of situations. The fact that the findings of Batterham et al. cannot easily be replicated calls into question the potential value of an anti-obesity approach that is based on administration of PYY3-36.     

The effectiveness of imprecise probability forecasts

In this paper we investigate the feasibility of algorithmically deriving precise probability forecasts from imprecise forecasts. We provide an empirical evaluation of precise probabilities that have been derived from two types of imprecise probability forecasts: probability intervals and probability intervals with second-order probability distributions. The minimum cross-entropy (MCE) principle is applied to the former to derive precise (i.e. additive) probabilities; expectation (EX) is used to derive precise probabilities in the latter case. Probability intervals that were constructed without second-order probabilities tended to be narrower than and contained in those that were amplified by second-order probabilities. Evidence that this narrowness is due to motivational bias is presented. Analysis of forecasters' mean Probability Scores for the derived precise probabilities indicates that it is possible to derive precise forecasts whose external correspondence is as good as directly assessed precise probability forecasts. The forecasts of the EX method, however, are more like the directly assessed precise forecasts than those of the MCE method.