Episode 126 - Andrew Ferguson: "Fortune favors a prepared mind."

Professor of Molecular Engineering and Vice Dean for Education at the UChicago Pritzker School of Molecular Engineering, Andrew Ferguson, talks about his parents encouraging him to pursue the path he wanted, studying chemical engineering and now researching immunoengineering. In this episode, Professor Ferguson breaks down the complexity of studying proteins, the potential careers one could have in his field, and the joy of nurturing other scientists.

Transcript

Stephen 00:00
Hello, and welcome to The Course. I'm your host, Stephen, and today I'm speaking with Professor Andrew Ferguson, Professor of Molecular Engineering, Vice Dean of Equity, Diversity, and Inclusion, and Chair of the Admissions Committee in the UChicago Pritzker School of Molecular Engineering. Professor Ferguson was named the Institution of Chemical Engineers North American Young Chemical Engineer of the Year in 2013, and has collected numerous other awards since, including most recently the UIUC College of Engineering Dean's Award for Excellence in Research.

He's here today to talk to us about his investigations into what he calls the little machines that biology has adapted to do the functions of life, and of course, how he became a University of Chicago professor.   

Professor Ferguson, welcome to The Course. How are you this morning? 

Andrew Ferguson 00:42
Very well. Pleasure to be here. 

Stephen 00:43
Thank you for joining me. 

Could you tell mewhat your role is at UChicago and in layman's terms, what that actually means, like what you do?

Andrew Ferguson 00:52
Yeah, great. So I'm professor in the Pritzker School of Molecular Engineering. So I'm trained as a chemical engineer, but at UChicago, we call engineering by a slightly different name, and so we call it molecular engineering. The reason being the school is very new. So we're only sort of 12, 13 years old now. And so when the university decided to start an engineering school, we decided to do it a little bit differently. Not sort of have the canonical divisions between electrical, chemical, mechanical engineering, but have a school that was sort of focused on sort of more tightly molecular scale phenomena.

And so we have three academic themes focused on Immuno Engineering, which you can think of as a subset of bioengineering, sort of dedicated to engineering the immune system. So using the principles of engineering. to modify how the human immune system responds to infection or sort of tolerizing against auto immune diseases.

We have quantum engineering, which is sort of working on quantum information and quantum computing, and then sort of materials engineering, largely with a focus towards sustainability and health. And so that's things like, how do you engineer clean water, clean energy? How do you devise sort of new molecular machines to work on particular bioengineering problems, for example.

And so, in my work, I sort of straddle between the immunoengineering theme and the materials engineering theme. And so, our research interests are largely using molecular modeling and simulation using artificial intelligence and machine learning. to try and discover and engineer new molecules and materials.

And so, although I'm trained as a theorist and a computationalist, we collaborate very heavily with experimentalists. And some of the most fun that I have in my academic life is to, you know, make a prediction or try and discover a new molecule on the computer and then we try and partner up with an experimental buddy and we put it into the lab and we see that it works.

And so having the synergy between the experiment and the computation, I think is very rewarding and exciting and something that the molecular engineering school at UChicago really excels at this sort of cross disciplinary collaboration. 

Stephen 02:47
Before we get ahead of ourselves, I want to go back into your own personal history. When you were a kid, maybe like a middle or high school age, you know, if you're in the States, what did you imagine you might be doing? And does it bear much of a resemblance to what you do now?

Andrew Ferguson 03:05
Yeah, it's a good question. So, I grew up in Scotland, and so I grew up just east of Edinburgh in a small town called North Barrett, and so if anyone follows professional golf, there's a famous golf course called Muirfield, where they frequently play the British Open, which is right next door to where I grew up. My brother was actually a greenskeeper there for a couple of summers, and so, I was never much of a golfer, but this is where folks may know my town from. 

Think I had a pretty boring track in a sense when I was getting it towards the middle or the end of high school. I was sort of thinking about, you know, what do I like to do? My parents were both the first in their family to go to college. They both became pharmacists. So, they were community pharmacists. 

So they were very supportive of me sort of going to higher education. I sort of enjoyed sort of math, physics, chemistry. When you grow up in the UK and you do quite well in school, the normal track is to go to become a medical doctor. If you get sort of A's in high school and sort of STEM technical subjects, that's the standard thing to do.

And unlike the US, you can actually go directly to medical school straight out of high school. So I kind of thought that maybe what I wanted to do, there was some cultural pressure, I think, to sort of do that. But my folks were very supportive and they said, you know, you do whatever you want. 

And I'm a little bit better now, but I think I was quite a sort of introverted kid to some degree and didn't really have, what one might consider the best bedside manner for a physician. And so, I was sort of interested in perhaps doing something a little more technical. And so, engineering was sort of a natural fit and chemical engineering in particular sort of appealed to me. And so, I ended up going to college for chemical engineering and sort of had the idea, maybe I would like to do research in the future. 

Stephen 4:44
What do you think drew you to molecular engineering specifically? I mean, what drives people to work with, you know, like the, sort of the smallest, smallest things imaginable. 

Andrew Ferguson 04:54
I started graduate school thinking I was going to do something in fluid mechanics. And so I was sort of interested in working with some mathematical modeling of systems. And so I think that sort of came from an experience when I was an undergraduate that, so I did chemical engineering undergraduate at Imperial College in London. And in the UK, there's sort of a strong tradition of sending folks to big oil companies or big pharma companies.

Graduate school when I was there was sort of less of a well trodden track, but I had a really excellent mentor who said, you know, you may want to consider this. In my third year, I did a one year exchange program to Carnegie Mellon in Pittsburgh and sort of really enjoyed the U. S. system. And I ended up applying to U. S. graduate schools in particular because I actually, I liked the idea of it being a longer program. And so in the U. K. and Europe. You can go through a Ph. D. program in approximately three years, and in the U. S., it's typically five years. And the reason for that is largely because you're taking more classes, you're doing some TA-ing rather than sort of just, you know, full time research. And I sort of like that idea, to be able to sort of have a little more time to do these things, take some extra courses. And I also felt a little young, maybe a little immature, and so I felt having a little more time was attractive. 

And so, yeah, when I started there, thought I would do something technical, but I had an experience where I was working at Rolls Royce fuel cell systems for a senior year internship in my undergrad, and we were working, you know, developing these benchtop reactors to do some reforming reactions for solid oxide fuel cell.

Basically, this idea from static power generation, you can sort of hook this thing up to a natural gas line and generate electricity, you know, and perhaps, you know, remote regions where there's not good, electrical infrastructure. And I didn't really enjoy the experimental stuff. And so, it was more the mathematical modeling that really, really appealed to me. And so that was actually a very formative experience that taught me this maybe is not the right track for me, and so I, I did want to do something more theoretical. 

And so I got to graduate school. I went to Princeton for my grad work. So a bunch of presentations from faculty members. And then, the person who, who became my PhD advisor gave this amazing presentation about molecular simulation and modeling, which is not something I'd really even been exposed to as an undergraduate, and it just sort of blew my mind that you can use sort of computers to discover how the inner workings of proteins or polymers or these things that you can't see with the naked eye, but of course underpin all of our natural world and all of biology and physics and chemistry could be simulated on the computer and use that to sort of do some useful engineering.

And so, I sort of sheepishly approached him and said, you know, I've never really. Done any, any of this before. But I think it's really exciting and I would love to be involved in this. And he said something that I say to my prospective students that he said you know, you're all smart people coming here.

You've earned your place here. Even though you may not have done that as an undergraduate, you're certainly qualified to do it and we'll give you the skills to be successful and it we'll train you to do that. And so that ended up being the route that I took. And so, I sometimes look back and think, you know, there's a lot of stochasticity.

If that person had happed to be on sabbatical or wasn't taking students, I think my life would have been very different. I probably wouldn't be happy doing lots of things, but that was sort of how I fell into it and yeah, I fell in love with it from there.

Stephen 08:10
You've mentioned a couple of different mentors already, don't feel like you need to go back and summarize, but I'm curious, like, you know, who you think of when you think of your mentors and like what qualities do those people have that you now try to emulate?

Andrew Ferguson 08:28
You know, when folks ask, you know, how did you end up where you are today? I think the two biggest things are definitely mentors and then just a lot of happenstance. You just get a get a lucky break or you wind up sort of being in the right place at the right time and definitely chance fortune favors a prepared mind.

But there is, there is sort of lucky breaks that I took. I could be in a very different place, but I think mentorship is a big part of that. And so having people that can nudge you in the right direction or provide the ability to open doors for you. And so I think it's probably one of the most critical things of certainly an academic career track and probably most career tracks, to be honest, this is to have people that do that.

And so I would say that the thing that sort of links all these people together. People in my mind is sort of them just taking an interest in you and that can be a relatively selfless thing typically that it doesn't necessarily benefit them directly, but taking the time just to provide you some guidance, understand what your motivations are, how they can help you and just nudge you in the right direction.

Not wanting to make things too easy for you is also an important thing that you sort of have to have to earn it that, you know, just opening the door so wide that you can just fall through it rather than you having to sort of work hard and push yourself to sort of get to the right place. I think is important.

And then just being being sort of, you know, cheerleaders, supporters, people that you can sort of rely on. And so, I was very fortunate to have three or four of these people in my life. And I still have relationships with all of them actually. And I'm very grateful for what they've done.

Stephen 09:56
Looking At your current role at UChicago, can you just give us a sense of, what a typical day or week looks like if such a thing exists and kind of what the balance is for you right now between teaching, research and, you know, your various other responsibilities. 

Andrew Ferguson 10:16
Yeah, I think there's, yeah, it's hard to talk about a typical day, which is actually one of the nice things about, about the job, I would say, you know, where we typically, at least, within my unit, we teach two quarters out of the year. And so, you know, you're teaching a class, 20 weeks out of the year. But, and sort of the main part of the job, I think is then beyond the teaching is research.

And so then we're writing grants to support research. We're writing papers which is research outcomes. We're working with students to sort of prepare presentations, prepare papers, actually do the research itself. And so a good fraction of my week is actually I'll spend the rest of today meeting with each of my students to talk about their research progress, how they're doing, do some troubleshooting, talk about some exciting results, and then also try and provide some professional mentorship to them.

And so, sort of modeling how it looks to sort of be a PI, how one sort of pursues research, how one decides, you know, how to formulate hypotheses, et cetera, and gear that towards what they want their future success to be. And so I think there's got to be a research component, but also sort of a personal component to sort of interacting with students.

And then more and more, as you sort of get more senior through the through the ranks, there's more administration. And so there's lots of lots of emails, unfortunately, and so I sometimes wonder what this job would have been like, prior to prior to emails but that's a good fraction of my day is responding to those things. And then, you know, writing memos or, you know, working with people across the unit to sort of advance particular activities.

And so I think, you know, somebody once said to me when I was sort of coming out of the process of becoming more senior in academia is just an increase in bandwidth. You just have to do more and more things. Nothing goes away. You just get more and more on top. And I find it rewarding and exciting, but it's also, you need to make decisions about, you know, what you can reasonably expect to be able to do within the time available to you in a day and still remain sort of a functional human being, have a personal life, have some hobbies, et cetera.

And so to the other the big initiatives that I've taken on are getting into academic publishing. And so I'm a deputy editor in chief for Royal Society Chemistry Journal. And so that's been sort of rewarding seeing the other side of sort of publishing, sort of handling papers and working with authors and editors.

And then I've also served for the last few years as the Vice Dean for Equity, Diversity and Inclusion within the Pritzker school of molecular engineering. And so that's been a sort of rewarding role to try and sort of work with our EDI initiatives across the school at the undergraduate, graduate staff, faculty level.

In the academic publishing. It's something that I'd thought about doing for a while I'd been involved with a few journals and then I had the opportunity to become a sort of a junior editor at a different journal at an ACS Journal, American Chemical Society Journal, and didn't, I wasn't sure if I was going to enjoy it or not. And so it was a temporary position. It was, you know, renewable after a year but I did enjoy it more than I thought I would. It was sort of it was nice to be able to see different papers, you know, work with author, have a hand in actually shaping the future of the journal, what the journal publishes. And it's a journal that really supported me a lot in my more junior career. And so, it was actually a privilege working with them as a junior editor of physical chemistry. 

And then there was an opportunity with this Royal Society Chemistry Journal. It's called MSD - Molecular Systems Design and Engineering. It's a journal that I published in, that I was involved in the advisory board editorial board. And so, there was a turnover in leadership. And so, I ended up stepping into the deputy editor in chief role. And similarly, I felt I felt sort of indebted to the journal. It had given me some awards as a younger person.

It's a newer journal. And so it was nice to be able to be involved relatively early and to shape the journal and work with it. And so it's been a good experience and I see it in, in some regards as a service to the community providing some sort of ability to help the community and publish papers so, yeah, it's been enjoyable. It's certainly some additional overhead on my week, but it's been fun to do that. 

And yeah, and then on the Vice Dean for Equity, Diversity, Inclusion, this was a new position within the PME, the Pritzker School of Molecular Engineering, it was established in 2020 by our previous dean in response to the George Floyd murder and the civil unrest that was happening around the country.

And I think the feeling was to, of course, we've been doing things around equity, diversity and inclusion, but actually formalize that within sort of a cabinet position role within the school, basically a vice dean position and try and provide some overall sort of guidance and central authority to sort of collect these efforts and also initiate new efforts.

And so, again, that's been quite a lot of work, but it's again, been quite rewarding. And so it's been nice to work with a lot of folks across the different constituencies within the PME, undergraduates, graduates, postdocs, teaching staff, administrative staff, faculty. Largely on a volunteer basis, people, people actually really want to do this and spend some of their time working with us, to do these efforts and I think we've been quite successful. It's, I had to come to terms quite early that we were never going to sort of complete this process. It was never going to be something that you declare victory.

It's always going to be a work in progress. It's also something that unlike, you know, research endeavors, having measurable success is difficult and or slow. And so how do you sort of, you know, measure how successful you've been in these things? I think over a long time scales, you can see things like, what is the representation within different underrepresented groups? Is that changing within the demographs of your unit? Is this sort of cultural surveys, sort of climate surveys, or are they showing it, an improving trend. Those are on time scales of years rather than sort of weeks or months. 

And then also just being open to make mistakes. And so, you know, I'm an immigrant to this country. I'm a white male. I don't share sort of the experience of a number of the groups that are, you know, perhaps disenfranchised or have had less opportunity and success. And so just being aware that I'm going to have to ask people and you know, trying to be sensitive about different backgrounds, different communities and try my best to be understanding and, you know, within the confines of my own identity, sort of be an advocate for different constituencies and work as well as I can to sort of improve representation. Roles of equity, diversity and inclusion within the school. 

And so, I've been quite proud of what we've achieved just in the last few years. We have sort of new tutoring and mentoring programs. We have new partnerships with different institutions to try and diversify our classes, try and provide some support for students from varying backgrounds.

We have new initiatives within the staff to try and improve the staff experience within the school, such as, you know, uh, new ability for staff members to do additional professional development activities and receive credits to do that.

And so, I think there's a lot of work still to do, but it's a really valuable work. And I've said before, I think, aside from our science and engineering mission, I think it's one of the most important things that we can do at the school. And you can see that from a moral perspective that I think we're just, you know, as an research university, it behooves us to try and, you know, respond to the current climate and actually provide opportunities for folks that may have not historically had such opportunities.

Or you can just see it from a very instrumental perspective that there's an enormous literature that diverse teams are just better teams. And so, particularly in the business world. So having a diversity of different backgrounds and opinions really actually drives us all to be more successful. And so whichever way you slice it, I think you can only benefit us to sort of do this work.

Stephen 18:00
Yeah, well, that's very cool to hear both your enthusiasm and also it sounds like there's a real enthusiasm for it like across your department, which is great. When you do have time to work on your own research what are you researching at the moment? And like, what are the kind of big questions or like experiments that are motivating you at this time?

Andrew Ferguson 18:21
Yeah, so I think the two sort of big themes that we've been working on for any years. And so one is without getting too technical about it, but we do a lot of molecular dynamics simulations, since this is, if you're familiar with Newton's equations of motion, basically F equals MA, sort of in high school physics, sort of cannonballs travel through the air, et cetera.

It turns out those equations of motion actually work quite well. really well down to the atomistic scale. If you get down to sort of, you know, chemical bonding or conductivity or things, you need to do quantum mechanics because you're worrying about electrons. But if you're worried about atoms and molecules, it turns out these classical mechanical equations actually work very well.

And so we use these to simulate how molecules behave. And so I've biomolecules or proteins in particular which are molecular machines, essentially. And so it's the little machines that biology has adopted to do all the functions of life. They help us digest our food. They serve as sort of antibodies. They help us sort of provide structural materials, your hair, your fingernails. And so if you think about like the DNA is carrying the blueprint for life, the proteins are the ones that actually execute all these functions. So it's a long standing sort of grand challenge, if you like, in bioengineering that can we sort of co opt these proteins and ask them to do things that maybe nature doesn't care about, but we as humans do.

So can we ask them to, you know, fight new diseases? Can we ask them to catalyze reactions and bioreactors at high temperatures and pressures that nature doesn't care about, but we care about and we use 'em to help us solve the energy crisis by sort of helping us digest things like switchgrass to make biofuels.

And so because of their flexibility and their power, it's a really sort of the witching possibility that if we could sort of realize new functions and proteins and that's, that's a very exciting new potential. So the two areas I've been very interested in the context of this problem is number one, how do we make our simulations more efficient?

And so even on the world's fastest supercomputer is we can't simulate more than, say, a billion atoms for more than, you know, a millisecond at the very most. Um, and so this sounds like a very big simulation, but actually, if you think about the macro scale, so the experience that we have every day.

That the number of atoms are involved in that is something called Avogadro's number, which is 10 to the 23, which is just a fantastically large number. And we're talking about doing simulations of 10 to the nine. And so, you know, we were sort of off by many orders of magnitude and what we can actually simulate.

And so how can you make these simulations? Go bigger and go faster. And so, I think tools from machine learning, artificial intelligence have been really powerful in this regard. It allows us to sort of, learn the key determinants of these simulations and make sort of efficient dynamical models or surrogate models that allow us to simulate a longer time scales and open up new phenomena that would not be possible using even state of the art computational facilities. And so, we've done a lot of that. 

And so it involves basically dimensionality reduction is taking these very complex, large systems and learning what is the key fundamental variables that are underpinning them. Maybe a handful of variables, two, three, four, five that allow us to get a toehold and actually do some much larger scale sampling.

And this is intimately sort of connected with the Complexity theory and emergence. This idea of sort of an emergent simplicity and very complex systems. And so despite the fact that these systems contain, you know, billions of atoms, they're all interacting and doing their own thing, there's sort of an emergent beauty or simplicity. And so, I think that's a sort of a beautiful concept that has motivated a lot of our work. 

And then I think the second thread is does one engineer materials? And so, in the first case, you can think about that maybe as more understanding, but in the second case, it's maybe more engineering.

So how does one then change the protein or sort of change the conditions to sort of modify the function or the behaviors? So how can we convince them to, you know, capture perhaps contaminants in our wastewater, or perhaps act as an improved dye material for solar cells, or perhaps, you know, biological sort of staining of membranes, or how can we get the proteins to execute supernatural functions beyond what nature cares about.

And so a lot of that has been also motivated by AI and machine learning, which is how do you efficiently search large molecular search species or make modifications that can improve the behaviors of these things? And we frequently do this in a sort of virtuous feedback loop, which goes by the name of active learning or Bayesian optimization or sequential learning.

Or you can actually use a machine learning model to help you design new molecules, and then you can assay them by doing a computational experiment. I mean, measuring a binding free energy or something, or a wet lab experiment, actually going into the lab and sort of measuring the activity of a protein, and then closing the feedback loop, and then sort of re-educating the model, say, okay, we did these tests. Here's how these guys behave. Now what should we do next? 

And so, it's almost like an optimal experimental design, and by going around this virtuous cycle, the model gets better and better at picking good candidates, and then, you also discover better and better candidates. And so, we've used this in a number of places to discover better vaccines with some of our collaborators in PME and engineer better proteins, discover better molecules.

And we've actually used this technology to co-found a company with one of my collaborators, Rama Ranganathan, called Evozyne, which is a startup company doing data-driven protein design in Chicago. And so we're now sort of, you know, three or so years old and we've been using these principles to try and design new proteins for applications largely in sort of therapeutics.

Stephen: When you're doing this? Are you searching for things that you think exist or like, attempting to create new molecules or like I just if you could kind of talk us through again, in layman's terms, the loop that you mentioned between like running simulations and then actually like going into a lab and where the discoveries are in that process.

Andrew Ferguson 24:32
Yes. So in the context of proteins, it's maybe useful to sort of to make a concrete example that, there are, you know, a protein of lens, just a few amino acids, you know, maybe 20 amino acids long. There are sort of more possible sequences available than there are proteins. protons in the observable universe.

And so it's just fantastically large numbers. In fact, I think that the numbers are so large that even over the entire, you know, 4.5 billion year history of earth, there's just not sufficient time to explore them all. And so what that tells us is that nature has done a fantastically, you know, valuable job in identifying those proteins that do underpin life through natural selection, of course.

But it also tells us that there's a huge palette of unsampled sequences that could be available out there that could do things that are beyond nature, supernatural. And so it gives us confidence that we can discover new functionalities, new things that that are perhaps useful to us as humankind. And so what we typically do is sort of use nature as a springboard and say, okay, imagine you want to design a protein that perhaps works at very high temperatures and pressures, um, in your bioreactor to catalyze some reactions.

So maybe you go and look at some thermophilic organisms, maybe some hydrothermal vents at the bottom of the ocean, where you have some proteins that do something similar. You know, quite high temperatures. And then you say, what modifications can I make beyond that to achieve the functional goals that I want?

And so what we do is sort of, we build some models. We take these machine learning models and say, let's learn over what nature has done. And then we try and extrapolate them. So then we say, well, okay, let's make some predictions and let's try and push the envelope. Can you sort of tell us what sequences we should look at next that might have even better function?

Sort of like advancing the frontier of what, what's possible. Cool, the models are imperfect. They don't always make the best predictions. Some of them fail. Some of them are moderately successful. Some of them are very successful. But then what we do is actually when we test them either by an in silico experiment by doing some computer simulations and or by doing an in vitro or in vivo experiment, you know, a real wet lab experiment.

We get some information on how those designs perform, and then we take them back to the model and re educate it and say, okay, here's the ones where you made good predictions and they worked out, here's the ones where you made not so good predictions, let's re-educate the model, and then let's ask it again to make some new designs within this virtuous sort of design build cycle.

Test learn cycle. And so you can think about it as sort of advancing the frontier incrementally towards the, uh, the goals that you want. And in some cases, there's lots of space to do optimization. In some cases nature has reached a natural, um, optimal, uh, um, uh, solution. So in some cases there are enzymes that are literally operating at the speed of limits that you can't make them any more active because, um, they're limited by how quickly the reactants can get in and the products can get out.

And so in that case, you might not be able to engineer any more activity, but there are plenty of cases where you can. And so I think it's important to combine sort of this data science design, build tests. learn loop with a lot of domain science, sort of understanding the details of how these proteins work, what does the literature over the last, you know, a few decades tell you, so that you're picking your problems smartly, and that you, can actually deploy those, these, uh, these, uh, these, uh, protocols successfully. 

Stephen 27:50
That's fascinating, just as we come to a close here, what would you say, is the most fulfilling aspects of what you do?

Andrew 27:57
I think that's pretty much it. Probably changed some over time. And so certainly when I was more junior in my career, it was really the research itself. And so I was, you know, always sort of in love with the research I was doing. It was a privilege to be able to do that research. And that's still absolutely true today.

But I think what's becoming more and more sort of fulfilling to me is actually enabling the science of others, in particular, the students, and the trainees in my group. And so, you know, in a lot of cases that the research is more and more collaborative that students, you know, postdocs, graduate students, undergraduates are coming with ideas.

And we work on them together. And so it's not me sort of directing top down. This is what we're working on. Like it was when I was a little more junior in my career. And it's enabling these, you know, wonderfully talented scientists and engineers to execute their own visions and be successful.

And then it's really rewarding to see them go on and do exciting things in their own careers that you know, they do some exciting research with us, but it opens doors for them, whether that's in the academic realm, going on to new postdoc positions, or I have a few students who are now independent faculty members, and that's really rewarding to see or in industry.

And so it's I think, you know, as engineers, we send a lot of folks to industry and it's a terrific career track. Also, I think we shouldn't be over privileging the academic route, that industrial route is equally successful and equally exciting. And so I have students working at big biotech companies, doing fantastically exciting things and machine learning and AI.

And it's really rewarding to see them go on and do those things. 

Stephen 29:30
Thank you, Professor Ferguson, for your time today. And Course Takers, if you enjoyed today's interview, please check out the others. Leave us a comment, subscribe, follow, and share this episode with your friends and family. You can find out more about the University of Chicago through uchicago.edu or the university's campus in Hong Kong through uchicago.hk. Stay tuned for more, and thanks for listening.