ERC FUNDED PROJECTS

I intend to investigate what cognitive mechanisms give us combinatorial speech. Combinatorial speech is the ability to make new words using pre-existing speech sounds. Humans are the only apes that can do this, yet we do not know how our brains do it, nor how exactly we differ from other apes. Using new experimental techniques to study human behavior and new computational techniques to model human cognition, I will find out how we deal with combinatorial speech. The experimental part will study individual and cultural learning. Experimental cultural learning is a new technique that simulates cultural evolution in the laboratory. Two types of cultural learning will be used: iterated learning, which simulates language transfer across generations, and social coordination, which simulates emergence of norms in a language community. Using the two types of cultural learning together with individual learning experiments will help to zero in, from three angles, on how humans deal with combinatorial speech. In addition it will make a methodological contribution by comparing the strengths and weaknesses of the three methods. The computer modeling part will formalize hypotheses about how our brains deal with combinatorial speech. Two models will be built: a high-level model that will establish the basic algorithms with which combinatorial speech is learned and reproduced, and a neural model that will establish in more detail how the algorithms are implemented in the brain. In addition, the models, through increasing understanding of how humans deal with speech, will help bridge the performance gap between human and computer speech recognition. The project will advance science in four ways: it will provide insight into how our unique ability for using combinatorial speech works, it will tell us how this is implemented in the brain, it will extend the novel methodology of experimental cultural learning and it will create new computer models for dealing with human speech.

1 276 620 €

Start date: 2012-02-01, End date: 2017-01-31

Synthetic biology is an emerging discipline that offers powerful tools to control and manipulate fundamental processes in living matter. We propose to develop and apply such tools to modify the genetic code of cultured mammalian cells and bacteria with the aim to study the role of lysine acetylation in the regulation of metabolism and in cancer development. Thousands of lysine acetylation sites were recently discovered on non-histone proteins, suggesting that acetylation is a widespread and evolutionarily conserved post translational modification, similar in scope to phosphorylation and ubiquitination. Specifically, it has been found that most of the enzymes of metabolic processes—including glycolysis—are acetylated, implying that acetylation is key regulator of cellular metabolism in general and in glycolysis in particular. The regulation of metabolic pathways is of particular importance to cancer research, as misregulation of metabolic pathways, especially upregulation of glycolysis, is common to most transformed cells and is now considered a new hallmark of cancer. These data raise an immediate question: what is the role of acetylation in the regulation of glycolysis and in the metabolic reprogramming of cancer cells? While current methods rely on mutational analyses, we will genetically encode the incorporation of acetylated lysine and directly measure the functional role of each acetylation site in cancerous and non-cancerous cell lines. Using this methodology, we will study the structural and functional implications of all the acetylation sites in glycolytic enzymes. We will also decipher the mechanism by which acetylation is regulated by deacetylases and answer a long standing question – how 18 deacetylases recognise their substrates among thousands of acetylated proteins? The developed methodologies can be applied to a wide range of protein families known to be acetylated, thereby making this study relevant to diverse research fields.

1 499 375 €

Start date: 2016-07-01, End date: 2021-06-30

With the recognition that wind energy will become an important contributor to the world’s energy portfolio, several wind farms with a capacity of over 1 gigawatt are in planning phase. In the past, engineering of wind farms focused on a bottom-up approach, in which atmospheric wind availability was considered to be fixed by climate and weather. However, farms of gigawatt size slow down the Atmospheric Boundary Layer (ABL) as a whole, reducing the availability of wind at turbine hub height. In Denmark’s large off-shore farms, this leads to underperformance of turbines which can reach levels of 40%–50% compared to the same turbine in a lone-standing case. For large wind farms, the vertical structure and turbulence physics of the flow in the ABL become crucial ingredients in their design and operation. This introduces a new set of scientific challenges related to the design and control of large wind farms. The major ambition of the present research proposal is to employ optimal control techniques to control the interaction between large wind farms and the ABL, and optimize overall farm-power extraction. Individual turbines are used as flow actuators by dynamically pitching their blades using time scales ranging between 10 to 500 seconds. The application of such control efforts on the atmospheric boundary layer has never been attempted before, and introduces flow control on a physical scale which is currently unprecedented. The PI possesses a unique combination of expertise and tools enabling these developments: efficient parallel large-eddy simulations of wind farms, multi-scale turbine modeling, and gradient-based optimization in large optimization-parameter spaces using adjoint formulations. To ensure a maximum impact on the wind-engineering field, the project aims at optimal control, experimental wind-tunnel validation, and at including multi-disciplinary aspects, related to structural mechanics, power quality, and controller design.

1 499 241 €

Start date: 2012-10-01, End date: 2017-09-30

This proposal outlines a highly ambitious and path-breaking research program. Through a tightly integrated package of basic theoretical work, strategic empirical research projects, international workshops, and a large number of publications in leading journals, the research program seeks to move sociology in a more analytical direction. One part of the research program focuses on the epistemological and methodological foundations of analytical sociology, an approach to sociological theory and research that currently receives considerable attention in the international scholarly community. This work will be organized around two core themes: (1) the principles of mechanism-based explanations and (2) the micro-macro link. The empirical research analyzes in great detail the ethnic, gender, and socio-economic segregation of key interaction domains in Sweden using the approach of analytical sociology. The interaction domains focused upon are schools, workplaces and neighborhoods; domains where people spend a considerable part of their time, where much of the social interaction between people takes place, where identities are formed, and where important resources are distributed. Large-scale longitudinal micro data on the entire Swedish population, unique longitudinal data on social networks within school classes, and various agent-based simulation techniques, are used to better understand the processes through which schools, workplaces and neighborhoods become segregated along various dimensions, how the domains interact with one another, and how the structure and extent of segregation affects diverse social and economic outcomes.

1 745 098 €

Start date: 2013-03-01, End date: 2018-02-28