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University of Tartu
Country: Estonia
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350 Projects, page 1 of 70
  • Funder: EC Project Code: 894987
    Overall Budget: 213,290 EURFunder Contribution: 213,290 EUR

    The human genome is over 3 billion nucleotides long, yet only 1,5% of it codes for proteins. In recent years, a striking number of regions of the genome have been discovered to be capable of being transcribed and translated into short polypeptides. These micropeptides comprise of less than 100 amino acids and to date, more than 160 000 different micropeptides have been catalogued within human tissues. These protein products are hypothesized to participate in numerous molecular, cellular and physiological processes, yet the function of but a few micropeptides has been identified. Subsequently, due to its largely unknown functionality, the micropeptidome is commonly overlooked during genomic studies. Due to increasing life expectancy and detrimental lifestyle habits, the European population can be considered to be a high-risk population for cardiovascular diseases, which cause millions of deaths per annum, while taking a tremendous financial toll on the regional economy. GENOMEPEP aims to pinpoint novel micropeptides participating in the pathogenesis of cardiovascular diseases by investigating the genetic variation within the micropeptidome-encoding genome in correlation to existing common cardiovascular phenotypes in population. This will be achieved by establishing a computational analysis pipeline based on biometric, genotype and health records data available within the Estonian and Finnish biobanks. The identification of novel pathogenic genes and the development of guidelines to investigate the micropeptidome would assist in the advancement of research, diagnostic medicine and pharmacology both in public and private sectors. The results of GENOMEPEP will address the CVD research aspect highlighted in “Societal Challenge 1” work program of Horizon 2020, as well as improve other research priorities set by Horizon 2020, e.g. the progression of personalized medicine and support the decrease of economic burden by healthcare.

  • Funder: EC Project Code: 241025
  • Funder: EC Project Code: 101096403
    Overall Budget: 3,498,880 EURFunder Contribution: 3,498,880 EUR

    Nitrous oxide (N2O) is a powerful greenhouse gas and dangerous stratospheric O3 depleting agent. Agriculture and forestry in peatlands are the main sources of N2O emissions. Climate extreme events may boost the emissions but knowledge on their effect is scarce. N2O is a product of a variety of soil processes, including denitrification, nitrification and less studied mechanisms. Partitioning of N2O fluxes between all these different mechanisms is still a major challenge. Microbial processes are of particular importance for N2O budgets. The role of canopy and tree stems in N2O budgets is currently unknown. Novel flux measurement techniques implemented at different levels in combination with remote sensing methods can provide a solid basis for adequate estimation of long-term N2O fluxes in peatlands from local to the global scale. The ground-breaking nature of the proposal lies in integrated use of a combination of innovative methods yielding a pioneering synthesis and modelling of nitrous oxide fluxes at various spatial scales, linked to microbial processes. PeatlandN2O project will: (1) determine the role of rapidly changing environmental factors (soil moisture, freeze–thaw, canopy effects) on N2O emission, particularly in hot spots and hot moments; (2) distinguish between and quantify key N2O production and consumption processes using labelled nitrogen, isotopologues, and microbiome structure; (3) integrate results of experiments and novel measurement techniques (automated chambers, stationary and mobile eddy covariance towers, canopy profile analysis) into the PEATN2O model of N2O fluxes and related environmental factors to enable prediction of hot spots and hot moments of N2O emissions; (4) upgrade IPCC emission factors and suitable land-use strategies to mitigate N2O emissions in peatlands, also considering other greenhouse gases; (5) predict global distribution of N2O emissions according to the land-use and 5 climate change scenarios for 100-year time horizon.

  • Funder: EC Project Code: 205773
  • Funder: EC Project Code: 834141
    Overall Budget: 2,349,960 EURFunder Contribution: 2,349,960 EUR

    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.

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