Recent progress in parton distributions and implications for LHC physics

I outline some of the most recent developments in the global fit to parton distributions performed by the MRST collaboration

Vertebrate fauna associates of the Wedge-tailed Shearwater, Puffinus pacificus, colonies of Rottnest Island: influence of an ecosystem engineer

Wedge-tailed Shearwaters, Puffinus pacificus, engineer the ecosystem by digging burrows in which they nest. This has been previously shown to affect the soil and vegetation properties of their colonies. Here we report on field surveys employed to investigate how associated vertebrate fauna respond to physical habitat modification by shearwaters. The study area was species poor, with only one mammal, and three reptile species detected in 1440 Elliott trap and 720 pitfall trap nights across a 13-month period. Nineteen bird species were recorded from 98 survey days. Relative to an area of uncolonised heath, we observed an increase in the abundance of King's Skinks, Egernia kingii, and a decrease in the abundance of House Mice, Mus musculus, and West Coast Ctenotus, Ctenotus fallens, in the shearwater colony. The survival rates of King's Skinks and House Mice were not affected by Wedge-tailed Shearwater presence. Bird species richness was less in the colony (9.2 ±0.5 species month-I) than the heath (11.5 ±0.2 species month-I), and the composition of the two communities was different. We suggest that ecosystem engineering by Wedge-tailed Shearwaters is a major determinant of fauna associates of their colonies and offer direct and indirect mechanisms to explain the patterns of species occurrence observed

osl-dynamics: a toolbox for modelling fast dynamic brain activity

Neural activity contains rich spatio-temporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of a tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modelling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events is often a priori unknown. Here we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behaviour and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modelling of fast dynamic processes

Resource use data by patient report or hospital records: Do they agree?

Background: Economic evaluations alongside clinical trials are becoming increasingly common. Cost data are often collected through the use of postal questionnaires; however, the accuracy of this method is uncertain. We compared postal questionnaires with hospital records for collecting data on physiotherapy service use. Methods: As part of a randomised trial of orthopaedic medicine compared with orthopaedic surgery we collected physiotherapy use data on a group of patients from retrospective postal questionnaires and from hospital records. Results: 315 patients were referred for physiotherapy. Hospital data on attendances was available for 30% (n = 96), compared with 48% (n = 150) of patients completing questionnaire data (95% Cl for difference = 10% to 24%); 19% (n = 59) had data available from both sources. The two methods produced an intraclass correlation coefficient of 0.54 (95% Cl 0.31 to 0.70). However, the two methods produced significantly different estimates of resource use with patient self report recalling a mean of 1.3 extra visits (95% Cl 0.4 to 2.2) compared with hospital records. Conclusions: Using questionnaires in this study produced data on a greater number of patients compared with examination of hospital records. However, the two data sources did differ in the quantity of physiotherapy used and this should be taken into account in any analysi

On the flexibility of the design of Multiple Try Metropolis schemes

The Multiple Try Metropolis (MTM) method is a generalization of the classical Metropolis-Hastings algorithm in which the next state of the chain is chosen among a set of samples, according to normalized weights. In the literature, several extensions have been proposed. In this work, we show and remark upon the flexibility of the design of MTM-type methods, fulfilling the detailed balance condition. We discuss several possibilities and show different numerical results

Emotions and Digital Well-being. The rationalistic bias of social media design in online deliberations

In this chapter we argue that emotions are mediated in an incomplete way in online social media because of the heavy reliance on textual messages which fosters a rationalistic bias and an inclination towards less nuanced emotional expressions. This incompleteness can happen either by obscuring emotions, showing less than the original intensity, misinterpreting emotions, or eliciting emotions without feedback and context. Online interactions and deliberations tend to contribute rather than overcome stalemates and informational bubbles, partially due to prevalence of anti-social emotions. It is tempting to see emotions as being the cause of the problem of online verbal aggression and bullying. However, we argue that social media are actually designed in a predominantly rationalistic way, because of the reliance on text-based communication, thereby filtering out social emotions and leaving space for easily expressed antisocial emotions. Based on research on emotions that sees these as key ingredients to moral interaction and deliberation, as well as on research on text-based versus non-verbal communication, we propose a richer understanding of emotions, requiring different designs of online deliberation platforms. We propose that such designs should move from text-centred designs and should find ways to incorporate the complete expression of the full range of human emotions so that these can play a constructive role in online deliberations

osl-dynamics, a toolbox for modeling fast dynamic brain activity

Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes