JWST's "Little Red Dots," TimeVaults, and the Dawn of Math

Intro — what we’re covering today

0:00Hello internet. This is your captain speaking Lester Nar joined as always by my co-host and our resident PhD Krishna Chowdery. We have three great stories lined up for you today along with the rundown. We're going to hit astrophysics genomics ancient mathematics. Couple of housekeeping items at the top of the show. Our new collaboration episode with the guys over at Curiosity Theory is now available on their channel. The episode was focused on the future of intelligence. It's part one of two. It's a great conversation, definitely worth a watch. And at the end of this story, we're going to do a follow-up from episode 22 as it relates

0:42to the Cloud9 story that we covered, the gas cloud. Yeah. Because we got some interesting insight about the naming conventions and some of the background from the scientists who put the paper out. >> Yeah, that was really cool. Um, we sent out the video to the scientists, the authors of that study, and they were gracious enough to give us some of the backstory. So, it's really interesting. As always, you're going to learn something today because this is from [music] First Principles. [music]

1:22[music] For our first story, we're going to dive

Story 1 begins — JWST’s “Little Red Dots”

1:28into the James Webb Space Telescope in an astrophysics story uh that was a research paper published in Nature about the little red dots as young super massive black holes in densed ionized cocoons. This is a paper out of the University of Manchester, University of Copenhagen, and the Cosmic Dawn Center. And it's a nature study that reveals that these young super massive black holes are hiding in dense cosmic cocoons that we were able to sort of get more insight from because of this highfidelity highquality JWST uh ST spectra. So what's this story all about?

2:09>> Yeah. So I don't know if you remember but back in July 2022 JWST came online. I was traveling in Europe and it was a whole frenzy. And within two weeks of when the data was released, we had headlines like this from the scientific American that said the first glimpses of early galaxies could break cosmology. You know, JWST was seeing universe breakers. And it was like all of astrophysics was, you know, oh my god, what's going to happen? What's going to happen? And what it was all about was there were these spots of red light everywhere at really high red shifts. And if they're really far away, that means that they're really old, but that

2:50also means that they're very young for the universe. Right? Because the universe was only less than a billion years old. And we were seeing galaxies. And in order to make galaxies, you need a lot of time, right? Right. And so even there was like creation creationists that were like going gang busters saying that oh this is proof of um God created the universe and all of that stuff. So the focus of our study is something called little red dots and that is the technical term. >> Is it really? >> It is. It is literally the technical term. These are little red dots in the JWST deep field surveys and they're at a really high red shift which means they're very young in terms of when the

3:32universe was born. So this is you know about a billion years less than a billion years after the big bang. And the problem is they're really compact. And the debate is if these are star-filled galaxies, the galaxy sees the galaxies shouldn't have had enough time to become galaxies, right? >> Okay? Because it takes time to form stars and then from those stars to a mass and become a clustered gravitationally attracted object like a galaxy. And and this idea is based off of our current like cosmological models of how the early universe the mechanics and the dynamics of the early universe. >> Yes. Yes. Based on how much we know dark

4:12matter is there, how much we know dark energy is there. In the beginning there was not a lot of dark energy because the universe was small and dark energy scales with how big the universe is. So in the beginning there was really just a lot of dark energy. Sorry. There was a lot of dark matter and a lot of normal matter, >> but more dark matter than normal matter.

Balmer break & why these objects fooled astronomers

4:32And those models of our universe tend to not have galaxies. >> Mhm. >> So young, >> right? >> Okay. So, these became these universe breakers. And then the debate was, okay, maybe they're not star-filled galaxies, but maybe they're just weird massive black holes. >> Okay. >> And you would think, okay, like maybe that's going to solve this universe breaking thing. No, it still doesn't because those black holes are again too big for how young the universe was, right? >> So, it was a real conundrum for a while and this paper might have the answer. Okay, that's the idea. And so it it's quite a big deal if if it, you know, holds up to scientific scrutiny >> because it's both it's going to solve this new problem that has arisen as

5:13we've gotten higher quality data that's giving us ex experimentally in the real world information that doesn't quite map onto our existing models of the early universe. >> Exactly. And that's why I mean people were so excited when the James Webb Space Telescope went online because we knew there was going to be all sorts of random stuff that we were going to see that we had never seen before and you know we're at the forefront of technology. It was launched in 2021, late 2021. It came online in um July 2022 and these deep space fields were crucial. This is a photo of the James Web. It was folded up. Incredible

5:54engineering feat, by the way, to even get such a scientific instrument to be folded up. Yes. >> Inside a rocket and then have it go to a lrangee point. So, not even in Earth orbit, really far away from Earth orbit, >> get it parked there, have it unfold like origami, no issues. And Biden made an announcement being like, "We got this amazing data." And two weeks after that amazing data was published, everyone was like, what's going on? [laughter] Right? So, one of the crucial things about the James Webb Space Telescope is it is an infrared telescope unlike the Hubble which is optical. The James Web is an infrared telescope. And the reason why it is sensitive is in infrared is

6:36because infrared is wavelengths of light that are longer than optical. Mhm. >> And [clears throat] we want it to be that way because we want to look at the very depths of the universe. Okay. Really, really far back. And because the universe is expanding, you have something called a gravitational red red shift. Actually, no, it's called a cosmological red shift. What ends up happening is light that is coming from really far away. As the universe stretches, you can imagine a little sine wave and then I stretch that sine wave like a spring. So the wavelength is going to stretch out. >> So if I'm infrared, I can catch these highly redshifted

7:18light. >> And that's exactly what we see in this photo. And we see as we get farther and farther into the universe, closer and closer to the big bang, >> the spectra, these unique fingerprints where oxygen should be emitting light or where hydrogen should be emitting light, that stuff gets stretched more and more into the infrared. Mhm. >> The James Web being an infrared telescope means that we are sensitive to that infrared. >> Got it. Okay. >> Right. So we we we sort of have instead of seeing a fuzzier image of things kind of blending, there's more discreetness in what we're looking at because it it's >> we're not limited to light has to travel and things are getting squished at these

7:58these scales. Stretched sorry stretched

Spectra 101: what JWST actually measures

8:01at these scales. And so we need to expand what we're looking at in order to capture just more clarity. Yes. And understanding what what >> we want to be sensitive to that correct part >> of the of the spectrum. Right. That makes sense. >> And so that's where these little red dots come in. And they're literally called little red dots. Okay. Weird thing about them is they they show up all over the p place about 600 million years to 800 million years after the Big Bang, but then they vanish out of existence 1.5 billion years after the Big Bang. And we don't see any of them. >> Interesting. >> It's kind of like dinosaurs where like dinosaurs were alive for, you know, 300 million years to 65 million years before us and then they're just gone.

8:42>> So it's kind of these little red dots are like dinosaurs from the early universe that are no longer there in any nearby galaxies. >> Interesting. >> So it's very weird. Right. >> That is curious. >> The other thing about them is they're really small. Okay. They're really, really small. If you look on the right hand side, on the left hand side we've got one of the JWSD deep fields and on the right hand side we have a closeup of one of these red dots. And you if you look really closely, that red dot has a sort of hexagonal hue around it. That hexagonal hue is an artifact of the James Webb Space Telescope's design because >> the James Web has a primary mirror and then a secondary mirror that sort of the

9:23light comes in primary goes to the secondary and then goes to the detector and the scaffolding that is holding up that secondary mirror has three sort of rods that hold it holds it up. So those rods are going to cause something called defraction spikes. They're just artifacts of the fact that the secondary mirror has these three rods holding it up. In the Hubble, there's four. >> Mhm. >> And so you see for the Hubble Hubble photographs, the defraction spikes are >> Yeah. Right. >> in diamond shape. >> But in the James Web, they'll be in hexagonal shape. >> Makes sense. >> Okay. So, but the fact that we're seeing

10:03those diffraction spikes for those little red dots means that they're localized point sources. And we can actually calculate how big they are and they'd be about 150 to 500 light years across. That's really light light years across. >> Years, >> right? The Milky Way is on the order of 100,000 light years. >> Okay. >> Okay. The Milky Way is much bigger >> than these things by a factor of 100 to a thousand. >> Okay. >> So, perhaps they are just like black holes. This is the first clue that maybe they're black holes. They're really compact objects, right? Um they have a lot of activity and a lot of mass. So >> yeah, >> seems like a black hole. >> Okay, so if it's a black hole, we'd like to calculate its size,

10:44>> right? >> Okay. How do we calculate a black hole size? Well, in the Milky Way, the way we calculated the black hole size is we had Andrea GZ from UCLA. She watched the black hole for about 20 years and charted the path of stars that were nearby. And then you just do Newton's laws and you figure out how big is the compact object on the inside. Now, if we've got a black hole

Broad lines & dense gas: what the data imply

11:08that is at the edge of the universe 13 billion lighty years away, how are we going to do it? Well, we use something called the Doppler broadening of spectral lines. Okay, effectively what's happening is this. We look at a certain spectra, let's say H alpha, which is a intrinsic piece of light that comes out of hydrogen. Okay? Now, if the hydrogen is not moving at all and it's static, that line is going to be really sharp. >> But in a black hole, there's going to be stuff moving around in an accretion disc, right? So, there's going to be hydrogen that's moving away from us and hydrogen that move that's moving towards us. So, there's going to be some bits of Halpha, the light that is going to be blueshifted. So the wavelength will be

11:50shorter and then there's going to be some bits that are redshifted because it's moving away and the wavelength is going to be longer. And what's that going to what that's going to cause is my H alpha line which used to be just a really well- definfined frequency. It's going to broaden. >> I see. >> Right. And that's called Doppler broadening. >> This is what we're seeing here. >> And that's what we're seeing here. We we get a nice Gausian profile of our spectral line. and that gausian profile from there we can then back calculate how fast does the gas need to be moving around to create that broadening right and then you can apply the varial theorem which is something in basic physics the mass is proportional to the velocity squared and from that you get something like a

12:30number that is 10 7 to 10 9* the mass of the sun so it's like you know a billion to 100 million tens of million times the mass of the sun this is for something that is moving at 2,000 km/s. That's what we can calculate back from the broadening is the velocity and from the velocity we calculate back the mass. >> Got it? >> Okay. So, we're getting 10 7 * or 10 the 9 mass of the sun >> as the as the scale of these of of >> how big these black holes should be. >> Should be, >> right? >> How much how big they should be. >> Okay. [snorts] So, you would think that okay, we're good, right? Like they're not galaxies, they're just black holes. >> Mhm. Not so fast. Okay, there's two

13:12problems with these masses. >> For one thing, these black holes are way too big for whatever galaxy this little red dot is. If this little red dot is a galaxy and at the center is this active galactic nuclei that's, you know, churning up a bunch of mass and then spewing it out as radiation, then those black holes are about 10 to 100% the host stellar mass. >> Mhm. [clears throat] >> Okay. In contrast, for our Milky Way, the center of the galaxy where the black hole is, that black hole is only 0.1% of the entire mass of the Milky Way. >> So, this is the math is not math. >> The math is not mathing. Okay. Sagittarius A star at the center of our

13:53Milky Way is only 0.1% of the total mass of the Milky Way. And here I'm getting 10% to 100% the total mass. Like the whole thing is a black hole. >> Black hole. They're way too massive. >> Yeah, >> they're Okay. >> It doesn't make any sense. >> It does. Okay. >> Okay. Yeah. >> The second thing is, okay, so you're you're telling me that these black holes are 100 million times the mass of the sun, right? How did it get that big? Well, the way that black holes get big is through accretion, right? It's like taking in mass and it's eating up mass. And as it eats up all this matter, it gets bigger and bigger. There's a caveat to it, though. Okay. It can't just like eat endlessly. It's not like those video games where you just eat the whole city

14:34around you and >> Yeah. In like a single Yeah. In like a single moment. You can't do that. You've got to take your time with your meal. That's the point. There's something called the Edington limit. Okay. And it says there's a maximum luminosity to accretion rate >> before I'm eating too much.

Eddington limit & the early SMBH growth paradox

14:52>> And when I eat all that much, I'm going to spew out radiation. That radiation is going to shock all of the rest of my food away. M >> you see what I'm saying? It's the same argument of hydrostatic equilibrium that we've been visiting a lot on this podcast. The idea is >> if I eat too fast, >> then the radiation from [snorts] all of that eating because the black hole is going to accrete all this mass. The mass is going to start heating up. That's going to cause radiation. That radiation is going to be enough to shoe all of the rest of my mass away. So, there's a maximum limit at which I can grow. >> 3,000 calories a day max. >> Max. Right. And if you do too much, then

15:33there's no food around you. >> Right. Right. >> Okay. That's the idea. >> Look, that's very interesting. >> Okay. So, that's something called the Edington limit. It's named after Arthur Edington, who's a uh amazing man of science in history. Also a bit problematic. And by a bit, I mean a lot. But [laughter] that's um >> for another episode. >> For another episode. In any case, he finds this sort of limit to how how much stars can grow. What's the luminosity of these stars? And you can apply that same logic to black holes. >> Got it? >> Okay. So, from that we get an accretion rate. It's like how much am I eating? >> Yes. >> And from that I can have a characteristic growth time scale >> of like how how much time do I need to

16:14grow by a certain factor >> because you know what your limit on eating is. And so you'll know if I'm trying to gain 10 pounds or lose 10 pounds. >> Yeah. How long is that going to take? >> Take, >> right? >> Cuz you're you have a max. >> Yeah. And and for this by a factor of e because everything is an exponential exponential to the e. So if I want to grow by a factor of e, which is like 2.7ish, I need to wait 50 million years. >> Yeah. >> All right. 50 million years to to to grow by a factor of 2.7. >> All right. Now, these red dots that we see in the universe, they're at a red shift of seven, which means they're approximately 800 million years after the Big Bang. 800 million years is not a lot of time. >> No.

16:55>> Even especially if you're growing at only 50 million years per factor of 2.7, right? >> Okay. >> Right. >> So, there's two possibilities. >> One is you've got these light seeds. Light as in like light instead of heavy. >> Okay. >> You have the first stars. These are population three stars. They're the remnants of the very first stars. When the very first stars that are, you know, about, let's say, 100 mass, 100 times the mass of the sun or a thousand times the mass of the sun, very, very old stars, when they blow up, they're going to make a black hole. And that black hole is going to be about 100 times the mass of the sun. And so, in order to reach 10 the 9 times the mass of the sun in 800 million years, you got to be

17:37continuously eating at the Edington limit. >> I see. Stuffing your face. stuffing your face for like hundreds of millions of years. >> You got you got to be going like like the the hot dog eating competitions. Have you seen those? >> Kobayashi. >> Yeah, dude. So So you got like you got to be in the zone. >> Yes. >> For hundreds of millions of years. >> Constantly. >> Constantly. >> Very improbable. >> Seems Yeah. >> Seems improbable to just be going at it. Yes. >> Right. Okay. So the second possibility is there's heavy seeds which is this direct collapse. You don't make a star. You just got a gas cloud. The gas cloud maybe runs into another gas cloud

18:18reaches critical mass and then the whole thing just falls inside the Schwarz load radius. You get a black hole. No star needed. >> Mhm. >> For that the advantage is you you can reach that high mass 10 the 9 mass of the sun. You can reach that but the conditions got to be perfect. You got to have really pristine gas. It's got to be like sort of spherically symmetric and um you need a a lot of UV background so that the the gas doesn't cool down to form stars. It has to like really just be focused on making a black hole. Again, >> improbable, >> right? Right. So, the idea is the current the the light seed and heavy seed options >> uh don't pass the sniff test.

18:59>> Yeah. They they really don't. It's like both are like super improbable. On the one hand, you have you have like a black hole that is just eating max out like in the zone and then the second the the conditions to create that first black hole are like perfect. Okay. And the number of these little rod dots that we're seeing it just doesn't make sense. There's too many. >> There's too many for all of them to have just this perfect >> like condition. >> The idea is we're seeing so many of them. So you're saying this thing that's improbable is happening like frequently. >> Yeah. Which is the whole point. Yeah. So, what are we doing? All right. So, so this was the big conundrum, right? >> Yes. >> So, what can we do? Well, let's point an X-ray telescope at it. So, they had the

19:40Chundra X-ray telescope pointed at it. No X-rays. That's weird. >> Okay. >> Black holes spew a lot of X-rays. >> Okay. >> Because stuff gets really hot and if stuff gets really hot, it's going to like >> spew out very high energy radiation. It also there's no radio emissions. You look at you look at it from the Earth with really nice radio telescopes that can localize that point source. No radio emissions. Black holes should have radio emissions because there's high magnetic fields. And if charged particles are spinning around in that high magnetic field, >> it's going to shoot off. >> They're going to shoot off radio waves. So no X-rays, no radio waves. What do we have? >> Well, let's look at the photo. Like what do we have? Well, the James Web Space

20:21Telescope has a really nice spectrograph, right? So we can look at the phototric spectra and this is from a particular little red dot called the cliff. >> The cliff. And the cliff you can see the cliff. Right. >> Yeah. >> So at shorter wavelengths you've got a little bit of light coming through in the ultraviolet. >> Yeah. >> And then right at about 300 and like 380 >> nanometers you get a you get a really high spike. Everything that's over that 365 sorry 365 nanometers you get a lot of light coming in. >> Yeah. >> Okay. So you get this like distinct Vshape. This particular one is called the cliff because it's such a nice

21:02spectra. >> Okay. So the question is what would actually cause >> a spectral like this where >> you've got a little bit of ultraviolet but right at 365 there's nothing. >> Mhm. >> Almost nothing. And then for any wavelength longer than 365, you've got a lot of light coming through. Well, 365 nanome and this is in the rest frame. So they had to, you know, shift everything to suppose I was suppose this thing wasn't moving away because in reality that light is going to be somewhere in the infrared, right? Because it's moving away. But you can shift it back into the rest frame and you can say, okay, at 365 there's nothing. And then anything higher than 365 nanometers which means

21:43lower in energy because higher wavelength lower energy we're getting a lot of light. >> Let there be light at 365. >> So 365 nanometers is something called the Balmer limit. Okay. This is what happens because any piece of light that is higher energy than the 365 nanometer wavelength, it ionizes hydrogen. that photon has enough energy to kick out an electron if the electron is in the second shell of

The ionized-cocoon model (why it reddens light)

22:14hydrogen. Okay? >> So any photon that is in lower energy, meaning longer wavelength, >> that's not going to be able to kick out that hydrogen and it's not going to be able to ionize. And this is actually something that we see a lot in stellar atmospheres because stellar atmospheres have a lot of ionized hydrogen. This is a blueprint of a blue super giant spectrum. And there you can see right at about >> uh that Balmer limit, you're going to get a massive discontinuity. >> Yes. >> Where there's a bunch of stuff at a higher wavelength because those photons don't have enough energy to ionize hydrogen. But the stuff that's with a smaller wavelength, so higher

Story 1 wrap-up — what it means for early-universe black holes

22:55energy, those things ionize hydrogen, no problem. And the electron goes off and does whatever it wants to do. Right? >> So that's the key. The key here is that the ionized hydrogen >> around a star >> could be causing this. >> Okay. >> Okay. >> Yeah. Yeah. >> So, the key insight of this paper is to say, all right, >> suppose I were to take that ionized hydrogen idea >> and I were to put that ionized hydrogen envelope around a black hole. >> I see. I see. >> What would that mean? >> What would that mean? >> Okay. And that's the key insight of this paper. It's funny because we've been talking about uh reionization and hydrogen in in

23:40astrophysics cosmology and space in a couple of our previous episodes. So folks will have a good grounding for this conversation based on several of our past >> Exactly. >> stories. >> Yeah. And and this paper came out in um I I think it was nature. We have the >> Yes, it was in nature January uh 13th, 2026. >> Right. So So very recently. And this is photo 13 just to show our folks what the what the paper looks like. Little red dots as young super massive black holes in dense ionized cocoons. That's where the ionized cocoons comes in. It's an ionized cocoon of stuff around my black hole. >> That okay. >> And the key is that that hypothesis

24:20then lays to rest all of the different conundrums that we've had. >> I see. Okay. >> I see. like when we talked about earlier about it's over massive and too big uh the Edington limit so it can't accrete enough to like be able to get to that size but the the the note here is that this the the ionized hydrogen envelope that's around it is what is making what we're measuring or seeing look the way it is >> look the way it is and so perhaps these black holes aren't as big >> as we thought they were. >> I see. I see. That's the idea. >> Okay. >> Okay. >> So, the inside of the ionized gas cloud means we can we can now recalculate the

25:01mass of these black holes because the line broadening before we thought the line broadening just had to do with the the hydrogen spinning around the black hole and then from that we calculate what the mass should be for it to spin around at that rate. >> Okay. Well, if there's ionized hydrogen, then what we're going to see is not the actual photons from the accretion disc, but we're going to see those photons get released, and then they're going to bounce around in the ionized hydrogen because the ionized hydrogen is just plasma. And plasma means that light doesn't really have a free path to travel all the way to us, >> right? It's going from here to there. And so the photons undergo this random

25:43walk of free electrons in a highly ionized environment. So you've got the black hole in the center >> and it releases a gausian profile. Yes. Because of the accretion, but by the time it gets to us, by the time it gets to the James Web, it's going to be bouncing around this hydrogen atom, then this electron, then this hydrogen, then this electron electron, blah blah blah. And you're going to get this Thompson Thompson scattering. The electron the free electrons are going to scatter around. >> Yes. >> Okay. And when that happens, the Gausian profile is actually going to resemble more of an exponential decay on both sides. >> So the way to test this and in physics

26:25and in in science really a lot of times what it comes down to is can you get good enough data to discern one theory from another. So the two theories that are competing in this case are it's a gausian profile so it's a bell curve and the other one is it's a exponential decay >> the James web style telescope is g giving us good enough data where we can discern between those two theories >> and that's why it's so important to get really good data >> and [clears throat] which which means we have to build really great instruments. >> Yeah. And the James Web is just a phenomenal instrument. And so here you can see from their paper they fit an

27:06exponential and then they fit a gausian >> and the errors when it comes to fitting an exponential are basically zero. That's the teal color. And then if you look at the purple there's plenty of error. >> Yeah. Yes. >> The exponential fits this profile a lot better. the the model the exponential model of the theory to explain why we're seeing the what we believe to be these you know black holes look the way they do this ionization of hydrogen >> which then ends up having this exponential decay uh on this that >> that we did uh theoretically on paper. >> Yeah. We're like, we're like, okay, if

27:46there's a cocoon of hydrogen that's ionized, so a bunch of plasma that's like diffuse, what would it look like? Well, it would look like an exponential. So, let's fit an exponential. >> And then we got the experimental data of high fidelity from JWST that now it's like, oh, yeah, that's exactly what we're seeing. >> That's exactly right. Yeah. And now instead of 2,000 km per per second, Yes. that the accretion disc is going around. >> Yes. >> Instead, it's something like 300 km/s. Okay. So, actually the accretion disc isn't moving all that fast. The broadening is happening because of the plasma that's all around >> that's surrounding it, not the actual internal structure of the black hole itself. So, it I guess to the intuition is then that means the black holes are

28:28much smaller and much younger because they're not super massive and they're not going very very quickly. That's all being sort of obvisiscated by the amount of plasma ionized hydrogen that's around this. She's really young small black hole. >> That's exactly right. >> Okay. Okay. >> Yeah. And and so when you when you redo the estimate for the mass, you get something like 10 to the 5 >> which is you know several orders of magnitude below right >> what we had earlier. >> And now this makes sense. >> Yeah. Now it's now it fits. >> Now it fits. It's like now you could have that light seed of the population three stars. But this thing this guy doesn't have to constantly eat like a hot dog contest for hundreds of million years. He can take breaks, you He he he

29:09can go sub Edington limit and like he'll be fine. He'll he'll make it he'll make it to this far. Yes. You know >> so and and given the volume of these that we're seeing it then also matches the like frequency >> of of its appearance like okay these are >> this is probable now. >> Yes. >> You know and then and then now it kind of explains why there's so many of them. The missing X-rays also makes sense because all of these X-rays are now going to get absorbed by the plasma so we don't see the X-rays. The red color makes sense because the cocoon absorbs the high energy light and then remits it in the lighter part of the spectrum. The the redder part of the spectum. >> I was going to ask earlier, I'm like I thought black holes were like black holes of light, but I get the accretion

29:50disc and then stuff happens there. But I'm like why is it why was it red was kind of >> Yeah. Yeah. But it's just I mean the the stuff around the black hole is so hot that it's releasing a bunch of a bunch of light, right? And then it also explains the black hole mass problem because now you don't need this accretion and the host mass. You know, before we used to think, oh, this thing is like 10% to 100% of the of the host galaxy >> as opposed to the 0.1 we see. >> Yeah. Yeah. And now you can see in the final figure in their paper is actually showing. So the the >> the gray circles >> Yes. >> are what it would be without their hypothesis. >> And you can see the mass is decreasing. Yes. because they use this exponential

30:30fit and they're saying actually these black holes are not that >> massive and the blue line is what the theory says, >> right? >> It's like lining up very nicely with theory. >> Yeah. Yeah. Yeah. Right. Yep. Yes. >> Now, there's caveats. They only analyze 12 of these and we've seen, you know, several hundred. >> So, it could be that there's a bunch of different things >> and not it's not like a one um one answer fits all little red dots, right? Some of them could be young galaxies and then there's always more questions. But this at least puts a lot of the black hole theories. >> Yes. >> In to rest >> like it's making sense now. Okay. The

31:11universe did the big bang did start 33.6 billion years ago. And >> right after the big bang within a billion years there were black holes but they weren't that big. It was fine. But because there was so much gas around at the time you could have these ionized cocoons. Nowadays you can't because the gas is so sort of diffuse. >> Yeah. We have we have Pepto-Bismol now. So it's >> exactly the universe [laughter] is 13.6 billion years old. The universe needs some Pepto-Bismol, [laughter] you know. But I thought I thought that was really cool just the way that you know it it's it's a recurring theme in um astrophysics research actually that the closer you measure something the better you can discern between two competing

31:52theories. >> Right. >> Right. Right. And like it goes all the way back to Kepler and Taiko Bry. Taiko Bry was this really rich guy who had a very nice observatory, not with lenses and stuff, but an observatory to point out locations of planets. And he tracked the locations of planets to a really high degree of accuracy. And then Kepler, who was the mathematician, took that data and he was trying to fit Capernacus's model of circles. Yep. And he's like, "The circles don't fit, >> but the ellipses do fit." >> And the difference between the ellipse and the circle was really small because the the orbits of the planets are

32:33basically circles, but they're more ellipses than there are [laughter] circles. Right. >> But there's a very famous quote by Taob O'Bri. I'm going to paraphrase. He said that this error is too big >> Yeah. >> for Taob O'Bri. >> Yeah. Yeah. Yeah. >> Right. like the error between the circle and the ellipse is big enough that I'm pretty sure Taiko Bry's error bar is smaller than the difference between these theories. So I'm pretty sure these things are ellipses. >> Mhm. >> Yep. >> And then he came up with Kepler's three laws and then you know Newton ran with it. So it's it's an amazing like sort of it's the tradition holds true. The better data you get the more you can discern between one or the other theory. >> This is why we need to continue to

33:15support and invest in frontier research. It's not necessarily tied to commercial impact. No, >> because the better instruments will eventually somehow someway always is dual use. Yeah, >> it's always dual use. Our better the better we understand the world around us, the better we can get all the conveniences we love every day. >> Yeah. And this is this was >> I mean it was just so cool that now we know that there were black holes >> you know 800 million like like an infant universe >> had just a bunch of black holes >> that were just like going gang busters. >> Yes. Yes. And then started spitting out and creating all of this and >> again and we can see it we can literally

33:56>> see it now. >> Yeah. Um, I still get so my mind still gets so weirded out by the idea that because of the speed of light, when we look far enough, it's like looking in the past. >> Oh, dude, it's still it's still weird to me. >> It's very, >> it's weird to me. >> It's very hard for my brain. >> Um, like it makes logical sense. >> Yeah, of course. But >> but like then you're like, are [laughter] you sure? >> That's our astrophysics story of the day. I think we're almost averaging one per one per week at this point. Um, great story one. Um, we're going to move now into the rundown. Uh, before we get to story two, we can't cover every story

34:36happening in the frontiers of research labs, not only in the US, but across the globe. It's an incredible time and it's really important that we kind of keep track of what's going on because these things are eventually going to impact our lives one way or another. And we're going to start the rundown. These are some fun stories this week covering a couple different areas. Our first story in the rundown is about a prehistoric wolf's gut. >> Yeah. >> Frozen in time and revealed an ice age giant. This is from the center of paleo genenetics in Stockholm, Sweden and Stockholm University that was published in Genome Biology and Evolution. The

35:19summary here is that a wolf pup that died 14,000 years ago close to when the woolly rhino >> Yeah. >> went extinct. >> Went extinct. >> And there's some hair and stuff. >> Yeah. That's still in the stomach. >> Still in the gut. And which means that the DNA apparently is well preserved.

The Rundown begins — quick hits from science this week

35:38>> Yeah. Because apparently the wolf died like pretty soon after having dinner. This wolf pup. Okay. And they don't think that it like died from predation or something like that. They think a landslide just came and like knocked its home. >> And so it had just had dinner and then the wolf pup died along with its sister and it was found like right next to each other. And what's really cool is the >> the hair and all of this biological material was actually intact in the the gut of this wolf pup. Okay. So from there we can we can do genetic >> sequencing. Yes. >> And find the genetic remnants of this woolly rhino. And the key thing is if

36:19the woolly rhino went out because the population dwindled, right, and there were not that many viable individuals in the population, then what you would expect is a lot of inbreeding. And that would leave markers in the genomics. [clears throat] >> But what they found was there was no evidence of inbreeding, which means that there was a healthy population even as >> recent as 14,000 years ago. And that's right around the time when all of them died off. And that's also right around the time when the ice age ended. Right. >> So, it's probably due to climate change that this massive massive species that used to roam Siberia and Canada is now is now

36:59gone. >> I didn't even know the woolly rhino was a thing. >> Yeah, it's pretty crazy. It's a rhino but like >> like hair like hair. Yeah. I mean, it's a mammal, so it's got hair, but this has a lot of hair because it lives in the, you know, the winter. Did this I just found this might be totally nonsense. >> A pig can become a wild boar if you leave it in the wild. Like like like there's it's it's about the environment that transitions them from being like >> a generic domesticated >> domesticated pig versus like what we think of as a wild boar. Like they're not necessarily different geni or whatever. >> Oh, really? I this look someone in the

37:39comments please correct me if I'm wrong. I saw it scrolling and I was like this makes >> that's pretty crazy. So that mean that means that like certain genes turn on and off based on environment, >> right? >> Wow. >> Which >> story for another day. Yeah. >> Uh number two in the rundown. Uh and we're going to be doing a lot of this Aremis story because we're coming up on our return to the moon. NASA rolled out an 11 million pound moon rocket in preparations for the astronaut's launch. There's no journal or institution here, just one of our favorite government

Rundown: Artemis II rollout / moon rehearsal

38:13agencies. NASA moved a 322 ft tall rocket out to the launch pad on Saturday. A key step as the agency prepares for its long-awaited mission to send four astronauts around the moon on the widest orbit it's been around the moon. Not on the moon. We have been to the moon, but not at this widest orbit. There were so many companies just >> guys, it's it's fine. >> It's [laughter] we've we've been to the moon, but now we're going really far away. That's the idea. >> That's that's the idea here. Uh we did, I think, see have someone in our comments that was mentioning that they were uh a rocket scientist and they were actually driving down.

38:53>> Yes. >> To to basically get ready for the launch. >> Yeah. It was really cool that some of them like they worked at Huntsville, Alabama, which which is involved with the Artemis program. >> Yes, >> it's very cool to get feedback like that. >> Um, sorry for not immediately being able to pull the name, but comment again and we'll make sure to get your name next time. Uh, we will have a deep dive on uh the Artemis launch and everything soon. So, we will take care of all the moon people who want more moon info. Story number three is about teenagers. >> Yeah. Uh scientists have found hidden synap hotspots in the teen brain. This

Rundown: teen synapse “hotspots”

39:30is out of Kyushu University in Japan. It was published in Science Advances. Scientists have discovered that the adolescent brain does more than prune old connections. During the teen years, it actively builds dense new clusters of synapses in specific parts of neurons. These clusters emerge only in adolescence and may help shape higher level thinking. When the process is disrupted, it could play a role in conditions like schizophrenia. Yeah, this is this was very exciting to me because, you know, by normal neurological dogma, we grow a lot of synapses as we get into our teen teenage years. So like when we're children, when

40:11we're toddlers, and then during teenage years, the the sort of main understanding is the synapses that don't have a lot of traffic, synapses are ways to connect from one neuron to another. And when those synapses don't have a lot of traffic, those synapses get deleted. Right? This is saying that actually in certain parts of the brain, new synapses form, not new neurons, but neurons are making new connections >> while we're teenagers. This kind of tracks with how I was as a teen, you [laughter] know. Um, specifically they looked at layer five neurons and the cerebral cortex. Cerebral cortex is the the neoortex part of our brain. That's the newest evolved for us mammals. The

40:53experiments were done in mice and layer 5 neurons are very cool because these are the neurons that collect a lot of data from the other layers and then send it out to other different parts of the brain. So this is very important for trafficking information and making sense of different sensory stimuli or different ideas and so on and so forth. The the authors do mention that this is experiments in mice and you know not everything that happens in mice translates to primates and humans but it's something to look into. Even in mice I think it's a pretty big deal because this kind of stuff I would assume is conserved across the mamalian lineage right something as deep as making new synapses. Yeah,

41:34>> I wouldn't be surprised if we found it now in primates, but this this is, you know, something that we can study further on. I think it's I think it's very cool. >> It definitely maps on to the behavior we see in teenagers for sure. >> Uh from a from a purely non-scientific perspective, it tracks. >> Yeah. >> Uh story number four is from Penn, University of Pennsylvania and University of Michigan. Penn, Michigan, create the world's smallest programmable autonomous robots. Uh because we need more robots. Uh this was published in science uh science robotics and proceedings of the national academy of

42:15sciences. Uh researchers at UPUPAN and University of Michigan have created the world's smallest fully programmable autonomous robots. Microscopic swimming machines little Michael Phelps is

Rundown: microrobots & targeted navigation

42:25running around that can independently sense and respond to their surroundings independently. H and operate for months and cost just a penny each. Not going to take off in the US. >> No way. We don't got pennies anymore. >> We don't even have you got to buy in bulk. [laughter] No, >> but okay. Uh all jokes aside, this is pretty crazy. Okay, you saw the photo, right? These these things are smaller than a grain of salt. They're powered by light and they've got a little computer that can let it move in sophisticated ways. This thing is this thing is insane. And they operate at the biological level. So, you know, they say you can monitor individual cells. You can have it go inside your body. Um, you

43:07can help with smallcale manufacturing. Like imagine a a laboratory that's the size of like, you know, microns by microns. I think it's definitely uh look it's it's a cool technological advance but all those people who are like there's a chip in my body like >> this is a this is weird it's a little bit weird >> now there's some experimental research >> yeah now there yeah and and they operate you know with just light >> meaning >> so they've got little tiny solar panels and then they've got a computer that lets them figure out where they want to go and things like that. Um, one thing that I thought was really cool is how do you how do you make something that small move around in water? Okay, because you run into the low Reynolds numbers,

43:50right? The Navier Stokes equations when you go down to that scale, water is basically like honey >> and so it's really hard to move around. So, how are they actually doing it? Well, if you have movable parts, like little flippers and stuff, one, that's a really inefficient way to move around in Honey because like as I move this way and then I retract my flippers, I'm actually going to go backwards. Um, the other thing is movable parts are really bad because they're not durable. They can break, especially if you're moving around in honey. >> There's no nanob nano robotic autozone. >> Yeah. Yeah. [laughter] Yeah. And these things are lasting for months on end, right? The way they do it is they generate electric fields around their chip and then the electric fields then move around ions because our body has a

44:32bunch of ions and then that moves around water which propels propels them forward. So there's no moving parts. >> I just am so mad at how clever that is. >> That it's pretty clever. I got to say it's a >> bit weird that that's what we're going for but at the end it it is pretty clever and I think for the right use cases it can be huge right for delivering let's say medical cargo for

Rundown: Leonardo da Vinci DNA / ancestry

44:54to one specific site >> of the body versus another things like that it could be very useful >> we've been hearing about the potential of nanotechnology since I think I was like in middle school about how it's the next frontier and all these things um this is a real practical um you know it reminds me of the programmable protein story we covered from Yale in last season. >> Uh we're doing >> greater and greater things at smaller and smaller scales. >> Uh and again the >> the applications, you know, >> eat your heart out. It's it's it's endless. Uh that's a really really good story. Our last story for the rundown today is about Leonardo DiCaprio. Uh

45:37genetic clues >> da Vinci. >> Oh, excuse me. [laughter] It's Golden Globes. Excuse me. I saw that clip of him talking about K-pop demon hunters. >> Da Vinci, the one from the 1500s. >> Yeah. >> Genetic clues may be hiding in his artwork. Uh there were many institutions involved in this one. Uh the Venttor Institute, Vanderbilt uh uh Vanderbilt University Medical Center and the University of Maryland. Uh this was out in bioarchchive. And the idea here is that the artist, inventor, and uh uh all-around genius uh Da Vinci was sort of the definition of the Renaissance man. Obviously, we have the Da Vinci Code, all these books, all

46:17this stuff. And scientists have been aiming to unlock the secrets of his genius on a genetic level. Uh but there's just one small problem. Uh more than 500 years after his death, uh his DNA has proved virtually impossible to locate. >> Yeah. >> No kids. His grave was destroyed during the French Revolution. >> Yeah, that's not the only thing that was destroyed in the French Revolution. Let me tell you that. [laughter] But uh you know, that's my jab at the French. There's going to be another one later in the episode. [laughter] In any case, I don't know why, but like there's this thing called the Leonardo Project, and there they're obsessed with all things Leonardo, and they really want his DNA. Okay, they want to sequence his DNA. Um, what the project's

46:58team did was they swabbed letters that were written by a distant Leonardo relative and they also swabbed something called the holy child, which is a drawing that was possibly created by Leonardo. >> And they found um a Y chromosome, >> okay, >> which is the chromosome that >> is in all males and there's not a lot of mutations. So, if there's a distant Leonardo relative and then this swab from the letter is actually matching, then it's very highly likely that it is Leonardo. And this kind of reminded me of there was this documentary called The Lost Leonardo. >> It was about this painting, Salvador Mundi. >> Mhm. >> That was apparently the lost Leonardo da

47:40Vinci painting. He he only made a handful of paintings, something like 10 or 15, less than 15. And each one of his paintings are masterpieces. The first painting that I ever saw was Madonna with child in the um gallery at Lond in London. And it it is it is like quite amazing just the way that he shades painting paint together and the the the faces that he makes are just like incredible. Um this particular painting, the Salvatore Mundi, it's sold for $450 million. It's the most expensive painting to have ever been sold by Christies and it is currently owned by Muhammad bin bin Solomon of Saudi Arabia. He's trying to open a new um art

48:23gallery in Saudi Arabia and this is going to be the big masterpiece there. It's debated whether Salvatore Mundi is from Leonardo. Okay. >> Okay. But if we could figure out like a scientific way to say this is Leonardo or not, then you know you could actually see whether he made the bet was correct or not. >> We are giving new meaning to the common phrase the Da Vinci Code. >> Yeah. >> Um >> Oh yeah, nice one. [laughter] >> I do this professionally. >> Uh great five stories on the rundown today. prehistoric woolly rhino.

49:05The roll out of an 11 million pound rocket in preparation for Artemis 2 launch. We figured out why teens get to communicate better in their brain because of all these new synaptic connections. Nanobots are here. >> Yeah. >> And we may finally have the DNA of Leonardo da Vinci so that we can genetically replicate him and have a bunch of geniuses running around. We are now going to move into our next story which is about something very fascinating that I've never heard of which is time vaults. >> Yeah.

Story 2 begins — TimeVaults: recording a cell’s transcriptome over time

49:41>> So in a new paper out in science from both Harvard and the Broad Institute of MIT is this new genetically encoded device for transcriptto storage information storage >> in mamalian cells. This this story is super interesting. >> Yeah, this is this is pretty incredible because I think it is going to dare I say if this is what it purports it to be, this might be something that is Nobel Prizeworthy. >> Okay. Wow. That's >> I'm going to start there. There's something called the measurement problem in biology. In physics, we have the measurement problem, right? Which is like you measure something and then that destroys the system. Well, in biology,

50:23we've got something very similar, which is the destructive nature of observation. If I want to measure what is happening inside of a cell, I basically have to kill the cell, get all of its contents out, and then do RNA sequencing to figure out what kind of RNA is inside and all that other kind of stuff. So, we can never observe the same cell twice. And what we have to do is we have to infer biological dynamics using static snapshots of stuff. >> You got to break down and then you got to break down here and then you got to sort of connect the dots in time. That's I actually did not know that. That's interesting. >> If you think about it, that kind of makes sense, right? How am I going to observe a cell? Well, I got to break all of its contents. I got to get all the

51:03proteins and then sequence the proteins. Oh, there's this much amount of this protein. There's this much amount of this. This is the mRNA profile. This that which is the transcriptto. So, it's like super nonlinear. And if you have stuff like cancer and embryionic development which is nonlinear and non-erotic these systems you can't really piece together a time sequence that confidently. >> You can't really understand what's happening in time. Uh you can understand what's happening in snapshots of time. >> Exactly. So there's a lot of information loss. Right. There's cellular decisions that happen let's say now. Yes. And then >> the result of that cellular decision is only going to come forward in a few days. >> Yes. Right? So, how do I connect those

51:46two things? And that the existing solutions are something like DNA editing. In 2014, there was this crisper based tape recorder, >> okay, >> that [clears throat] what you could do is you could say, okay, I want to watch these specific genes and how they're transcribed. And there was a tape recorder that you could literally like be like, oh, okay, this thing was transcribed, but this time I'm going to like keep it and save it in some sort of memory. But you had to choose beforehand what to record. Uh it wasn't okay. >> Right. Yeah. And and there's only a few events that you could record before the memory went out. >> Okay. And that's where the breakthrough comes in here. It's called time vaults. Okay. Genetically encoded molecular archives. It's a time capsule for st

52:28cells that stores the secret experiences of their past. >> Very very cool. And it's repurposing something called the vault particle to physically encapsulate mRNA and store it as the cell just goes about its daily business. >> Well, you created almost a cellular backup system like time machine on your Mac. >> Yeah, ex dude. It's exactly that. It's a time machine on your Mac, but now at the cellular level. >> That's incredible. >> It's insane. >> No, that's incredible. Especially given before we were only getting snapped like the the order the the scale of of difference of capability. >> Yeah. Okay. It's >> it's very cool. Okay. So, let's talk about like why this is important, right?

53:08Yeah. So, in physics, we like to think of systems as state vectors. Like all of the particles in a room are going to have position and momenta. And if we know those position and momenta, then we'll know everything about that system. Well, in biology, the state vector is really something called the transcryto, which is the mRNA counts. There's about 20,000 genes that are getting expressed in the human body. And if you could count which genes and how much of that gene is getting expressed in a cell, you could have something like a state vector for that cell. It could be like what is the cell state in >> the the cells in our skin will have a bunch of genes that express for the

53:49proteins that are responsible for our skin. >> The cells in our blood will have the blood genes expressed. The DNA is the same but how much of which gene is expressed is what matters in terms of what that cell is doing. >> It's it's what differentiates how cells be have this multi have these different functions. >> Exactly. Yeah. And so in nature you can think of DNA as like a stable read only memory. So that's your ROM and then your mRNA is a volatile random access memory right that's your RAM. Now mRNA has a halflife that's about 5 to9 hours which which means in about every 5 to9 hours half of the amount of mRNA that was there is going to be gone. It's going to

54:30be degraded. And this is a feature and not a bug because the cell can then quickly respond to an environment and forget what it was doing 5 hours ago. You want to be able to, you know, quickly change what you're doing in case there's some outside stress that comes along. Right. >> It's sort of this adaptive property. >> Exactly. And there were ways that we could tag the new mRNA to to be like, okay, I want I want to check how much of this new mRNA is going through. You could tag it with something called for thiouodine, which is a specific type of nucleotide that goes into the mRNA and it kind of saves stuff. But that signal was limited by about 17 hours. So it's like undetectable after 24 hours. So your memory was only really 24 hours

55:12because you've got these things called exoomes in your cell that are basically the trash collectors and they collect all the trash and then they they put it out. Okay, so okay, how do we how do we

The “vault” concept: protecting RNA inside protein shells

55:23extend the recording window? Well, one naive way to do it is just be like delete all the exoomes, all the trash collectors. That's a really bad idea. >> Okay, [laughter] imagine a city without trash collectors. >> Yeah, we don't want that. >> We don't want that. We don't want that. >> Some people say that's what LA is today. [laughter] I will have you know we still have trash and recycling. >> It's not that bad. It's not that bad. But you can imagine that's that's a really bad way of doing things. You can't stop degradation because that stuff is essential for life to keep moving around. So instead, >> what if we could spatially segregate the mRNA and keep it away from the trash collectors? >> Okay. >> Right. We could put it in a vault. Uh-huh. I see. Although although the name vault is actually not for like you

56:05know a vault to store stuff, it actually comes from this 1986 paper by Kerodasha and Rome um at UCLA in the journal of cell biology. This was 1986. Okay. What they were doing was studying coded vesicles which are like just transport spheres that like package a bunch of stuff and then and then move it around the cell. And they kept coming up with this contaminant. Okay. There was a contaminant in their sample all the time. And when they looked really closely at this contaminant, there were these large barrel-shaped organels. >> Okay. >> Okay. And when you went into a electron microscope and took a look at them, these organels resembled vaulted ceilings of Gothic cathedrals. So they

56:47named them vaults. And these things are massive. Okay? They if you look at the scale bar there, that's 100 nanometers. That's like the size of some cells. And these organels are that big, right? And they're the largest nonviral >> ribboucleic protein complex. >> They've got a mass of approximately 13 megains, which is three times the size of a ribosome. The ribosome is the factory that makes proteins out of mRNA. And they're pretty big for just like for for just like single units of stuff, right? Not not really single units. There's like subunits that form together to create a ribosome. But at the end of the day, it's not a fullyfledged organel

57:28with like a membrane and like a a full like, you know, inside and outside. >> This thing 13 megodulins at the dimensions of tens of nanometers and there's a bunch of them in our cells. 10,000 to 100,000 per cell. >> Jesus. >> Okay. A lot of our immune system cells have overexpression of this stuff. >> Okay. >> And what's weird is we still don't really know what they're there for. And they're not essential because if you knock them out in mice, like you just get rid of the protein that makes these vaults, the mice are fine. >> So there's some, you know, there's a variety of things. It's either, you know, evolutionarily over time, we've, you know, no longer

58:10need these things, but they're still around. Yeah. >> Or there's some functionality that we just haven't figured out. We don't really quite know yet. >> And it's it's more likely the latter because there's a lot of stuff that, >> you know, our DNA is made up of introns and exxons. So there's stuff that the DNA keeps inside the nucleus and then there's stuff that gets expressed. This stuff is getting expressed, right? If you have 10,000 to 100,000 per cell, clearly like >> evolution by now should have figured out if if there's no use for this, why waste time and energy like making this stuff, but it's it's being expressed. So there is some reason. We just haven't quite figured out what that is. But the advantage is because it's not critical for function, you can use it as this kind of ideal robust non-toxic chassis

58:53for stuff. >> Okay, so let's get into this vault protein. What does it look like? There's there's a single protein that makes it up that's called a major vault protein. You get 78 copies of this protein and it self assembles. It doesn't need like something to come in and assemble it.

Capturing mRNA + barcoding the record

59:11It's like a bunch of tiny little magnets. 78 of them come together and make this barrel shape. Beautiful structure, >> right? This very beautiful symmetric barrel-like structure. They're all just coming together. There's these two subunits that come together, one on each side, 39 on each of them. >> And the Young's modulus of this thing, like the stress and strain, like how hard it is, it's about as hard as like some plastics or like viral capsids, which is very good because >> it's, you know, it's quite sturdy. >> Yeah. And the other thing is unlike viral capsids when you like poke them the viral capsids are kind of you're going to go all the all over the place and they're not really going to be able

59:51to recover. With these major vault proteins there's interactions with that protein. the way it sort of just magnetically locks into place with all of the others that ensures that if I were to like poke it with let's say a little um tip like an AFM tip or something like that with atomic force microscopy tip the vault protein will break and then when I remove the stress it'll just form back up just naturally. Biology has figured out this exact design to make this beautiful barrel-like structure that is >> resistant to stress. >> It's incredible. And we still don't know what it does. Like 40 years later,

1:00:33>> 1986, 40 years later, we still quite don't know what it does. >> But it is clearly uh fantastically engineered through evolutionary biology. >> Yeah, it's it's it's absolutely amazing. Right. So, how do how do we get from vaults to now time vaults? >> Yes. >> This is a really funny story. Okay, so one of these students at the Harvard Lab found Leonard Rome's YouTube channel. >> Mhm. >> He's called the Vault Guy. >> The Vault Guy >> on YouTube and he makes a bunch of YouTube videos about vault proteins and just like molecular biology in general. >> Shout out to the Vault Guy. >> And yeah, I guess one of these like grad students in the Harvard lab found the YouTube channel and was like, "Yo, this Vault guy is making some pretty cool stuff. Maybe we could use him for our

1:01:15purposes, right?" And that's where they got the idea of time vaults. And then this paper has come out in science, a genetically encoded device for transcriptto storage in mamalian cells. The idea is we're going to use these vaults as our Mac time machine. >> Yes. >> Right. >> Yes. >> Very cool. So how do we do that? How do how exactly mechanistically does it work? Because you know on this podcast we like to focus on just the Lego blocks >> Yes. of biology and how you put together a bunch of Lego blocks that actually work the way that they're supposed to >> because because the the concept of what what they're trying to accomplish and the reason why makes total sense.

1:01:56>> Yeah. >> Uh and then the entry point of, >> you know, these vaults being a possible conduit >> Yeah. >> to an engineered kind of functionality >> is interesting, but there's a big gap between those two things. >> Yeah. How am I going to get the mRNA inside and then how does it stay inside and all this other kind of stuff, right? This is again at the molecular level. >> Okay. Well, at the molecular level, everything is Lego blocks. So, let's talk about mRNA. One of the properties of mRNA is when it comes out, it has something called a poly A tail, which means at the end of the mRNA after it gets transcribed from the DNA, there's a bunch of aa the addinine, there's just

1:02:37repetition of adinine. And that poly a tail is kind of like a handle. >> Mhm. >> For figuring out where to take this mRNA and things like that. You you need to be able to grab it without messing with the genetic information that's later. Right. So you need some kind of handle for the sword. >> It's it's Yeah. It's Yeah. I was going to say it's it's like on a knife. You don't want the end to be a blade that you're grabbing. >> Yeah. Yeah. Because then you'll mess with the blade or you'll like kill yourself, you know. So, so the polyatail is there and the stuff that interacts with the polyatail is something called the polya binding protein pab. And what these guys figured is we could have a container and a bait. Okay, the

1:03:20container is my vault protein shell and my bait would be this protein that interacts with the poly tail and also has a little domain on the protein that interacts with the vault. Mhm. >> Okay. So, the PABP, that binding protein, is going to grab the poly A tail >> and then part of it is going to get stuck to the vault. >> Yes. >> Okay. And all of this we can put in a single piece of DNA >> such that when it gets transcribed, the vault protein gets transcribed to make the vault and also the poly A >> binding protein gets transcribed. >> Goodness. >> You see, >> that is so good. >> And so when I turn this thing on, it it makes the whole package. It makes my

1:04:01vault and it makes the thing that grabs the mRNA and puts it inside the vault. >> We've created a vertically integrated stack at this molecular level to effectively go through the creation of the individual component parts necessary. We don't have to hijack something existing. We're literally able to say, "No, we want the time vault. We want the grab pro the transporter." Yeah. >> Uh and so it's fully self-contained as a process. >> Yeah. Fully self-contained. >> Fascinating. Amazing, right? And then what you can do is this entire genetic circuit you can turn on and off based on this TET on TET off mechanism which is effectively what you do is you introduce an antibiotic called doxycyc and then

1:04:42when you introduce it on this genetic pathway gets triggered. So it makes the vault protein and it makes the interaction protein right and then and then there's another called docs off where I can introduce docs off and then it's going to stock stop the vault protein. And so now I have a controlled time window where I can make this time machine. I can trigger it. It can turn on. It can make all this stuff and it'll just grab a bunch of mRNA, stick it inside the vault protein, and then I'll I can make it stop. >> For some listeners may have heard of doxy cycling before because it's like a it's an antibiotic you'll get for like a variety of use cases. So I'm like it's

1:05:24simply just doxy triggers the initialization of the storage. That's again it's it's you're not inventing like we're taking things that are already there >> but [clears throat] just putting these Lego blocks together in a really clever way right? >> And then now you can see if this time vault stuff works, right? >> So you can you can show that there's no new capture. This time vault the halflife of the mRNA and the time vault is now 132 hours which is 5 days instead of the 5 hours that we had before. So the mRNA is sticking around because it's been captured. And if I now lice the cells, it's still in there. >> Mhm. Mhm. >> Right. >> Yes. This is good. >> So you you can now you can now basically

1:06:05tell a cell >> Yes. >> I want you to save all your memory right now. >> Yes. Yes. Simply by introducing. >> Yeah. Yeah. It'll save all the memory and then you can just have the cell go on its merry way because all of the stuff is inside the vault protein. It's not interacting with the stuff outside. And we know that the vault protein is not toxic to the cells themselves because it was already there in the first place. >> So it can just hang around doing the function we want it to because it's non-toxic and it's self-contained meaning like the mRNA inside cannot

Extending RNA lifetime — turning minutes into days

1:06:34escape. And also if it gets poked it'll reconstruct. So it's it sort of has security in that context. So the point being you want to be careful when you start doing things like this to not introduce >> you know new variables that the system will attack or not prepared for. But the the the structure we've already walked through really identifies that this is not going to interrupt regularly scheduled programming while still enabling the capability of capturing the memory of a cell over this long much longer period of time. >> Exactly. And so now you can ask this the mRNA that's inside the vault protein, right? How long does that last? Well, the thing is lasting for about 17 days.

1:07:14Okay, the stuff that's inside the vault protein. And you ask, okay, why is it lasting that long? Well, that's actually the inherent physics of RNA. >> Okay. >> Okay. RNA is ribboucleic acid versus DNA is deoxxyribboucleic acid. The one of the big differences is of course the DNA is double stranded and RNA is single stranded. So DNA is a bit more stable obviously because you've got you know a fullyfledged ladder that's sort of protecting the inner bases. The other big thing is that the ribos sugar which RNA has and DNA has deoxyibbos sugar. The rival sugar has an extra O. >> Okay. It's got an extra hydroxal group on the on the two of that pentagon.

1:07:54Right. Yep. >> And that extra O that oxygen oxygen is always just bad. Okay. It just attacks random crap. And so the RNA, that extra oxygen goes and attacks the phosphate backbone. >> And that process >> has a characteristic time scale of 17 days. So, we're reaching the theoretical limit of how long RNA can just hang around before the chemical RNA itself starts attacking itself. >> We we're we're literally at the what did we call the the ed the Edington limit of your of your in this case it would be the thermodynamic limit of of RNA stability. But what it's what it's showing is that there's no enzyatic

1:08:36degradation of mRNA, right? There's no other stuff. There's no exoomes that are coming in trying to like take it out. This is just pure physics. It's going to last about 17 to 18 days. >> It's locked down but for the fact that uh the structure of uh ribos itself will degrade >> will degrade the iron >> which we that's a bigger that's a whole >> Yeah. That's like I don't know what to do about that. Right. [laughter] >> Yeah. So so here you can show that like you know this >> this is really happening right. So the critical step is once you have once you have like the RNA that's present in the cytool let's say versus RNA that's present in the time vault. How do you

1:09:17tell the two apart? >> Right. Okay. >> Right. Cuz if I were to now take the cell and I were to say, okay, I'm I want you to record now and then in 5 days I want to read it. >> Right. How do I how do I >> tell what's already there versus what's in the vault? Well, I can introduce something called RNA, which is an enzyme that eats RNA. >> If I make it eat RNA, the stuff that's inside the time vault is protected from this enzyatic degradation. >> Oh my god, bro. This is so good. >> It's so good. And and so now the if you add RNAs, the only stuff that we're going to be able to read afterwards is the stuff [clears throat] that's inside the vault protein. >> God, that's [sighs] so good.

1:09:58>> It's so good. I this is this is this is um you're watching masters at work here. >> Yeah, this is great. >> I I can see why you started off with such a high high bar. >> High bar, right? There are some there are some caveats. For example, it's not going to it's not going to take in mitochondrial RNA because the mitochondrial RNA is inside the mitochondria, right? And it's not going to take in >> um any RNAs that lack a polyatail. There's some there's always some that are exceptions to the rule. And so, you know, the the interaction protein is not going to be able to grab that mRNA because there's no polyatil to actually grab. Um, the capture efficiency is also quite low. It only takes in about 3% of

1:10:40the total mRNA, right? Because there's so much mRNA. So, it's only going to take in a small sample. And so, that low capture rate means that you really need to pull together thousands of cells to prevent this statistical bias, right? where if you were to do do just one cell, how do you know that you got all of it if it's only taking 3%. But if you do a thousand cells, then you're pretty sure that you've got all of the mRNA covered. >> That's well that's a solvable. We'll solve that problem. >> And and there's there's ideas later that'll that'll help you solve it. But I just want to let you know that in at least in the first iteration of this, this is still pretty >> No, this this is this is >> cuz now that this base is there, like I I'm just shocked at how clever that is.

1:11:20>> Yeah, it's it's really nice. Okay. And so the way so how do we know that it works? Well, they did a really simple experiment. What they did was heat shock these cells after um after and then right after the heat shock they had the RNA go into the vault proteins. >> Okay? And then they compared the vault proteins right after the heat shock to when you and then you know you wait five days and you sequence the mRNA that's in the vault proteins and then you compare that to the heat shock and then right after you you lice [clears throat] it and then you do it right. If this whole thing worked, >> yes, >> then the mRNA that's in the vault proteins right after the heat shock should match

1:12:01>> normal cells right after the heat shock. The transcriptto should match. And that's exactly what they found, right? Is that the present transcriptto, if you look at >> 5 days later, the present transcriptto forgot that it was heat shock, >> but the stuff that's inside the vault remembers that it got heat shocked because you did the time machine thing. >> You did the backup >> and you did the backup right then. >> Right. Very beautiful. And the figures, I got to say, the aesthetics of the figures are also really nice. Like the color coordination of the blue and the red, I liked it. I don't know. >> Look, look at we like design and aesthetic, both experimental and visual. >> Yeah. Yeah. So, so I thought this was really cool. So now applications, right? They actually went further and went into applications. This is the one where

1:12:42people talk about, you know, real world applications. So Harvard when when it came out with the story in their press, they talked about how did that cancer cell become drugresistant? Mhm. >> One of the first things you can do is apply this thing to cancer cells. Okay. >> Whenever you do cancer cells and you treat the cancer cells with a drug, there's always 1% of cancer cells that are drug tolerant persisters. >> You know how like you have like the soap that's like kills 99% of germs. Well, there's always 1% of germs that get away, right? And the same thing happens

Use cases: development, drug response, cell-state transitions

1:13:12with cancer. If you target therapy with something called called oymeib which is a drug for cancer 1% of these cancer cells survive. Now the debate is the following. Were these cancer cells pre-addapted >> or was the fact that I introduced the drug causing them to adapt? >> This is a debate that goes all the way back to Charles Darwin and Jean Baptiste Lamar. Um there was the idea of how do organisms gain traits? The Darwinian model was the one at the top that you see, which is let's take a population of giraffes, right? Why do giraffes have long necks where well, you could have a population that has a bunch of different

1:13:53neck sizes and then the longer necks could reach the trees and eat the trees and so then the shorter necks died off and then the subsequent populations got longer and longer necks. That's actually the one at the bottom. Okay. Now the Lamarian hypothesis is the hereditary nature of acquired characteristics. Meaning the giraffes like kept stretching their necks and then that stretching of the necks made the subsequent population stretch their necks >> and so now that's why we have longer neck giraffes. >> Would it be almost like an epigenetic

1:14:33kind of theory? Like more of an epigenetic theory? >> Exactly. Yeah. And for what we know from large scale populations, it's really a Darwinian thing. But the Lamar hypothesis is not completely off ground, right? At the cellular level, we have examples of that. For example, with bacteria, when we heat shock bacteria, the bacteria just start taking in DNA from their environment, right? So that's kind of Lamarian because I've introduced a stress and then that stress is causing the genetics to change. So the question is what's happening with these cancer cells, right? And currently we can only record the bulk population which is mostly dead >> and [clears throat] then we can record the survivors which is after the fact. So to answer this question in cancer biology you have to be able to record

1:15:14what's happening before the drug was introduced >> and then probe what the survivors were doing >> doing. Yeah. Yes. >> Does that make sense? And this is perfect for the time vault. So what they did was they had a protocol where you record for 24 hours with no drug and then you introduce this cancer drug oimon. You record this cancer drug for 4 days 99% die. Yes. >> And then the ones that survive, let's read out what's inside their vaults. >> Yes. Yes. >> And it was definitive evidence for the selection model, meaning that these cancer cells had previously adapted. >> The cancer cells have a very high mutation rate. And so they're exploring

1:15:55all of these different possibilities and the 1% of cancer cells that survived >> had a distinct transcriptional signature before the drug treatment. >> Right? And so now the focus for therapeutics can shift from adaptation to targeting that pre-existing state. And what they did was they they looked at some of the genes that were expressed in those vaults, specifically FN1. >> They knocked that out >> and then now none of the cancer cells survive that drug. >> This is so it's like literally working. This this is actually un unreal. I want people to understand how unreal this is because now you're B because we can now DVR ourselves. We can

1:16:35>> we can MC time machine backup ourselves. It means we can actually see what happens before like we can understand after you introduce some external factor >> like what the changes are and particularly in the cancer research example you just brought up. This is so fascinating. You have all of them you you you do the recording in the time vault. You're doing it over 24 hours. You have all of them there. Then you introduce the current treatment. >> Yeah. 99% die. >> 99 are gone. 1% are left over. You can look at the 1% that are left over and say, "What is it about these guys that's different?" And then now we can attack the thing like the FN1 that's different because we are able to now actually see

1:17:16over time do it again and now they all die. That's incredible. >> Yeah, >> that's incredible. Do you understand what we can do with that? >> Yeah, that's so cool. You can you can use this for like and the fact that these vault proteins are just ubiquitous in malian cells means that you can just apply this all over the place. >> Exactly. This almost feels uh structural conceptually as I try to put it into my brain. It feels crisperesque as being a platform versus a single point solution. >> Exactly. Yeah. >> This is really a really big deal. Once it, you know, all the once it percolates and people poke a hole, reviewer two pokes a hole that >> No, but people people are very excited

1:17:56from the from the press coverage that I've seen, people are very excited. >> This is I mean, holy moly. >> Yeah, it's very cool. >> And what a great name. Time vaults. >> Yeah, great name. Well done. Well done. Well done on that one. >> Whoever was doing the branding, right, >> that the branding on that one is great. >> So, what's the future? Well, we want to overcome this 3% capture efficiency, right? So, one thing that I thought was really cool, the idea was you can put in inside the vault protein, you can put in something called reverse transcriptise, and what that's going to do is take the mRNA that's getting stuck inside the vault protein and reverse transcribe that into DNA. >> Now, the DNA is going to last much longer than the 17 days that's the thermodynamic limit for RNA. And so, you

1:18:38could like have weeks >> of time vaults, right? This so it's it's almost like there's a a short-term memory in the mRNA storage that get and then sort of like how we record. We record onto a small hard drive and then we dump it onto our archive drive for long-term storage. >> It's exactly that. >> Yeah. The other thing could be like you could have molecular identifiers per vault >> per cell >> and then it's kind of like a barcode for each cell. And then you could be like these mRNA came from this cell and these mRNA came from this cell. So you get the aggregate but you also get single cell resolution. kind of cool, right? >> To think about >> you could combine this time vaults with super resolution and expansion microscopy. So then you could locate

1:19:20where the time vaults were in a cell. Right now they're just sort of len in a test tube. But imagine, you know, you stick it under a microscope. You could locate where in the cell it is. Is it next to the nucleus? Is it next to the endopplasmic reticulum? So on and so forth. Is it next to the the cell membrane? >> Synthetic organels. You could have programmable delivery vehicles >> that will just like go through. >> I think it's it's it's very very cool. >> My mind is just >> Yeah, there's so much that you can do with this. >> This this this was a good Again, we don't always like to give credit to our two of our arch rivals in Harvard and MIT. >> But kudos, guys. You know, >> this was a good one.

1:20:00>> Still not Princeton, but you know. [laughter] >> Yeah, we'll give credit where credit's due. >> Yeah, this one's a good one.

Story 3 begins — ancient pottery and the dawn of mathematical thinking

1:20:07I thought that was really cool. >> That was that was fantastic. Time vaults storing. Oh my my brain every week. I know for those of you who watch and listen, my brain's constantly being my mind is constantly being blown, but like the the implications of some stuff we talk about is just insane. We're going to go to our last main story of the day, which is going to be about ancient mathematics. Ancient maths. Yeah. as they say, uh, over over the across the pond, which right now we're having some sibling rivalry with. This story is in the journal of world prehistory. Uh, the dawn of art and mathematics, ancient Mesopotamian pottery reveals early

1:20:50mathematical thinking. >> Yes. >> So, we've been thinking about math, the maths for quite a while. >> For quite a while. And it it used to be that we thought math from the 4th millennium BC, right? The Sumerian city states, they had these like tablets that had stuff like the Pythagorean theorem. They even had the quadratic formula like figured out which I think is very very cool, right? And they had a base 60 which we still use today when we talk about like uh minutes to an hour, seconds to a minute, um angles, [snorts] things like that. We still use B 60. This is a new discovery that says that Halifian pottery, which is 6,000 BC.

1:21:32>> It's in northern Mesopotamia. This might contain geometric sequences and the first evidence of humans doing math. This is a,000 to 2,000 years before the Sumerian city states. Okay? And it's a pretty cool study and we're going to get into it. I have some I don't know much about anthropology and so maybe I'm >> maybe I'm not getting it >> but I think it's still a really cool study and I we'll get into some of the qualms that I have later on but it's not much. Okay, so this time period is 6th millennium BC, late Neolithic revolution right? >> The Halifians were this culture that were widely dispersed in a network in northern Iraq and Syria. So this is

1:22:12above the Euphrates and the Tigress rivers. >> Sixth century, sixth millennium BC, way back. Okay, so this is right when we're coming out of the post ice age stabilization. And the climate is becoming sort of stabilized and the world world population is about 40 million. It's quadrupling. It's like growing really fast. Humans are, you know, getting better. They used to live in these distinctive circular tolloy dwellings, mud brick dwellings, agricultural villages. They used to have dry farming, so no irrigation as of yet. They were just relying on the rains to make wheat and barley. They used to have hering for sheeps and goats and before

1:22:54the halafians the most of the art was dominated by animals. Okay, this is an example of cave paintings from 40,000 years ago from Lascal France. [laughter] I believe that's how they pronounce it over there. That's how it's spelled anyways. So I don't know any better. But 40,000 years ago, they were making a bunch of cave paintings that were a bunch of really beautiful animals and things like that, right? Halapian pottery comes in 6,000 BC. Very beautiful. First of all, the pottery itself is like really, really nice. Okay. And this pottery contains a shift between just focusing

1:23:34on animals to now also focusing on the botanical world. >> Okay. >> Okay. So if you go to the next slide, there's examples of plants on pottery, plant art. And this is a really fundamental cognitive shift from this hunter gatherer, animal- centered view to now farmers and plant-c centered, >> the vegetarians, man. >> Yeah. I mean because farming be becomes the way of life, right? To create civilization% >> to create population. So the idea with this paper is maybe this change is what's creating now mathematics and the need for mathematics >> because you're shifting into this system

1:24:16that naturally benefits from some foundational mathematics >> mathematics right because now you have like communal stuff >> that you need to split up. >> Yeah. Yeah. >> Okay. So let's get into the history of human mathematical thought right. um paleolithic. There was this thing called the Ishango bone which was discovered in the Congo in 1960. This is it's thought that the first example of mathematics. Now it's in a Belgian museum because of course the Belgians >> yeah the Belgians did great things for the Congo, right? And um for for those who are listening, my my face is very sarcastic. [laughter] Okay. The Belgians were probably the worst colonizers of

1:24:56all the colonizers. So, but they got the bone and now it's in one of their museums. Great. So, why do we think this is a mathematical tool? Well, it's got all these like notches on it that happen

Geometric sequences (4–8–16–32–64) and why that’s “math”

1:25:08to be prime numbers, >> which is very cool. >> Yeah. >> Right. >> Yeah. >> Like 20,000 years ago, they're putting in these notches that are prime numbers. It could be like a counting tool. This is very advanced, >> right? >> For 20 Anyway, >> 20,000 years ago, right? There's some criticism that maybe it's just a tally stick or like the notches are for better grip, but the prime numbers are kind of >> specific >> specific, right? So, I like that one. [laughter] >> Okay, I'm just going to go ahead and say I like that one. Then there's there's an understanding about Neanderl yarn. This came out and what what they were doing was microscopic analysis of yarn that

1:25:48they found in Neanderl cave and it showed evidence of an intertwined three ply cord. >> Okay. >> And if it's intertwined like that, you know, where it's like you're doing braids, >> then there's some understanding of mathematics, right? You got to do three and then one under and then two and then one under, so on and so forth. >> Okay. >> So maybe the Neanderls had basic mathematical concepts, right? Then we get into about 8,000 BC. There's something called the token system which is again in Mesopotamia these early farmers used geometric clay tokens like triangles cones spheres for onetoone accounting and there's evidence that three cones would mean three measures of barley and so on and so forth right and

1:26:29amidst this is where we get the halafian gap which is where this paper sort of fits in okay it's a paper that was out very recently and it's showing that the halafian period which is about 6,000 to 5,000 BC. It predates the Sumerian writing >> and perhaps >> the pottery is evidence that it's not just aesthetics that they were going for, but they were going for some kind of mathematical codification >> for their agricultural society. >> Okay. >> Okay. >> And this is we're getting into something called cognitive archaeology, which is reconstructing ancient thought. It's always very hard to do. >> Yes. um ethnommathematics which is inferring math from indirect indications

1:27:11like we did with that bone from the Congo. So it's very hard to do. All right. So this is not an exact science but I still think it's incredibly cool because I love thinking about how we got here. >> How do we get out of the cradle of civilization? >> Yeah. Yeah. And how do we form this kind of amazing society? Right. To think about something that happened 8,000 years ago is is I think very very cool. Yes. >> So >> let's get into the paper. They did a survey of pottery from 29 different agricultural sites across northern Mesopotamia. >> Archaeological sites. >> Yes. Yes. What did I say? >> Uh agricultural. >> Oh, yeah. Yeah. Archaeological sites. I guess they were agricultural, but you're right. The this is archaeological sites where they analyze tens of thousands of

1:27:54painted shards of pottery. Okay. So, they have one thing that's very important is they have a very large statistical sample. This is quite rare. I was going to say that's I was literally going to say I can't imagine there's a lot of archaeological sites where we have that volume Yes. >> of stuff. >> Yes. So this is a lot of data >> from tens of thousands of shards from all over this geography. And what we find what they find is vegetital motifs. So motifs of plants that are a shared cultural language. >> The entire Halafian territory had this language. If you look at pottery from one end all the way to the other, >> you've got plants. So that means this isn't like a one-off thing, right? >> Okay. This is a kind of cultural

1:28:35language that goes across the entire civilization, right? And there's an average frequency of these motifs which is pretty significant. About 4 to 6% of all of the pottery has this. And given the amount of data that we have, we can actually be very confident that this is there. Right?

Skeptic’s corner: are we over-interpreting patterns?

1:28:52>> Now the motifs are classified into four basic categories. And there's a critical subgroup. There's a bunch of large flowers. Okay. And these flowers are on big bowls and the bases of these bowls and they happen to be in radial patterns. >> Yes. >> Here you can see there's four. Then you have eight. So you get this doubling, right? You get four which is a cross and then you half each of the crosses. So you get a doubling and you get eight. And then these radial encodings don't just stop at eight. They keep going on and on. So then you get 16, you get 32. And the one that's really crazy for me is all the way to 64. >> Okay. >> Getting 32 pedals. I can imagine you do

1:29:33four, then you, you know, you do the pizza cut for eight, and then you do another pizza cut for 16, then you do another pizza cut for 32. But if you've ever cut pizzas for like a large group [laughter] of people, and you've only got one pizza, which happened to me once, it was the worst party ever. You can't you can't get to 64 because then at that point it's like you're you're doing like atoms of bread, [laughter] you know? And so what they did with the 16 with the 64 is they they divided it into four quadrants >> and then they had 16 in each a 4x4 sort of >> Mhm. >> a 4x4 grid in each of the four quadrants to give 16 * 4 which is 64. This is pretty convincing that there is some mathematics.

1:30:14>> Yes, >> that's happening, right? >> Yeah. That level the complexity once you start getting to that size. I mean also these designs are beautiful. >> These designs are beautiful for 8,000 years ago. >> Yeah. They're not just >> like the patterns are amazing. The the checkerboard, the radially outward, like >> even in terms of symmetry. Yes. >> 8,000 years ago, this is incredible symmetry. >> I I totally agree. It's I mean, I can barely get that out of chat GPT today. >> Yeah, [laughter] exactly. So, so the the authors argue that there's some kind of consistent use of these two to the power of two, two to the^ [clears throat] of three. So, you get four, >> 8, >> Mhm. >> 16, >> yep,

1:30:54>> 32, and 64. And these are not these are not by coincidence. There's some kind of having logic, right? Where the sequence emerges because of some intuitive geometric operation where you're dividing a circle into a bunch of parts, >> right? And now comes the theoretical side of things that these halofen villages used to have communal sharing economy and then the crops like grain or barley had to be shared out fairly to everyone in the population. >> So they had they had the sharing economy. What do we call it now with the the the Uber and the Instacart? >> Oh yeah. What are those called? >> It's not sharing econ. It's is it >> Uber pool? Uh >> no. The the creator economy. Anyway.

1:31:35>> Yeah. >> They used to have that. >> They used to have that. Yeah. all the way back then, right? And there's there's more um there's more evidence that there used to be complex geometric subdivisions because if you look at the stamp seals from the Halafian period, the stamp seals also have this very nice geometric pattern and these were seals that were used to mark property and secure trade items. So it supports this idea that there's some sophisticated economic administration and perhaps this kind of economic administration would require basic mathematics of 2 to the^ of whatever. Yes. Okay. Okay. >> Now, there are examples of other numbers. >> Okay. >> Okay. Like there are examples of six or

1:32:17seven >> six >> or like 12, 13. And what the authors argue is that these show less meticulous craftsmanship. This is where I'm like, >> I don't know. That still looks pretty good to me, [laughter] right? Six, seven, 12, 13. The these still look very beautiful. So, I don't know. I'm not a anthropologist. Maybe there's some way to quantify, right, that these are less beautiful than the ones that were 16 and 32. Um, there's also some push back in the CNN article. There was a professor Jensen's Herup from um Roscadike University in Denmark. He specializes in meth Mesopotamian

1:32:59mathematics and he was not convinced. He said that the symmetry is just an isolated incident of mathematical technique rather than the evidence of a broader mathematical reasoning. If you just got to divide the circle nicely, of course, you're going to get >> powers of two. Now, he's an expert in Mesopotamian mathematics, which tells me that he's probably of the Sumerian citystates camp, and he wants the Sumerian citystates to be first. So, you know, there obviously with science, there's a lot of like >> human tension. >> Yes. And it's worth something to think about like you know is this just symmetry or is consistently dividing a circle into 32 equal parts and then 64

1:33:40it's inherently performing a mathematical operation without written symbols. >> Right. >> Yeah. Yeah. >> It's an open question. I I think it's the symmetry is clear to me and to me we've had a lot of discussions about symmetry on this podcast and so symmetry is inherently a mathematical principle. >> Yes. So to me, I I think this is pretty cool right? >> If you have not listened to it, check out our Chenyang episode. I think it's episode 18 or 19 >> from season >> from season one where we go deep into the the concept of symmetry and and it's it's that was a lot of fan favorite episodes. I what's interesting here is I do I think get the tension of like

1:34:22there's it looks aesthetically good versus understanding a broader mathematical context of why it looks so good. >> Yeah, >> that feels fair to me. >> Yeah. >> Like I I can sort of see like Yeah. Like you know I make things that look good all the time. I don't know the math behind it. >> Yeah. >> But given how early it is even being able to compute aesthetic value >> Yeah. and have it all over the civilization right? >> And clearly it was a part of some aspect of culture whether it's economics or otherwise. >> Yeah. >> That it's not just um for >> it's clearly a shared visual language. >> Yes. >> Right. Yeah. >> Yes. Yes. And so I this this is

1:35:04interesting. So what what is your what is your conclusion here? >> My conclusion is that I do think that they were doing something mathematical because of the 64. >> Okay. Yeah. the 64 bowl with the four quadrants and then the 4x4 grid in each of them. That was like >> that was that was pretty like oh okay that's like you know they're doing something. >> Yeah. No, >> you know but again it could just be symmetry. I like to think that there's mathematics because I just like to find the beauty in things but um >> you know who knows. For those who have stayed this long and worked through mathematics, which you've always said we're never going to cover mathematics, and you broke your rule because it's so

1:35:44good and it was kind of light mathematics. Light, you know, mathematics light. What do you guys think? Do you think does Halafi work on the pottery and stuff? Do you think it's math? Do you think it's art? Do you think it's art? Math. Math. Art. Uh, let us know in the comments. As always, we like to know for how folks get to the end of the episode and it just helps us with that little comment.

Follow-up — Cloud9 update: RELHIC vs. “dark galaxy” debate

1:36:09We are going to wrap up here today with uh a followup from the Cloud9 story uh potential for a dark matter gas cloud from last episode. I'll let you take it away here. Yeah, I mean we at the end of the last episode, we asked our audience, what do you guys think caused the name Cloud9? And you know, uh, some of the authors of the episode that we sent the link to, they actually made it that far as well, >> which is great. >> And they were asking, they actually let us know, >> yes, >> what is the story behind the name Cloud9? So this particular response

1:36:51comes from Alejandro Lombbe, but actually most of the authors of that study responded with very similar takes. I'll just sort of paraphrase how he put it. So back in February 2023, he read a paper about a potential dark galaxy candidate that was identified by the fast telescope, which is the big Aerosibo like telescope in China. And the authors were initially unaware of Alejandro's earlier paper where he had described this idea of relics. Yes, >> he was one of the original authors of that relics paper being like, "Hey, there should be something like this out there." So, he cloud that he emailed them about turned out to be a galaxy. So, when you point an optical telescope, you see stars and

1:37:31it's just a galaxy. Then the same fast guys, >> yes, >> in China, they look at this object, Cloud9. And Cloud9 was named Cloud9 because it was the ninth object that they had seen in that direction. Okay. So, it was just the Cloud9. And in China, there's no sort of cultural meaning for the phrase Cloud9. The Fast Guys then emailed Alejandro back being like, "Hey, this could be a new candidate." And this was the ninth cloud in their radio survey. The authors were aware of Alejandro and then they sort of teamed together. They went looked at the analysis from the VA. the VA actually could pinpoint where Cloud9 was because the the fast

1:38:12telescope is this giant thing that's just sort of like stuck to the Earth, right? So, it doesn't have very good angular resolution on where this thing is coming from. VA could then pinpoint and be like, "It's actually right there." Once you have those coordinates, then you can go to the Hubble guys at NASA and be like, "Hey, look, I I actually know where it is and I'd really like to look at it." The Hubble guys gave the authors some time and that's where we get that paper from Cloud9. They kept the name because you know if it turns out that Cloud9 is a relic then the authors would be on Cloud9. So they they kept that name for that reason but that's that's that's what's behind it which is which is very cool. >> We always love uh naming conventions on this show. Yeah.

1:38:53>> Um and it was great to get outreach from everyone on the team. >> Yeah, that was awesome. If you if we happen to cover if you're listening now and you happen to be um in a lab on a research team having had a story by us covered at some point and there is a correction, you can always make sure you reach out to us to to uh facilitate that correction in a future episode. Um we do our best to cover it as accurately as possible, but there's no one better than the experts. And so we are really grateful um that the team for the relics paper uh were just so uh enthusiastic in providing feedback and loving the coverage.

Wrap-up — week’s throughline: inference from scarce data

1:39:31>> Uh this was a fantastic episode 23. >> Yeah, >> the time vault story is still so nuts to me. Uh wrapping notes for housekeeping. Again, if you want us to cover a paper, you can go to ffpod.comsubmissions. We have a Reddit style upvote down vote ranking board on there that can help you in the community provide us stories you want to cover us to cover in the future. As we mentioned earlier in the pod, we do have a an Artemis special coming up uh next week. Uh so that will be a onetory deep dive. Uh we will probably

Outro + closing credits

1:40:10still do a rundown as well. Um, but just if you're listening this far, you're a fan, just be aware of that so you're not disappointed when you tune in next week. We're going to get into a bunch of weeds. I am your host, Lester Nar, joined as always by my co-host and our resident PhD Krishna Chowdery. This is from First Principles. [music]

1:40:40Hey, [music]