Systemic sclerosis (SSc) is an autoimmune disease which is characterized by vasculopathy, tissue fibrosis and activation of the innate and adaptive immune system. Clinical features of the disease consists of skin thickening and internal organ involvement. Due to the heterogeneous nature of the disease it is difficult to predict disease progression and complications. Despite the discovery of novel autoantibodies associated with SSc, there is an unmet need for biomarkers for diagnosis, disease progression and response to treatment. To date, the use of single (surrogate) biomarkers for these purposes has been unsuccessful. Combining multiple biomarkers in to predictive panels or ultimately algorithms could be more precise. Given the limited therapeutic options and poor prognosis of many SSc patients, a better understanding of the immune-pathofysiological profiles might aid to an adjusted therapeutic approach. Therefore, we set out to explore immunological fingerprints in various clinically defined forms of SSc. We used multilayer profiling to identify unique immune profiles underlying distinct autoantibody signatures. These immune profiles could fill the unmet need for prognosis and response to therapy in SSc. Here, we present 3 pathophysiological fingerprints in SSc based on the expression of circulating antibodies, vascular markers and immunomodulatory mediators.
The sharing economy holds promise for the way we consume, work, and interact. However, consuming in the sharing economy is not without risk, as institutional trust measures (e.g. contracts, regulations, guarantees) are often absent. Trust between sellers and buyers is therefore crucial to complete transactions successfully. From a buyer ́s perspective, a seller ́s profile is an important source of information for judging trustworthiness, because it contains multiple trust cues such as a reputation score, a profile picture, and a textual self-description. The effect of a seller’s self-description on perceived trustworthiness is still poorly understood. We examine how the linguistic features of a seller’s self-description predict perceived trustworthiness. To determine the perceived trustworthiness of 259 profiles, 189 real buyers on a Dutch sharing platform rated their trustworthiness. The results show that profiles were perceived as more trustworthy if they contained more words (which could be an indicator of uncertainty reduction), more words related to cooking (indicator of expertise), and more words related to positive emotions (indicator of enthusiasm). Also, a profile’s perceived trustworthiness score correlated positively with the seller’s actual sales performance. These findings indicate that a seller’s self-description is a relevant signal to buyers, eventhough it is cheap talk (i.e. easy to produce). The results can guide sellers on how to self-present themselves on sharing platforms and inform platform owners on how to design their platform so that it enhances trust between platform users.
Young people spend a large part of their day sedentary, both at school and at home. The aim of the PlayFit project is to persuade teenagers to lead a more active lifestyle by using digital as well as non-digital games and play. In this position paper, we describe in detail the three key principles of our vision concerning the design of game-based interventions for stimulating physical activity: playful persuasion, ambient action and play profiles. In our vision teenagers take part in playful activities and games throughout the day. In these activities, casual action is inherent to the fun experience, thus reducing teenagers' sedentary behavior. Relevant information about their activities and preferences is stored in a personal play profile, which affects the games they play and through which they can communicate to their peers. We illustrate this vision by means of several innovative game concepts.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
