Glioblastoma (GBM), is the most aggressive form of primary brain cancer and is diagnosed in 22,000 people per year in the EU. GBM is the most aggressive primary brain cancer and, by annual incidence, the most common type of malignant brain tumour. Despite aggressive treatment, the cancer always recurs. The average survival after diagnosis is 12 to 15 months, with less than 3% to 5% of patients surviving longer than 5 years. The current standard of care extends overall survival to ~14 -16 months. Current valuation of the global GBM treatment market is estimated to be €615 million per year, and it is predicted to increase to over €3.08 billion by 2024 (compound annual growth rate of 17%), based on the projected approval of new therapies including Opdivo and Optune. The main aim of this ERC PoC proposal is to probe the commercial viability of two types of tumour-targeted payload hybrids, one as an improved chemotherapeutic treatment, and the other as a precision-guided imaging agent for PET/MRI-based diagnosis. Our goal is for the former to become the clinician’s chemotherapy of choice in the adjuvant phase of GBM treatment; and for the latter to be incorporated into PET and MR-imaging procedures as a method for identifying and diagnosing GBM, as well as a companion test for stratification of patients for therapy.

The SynBioTEC project aims to recruit an experienced research manager to act as the ERA Chair and a team of experienced researchers at University of Tartu Institute of Technology (TUIT) in order to establish the multidisciplinary Centre of Synthetic Biology. The ERA Chair and the newly recruited team of researchers will bring about the necessary structural changes at TUIT to merge the competences of existing research groups on molecular biology to foster excellent research for development of industrially relevant designer cells, focusing on cell factories and biosensors. The Centre of Synthetic Biology will combine the experience of the four core facilities in synthetic biology, proteomics, cell culture and microscopy, yielding an excellent platform for development of synthetic designer cells for industrial applications and spin-off companies that will move forward with the product development phases. In order to achieve the objective, at least five experienced researchers will be recruited at TUIT for a total of 246 months to enhance the research capacity of the Institute. The synthetic biology core facility will be upgraded and an innovative doctoral training programme on biomechanical engineering will be established. The ERA management principles will be established and introduced throughout TUIT and beyond in the university, the Research and Innovation Strategy of the new Centre devised and multiple dedicated training sessions organised. Special attention will be paid to cooperation with local and national stakeholders and industry. As a result of the SynBioTEC project, the competitiveness of TUIT will be increased through enhanced research capacity, enabling TUIT to contribute to the development of cell factories and cell-based biosensors. The spill-over effects to other research groups at University of Tartu and industry will enhance the competitiveness of the whole country, contributing to the objectives of the Estonian Smart Specialisation Strategy.

MenNaRum aims to test the mental navigation framework (Aru et al., 2023) in the context of rumination. The theory proposes that the spatial navigation related activation in the hippocampus is indicative of a universal navigation mechanism that underlies spatial and non-spatial navigation, i.e. navigation between concepts or thoughts. The navigation process is goal directed and can lead to sudden solutions - insights. When ruminating, people think about their personal problems repetitively, often referred to as "being stuck in a loop", potentially indicative of the underlying navigational phenomenon. Rumination has been characterized by dwelling on negative autobiographical memories associated with hippocampal activation. So far, the link between rumination and navigational processing has not been established. MenNaRum aims to (1) establish a link between rumination and spatial navigation by applying a data-driven network analysis approach in an online study, (2) experimentally test the effects of insight induction on rumination, and navigation within a novel virtual-reality task, and (3) systematically review human and animal literature to uncover potential common rudimentary mechanisms for rumination and spatial navigation. In this project, I will integrate experimental psychopathology, behavioral neuroscience, and information technology approaches. MenNaRum will generate new hypotheses, and potentially shift rumination research paradigms towards integration, and interdisciplinarity. Implications include (1) a better understanding of the basic mechanisms of rumination, (2) novel VR-based measurement tools, some of which I will test in the project, and (3) inspiration for new interventions which can relieve the socioeconomic burden of affective disorders. MenNaRum will provide me with the important transferrable, virtual-reality, translational analysis and reviewing skills which will enhance my future interdisciplinary research on rumination and related processes.

Business processes are the operational backbone of modern organizations. Their continuous improvement is key to the achievement of business objectives, be it with respect to efficiency, quality, compliance, or agility. Accordingly, a common task for process analysts is to discover and assess process improvement opportunities, i.e. changes to one or more processes, which are likely to improve them with respect to one or more performance measures. Current approaches to discover process improvement opportunities are expert-driven. In these approaches, data are used to assess opportunities derived from experience and intuition rather than to discover them in the first place. Moreover, as the assessment of opportunities is manual, analysts can only explore a fraction thereof. PIX will build the foundations of a new generation of process improvement methods that do not exclusively rely on guidelines and heuristics, but rather on a systematic exploration of a space of possible changes derived from process execution data. Specifically, PIX will develop conceptual frameworks and algorithms to analyze process execution data in order to discover process changes corresponding to possible improvement opportunities, including changes in the control-flow dependencies between activities, partial automation of activities, changes in resource allocation rules, or changes in decision rules that may reduce wastes or negative outcomes. Each change will be associated with a multi-dimensional utility, thus allowing us to map a process improvement problem to an optimization problem over a multidimensional space. Given this mapping, PIX will develop efficient and incremental methods to search through said spaces in order to find Pareto-optimal groups of changes. The outputs will be embodied in a first-of-its-kind tool for automated process improvement discovery, which will lift the focus in the field of process mining from analyzing as-is processes to designing to-be processes.