Despite the growing economic inequality, there is no evidence of growing public demand for wealth redistribution. One proposed explanation for this paradox is the popular belief that inequality is the outcome of a fair process where societal success reflects talent and effort. Across countries, the popularity of these meritocratic beliefs bears no relationship to the actual social mobility and is associated with increased levels of within-country inequality. On the other hand, these beliefs negatively correlate with the extent of social spending and redistribution in each country. In light of this correlational evidence, this project employ an experimental approach to investigate the potential role of redistribution policies in the formation of biased meritocratic beliefs and the psychological mechanisms that underlie this process. Building on research in Psychology and Economics, I first examine whether the absence of redistribution incentivizes agents to become more tolerant of inequality. Drawing on the theory of cognitive dissonance, I then explore whether high inequality tolerance leads to endorsement of meritocratic beliefs. After establishing the role of redistribution on beliefs, I use functional neuroimaging to shed light on the neurocognitive mechanisms that underlie the formation of biased beliefs. Overall, this research tries to address a long-standing question about the psychological processes that lead to inequality tolerance and fits the EU priorities of a deeper and fairer economic Union. My strong background in Neuroscience, combined with my supervisor’s extensive expertise in Behavioral Economics and the excellent training environment at UM guarantee the success of this project. This project will critically contribute to my development as an independent researcher in the field of Neuroeconomics and Behavioral Economics and will allow me to forge a new collaborative research line at the MPE.

Most people who develop ischemic HF have (or had) an ischemic heart condition first (e.g. myocardial infarction, MI). Restoring damaged heart muscle tissue therefore represents a fundamental mechanistic strategy to treat HF. Cardiac regeneration after ischemic damage can be achieved by stimulating the proliferation of already existing (endogenous) cardiomyocytes rather than having to rely on the implantation of exogenous cells (i.e. stem cell therapy). Compelling evidence supports that expanding the endogenous cardiomyocyte proliferation capacity by gene therapy appears to represent a promising approach to achieve endogenous cardiac regeneration. Species of non-coding RNA molecules, microRNAs, have been demonstrated to stimulate cardiomyocyte proliferation and the recognition of microRNAs as potential regenerative targets marks a major step towards fundamentally new therapeutic concepts that SUMMA addresses in ischemic heart disease. In the context of the ERC-Starting Grant project CALMIRS, we identified the miR-106b~25 cluster with high potential as new target for therapy to stimulate regeneration of the heart by promoting cardiomyocyte proliferation. Within SUMMA, we aim to advance these valuable research results on microRNA cardiac gene therapy in mice towards commercial proof-of-concept. During this project, proof-of-concept efficacy studies in a large animal (e.g. porcine) model of cardiac ischemia will be performed, while simultaneously market research, IP strategy development and business development activities take place to maximize the value of the project’s results. Different business models will be studied in terms of market research, IP strategy and business development to eventually consolidate a commercial strategy and business case for presenting our business proposition to strategic partners or venture capitalists.

Artificial Intelligence (AI), deep-learning in particular, is propelling the field of radiology forward at a rapid pace. In oncology, AI can characterize the radiomic phenotype of the entire tumor and provide a non-invasive window into the internal growth patterns of a cancer lesion. This is especially important for patients treated with immunotherapy as, despite the remarkable success of these novel therapies, the clinical benefit remains limited to a subset. As immunotherapy is expensive and could bring unnecessary toxicity there is a direct need to identify beneficial patients, but this remains difficult in clinical practice today. Radiomic biomarkers could address this, as, unlike biopsies that only represent a sample within the tumor, radiomics can depict a full picture of each cancer lesion with a single non-invasive examination. Previous work found significant connections between radiomic data, molecular pathways, and clinical outcomes. However, a direct link between radiomics and immunotherapy response has not yet been established. This project will address this problem by analyzing unique multicentre clinical data, including non-invasive imaging, clinical outcomes, and extensive biologic characterization of patients with lung or melanoma cancer. Specifically, I will develop deep-learning radiomic biomarkers to predict immunotherapy response using baseline (WP1) and follow-up imaging (WP2). I will also investigate if radiomics can characterize underlying biological factors, and, in turn, can be used to improve response predictions (WP3). Successful completion of this proposal will demonstrate the potential of radiomics to help physicians in selecting patients who will likely benefit from immunotherapy, while sparing this expensive and potentially toxic treatment for patients who don't. This work has implications for the use of imaging-based biomarkers in the clinic, as they can be applied noninvasively, repeatedly, and at low additional cost.

Recent literature has underlined the interplay among climate mitigation, adaptation, and finance, as well as between climate action and other development agendas, including sustainable resource use, human development and equity, and environmental pressures. Such an interconnected policy environment requires an integrated ecosystem of disciplines, methods, and tools. Despite the significant evolution of integrated assessment models (IAMs) in the last decade, there remain several criticisms on their design, use, and adequacy to respond to unaddressed and emerging questions in the light of the Paris Agreement and net-zero ambition. These include openness, legitimacy, and ownership, as well as technical feasibility to represent demand-side and broader societal transformations, cross-sectoral interactions, physical impacts and adaptation, climate finance and labour dynamics, and other sustainability goals. DIAMOND will update, upgrade, and fully open six IAMs that are emblematic in scientific and policy processes, improving their sectoral and technological detail, spatiotemporal resolution, and geographic granularity. It will further enhance modelling capacity to assess the feasibility and desirability of Paris-compliant mitigation pathways, their interplay with adaptation, circular economy, and other SDGs, their distributional and equity effects, and their resilience to extremes, as well as robust risk management and investment strategies. This will be done via integration of tools and insights from psychology, finance research, behavioural and labour economics, operational research, and physical science. We will develop a transdisciplinary scientific approach to legitimise the implementation process and co-create research questions that stretch the frontiers of climate science, as well as establish vibrant communities of practice to transparently open model enhancements and to develop capacities, thereby lowering the entrance barriers to the established IAM community.