A Bayesian model predicts the response of axons to molecular gradients

Axon guidance by molecular gradients plays a crucial role in wiring up the nervous system. However, the mechanisms axons use to detect gradients are largely unknown. We first develop a Bayesian “ideal observer” analysis of gradient detection by axons, based on the hypothesis that a principal constraint on gradient detection is intrinsic receptor binding noise. Second, from this model, we derive an equation predicting how the degree of response of an axon to a gradient should vary with gradient steepness and absolute concentration. Third, we confirm this prediction quantitatively by performing the first systematic experimental analysis of how axonal response varies with both these quantities. These experiments demonstrate a degree of sensitivity much higher than previously reported for any chemotacting system. Together, these results reveal both the quantitative constraints that must be satisfied for effective axonal guidance and the computational principles that may be used by the underlying signal transduction pathways, and allow predictions for the degree of response of axons to gradients in a wide variety of in vivo and in vitro settings.

Helping someone with problem drinking: Mental health first aid guidelines - a Delphi expert consensus study

Background Alcohol is a leading risk factor for avoidable disease burden. Research suggests that a drinker's social network can play an integral role in addressing hazardous (i.e., high-risk) or problem drinking. Often however, social networks do not have adequate mental health literacy (i.e., knowledge about mental health problems, like problem drinking, or how to treat them). This is a concern as the response that a drinker receives from their social network can have a substantial impact on their willingness to seek help. This paper describes the development of mental health first aid guidelines that inform community members on how to help someone who may have, or may be developing, a drinking problem (i.e., alcohol abuse or dependence). Methods A systematic review of the research and lay literature was conducted to develop a 285-item survey containing strategies on how to help someone who may have, or may be developing, a drinking problem. Two panels of experts (consumers/carers and clinicians) individually rated survey items, using a Delphi process. Surveys were completed online or via postal mail. Participants were 99 consumers, carers and clinicians with experience or expertise in problem drinking from Australia, Canada, Ireland, New Zealand, the United Kingdom, and the United States. Items that reached consensus on importance were retained and written into guidelines. Results The overall response rate across all three rounds was 68.7% (67.6% consumers/carers, 69.2% clinicians), with 184 first aid strategies rated as essential or important by ≥80% of panel members. The endorsed guidelines provide guidance on how to: recognize problem drinking; approach someone if there is concern about their drinking; support the person to change their drinking; respond if they are unwilling to change their drinking; facilitate professional help seeking and respond if professional help is refused; and manage an alcohol-related medical emergency. Conclusion The guidelines provide a consensus-based resource for community members seeking to help someone with a drinking problem. Improving community awareness and understanding of how to identify and support someone with a drinking problem may lead to earlier recognition of problem drinking and greater facilitation of professional help seeking.

Margin-adaptive model selection in statistical learning

A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of model selection, in a general learning framework. Actually, we consider a weaker version of this condition that allows one to take into account that learning within a small model can be much easier than within a large one. Requiring this “strong margin adaptivity” makes the model selection problem more challenging. We first prove, in a general framework, that some penalization procedures (including local Rademacher complexities) exhibit this adaptivity when the models are nested. Contrary to previous results, this holds with penalties that only depend on the data. Our second main result is that strong margin adaptivity is not always possible when the models are not nested: for every model selection procedure (even a randomized one), there is a problem for which it does not demonstrate strong margin adaptivity.

The Bayesian conditional independence model for measurement error: applications in ecology

The measurement error model is a well established statistical method for regression problems in medical sciences, although rarely used in ecological studies. While the situations in which it is appropriate may be less common in ecology, there are instances in which there may be benefits in its use for prediction and estimation of parameters of interest. We have chosen to explore this topic using a conditional independence model in a Bayesian framework using a Gibbs sampler, as this gives a great deal of flexibility, allowing us to analyse a number of different models without losing generality. Using simulations and two examples, we show how the conditional independence model can be used in ecology, and when it is appropriate.