Thanks, and good morning, all. As Sujal shared, in Q1, we made excellent progress against our development goals on our core platform, tau proteoform assay, and broad scale proteome assay. This quarter in particular saw significant progress on transitioning both our TAO assay and core platform towards readiness for larger scale application, to enable our partners to ask and answer important biological questions. This readiness was evaluated using a rigorous verification and validation VNV, process of the tau proteoform assay has run on the latest generation of our instrument. We additionally performed a number of pilot biologic studies with the platform including our first ever analysis comparing samples from brains of patients with Alzheimer's disease to control samples. I'd also like to note that the extensive foundational work we have put into reliability, reproducibility, and scale across every component of our platform from reagents to software over the past several years, bore fruit that was clearly demonstrated in our VNB. Also, as a reminder, the instrument that was used in this TAO assay VNB is the same instrument used for the broad scale proteome assay. The only differences between the targeted proteoform assay and the broad scale proteome assay are the consumables. Consequently, though focused on the tau assay, this VNB was in no small part a significant validation of our entire platform. Our verification and validation evaluation looked at a number of key customer and product requirements. Specifically, from customer feedback, it was clear that customers prioritize reproducibility, accuracy, dynamic range, number of proteoforms measured, sample compatibility, sample input requirements, turnaround time, reliability. The assay exceeded expectations on all criteria and is ready for broader application. Below, I will describe some of the key criteria and observed performance, in greater detail. The tested assay included twelve distinct affinity regimens targeting a range of locations, isoforms, or position-specific post-translational modifications of TAO. Giving a theoretical maximum of four thousand ninety-six proteoforms. To estimate key figures of merit, such as accuracy and reproducibility, sets of recombinant protein, representing a range of proteoforms mixed into a background substrate such as cell lysate in varied ratios. Based upon customer feedback, we had set a target accuracy to have percent errors less than thirty percent. We observed that across all runs, our median percent error was approximately ten percent, exceeding our target by a factor of three. For reproducibility, we set our target as measured by coefficient of variation to be better than twenty percent. We examined different types of reproducibility, including between lane between runs on the same instrument, between instruments, between flow cell lots, between reagent lots, between sample preps, with the same operator, and between sample preps with different operators. We note that such extensive testing affirms our steadfast commitment to an incredibly rigorous process to ensure that we will be generating extremely high-quality data for our customers. We significantly exceeded our reproducibility targets. For example, our observed across lane median CV was one point five percent. Likewise, our median CV across library prep replicates was one point seven percent, more than fifteen x better than our targets. We've heard extensively from our customers about the importance of reproducibility to them for a number of reasons. The primary driver of customer demand for high reproducibility is that it is generally perceived as an indicator of high quality, reliable data. In addition, it enables customers to expand the size of their cohorts, rather than spending valuable runs on technical replicates. Furthermore, low CVs make it possible to observe in a statistically significant manner small but important biological differences. Reproducibility, particularly across operators and instruments, is critical when generating datasets for training AI. One of the key differentiators of our platform is its dynamic range. While you will often hear people talk about across analyte, dynamic range, such as the ability to measure both high abundance proteins alongside low abundance proteins, Within analyte, dynamic range is often more important. Within analyte dynamic range, describes the ability to measure and a given analyte accurately across a range of concentrations. For example, if an analyte has a hundred-fold difference between samples, are you able to accurately measure a hundred-fold difference? Or does it instead appear to be solely a factor of five? This sort of compression occurs within TMT-based mass spectrometry studies. The consequence of this is that large biologically impactful differences in abundance cannot be accurately measured. Leading to effect sizes that are inaccurately compressed and thereby missing important biology. Additionally, on the low end, small changes in abundance may not be well quantified. Obscuring researchers' ability to find important biological differences. During our testing, we observed within analyte dynamic range exceeding four orders of magnitude on our platform. This was well beyond our expectations, and several orders of magnitude better than current methods. As remarkable as this early dynamic range is, we anticipate significant advancements as we continue to mature and evolve the assay. Regarding sample compatibility and input, we applied the assay to a human cell line, iPSCs, organoids, humanized mouse brain, and to human brain samples. We demonstrated excellent data quality across this wide range of sample types. Regarding sample amount, the assay was shown to be performant as little as ten micrograms total protein input that contained point zero three percent tau. Lastly, I'll note that performing a VNB study on this scale required a massive number of instrument runs. And allowed us to evaluate factors such as instrument and software reliability as well. The instrument software were highly performant, with reliability well above our target of eighty percent. Through the process, we also significantly accelerated and matured both the instrument control software and the data analysis pipeline software. This result speaks to the increasing maturity and readiness of what will be our core commercial platform. These results will enable us to provide clear guidance around performance specifications for our TAO proteopharm assay and to engage in significant partnerships in drug and biomarker development. As part of such a partnership, we performed our first pilot study of human Alzheimer's disease brain, The proteome landscape of AD, Alzheimer's disease, has become of increasing interest given the recent attention drawn to tau p two seventeen serum assays and their potential for improving AD diagnosis. Furthermore, the field has generally hypothesized the importance of hyperphosphorylation of tau, a critical aspect of disease progression. But how that hyperphosphorylation manifests as a single phosphorylation on a significant portion of tau molecules or instead as combinations of many modifications is not widely known. It is anticipated that the order and timing of multiple phosphorylation, could generate a unique PTM code that could help us better define the most critical drug targets for AB as well as provide biomarkers that can be used not just for diagnosis, but also for prognosis and as a biomarker companion stratifying patients which may respond to a given therapy. This study, though very early, is already showing an intriguing new look at AD. In particular, our analysis of the proteoform landscape of a patient with extremely aggressive disease showed the distinctive presence of molecules that demonstrated a more complex phosphorylation pattern than has ever been reported. A result not achievable on any other platform. With the conclusion of our VNB, we look forward to continuing to support our partners in their efforts to make significant progress in combating neurodegenerative disease. We anticipate submitting a manuscript summarizing our VNV and biological pilot work this quarter. Moving on to our broad scale efforts. As we noticed on our last call, we reported that we would be optimizing our assay configuration to better align with the characteristics of our probe library. These optimizations include the fluorescent labels, used within our platform, the chemistry used to attach probes to these labels, the chip surfaces themselves to maximize specific binding, and the buffers used during binding and measurement. Q1 saw steady progress on improving our broad scale assay configuration, The recent data on the evolved configurations continues to be promising. This progress represents a critical step on the path to achieving the milestone of quantifying a significant number of proteins from a complex sample like cell lysate. With that, I'll turn the call back to Sujal.