Julkaisuarkisto

Four αI domain-containing integrins (α1β1, α2β1, α10β1, and α11β1) have evolved to recognize various members of the collagen family. A defining structural feature of these receptors is the presence of the αC helix within their αI domains (Glu284–Arg288 in the α2I domain), a structure solely found in collagen-binding integrins, whose functional mechanism has remained unclear. To elucidate the functional role of the αC helix, and to assess the contribution of the mechanistically important Arg288-Glu318 ion pair in α2I domain activation, we created two variants α2IR288A and α2IE318A. Functional solid-phase binding assays and surface plasmon resonance revealed that these variants exhibit strikingly increased avidity for collagens I and IV, whereas affinities for triple-helical GFOGER peptides, representing a single binding motif, were only slightly increased. The variants displayed distinct ligand-binding profiles, including differences in association and dissociation constants. To understand these differences at a structural level, we determined four novel X-ray crystal structures of the variants, including both ligand-free and ligand-bound (succinic acid or malic acid) states. These structures revealed differences in the folding of the αC helix region, suggesting that its conformation regulates α2I domain activation and ligand specificity, consistent with our binding assays. Additionally, the structures captured the movements of the catalytic metal ion in the metal ion dependent adhesion site, providing a detailed view of the sophisticated activation mechanism of the α2I domain.

This paper presents a longitudinal ethical analysis of Untappd, a social drinking application that gamifies beer consumption through badges, streaks, and social sharing. Building on an exploratory study conducted in 2020, we revisit the platform in 2025 to examine how its gamification features and ethical framings have evolved. Drawing on traditional ethical theory and practical frameworks for Software Engineering, we analyze five categories of badges and their implications for user autonomy and well-being. Our findings show that, despite small adjustments and superficial disclaimers, many of the original ethical issues remain. We argue for continuous ethical reflection built embedded into software lifecycles to prevent the normalization of risky behaviors through design.

Photovoltaic (PV) production grows rapidly in the Nordics, but literature on calculating long-term PV performance losses remains limited in these conditions, leading to inaccurate yield estimations and economic losses. To fill this knowledge gap, this contribution (1) shows typical PV performance characteristics, (2) obtains performance loss rates (PLRs) from multiple systems, and (3) assesses the best practices of PLR calculation under these conditions. For these objectives, we compared various methods for all steps of PLR calculation, including data filters, performance metrics, aggregation intervals, and statistical models for five systems (with 2.5–6 years of data) across Finland, from 60° to 67° N. Dark winters and snow coverage caused gaps and anomalies to the performance time series during wintertime, resulting in unrealistic PLRs. Excluding these anomalies with performance threshold filter improved the results. As the different methods can drastically affect the PLR value, an ensemble method consisting of an assessment of average PLR with over 700 calculation approaches was validated to be essential in identifying robust PLRs. Using this ensemble method, the system PLR estimates ranged from −−0.41%/year to −−2.65%/year. The data and codes are made publicly available, contributing to open access of PV datasets and allowing wider application of the PLR methodology.

Understanding the demographic mechanisms underlying population decline or recovery is critical for conservation and management, yet similar population trends may arise from very different underlying processes. Comparing populations of vulnerable species groups like long-lived raptors in different environments can reveal how species respond to change. Here, we explore the demographic drivers of population recovery in two white-tailed eagle Haliaeetus albicilla populations, for which we have comprehensive data available: a mainly coastal population in the Baltic Sea region and an Arctic inland freshwater population primarily in Lapland. We analysed over 50 years of data, including breeding pair counts, productivity, ringing records, visual resightings, and genetic identification of individuals at natal nests and breeding sites. These data were combined in a Bayesian age-structured integrated population model to estimate key demographic parameters, population trends, and conduct prospective and retrospective analyses. Both populations experienced similar growth rates (Baltic Sea: 8.7% (CRI = −1.1%–16.8%), Lapland: 8.8% (CRI = −7.0–28.0%)) primarily supported by high adult survival typical for long-lived species. Despite these similar growth rates, demographic pathways differed markedly between populations: subadult survival being higher in the Baltic Sea, whereas adult survival was higher in Lapland. Both populations showed evidence of negative density dependence, and in the Baltic Sea population this appeared to operate primarily through delayed recruitment. This was associated with an increase in the non-breeding floater cohorts, suggesting increasing constraints on recruitment into the breeding population. Our results demonstrate that comparable population growth can be sustained by contrasting demographic structures, implying that monitoring and management strategies should account for population-specific processes rather than relying on trends alone. More broadly, this study highlights the value of long-term, multi-source data and integrated demographic modelling for identifying density-dependent mechanisms and understanding recovery dynamics in long-lived species.

In this study, we analyze transparency of chatbots on Finnish well-being services counties’ websites. We study how well the transparency obligations under the EU Artificial Intelligence Act (AI Act) becoming applicable in August 2026, and the General Data Protection Regulation are met. We examined 12 chatbots and websites to assess whether they appropriately inform the user of the AI use and follow the transparency requirements of GDPR. The results indicate that, at this point in time, users are not informed transparently about the AI use. Websites also were not transparent enough about the personal data they send to third parties. These shortcomings highlight the need for clearer instructions and better design practices, so that transparency is achieved in the web-based social and health care services and the chatbots they use.