 Okay, Brandi, do you want to, or those high-fives, next slide? I agree with a lot of what Dan just said and so impressed with what both Rex and Dan presented about potential for discovery in eMERGE using the EMRs. I do want to start off making the point that I think that it possibly may take even more resources to fully utilize the data in the EMRs to do the kind of discovery that we can do in clinical trials. Next slide. So an example of this is the example of topoisomerase 2 inhibitors such as a topocyte or anticycline induced secondary leukemia. This is an event that was discovered over 20 years ago and it can have as high of a frequency as 20% of patients treated with these drugs and uniformly fatal disease. So finding out the risk factors for this disease was critical. Next slide. And I want to give you an example of a protocol. So this is the clinical trial that randomized patients to two different schedules of exactly the same drugs. So these patients get treated with weekly chemotherapy for 120 weeks and the only difference between the patients randomized to arm 2 versus arm 3 was that arm 2 had the same pairs of drugs rotated every week and arm 3 had the same pairs of drugs rotated every six weeks. So the cumulative doses over a two-year period of all of these drugs were absolutely identical. Next slide. But the frequency of secondary leukemia was much higher in the top curve in those who had the six-week slots to drug versus those who got the rotations every week. And I use this as an example to point out that if we're going to do things like survey EMRs for drug-related risk factors for adverse events, including fatal ones like this, looking at cumulative dose alone is not enough. Next slide. And the same thing was true in another fatal adverse event, the secondary brain tumor in patients treated with leukemia. This slide depicts the frequency of secondary brain tumors in four different front-line clinical trials as childhood AOL. They all went on for two and a half years, and they all had exactly the same dose of cranial irradiation, which is the primary risk factor that was known for development of secondary leukemia. But you can see in the top curve, this total 12th trial had a ridiculously high frequency of severe fatal secondary brain tumors compared to the other trials, even though the dose of radiation was identical. Next slide. And we found actually a genetic feature in that study, a defect in TPMT, which you just heard about, which predisposed patients to develop this secondary brain tumor much worse than the patients who did not have an inherited defect in TPMT. Next slide. However, these patients that have a defect in TPMT have always been around, and it's about 10% of the population that have that defect. So what was different about the total 12th study that made that genetic variant shine through and have its penetrant effect on this adverse drug effect that was not true in other studies? Next slide. So by looking carefully at exactly what therapy was given per protocol, among these four protocols, during the time period of irradiation, we could identify that it was the degree of anti-metabolite intensity only during the two weeks of radiation that was different about total 12th compared to the other studies that had a reasonably low level of secondary brain tumor. So again, an overall chart review that looked at just cumulative doses of drugs or cumulative doses of irradiation never would have found this schedule-dependent interaction that was a strong risk factor for this fatal complication. Next slide. And so here we have this interaction of post-genetics only shining through with its adverse event on a secondary brain tumor that was contributed to by the use of thiopurine drugs during the cranial irradiation because of the unfortunate mixture of these drugs during the period of cranial irradiation, very difficult to identify this kind of detailed drug therapy interaction from chart review alone. Next slide. So I guess the point that I would make is that I think that the EMR data being accumulated by eMERGE is absolutely fantastic and unparalleled that the tools that will be needed to query it to come up with detailed information about schedule and timing of doses versus other interventions is probably going to take even more resources. Now as Dan just alluded to, the other point is can you implement something if you don't already know that it works? Next slide. And we are of course looking at things like some of these pharmacogenetic gene drug pairs which we've worked on with the CPIC supported by the Pharmacogenomics Research Network. Right now we've got about 13 genes affecting about 60 drugs that are affected by CPIC guidelines and they are ready to implement now. However, I definitely agree with Dan that for rare variants of these genes we don't know necessarily to implement them and some of the data have been generated in ancestrally non-diverse groups which would make implementation in all ancestral groups potentially problematic. However, I do think that there has been a difference between doing implementation for that which one can with relatively common variants in well-studied ancestral groups and collecting new data, trying to find new associations between rare variants and those same genes affecting those same 60 drugs. I do think that some of the trials ongoing not necessarily be merged but elsewhere where patients are being randomized to have clinical implementation of genomics especially pharmacogenomics versus not are potentially problematic because when things are ready to implement they should really be implemented and it wouldn't really be ethical to withhold something that's ready to be implemented. Now some sites are getting around this by sort of capitalizing on our unfortunate non-uniform healthcare systems to take advantage of natural randomizations where some sites can do genomic medicine and others can't but that's really just an accident of our non-uniform healthcare systems. And studies that purport to look at the effective genomic medicine on clinical outcomes versus historical control I think really risk having all of the problems of poor study design coming up with misleading answers because the non-genetic clinical covariates are so critical in deciding whether something works or not that aren't controlled from these studies are really a problem and can never be addressed by a historical control or and that also affects of course these system randomizations. Next slide. So I guess I would just end there by saying that I think we have to I think capitalizing in emerge on both discovery and on implementation as it sounds as if you're doing is a fantastic approach but let's not mix up the two. When something is ready to implement it's used as a clinical tool and generating additional clinical outcome data may not be the best use of resources. If something's not ready to implement it's worthy of clinical research and emerge is better poised to do that than anyone. I'll stop there. Okay with that thank you and we'll