 So, I'm now going to hand over to my colleague, Paul Collegius, who's going to talk about medical informatics. Hi, Rosie. Thanks, Paul. Oh, you got one. Hi there. So, it's really exciting today to be here talking about research computing at the OpenStack Summit, because I feel OpenStack technologies are now poised to make significant impact in the research and innovation domains. So, we started our OpenStack journey in Cambridge about 18 months ago when we started to build a new research computing platform called the Cambridge Biomedical Cloud. The task of this platform is to take clinical data and applications from the university hospital environment and move those across to the research computing environment to drive medical analytics and computationally intensive biomedical research. So, why would we use OpenStack in the research computing environment? Well, from my perspective, from a provider's perspective, OpenStack technologies make computing data and applications more accessible, more flexible and more secure. From the researcher's perspective, it makes research computing and data easier to use, easier to share for collaboration, and this decreases the time to science and increases innovation. So, within our university hospital environment, we produce huge amounts of data, which is all now held in electronic record systems. This provides us with an ideal opportunity to apply big data technologies for improved health outcomes. But to do this, we need an IT platform which is secure, flexible, elastic, and lends itself to a sandboxed research computing environment. And OpenStack really meets all those requirements. So, if we take a look at the Biomedical Cloud, we can see that it's a heterogeneous architecture with three main elements. There's a 2,000-core OpenStack element using 50 gigabit ethernet that's RDMA-enabled for performance. We have a traditional HPC cluster, static image-based system using 56 gigabit infinity band. That's 1,000 cores. And we have a currently quite a small Hadoop cluster, which will be growing at the beginning of next year. And there's quite a complex ethical sign-off and data-sharing platform that takes data from the hospital network and moves that across to the research network under the correct regulatory compliance regimes. And then once we have that data in the research network, we can do interesting things with it. So, how do we use that platform to develop new predictive medical analytics techniques? So, firstly, we take various data warehouse products from Epic on the upper right-hand part of that diagram. These are patient test results, medical records, live telemetry feeds from the operating theatre. And we run that through predictive modeling techniques to produce predictions. We can then test those predictions and enter into a device trial loop where we assess and refine that model until in the end we have something that we think can stand up as a new clinical treatment. So a really good example of this is work done by Dr. John Cromwell at Iowa University Hospital. John's developed a statistical model looking at surgical site infections that they run live while a patient is undergoing a procedure. And by using this model, they can cut surgical site infections by 58%. That's a really good example of how quite low-hanging fruit can develop large benefits in the healthcare environment. So another use case we're working on in Cambridge is population scale genomics analysis and we developed a new genomics analysis platform called OpenCB using Hadoop infrastructure. This has been developed with Genomics England to undertake one of the largest population studies in the world, the UK 100K genome project, where we would be looking at the genomes of 100,000 people. This OpenCB technology was already deployed on the bio cloud and we're running that over a UK 100K precursor project called the bridge project where we're looking at 10,000 patient's genomes of rare diseases. And we're seeing two orders of a magnitude performance increase over previous platforms. So the last use case is actually again very interesting. Medical use case from the hospital that deployed a new brain imaging machine. That's the brain imaging machine being installed on the left hand side. This produced vast amounts of data, so the center needed a step function increase in its computing data capability. OpenStack medical imaging VMs provides that step change and that's now going into production, I think, in about a month's time. So when we started our journey, the scientific computing community was really quite nascent. That community has now been developed through the scientific working group. I'd like to present Stig Telfer who'll tell us a little bit about that now. Thank you. The science we've seen today ranges from the subatomic to the breathtakingly cosmic. From the beginning of time to the future of healthcare. Yet the computational challenges that these projects face have more in common than indifference. At an infrastructure level, they all face pretty much the same problems. These use cases are pretty typical in research computing, but they are not the default OpenStack use case. When you deploy OpenStack out of the box, this is not what you get. The scientists have to work a little bit harder at their configurations than the rest of us. This is the driving force behind OpenStack's scientific working group. Our open membership is drawn from institutions around the globe who use OpenStack to support science and research of this kind. We share knowledge with each other. We help each other out. We know what works and what doesn't. We fix a few things and we share the results. Together as a working group, we advocate our use case for research computing among the wider OpenStack and research computing communities. I cannot believe that it has just been a short year and all this has happened. I cannot believe how much it has already helped us at Cambridge and our other members. With our working group now established, I know that we want to make an even bigger impact in the year to come. Our quest to understand the universe and to improve our little corner of it is going to be moved forward by ambitious scientific projects such as these we've just heard. To help them achieve this, compute hardware is going to get deployed on a massive scale. And upon that hardware, a platform must be built that meets the scientists' needs. In our group, we believe that platform should be OpenStack. And if you're interested in the problems and the solutions, I hope you'll join us in the working group and together we'll make it happen. Thank you. Well, thank you so much, Paul and Stake, for all of your contributions. We really appreciate it. And I just wanted to highlight one of the collaborative efforts that we did recently with the scientific working group and including the foundation staff put together a book about HPC and OpenStack. Maybe you can tell us a little about it. Yeah, we started out, we thought we'd do a survey of what was out there, get a baseline of knowledge. And we thought we'd write a few papers and get a few articles together. We found so much that we actually put together a book on the subject. And here it is. So this is a book which is drawn from the expertise of a lot of the subject matter experts who are members of the working group. We've got together, we've contributed to create this publication. Well, excellent. Well, thank you so much for your work. We're actually going to have this available online and at the Supercomputing conference in just a couple of weeks. So excited to participate there as well in your industry. And if you're interested, a lot of the authors will be at the Scientific Computing BoF, which is Wednesday afternoon at 2.15. Great. Thank you so much. Thank you.