The effect of artificial lighting on the arrival time of birds using garden feeding stations in winter: A missed opportunity?

The proliferation of artificial lighting at night is one of the key anthropogenic changes associated with urbanised areas as well as some non-urban areas. Disruption to natural light/dark regimes can have considerable effects on the timing of different behaviours of birds, particularly during the breeding season. However, the effect of artificial lights on the timing of behaviours during winter has received relatively little attention, despite the fact that time partitioning of foraging can have implications for avian winter survival. In this study, we assess at a landscape scale during winter, whether birds arrive at feeding stations earlier in areas with increased levels of artificial lighting using data from a citizen science project. Arrival times of the ten most commonly recorded species were associated with a combination of the density of artificial lights, temperature, rainfall and urban land cover. We found no evidence that birds advance the onset of foraging in gardens with more artificial lights nearby; contrary to our prediction, birds generally arrived later into these areas. This is possibly a response to differences in food availability or predation risk in areas with more artificial lights. We conclude that artificial light at night may not be as important for driving the timing of foraging behaviour in winter as previously thought, but it remains to be seen whether this represents a missed opportunity to extend the foraging period or an adaptive response.     

NONLINEAR TUBE-FITTING FOR THE ANALYSIS OF ANATOMICAL AND FUNCTIONAL STRUCTURES

We are concerned with the estimation of the exterior surface and interior summaries of tube-shaped anatomical structures. This interest is motivated by two distinct scientific goals, one dealing with the distribution of HIV microbicide in the colon and the other with measuring degradation in white-matter tracts in the brain. Our problem is posed as the estimation of the support of a distribution in three dimensions from a sample from that distribution, possibly measured with error. We propose a novel tube-fitting algorithm to construct such estimators. Further, we conduct a simulation study to aid in the choice of a key parameter of the algorithm, and we test our algorithm with validation study tailored to the motivating data sets. Finally, we apply the tube-fitting algorithm to a colon image produced by single photon emission computed tomography (SPECT) and to a white-matter tract image produced using diffusion tensor imaging (DTI).     

Selecting social work students: lessons from research in Scotland

The issue of selection of students to social work programmes is one that remains highly contested. While it is clear that there is no single way of choosing the next generation of social work students, nevertheless, there are a number of strongly held beliefs about what ‘best practice’ means in this fraught field. These can be difficult to challenge, and even harder to shift, in spite of contrary evidence. This paper presents research conducted in Scotland in 2016 as part of the Scottish Government-sponsored Review of Social Work Education. The research set out to consider what selection processes were being used in Scotland and why; more fundamentally, it sought to explore the views of those involved in social work education alongside evidence about the outcomes of the selection processes (that is, data on student retention and success). The article concludes that while there is little evidence that one method of selection to social work programmes is intrinsically better than another, issues of fairness and transparency in selection, as well as diversity, remain pressing.     

‘Sit in the corner and don’t eat the crayons’: postgraduates with dyslexia and the dominant ‘lexic’ discourse

The lack of cultural diversity in higher education is recognised by policy objectives and a current focus on the development of widening participation for a range of students, including those with disabilities. Amongst this group are those with dyslexia who might previously have been disenfranchised from formal education and under‐represented within it. This paper explores the personal narratives and learner histories of six postgraduates and academics with dyslexia from their earliest memories of learning to their present experiences. It examines how literacy, as a dominant form of discourse, has defined concepts of academic ability resulting in the early exclusion of these learners from formal education. It is argued that this dominant discourse can be challenged by non‐authorised, informal learning resulting in stories of resistance.     

Predicting treatment efficacy via quantitative magnetic resonance imaging: a Bayesian joint model

The prognosis for patients with high grade gliomas is poor, with a median survival of 1 year. Treatment efficacy assessment is typically unavailable until 5-6 months post diagnosis. Investigators hypothesize that quantitative magnetic resonance imaging can assess treatment efficacy 3 weeks after therapy starts, thereby allowing salvage treatments to begin earlier. The purpose of this work is to build a predictive model of treatment efficacy by using quantitative magnetic resonance imaging data and to assess its performance. The outcome is 1-year survival status. We propose a joint, two-stage Bayesian model. In stage I, we smooth the image data with a multivariate spatiotemporal pairwise difference prior. We propose four summary statistics that are functionals of posterior parameters from the first-stage model. In stage II, these statistics enter a generalized non-linear model as predictors of survival status. We use the probit link and a multivariate adaptive regression spline basis. Gibbs sampling and reversible jump Markov chain Monte Carlo methods are applied iteratively between the two stages to estimate the posterior distribution. Through both simulation studies and model performance comparisons we find that we can achieve higher overall correct classification rates by accounting for the spatiotemporal correlation in the images and by allowing for a more complex and flexible decision boundary provided by the generalized non-linear model.     

Prioritizing Environmental Health Risks in the UAE

This article presents the results of a comparative environmental risk‐ranking exercise that was conducted in the United Arab Emirates (UAE) to inform a strategic planning process led by the Environment Agency‐Abu Dhabi (EAD). It represents the first national‐level application of a deliberative method for comparative risk ranking first published in this journal. The deliberative method involves a five‐stage process that includes quantitative risk assessment by experts and deliberations by groups of stakeholders. The project reported in this article considered 14 categories of environmental risks to health identified through discussions with EAD staff: ambient and indoor air pollution; drinking water contamination; coastal water pollution; soil and groundwater contamination; contamination of fruits, vegetables, and seafood; ambient noise; stratospheric ozone depletion; electromagnetic fields from power lines; health impacts from climate change; and exposure to hazardous substances in industrial, construction, and agricultural work environments. Results from workshops involving 73 stakeholders who met in five separate groups to rank these risks individually and collaboratively indicated strong consensus that outdoor and indoor air pollution are the highest priorities in the UAE. Each of the five groups rated these as being among the highest risks. All groups rated soil and groundwater contamination as being among the lowest risks. In surveys administered after the ranking exercises, participants indicated that the results of the process represented their concerns and approved of using the ranking results to inform policy decisions. The results ultimately shaped a strategic plan that is now being implemented.     

Small-Sample Properties of GMM-Based Wald Tests

This article assesses the small-sample properties of generalized-method-of-moments-based Wald statistics by using (a) a vector white-noise process and (b) an equilibrium business-cycle model as the data-generating mechanisms. In many cases, the small-sample size of the Wald tests exceeds its asymptotic size and increases sharply with the number of hypotheses being jointly tested. We argue that this is mostly due to difficulty in estimating the spectral-density matrix of the residuals. Estimators of this matrix that impose restrictions implied by the model or the null hypothesis substantially improve the properties of the Wald statistics.