A neurally plausible model of the dynamics of motion integration in smooth eye pursuit based on recursive Bayesian estimation

In this study, we describe a model of motion integration in smooth eye pursuit based on a recursive Bayesian estimation process, which displays a dynamic behaviour qualitatively similar to the dynamics of the motion integration process observed experimentally, both psychophysically in humans and monkeys, and physiologically in monkeys. By formulating the model as an approximate version of a Kalman filter algorithm, we have been able to show that it can be put into a neurally plausible, distributed recurrent form which coarsely corresponds to the recurrent circuitry of visual cortical areas V1 and MT. The model thus provides further support for the notion that the motion integration process is based on a form of Bayesian estimation, as has been suggested by many psychophysical studies, and moreover suggests that the observed dynamic properties of this process are the result of the recursive nature of the motion estimation.     

Applications of omics approaches to the development of microbiological risk assessment using RNA virus dose–response models as a case study

T e in the amount of ‘omics’ data available and in our ability to interpret those data. The aim of this paper was to consider how omics techniques can be used to improve and refine microbiological risk assessment, using dose–response models for RNA viruses, with particular reference to norovirus through the oral route as the case study. The dose–response model for initial infection in the gastrointestinal tract is broken down into the component steps at the molecular level and the feasibility of assigning probabilities to each step assessed. The molecular mechanisms are not sufficiently well understood at present to enable quantitative estimation of probabilities on the basis of omics data. At present, the great strength of gene sequence data appears to be in giving information on the distribution and proportion of susceptible genotypes (for example due to the presence of the appropriate pathogen‐binding receptor) in the host population rather than in predicting specificities from the amino acid sequences concurrently obtained. The nature of the mutant spectrum in RNA viruses greatly complicates the application of omics approaches to the development of mechanistic dose–response models and prevents prediction of risks of disease progression (given infection has occurred) at the level of the individual host. However, molecular markers in the host and virus may enable more broad predictions to be made about the consequences of exposure in a population. In an alternative approach, comparing the results of deep sequencing of RNA viruses in the faeces/vomitus from donor humans with those from their infected recipients may enable direct estimates of the average probability of infection per virion to be made.