A case for increased private sector involvement in Ireland's national animal health services

Non-regulatory animal health issues, such as Johne's disease, infectious bovine rhinotracheitis (IBR) and mastitis will become increasing important, with ongoing globalisation of markets in animals and animal products. In response, Ireland may need to broaden the scope of its national animal health services. However, there have been concerns about the respective roles and responsibilities (both financial and otherwise) of government and industry in any such moves. This paper argues the case for increased private sector involvement in Ireland's national animal health services, based both on theoretical considerations and country case studies (the Netherlands and Australia). The Dutch and Australian case studies present examples of successful partnerships between government and industry, including systems and processes to address non-regulatory animal health issues. In each case, the roles and responsibilities of government are clear, as are the principles underpinning government involvement. Furthermore, the roles and responsibilities (financial and otherwise) of the Dutch and Australian industry are determined through enabling legislation, providing both legitimacy and accountability. There are constraints on the use of EU and national government funds to support non-regulatory animal health services in EU member states (such as Ireland and the Netherlands)

A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

Bayesian models have advanced the idea that humans combine prior beliefs and sensory observations to optimize behavior. How the brain implements Bayes-optimal inference, however, remains poorly understood. Simple behavioral tasks suggest that the brain can flexibly represent probability distributions. An alternative view is that the brain relies on simple algorithms that can implement Bayes-optimal behavior only when the computational demands are low. To distinguish between these alternatives, we devised a task in which Bayes-optimal performance could not be matched by simple algorithms. We asked subjects to estimate and reproduce a time interval by combining prior information with one or two sequential measurements. In the domain of time, measurement noise increases with duration. This property takes the integration of multiple measurements beyond the reach of simple algorithms. We found that subjects were able to update their estimates using the second measurement but their performance was suboptimal, suggesting that they were unable to update full probability distributions. Instead, subjects’ behavior was consistent with an algorithm that predicts upcoming sensory signals, and applies a nonlinear function to errors in prediction to update estimates. These results indicate that the inference strategies employed by humans may deviate from Bayes-optimal integration when the computational demands are high

Efficient Coding and Statistically Optimal Weighting of Covariance among Acoustic Attributes in Novel Sounds

To the extent that sensorineural systems are efficient, redundancy should be extracted to optimize transmission of information, but perceptual evidence for this has been limited. Stilp and colleagues recently reported efficient coding of robust correlation (r = .97) among complex acoustic attributes (attack/decay, spectral shape) in novel sounds. Discrimination of sounds orthogonal to the correlation was initially inferior but later comparable to that of sounds obeying the correlation. These effects were attenuated for less-correlated stimuli (r = .54) for reasons that are unclear. Here, statistical properties of correlation among acoustic attributes essential for perceptual organization are investigated. Overall, simple strength of the principal correlation is inadequate to predict listener performance. Initial superiority of discrimination for statistically consistent sound pairs was relatively insensitive to decreased physical acoustic/psychoacoustic range of evidence supporting the correlation, and to more frequent presentations of the same orthogonal test pairs. However, increased range supporting an orthogonal dimension has substantial effects upon perceptual organization. Connectionist simulations and Eigenvalues from closed-form calculations of principal components analysis (PCA) reveal that perceptual organization is near-optimally weighted to shared versus unshared covariance in experienced sound distributions. Implications of reduced perceptual dimensionality for speech perception and plausible neural substrates are discussed

The incidence of unpleasant dreams after sub-anaesthetic ketamine

Ketamine is an N-methyl-D-aspartate (NMDA)receptor antagonist with psychotogenic effects and for whichthere are diverse reports of whether pleasant or unpleasantdreams result during anaesthesia, post-operatively or aftersub-anaesthetic use. The aim was to assess in healthy volunteers the incidence ofunpleasant dreams over the three nights after receiving asub-anaesthetic dose of ketamine, in comparison to placebo,and with retrospective home nightmare frequency as acovariate.Thirty healthy volunteers completed questionnairesabout retrospective home dream recall and were then giveneither ketamine or placebo. Ketamine resulted in significantly more meandream unpleasantness relative to placebo and caused athreefold increase in the odds ratio for the incidence of anunpleasant dream. The number of dreams reported over thethree nights did not differ between the groups. Theincidence of unpleasant dreams after ketamine use waspredicted by retrospectively assessed nightmare frequencyat home.Ketamine causes unpleasant dreams over thethree post-administration nights. This may be evidence of aresidual psychotogenic effect that is not found on standardself-report symptomatology measures or a result of disturbedsleep electrophysiology. The results have theoretical implications for the relationship between nightmares and schizotypy