3rd Interstellar Visitor, Rubin’s Sky Camera, AI CRISPR Boost & T. rex Blood Vessels (EP. 4)

Hello Internet, this is your captain speaking Lester Nare. I am joined as always by my co-host and our resident PhD, Krishna Chowdhury. This is from First Principles. We have some great stories for you this week, starting off with the third interstellar object to pass through our neighborhood. 3i atlas is it an alien spacecraft we'll soon see we'll follow it up with a story about the vera c rubin observatory which is related to our initial first story on the third interstellar object we will follow that up with moving into ai ai meets crisper for precise gene editing maybe you can fix my knees a little bit a little rusty and And we will end with a great story.

Shout out to Jurassic Park for a rare T-Rex blood cell vessel found in fossils that show that dinosaurs had the ability to heal their own injuries. And we will end with a mystery box. This is from First Principles. How are you, my friend? Doing pretty good. Episode four. Yeah. Yeah. We're still here.

Shout out to everybody who's tuned in on YouTube, TikTok, Instagram. Yeah, we really do appreciate it. It's been a great, great commentary, great discussion. And we're going to start off with a story that's near and dear to my heart, which is about this interstellar visitor. That's right. So just to kind of set the table for context, because there's a certain Harvard physicist who has been used in a lot of the headlines around this story, Dr. Avi Loeb, who is the professor of astrophysics at Harvard.

And funny enough, for those who might not know, I am the director of operations at a nonprofit called the UAP Disclosure Fund, where we are actively working with members of Congress and the executive branch to push for research. Disclosure around these weird things that are flying around. And it just so happens that our favorite Harvard astrophysicist, Avi Loeb, is one of our advisory board members.

So as soon as I saw the headlines of Harvard astrophysicist says – I knew it was Avi. Yeah. And you've met him. Yeah. He's extremely – he's hilarious. Yeah. He's just so funny, witty, charismatic. It's not a surprise he's been as successful as he has because in the industry it's both. You have to do the work but also be able to sell it.

Yeah. But I think what's been interesting is how this story has cascaded. All over the place. Yeah. It started with an initial observation, and it seems like we've had some follow-up observations from things like the Hubble Space Telescope. So I'm really excited to kind of get your perspective on what exactly is going on here.

Yeah. So I hate to break it to you, man, but it's probably not aliens. I tell you this every time we've known each other for a while and every time it's been like, not this time. Yeah, not this time. Not this time again. It's probably an interstellar comet. Okay. But it is still really, really, really cool.

Okay. And I want to see more pictures. Okay. Putting it out there could be other stuff, but it is very likely just an interstellar comet. And so I think what's interesting about this whole concept of interstellar objects coming into our – into a near-Earth or into our solar system, it's a relatively rare – at least from our ability to track it.

Our ability to track it. That's the thing. It is. It is. The first one was Oumuamua, I believe it was called. Yes. That was in the late 2010s. Yes. So just maybe five. No, like probably less than 10 years ago. Yes. And then the next one that came out was 2i Borisov. Yes. And then this is the third one.

Both of which, Avi was one of the first early movers on really working on them. And so we don't really have a lot of context or data by which to sort of do analysis on this, which is why it's so important that we've had astronomers using Hubble. Yeah. To sort of point out and say, hey, what data can we gather around?

Yeah. This one is this one is really nice because we caught it on its way in. Right. OK. Right. Oh, more, more. We caught it on its way out, so we can't get good data anyways. The data has really high error bars where we don't know if what we saw is like if the number that we got is the true number, if it's plus or minus some big thing now.

we see it coming in. So it's really nice that we can chart its trajectory and we can like schedule observations. Yep. Okay. And that's, I think, going to be key to really understand what this thing is because, you know, this thing is most likely an interstellar object that is just like going through the Milky Way at an incredible speed.

That's one of the things I think that's been pointed out in a lot of the news stories is that it's moving so quickly. Yeah. It's moving extremely quickly. And I guess one of the one of the questions becomes, how do we do analysis on or what are the characteristics that we would look for in an observation like this to distinguish it from just saying like, or what are the attributes that make you sort of make that conclusion to say this has attributes that are similar to comets or other sort of rocky objects that we've seen even locally no that's that's a good question um to to really get into that let's talk about sort of the history of how we yeah found this thing right so we found this thing back when it was about four or five astronomical units away from the sun an astronomical unit is the distance from the sun to the earth okay so it's like four or five radii away from the earth okay that's when we first spotted it it was like out at near the orbit of jupiter

So, I mean, we spotted it relatively close. Relatively close, yeah. It's moving extremely fast, 58 kilometers per second. So that's one Earth orbit, like one, yeah, one Earth orbit radius a month. Okay. Which is like really fast. Like it takes us years to get to Jupiter. And this thing is going to be here in like four months.

And I want to be specific. When you say orbit, you mean orbit around the sun, not the rotation. No. Yeah. Yeah. Yeah. This is the radius of our orbit around the sun. It's take it's it's doing that in a month. So what would take us 12? Yeah. Well, it would take us like years. Yeah. Yeah. Yeah. Yeah. Makes sense.

To get from here to Mars usually takes us a year. Fair. This thing is going to do it. Oh, because you're talking about traveling through space at a target. Yeah. And this thing is just coming at us like in a month. So this thing is incredibly fast. Okay. When it was really far away and it was first spotted, we thought it was like 20 kilometers big.

Okay. Which is massive. Yeah, yeah, yeah. Okay? That's like a big, big comet, big asteroid. I think in the movie Deep Impact, the asteroid that was hitting Earth or whatever was like one to three. It was significantly smaller. Significantly smaller, and that thing did damage. Right? So this thing is like a, you know, at least it's a life killer.

It's a mass extinction type thing if it came. Especially at that speed, like we'd just be wrecked. So it's incredibly fast and we thought it was incredibly big. The other thing that we thought was, you know, it was really far away. We couldn't see any gas coming out of it like what normal comets do when they come out from way out there.

They usually have a buildup of ice and then when they get closer to the sun, they should like, you know, start having this tail, which is from all the ice. Famously known as the cometary tail. Yeah. Which was what was originally interesting about Oumuamua was and the problem was we were catching it late.

So we couldn't have enough data to tell. But the lack of a commentary tale was one of the reasons by which it was like, well, it doesn't look like the other rocks that we've seen flying through space. Yeah. So this one, it was similar. Right. And that's when Professor Loeb came out and was like, you know, this could be something weird.

We should take a closer look. It could be it could be aliens. And a lot of astronomers at the time came out and said that, well, it's like too far away for us to really get good data. OK, with astronomy, that's always the name of the game. Like it's like it's hard to get data on these things. Now, I think it's been a month since since then, a month or two probably since then.

It's come closer. Now we see a tail. OK, we do. We do see a tail. We see a tail. We see we also see sun facing. Tail. How do you mean? Okay. So if there's the sun, the comet is moving like this. You'd expect there to be a tail that's going in the back, right? Because it's like, like leaving stuff behind behind.

There's also a tail facing towards the sun. Got it. And that is something that we know comets do because of something called sublimation. If you've ever seen dry ice, like dry ice just goes straight from solid to vapor carbon dioxide. Right. Right. Right. It doesn't go in. There's no liquid CO2. Right.

Because the air pressure is actually just too low to have that. So the phase changes go straight from solid to gas. Same thing is happening on this comet. Like the water ice is getting bombarded by the sun's rays and then it's just boiling off. There's no like so it's called sublimation. And that creates this like gaseous sort of water vapor halo around the comet.

The Hubble Space Telescope made an observation of it not too long ago. And it updated the size of this thing. The size of this thing, we thought it was 20 kilometers. It's now something like 0.3 to 5 kilometers. Significantly smaller. And it is most likely smaller than 1 kilometer. Okay. Now, also, what's interesting is the error bar is so massive.

It's going from 0.3 kilometers to 5 kilometers, right? That's a pretty wide range. It's a pretty wide range. It's almost tenfold in the variation. And that's just because, like, with comets, the nucleus is shrouded now. In this… Like, if you look at that photo, right? Yes. The nucleus is shrouded by this gas now.

Right. So now it's really hard to resolve, like, what's happening on the very inside. Right. It's a force field. Yes. Yeah. It's something like that. It could be. But I think what's interesting here, I mean, this this kind of goes to why the instruments we use for detection really matter, because it's what allows us to get sort of a high fidelity data set.

Yeah. To then be able to do analysis and also being able to do so repeatedly over time such that we can continue to refine. Yeah. What analysis is. And as it gets closer and closer, we're going to be doing more and more because this is I mean, at the end of the day, this is an incredibly interesting object.

OK. Right. If we were to run back the clock on where this object came from, this three eye atlas. OK. It's an interstellar object. And if we were to run back the clock on where it came from. It would come from the depths of the Milky Way and especially in this region of the Milky Way that's like right above our disk.

Okay. Okay. It's called like the thick disk. Okay. So the Milky Way is like this, right? You've seen the Milky Way across the sky. Yes. Most of that stuff is in the thin disk. Okay. That's where the, like, stars and the gas sort of is. So when you look up at the night sky in a national park or in a really dark area, you'll see it going across the night sky.

That's usually just the thin disk. Right above that thin disk is something called the thick disk. Okay. Okay. And this has an old population of stars. Okay. Really old. Okay. Okay. Like older than our solar system. And if this object came from up there and it's like sort of, you know, crossing down into our neighborhood.

Yes. Then it came from those objects, which means that, you know, we're estimating this thing to be 7 billion years old. Wow. Okay. Which is older than our solar system. Right. So this is actually the oldest object that we've seen in our solar system. It's older than everything else. That's around. It's basically like it's a quarter of the age of the.

No, about half the age of the universe. Yeah. Which is fascinating. Yeah. I mean. We're about a third, right? Right. But like, yeah, this thing's like. Half. Look, I'm not saying it's aliens, but they've had time. They have had time. And they might live up there. But we definitely know that it's old.

Like if it's a naturally occurring rock and it came from up there, it's probably really old. Very old. Fascinating. The other thing that's kind of crazy is like how fast it's going. So how do we explain that? Right. Well, that can be explained by just close encounters of the gravitational kind. Right.

OK. So it was passing through, you know, through from the thickness through the thin disk. And at some point it passed a very, very massive object. Yeah. It passed a very massive object in exactly the right. I mean, this thing's been around for seven billion years. Right. So and if it's like out here, who knows how long it's been roaming the cosmos.

Right. Right. So, you know, in the three body problem, if you remember, right, like tiny little deflections can cause something to just shoot out. Right. You can imagine the same thing here, like spacecraft. NASA uses these things called gravity assists to boost the speed of spacecraft all the time. This thing could have had a very close encounter with a massive object, let's say another star, and it was just like shot out.

And then ended up being shot out in our direction. In our direction. Now, the direction that it was shot out in is very peculiar. Okay. Because the sun and the planets form an ecliptic plane. Yep. where we all sort of like this is the primordial angular momentum of the solar system coming to fruition as like one plate where all the planets reside.

And this thing is coming in at like a really shallow angle, right? Of all the angles that it could have chosen, it did choose this weird shallow angle. Okay. That's a little weird. Okay. Okay. I'll take it. Awomua did not do that. Okay. Right. So it's, you know, you could say, well, you didn't use it for that one.

And then now you're using, you know, so like it, it, again, it could just be coincidence. This is the first three that we've done. And this is one that like, it's, um, as, as people are saying, it's like taking a tour of the solar system, right? It's going through Jupiter. It's going to visit Jupiter.

It's going to see Mars and Venus. It's not going to see earth. We're actually going to be on the other side of the sun. Got it. So it's going to like, if the sun is here and we're here, it's going to like move. Over there. It'll pass through generally where our orbit would be. We're just in the wrong place.

Yeah, we're just in the wrong place at that time. But then it's going to go see Jupiter later. And actually, there's a proposal out in astrophysical letters, I think by Professor Avi Loeb, that – wants to get the juno spacecraft which is around jupiter yes they they just want to like boost it a little yes and they calculated how to do it you boost a little you get it into a higher orbit around jupiter and then it'll make a really turn it counter and yeah it'll make a real close encounter I think that would be really cool, actually.

Personally, I think that would be a great use of Juno. What's so—I mean, it seems like—and this is a call to those who may be watching that are in the annals at NASA making decisions. Given we don't have— A lot of historical data about interstellar objects passing through our solar system, it seems like, and the fact that there are always chance opportunities, this seems like an incredible opportunity with relatively low lift.

We've done the math. We've done the math, yeah. It can be done. Do we want to waste the fuel? Hey, look, when's the next time we're going to get a chance to capture an interstellar object at that proximity? Yeah. Especially given where our orbit is, that means like, on earth observatories. Yeah. We're not going to be able to, cause we can't like point at the sun, right?

It's going to be behind the sun. So like, what are we going to do? Um, already though, like so many people have like imaged it. Hubble space telescope image did the very large telescope, the VLT that we talked about in one of the previous episodes that imaged it. Um, Gemini North, another telescope that we've talked about in previous episodes that imaged it, um, in Hawaii.

So we've had like tons of interest in this thing. Because it's obviously interesting. It's a seven billion year old object that's coming to visit us. Right. Right. It's incredible. We want to we have to have the doorman to check to make sure security is tight. You know, who are you and what intentions do you have with our daughters?

Yeah. And it appears that it's going to be making its closest sort of approach to us in the late October, early November 2025. Yeah. Time frame, which is why everyone's freaking out that there's an alien invasion. Yeah. In November. Yeah. I mean, if we find out it's aliens, then the stock market crashes.

I don't know what happens to Bitcoin, you know, but we will we will find out soon. It's not too long away. Yeah, it's not too long away. And actually, you know, it's hilarious. Sixth of August. What's today's date? The 12th. Yeah. So six days ago, the James Webb telescope imaged it. Oh, interesting. But they haven't released a three month embargo on whoever gets the time.

Like so whoever whoever like had that time was like, I'm going to put it here and then like image it. And I'm sure that if like like that's another thing that gives me like some security is like I'm sure if that dude. Like saw something weird. It would have. NASA would have been like, no. Okay. Yeah.

It would have. We would have like someone would have said something. Right. By now. So it's been six days. It's probably something extremely interesting. And this guy's scrambling to get his paper out before the public gets the data. Right. That's. I don't know. But it was like six days ago. And it's also going to image it again in December.

So it's actually scheduled in December to do it. Perfect. Perfect. Because that's when the Earth is going to be at exactly the right spot to see it again. Because James Webb is also – it's still tied to Earth. It's not like just out there. It's out there. So it's like now, and then we're going to have like a few months where we can't because we'd have to point it towards the sun.

Which is why Juno is so important. Yeah. NASA, send Juno out there. Let's take some pictures. Yeah. We want to see it. It does look really beautiful and brilliant in the imagery we do have from Hubble. Yeah. And you can see it's clearly moving, right? Right. Because, like, the streaks that you see there, those are stars.

Right. That are not moving. Right. So the telescope had to, like, track it. Yes. And then as it was tracking it, the stars were, like, making streaks. Nice space-long exposure. Yeah. So while it may not be aliens yet, it will give us more data. It's sort of like that in the sort of AI analogy, it's like that training data set of what we know as sort of prosaic data.

And such, it gives us a better way to then – tune for future potential visitors if they're not already here. Yeah. And it's going to be really interesting to see, like once it gets closer and we take more data, it'll be really cool to see what this object is made of. Right. Because we know what solar system comets are made of, but are these older solar systems somehow different?

Do they have different characteristics? We've already found water on it, which is very characteristic of a comet. So, you know, in that respect, it's very similar. And we're expecting to find CO2 and carbon monoxide. But it'll be interesting to see what else comes out, right? Like maybe there's like some other exotic chemical that we didn't even think a comet would have.

This actually ties to the story we previously covered about the importance of discovering the first molecule in the universe and how that then has this sort of butterfly effect in terms of how everything forms downstream. And this would be great insight to understand those earlier solar systems and what's kind of going on there.

This also makes me think we should do another episode on the idea of panspermia, where it's the idea that life is seeded from these comets that are traveling interstellar and carrying things like water. I mean, this thing is carrying water. What else has it got? It'd be so interesting to see. And like, you know, the other thing I want to say is We've only seen three, right?

And the reason we've only seen three is probably our inability to see the rest. Fair. Right? This might be super common. We've only had like a giant network of telescopes doing this kind of stuff for not very long. Right. Right. Right. So it's not like all of a sudden someone turned on the interstellar button in 2015, and now we're just starting to see stuff.

And for that, we need telescopes that are dedicated to just finding this kind of stuff. Which means we need to fund science. Yes. Yes. This is the – For that, we actually have a telescope now. Right, and this is our second story, which is, this is an NSF, National Science Foundation, and DOE, Department of Energy, collaboration for the Vera C. Rubin Observatory.

So the sort of headline on the NSF.gov website is, beginning in 2025, NSF DOE Rubin Observatory will embark on the legacy survey of space and time, a 10-year survey of the night sky using the biggest camera ever made, capturing an ultra-wide, ultra-high-definition time-lapse record of the universe, which sounds incredible.

And so help me understand the importance of this new observatory and what it actually is. You know that previous story we were talking about? Yes. With 3i Atlas? Yes. If Verice Rubin Observatory had gone online a month before it did, it went online in July. If it went online in June, it would have found…

this object within three days. That's incredible. Okay? That's what this observatory is doing. This observatory is American exceptionalism at its finest. Hoorah! It really is. Nice shirt. Nice jersey. You see these four stars? That means we're winners. How do we get four? Wait. The women's team. Okay, got it.

All right. Well, hey. We'll take what we can get. We'll take what we can get. Yeah, this observatory is – yeah, it's like one of the greatest observatories that mankind has ever built. It is very different from other observatories, very, very different in that traditional observatories – The whole point of the observatory is like the way it works is you're an astronomer.

You got some idea for a bit of research that you want to do. So you write to the observatory funders or like whoever's in charge of like allocating time. And you say, this is what I want to look at. This is how many nights it's going to take. This is why it's important for science research, blah, blah, blah.

And then they get a bunch of proposals. And then they allocate the yearly time budget to whatever you want to see. This observatory has no such thing. Okay. Okay. There's astronomers can't bid for time. And yet astronomers are super excited about it. Okay. This observatory's only job is to take a picture of the night sky, the entire night sky, every three nights.

And that's it. That's it. That's it. Like robotic clockwork. Okay. It's almost like just a robotic telescope. Yes. It takes a picture of the entire night sky. Yes. Every three nights. Mm-hmm. Now that is impossible with other telescopes. It was OK. This thing had to be engineered specifically for that task.

It seems like a brilliant use of time and money because, you know, the it creates this baseline data set that's very robust that creates a treasure map effectively for people to then say, OK, If I look at what's going to come out of the Rubin Observatory, you can get a baseline reference point and say, hey, this looks interesting over here.

Yeah. And then we can zoom in with other tools that are maybe more specialized for any number of different types of observations. Yeah. And it's not just a baseline. It's like… It's actually going to find new things out of out of the box actively. So people can just look at it and be like, oh, that's new.

Yeah. And in fact, I mean, it's going to have so much data that like people can't afford to look at it. Right. There's just too much data. What they're actually going to have artificial intelligence looking through and combing through the data. Okay. So let's get into it because there's there's so much.

This thing started 15 years ago, 15 to 20 years ago. There's these things called decadal surveys in astrophysics. OK, so I mean, you know, people might think that like scientists are just like they just have like ideas. And then the government's like, oh, OK, here's like four hundred million dollars.

Right. That's that's not how it works. A bunch of scientists have to come together because this is a lot of money. Observatories are incredibly expensive. Right. Every little thing you're doing is at the cutting edge of science and technology. You're making the biggest camera. You're making the biggest lens.

You're making the largest system for like distributing data and like finding stuff in data. Right. All of this stuff is at the cutting edge. It's going to employ hundreds and hundreds of researchers, sometimes thousands. So in order to justify this kind of spending, there has to be a consensus in the community to actually say, look, this is the total budget that NSF and the government are giving us.

How are we going to make use of it with big projects? Because this stuff is big. This is hundreds of millions of dollars. And I want to make the note that I don't know if it's hundreds. I don't know. I don't know how many million this is actually. This is also work that the private sector won't do. No.

Why would they? Because there's no clear profit, you know, immediate profit value to investing that amount of CapEx. No, this one, this particular one was really nice because, you know, Congress has a mandate that I think by 20. 20, 30. They want to catalog all like 90 percent of all near Earth objects that are bigger than 140 kilometers.

OK. They want to just. Get a catalog of them all. And NASA was like, all right, well, if you want that, we're going to need money to make an observatory exactly like this. This thing is going to catalog something like 65 percent of it. OK. OK. And. What was funny about this particular project is in 2018, Congress actually gave them more money than they asked for because they wanted it done faster.

Yeah. But at that point, they weren't limited by money. They were just like, no, we got the best people here. There's not anyone else who can do what we're trying to do. Like making a 32 gigapixel camera takes time. Grinding this lens takes time. This is not a throw money at the problem kind of problem.

This is now we have the money, but it requires time to get this right so that when we see first light with this telescope, it's going to be banger. And it's so funny because this is the one time Congress was like – for scientists. They were like, you know what? Yeah, here, have more. And it's the one time that we were like, I wish – there are so many other – I think what's interesting about this is this idea – how times change, right?

It's not a guarantee that the funding pool will always be there. And this is 2018. So this is during the first Trump administration. Right, right. So it's not like – it's not a party-type thing. It's just some – like sometimes we really like science, and then all of a sudden, I don't know, something happens.

COVID in this case. I will note that on the NSF.gov website, they mentioned the amount of data gathered by the Rubin Observatory in its first year alone will be greater than the data collected by all other optical observatories combined. Yeah. Which is a massive, massive, massive amount of data. It goes on to say countless discoveries in improving our understanding of the nature of dark matter, dark energy, and other longstanding cosmic mysteries.

Yeah, yeah, yeah. It's got four mandates. Okay, the first thing is so okay first actually let's get into let's get into what this thing is doing. Okay. Okay. So this this very Rubin telescope it's It's an eight meter telescope. Okay. It's it's not like the Keck or the James Webb where it's a bunch of segmented mirrors This is a single solid mirror Wow on the bottom.

Okay. Okay. It was made in the University of Arizona Okay University of Arizona, there's a lab there that is the world's best at making single big mirrors. Okay. Okay. And so when time came to make this, it's like, okay, we all know University of Arizona is going to do that. That's your job. You do it.

Okay. They made this massive mirror. This mirror is extremely wide, right? Eight meters. But it's also like – the field of view of the camera, The way the mirror is shaped means that the field of view is extremely large. Okay? What does that mean? That means like – so most telescopes, when they look at the night sky, they're looking at a patch of the sky that's smaller than the full moon.

They're looking at a very narrow – Yeah, very – like you hold your thumb out and it's like that big. And they're looking like that. Okay? So even sometimes smaller. Same thing with James Webb, the Keck Telescope in Hawaii, Hubble, same thing. This thing is going to look at – a spot in the sky that's like 45 full moons.

Oh, wow. At the same time. It's like a palm. Several hours of magnitude. Yeah, it's almost like a palm. Okay. As big as a palm. Mm-hmm. Right? It's a thousand times the field of view of Hubble. Mm-hmm. Okay? Now... If I want to look at something really close for a really long time, this isn't the thing to do it.

But if I want to see the whole night sky in three nights, this is the thing to do it. What this thing is going to do is 30 seconds. Then it'll take five seconds to move 30 seconds. Then it'll take five seconds to move 30 seconds, five seconds to move 30 seconds. And it'll map out the entire night sky every three nights.

In all of the filters. It's making almost this giant – panorama is not the right word, but this giant mosaic of these 30-second snapshots that ultimately will create this sort of 3D. Yeah, it'll be like the Vegas sphere. Yeah, yeah, yeah, yeah, yeah, yeah, yeah. Yeah, yeah. But like every night, every night.

OK, it's 60 petabytes of data over 10 years. I was literally going to say I don't envy the data. 60,000 terabytes of data like every night. It's going to it's going to be making like incredible, incredible discoveries because it's observing the whole night sky, which is something we've never done in this way before.

Yeah. Yeah. And each of these each of these pictures is going to be 32 gigapixels. Right. So that's thirty two thousand megapixels in my palm. Right. That I'm like with a giant eight meter aperture. Right. Right. The camera, actually, the camera was built by Slack. Oh, Stanford Linear Accelerator Lab.

Different Slack. Yeah. Yeah. Not not not the not the. Not the not the works, works, not the software company. Yeah. Again, the private sector isn't going to do this. Right. But the government and Slack Slack is part of the DOE. So Slack made this camera. The camera is like going to fit in this room. OK. OK.

It's like as tall as you are. You are. OK. And it's like as as wide. You can imagine like it's the world's largest camera. Yes. Thirty two gigapixels. Yes. Right. I mean, in 30 seconds, we want an exposure on this small patch of the sky. It's incredibly detailed, dude. You see like galaxies you've never seen before.

In the first two days, it discovered like thousands of galaxies. Right. In the first two days, it discovered 2000 asteroids that we've never known existed. And it's like, wait, wait, wait, wait, wait. Like, you're right. Those are asteroids like that could, that could be bad. Right. But in the, in the first two days, 2000 asteroids that we should definitely know and we don't, there's a million known asteroids right now.

This thing is forecasted to get 5 million more in the next 10 years. So over the past 200 years, we've observed a million. And now in the next 10, we're going to observe 5 million because of this thing. Because all it's doing is taking a picture of the entire night sky every night. Yes. And then it's going to look for – it will be super obvious when an asteroid moves, right?

When a point in the – when a small point of light moves. We can literally track it over the whole year. This seems like a perfect use case for AI detection and anomaly detection algorithms because we have a decent amount of data to create sort of structured algorithms as a starting point that will identify things like asteroids, et cetera, et cetera.

And then we'll put them into the buckets. Yeah, and AI is really good at that kind of stuff, right? Anomaly detection is like huge. And so we'll have these buckets of stars, asteroids, galaxies, and then there'll be this fun other bucket where it'll just seem to, I mean, it's like, it's like putting on glasses when you're like really blind or turning on the lights further, further down into a room where it's like, we've, we've seen some stuff, but we've not really seen at all.

And it is going to be in an incredible amount of the, the fidelity to me is what's crazy. Um, Yeah, and the clarity of the images and the amount of light-gathering power that we have here is going to be insane. The first thing – there have been surveys in the past. Sure. Right? Harvard has the Harvard Plate Archive, which is back when there were photographic plates.

Yes. Palomar has one, Mount Palomar. You know about that one. Yes. And then the most famous one before this was the Sloan Digital Sky Survey. That was in New Mexico. And back then, like, you know, there's no – I mean, there's internet, but – For this amount of data there isn't and so like dudes literally had like dude literally had to like fill up luggage with like The data and then bring it to the university like physically right now This thing takes a photo within a few seconds.

It's to California to France And then within another few within another like few seconds. It's gonna be checked across the Every other photo that we've taken of that spot in the sky, because the telescope is tracking where it looked, right? And then maybe Hubble looked there. Maybe VLT looked there like five years ago or ten years ago.

You check around there. And then if there's anything different, it's going to flag and go across to all the astronomers everywhere being like, hey, there's something cool here. I want to be on that email thread. Every every like minute that it's observing, we're going to be we're going to be seeing these things.

And the cool thing about this is there's no embargo on the data. OK, you know how I was saying about like the James Webb telescope. Yes. Three months. You get three months to massage your data. Get your paper out here. It's like it's out. It's out like that night. It's not only a new technical innovation in and of itself.

It's the first of its kind observatory. But also the workflow that is accompanying the observatory in terms of data access to a wide community of scientists is also fundamentally different. It's fundamentally different. And it's like it took like the software, the kind of software that was required to do this and the kind of electrical – experience and all of that stuff that could only happen in America.

Like the amount of cloud computing that is happening because of this thing is like something that it's actually being handled by Fermilab and the University of Illinois because like particle detectors, they have experience with like ridiculous amounts of data and how to make sense of it. Yep. Right. This is a great.

Problem that now they can solve. So the University of Illinois is actually in charge of this whole data pipeline that's happening. And they're still massaging it out right now. Like the telescope is live. Right. But like this is a part of the pipeline that they couldn't really test until they started getting the photos.

It's still in beta. Yeah. Like so everyone was focused on, OK, let's just get the photos. Right. Now it's like, okay, how do we get the photos to everyone? And it's slowly coming about. Now, you know, I said something about how there's no embargo on this data. So the telescope takes a photo tonight. It's out.

Now we have public access to this data through the Vera Rubin website. There's actually like citizen science efforts going on because, as you said, right, machine learning is going to be really, really powerful. Yes. for detecting anomalies and detecting new objects in this data, but what does machine learning need?

Training data. Training data sets. Training data. Yeah. And so how is the, like all of the training data, like, you know, the LLMs. The training data is coming from humans writing on Reddit and all this other crap, right? The internet. The internet. Yeah, right. So, like, there's all this text on the internet.

That's your training data. But that text fundamentally had to come from human beings for it to learn how to speak like a human being and, like, language and logic and all that other stuff. Right. This, we're going to have citizen scientists doing, like, training annotations on the data. So, already something like 100,000 images.

It's only been, like... Less than a month. Yeah. But already 100,000 images have been looked at and labeled for comets. Yep. Okay. Because comets look a little bit different from asteroids. Okay. So there's a bunch of people on there. And you guys can, like, go and, like, be a part of this effort. Right.

Like, you basically identify, is it an asteroid, which is just a point-like thing that is moving, or does it have a little bit of fuzz around it? Right. Okay. And that's your job. We need more reinforcement learning from humans. Yeah. And so I actually didn't know that. That's fascinating. Yeah. So these guys are just – this is just, yeah, classic data annotation.

Yep. And then we can then train the machine learning algorithm and the AI for the future. Yes. And then we won't need – we can have them, like, do other stuff. Yes. Like, okay, is this a spiral galaxy or an elliptical galaxy? You know, all sorts of stuff. This is – I think one of the things that's so fascinating about that part of the story is it is this reminder we always talk about, which is that science is for everybody, right?

And it should be accessible for everybody. So it's actually really heartening to hear that. Yeah, this one – I really like that. They got all this funding from the American taxpayer, but they're giving it back, not just to the American taxpayer, but to the world. Right. Right. Kind of cool. Very. I think I think I think it should make us very proud as Americans that we are still at the forefront of astronomy.

Yes. You know, ever since the 1920s. Yes. Like we built the biggest telescope in the 1920s and we haven't stopped. One day, the biggest, the best. Yeah, that I think we really do understand that adjective. Biggest. Biggest. It's like, OK. Bigly. Yeah. We're going to make the biggest. Although we didn't understand that when we tried to build a superconducting supercollider in Texas.

We almost built it. Then CERN. And then we ran out of funding during the Clinton administration. When we had the crazy budget surplus. Yeah. Interesting. Like Reagan actually gave money for it. But that would be fine. That was during the Cold War. So maybe, you know, one can. But basically, once the Cold War ended.

Yeah. Yeah. Well, even even the Democrats were like, oh, I guess I guess we don't need science anymore. You know, it's it's it's so annoying. It's like now there's just a giant tunnel underground in Texas. There's a giant tunnel with graffiti and stuff. Don't tell you bored. We bored a tunnel, but we just didn't put the detectors in because like we ran out of money.

It's this it's it would have been bigger than CERN. You understand? Yeah, it would have. Dude, we would have found the Higgs like 20 years ago or something. This is why we need to fund science. It's incredible. And, like, it would have been a larger search space. Like, we would have answered questions about supersymmetry, the fact that it doesn't, like, isn't there in these.

Like, we would have answered all that because the luminosity for that Texas superconducting supercollider was higher than CERNs is right now. it's been ramped up. Like when we do stuff, we planned to like, it was going to be bright and it was going to be big. Big. And then, and then, and then, and then money ran out and then we didn't give the money.

And then that's when I think we learned our lesson and that's when NSF was like, no, we're going to do with whatever limited budget we have, We're going to take big risks and get big rewards. And that's when LIGO sort of got in the mix. And that's when people started asking, OK, can we make this gravitational observatory?

This is the gravitational wave observatory. And finally, LIGO got funding. I think it was sort of a backlash from that failure that we had as American scientists. Yeah, we were just like, ah. We really dropped. And now at CERN, we're kind of like second-class citizens because… And to be like part of the fold, you have to contribute something like 1% of your GDP, which is fine for something like the Netherlands.

But like the US is all of Europe plus more. It's like you want 1% of our GDP. No. You know? But it's like – but they need our magnets. Like CERN couldn't work without our magnets and like a lot of the tech and a lot of the like big electronics was coming from America. So, yeah, I think that – we didn't really like that.

And then like we made LIGO. I love that in astronomy we're still like at the forefront. Like we're making James Webb. We made Vera Rubin. The Keck telescopes in Hawaii. Like – We're still going at it. There's a new one that's planned. It's called the Nancy Grace Roman Telescope. That's going to be in space.

It's going to be very similar to the Vera Rubin Telescope because it's going to have a really big field of view. Um, but unfortunately I think that one is, has been put on pause cause it hasn't been launched yet. Right. And the funding, and the funding has stopped. So, so that's something that like, we clearly should like really like, you know, invest, invest in.

Yeah. Oh, you're looking at the photos. I want people to understand. Dude, look at all of those dots. Yes. Like all of those dots are previously unknown galaxies. It is incredible. And what you can see is – That's a single night. And it's basically this map view. With part of it filled in from where the photos have been taken, and it's just going to continue to expand and expand over time.

Yeah. And you can just zoom in, and it's pixelated for a little bit, and then it gets hyper-and you just keep zooming in. Yeah, and then finally you get to the signal, the noise of the detector. Right. Right. But, I mean, there is an incredible amount of objects in this view. Yeah. You see some of these diffuse galaxies here?

Yes, yes. Those were so diffuse that they weren't even caught in the Sloan Digital Sky Survey. This is incredible. Right? But that's like – some of those are totally new galaxies that are pretty close to us. And we just didn't even see. That we didn't even see because, like, they were just sort of like blobs of, like, you know, particulate stars.

Yes. With no real structure. So they weren't bright enough. But now this thing, this thing is incredible. And the thing is, it's going to take – pictures of the same spot in the sky, right? Right. At the end of the day, it's over 10 years. Right. You can start averaging, not even looking at the movie aspect of this, right?

Like the time lapse aspect of it. You can start averaging the same spot. Yep. Right? And then get insane signal to noise. Right. Right. And then start seeing really faint stuff. Yep. Right? Yep. And so – and this is – it's only been a month. And already we have, like, thousands of new asteroids, thousands of new galaxies.

This is incredible. I mean, this imagery is unbelievable. It's unbelievable. And the amount that we're getting. Right. The sheer amount. Like, you know, it's doing a whole night sky – And then the other thing we got to remember is like, you know, let's say the sun is here. Yeah. Yeah, let's say the sun is here and the earth is over here.

Yeah. Okay, it's in the southern hemisphere. Yes. So it's only going to be able to see like this part of the sky in one night, right? Yep. And then as the earth rotates, it's going to basically see all of this, all of this down here. So there's going to be parts like near the equator that we're going to have like a movie for like – we're going to have all the photos for five months and then we're not because it's going to be on the wrong side.

And then we'll be back. So then like, you know – We'll see like – but then there's parts near the South Pole that are just always going to be visible. So we'll just have like a 10-year full-on movie time lapse of like the universe in this like down here. Right. Because it will always be nighttime down there.

Right. Right. And so that'll be really cool. That's going to be. I mean, the image, like even already. Yeah. It's going to be so awesome, dude. This is incredible. I want to end this story with this little bio snippet that the NSF put out on. Who was Vera Rubin? Yeah. The Rubin Observatory is named in honor of Vera Rubin, a pioneering American astronomer whose observations provided convincing evidence of dark matter.

Yeah. An invisible substance that makes up over 80% of all the matter in the universe. Rubin's work in the 1970s showed that galaxies were rotating too much. fast to be held together by visible matter alone suggesting the presence of an unseen mass yeah her discovery reshaped our understanding of the universe dark matter is unlike any known type of matter and its true nature remains one of the greatest mysteries in science today inspiring generations of researchers and science lovers and one of the greatest observatories that humanity has ever ever created to date.

And so in honor of Vera Rubin, a woman in science. Yeah, she was snubbed for the Nobel Prize, which is she should have gotten the Nobel Prize. It's an incredible discovery. She did these what they're referring to as galaxy rotation curves. OK, it was the first sort of real inkling that, OK, there's something real wrong.

Where is everything? Why can't we see it? Basically, what she was it's a very simple. Very simple observation that she was making, which is that stars that are farther away from the galaxy center should be moving slower. Jupiter moves way slower than Earth. Mercury moves really fast around the sun because the closer you are, the stronger the gravity.

And so the stronger the acceleration, which means you're going to move faster. Okay, fine. What she noticed was the galaxy rotation curves, instead of doing this, instead of trailing off as you go farther out, they remained constant. So what does that mean? It means one of two things. Either Einstein is wrong, unlikely, or there's a bunch of mass there that is contributing to this gravitational pull that we're not seeing.

Right. Okay? Right. And that's where sort of the dark matter debate started. Got it. It's like, is it dark matter or is Einstein wrong? The Vera Rubin Observatory, which is named after her, one of the key pillars of its creation – Is this idea of answering that question. Okay. It's like it's, you know, I mean, you saw those photos, right?

Galaxies upon galaxies of stuff. What it's going to be able to do is map out the entire universe from extremely large distances. Yes. And what we're looking for is structure, large-scale structure. What that means is like – so it turns out the universe is not like completely isotropic in that it's not completely the same everywhere.

There's clumps of stuff. There's clumps of galaxies. And then there's giant voids that are like – Millions of galaxies big where there's nothing. Okay, not you know, you know my low density high density Yes, and so if we can map out that structure. Yes of like where the high density is that's you That's where the dark matter is and then where the low density is then we can start figuring out how much dark matter there is yes and and how that dark matter evolved from the beginning of evolution.

We can start making – not evolution, from the beginning of the Big Bang, from the evolution of the universe from the Big Bang until now, like how that dark matter evolved, how it shaped the creation of this large structure. Yes. And all this other kind of stuff, right? Yes. That's one of the things it's doing.

The other thing it's doing is because it's like – just going through galaxy after galaxy, it's going to catch a lot of gravitational lensing. Right? It's going to catch these, like, instances where the light is bending because of the mass in front of it. And then once we have that, we can have a better estimate on how much mass there is to bend that light.

And then we can start discovering things about dark matter. The more data that we get, the more closely we can start scrutinizing stuff like Einstein's general relativity, which we sort of take for granted. thousands of galaxies. I think it might honestly be, somebody did the calculation, it's like on the order of millions of galaxies worth of data to really start honing in on this question.

And Vera Rubin, given that it's doing this, it's going to be able to do that. It's going to be awesome. Unbelievable. Unbelievable. I, I, we're going to come back to this. Oh yeah. This, this thing is going to be in, in our zeitgeist for the next 10 years. Right. It's going to, it's going to be finding crazy stuff.

Like, um, yeah, it's going to be insane. Like ice cube and LIGO. We've talked about ice cube and LIGO on this podcast before. Um, the only time like Vera Rubin is it's, it's on a mandate, right? It's doing this. That's it. That's it. It's not going to – you got something nice to see, go look somewhere else.

Except for when Ice Cube or LIGO see something crazy. Okay? If they see something crazy and Ice Cube is like, yo, there was a massive neutrino that just came out of there. Yeah, yeah, yeah. Then Vera Rubin is going to be like, all right, I'll take a break, point it there. And because of Vera Rubin's, like, big field of view – because, like, when stuff comes into Ice Cube – We kind of generally know the direction it came from, but it's not like other things where it's like, oh, you got to go to that star, look a little bit left, look a little bit down, you're good to go.

No. So Vera Rubin's got this giant field of view. It can just point and start perusing that path of the sky, waiting for the signal. It doesn't have to be super specific. It can have a general region of the sky to look at, and because it's so high fidelity, it's going to just get everything, both where those neutrinos are coming from, Yeah, and it might see like – and it'll have – so the neutrino probably came from like a massive supernova or like some giant quasar bursting or something like that.

This thing can look and it'll have the image from the previous night. Right, right. So it'll just compare immediately and be like, oh, that's different. Right, right, right, right. You know? And then it could tell all the other observatories, yo, look over there. Yep, because it'll actually get the – like it'll have – Yeah.

basically the direction of like, oh, yeah, we have all this historical data from the Rubin Observatory. Ice Cube told us to look. Something is different, and the different thing is right there. Yeah. Yeah. Like, if neutron stars... create gravitational waves. LIGO is going to find out. LIGO and Kagura and Virgo.

But their resolution is also based on whether the gravitational wave hit this thing first before this thing. Their only resolution of direction is the timing difference between the two. And so they're just like, it's in this general direction. Vera Rubin can go and be like, okay, let's find it. It's a long time coming.

That's what I was saying. In the decadal survey, 15 years ago, the astronomers came together and were like, we need something like a large survey telescope whose only job is to survey the night sky because we're tired of living in still pictures. We want to upgrade to movies now. Right. Right. And so we need one telescope that does exactly that.

That's actually a great analogy. We're moving from photography to videography in space. It's a loose analogy. No, no, no. I mean – and so actually it was – at first it was called a large synoptic survey telescope. Lame. And then and then and then they renamed the observatory Vera Rubin Observatory. And then the the program now they kept the LSST.

Yeah. And they called it now the Legacy Survey of Space and Time, which is sick. That's that. That is. I love that. Shout out to the acronym creators. Yeah. Yeah. Yeah. They love their acronym so much. They kept it, but they just like upgraded like what it stood for. It's a great it's a great story. I mean, I'm so glad this thing is up.

You can go see it. It's it's up and running. Yeah, you can go right now to NSF dot gov and look at the Rubin Observatory page and literally see the the the field of view explorer of right now. They have their first look first images on there from January 23rd. One day. I can't you guys have to go look at this.

The amount of stuff that it captured in one day is crazy. And it's the first time the humans have ever seen that. It's crazy. A lot of that stuff, right? There's also another spot. I don't know if you can find it, but, like, there's an asteroid, like, explorer where they map out, like, all the asteroids that Vera Rubin has seen.

That's incredible. And so you can just go look. I mean, it's really awesome. Like, these are your taxpayer dollars at work. You know what will be? And it's – It's an incredible gift. It is. I can't wait for – there's this game, video game. I think it's called Star Citizen that is a procedurally generated universe, but it's, like, highly – Oh, dude.

I think I've heard about this. It's, like, super high fidelity, and, like, you travel at real space-time. Yeah. Like, you know, real – it's – equivalent to like the actual traveling distances and stuff like that modified a little bit but what what i would love to eventually see is someone take this and then replicate a 3d explorable 3d environment yeah with this as the backing data that yeah because now we have we'll have five million asteroids right right and they'll be cool to sort of anyway and they'll be like real right right you know with names and shit oh god i want to name one let's name one the uh christian lester asteroid Yeah, the first principles.

The first. FPPA. An amazing story about the brilliance again. A little American exceptionalism in there. We'll give the old badge and a nice kiss. No, we deserve it. That's dope. This was a good one. This was a good one. We're going to go again like we normally do or regularly end up doing from very, very large and very, very far to very, very small and very, very close.

Very, very small now. With our next story out of the University of Zurich, which is headlined AI meets CRISPR for precise gene editing. And the byline is a research team headed by the University of Zurich has developed a powerful new method to precisely edit DNA by combining cutting edge genetic engineering with artificial intelligence and the paper was recently published in nature biotechnology.

Yeah. So we've talked about CRISPR before. I think most viewers who have come across this video will be relatively familiar with what CRISPR is. So maybe we won't do a deep dive on the basics. Yeah. But we can touch on what is the insider improvement here that has allowed them to increase that precision of what has been a revolutionary discovery with the underlying CRISPR sort of work that has already been done.

Yes. Yeah. So CRISPR, it's it's kind of an old technology now. Right. Oh, it's almost ten years old. And and OK, CRISPR. OK, you guys happy? You know, you know, it's like I've been getting all the shit. That's true. From from from these tick tock comments. It is spelled, you know, with it was Chris. That's it.

Yeah, it's an acronym. It's an acronym that stands for Clustered Regularly Inter-Spaceed Short Palindromic Repeats. It's it's not a real word. I can say it however I want. But I'm going to say CRISPR now. I got bullied into it. The peer pressure works. Yeah, the peer pressure works, dude. So CRISPR, it's pretty old technology now, almost 10 years old, maybe a little bit more.

Basic idea, you get to cut DNA wherever you want in, like, the spot that you want. Mm-hmm. Um, before you could only cut DNA if you looked at certain base pairs and then recognize those, um, with like restriction enzymes. Now you've got a custom restriction enzyme that you can be like, okay, I want you to look for ATC GGA and then wherever that is, find it and cut it.

That's what CRISPR can do. CRISPR only cuts. And once it's cuts, it's, other stuff comes in to repair. Okay. And so far, it's just been a sort of cut and hope. Okay. You cut it at the spot and you hope that whatever machinery is in the cell already is going to come and repair this DNA. And there's not going to be any anything weird that happens.

Of course, that's not always going to be the case. Of course, you're going to get lucky. Most of the time, you're going to get lucky if it's a healthy cell. But in certain situations, it's not going to go well. Right. There's going to be these repair mechanisms. What they're going to do is sometimes delete a nucleotide or two and then your whole gene is completely wrecked.

Yep. You might be working with something called like non-proliferating cells, so cells that don't actually cell divide, like your neurons. Skin cells cell divide. Most of the cells in your body, muscle cells obviously cell divide. Your neurons do not cell divide. So they don't have half the machinery necessary for DNA repair.

that a dividing cell would have dividing cells they're they're constantly like replicating dna checking to make sure the dna is correct and all this stuff but in your neurons it's like you don't need that this neuron is going to live until you die and it's not going to make any more so for those cells if we want to use crispr and these new genetic technologies we're going to need a more robust way to control for the repair after the cut.

No more cut and hope. Yeah, no more cut and hope. We want to cut and then engineer. Right. Right. The cutting was an engineer. Before it was like the cutting was a hope too. And then we upgraded. It's like, okay, now we can engineer the cut. Now this is like sort of going towards engineering the repair.

Okay. They made this AI called Pythia. Okay. I hope I'm pronouncing that right. Okay, whatever. It's named after the Oracle at the Temple of Apollo in Delphi. Yep. You know, have you seen 300? Yes. Remember when he goes to the Oracle and he's like, should I go to war? And then he's like, no, you're going to lose or something like that.

But turns out he was bribed by the Persians. Right. Anyways, the Oracle of Delphi is supposed to tell the future. Okay. And predict the future. So this thing is predicting how the repair is going to go. Ah. Okay. Because the repair mechanisms in cells is extremely complicated. Yes. But at the end of the day, there is a hidden pattern.

Right. Right. Okay? And that's what AI is all about. Like we just talked about. Yes, it's got to be a complicated set of data, but there has to be some hidden pattern, and AI is really good at picking out those hidden patterns. The pattern could be based on the cellular environment. It could be based on, like, where along the genome that we need this cut to happen, or, like, how the chromatin, which is, like, the proteins that – wrap the DNA around how that stuff works, right?

So all of those things are what go into this AI. And what comes out is a way to predict how the repair is going to go, where we should even do the cut so that the repair is best, right? And like minimal amount of work. And the other cool thing is they've made this kind of like – Um, glue, this repair glue.

What it does is it's, it's, it's a custom piece of DNA that guides the cells DNA repair mechanisms to come and do it right. So we're not just like hoping it's not right. It's now it's like, it's like, nope, it's over here. It's like a runway for the, the, the enzymes to like come in and be like, oh, okay, cool.

I'm gonna, I'm gonna do this repair over here. It's going to be really nice. There's going to be no more scars. That's what we call them. Like when CRISPR makes a cut and then some random crap happens, it's called a scar in your gene. And it's not a good thing, right? It might even kill the whole project itself.

This makes me think of the scene and foundation. If there's any foundation fans out there, the Apple TV show. That's a great. Yeah. Isaac Asimov books. There's a there's spoiler alert. One of the people that serves the the main, you know, royal family is this like sentient robot. And. Anytime she gets injured, it's just the healing kind of happens in real time.

And just the visual of that makes me think of this kind of – this new glue, the ability to basically control the healing process. Yeah, at a molecular level. At a molecular level. Yeah. which is incredible in its own right. And so we're sort of expanding the toolkit for gene editing to not just be able to – we have the map of DNA.

We know what different aspects of our DNA do, so we can be like, oh, if we were to cut here, certain outcomes are going to happen. We then created the tool to do the cut, and now we are beginning to develop a sort of planning tool with the AI system that helps us simulate what are the outcomes going to be to a cut and how the body will respond as one aspect.

But as a second aspect, we've also created some helpers to guide that process in addition to being able to sort of look at how that process is going to transpire. Yeah. So it's not only a planning benefit, but there's also an execution. Yeah, there's a part of this that is execution itself at the molecular level.

Which is – And so now when you can cut and heal and have some level of efficacy. In the entire top-down process. In the entire top-down process from identification to extraction to repair, obviously my mind goes to the implications. You can now have more. consistency in expected outcome to changes made by CRISPR.

That's exactly the point. Yeah. Which obviously makes it more viable as a therapeutic tool or whatever you want to call it for any number of reasons. Yeah. Now we can now we can actually start like really targeting and in a safe way. Right. Right. It wasn't safe before. CRISPR, it's – I mean it's – it obviously has its applications.

But if we want to go into like real clinical therapies where we're starting to like now go into trials like human trials and things like that, we needed this kind of mechanism. Yes. That would control for the aftermath. of that DNA cut. Right. Right. Not just because, you know, biology, if you just do one part, it's – biology is a nested – somebody in molecular biology once told me, biology is nested for loops and if statements.

Okay? And you don't know where there's a random break that's just written that'll just, like, crash your whole program. Right. Right. And so you need to be extremely careful when you start editing this program. Right. Right? To make sure you, like – Don't get to that breakpoint. Right. Right. And so this is extremely important for that reason.

It's it's it's paving the way to these targeted gene therapies. Yes. It's paving the way for clinical application. We need it to be safe. And this this is this is definitely like on the road to that. Right. At least in the U.S. We can't speak for other massive nation states and their. Right. Of course.

Of course. On where the ethical line is. Yeah. You know, start implementing this in humans. Yeah. But here in the US, in this country where we make big, beautiful stuff. Yeah. Although this was in Zurich, but whatever. Well, we're going to we're going to we're going to we're good at borrowing. Yeah. And we'll make it back.

But this is this is, I think, a really important story, though, because I hate Science always feels to me like you're playing with God, right? We're starting to get to a place where we're sufficiently advanced in what we can manipulate and what we can engineer. Yeah, it's getting insane. We're getting into weird territory because, again, the tool scales.

Once it exists and it gets to that level of efficiency and efficacy – It can be applied to a number of different use cases. This is not like just for X or Y. It's like it is a fundamental concept. Yeah. Actually, on that note, on that note, one of the – so they tested it on three working models. Human cell cultures.

Okay. It worked. So this is in the Petri dish. We grow human cells. Okay. on the tropical frog, which is a model organism. I think it's called Xenopus. Yeah, it's the Xenopus frog. It's a model organism that's used for generational studies. Yep. Okay. So they messed around with the DNA here, and then the kids, and it was passed down, and it was like, okay, everything's good there.

And then, so it's called germline studies. Yes. And then it also worked on non-dividing adult mouse studies. neurons from the brain. Oh, wow. Which is this thing where, like, you know, these cells don't have that as robust of a DNA repair mechanism as cells that do divide, and we'll need that on a day-to-day.

And it worked in those cells as well. So now you can start targeting therapies, like, In neurodegenerative diseases. Right. Which would be an unbelievable. That would be unbelievable. It would be unbelievable. Yeah. Like this is – That's actually a really important small note. Yeah. That it does work on those non-dividing neuronal cells.

Yeah. Like this sort of like guide, this scar edit that it does where it's like guiding whatever the DNA repair – because there's some obviously. Every cell has some. It's just not as robust. Yes. But like with this guide – Even that can now do the work. That is fascinating. Yeah. A really, really important story out of the- It just came out.

Just, this is, we're looking at August 12th. Literally, I mean, today. Yeah, today. It came out today. It came out today. An unbelievable story that's going to have major implications. It's funny, biotech continues to be super fascinating to me. And while Jurassic Park did involve manipulating amphibian DNA in order to bring back the dinosaurs.

Our final story of the day is not quite as salacious. No. But interesting nonetheless. I think it's cute. And the story headline here is, Rare T. rex blood cells found in fossils show how dinosaurs eat. healed injuries the byline blood blood vessel structures found preserved in a famous t-rex fossil are helping scientists understand how dinosaurs healed from injuries so there's no engine involved there's no isla sorna isla nublar no we're not sending mercenaries there's no there's not even any um dinosaur dna no dinosaur dna this is blood vessels yeah And so help me understand.

It looks like this study was published in Scientific Reports from this well-known T. rex that was unearthed in Saskatchewan, Canada in the 1990s. Yes. I like this story because I really like dinosaurs. You and me both. One. I really like dinosaurs, and I really like physics, particle physics. And this is a combination of dinosaurs and particle physics.

I'll take it. Okay? And I was like, whoa, what? What? Okay, let's start with the dinosaur. Yes. Okay, his name is Scotty. He's the largest T-Rex ever found. He was found in Saskatchewan, Canada in the 1990s. He's named Scotty because once they found him, the paleontologists opened a bottle of scotch to celebrate that they had found this T-Rex.

I thought they were going to name him Scotty because he doesn't know. Don't tell him. He probably died 66 million years ago. Okay. So just before the Great Mass Extinction, late Cretaceous era, he was probably about 20,000 pounds. Lived until his 30s, which is pretty old for a T-Rex. This guy had a crazy life back 65 million years ago, 66 million years ago.

There's like all of his bones have like scar tissue, which means he got in a lot of fights. Yeah, yeah. He was pretty big. So, you know, it's probably like one of the alphas. Yep. A lot of injuries. In 2019, they were doing scans of his bones and they found an anomaly in his ribs. Okay. Okay. Where it seemed that something like angiogenesis had happened.

Do you know what angiogenesis is? I do not. It's basically the formation of new blood vessels during healing. So if we break a bone or something like that and we're trying to heal, new blood vessels will form in that spot to bring the repair mechanisms to that spot to create new bone and things like that.

So it seems that in this part of the ribs, maybe fractured a rib and then it healed. And there was scar tissue from new blood vessels forming within that rib. And that's very interesting because in the fossil record, it's incredibly difficult to preserve soft tissue. Right. Okay. Like the stuff that gets preserved are like shells, teeth, and bone.

Mm-hmm. Why? Because they have like these like higher elements. Right. They've got like calcium basically. And calcium is easy to preserve because it doesn't really decompose. The calcium just sort of stays there and it becomes part of the fossil. And then that's it. With soft tissue, you can have like bacteria, fungus, whatnot, eating you up.

Yep. And so you're not going to get it. But this was this was blood, blood vessels. And because the blood vessels have a high bit of iron, there's a chance that this got preserved because of that iron. Oh, because of the high level of iron. Because of the high level of iron in there. Now, the question is, why haven't we seen this kind of stuff before?

Well, it was sort of just assumed that blood vessels would not be preserved because they were soft tissue. But this has sort of been encased inside of a bone. Mm-hmm. Right. So it's this rare glimpse into the vasculature of a T-Rex, which I think is like incredible. Yeah. OK. So now the question is this stuff is inside bone.

How do we get to it? This is where the particle physics comes in. OK. So this group at the University of Regina in Canada. They used particle physics to look inside the bone and identify the blood vessels, identify, like, the kinds of compounds in the fossil. It was through particle physics. That's fascinating.

It's incredible. So they – I mean, basically what they want to do is they want to x-ray this thing. But you can't use, like, lab-grade x-rays because x-rays are too dim, and this fossil is huge. Yeah, yeah, yeah. Okay? It's not going to, like, go through that bone. Yep. Yep. So what they did was they got a synchrotron from the Canada Light Source, also in Saskatchewan.

That synchrotron is used for, like, all sorts of stuff. Like, you know, if you want to send, like, electronics into space, you want to see that it can handle space weather. Yeah. Like space radiation. So you stick it in there, you blow it up, and then see if the electronics last. So I don't know. I don't know what it must have been like when they got an email from the museum.

We want to put a T-Rex bone and they're like, what are you talking about? But yeah, they got this synchrotron. A synchrotron is basically it's like a a way to accelerate electrons really fast, round around in a circle, extremely fast with magnets, alternating magnets. And then what you do is when the electron accelerates because of electromagnetism, there's a charged particle accelerating.

So it's going to give off radiation. And that radiation is in the X-ray. It can actually be in a very broad range, but you can tune it extremely precisely by tuning the way in which these electrons, the speed and the curvature of the path of the electrons. This particle detector is really nice because you get like sort of monochromatic x-rays.

Okay. Mono, like the same color in some sense, right? It's the same frequency of light and it's an incredibly powerful beam. So the first thing they did was they just put it there, they x-rayed it, then you rotate the fossil, x-ray it again, rotate the fossil, x-ray it again. You get 2D projections each time.

And then with a sophisticated computer algorithm, you put it together and you can reconstruct. the 3D structure and you can see inside because this x-ray beam is so powerful that you can actually see inside and you can actually start like looking at T-Rex blood vessels which is pretty cool like we're seeing blood vessels from an engineering that happened 66 million years ago using like electrons and x-rays I mean, it's clever.

It's a great story. Yeah. No. Very, very clever. Yeah. Dr. Hammond, the founder and creator of Jurassic Park, would be very proud. You'd be very proud. Yeah. Yeah. It'd be like this is this is good old fashioned science, you know. And the other thing that they can do is they can they can use the x-rays to talk about like x-ray fluorescence.

and get into like what are the compounds that are in there. Okay. Okay, because what you can do is you can tune an x-ray to like start kicking out whatever like let's say there's like some atoms in there like heavy atoms. You can kick out an electron. It's in its lower shell. And then now you've created a hole in its lower shell.

In order to fill that the atom borrows an electron from the very top and sticks it in there. Now that's going to release an x-ray. And the color or the frequency of that X-ray is going to tell you what kind of element did that. Right. And then from that, you can tell the ratios of the elements. And from that, you can then tell stuff about the fossilization process of how exactly this thing got preserved.

Because if we can tell that, then when we find new dinosaur bones, right, we can, like, be more careful about, like, Not just like, you know, manhandling it in some way that like destroys this kind of evidence that we could be looking for later. Yes. Yes. This is there's almost this bumper cars thing where by shooting the x-rays in, we dislodge an electron and we use that detection of that dislodge electron to understand the underlying chemical composition that makes up.

That's right. What's in what's in the blood vessels, which I'm assuming is how we understood that it's had high iron. Yeah, yeah. And it had a high iron. We also detected calcium. We detected iron and manganese in the blood vessels. Like just really cool stuff, like stuff that we could never have done without this marriage of particle physics and paleontology.

I was going to say this is kind of like a crossover episode. Yeah. Because I imagine it's not necessarily super frequent that we are utilizing this methodology for… No, but now it's certainly is going to be used a lot more, you know. And so I think I think I think it's a really cool story about how we can we can take techniques that we use for one thing.

And then in a completely random scenario, you know, discover stuff about dinosaurs. Who said science wasn't created? Yeah. Yeah. I thought it was really cool. This is a really I, as you know, huge Jurassic Park fan. The new one was Scarlett Johansson and Mara Shaughnessy. I haven't watched it. I probably will.

It was I saw it in theaters. I love Jurassic Park. It was okay. Yeah. It was okay. The family was a little annoying. But I love the original Jurassic Parks. The new ones I've just. The original is classic. I think Lost World is underrated. That's the second one, right? That's an amazing one. I think that that one's underrated.

But fascinating story. And we're going to wrap up here with a special little mystery box. which means that one of us doesn't know what the story is. And this is not really a story, it's just the headline. Okay. And I'm bringing this up because of our initial first story around 3i Atlas, the third interstellar object we've detected.

And I thought this was hilarious because I saw this come up on my TikTok as people making memes about it. And I didn't really understand what was going on. Okay. And so the headline of this story is NASA scientist says he saw seven foot tall, black skin raced aliens that looked like African Americans stepping out of a mile long ship.

So the byline, A former NASA scientist is making explosive claims about an alien encounter at an Air Force base. He describes a mile-long ship landing and hundreds of beings getting off who were a black race, looked like African Americans, African-Americans. There's my African-American. He's on a ship.

He came from outer space. And we're around seven feet tall. He said they had an arrogant look of conquerors that had never been conquered. Oh, my God. So the only reason I brought this up is the memes are fantastic. I got to see these memes. You got to forward them. So you're now basically having a bunch of black people like me, right, acting as if the African-American aliens are landing and they're usually playing loud music.

The sub is just going crazy. The guy in a repair shop like me, like, come here, come here. You just hear this booming subwoofer music in the background. It's not a real story. That's so funny. The memes are fantastic. Please keep them coming. If you see any of them and you want to send them to me, send them to me.

It makes me laugh every time. Obviously, this is not an official NASA position. No, of course not. So NASA did not say if it was white people, though, they'd be like, oh, maybe. So the joke is this is why we can't get disclosure about UFOs. Because all the aliens are black. All the aliens are black. Look, everybody, we can't get disclosure.

The aliens are black. I hate to break it to you. This another fantastic episode, Interstellar Objects. the Vera Rubin Observatory, another follow-up on CRISPR, which continues to just have updates constantly, and a little fun with our T-Rex blood vessels and the intersection of paleontology and particle physics.

With this, we will end another fantastic episode of From First Principles. I am your host, Lester Nare, joined again, as always, by my co-host, fellow Princeton Tiger, and our resident PhD, Krishna Chowdhury. We'll see you guys next week.

3rd Interstellar Visitor, Rubin’s Sky Camera, AI CRISPR Boost & T. rex Blood Vessels (EP. 4)

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