ObjectiveCharacter strengths are positive personality traits that not only define our core identity, but also yield positive effects for us and those around us. Psychological research has often been one-sidedly focused on tackling health risk factors or maladaptive traits, disregarding the potential of fostering positive resources such as character strengths when aiming to influence health trajectories. We examine the predictive validity of character strengths for health-related outcomes using machine learning algorithms.MethodsUsing a sample of 4,830 adults from five countries, we examined the validity of character strengths for the prediction of 12 health-related indicators (e.g., sleep quality, feeling anxious, or healthy dieting) across two levels of measurement (items vs. scales), modeling approaches (multiple regression vs. three machine learning algorithms), and cultural contexts.ResultsThe outcomes could be predicted by character strengths with R² values ranging from .02 for the prediction of poor physical health to .28 for poor mental health. Character strength items rarely out-predicted their overarching scales. Machine learning algorithms were able to enhance predictive performance by means of regularization, but the results did not point to meaningful non-linear or interaction effects. The largest differences in predictive performance were found when evaluating models across culturally dissimilar countries.ConclusionsCultural context proved an important moderator of the association between character strengths and mental as well as physical health indicators. In contrast, the incremental value of character strengths at the item level or including complex relationships in the modeling compared to simpler modeling approaches is negligible.

was 51.1±11.2 years. Body weight decreased from 144.0±27.6 kg at baseline to 121.1±25.0 kg at 24 weeks (P<0.001), a mean total body weight loss of 15.9±6.0%, with a reduction in body mass index from 50.6±8.0 to 42.6±7.6 kg m (P<0.001). In patients with diabetes, haemoglobin A1c decreased from 66.3±13.0 to 48.3±13.5 mmol/mol (P<0.001) and diabetes medication use decreased significantly. There were significant improvements also in lipid profiles and reductions in antihypertensive medication use. Conclusion: These preliminary findings suggest that completion of a 24-week milk-based meal replacement program has large effects on important outcomes in adults with severe obesity. However, attrition was high. Prospective assessment of the efficacy, safety, durability and cost-effectiveness of this intervention seems warranted.

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Note to reader: We present here some guidelines on how to represent HDF4 objects in HDF5 and how to interpret HDF5 objects as HDF4 objects. It is meant to help in implementing software that has to deal with both formats in some consistent way, such as converting HDF4 files to HDF5, or adapting HDF4 tools to HDF5. Although the latest revision is more than 10 years after the previous one, we feel that most information in the previous revision is still valuable. The latest revision corrects errors and updates the out-of-dated information. The significant improvement is to represent SDS dimension scales with the HDF5 Dimension Scales. Please send comments and corrections to hdfhelp@hdfgroup.org.

This paper discusses aspects of a collaborative investigation of embodied computing and personal manufacturing. We describe the NeuroMaker 1.0, an artwork that playfully implements the concept of "translating of the designer's ideas into a product". Visitors to the installation were invited to use their own EEG to fabricate personalized physical objects. While primarily intended to provoke thought about the process of creativity, we also demonstrated that, with the right team, radical new interfaces are well within the reach.

The Tupelo semantic content management middleware implements Knowledge Spaces that enable scientists to locate, use, link, annotate, and discuss data and metadata as they work with existing applications in distributed environments. Tupelo is built using a combination of commonly-used Semantic Web technologies for metadata management, content management technologies for data management, and workflow technologies for management of computation, and can interoperate with other tools using a variety of standard interfaces and a client and desktop API. Tupelo's primary function is to facilitate interoperability, providing a Knowledge Space "view" of distributed, heterogeneous resources such as institutional repositories, relational databases, and semantic web stores. Knowledge Spaces have driven recent work creating e-Science cyberenvironments to serve distributed, active scientific communities. Tupelo-based components deployed in desktop applications, on portals, and in AJAX applications interoperate to allow researchers to develop, coordinate and share datasets, documents, and computational models, while preserving process documentation and other contextual information needed to produce a complete and coherent research record suitable for distribution and archiving.