Thanks, Mark. Just to add to Mark's comments, I'm very happy with our revenue performance this quarter, but I just wanted to provide extra clarity on the Motif noncash deferred revenue release. So, Motif is one of the companies that was founded on top of Ginkgo's platform. And with these deals, we received equity in the Company in exchange for preferred access to our platform. On top of that, we also paid cash R&D fees when we did work for Motif. The accounting treatment is such that we don't actually book the equity value as revenue until more work is completed across sort of a batch of programs. And now since we terminated the relationship with Motif now, we have a catch-up where all the work is considered finished and we get a bunch of the value all at once. We don't consider this equity to have value at this point given the substantial downturn in the alternate meat space and the status of Motif, which is winding down. So, I correct to account for it as we did. We've been clear that analysts and investors shouldn't think of it as cash revenue. Okay. Let's dive in on the strategic section. So similar to last quarter, I'm going to use the first strategic section to focus on Ginkgo's efforts to reduce costs and provide you with an update on our consolidation efforts. Next, I'm super proud of the work that we've been able to accomplish with customers despite our cost cutting, and I'm excited to highlight some cell engineering successes as well as new deals with blue-chip customers, while also discussing our new data points offerings that are beginning to see traction. Lastly, I'd like to focus on the new services that our biosecurity team has been focused on specifically surrounding the recent spread of H5N1. Okay. Let's get started. So, during our Q2 call, we reiterated our plans to cut spending back by a run rate of $100 million by the end of '24 and an additional $100 million expected by mid-'25. Now although our total cash burn has not reduced significantly in the quarter due to some of the one-off costs that Mark discussed, our key recurring costs have dropped $125 million on an annualized basis since Q1 of this year and we'll hope they'll continue to drop even more as we close out the year. I'm proud of the progress we have made overall and would like to highlight some of the key measures we took to reduce costs. First, we previously announced a 35% rift that has almost been fully executed now. Second, in terms of other expenses, we have reduced our contractor and temp labor pool by nearly 60%, while also cutting back professional fees by roughly 40% since Q1 of this year. We've also significantly reduced the number of software licenses and applications at Ginko, LDaaS software spending. And then lastly, we've seen great progress within our real estate consolidation efforts as we transition out of several offices. I just want to flag it. These cost takeouts do come at a cost. The -- and it's largely that we can no longer go out and sort of an open-ended way to sell our solutions offering. So previously, we book into a large company industrial like a Merck or something, an offer to do whatever type of cell engineering work they needed and then we'd expand the platform on the back end to meet that demand. We're being much tighter now about where and what we sell in the solutions business so that we can deliver more efficiently. In other words, selling things that we've done many times before. And then we're expanding via our tool sales, as I'll talk about in a minute. So, there's a little bit of free lunch here where it's just us tightening our belts. But I do think the big trade here for the cost takeouts is in sort of reducing the scope of what we offer on the solution side when we walk into a customer site and that's a strategic choice. Okay. I want to spend another minute on our site consolidation as that's moved about six months faster than we previously planned. So very happy about this. So, in September, we announced the sale -- I'll just go through a few of the you can see on the map, sites that are closed and also sites where we've been able to even sublease some of those closed sites. So, in September, when we announced the sale of Altar, our French subsidiary, to Lesaffre. This acquisition allows Ginkgo to save these costs and simplify our operations, but we still get to access the technology developed at Altar, which was important to us. So, I want to say congrats to the team for getting this deal done. Lesaffre is a great home for this technology to live, but also importantly, for the Ginkgo employees came in from Altar and are able to go to Lesaffre. And that technology will be deployed well in-house there. We have also worked to shut down our operations in the Netherlands and expect the site to be closed on December 1. And within the U.S., we expect to have fully consolidated our Boston area footprint by the end of the year, which is approximately six months ahead of schedule. We fully closed Atwell, have fully closed our Cambridge locations by the end of November, one is already done, and we're staying at our current site rather than moving into Biofab-1, that's the Parcel O site. We could get into Biofab-l one in the future as we expand our business. I hope we do. And in the meantime, there are a few of the floors that are ready for sublease. So please feel free to send people our way that are looking for great 60% lab, 40% office space here in Boston. I'm happy with the progress we made on our cost-cutting initiatives thus far, but we have more steps we can still take to further reduce costs. So, I mentioned we're looking to sublease. Again, don't hesitate to send people. We're also optimizing our lab supply and spend and reducing inventory. And lastly, we're looking to continue to rationalize across all our expenses, both people and non-people. We made an absolute ton of changes over the summer here, and we have a much better picture now of what's working and not working after those changes. And I still see lots of opportunity to boost productivity and capitalize on cost-saving opportunities throughout the whole company. If we do this, I hope we can even exceed our $200 million cost takeout target on an annualized run rate basis by the middle of next year, while still delivering well for customers and adding new customers. Okay. That's an update on where we expect our cost takes to come from. Next, I want to talk about our cell engineering business and where we're seeing customer success. So as a reminder of what we discussed last quarter, this is how I see our offerings at Ginkgo. So, on the y-axis here, you can see how customized and offering it is that we're offering to our customer, right? And so, as well as the amount of technical risk we take as you kind of go up that axis. As they go up, you get the most extreme form sort of a custom, high technical risk, B2B solution. In other words, developing a drug asset at a small biotech and hoping you can sell it to a large biopharma. That's sort of like the top of that curve. We don't do that at Ginkgo. We've never done it at Ginkgo. We're not planning to start adding a pipeline. But what we have done is what we call our cell engineering solutions. And in this business, we bear less technical risk than we would if we were doing this ourselves, but we do bear some, right? So, you saw that milestone we announced with Merck this morning. That's as a result of us achieving a technical goal that was uncertain whether we would be able to do that, okay? Because we're taking that risk, and because it's so custom for the customer, we are able to ask for commercial milestones and royalties in many cases, on the products that come out of our solutions deals. So that dotted line in the middle, you can see to the left of it, those are things where we can take those royalties. This is a good business, okay? It has tons of upside potential in the milestones and royalties. But the time that those products take to go to market, especially for biopharma is very slow, all right? So, a lot of our challenges have been -- we had a lot of this in the industrial sector that goes to market quicker. That dried up a lot over the last couple of years. We're doing more business in biopharma, but the time to those milestones and royalties takes a while. And so, a lot of our changes in solutions is to reduce our costs down somewhere closer to where the revenues get from the fees for these deals are. So, in other words, we don't need to -- we don't need to be in a rush for those milestones royalties to hit. We can just patiently wait and see those higher returns over time as our customers commercialize products. In the meantime, while we wait for that, we're finding ways to sell our same set of underlying assets, our robotics, our data and AI models, our ability to generate complex lab data at scale via our tools business model. And so, this is the right-hand side of that dotted lines. We're not asking for royalties. The customer owns the intellectual property developed in the project and so on, and we're just charging service fees or charging for equipment. In the last quarter, we made a ton of announcements launching our first products with our AI API, our robotics offering and our data points offering. I'm going to talk about data points in a minute, but first, I want to give a quick update on how things are going in solutions. So, I'm super proud of the work. The solutions team has been doing at Ginkgo, enormous amount of change in that group. And while all that's been having, they've been delivering. I'd like to highlight the announcement this morning that I mentioned with Merck. This program surrounds our previously announced biologic manufacturing work. The Ginkgo team has reached our first technical milestone. This is a real tour to force and very impressive to our customer and also comes along with a research milestone payment of $9 million in cash, which will be reflected in our Q4 financials. We're excited by the work we completed here. We're even more excited about the next stages of this program as we continue to build our relationship with Merck. Next, beyond reaching specific technical milestones with customers. We've also worked to add new customer deals, for example, the ones we're showing here with Novo Nordisk. So, this has happened over the past few years. Several times, we've expanded our relationship most recently announcing a new deal surrounding an umbrella agreement that allows us to add more programs with Novo more efficiently. In other words, we don't have to renegotiate it to launch new work. This has resulted in a new partnership with Novo focused on the discovery and development of proteins. Additionally, we also recently expanded our previously announced collaboration for expression systems for pharmaceutical products. So, it's expanding our old work, adding new work. This is the first time we are speaking about these new and updated deals publicly, but I'm really excited about the opportunity that this illustrates because I believe the ability to reach our revenue and EBITDA targets relies heavily on our ability to execute inside sales deals with key customers. In other words, when you see us signing up a new large biopharma for the first time, and we'll talk about we're doing some of that now with our data points products, that's kind of where we were back with Novo in June of '22, right? Your first getting that relationship going, you're proving yourself to customer, but success breeds success in these relationships particularly in solutions given the close relationship with the partners team needed in these complex R&D partnerships. So, I'm really excited about this. I think you'll see us keep doing this again and again, particularly with the large biopharma customers. Okay. So now, I want to talk about our new tools offerings and in particular, data points, which has been getting rapid traction in the market. Many customers, both start-ups and large biopharma companies are looking to produce large data sets to train AI models for both target discovery, and therapeutic development, okay? In other words, they want to generate these big data sets so that they can take the data in-house in their own models to get better at these two activities. So, our first data points product is what we call functional genomics, and it provides large perturbation data sets to power really target discovery AI models at our customers' sites. This product addresses key problems customers face when training these AI models, data availability, quality and uniformity. We generate large high-fidelity transcriptomic and phenotypic data sets in the disease context of your choice as the customer. We introduced genetics changes, in other words, like CRISPR edits and chemical perturbations such as a compound library in major cell types, including your own internal cell line, if that's what you're interested in. And we provide you with your readouts of choice. Our highly automated workflow means we can deliver data as quickly as three weeks for chemical screens and within three months for genetic screens, depending on the type of cells used and the type of genetic perturbation you're looking for. And the great thing is we can do this over 10,000 perturbations for both chemical and genetics. So, the sort of scale of data you need really for AI model training. And we think this is really a unique service offering in the market today. We also have a sample data set that you can just go to our website and download and interact with that includes a compressed data set, metadata plus raw UMI counts and a short report detailing the workflow and data with a link to the full data set. Customers have really been loving this -- so you should go over and download it if you're a bioinformatician or a drug discovery expert that wants to take a look. Finally, I'm excited to report that we recently signed deals with a leading tech bio company as well as a top 10 pharma company for functional genomics, and these will be reflected in our Q4 program count, okay? The second data point product we launched just a couple of weeks ago is antibody developability, okay? This is applied more on the development of a new drug asset. Our world-class wet lab infrastructure offers customers the scale and breadth to unlock AI in this area. We basically ask a customer to send us their antibody sequences as input, okay? And then we perform wet lab workflows and high throughput and then give back that AI-ready data set. And so, in this case, we are synthesizing, expressing and purifying the antibodies. We're performing broad biophysical characterization across 10-plus developability assays to analyzing and integrating that data and then giving it back in a way that your ML team can work with. And just like functional genomics, we've already created a sample data set. We produced and tested 20 IgG antibodies that were previously characterized in Jain et al, sort of a famous antibody paper back in 2017. And you can just download that data set on our website right now. But we also expanded on this and tested another 200-plus commercial antibodies with the same set of developability assays. And if you reach out to e-mail us, you can get access to that, too. Okay. The downloadable data set includes assay parameters and data output for each of these 10 different assays. There's also read me file with more details on the assays and descriptions of the fields and the data set. Similar to our functional genomics offering. We've already signed a pioneer deal with a top 25 pharma company which will be in our Q4 program count. And one of the things that really excites me is that because the data point deals are initially smaller than solutions and don't involve royalties, we're able to sell them to customers via traditional procurement channels. In other words, it's a much faster sales cycle than we see in solutions where we're really working with the BD group at our customer and sort of doing a longer deal and partnership. It can be as short as a few weeks to close one of these deals. This is especially exciting when it allows us to get a first deal with a new large biopharma that we don't have a relationship with, and we had a couple of those. And so, if we can prove ourselves in data points, I think it will help us sell solutions or robotics offerings in the future. So super excited about this. And again, congratulations to the data points team at Ginkgo for getting those projects launched and adopted so quickly. All right. So that illustrates the catalyst we're seeing within our cell engineering business. I'm now going to turn to what we're seeing within our biosecurity business especially with the recent spread of H5N1 in the U.S. and now actually recently in Canada, too. Now, we talked about H5N1 last quarter. But here, you can see how the spread of this disease has evolved just over the last few months. We've gone from how the flu is spreading and how it's impacting birds and livestock and wildlife to large detections of the disease in humans and how this could turn into the next pandemic if we don't take it seriously. Last quarter, I spoke about how we were working to validate our novel method for genomic surveillance of H5N1 among dairy cow populations. You can see by our map on the left, which pulls together a number of public databases that the spread is impacting several species now, including humans. Because of this rapid spread, we're updating our offering to include DNA sequencing of raw milk, bioinformatics as a service and then comprehensive analyzed data sets. Our first testing method detects and provides genomic information for flu A in raw milk. Our genomic sequencing delivers necessary information to accelerate development of diagnostics, vaccines and therapeutics and also gives early warnings. This is, I think, a pretty clever approach where we test raw milk after it's all pulled together from a bunch of different forms. This protects farmers privacy. People are concerned. There's a lot of analogs here to what we saw with COVID where people don't necessarily want to know that they have this because it has economic impacts. And so, this protects farmers privacy as well as offering insights that might not otherwise be gleaned if only tracking symptomatic cows. Next, we have bioinformatics as a service, our bioinformatic specialist give in-depth analysis of genomic information through a suite of capabilities and tools, including data and insights generation from any samples, it could be from raw milk or wastewater, state-of-the-art AI tools to process that data and then finally, custom analysis of the genome. In other words, is this a viral sequence I should be essentially worried about or not? And lastly, we can offer comprehensive analyzed data sets. We bring in a bunch of data sets and data streams at scale, including our proprietary international nodes for aircraft wastewater that we've talked about before, also wildlife surveillance and so on. And this data has been integrated, analyzed and standardized to give consistent results and comparability across sets. And with that, this landscape of integrated data, we can create sort of a common operating picture to give actual insights to decision-makers typically in governments. With last week's CDC announcement, highlighting the need to identify and implement strategies to prevent transmission among dairy cattle to reduce worker exposures, which is focused on timely identification of infected herds to support the rapid initiation of monitoring testing and treatment of human illness. What -- I think you're seeing CDC start to lean in. We're hopeful Ginkgo can play a role in these efforts. Look, the thing we're trying to avoid -- everyone wants to avoid here is human-to-human transition of this disease. And what's happening is it's spreading in livestock, different animals, humans that are close to that, are occasionally getting it. And the question is, will it mutate in a way that allows a human to spread it to a human. If that happens, that is very bad. So, we're continuing to socialize these threats with the U.S. government so you can hear things like what I just said and we're currently in discussion with potential commercial partners to expand our testing capabilities. As you can see on the slide there some but that allow us to get into sort of about 50% of the milk supply for monitoring. Bottom line is there currently low participation in the government offered voluntary programs that are happening today. So, this is not a business for us yet here, but we think our approach offers a very common-sense solution that sort of protects farmers and producers, but allows this milk supply to still be tested to help us mitigate spread and reduce the odds of human-to-human transition. Our pooled testing approach has quick turnaround on results. We know this from our K-12 testing program, large-scale surveillance can really work. We got a lot of reps handling the complexities of testing and privacy during COVID-19, let me tell you. So, we stand ready to support the dairy industry with the tools that can be used the same way for herds as well as exposed farm workers. This is a good idea, and I hope the U.S. government stays on top of it. Okay. In conclusion, I'm extremely proud of our execution this quarter as we continue to cut back our cost while still delivering on our revenue target and on critical technical milestones for our customers. We deliver for our customers, both with our traditional offerings and our new tools offerings such as data points and I'm excited for the opportunity we have ahead of us as we continue to strive towards adjusted EBITDA breakeven by mid-2026, while maintaining that cash margin of safety. All right. Now I'll hand it back to Megan for Q&A.