‘What’s it like to have ME?’ The discursive construction of ME in computer-mediated communication and face-to-face interaction

ME/CFS (chronic fatigue syndrome) is a debilitating illness for which no cause or medical tests have been identified. Debates over its nature have generated interest from qualitative researchers. However, participants are difficult to recruit because of the nature of their condition. Therefore, this study explores the utility of the internet as a means of eliciting accounts. We analyse data from focus groups and the internet in order to ascertain the extent to which previous research findings apply to the internet domain. Interviews were conducted among 49 members of internet (38 chatline, 11 personal) and 7 members of two face-to-face support groups. Discourse analysis of descriptions and accounts of ME/CFS revealed similar devices and interactional concerns in both internet and face-to-face communication. Participants constructed their condition as serious, enigmatic and not psychological. These functioned to deflect problematic assumptions about ME/CFS and to manage their accountability for the illness and its effects

Superprocesses as models for information dissemination in the Future Internet

Future Internet will be composed by a tremendous number of potentially interconnected people and devices, offering a variety of services, applications and communication opportunities. In particular, short-range wireless communications, which are available on almost all portable devices, will enable the formation of the largest cloud of interconnected, smart computing devices mankind has ever dreamed about: the Proximate Internet. In this paper, we consider superprocesses, more specifically super Brownian motion, as a suitable mathematical model to analyse a basic problem of information dissemination arising in the context of Proximate Internet. The proposed model provides a promising analytical framework to both study theoretical properties related to the information dissemination process and to devise efficient and reliable simulation schemes for very large systems

Finite-size scaling of meson propagators

Using quenched chiral perturbation theory we compute meson correlation functions at finite volume and fixed gauge field topology. We also present the corresponding analytical predictions for the unquenched theory at fixed gauge field topology. These results can be used to measure the low-energy parameters of the chiral Langrangian from lattice simulations in volumes much smaller than one pion Compton wavelength

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives