Gut microbiota of Type 1 diabetes patients with good glycaemic control and high physical fitness is similar to people without diabetes: an observational study

Type 1 diabetes is the product of a complex interplay between genetic susceptibility and exposure to environmental factors. Existing bacterial profiling studies focus on people who are most at risk at the time of diagnosis; there are limited data on the gut microbiota of people with long-standing Type 1 diabetes. This study compared the gut microbiota of patients with Type 1 diabetes and good glycaemic control and high levels of physical-fitness with that of matched controls without diabetes.Ten males with Type 1 diabetes and ten matched controls without diabetes were recruited; groups were matched for gender, age, BMI, peak oxygen uptake (VO2max ), and exercise habits. Stool samples were analysed using next-generation sequencing of the 16S rRNA gene to obtain bacterial profiles from each individual. Phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) was implemented to predict the functional content of the bacterial operational taxonomic units.Faecalibacterium sp., Roseburia sp. and Bacteroides sp. were typically the most abundant members of the community in both patients with Type 1 diabetes and controls, and were present in every sample in the cohort. Each bacterial profile was relatively individual and no significant difference was reported between the bacterial profiles or the Shannon diversity indices of Type 1 diabetes compared with controls. The functional profiles were more conserved and the Type 1 diabetes group were comparable with the control group.We show that both gut microbiota and resulting functional bacterial profiles from patients with long-standing Type 1 diabetes in good glycaemic control and high physical fitness levels are comparable with those of matched people without diabetes. This article is protected by copyright. All rights reserved

Mixed-initiative approaches to on-device mobile game design

Playing casual games is a wildly popular activity on smartphones. However, designing casual games is done by a smaller group of people, usually on desktop computers, using professional development tools. Our goal is to bring these activities closer together, in terms of who does them and how they do them. Our Gamika Technology platform is a 2D physics-based mobile game design environment. It comprises a 284-dimensional parametric design space, and poses mobile game design as the problem of navigating this space. We have built three mobile apps thus far to experiment with on-device, mixed-initiative navigation of the Gamika design space and some of its subspaces. We describe these apps here in terms of the initiatives that go into making a game with them, and how these are split between people and underlying AI software. Our overall goal is to democratise game design, so that anyone and everyone can make casual games directly on their mobile phones or tablets

Does landscape-scale conservation management enhance the provision of ecosystem services?

Biodiversity conservation approaches are increasingly being implemented at the landscape-scale to support the maintenance of metapopulations and metacommunities. However, the impact of such interventions on the provision of ecosystem services is less well defined. Here we examine the potential impacts of landscape-scale conservation initiatives on ecosystem services, through analysis of five case study areas in England and Wales. The provision of multiple ecosystem services was projected according to current management plans and compared with a baseline scenario. Multicriteria analysis indicated that in most cases landscape-scale approaches lead to an overall increase in service provision. Consistent increases were projected in carbon storage, recreation and aesthetic value, as well as biodiversity value. However, most study areas provided evidence of trade-offs, particularly between provisioning services and other types of service. Results differed markedly between study areas, highlighting the importance of local context. These results suggest that landscape-scale conservation approaches are likely to be effective in increasing ecosystem service provision, but also indicate that associated costs can be significant, particularly in lowland areas

Cosmic strings from pseudo-anomalous Fayet-Iliopoulos U(1) in D3/D7 brane inflation

We examine the consequences of recent developments on Fayet-Iliopoulos (FI) terms for D-term inflationary models. There is currently no known way to couple constant FI terms to supergravity consistently; only field-dependent FI terms are allowed. These are natural in string theory and we argue that the FI term in D3/D7 inflation turns out to be of this type, corresponding to a pseudo-anomalous U(1). T he anomaly is canceled by the Green-Schwarz mechanism in 4 dimensions. Inflation proceeds as usual, except that the scale is set by the GS parameter. Cosmic strings resulting from a pseudo-anomalous U(1) have potentially interesting characteristics. Originally expected to be global, they turn out to be local in the string theory context and can support currents. We outline the nature of these strings, discuss bounds on their formation, and summarize resulting cosmological consequences.Comment: 10 pages; minor changes to match published versio

Two-loop RGEs with Dirac gaugino masses

The set of renormalisation group equations to two loop order for general supersymmetric theories broken by soft and supersoft operators is completed. As an example, the explicit expressions for the RGEs in a Dirac gaugino extension of the (N)MSSM are presented.Comment: 10 pages + 24 pages of RGEs in appendix; no figure

Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201