The shift from hunting and gathering to an agricultural way of life was one of the most profound events in the history of our species and one which continues to impact our existence today. Understanding this process is key to understanding the origins and rise of human civilization. Despite decades of study, however, fundamental questions regarding why, where and how it occurred remain largely unanswered. Such a fundamental change in human existence could not have been possible without the domestication of selected animals and plants. The dog is crucial in this story since it was not only the first ever domestic animal, but also the only animal to be domesticated by hunter-gatherers several thousand years before the appearance of farmers. The bones and teeth of early domestic dogs and their wild wolf ancestors hold important clues to our understanding of how, where and when humans and wild animals began the relationship we still depend upon today. These remains have been recovered from as early as 15,000 years ago in numerous archaeological sites across Eurasia suggesting that dogs were either domesticated independently on several occasions across the Old World, or that dogs were domesticated just once and subsequently spreading with late Stone Age hunter gatherers across the Eurasian continent and into North America. There are also those who suggest that wolves were involved in an earlier, failed domestication experiment by Ice Age Palaeolithic hunters about 32,000 years ago. Despite the fact that we generally know the timing and locations of the domestication of all the other farmyard animals, we still know very little for certain about the origins of our most iconic domestic animal. New scientific techniques that include the combination of genetics and statistical analyses of the shapes of ancient bones and teeth are beginning to provide unique insights into the biology of the domestication process itself, as well as new ways of tracking the spread of humans and their domestic animals around the globe. By employing these techniques we will be able to observe the variation that existed in early wolf populations at different levels of biological organization, identify diagnostic signatures that pinpoint which ancestral wolf populations were involved in early dog domestication, reveal the shape (and possibly the genetic) signatures specifically linked to the domestication process and track those signatures through time and space. We have used this combined approach successfully in our previous research enabling us to definitively unravel the complex story of pig domestication in both Europe and the Far East. We have shown that pigs were domesticated multiple times and in multiple places across Eurasia, and the fine-scale resolution of the data we have generated has also allowed us to reveal the migration routes pigs took with early farmers across Europe and into the Pacific. By applying this successful research model to ancient dogs and wolves, we will gain much deeper insight into the fundamental questions that still surround the story of dog domestication.

Synthetic biology engineers living systems to perform useful functions. For example, we engineer small bacteria's genomes to produce expensive vitamins or to degrade plastic waste. However, cells do not behave the same even when their genetic information is the same. For example, when we engineer cells to produce a specific molecule, some cells produce it efficiently while other cells do not. This is a problem because the overall yield of production is reduced because of inefficient cells. This increase in the production cost is one of the major obstacles that need to be overcome to commercialise many synthetic biology applications. To solve this problem, we need to know what is happening inside each cell. However, it is not an easy task because a cell is a complex object. Even a simple bacterial cell has more than one million molecules inside its cytoplasm. In this proposal, we will develop a simple cell mimic - an artificial cell system made from scratch using synthetic elements - to observe what is happening inside a cell. This will help us to understand why cells show different responses despite sharing the same genetic information. A microfluidic device will be used to produce artificial cells at a scale large enough to analyse different populations. Then we will observe individual cells and their responses. The result will be analysed with mathematical modelling to understand why certain cells behave differently from other cells. This knowledge will allow us to engineer cells that exhibit homogeneous and consistent behaviour. In a long term, this work will help commercialise a lot of synthetic biology applications by reducing their production costs.

Measuring the length of an Olympic swimming pool doesn't affect how much water it has in it! We normally don't expect measuring things to change them. In the quantum world, things are very different.Quantum mechanics tells us how the world works at its most fundamental level. It predicts very strange behaviour that can typically only be observed when things are very cold and very small. It has an inbuilt element of chance, allows superpositions of two different states, and includes super-strong correlations between objects that would be nonsensical in our everyday world - entanglement . Despite this strange behaviour, quantum mechanics is the most successful theory that we have ever had - it predicts what will happen almost perfectly! However, it is not completely understood, and some of its implications are still being discovered.One of the great mysteries of quantum mechanics - The Measurement Problem - seeks to answer the question Why don't we see superpositions in the everyday world? ( alive and dead for example). Measurements play a special role in quantum mechanics and have been the subject of intense debate since the theory's development early last century. Recently quantum measurements have emerged to become an important practical issue. This is the result of the advent of quantum information science , which seeks to answer the question What advantage can be gained by specifically harnessing quantum mechanical effects in the storing, transmitting and processing of information? Anticipated future technologies include quantum computers with tremendous computational power, quantum metrology which promises the most precise measurements possible, and quantum cryptography which is already being used in commercial communication systems, and offers perfect security.Unlike measuring the length of a pool, measuring a quantum system necessarily disturbs the system. For example a standard measurement of a system in a superposition of two states finds the system in one of those states with some probability. After the measurement, the system is no longer in a superposition, but is in the state it was measured to be in with certainty. The original superposition state can never be recovered, and that information is lost.More general quantum measurements involve a payoff between the information gained and the disturbance of the system. Quantum mechanics also allows entangling measurements on two or more systems, that leave them in an entangle state. Finally, we can intentionally manipulate the system being measured depending on what the measurement tells us - feedback.These general quantum measurements could play an important role in future quantum technologies: the security of quantum cryptography relies on detecting an eavesdropper by the disturbance their measurements must cause; quantum metrology requires entangled measurements; and some schemes for quantum computation proceed via measurements alone.Single particles of light - photons - are excellent system for developing new quantum measurements, because they suffer from almost no noise. They also have great potential for application in future quantum technologies: schemes for all optical quantum computers are leading contenders, and photons are the obvious choice for both quantum communication and for quantum metrology schemes for measuring optical path lengths. This project will realise new quantum measurements which are entangled, tuneable in the amount of disturbance, and include feedback. It will use an optical crystal to produce up to six photons, optical circuits to realise controlled interactions between them (with feedback), and standard avalanche photodiodes to detect them. A particular focus will be on developing practical schemes for efficiently extracting information from quantum measurements. Finally, the project will design and implement techniques for distinguishing between quantum processes on up to 4 photons.

The atmosphere changes on time scales from seconds (or less) through to years. An example of the former are leaves swirling about the ground within a dust-devil, while an example of the latter is the quasibiennial oscillation (QBO) which occurs over the equator high up in the stratosphere. The QBO is seen as a slow meander of winds: from easterly to westerly to easterly over a time scale of about 2.5 years. This 'oscillation' is quite regular and so therefore is predictable out from months through to years. These winds have also been linked with weather events in the high latitude stratosphere during winter, and also with weather regimes in the North Atlantic and Europe. It is this combination of potential predictability and the association with weather which can affect people, businesses and ultimately economies which makes knowing more about these stratospheric winds desirable. However, it has been difficult to get this phenomenon reproduced in global climate models. We know that to get these winds in models one needs a good deal of (vertical) resolution. Perhaps better than 600-800m vertical resolution is needed. In most GCMs with a QBO this is the case, but why? We also know that there needs to be waves sloshing about, either ones that can be 'seen' in the models, or wave effects which are inferred by parameterisations. Get the right mix of waves and you can get a QBO. Get the wrong mix and you don't. Again we do not know entirely why. Furthermore, we also know convection bubbling up over the tropics and the slow migration of air upwards and out to the poles also has a big impact of resolving the QBO. All of these factors need to be constrained in some way to get a QBO. The trouble is that these factors are invariably different in different climate models. It is for this reason that getting a regular QBO in a climate model is so hard. This project is interested in exploring the sensitivity of the QBO to changes in resolution, diffusion and physics processes in lots of climate models and in reanalyses (models used with observations). To achieve this, we are seeking to bring together all the main modelling centres around the world and all the main researchers interested in the QBO to explore more robust ways of modelling this phenomena and looking for commonalities and differences in reanalyses. We hope that by doing this, we may get more modelling centres interested and thereby improve the number of models which can reproduce the QBO. We also hope that we can get a better understanding of those impacts seen in the North-Atlantic and around Europe and these may affect our seasonal predictions. The primary objective of QBOnet is to facilitate major advances in our understanding and modelling of the QBO by galvanizing international collaboration amongst researchers that are actively working on the QBO. Secondary objectives include: (1) Establish the methods and experiments required to most efficiently compare dominant processes involved in maintaining the QBO in different models and how they are modified by resolution, numerical representation and physics parameterisation. (2) Facilitate (1) by way of targeted visits by the PI and researchers with project partners and through a 3-4 day Workshop (3) Setup and promote a shared computing resource for both the QBOi and S-RIP QBO projects on the JASMIN facility

The West Antarctic Ice Sheet contains enough ice to cause 3.3 meters of sea level rise. The ice streams of its Amundsen Sea sector, which alone could contribute up to 1.2 meters of sea level rise, are thinning faster than in any other region on earth, and have the potential for rapid collapse due to inland-deepening bedrock. Using a combination of novel inverse modelling, a comprehensive ice-sheet model, and remote sensing we will: 1) Estimate the present state of the critical Amundsen sector 2) Predict its future behaviour 3) Quantify the uncertainty of these estimates and predictions The physics of ice-sheet retreat is qualitatively understood, but the detailed behaviour is dependent upon a very large number of parameters that cannot be measured directly (e.g, spatially-varying basal traction and ice stiffness). However, numerical ice sheet models have now evolved to the point where a number of relevant physical processes, such as grounding line movement and ice-sheet response to ocean forcing, can be represented accurately. Moreover, the satellite-observational record continues to grow, creating opportunities for assimilation of this new data into models. Such a model-data synthesis can allow key underlying and hidden physical parameters to be determined, facilitating data-driven prediction of future ice-sheet contribution to sea levels. However, techniques for the assimilation of data using ice sheet models remain at an early stage. A considerable amount of data remains unused and fundamental questions, such as the specific information required for reliable predictions, remain unanswered. Moreover, model simulations of future behaviour of ice sheets generally do not account for the uncertainty inherent in estimates of hidden parameters, which can potentially grow with forecast horizons. Accounting for these uncertainties is vital so that informed risk and cost-benefit analyses of sea-level rise protection and adaptation can be carried out. In the proposed project we will develop a model-based framework which will efficiently assimilate the data record for the Amundsen sector (Fig. 1), providing estimates of key physical quantities, and predictions of future behaviour. Crucially, measures of uncertainty will be provided for the estimate and predictions. We will further study the impact that different observations have on our model predictions and uncertainty therein, providing information that will be of value to future observational campaigns. While the Amundsen region is chosen as a focus in the interest of critical relevance and timeliness, the methodology can be applied more generally in other regions of Antarctica, or Greenland.