Improving real-time estimates of output and inflation gaps with multiple-vintage models

Real-time estimates of output gaps and inflation gaps differ from the values that are obtained using data available long after the event. Part of the problem is that the data on which the real-time estimates are based is subsequently revised. We show that vector-autoregressive models of data vintages provide forecasts of post-revision values of future observations and of already-released observations capable of improving estimates of output and inflation gaps in real time. Our findings indicate that annual revisions to output and inflation data are in part predictable based on their past vintages.

High speed keck spectroscopy of flickering in AM Her

We present fast (72 ms) spectroscopy of AM Her obtained at an intermediate brightness state just before a rise to high state. Interesting features in the line behaviour of AM Her are noted and the variability spectrum is presented and compared to that of SS Cyg.

Do location specific forecasts pose a new challenge for communicating uncertainty?

In the last decade, the growth of local, site-specific weather forecasts delivered by mobile phone or website represents arguably the fastest change in forecast consumption since the beginning of Television weather forecasts 60 years ago. In this study, a street-interception survey of 274 members of the public a clear first preference for narrow weather forecasts above traditional broad weather forecasts is shown for the first time, with a clear bias towards this preference for users under 40. The impact of this change on the understanding of forecast probability and intensity information is explored. While the correct interpretation of the statement ‘There is a 30% chance of rain tomorrow’ is still low in the cohort, in common with previous studies, a clear impact of age and educational attainment on understanding is shown, with those under 40 and educated to degree level or above more likely to correctly interpret it. The interpretation of rainfall intensity descriptors (‘Light’, ‘Moderate’, ‘Heavy’) by the cohort is shown to be significantly different to official and expert assessment of the same descriptors and to have large variance amongst the cohort. However, despite these key uncertainties, members of the cohort generally seem to make appropriate decisions about rainfall forecasts. There is some evidence that the decisions made are different depending on the communication format used, and the cohort expressed a clear preference for tabular over graphical weather forecast presentation.

Design and baseline characteristics of the Food4Me study: a web-based randomised controlled trial of personalised nutrition in seven European countries

Improving lifestyle behaviours has considerable potential for reducing the global burden of non-communicable diseases, promoting better health across the life-course and increasing well-being. However, realising this potential will require the development, testing and implementation of much more effective behaviour change interventions than are used conventionally. Therefore, the aim of this study was to conduct a multi-centre, web-based, proof-of-principle study of personalised nutrition (PN) to determine whether providing more personalised dietary advice leads to greater improvements in eating patterns and health outcomes compared to conventional population-based advice. A total of 5,562 volunteers were screened across seven European countries; the first 1,607 participants who fulfilled the inclusion criteria were recruited into the trial. Participants were randomly assigned to one of the following intervention groups for a 6-month period: Level 0-control group-receiving conventional, non-PN advice; Level 1-receiving PN advice based on dietary intake data alone; Level 2-receiving PN advice based on dietary intake and phenotypic data; and Level 3-receiving PN advice based on dietary intake, phenotypic and genotypic data. A total of 1,607 participants had a mean age of 39.8 years (ranging from 18 to 79 years). Of these participants, 60.9 % were women and 96.7 % were from white-European background. The mean BMI for all randomised participants was 25.5 kg m(-2), and 44.8 % of the participants had a BMI ≥ 25.0 kg m(-2). Food4Me is the first large multi-centre RCT of web-based PN. The main outcomes from the Food4Me study will be submitted for publication during 2015.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.