ERC FUNDED PROJECTS

Actual implementation of impactful applications for microfluidic devices in a commercial setting has been surprisingly limited so far. The cause can be to a great extent attributed to the main feature of microfluidic devices: their small dimensions. While miniaturized structures are essential in generating key functionalities, they are also ideal nucleation and anchor sites for solid material present in the liquid that flows through the channels, a phenomenon called fouling. This subsequently results in a reduced or loss of functionality and eventually plugging of the entire flow system. The solution to avoiding fouling is measuring the flow in microfluidic devices in 3D, by particle image velocimetry (PIV), either when designing or using them. However, achieving 3D imaging of flows is currently an extremely difficult task due to the amount of work, high costs and lengthy timelines required. Our value proposition in the ERC Proof of Concept project ‘3D-PIV’ is a table-top device able to efficiently analyse the velocimetry of particles in 3D, offering an unprecedented level of detail of the fluid motion through micron-sized channels/inlets/outlets, opening new possibilities in microfluidics design and validation with significant impact on multiple applications. One of the killer applications we envision, and our focus in this ERC Proof of Concept project, is in the pharmaceutical and chemical industries, for the manufacturing of drugs or chemical components, to enable, adjust or improve their separation. In this project we will focus on building a strong business case for our 3D-PIV technology through prototyping, optimizing software, market analysis and business development.

150 000 €

Start date: 2020-01-01, End date: 2021-06-30

Making accurate predictions is a crucial factor in many systems (such as in modelling energy consumption, power load forecasting, traffic networks, process industry, environmental modelling, biomedicine, brain-machine interfaces) for cost savings, efficiency, health, safety and organizational purposes. In this proposal we aim at realizing a new generation of more advanced black-box modelling techniques for estimating predictive models from measured data. We will study different optimization modelling frameworks in order to obtain improved black-box modelling approaches. This will be done by specifying models through constrained optimization problems by studying different candidate core models (parametric models, support vector machines and kernel methods) together with additional sets of constraints and regularization mechanisms. Different candidate mathematical frameworks will be considered with models that possess primal and (Lagrange) dual model representations, functional analysis in reproducing kernel Hilbert spaces, operator splitting and optimization in Banach spaces. Several aspects that are relevant to black-box models will be studied including incorporation of prior knowledge, structured dynamical systems, tensorial data representations, interpretability and sparsity, and general purpose optimization algorithms. The methods should be suitable for handling larger data sets and high dimensional input spaces. The final goal is also to realize a next generation software tool (including symbolic generation of models and handling different supervised and unsupervised learning tasks, static and dynamic systems) that can be generically applied to data from different application areas. The proposal A-DATADRIVE-B aims at getting end-users connected to the more advanced methods through a user-friendly data-driven black-box modelling tool. The methods and tool will be tested in connection to several real-life applications.

2 485 800 €

Start date: 2012-04-01, End date: 2017-03-31

ACADEMIA proposes a pioneering study of a neglected corpus of manuscripts stemming from the practice of quodlibetal debates held at Faculties of Arts of European universities, flourishing from the 14th to the early 16th century. As prescribed by the university statutes, dozens of professors participated periodically in these unique collective works of the Middle Ages, which encompassed all the disciplines pursued at the university, from logic to medicine to theology. The PI hypothesises that the professors presented at the hitherto mostly ignored quodlibets their recent scientific innovations, which they then published in the first collective volumes of European academia. The PI thus proposes a novel theoretical framework for understanding the quodlibets: they stand at the origin of the modern concept of science as a collective intellectual enterprise, similar to modern conferences and the subsequent dissemination of results. This makes them and their written form critical for understanding European intellectual and scientific traditions, both past and present. ACADEMIA’s ambition is to establish the corpus of these debates as a new field of study through an extensive examination of manuscripts, thus filling a substantial gap, radically extending the fields of the history of universities and intellectual history, and reconstructing the roots of the modern practice of fostering collective science. A complex analysis of the corpus will bring about a substantial change in our understanding of medieval practices of the production and sharing of knowledge. Aiming to examine the quodlibets as a phenomenon successively interconnecting European intellectual space, ACADEMIA focuses on fourteen universities at which the PI has identified the tradition so far and on their mutual relations and development. ACADEMIA employs an interdisciplinary team and an innovative combination of approaches from history, codicology, palaeography, philology, hermeneutics and Digital Humanities.

1 260 485 €

Start date: 2021-07-01, End date: 2026-06-30

Tumor Necrosis Factor (TNF) is a homotrimeric pro-inflammatory cytokine that was originally discovered based on its extraordinary antitumor activity. However, its shock-inducing properties, causing hypotension, leukopenia and multiple organ failure, prevented its systemic use in cancer treatment. With this proof-of-concept study we want to evaluate a novel class of cell-targeted TNFs with strongly reduced systemic toxicities (AcTafactors). In these engineered immuno-cytokines, single-chain TNFs that harbor mutations to reduce the affinity for its receptor(s) are fused to a cell- specific targeting domain. Whilst almost no biological activity is observed on non-targeted cells, thus preventing systemic toxicity, avidity effects at the targeted cell membrane lead to recovery of over 90% of the TNF signaling activity. In this project we propose a lead optimization program to further improve the lead AcTafactors identified in the context of the ERC Advanced Grant project and to evaluate the resulting molecules for their ability to target the tumor (neo)vasculature in clinically relevant murine tumor models. The pre-clinical proof-of-concept we aim for represents a first step towards clinical development and ultimately potential market approval of an effective AcTafactor anti-cancer therapy.

149 320 €

Start date: 2015-11-01, End date: 2017-04-30