Spatio-temporal judgements of observed actions : Contrasts between first- and third-person perspective after motor priming.

When observing actions, motor simulation processes aid the prediction and understanding of future events. A central issue concerns whether such action simulation serves social functions of interpreting other people, where performance is predicted to be better when third-person perspective (3PP) actions are viewed; or whether it is most beneficial to guide self actions, whereby the first-person perspective (1PP) would be advantageous. We show that in a spatio-temporal judgement task there is an advantage for the prediction of 1PP. However, this is only detected after motor priming whereby participants perform the observed actions prior to making spatio-temporal judgements. The results, firstly, confirm that we draw on our motor experience for the accurate simulation and prediction of action. Secondly, the results suggest that such experience facilitates more accurate state estimation for actions perceived in the 1PP which map more closely onto visual input of self-generated action. More forward prediction error is retained for 3PP viewed actions, which may however have the benefit of compensating for the uncertainty involved in interacting with others

Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future

Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact