Sure, thanks, Chad. So in terms of the research side and the development side, we're working hard to get an economy of scale by sort of standardizing our methodology. And so for example, we're able to now each different disease class requires algorithmic development, and it developed chemistry, the chemistry is united among all of our all of our different essays right now, we literally have one delivery mechanism, which is just the same for T-Detect COVID is that will be T-Detect everything. So same sample type, same chemistry, et cetera. The only thing that potentially can change the algorithms, and we're uniting, at least within the disease class, those algorithms, which is why we've been able to really expedite on the infectious disease side, we have a few that are COVID on market line coming soon, we just published a paper on that. And there's multiple other infectious diseases that we have really nice, probably clinical grade signals already. Separately in the auto immune space, we're making progress across multiple different autoimmune space. We talked about IBD, we talked about MS. We have some others that we're working on in the same concept, right? We're uniting and trying to get learn cross learning's from one where we can then get much faster at the others. So what does this require, this requires us to get a certain amount of samples with patients with those diseases. And we've been collecting those for quite a while now. And we have a either samples sequenced already, as we told you already in multiple books, in MS as well as in IBD. But we have others coming in other autoimmune diseases, and other infectious diseases as we go. So all of those are progressing really nicely and we're very pleased with the direction they're going. You asked about oncology, oncology, as a much broader set of questions. And we are working in the research setting here, the class of drugs is enormous. And the number of companies working on them is huge. And so we're working to help all those companies really understand how to use those drugs, et cetera, in terms of, but that does feed on and stems from our connecting T-cells to antigens at scale. And the issue here is simply that the immune response to cancer is so broad, because the potential antigenic space, the things that your immune system can see is huge. So it's just a harder problem. We are making progress, but it's going to be on a longer timeframe just because of the scale of the project. So we're moving, it's coming, but it's going to be a little while on that side, we're going to have more near term success, successes that you'll see hit in infectious disease and autoimmune space.