This proposal aims to provide insight into persister resuscitation. Persisters are multidrug-tolerant cells that are transiently non-growing and able to generate viable offspring by resuming growth when antibiotic pressure is removed. Despite their implication in relapses of many infectious diseases, and progress in understanding how persisters form through the action of toxin-antitoxin modules, the mechanisms underlying resuscitation of these persisters are still unknown. The interaction between Salmonella and host macrophages has proven to be a powerful and relevant model to study persister biology since the bacteria specifically respond to engulfment by the host defence cells by forming high proportions of persisters. Upon encounter with the host, Salmonella activates 14 toxins to arrest growth and persist in this environment. With the recent discovery of a detoxifying enzyme (Pth) counteracting the action of a persister-inducing toxin (TacT), thus allowing growth resumption of Salmonella persisters, we can now begin to address the pending question of persister regrowth. The consolidation of my research group around the experimental plan proposed here, will enable us to dissect the balance between intoxication vs. detoxification or entry vs. exit from persistence induced by Tact and Pth respectively. This is the first couple of toxin/detoxifying enzymes to be identified. I intend to extend this knowledge to the whole repertoire of toxins involved in Salmonella persister formation through systematic identification and characterization of the detoxifying mechanisms allowing resuscitation. Additionally I will investigate the lag phase leading to regrowth of persisters; and target the toxins involved in persister formation to force persisters out of growth arrest. This work will unravel persister resuscitation and could ultimately provide ways to force persisters out of that state so they become re-sensitized to antibiotics.

Dr Kangkang Zhang will carry out a project to concern cybersecurity protection for cyber-physical systems against integrity cyberattacks (CSP-CPS-A-ICA) by revealing the stealthiness of integrity attacks and then developing detection and distinction methodologies and schemes from control perspectives. This research aims to provide effective, efficient and reliable cybersecurity protections for industrial control systems (ICSs). The project will be carried out in the Control and Power (CAP) research group, within the Department of Electrical and Electronic Engineering (EEE), at the Imperial College London (Imperial), under the supervision of Prof. Thomas Parisini. As a part of the project, I will carry out two 3-month secondments in the KIOS Research and Innovation Center of Excellence (KIOS CoE), at the University of Cyprus (UCY). The expected benefits of this project are to achieve the above research aims and enable me to make a leap in my career as well as to create a close collaboration between Imperial and UCY in the cybersecurity-related research areas.

The MultiCAMS project will develop an advanced computational strategy including numerical modelling and innovative model calibration for realistic assessment of historical unreinforced masonry structures. The presence of relevant historical heritage in Europe, partly still in use, together with high seismic risk in the Mediterranean areas highlights the need of assessing such structures considering current safety requirements and if necessary strengthened to prevent critical failure. The proposed assessment approach is based on the use of models with different levels of sophistication, comprising detailed mesoscale models where masonry units and mortar joints are modelled separately (low level), homogeneous shell/solid-element models in which masonry is considered as a continuum (intermediate level), and efficient macroscale models where entire structural parts (i.e. piers, spandrels) are represented by appropriate macro-elements (high level). In this respect, I will develop a novel numerical description with macro-elements for an efficient and practical analysis of masonry structures accounting for the interaction between the nonlinear in-plane and out-of-plane behaviour of different URM components. An innovative model calibration strategy utilising inverse analysis techniques will thus be developed to couple the efficiency of the novel simplified mechanical model and the accuracy of existing detailed models, such as the parameters of each model will be determined from the inverse analysis of the numerical response of the lower-level model. The main outcome of MultiCAMS will be a comprehensive and accurate methodology for the seismic assessment of historical masonry buildings, which represents an urgent requirement for the safeguard of human lives in many European urban areas, as shown by recent catastrophic events.

The nature of dark matter (DM) is one of the greatest mysteries in modern physics. It is well established to constitute 26% of the total energy-matter of the universe, to drive the motion of stars and galaxies, and to have shaped the universe as we observe it today. We know how DM interacts gravitationally with standard matter, but what we do not yet understand are its nature and particle properties. A general prediction of theoretical DM models is that dark matter particles self-interact producing high-energy photons and charged particles at rates detectable by current ground- and space-based telescopes. Discovering such a signal would allow us to study the particle properties of DM for the very first time. In a recent exciting development, an anomalous emission in the gamma rays coming from the centre of our Galaxy has been measured, the so-called Galactic centre GeV excess: this could be the very first signature of the presence of DM there. My project will carry out definitive tests of this putative first non-gravitational signature of the DM particle nature by studying the gamma-ray sky. Firstly, I will improve the sensitivity and reliability of searches for DM in the inner region of the Galaxy - one of the most promising targets - thanks to novel analysis methods. The unprecedented accuracy of the data available requires theory to provide robust predictions for the DM distribution in the Galaxy, which are currently missing. I will remedy this by using the most recent simulations of galaxy formation to predict the DM induced signal. My project will considerably boost astroparticle physics research towards the identification of the much-sought after DM by developing new methods for gamma-ray data analysis and for DM models testing. Owing to the designed program, I will bring new expertise to the Imperial College Astrophysics Group and use this experience to grow into a senior researcher.