TwinAIR aims to improve urban life by tackling the challenge of indoor air quality (IAQ) improvement by understating its complex interrelationship with external factors. This is achieved by introducing a novel set of tools for identifying sources and tracing a variety of pollutants and pathogens, for enhancing understanding of their effects and assessing their impact on health, for controlling building management systems and services in ways that mitigate part of the impacts and for helping citizens to develop better insights into pollution impacts, along with encouraging healthier, more sustainable choices. TwinAIR embraces bleeding edge innovation in urban sensing (chemical and environmental sensors), data analytics and visualisation (digital maps and real-time video analysis), smart buildings (digital twins and virtual sensors) and behavioural insights (citizen participation, gamification) to deliver a nascent solution. It is implemented across six diverse pilot sites in Europe (ES, IE, UK, SE, DE, EL) with demonstrations covering residential dwellings, public administration buildings, hospitals and schools, along with selected types of vehicles (buses, vans). TwinAIR?s toolsets will empower students and their parents, commuters, workers and residents to make more health-aware personal decisions about their everyday mobility options and use of indoor spaces, through access to insightful analytics and engaging visualisations of their data, as well as by their participation in educational events and activities. At the same time, it will provide rich evidence to transport planners, facility managers and policymakers about factors influencing IAQ and effective interventions for mitigating its effects on health and wellbeing. By democratising cutting edge innovation in sensors, digital twinning and visual analytics, TwinAIR will enable better decision-making about future mobility policies, built environment management and incentivisation of citizens. Project TwinAIR is part of the European cluster on indoor air quality and health.

Effective personalized medicine for paediatric cancers must address a multitude of challenges, including domain-specific challenges. To overcome these challenges, we propose a comprehensive computational effort to combine knowledge-base, machine-learning, and mechanistic models to predict optimal standard and experimental therapies for each child. Our approach is based on virtual patient models–in-silico avatars whose analysis can inform personalized diagnostics and recommend treatments. Our platform will also allow care givers to query models and infer benefits and drawbacks for specific treatment combinations for each child. To construct these models, we will combine state-of-the-art computational methods and data from molecular assays, and clinical and preclinical studies. We will test their predictions prospectively on data from clinical trials and test therapies in pre-clinical settings. We will focus on a select panel of paediatric tumours including both high-incidence and high-risk tumour types. To accomplish our goals, we have assembled an interdisciplinary team consisting of basic, translational, and clinical researchers—all amongst the leaders in their respective fields—and established strong relationships with European Centres of Excellence, patient organizations, and clinical trials focus on personalized medicine for our proposed case studies. We will produce, assemble, standardize, and harmonize accessible high-quality multi-disciplinary data and leverage the potential of Big Data and HPC for the personalized treatments of European citizens. We will make our models and data available through a cloud-based platform, whose exploitation will be maximised through a collaboration with the European Open Science Cloud initiative. In summary, iPC will address the critical need for personalized medicine for children with cancer, contribute to the digitalization of clinical workflows, and enable the Digital Single Market of the EU data infrastructure.