What helps adolescents with obesity to enjoy and participate in physical activity? - Constanze Greule; Gorden Sudeck; Katrin Ziser; Rebecca Erschens; Peter Martus; Katrin Elisabeth Giel; Isabelle Mack; Alisa Weiland; Guido Zurstiege; Stephan Zipfel; Stefan Ehehalt; Florian Junne; et al. (German Journal of Exercise and Sport Research, 2026)
Purpose The main purpose was to identify clusters of adolescents with overweight or obesity characterized by the satisfaction of basic psychological needs, exercise-related barriers, and social support by family members and friends. This served the overarching goal of informing hypotheses for tailored research approaches in physical activity promotion considering a stronger focus on physical activity enjoyment. Methods Cross-sectional baseline data from 125 adolescents with overweight or obesity aged 12–17 years participating in the STARKIDS study were analyzed. A hierarchical cluster analysis was carried out, and the resulting number of clusters was optimized using the K‑means method. Subsequently, differences between individual cluster membership regarding physical activity enjoyment and physical activity levels were analyzed to further describe the identified subgroups. Results Four psychosocial clusters were identified as (1) overall supportive, (2) overall hindering, (3) individually supportive, and (4) socially supportive. The clusters differ significantly in terms of physical activity enjoyment ( p η 2 = 0.370) and physical activity behavior ( p η 2 = 0.145), with the highest values of enjoyment in clusters (1) and (3). Conclusion The present results indicate promising opportunities for future research on promoting physical activity enjoyment considering adolescents’ individual and social resources.

Designing a blended microbial culture to bioremediate distinct environmental matrices from a complex industrial site contaminated with amino-, nitro-, and chloroaromatic compounds - Isabelle Câmara; Natanna Melo; Line Lomheim; E. Erin Mack; Elizabeth A. Edwards; Jim C. Spain; Osmar Menezes; Savia Gavazza (Environmental Research, 2025)

Argo Data 1999–2019: Two Million Temperature-Salinity Profiles and Subsurface Velocity Observations From a Global Array of Profiling Floats - Annie P. S. Wong; Susan E. Wijffels; Stephen C. Riser; Sylvie Pouliquen; Shigeki Hosoda; Dean Roemmich; John Gilson; Gregory C. Johnson; Kim Martini; David J. Murphy; Megan Scanderbeg; T. V. S. Udaya Bhaskar; et al. (Frontiers in Marine Science, 2020)

TRY plant trait database – enhanced coverage and open access - Jens Kattge; Gerhard Bönisch; Sandra Díaz; Sandra Lavorel; Iain Colin Prentice; Paul Leadley; Susanne Tautenhahn; Gijsbert D. A. Werner; Tuomas Aakala; Mehdi Abedi; Alicia T. R. Acosta; George C. Adamidis; et al. (Global Change Biology, 2020)
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.