Do third-person perceptions amplify exemplification effects?

The presumed underlying mechanism of exemplification effects is that people generalize single-case media depictions and overestimate their position of social relevance, while at the same time neglecting more valid base-rate information. A 2 × 2 between-subjects experiment with n = 112 participants explored whether these exemplification effects can be explained by presumptions of strong media influences on others. Participants were shown a “rate my professor”-type website stimulus in which a single user had commented on a university course. Results show that fundamental assumptions of exemplification research interact with presumed media influences: exemplification effects can be amplified by third-person perceptions, particularly when people assess public opinion

A Feminist-Driven Computational Urban Design Framework for Mapping Gender-Inclusive Urban Places

A significant hurdle to employing data-driven and computational methods in urban design for people-place relation analysis is when the research is driven not by in-depth knowledge and theory of the field, but by data, which could lead to data autocracy. This paper aims to develop a feminist-driven framework for computational urban design to map, measure, and analyze gender-inclusive features of urban places. The framework suggests that data requirements for a computational urban design assessment need to be initially determined from domain theory patterns. The results demonstrate that the integration of multi-type, multi-scale, and multi-source datasets is needed to address all gender-inclusive features of urban places. Finally, we conclude that by adopting a theory-driven approach, it is possible to define a research system through which the re-searcher can control the data flow, guide the research path, and benefit from opportunities of geospatial big data and data-driven methods for conducting computational urban design

Dietary supplementation with a specific mannan-rich yeast parietal fraction enhances the gut and skin mucosal barriers of Atlantic salmon (Salmo salar) and reduces its susceptibility to sea lice (Lepeophtheirus salmonis)

Background: Increasing reliance on non-medicinal interventions to control sea lice in the Atlantic salmon (Salmo salar) farming industry imposes a high level of skin mucosal disturbance and indirect health issues. Dietary supplementation with yeast-based MOS products is widely used to support intestinal homeostasis across farmed species. Evidence of their effect on skin mucosa is increasing in aquatic species but it remains inconsistent and someway short of a clear contribution to sea lice management. A tank-based trial was performed to test the effect of a yeast-based MOS functional compound (sMOS) on the skin mucosal layer and its protective effects against sea lice (Lepeophtheirus salmonis). Results: The test compound significantly increased skin mucus (+46%) and goblet cell density (+25%) after 6 weeks of dietary supplementation when positive effects on intestinal villi-length (+10.9%) and goblet cell density (+80.0%) were also documented. Following dietary supplementation, a 16.6% reduction in susceptibility to an acute standard copepodid challenge was measured alongside an earlier increase in skin lysozyme activity widely used as an index of innate immunity. Conclusion: The study provides functional evidence that the benefits of dietary sMOS reach beyond the intestine to the skin mucosa. Bolstering of the Atlantic salmon skin barrier and immune functions and the resulting lower susceptibility to sea lice has the potential to reduce the need for delousing interventions and the impact of non-medicinal interventions on the animal's health and welfare

Assessing fatigue and sleep in chronic diseases using physiological signals from wearables : A pilot study

Problems with fatigue and sleep are highly prevalent in patients with chronic diseases and often rated among the most disabling symptoms, impairing their activities of daily living and the health-related quality of life (HRQoL). Currently, they are evaluated primarily via Patient Reported Outcomes (PROs), which can suffer from recall biases and have limited sensitivity to temporal variations. Objective measurements from wearable sensors allow to reliably quantify disease state, changes in the HRQoL, and evaluate therapeutic outcomes. This work investigates the feasibility of capturing continuous physiological signals from an electrocardiography-based wearable device for remote monitoring of fatigue and sleep and quantifies the relationship of objective digital measures to self-reported fatigue and sleep disturbances. 136 individuals were followed for a total of 1,297 recording days in a longitudinal multi-site study conducted in free-living settings and registered with the German Clinical Trial Registry (DRKS00021693). Participants comprised healthy individuals (N = 39) and patients with neurodegenerative disorders (NDD, N = 31) and immune mediated inflammatory diseases (IMID, N = 66). Objective physiological measures correlated with fatigue and sleep PROs, while demonstrating reasonable signal quality. Furthermore, analysis of heart rate recovery estimated during activities of daily living showed significant differences between healthy and patient groups. This work underscores the promise and sensitivity of novel digital measures from multimodal sensor time-series to differentiate chronic patients from healthy individuals and monitor their HRQoL. The presented work provides clinicians with realistic insights of continuous at home patient monitoring and its practical value in quantitative assessment of fatigue and sleep, an area of unmet need.publishedVersionPeer reviewe

THE ROLE OF TRANSPARENCY IN PRIVACY DECISION-MAKING UNDER UNCERTAINTY

Recent data breaches at online content and service providers (CSPs) such as Facebook or Uber illustrate the privacy risks associated with the disclosure of personal data. Yet, asymmetric information between users and CSPs makes it difficult for users to assess their privacy risks. Thus, in order to reduce uncertainty and assist users with increasingly complex privacy trade-offs, regulators and consumer protection agencies advise CSPs to be more transparent about their data collection, storage and use. In this context, Information Systems research has largely focused on the effectiveness of transparency measures in specific application scenarios (e.g. recommender systems, targeted advertising) by exogenously assigning subjects to scenarios with or without transparency. However, it is unclear whether users would actively choose a more transparent over a less transparent CSP, as they may prefer ambiguity regarding privacy risks and information avoidance. To advance research in this area, this paper presents an experimental design to study subjects’ preferences for transparency in a controlled laboratory environment. Drawing on the field of decision analysis and established theories on uncertainty and ambiguity attitudes, the present study contributes to a better understanding of human privacy decision-making

The Value of Personal Data: An Experimental Analysis of Data Types and Personal Antecedents

Experimental investigations of privacy decision-making can provide complementary insights to surveys but they raise the methodological challenge of which data type(s) to use in controlled laboratory environments. To this end, we conduct a laboratory experiment with 317 participants and elicit subjects’ data valuation (i.e., willingness-to- accept, WTA) of three different synthetic data types, which we create and collect in the laboratory. We find that subjects place positive, although different, monetary valuations on these data sets and show that these valuations are similar to subjects’ valuations of field data created at an online retailer. Moreover, we find that subjects’ WTA is significantly influenced by their gender, age, general risk attitude as well as stated privacy concerns. Finally, we find that information sensitivity mediates the relationship between data type and subjects’ WTA. Altogether, our findings provide guidance for future experimental designs on revealed privacy decision-making and the pricing of personal data brokers

Extending the Conversation: A Vision for Urban Accessibility for Diverse Mobilities through GeoAI

This paper envisions creating more inclusive communities through accessible urban places for not only those who identify as disabled but all equity-deserving groups. Concentrating on the street scale of the urban places, we propose identifying street scale accessibility features, and then, with the help of spatial data science and geospatial artificial intelligence, collecting and analyzing reliable data on these features to assess the accessibility of the urban places for movement diversity