Myotonic Dystrophy is the leading cause of muscular dystrophy in adults (1:8000). It is caused by a repeat expansion (mutation) in a gene called DMPK and results in severe muscle and heart disease, with significantly shortened life span. There is no treatment. We have developed new antisense compounds conjugated to short fragments of proteins able to deliver the therapy efficiently into muscle tissue, and we have shown the ability of these compounds to reverse the deterioration caused by DM1 in mouse models and in muscle cells derived from patients. We will now focus on completing the preclinical development of our lead compound with the safest toxicology profile. The recent approval of the oligonucleotide drug Nusinersen by the FDA and EMA for treatment of spinal muscular atrophy marks the start of a major revolution in the treatment of genetic diseases. Nusinersen has major clinical impact and keeps patients alive who would otherwise have died. There is now urgent need to address similar diseases which are currently untreated like DM1. Although these kinds of therapies have worked in vitro for several years the major challenge to successfully complete the clinical development an antisense compound is to being able to deliver the drug to the tissues in animals and patients. Our solution does exactly that, we have developed a novel platform technology based on short cell penetrating peptides (fragments of proteins), which when attached to the antisense molecule provide highly effective penetration into cells and into tissues such heart and diaphragm, difficult to reach for large drugs like antisense compounds. This is critical for effective therapy since life span in DM1 is reduced primarily due to respiratory insufficiency and cardiac failure. The final goal of this 20-month programme is to identify a drug suitable for testing in non-human primates.

Nanostructures are systems with one or more dimensions (particle-size, rod diameter, film thickness, pore-size) ranging from 1E-10 metres, the scale of atomic bonds, to 1E-7, the size of a typical biological virus. Most of the time such nanostructures are in their low energy ground state, but when they absorb light some electrons from the ground state can be excited to form a so-called excited state, which lies higher in energy. Excited states, however, are not stable and typically in 1E-15 to 1E-6 seconds the excited electrons will fall back to the ground state, filling the holes that they left upon excitation.The relaxation of an excited state can follow different paths: Firstly, the nanostructure can reemit light in a process called photoluminescence (PL). Secondly, the nanostructure can undergo a chemical reaction which results in a permanent rearrangement of its atoms. Thirdly, the excited electrons and/or holes can be transferred to a molecule adsorbed on the nanostructure and fourthly the nanostructure can heat up. These different relaxation paths have major practical implications. PL in inorganic nanostructures is successfully exploited in applications such as lasers and energy efficient solid state lighting. The transfer of excited electrons or holes to an adsorbed molecule is a critical step in both heterogeneous photocatalysis and in dye-sensitised solar cells. Finally, the structural changes induced by light impose a limit to the service life of solar cells and other devices that are routinely exposed to direct intense sunlight. Now, in spite of the enormous practical importance of the applications discussed above, fundamental knowledge of the different excited state relaxation paths is limited. For example the final structure of the excited state is often unknown. This knowledge gap arises because the inherent disorder of nanostructures and the short lifetimes of excited states make it difficult to characterise the relevant processes in experiment. As a result progress in photoactive materials development for these applications has been mostly through trial and error. The aim of my fellowship is to close the knowledge gap by using theoretical methods to generate microscopic insight into the photophysics and photochemistry of inorganic nanostructures. This will allow me to answer important practical questions such as on which part of the nanostructures the excited electrons are likely to get trapped, which material properties determine what relaxation path is dominant and how these could be successfully tuned experimentally, thus replacing serendipity by insight.In practice this means I will employ a theoretical method named time-dependent density functional theory (TD-DFT) to probe the geometry and chemical nature of the relaxed excited state in different nanostructures. This method, when properly validated, gives accurate results whilst at the same time being computationally cheap enough to efficiently study the systems of interest. Furthermore, where possible I will compare the obtained results, for example predicted PL spectra, to those obtained by my experimental collaborators. In a first step, I will study stoichiometric nanostructures for a range of sizes, shapes and compositions. Building on this work, I will then switch my attention to the fate of excited states in nanostructures that miss some atoms, are doped with foreign atoms or have molecules adsorbed on their surface. Study of these latter systems is especially important for understanding photocatalysis, an application that has to date not been studied with theoretical methods such as TD-DFT. Finally, as a latter part of the proposed work, I will apply the developed theoretical approach to realistic photocatalytic systems studied by my collaborators in the laboratory. Pooling our experimental and theoretical results will allow us to find, for example, new and improved water splitting catalysts for renewable hydrogen production.

As a defining challenge of our time, climate change has led to the 2015 Paris Agreement whose central policy goal is to keep global warming well below 2 degrees Celsius. The substantial remaining uncertainty in physical climate change projections, however, means that there is a very wide window of the dates within which this threshold might be passed. Assuming continuous greenhouse gas emissions, it could be within the next decade, or it might not be until well into the second half of this century. To inform their decision-making, policymakers urgently need this uncertainty reduced. Our research proposal, ML4CLOUDS, addresses the leading role of clouds in this uncertainty, and the coupled implications for climate variability. Clouds are ubiquitous phenomena covering around two thirds of Earth's surface at any time and, as such, play key roles in our climate system. Crucially, clouds are the single most important uncertainty factor in global warming projections under increasing atmospheric carbon dioxide (CO2) concentrations. Clouds are also key modulators of the main modes of climate variability, such as the El Niño Southern Oscillation (ENSO), which in turn drive regional climate and weather extremes. A better understanding of the response of clouds and their interactions with the atmospheric circulation and global warming has therefore been highlighted as one of the 7 Grand Challenges by the World Climate Research Programme. Constraining cloud-related uncertainties, and understanding the underlying physical drivers, would consequently be invaluable to society. The fundamental role of clouds primarily arises from their interaction with Earth's energy budget. Low-altitude clouds are highly reflective for sunlight (having a cooling effect on climate), while upper tropospheric clouds trap radiation emitted from the Earth (having a warming effect). Cloud formation itself releases latent heat to the atmosphere. It is the overall impacts of these processes on atmospheric temperature and the hydrological cycle that make clouds so important for the behaviour and evolution of the climate system. ML4CLOUDS aims to provide a better understanding of the complex physical control mechanisms driving cloud formation. This will improve our ability to predict how Earth's cloud cover will change under human influences such as increasing atmospheric CO2 and aerosol pollution, and thus reduce uncertainty in global warming. This reduction in cloud-related uncertainty will also feed back on our ability to model and comprehend present-day climate variability, and on how we expect the main climate modes, such as ENSO, to change in the future. We will achieve these goals through a novel approach incorporating artificial intelligence (or machine learning) methods, paired with targeted climate feedback analyses and state-of-the-art climate model simulations run on supercomputers. Specifically, our project will: 1. Use machine learning to derive cloud-controlling relationships from large climate model datasets and from space-based observations. These relationships will provide improved estimates of the cloud response and significantly reduced uncertainty in physical climate change projections. They will further provide new insights into the relative importance of distinct physical mechanisms behind the cloud response. Cloud-controlling relationships learned from observations will also be helpful to inform future climate model development, e.g. of the new UK Earth System Model (UK-ESM). 2. Improve our understanding of the role of clouds in modulating the main modes of climate variability. Next to its importance for extreme weather, climate variability is superimposed on long-term trends due to man-made climate change. A better understanding of the role of clouds in climate variability will therefore enhance our ability to detect and attribute historical climate change, and to predict future changes in climate and its extremes.

In atoms, molecules or biological systems, all structural changes will modify the properties of the entity (form, colour, capacity to react with other entities etc ...). These changes are due to electronic and nuclear dynamics known as charge migrations (rearrangement of electrons and/or protons within the entity). However charge migrations are very fast and can occurs within 1/1000 000 000 000 000 second meaning from few attosecond (1e-18 sec) to few femtosecond (1e-15 sec). As an example in the Rutherford model of the hydrogen atom, known as the "planetary" model, an electron is moving around a proton (first orbital). The duration the electron takes to complete period around the proton is 150 asec. What is particularly exciting is to be able to make "a movie" of this ultra-fast dynamic that no existing device is capable to follow. My interests are actually not only to observe the first instants of these structural changes but also to control them to go deeper in the understanding of how chemical reactions or biological phenomena take place. If such attosecond information is achieved it will be possible to approach very high-speed information transfer and why not studying how information can be artificially encoded (molecular electronics) or present (traces of cancers) in biological sample, a kind of bio computing?This research will give birth to a new type of Physics that will bridge the gap between many sciences. The technical challenges under this research area are leading international efforts in laser development that will have a huge impact on technological applications also in industry (electronic, communication), medicine technologies (Magnetic Resonance Imaging, proton therapy, pharmacology).Therefore I developed a research based on tools to observe and control the intra- atomic and intra-molecular electrons and nuclei motions. To capture this dynamics at the origin of any chemical or biological reactions, one has to capture snapshots of the system evolving, exactly as a camera will do. Unfortunately there is no such detector, but what is possible is to find a process observable, that can be affected by these changes and so that will carry the fingerprint of these changes. The ideal candidate for this is light, because emission of photons is highly sensitive to any changes, it is a fast process and it can be observable by looking at spectra (frequency equivalent to its colour). The process I choose is high-order harmonic generation (HHG) that occurs within 10's attosec to few fsec (appropriate time window). It occurs while an intense and short laser pulse interacts with an atom or a molecule. During this interaction, an electron is ionised (extract from the core), and follow a certain trajectory before coming back to the core where it can be recaptured, exactly as a returning boomerang. The excess kinetic energy the electron has acquired during its travel will be spent by the system (final atom or molecule) emitting a new photon which frequency (colour) will be an odd harmonic of the fundamental photon (the laser photon). These harmonic photons can be measured accurately so if a change in the core occurs during the electron travel, the characteristic of the photons emitted will be modified. I have been working in the study of high order harmonic and in particular in the understanding of electron trajectories during the process. I demonstrated experimentally that the ionised electron can not only follow one trajectory but many, giving rise to my technique of investigation called Quantum-Path Interferences first demonstrated in atoms. I will use this technique under different conditions to extract the information on charge migration in molecules within the attosecond timescale.

The project will link Warwick's large and very strong community of early modern scholars to other centres of early modern expertise in the US and France in order to create a virtual forum for the exchange of ideas about Britain, Europe and America in the period c.1500-c.1850. The network will link the research cultures of leading universities and centres of research excellence: Warwick, Yale, Boston, Vanderbilt, Huntington Library-USC and the Sorbonne, Paris. Rather than linking particular scholars the aim is to link the research communities more generally, creating an innovative form of cross-institutional collaboration. The project will make use of existing technologies, and in particular social networking, and apply them to the interactions associated with research in order to make the process of collaboration much easier. This could be interaction through forms of web-based video-conferencing or text-based collaboration in the form of shared documents, blogs, wikis and chats. These aids to research collaboration, it is envisaged, will become part of routine research culture though one spread out over large geographical distances, creating a new type of scholarly community. In turn, such collaborations can lead to new types of research output: alongside conventional articles we might expect podcasts, video-streamed events, and discussion pages overseen by a guest academic with a particular expertise. The project thus explores how a virtual network might transform the ways in which academic research takes place between institutions that are geographically far-flung and the types of research outputs such collaborations might produce. The project will seek to make use of the mass of digitised resources that already exist for the early modern period and that are available at Warwick and participating institutions. Rather than seeking to add new collections of digital material, the project will add value to the wonderful material that is already available. These databases enable new questions to be asked of such material and new answers. For example, the ability to search databases for particular words or phrases opens up exciting possibilities of exploring different uses of the same word. The network is based on existing partnerships (with Vanderbilt and Boston) but also with emerging ones (the Huntington Library-USC, Yale and the Sorbonne). The network will benefit both members of staff and postgraduates, who will thereby be able to pursue research themes and discussions with colleagues who they might otherwise only infrequently meet physically. In turn, institutions outside the UK benefit from Warwick's expertise and lively research culture.