 Thanks, Rick. And thanks for everybody to listening to our tag team here. This is how we see the big picture for the clinical translation and the different applications that can be done at different scale of activity. So we're on the left here of the smaller projects. Here is targeted clinical testing where you might have really a predetermined reason to go in and do detailed study, clearly affected families, for example. Here's where we would move that out into a healthcare setting where it's more generally available, perhaps without as many priors to begin the study. And here's truly ubiquitous application of our methods. The scale on the bottom here is a log scale, thousands, tens of thousands, hundreds of thousands, and eventually millions. Now, if we want to be out here, you might say, why can't we be? Since you just heard from Eric and as you all know that we're in this world right now where we're doing research in our $700 exome and $5,000 or so whole genome world, we're doing disease discovery in projects of scale of around 1,000 to 5,000 samples, and indeed are already looking at cohorts with 10,000 samples or more. And if we follow the future, as Rick has just pointed out with the developing technologies, we're going to move out here and be in this truly large-scale range that we can extend the studies. But that is the research domain, and so we're talking now what about the clinical translation, and there's something of a lag there. So here we are more in the 1,000 to 10,000 sample range right now, ably doing projects with a few hundred samples and only in a few places getting this up into the thousands of numbers. So why is this running behind the research arena? Well, it's clearly because there are different demands in the clinical translation space. I think you all know the clear-clap environment demands stable processes with 100% precision. The research 99% precision just doesn't cut it. That adds cost and reduces throughput. One has to deal with heterogeneous samples, not going to the freezer to get 10,000 samples out of plates but getting patients in one at a time. There's individual reporting and human input required at all steps. So there really are new challenges as part of the scaling of the clinical genomics. But it's not all bad news, of course. We've got good experience now with porting activities from our larger research genomics projects into clinical labs. The optimal practice, we developed those in the context of these high throughput genomics projects. We calibrate and stabilise the methods, document them, lock them down, declare we have standard operating procedures and then deploy those in a clinical environment. And we're seeing a few examples of that now indeed happening. So we could say, indeed, with some confidence, large-scale research studies do provide this foundation for clinical translation. What are some of the actual examples of those transitions and those throwing the technology over the wall into the clinical labs? We have the whole genome and whole exome experimental techniques at the lab. There's also been really strong developments in the variant calling and validation data handling methods. Managing reference databases rationally and effectively in a clinical setting to look for pathogenic variation and bringing just all of the other tools that we've learned for secure and proper management into process management phases in the clinical lab as opposed to the experimental homes that they came from. We've also been doing this, I think, very effectively in data sharing. You're hearing a lot of talk about cloud and large network applications. Really, the drivers for this are from the research end and they're now bringing those into the clinical end as well. So we have further opportunities to accelerate translation by more lessons drawn from the research area. What's the current state? We're seeing some of these activities going on in clear-clap environments partly supported by insurance companies but still being leveraged by these development programs. We're in a state now of a fraction of pediatric cases are being solved, about a quarter of them in our hands, but I think generally that number is around there in other centres as well. And maybe 5% of cancer cases are properly guided by these activities. So we have a lot of, I guess, the ceiling is high as the point here. We have a lot of room to transfer further this knowledge over into our clinical environment. I'd say globally there's only about 10,000 cases per year that are truly going through a clinical diagnostic environment bearing all the bells and whistles we've developed in the research environment. So what are some more opportunities? Well, here's just a selection that come from us. I think you can draw from Eric's talk and from Rick's talk other examples, but we certainly have examples where sequencing a few hundred cases in a different context has shown us that there are more genetics going on than we would have expected. We did a study on cerebral palsy that showed that 25% of the cases actually had a genetic contributor, which begs the question how ubiquitous are those genetic compounders in what is sometimes regarded as not a genetic disease but may have just a few Mendelian forms, maybe there are many more, how ubiquitous is that and what could you learn from a much larger study done with full clinical annotation? Another example of that is a real one with a few cases now where we have from de novo mutation identification recognised new syndromes. One case, four cases with a de novo out of 2,500 in our local clinical cohort. Now that begs the question, what's the real frequency of this disease? We're only seeing a fraction of possible cases. There's a lot of filters. If you extrapolate out from birth rates and overall likelihood of children that are in this class and might have the disorder, there could be 200 or so of those cases here in the US and many hundreds worldwide, simply not recognised as we extrapolate that. And what about building full genetic models by looking in various syndromic conditions where we know that not every case can be solved as a simple Mendelian case but this is not necessarily the same as complex disease but where we can find through detailed clinical annotation and deep sequencing and adequate sample sizes the kind of genetic data that can build full oligogenic models that can deeper our understanding of the disease pathology, doing genetics here somewhat from the ground up. So all this becomes possible as we move into this additional clinical space. So where we get samples for such things? Well, they're not exactly on the shelf like the cohort studies and the case control studies that Eric spoke about, but they're not so far away from us either. We certainly have private health care networks that are large that are already being tapped into in these large scale ways and actually Terry, you showed some beautiful examples from other countries. I wish we could be counted up there amongst those examples. And there are state screening programs. At the family study level we certainly have advocacy groups and clinical treatment groups that can bring us literally thousands of samples for these kinds of studies. The clinical clusters of developmental delay, pre-term birth and families with multiple different cancers, et cetera, et cetera, et cetera also represent big tunities to aggregate. Another opportunity that's represented from the previous slides, the menu on the previous slides is to extend these Mendelian studies to fully describe the mutations in low side known to cause rare disease. Eric showed the nice slide of the cancer genes where if you get enough mutations you begin to understand the function of the cancer gene. We need, for these new rare syndromes, we need to aggregate the information of the mutational spectrum from the hundreds of cases that exist so we can understand the relationship between the kind of mutation and exactly how the disease manifests. We can only do that by broadening our denominator here. And lastly, we can look most broadly at these sporadic cases with build-out oligogenic models in large sets of multiple patient admissions such as regular screening for whole medical center admissions. And that would be the very outer rim of our onion skin diagram with the largest input. And that sounds like hand-waving and extremely optimistic but it's exactly also what was shown in Terry's slides that's going on in the UK and in other countries. So we hope we can speed to there. So that's the end of our tag team for a prior to discussion and I guess we can take questions now if we still have some time left in our allocation ready.