Model Systems infrastructure of the German Center for Mental Health (DZPG): toward an integrated, mechanism-based framework for mental health research.
Oct 2026· European Archives of Psychiatry and Clinical Neuroscience· 0 citations· 88 references
Medicine
Abstract
Mental health is fragile and disorders are highly prevalent arising from complex interactions between genetic, developmental, psychological, social and environmental factors. The key mechanisms underlying resilience, vulnerability factors, brain repair systems, and recovery remain poorly understood, hindering the development of effective prevention and personalized treatment. The German Center for Mental Health (Deutsches Zentrum für Psychische Gesundheit, DZPG) addresses these challenges through a nationwide, interdisciplinary research network that integrates basic science with translational approaches across multiple sites. Its embedded "Model Systems" infrastructure provides a state-of-the-art platform to investigate brain-behavior mechanisms. Animal models are available to explore the links between genes, neural circuits, and behavior. Additionally, humanized model systems are established based on transgenesis and induced pluripotent stem cell (iPSC)-based models. Brain organoids further support research into neurodevelopment and multicellular interactions.Critically, when combined with high-content/high-throughput screening, iPSC-derived models link disease mechanisms to therapeutic targets. Advanced techniques, such as machine learning-based behavior quantification, single-cell and spatial multi-omics analyses, and genome editing enable in-depth analysis of disease/patient-specific cellular phenotypes and networks. Patient and public involvement embedded within the Model Systems infrastructure secures the scientific and translational relevance of the activities and helps to link experimental findings in our model systems to clinical symptoms and behavioral dimensions. By integrating animal, cellular, and human data on a multi-systems level with clinically relevant symptoms, the DZPG leverages model systems to accelerate mechanistic discovery and translation in mental health research.
Some claim that especially in the field of agile software development the research lags years behind of the practice. In this paper, we characterize the status and main challenges for research on agile software development, and propose a preliminary roadmap, focusing on providing more empirical research, primarily on e...
Torgeir Dingsøyr, T. Dybå, P. Abrahamsson· Agile Conference· 92 citations· ⚡7
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoptio...
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.