Allostery is a fundamental property of proteins, which regulates biochemical information transfer between spatially distant sites. Many emerging allosteric targets are large protein/nucleic acid complexes responsible for genome editing and regulation, whose underlying signaling remains poorly understood. Here, we focus on CRISPR-Cas9, a large nucleoprotein complex widely employed as a genome editing tool with enormous promises for medicine and biotechnology. In this system, an intricate allosteric signaling is suggested to span the multi-domain Cas9 protein and its associated nucleic acids, controlling the system’s function and specificity. However, in spite of extensive experimental characterization, the molecular basis for this allosteric response are largely unknown, hampering also efficient engineering for improving its genome editing capability. Allosteric-CRISPR will investigate the allosteric regulation in CRISPR-Cas9 by introducing a novel synergistic approach. This will implement the combination of state-of-the-art theoretical methods, including enhanced and multiscale approaches based on classical and ab-initio methods, with network models derived from graph theory and novel centrality analyses that are emerging as powerful to investigate allostery. This will create an innovative protocol that will enable determining the allosteric network of communication over multiple timescales, as well as the relation between allostery and catalysis, which remains unaddressed through classical approaches. This novel way to describe allostery can impact future studies of large nucleoprotein complexes, including newly discovered CRISPR systems, which are governed by similar allosteric rules and hold tremendous potential for genome editing. Finally, by delivering fundamental knowledge on the basic mechanisms underlying genome editing, Allosteric-CRISPR will help the design of improved genome editing tools, impacting their application across the field of life sciences.

Thermal barrier coatings (TBCs) have been widely used to protect the substrate of hot components against the hot and corrosive environment, which have extensive applications in power sectors, aerospace engineering and chemical industrials. They are, however, facing the paradox of conflicting competition between the strength and toughness, especially under high temperature. Aiming to develop high temperature TBCs with simultaneously improved strength and toughness, this project proposes an innovative strategy of engineering nano-twinned ceramics and examines their mechanical properties under high temperature with improved mechanistic understanding, which include: i) developing novel nano-twinned TYaO4 ceramic materials via hierarchical structures; ii) examining the mechanical properties of formed TBC materials via a unique high temperature nano-indentation system up to 2000 K; 3) establishing a multi-scale simulation framework to predict the macroscopic mechanical properties; and iv) developing a twin boundary affected hardening and crack growth model to reveal the influence of nanoscale structures. Four work programs are proposed ranging from experiments, simulations to theories to realize such an ambitious plan, intervened with a careful balanced training program, dissemination and management skill development. Properly implemented, the project shall reveal for the first time the effect of hierarchical nanoscale structures on the improved mechanical properties of TYaO4 ceramics up to 2000 K, which are much needed for the development of next generations of TBCs. The project exhibits strong interdisciplinary coupling among materials science and engineering, solid mechanics, and multiscale computation engineering. Not only bearing with significant scientific potentials, the mutual benefits from this program will booster the career of the researcher significantly and promote long term knowledge exchange and collaboration between Europe and China.

Despite their amazing success, we believe that computer vision algorithms have only scratched the surface in terms of understanding our world from images. While most research on 3D reconstruction has been concerned with recovering the surface geometry and reflectance, SIMULACRON is focused on inferring the underlying physics (masses, elasticity, momenta, forces, etc.) and a simulation of the observed action directly from videos. This not only provides a more profound understanding of the observed phenomena, but it also allows us to interpolate and extrapolate complex actions far beyond the observation: The inferred physical simulation can be employed for space-time super-resolution and for predictions into the future. SIMULACRON covers three lines of research: A) We will develop algorithms for deformable shape modeling. We will explore suitable representations of 3D shape and its evolution that enable the efficient computation of shape deformation, correspondence, interpolation and extrapolation. These techniques will form the basis for inferring physical simulations in parts B and C. B) We will develop variational methods for inferring physical simulations from videos. We will compute a reference shape and simulation parameters that generate the shape deformation that is most consistent with the observations. C) We will develop learning-based approaches for inferring physical simulations from videos. We will pursue two alternative approaches: First, we will generate synthetic training data by simulating deformable shapes and the associated camera observations. Second, we will devise self-supervised techniques for learning from real data without requiring labeled training data. By shifting from inference of 3D geometry to inference of physical simulations, SIMULACRON will give rise to a more profound notion of dynamic scene understanding in computer vision, robotics and beyond. We believe that we have the necessary competence to pursue this project.

Violence against women is a violation of fundamental human rights and substantially compromises women’s health, wellbeing, and empowerment. Globally, more than one in four women experience physical and/or sexual violence by a partner in their lives. This risk is considerably higher among women in low- and middle-income countries and in cultures with pronounced patriarchal gender norms. However, existing research has so far neglected an important dimension of intimate partner violence (IPV): economic abuse. This form of abuse includes denying women the right to participate in financial decisions, taking away their income or preventing them from seeking employment. The consequences are profound – economic IPV compromises women’s economic welfare and independence, traps them in abusive relationships, and adversely affects their mental health. To tackle this major global health concern, ECOVI has three objectives: first, to establish the prevalence of different forms of economic IPV; second, to develop a theory of economic abuse by investigating drivers of economic IPV and linkages with other forms of IPV; and third, to design and test a community-based prevention approach. To this end, I will focus on India, which is home to 670 million women and girls and exhibits high levels of gender discrimination that exacerbate women’s vulnerability to economic IPV. I will capitalise on a mixed-methods approach, including (i) systematic reviews and meta-analyses, (ii) conducting qualitative in-depth interviews and focus groups, and (iii) implementing a cluster randomised controlled trial and innovative survey experiments with husbands and wives in 150 Indian communities. ECOVI will generate the largest existing database on economic IPV and establish an evidence-informed prevention approach. This has the potential to yield ground-breaking scientific and programmatic evidence on how to alleviate the economic violence and associated economic hardship that women worldwide are facing.