Most modern computer applications depend in some way or another on computations that are performed by server applications on the internet. More and more of these server applications are now built as so-called microservices, which allow developers to gradually update or fix issues in unrelated parts of a larger application, and therefore, have become popular. Many of these microservices avoid certain types of concurrency issues by design. Unfortunately, they still suffer from other kinds of concurrency issues, for example when multiple online customers try to reserve the same seats at the same time. For software engineers, it is hard to test for all possible concurrent interactions. In practice, this means that only simple concurrency issues are reliably detected during testing. Complex issues can however easily slip through and make it into server applications and then handle client requests incorrectly. One example of such a concurrency issue appeared at Nasdaq when the Facebook stock was traded for the first time, resulting in the loss of millions of dollars. Our goal is to develop techniques that detect concurrency issues automatically at run time, to be able to circumvent them, and enable developers to fix them, using detailed information gathered by the detection techniques. Researchers have shown that one can detect and avoid issues, for instance by changing the order in which client requests are processed. In practice however, current techniques slow server applications down significantly, which make these techniques too costly to be used. Our aim is to dynamically balance the need for accurate information and minimize slow down. We conjecture that we can get most practical benefits while only rarely tracking precise details of how program code executes. In addition to automatically preventing concurrency issues to cause problems, we will also use the obtained information to provide feedback to developers so that they can fix the underlying issue in their software. Thus, overall the goal of this research project is to make server applications, and specifically microservices, more robust and resilient to software bugs that are hard to test for and therefore typically remain undiscovered until they cause major issues for customers or companies. Our work will result in the development of adaptive techniques that detect concurrency issues, and automatically tradeoff accuracy and run-time overhead, to be usable in practice. Furthermore, the detection techniques will be used to provide actionable input to the software developers, so that the concurrency issue can be fixed and therefore be prevented reliably in the future. To evaluate this work, we will collect various different types of concurrency issues and make them openly available. This collection will be based on issues from industrial systems and derived from theoretical scenarios for highly complex bugs. We include these theoretical scenarios, since such complex bugs are hard to diagnose and test for, they likely remain undiagnosed and undocumented in practice, but have the potential of causing major disruptions. Finally, we will build and evaluate our proposed techniques based on a system designed for concurrency research. The system uses the GraalVM technology of Oracle Labs, which allows us to prototype at the level of state-of-the-art systems, while keeping the development effort manageable for a small team.

The repeated formation, advance, retreat and disappearance of ice-sheets is the defining characteristic of the glacial cycles of the last million years. At the Last Glacial Maximum (LGM), 21,000 years ago, the extensive Northern Hemisphere ice-sheets had a major influence on global and regional climate, and global-mean sea-level was 120 m lower than present, mainly due to the much greater mass of water stored in ice on land. Ice-sheets and climate interact strongly. Ice-sheets are very sensitive to climate change through its effect on snowfall and melting. They feed back on regional and global climate change through several mechanisms; for instance, sunlight is reflected by the snow and ice, surface temperature is cooled by raised elevation, and meltwater running off the ice-sheet into the sea may influence ocean circulation. The enormous and complex changes in climate and ice-sheets which take place during glacial cycles are not understood in several important respects or in detail. Explaining them is an exciting intellectual challenge of Earth system science. The effect of anthropogenic climate change on the ice-sheets of Greenland and Antarctica could produce changes in global-mean sea-level of many metres over future centuries, with severe impacts on coastal populations and ecosystems. On the longer term, if climate change were reversed, the ice-sheets might regrow. Contemporary observations alone give us insufficient knowledge of the relevant processes to make reliable predictions, because changes during the relatively well-observed last century have been relatively small. Therefore the record of the larger natural variations that occur during glacial cycles is a crucial source of information about how ice-sheets may respond to and influence climate change in the future. The aim of this project is to investigate the co-evolution of the climate and the Northern Hemisphere ice-sheets during the last glacial cycle. For the first time we will do this using the type of climate model used for detailed future climate projections, coupled to a detailed ice-sheet model. The focus is on analysis of changes simulated by these computer models, which we compare with observational data. The intended outcomes will be (i) simulations of the last glacial cycle with a much more physically complete model than has been used before, including a quantification of the effect of model systematic uncertainty on the results; (ii) a consequent improvement in scientific understanding of ice-sheet change and its interaction with climate on timescales of centuries to millennia; (iii) an improved capability for modelling ice-sheet changes that will result from anthropogenic climate change. This has obvious practical socio-economic relevance, since we want to be able to make predictions for the future.

Anthropogenic emissions that affect climate are not just confined to greenhouse gases. Sulfur dioxide (SO2) and other pollutants form atmospheric aerosols that scatter and absorb sunlight, and influence the properties of clouds, modulating the Earth-atmosphere energy balance. Anthropogenic emissions of aerosols exert a significant, but poorly quantified, cooling of climate that acts to counterbalance the global warming from anthropogenic emissions of greenhouse gases. Uncertainties in aerosol-climate impacts are dominated by uncertainties in aerosol-cloud interactions (ACI) which operates through aerosols acting as cloud-condensation nuclei (CCN) which increases the cloud droplet number concentration (CDNC) while reducing the size of cloud droplets and subsequently impact rain formation which may change the overall physical properties of clouds. This consequently impacts the uncertainty in climate sensitivity (the climate response per unit climate forcing) because climate models with a strong/weak aerosol cooling effect and a high/low climate sensitivity respectively are both able to represent the historic record of global mean temperatures. On a global mean basis, the most significant anthropogenic aerosol by mass and number is sulphate aerosol resulting from the ~100Tg per year emissions of sulphur dioxide from burning of fossil fuels, but these plumes are emitted quasi-continuously owing to the nature of industrial processes, meaning that there is no simple 'control' state of the climate where sulphur dioxide is not present. On/off perturbation/control observations have, to date, been limited to observations of ship tracks but the spatial scales of such features are far less than the resolution of the weather forecast models or of the climate models that are used in future climate projections. This situation changed dramatically in 2014 with the occurrence of the huge fissure eruption at Holuhraun in 2014-2015 in Iceland, which was the largest effusive degassing event from Iceland since the eruption of Laki in 1783-17849. The eruption at Holuhraun emitted sulphur dioxide at a peak rate of up to 1/3 of global emissions, creating a massive plume of sulphur dioxide and sulphate aerosols across the entire North Atlantic. In effect, Iceland became a significant global/regional pollution source in an otherwise unpolluted environment where clouds should be most susceptible to aerosol emissions. Thus, the eruption at Holuhraun created an excellent analogy for studying the impacts of anthropogenic emissions of sulphur dioxide and the resulting sulphate aerosol on ACI. Our research will comprehensively evaluate impacts of the Holuhraun aerosol plume on clouds, precipitation, the energy balance, and key weather and climate variables. Observational analysis will be extended beyond that of our pilot study to include high quality surface sites. Two different climate models will be used; HadGEM3, which is the most up to date version of the Met Office Unified model and ECHAM6-HAM, developed by MPI Hamburg. These models are chosen because they produce radically different responses in terms of ACI; ECHAM6-HAM produces far stronger ACI impacts overall than HadGEM3. Additionally, the UK Met Office Unified Model framework means that the underlying physics is essentially identical in low-resolution climate models and high-resolution numerical weather predication models, a feature that is unique in weather/climate research. In the high resolution numerical weather prediction version, parameterisations of convection can be turned off and sub-gridscale processes can be explicitly represented. Thus the impacts of choices of parameterisation schemes and discrete values of variables within the schemes may be evaluated. The research promises new insights into ACI and climate sensitivity promising us great strides improving weather and climate models and simulations of the future.

The duplication of genes provides new genetic material that can be used for novel functions, allowing plants and animals to evolve biological innovations and adapt to environmental conditions. Whole genome duplication (WGD) is arguably the most dramatic mechanism for duplication, resulting in the production of a new copy of every gene in the nuclear genome. Around 430 million years ago, spiders and scorpions diverged from a common ancestor that had experienced a WGD. The retained duplicated genes from this WGD event (genes called ohnologs) can still be found in the genomes of the approximately 45,000 species of these animals alive today and may have contributed to their adaptation and diversification. Since then, some families of Synspermiata spiders have undergone at least two additional WGDs within a single lineage, reflecting a similar series of WGDs in vertebrates. This presents an opportunity to compare these events to determine whether there are general principals shaping the outcomes of WGDs and their contribution to animal diversification. In addition, Synspermiata represent a wide diversity of spiders that are understudied and poorly understood Therefore, the aims of this project are to identify spider ohnologs after multiple WGDs, explore whether and how they have contributed to the evolutionary success of these animals, and compare the outcomes of these events to repeated WGDs in vertebrates. We will first collect and carry out the first large scale detailed study of the morphology of Synspermiata spiders to better understand their evolution and phenotypic diversity. In parallel, we will identify the ohnologs that have been retained in spider groups after WGDs by comparing the repertoire and arrangement of the duplicated genes in these animals with relatives where there is no evidence of additional WGDs. As part of this aim, we will sequence the genomes of Synspermiata spiders that have undergone one (Pholcus phalangioides, Scytodes thoracica and Loxosceles reclusa), and two (Oonops pulcher, Segestria senoculata and Dysdera crocata) WGD, as well as the transcriptomes of Caponiidae species with two (Orthonops zebra) or three (Calponia harrisonfordi) WGDs. Since relatively little is known about these spiders this will provide new insights into the biology of these animals as well as their genome evolution. We will then compare the repertoires of genes retained after WGD between spiders and vertebrates to determine whether there are any similarities in the aftermath of these events. This information will help us to better understand the general consequences of WGD and the principles underlying their outcomes in terms of genes being preferentially retained or lost again. Identification of ohnologs will also allow us to ask if these genes have been subject to sub-, neofunctionalisation or specialisation during spider development and if their expression is associated with morphological diversification. Overall our project will provide new insights into the genomes of spiders and how WGDs in these animals have contributed to their morphological evolution. Our data will also allow comparisons to WGD events in other animals, including vertebrates, to better understand the general consequences of these events and their contribution to animal adaptation and diversification.

The Collaborative Computing Project for NMR (CCPN) was started in 2000 to improve the interoperability of software for biomolecular Nuclear Magnetic Resonance (NMR), and to promote a collaborative community for software users and programmers. Over the past fifteen years, the project has produced the CcpNmr suite of software for interactive NMR data analysis and software integration, which is now used worldwide by >1000 users. Through its conferences and workshops, CCPN also promotes best practices in both computational and experimental aspects of NMR, thus helping to maximise the impact of biological NMR research. CCPN has a leading role in the development of a NMR data-exchange format and coordination of NMR instrumentation proposals for RCUK and BIS. With the current proposal we seek to continue the CCPN project and to further expand its user community. Hence, over the next grant period we aim to: 1. Maintain and expand the CCPN code base. 2. Expand the capabilities and versatility of the CCPN software package. 3. Facilitate NMR-based scientific developments in collaboration with the partners of the project and the NMR community at large. 4. Promote and expand user uptake and user development of the software. 5. Provide support for research data management (RDM). 6. Support the training of researchers, sharing of knowledge and exchange of best-practices by the UK and international NMR community.