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Diffuse losses of nitrogen and phosphorus from agricultural areas contribute significantly to eutrophication of waterways, lakes, estuaries and coastal zones and water pollution is a growing and serious problem across much of the world. The role of wetlands in improving surface water quality is well known. The capacity of wetlands to improve water quality is dependent on a large number of parameters that have been widely studied, such as vegetation cover or type, water retention time, climatic variables, and also their size and spatial arrangement in the watershed. However, the question where wetlands should be located in agricultural catchments to achieve the most effective nutrient removal at the catchment level has not been clearly resolved. This project aims to determine the optimal sizing and location for wetlands in agricultural catchments to reduce nutrient (nitrogen and phosphorus) loads in catchments. The study consist of two parts performed on study areas with different landscape and climatic conditions. Firstly, potentially suitable wetland restoration/creation sites are identified by using high quality data and geospatial analysis techniques. Secondly, evaluation of the effectiveness of wetland nitrogen and phosphorus removal from surface waters at various potential locations indicated by the geospatial analyses under different hydrological regimes and land use scenarios will be done by using modelling with CLUES (Catchment Land Use for Environmental Sustainability model) and SWAT (Soil and Water and Assessment Tool). Important role in the study is also on using and integrating different datasets and modelling approaches.

The aim of the ECePS project – the ERA Chair in E-Governance and Digital Public Services is to strengthen the Center of IT Impact Studies (CITIS), a research unit within the Skytte Institute of Political Studies at the University of Tartu (UTARTU) so that it can act as a world leader in the field of research on e-governance, public e-services and data driven public innovation. We will do so by recruiting a leading expert in the field to serve as an ERA Chair for E-Governance and Digital Public Services who will in turn create a top-level research team capable of conducting cutting edge research that examines the fundamental questions of scientific and practical importance. The ERA Chair will trigger structural changes within UTARTU to support this effort by: • Initiating changes to CITIS research unit, including creation of a CITIS Supervisory Board, formation of advisory groups with members from industry, government and scientific community, and a Professorship position for the ERA Chair. • Integrating researchers from other departments relevant for e-governance research into the CITIS structure, including the SoBigData Research Infrastructure, the Institute of Social Studies, the School of Economics and the Faculty of Law as well as UTARTU’s High Performance Computing Center. • Building partnerships with governments and leading technology companies to create new models for attracting public and private research funding. The ERA Chair will also act as a role model to produce spill-over benefits for UTARTU to modernize rules and practices regarding the recruitment and performance measurement of researchers and professors, systematically implement processes to address RRI priorities and improve UTARTU’s gender policies and practices.

Core level X-ray Photoelectron Spectroscopy (XPS) is one of the most widely used experimental techniques in surface science and surface analysis. However, the interpretation of recorded spectra is challenging. Often the amount of chemical insight that XPS can provide is compromised by problems with assigning detected “peaks” to specific chemical environments. Theoretical modelling can provide an alternative means for determining the spectroscopic signature associated with a given chemical environment, and could therefore be used to overcome the long-standing peak-assignment problem. In this project, the accuracy of existing theoretical methods for guiding the interpretation of XPS spectra will be tested, and new methods for predicting satellite peaks and simulating vibrational effects in core level XPS will be developed. In particular, the accuracy of the Δ-Self-Consistent-Field (ΔSCF) method will be tested for solids and surface species; the ΔSCF method will be combined with Time-Dependent Density Functional Theory (TDDFT) to predict satellite structures in core level photoemission spectra; and a fully quantum mechanical method based on the Density Functional Theory (DFT) and normal mode analysis will be developed for the simulation of vibrational effects in XPS. Through the testing and development of computationally affordable theoretical methods, this study will provide impetus and justification for users of XPS to take full advantage of theoretical modelling when interpreting their experimental results. Several dissemination and communication activities have been planned to ensure that the theoretical work will reach its inteneded audience and ultimately help XPS users from a wide range of fields to gain greater insight into the systems that they study.

A growing economy and population in the world is causing landscape changes and an increasing pressure is put on water resources. Diffuse water pollution is considered to be one of the major problems for water quality in many countries. Modelling has been successfully used to simulate water quality in catchments to better understand the underlying landscape processes. The widely used Soil and Water Assessment Tool (SWAT) is a spatially distributed model that can be used to estimate flow and nutrient transport at a variety of scales. In current published studies typically only one or two parameters of precipitation, DEM, land use or soil properties are used in. The proposed project aims to investigate how spatial resolution of core input datasets of all types (precipitation, DEM, land use and soil) impacts SWAT modelling results and estimate the nutrient runoff on a local and global scale. Sensitivity analysis to all of precipitation, DEM, land use and soil will therefore be tested. The limitation to one or two parameters in current published studies is due to the computational demands. Due to the way the SWAT model is programmed using a tightly coupled Message Passing Interface (MPI) approaches the available computing power needs to accessible within specialised High Performance Computing (HPC) clusters of limited size. Thus, either scale or resolution is typically compromised. As for higher resolution or global scale data the computational effort becomes too large for automated calibration, we aim to develop a novel method to automate data processing and balancing computational load transparently between many computers. In order to surpass these limitations we test the MapReduce framework as a novel method for parallelization. This entails new ways of data management, model data partitioning and spreading the model partition computations transparently over multiple computing nodes fostering a loosely coupled distributed computation paradigm.

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.