I'm Olivier Elemento. I'm a Professor at Weill Cornell Medicine, and I'm also the Director of the Institute for Precision Medicine here at Cornell. Our institute is focused on making medicine as personalized as possible. We're looking to use technology such as high-throughput sequencing, technology such as AI, or technology such as Imaging Mass Cytometry as a way to get a tremendous amount of insight into clinical samples, and then use those insights as a way to personalize treatments to match each patient to the right treatment so that we can improve outcomes and treat disease in the most effective way. We are using Imaging Mass Cytometry, or IMC, for multiple projects. One of these projects is a COIVD project where we require tissue from patients with COVID, especially lung tissue, and we've been able to understand, using the technology, using IMC at single-cell resolution what the tissue looks like. We're able to observe the context of advanced disease with COVID in the lung. And we've been getting new insights into the pathophysiology of the disease, and especially the interplay between the immune cells, the infected cells, and for example, the large amounts of some [inaudible] immune cells that we did not expect to see. So, that's just an example of a project. We have other projects now across multiple types of cancers. We have big project in lymphoma, for example, where we've been able to image hundreds of lymphoma patient samples using IMC. We're able to observe at single-cell resolution what the tumor microenvironment of these tumors look like. And we're getting novel insight on how to treat those patients by dissecting the complexity of the microenvironments with our novel targets that we're trying to navigate now. We are using the Hyperion technology for multiple projects, from Alzheimer's to cancer to COVID. This is a wonderful technology that's allowing us to understand disease tissue with incredible resolution. There's a lot of things that this technology allows us to do that we couldn't do before. I mean, I think the most interesting and sort of relevant factor here is really the fact that we can see the diseased cells in a spatial context. You know, in the past when, we did single-cell analysis, we had to take the tissue, essentially dissociate the cells, so get, you know, individual cells, you know, millions of them if you want, and then we would analyze all these different cells. But when you do that, you lose the context, you lose the proximity between certain types of cells. And Hyperion allows us to keep that information. And so, that's very powerful because we know that cells having contact, you know, will typically engage in a communication or crosstalks, and this is really relevant and important when it comes to disease because disease, in most cases, is very complex and involves disease cells but also kinds of normal cells that come into the disease cells, for example, to repair things or to help the disease cells, you know, accomplish what they want to do. So, being able to understand this crosstalks, this communication, and the spatial layout of tissue with disease is very unique and, right now, only feasible using a technology such as Hyperion. One thing I'd like to share, and I think this is based on the experience, is that, obviously, Hyperion is a wonderful technology, but we rarely use it by itself. We often combine it with other technologies such as high-throughput sequencing, for example, to find the mutations that we see in a clinical sample or, you know, other types of analysis such as single-cel RNAseq which is the technology that I mentioned earlier, which is where you can understand individual cells without a spatial context but you can understand them with a lot of depth. So, you can measure essentially the expression of lots of genes. And in my view, really, the future of systems biology and the implication of technologies like IMC for disease is really in combination with many other technologies. So, what you get is kind of multiomic, multidimensional insight into disease, which I think is really essential to understand disease better.