ERC FUNDED PROJECTS

The detection of primordial gravity waves created during the Big Bang ranks among the greatest potential intellectual achievements in modern science. During the last few decades, the instrumental progress necessary to achieve this has been nothing short of breathtaking, and we today are able to measure the microwave sky with better than one-in-a-million precision. However, from the latest ultra-sensitive experiments such as BICEP2 and Planck, it is clear that instrumental sensitivity alone will not be sufficient to make a robust detection of gravitational waves. Contamination in the form of astrophysical radiation from the Milky Way, for instance thermal dust and synchrotron radiation, obscures the cosmological signal by orders of magnitude. Even more critically, though, are second-order interactions between this radiation and the instrument characterization itself that lead to a highly non-linear and complicated problem. I propose a ground-breaking solution to this problem that allows for joint estimation of cosmological parameters, astrophysical components, and instrument specifications. The engine of this method is called Gibbs sampling, which I have already applied extremely successfully to basic CMB component separation. The new and ciritical step is to apply this method to raw time-ordered observations observed directly by the instrument, as opposed to pre-processed frequency maps. While representing a ~100-fold increase in input data volume, this step is unavoidable in order to break through the current foreground-induced systematics floor. I will apply this method to the best currently available and future data sets (WMAP, Planck, SPIDER and LiteBIRD), and thereby derive the world's tightest constraint on the amplitude of inflationary gravitational waves. Additionally, the resulting ancillary science in the form of robust cosmological parameters and astrophysical component maps will represent the state-of-the-art in observational cosmology in years to come.

1 999 205 €

Start date: 2018-04-01, End date: 2023-03-31

Targeted therapy (TT) is frequently used to treat metastatic cancer. Although TT can achieve effective tumor control for several months, durable treatment responses are rare, due to emergence of aggressive, drug-resistant clones (RCs) with high metastatic competence. Tumor heterogeneity and plasticity result in multifaceted resistance mechanisms and targeting RCs poses a daunting challenge. To better understand the clinical emergence of RCs, my work focuses on the poorly understood events during TT-induced tumor regression. We recently reported that during this phase drug-responsive cancer cells release a therapy-induced secretome, which remodels the tumor microenvironment (TME) and propagates disease relapse by promoting the survival of drug-sensitive cells and stimulating the outgrowth of RCs. Consequently, intervening with combination therapies during the tumor regression period has the potential to prevent the clinical emergence of RCs in the first place. Here, we outline strategies to (1) understand how RCs emerge and (2) to leverage our findings on the TME remodeling for combination therapies. First, we will develop a novel and innovative parental clone-lookup method, that will allow us to identify and isolate treatment-naïve, parental clones (PCs) that gave rise to RCs. In functional experiments, we will assess (i) whether PCs were already resistant before or developed resistance during TT, (ii) whether PCs have a higher susceptibility to develop resistance than random clones, and (iii) the mechanistic basis for metastatic competence in different clones. Second, we will study the TT-induced TME remodeling, focusing on the effects on tumor vasculature and immune cells. We will utilize our results to target PCs and RCs by combining TT in the phase of tumor regression with other therapies, such as immunotherapies. Our study will provide new mechanistic insights into the biological processes during tumor regression and aims for novel therapeutic strategies.

1 500 000 €

Start date: 2018-03-01, End date: 2023-02-28

The evolutionary arms race has optimized and shaped the way animals attend to relevant sensory stimuli in an ever-changing environment. This is a complex task, because the vast majority of sensory experiences are not relevant. In humans, attentional disorders are a serious public health concern because of its high prevalence, but its causes are mostly unknown. In this proposal, I will explore the neuronal mechanisms used by the nervous system to attend visual cues to enable appropriate behaviors. We will combine cutting edge imaging techniques, optogenetic interventions, behavioral read outs and targeted connectomics to study the neuronal transformations of the mouse Superior Colliculus (SC), an evolutionary conserved midbrain area known to process sensorimotor transformations and to be involved in the allocation of attention. First, this work will reveal a detailed description of visual representation in the SC, focusing on understanding how defined retinal information-streams, like motion and color, contribute to these properties. Second, we will characterize sensorimotor transformations instructed by the SC. The combination of the previous two objectives will determine mechanisms of visual saliency and sensory driven attention (“bottom-up” attention). Finally, we will explore the neuronal mechanisms of attention by studying the modulatory effect of higher brain areas (“top-down” attention) on sensory transformation and multisensory integration in the SC. Taken together, this proposal aims to understand principles underlying sensorimotor transformation and build a framework to study attention in health and disease.

1 446 542 €

Start date: 2017-12-01, End date: 2022-11-30

The hydrological cycle, with its feedbacks related to water vapour and clouds, is the largest source of uncertainty in weather prediction and climate models. Particularly processes that occur on scales smaller than the model grid lead to errors, which can compensate one another, making them difficult to detect and correct for. Undetectable compensating errors critically limit the understanding of hydrological extremes, the response of the water cycle to a changing climate, and the interpretation of paleoclimate records. Stable water isotopes have a unique potential to serve as the needed constraints, as they provide measures of moisture origin and of the phase change history. We have recently spearheaded a revised view of the atmospheric water cycle, which highlights the importance of connections on a regional scale. This implies that in some areas, all relevant processes can be studied on a regional scale. The Nordic Seas are an ideal case of such a natural laboratory, with distinct evaporation events, shallow transport processes, and swift precipitation formation. Together with recent technological advances in isotope measurements and in-situ sample collection, this will allow us to acquire a new kind of observational data set that will follow the history of water vapour from source to sink. The high-resolution, high-precision isotope data will provide a combined view of established and novel natural isotopic source tracers and set new benchmarks for climate models. A unique palette of sophisticated model tools will allow us to decipher, synthesize and exploit these observations, and to identify compensating errors between water cycle processes in models. In ISLAS, my team and I will thus make unprecedented use of stable isotopes to provide the sought-after constraints for an improved understanding of the hydrological cycle in nature and in climate models, leading towards improved predictions of future climate.

1 999 054 €

Start date: 2018-08-01, End date: 2023-07-31