AU

Our understanding of the neural basis of human cognition and its relation to behaviour is limited by the extent to which we can observe its underlying components. Neural activity elicited by a given stimulus can be decomposed in parallel threads of cognitive computation, each specialising on a different aspect of the stimulus. Conventional methods are fundamentally limited to tease apart these components within the stimulus-specific brain activity, therefore obscuring our understanding of the underlying mechanisms. I will build a framework to distil these threads by modelling their (trial-by-trial) distinct spatiotemporal trajectories and the interaction between them. Furthermore, I propose that the way our brains process stimuli, and in particular how these different components organise and relate to each other, can be critical to characterise subjects at the psychological and clinical level. However, it is unclear how to relate these complex models of stimulus processing to the subject phenotypes. I will develop principled algorithms to automatically discover which specific aspects of the modelled brain activity are most relevant to the traits under study. In summary, this multidisciplinary project brings together modelling and prediction across different data modalities to offer a novel temporal analytic account of how different threads of brain activity give rise to cognition, and how the nature of these elements relates to population variability. I will tackle three important questions that are representative of the addressed methodological challenges: in the study of decision-making, the relation between value representation, decision-formation and attention; in sleep research, which specific aspects of the sleep cycle are most altered in insomniacs; in the field of pain perception, the disambiguation of nociception and salience, and how these diverge in chronic pain. Despite diverse, these questions are conceptually linked by ideas presented here.

Only 13.1 million km2 intact forests are left on Earth, and many risk degradations despite their importance for global biodiversity conservation, ecosystem services and human well-being. Meanwhile, a global increase in so-called forest transitions, where deforestation is replaced by reforestation, is observed. It is still unclear how and why forest transitions happen in some areas and not in others, to what extent the new forests will recover towards their intact state, and how transitions are affected by the intensifying societal and climate changes of our current human-dominated epoch, the Anthropocene. FAIR aims to deliver the first global map of forest transitions, i.e., de- to reforestation shifts and analogous shifts in intactness, and to test potential links between their dynamic trajectories to societal and climate change. Various remote sensing (RS) products across the globe and novel approaches will be used to answer the following questions: 1) How can RS be used to map forest transition types in human-altered forests? 2) How do transitioning forests differ from intact forests in structure and composition globally? 3) How do increasing societal pressure and climate change affect forest transitions and what are the implications for future restoration? FAIR will be hosted at Aarhus University with supervision of Prof. Svenning, a world-leading expert on macroecology with special interest in trees and forests. The combination of the professional backgrounds of Dr. Li (advanced RS science) and the supervisor will allow FAIR to develop an effective analytical framework and truly novel understanding of forest transitions, providing an important basis for better development of landscape planning to promote forest ecosystem services in the Anthropocene. Prof. Svenning’s strong experience in public outreach and dissemination will facilitate Dr. Li in developing an effective communication strategy for FAIR to contribute to future forest restoration policies.

Aerosol particles are ubiquitous constituents in the ambient atmosphere. Ultrafine particles (< 10 nm) show adverse effects on our public health, as inhalation leads to an elevated risk of lung- and cardiovascular diseases. Aerosol particles also influence the global climate, by scattering sunlight away from Earth's surface and acting as seeds for cloud droplet formation. Combined, these effects lead to an overall cooling of the Earth, directly counteracting the warming effect of greenhouse gases. According to the IPCC, aerosol particles pose the largest uncertainty in global climate forecast. This uncertainty is caused by the lack of understanding of the early growth behaviour of small (< 3 nm) particles. The largest source of aerosol particles (50-90%) is from nucleation of vapours in the air leading to a burst of freshly nucleated particles (FNPs) of 1-2 nm in size. However, even the basic fundamental properties of these FNPs remain unknown and cannot be studied using currently available experimental techniques. I propose a unique approach to target the properties of FNPs by applying a versatile suite of computational methods, ranging from quantum chemical calculations to application of conceptually new machine learning models. The scientific objectives are: 1) To determine the chemical composition and stability of FNPs. 2) To understand how FNPs evolve over time via exchange of vapours with the environment. 3) To investigate how FNPs transform as a consequence of chemical reactions occurring at the surface or inside the particles. The research will provide unprecedented insight into the molecular level properties of FNPs. This project will directly supply input parameters (chemical composition, thermodynamics and kinetics) for atmospheric models, which are crucial in order to constrain the large uncertainty in climate predictions caused by small aerosol particles.

Quantum information technologies have attracted much attention in recent years. Advanced fabrication technologies have made it possible to develop quantum architectures, such as trapped ions, color-defects in crystals (nitrogen-vacancy in diamond), and Rydberg atoms, where quantum information applications can be implemented. At the heart of this growing field stands quantum coherence. Maintaining coherence for longer times enables the realization of richer and more interesting quantum applications, varying from quantum gates for quantum computation, through quantum simulation of classical intractable systems, to quantum sensing schemes for medical applications. Noise, leakage and decay channels constitute the main sources for decoherence, which limit the fidelity of the desired quantum operations. In this project my main goal is to theoretically investigate ways to maintain coherence in the quantum systems mentioned above, while realizing a variety of quantum applications. This will be done using either dynamical decoupling or quantum error correction techniques. A numerical verification of the theoretical proposals will be undertaken using Runge-Kutta simulations of the systems together with the Orenstein-Uhlenbeck noise process. Importantly, I intend to collaborate on the realization of the theoretical proposals with the relevant experimental groups. In this way, I will enrich my scientific knowledge regarding the specific decoherence sources in the different experimental setups, and thus, my theoretical investigation can be adjusted specifically to the experimental needs. Eventually, these experiment-theory collaborations will end up in experimental verification and application of the theoretical proposals, which will have high impact on research within and far beyond physics.

The contribution of mental disorders to the Global Burden of Diseases (GBD) Study is now well recognized – mental disorders account for 6.8% of the total burden worldwide. Because the GBD’s health metric Disability-Adjusted Life Year (DALY) combines the impact of both morbidity (Years Lived with Disability; YLDs) and mortality (Years of Life Lost; YLLs), it has foregrounded the previously neglected contribution of mental disorders to the global disease burden. However, this health metric does not account for comorbidity adequately, which leads to an underestimation of excess morbidity and mortality associated with mental disorders. This bias has serious implications for service planning and resource allocation. The focus of FundaMentalHM is to use aggregated data from the GBD Study and register-based individual-level data from the whole Danish population to fill the existing methodological gaps, by developing and applying new methodologies in order to better estimate the burden of mental disorders. FundaMentalHM will (i) provide the most comprehensive detailed analysis of comorbidity between mental disorders and other general medical conditions, (ii) advance the area of science related to health metrics by incorporating dependent (instead of independent) comorbidity to the estimates, and (iii) use innovative methods to better estimate the excess mortality associated with mental disorders. In accomplishing FundaMentalHM’s objectives, I will provide a more accurate, unbiased view of the true burden – including true number of deaths – associated with mental disorders. These estimates are essential in order to guide the WHO Mental Health Action Plan 2013-2020 and the UN Sustainable Development Goals (SDGs). In addition, I expect that the suite of methods developed as part of FundaMentalHM project can be applied to a wide range of other disorders.