Yeah. Well, you know, I think, Kyle, it is a little hard to know exactly compare to nanopore-based approaches for protein sequencing given there are not any commercially available products that utilize that technology. There are certainly some very early-stage research papers that have been published on trying to apply nanopore technologies, but most of those papers, as is the case with early technology development, are focused on, you know, very simple samples, perhaps, you know, amino acids with spacers in them to improve the reading accuracy or the ability to detect the changes in amino acids. So, you know, a common technology feasibility approach, but not necessarily something that we know what the specs would look like in a commercial environment and how to think about positioning our technology against that. I think what we are focused on with our approach is, you know, unlike DNA, where nanopores have been applied, you know, you have, you know, four DNA bases to deal with. Everything is negatively charged. In amino acids, we are talking twenty. We are talking about a wide range of properties of those amino acids. And the sequence context of those amino acids is a very challenging endeavor. So while perhaps nanopore will get there, we think it is a significantly harder challenge than perhaps DNA in nanopore. In terms of how we focus on our approach, you know, what we like about our approach is a couple of things. One is, you know, our amino acid recognizers are capable. They typically detect, you know, two or three amino acids and the relevant sort of post-translational modifications or changes. That approach, you know, will prevent us from needing to have twenty different recognizers, which we think trying to scale up to these really large numbers of, you know, potential recognizers would be a very difficult biochemistry problem. So sort of our kinetic detection and that ability to have these multi sort of recognizers for different amino acids we think is definitely an advantage from a development perspective. And, you know, when we talk about sort of the engineering architecture, what we like about the direction we are going with Proteus is we really put ourselves on a consumable architecture that can be scaled to billions of reads, sort of the type of capacity you are bound to need to get the de novo sequencing, and then you are really leveraging a lot of the optical detection capability that has been evolved over the last decade or so. So we really like that sort of trying to leverage a lot of that capability that is out there getting onto that architecture that will scale, and, you know, underpinned by sort of our unique kinetic detection with those amino acid recognizers.