ERC FUNDED PROJECTS

The realization of “the internet of things” is inevitably constrained at the level of miniaturization that can be achieved in the electronic devices. A variety of technologies are now going through a process of miniaturization from micro-electromechanical systems (MEMS) to biomedical sensors, and actuators. The ultimate goal is to combine several components in an individual multifunctional platform, realizing on-chip technology. Devices have to be constrained to small footprints and exhibit high performance. Thus, the miniaturization process requires the introduction of new manufacturing processes to fabricate devices in the 3D space over small areas. 3D printing via robocasting is emerging as a new manufacturing technique, which allows shaping virtually any materials from polymers to ceramic and metals into complex architectures. The goal of this research is to establish a 3D printing paradigm to produce miniaturized complex shape devices with diversified functions for on-chip technologies adaptable to “smart environment” such as flexible substrates, smart textiles and biomedical sensors. The elementary building blocks of the devices will be two-dimensional nanomaterials, which present unique optical, electrical, chemical and mechanical properties. The synergistic combination of the intrinsic characteristics of the 2D nanomaterials and the specific 3D architecture will enable advanced performance of the 3D printed objects. This research programme will demonstrate 3D miniaturized energy storage and energy conversion units fabricated with inks produced using a pilot plant. These units are essential components of any on-chip platform as they ensure energy autonomy via self-powering. Ultimately, this research will initiate new technologies based on miniaturized 3D devices.

1 999 968 €

Start date: 2019-09-01, End date: 2024-08-31

Run away, sidestep, duck-and-cover, watch: when under threat, humans immediately choreograph a large repertoire of defensive actions. Understanding action-selection under threat is important for anybody wanting to explain why anxiety disorders imply some of these behaviours in harmless situations. Current concepts of human defensive behaviour are largely derived from rodent research and focus on a small number of broad, cross-species, action tendencies. This is likely to underestimate the complexity of the underlying action-selection mechanisms. This research programme will take decisive steps to understand these psychological mechanisms and elucidate their neural implementation. To elicit threat-related action in the laboratory, I will use virtual reality computer games with full body motion, and track actions with motion-capture technology. Based on a cognitive-computational framework, I will systematically characterise the space of actions under threat, investigate the psychological mechanisms by which actions are selected in different scenarios, and describe them with computational algorithms that allow quantitative predictions. To independently verify their neural implementation, I will use wearable magnetoencephalography (MEG) in freely moving subjects. This proposal fills a lacuna between defence system concepts based on rodent research, emotion psychology, and clinical accounts of anxiety disorders. By combining a stringent experimental approach with the formalism of cognitive-computational psychology, it furnishes a unique opportunity to understand the mechanisms of action-selection under threat, and how these are distinct from more general-purpose action-selection systems. Beyond its immediate scope, the proposal has a potential to lead to a better understanding of anxiety disorders, and to pave the way towards improved diagnostics and therapies.

1 998 750 €

Start date: 2019-10-01, End date: 2024-09-30

The aim of this proposal is to understand a novel regulatory signaling network controlling insulin secretion, fat accumulation and energy balance centered around selected components of the TGF-² signaling system, including Activins A and B, GDF-3 and their receptors ALK7 and ALK4. Recent results from my laboratory indicate that these molecules are part of paracrine signaling networks that control important functions in pancreatic islets and adipose tissue through feedback inhibition and feed-forward regulation. These discoveries have open up a new research area with important implications for the understanding of metabolic networks and the treatment of human metabolic syndromes, such as diabetes and obesity. To drive progress in this new research area beyond the state-of-the-art it is proposed to: i) Elucidate the molecular mechanisms by which Activins regulate Ca2+ influx and insulin secretion in pancreatic ²-cells; ii) Elucidate the molecular mechanisms underlying the effects of GDF-3 on adipocyte metabolism, turnover and fat accumulation; iii) Investigate the interplay between insulin levels and fat deposition in the development of insulin resistance using mutant mice lacking Activin B and GDF-3; iv) Investigate tissue-specific contributions of ALK7 and ALK4 signaling to metabolic control by generating and characterizing conditional mutant mice; v) Investigate the effects of specific and reversible inactivation of ALK7 and ALK4 on metabolic regulation using a novel chemical-genetic approach based on analog-sensitive alleles. This is research of a high-gain/high-risk nature. It is posed to open unique opportunities for further exploration of complex metabolic networks. The development of drugs capable of enhancing insulin secretion, limiting fat accumulation and ameliorating diet-induced obesity by targeting components of the ALK7 signaling network will find a strong rationale in the results of the proposed work.

2 462 154 €

Start date: 2009-04-01, End date: 2014-03-31

Algebras of operators on Hilbert spaces were originally introduced as the right framework for the mathematical description of quantum mechanics. In modern mathematics the scope has much broadened due to the highly versatile nature of operator algebras. They are particularly useful in the analysis of groups and their actions. Amenability is a finiteness property which occurs in many different contexts and which can be characterised in many different ways. We will analyse amenability in terms of approximation properties, in the frameworks of abstract C*-algebras, of topological dynamical systems, and of discrete groups. Such approximation properties will serve as bridging devices between these setups, and they will be used to systematically recover geometric information about the underlying structures. When passing from groups, and more generally from dynamical systems, to operator algebras, one loses information, but one gains new tools to isolate and analyse pertinent properties of the underlying structure. We will mostly be interested in the topological setting, and in the associated C*-algebras. Amenability of groups or of dynamical systems then translates into the completely positive approximation property. Systems of completely positive approximations store all the essential data about a C*-algebra, and sometimes one can arrange the systems so that one can directly read of such information. For transformation group C*-algebras, one can achieve this by using approximation properties of the underlying dynamics. To some extent one can even go back, and extract dynamical approximation properties from completely positive approximations of the C*-algebra. This interplay between approximation properties in topological dynamics and in noncommutative topology carries a surprisingly rich structure. It connects directly to the heart of the classification problem for nuclear C*-algebras on the one hand, and to central open questions on amenable dynamics on the other.

1 596 017 €

Start date: 2019-10-01, End date: 2024-09-30