Cloud9 Dark Matter Halo, Jellyfish Sleep, and String Theory Hidden in Nature

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. This is episode 22. We have a fantastic threestory episode setup plus the rundown. Today we're going to touch on string theory, biological networks, three-dimensional geometry. We have a neuroscience and sleep story. And as always, we can't forget astronomy with an interesting star formation topic, my friend. >> How's it going? >> It's going well. It's going well. We're in January. It's 2026. >> Yes. >> It's 80° outside. >> It's pretty nice.

0:41>> Pretty nice. There was no snow in Big Bear. >> Oh, yeah. >> Last weekend. That was a little bit unfortunate. >> Yeah, that's kind of surprising given like how much precipitation we've had in Southern California. So, >> Exactly. I was like, "Oh, is it going to be snow?" It was all in the desert. >> Yeah. >> Uh, couple of housekeeping notes for this episode. If you didn't catch it on social, our new website, ffpod.com, is now available. It's great. You can get all of our past episodes. You can also see and search through all of the research papers that we cover in each episode. Uh, we have a whole variety of details, key takeaways, links out to the original paper. Um, and we also have

1:21introduced our new submissions board. >> Yeah, >> so many of you have commented about, "Hey, can you cover this paper? Can you cover that paper?" So, you can go to ffpod.com/submissions. You'll see a ranking leaderboard of the most popular papers. You can upvote the papers you want us to cover. You can submit your own. We're trying to engage the community here. So, we're super excited about that. you haven't taken a look, take a look because we will have more coming to the site soon. I don't know, maybe a a leaderboard or something. >> Yeah, something like that. >> So, I I think what's so fascinating about the two-way street of like the social media era is like the response to

2:02our stuff is real time. >> Yeah. >> Right. So, you know, immediately the day an episode comes out, we're getting people like this is fantastic. Like the econo physics feedback from the last episode was fascinating. >> Yeah. Yeah. Yeah. There were a lot of people who I think are probably in finance that had technical degrees that um are watching. And hey, welcome, welcome. >> We're going to learn something today. We have some fantastic stories. And as always, this [music] is from first principles. [music]

2:40>> [music] [music]

Story 1 begins — physical networks follow string-theory-like math

2:46>> Okay, so for our first story, we are going to go into the world of string theory in a paper out in nature published on January 6th. Paper is about nature's hidden blueprint. How physical networks follow string theory mathematics. Scientists have discovered that biological networks from brains to blood vessels violate simple wiring rules. >> Yeah. >> And instead follow complex surface minimization patterns predicted by string theory. >> Yeah. >> Really good at predictions. Uh this one's out of uh Wrenchler Polytenic Northwestern, the University of Chicago and Northeastern. So a nice Marvel

3:28compilation. >> Yeah. Um, and the the idea is >> that they can now reveal how surface optimizations shape the architectural architecture of physical networks impacting both the brain and our vascular system. This is fascinating. >> It's it's really cool because it's not just the brain and vascule. It's all over biology. If you look at like mycelia and fungal networks, this has something to say about that. If you look at how trees branch out into patterns, it this has something to say about that. And you know, we've been trying to figure out the mathematics behind how these physical networks branch out. And

4:10the unlikely savior came in the form of string theory. And it's really wild because it's all over it's all over the news because you know string theory has kind of a a bad rep given that it doesn't predict anything at least in terms of the fundamental physics that it promised to predict. But it's kind of cool that you know this mathematical physics approach is now being used for biological problems. And one of the reasons why I really loved this story is, you know, I'm a biohysicist by training and there's so many examples of the mathematics of physics being used for something totally different being applied to

4:52biology. You know if you look at hotfield networks and associative memory in the brain that uses the mathematics of Ising models that was being developed to describe magnetization or if you look at the reormalization group which is being used for phase transitions or deep quantum field theory that's being used to describe the stling flocks and how they do murmmorations in the sky. So, it just it's one of these really cool examples of how mathematical physics research for something totally different can be applied to biology. And we're just discovering how life is so clever, you know,

5:32>> and this sort of translational translational nature of the frameworks of of physics being applied in other spaces. >> Yeah. Yeah. Yeah. And I I think I think that's just that's just really cool. There's there's a there's a neat line between the mathematics when we go really deep down, you know, I I think that's really cool. So, the core subject has to do with networks. Okay, networks are basically things and then those things have relationships. And usually when we talk about networks, what we use is graph theory. Okay, one of the famous networks that we all use every day is the internet, right? There's links from one page to another page. That's the

6:13relationship. And the things the nodes of these networks are web pages. and the very famous page rank algorithm that was developed by Larry Page um out of a Stanford PhD thesis that became the bedrock of Google, a trillion dollar company now realized that what they could do is use the mathematics of graph theory to understand relationships on the internet and then rank pages based on their connectivity rather than you know some naive approach that if you remember ask Jeieves >> you know we're old enough to remember ask >> we are we are old enough >> or like the Yahoo search they weren't as good because they weren't realizing this

6:54underlying graph structure Google was good because it was ranking pages with the attitude that the graph structure of the internet is what's important >> and this ended up being confirmed to be true not only by the fact that arguably the best business model ever invented in humanity with the best margins >> is Google and that same concept then ultimately got applied to social graphs. >> Yeah. >> In the rise of social media, Zuckerberg being the first to apply the same concept. >> Yeah. >> Uh but in the context of social networking which then became social media. >> Yes. Exactly. And all of these things at the end of the day there are graphs.

7:34They're vertices. In the case of social networks, there would be individual profiles and then a connection would be like a friendship between the two. Right? Now when it comes to physical networks, physical networks are embedded in 3D space. I'm talking about the brain which has neurons connecting from one to another. But this isn't isn't some abstract link. There's a little physical connection of a syninnapse between a neuron A connecting to a neuron B. Right? If you look at vascule, that's blood vessels that are physically connecting. And all of these physical networks are constrained by 3D geometry, right? They're constrained by thickness. How much room do I have to move in the brain, in the body? And it's not just

8:15these like 1D abstract links. So Ramoni Kahal in 1899, he's one of the pioneers of neuroscience. He's the first guy to um establish the neuron doctrine, which is the idea that the nervous system is made out of tiny individual cells called neurons that connect to each other and it's not just one giant network. And he prov he proposed a neuron morphology that's driven by minimizing wire volume. These are some great drawings that he drew. He was an artist and he could literally trace out using these incredible techniques where he would stain individual neurons and he could trace out how these neurons connected to one another and you could

8:55recreate things like the hippocampus, things like the optic nerve. >> That's incredible. >> And a lot of what he found even back then is true today. like he in the hippocampus for example he figured out that there's this cell layer called CA1 and it gets input from the dentate which is another part of the hippocampus and another part of the hippocampus called CA3 and so on and so forth. It's a he he was an incredible scientist and he proposed that the way that these biological networks um figure out where to go and what connections to make is by minimizing wire volume, which basically turns into what's the minimum distance between two points. Like if I want to connect this point and this point, I

9:35just want to figure out what the minimum distance is. >> So you're saying the shortest distance between two points is a straight line. >> Yes. And that's exactly what what he figured out that biology would >> at some point figure out like evolution would optimize for this right um he wasn't the only one in 1926 there was Murray who established Murray's law which governs vascular branching in the blood and he figured out that you know you can minimize work that the body has to do by doing this minimum distance kind of thing right where using that heristic what you can do is say with this network architecture, the blood can maintain the blood volume, but

10:18at the same time, it does the least amount of work to get over the friction of pushing fluid through my blood vasculature. >> It's it's sort of like an optimiz biological optimization. >> Yes, exactly. And he's like he's like the the the simplest optimization of finding the shortest link between two nodes that actually works. >> Okay. Mhm. >> So it was an old paradigm and in graph theory, this is effectively what's called the Steiner tree problem. Okay. If you've got a graph of points in uklidian space, these are the blue points in this photo. Then if I want to connect them with edges such that the sum of all the edges is smallest. So

10:59it's the it's the most optimized graph that minimizes the distance between all these edges. Then what I can do is introduce little points called Steiner points in between the vertices that I want to attach. Okay. And there's a certain rule that all of these Steiner points follow. Okay. If I basically if I want to have an algorithm that makes a graph such that all of the edges are least distance. Okay, then there's two things that we notice. Okay, first is that all branching points have only three nodes. They're called bifurcations, meaning one path is going to lead out to two. There's never one

11:39path leading out to three >> because one path leading out to three, you could actually decompose that into one path leading out to two and then this branch leads out to two. >> It's it's suboptimal to have three >> coming out of out of a single node. >> Yeah. The a fork is always two. >> It's right. >> A fork in the road is always two. There's never three paths out of a out of out of an input. Okay. So that's the first thing that's just a mathematical truth if you want to solve the Steiner tree problem. Okay. >> Okay. The second point is all of these >> intersections happen at 120°. >> Okay. And that kind of makes sense because if you've got three paths coming out, a circle is 360°. 360 / 3 is 120,

12:22right? So that it all like kind of makes sense. Okay. >> The problem is if you look at empirical data from biology, this is not true. Okay. In recent times, we've done incredible work mapping out the 3D morphology of biological networks. This right here is a neuron from the human conneto project. They've mapped out a human neuron. And what you can see is violations of that stiner tree. Yes, you've got triurcation. So, a single path leading out to three. And you've also got these orthogonal connections where the angle is not 120 but 90 degrees. >> So it it is structurally it appears to be suboptimal. Yeah.

13:03>> From an efficiency perspective. >> Yes. And if you were to actually calculate the minimum Steiner tree and then add up the lengths of all of these guys, the length the total length is about 25% more than what the minimum would be. So it's requiring more surface area to have the output that equivalent to the optimal version. >> Yeah. Yeah. Well, you're jumping the gun here a little bit because >> it's requiring more length. >> Okay. >> Right. >> I >> like the length is what is what is what is not optimal. >> I understand. >> You see what I'm saying? >> We're separating different degrees of measurement because they have different implications. >> Exactly. And and the older paradigm was

13:45that I want to minimize length. But you got there by saying, well, what if I want to minimize surface area instead? Now, let's think about it. Why would I want to minimize surface area? If I'm a living being, why would I want to minimize surface area rather than length? Well, the reason is surface area is the actual thing that requires material. Ah, you know, I need a cell membrane and that cell membrane is the thing that I want the least of. [clears throat] >> Doesn't really matter what the length is. >> Right. >> Right. Because if I can if I can minimize surface area but have a longer length maybe that's fine because I'm using less material. >> Okay. >> And that is the insight of this new

14:25paper. >> Interesting. Okay. >> Okay. The new papers introduces the fact that maybe if we minimize surface area then we can replicate biological networks. And what's really cool is they introduce an exact mathematical ma mapping between this local network design of minimizing surface area and the highdimensional fineman diagrams that we see in string theory. Okay, that's the key insight. The key insight is string theorists have been worried about this problem for like 30 years. >> Mhm. And if we can just borrow all their mathematics >> and apply it >> here >> instead of in an abstract space in a in a measurable space >> then then all of a sudden we've got a

15:06heristic a mathematical heristic that solves for why biology is doing this. >> It's it's a language to interpret the observations of biology where we don't necessarily have a coherent cohesive like language to describe it mathematically currently. >> Yeah. Currently but now maybe we do right that's the idea. Yes. So let's think about this a little bit more in detail. Okay, biological networks are 3D manifolds, right? There are these 2D structures embedded in 3D. So you've got tubes bounded by membranes, bounded by endothelium, things like that. And what we want to do, the optimization is to find the minimal surface. This is what's called a plateau problem. And physical

15:46structures do this all the time. Here you see a bubble in between two rings. Mhm. >> That bubble naturally is going to minimize the surface area of the bubble because of surface tension. >> So we sort of see this concaveesque uh cuz a bubble when you say bubble it's >> it's a spherical thing. But imagine now two rings like the bubble generators, right? And they're right next to each other. You get this sort of concave surface. >> Yeah. Right. >> Cuz it's trying to minimize >> Yeah. the surface area. And this is a a natural example of property I mean the reason why a bubble is spherical is because a sphere minimizes surface area right so the same mathematics maybe we can use for this right and the other

16:28constraint is that the surface curvature has to be continuous so you can't have like kinks you can't have like singularities like little points like you know you want a smooth surface everywhere >> you want a nice smooth bald head. Yeah. Yeah. Yeah. Yeah. Yeah. [laughter] And so with that in mind, let's now do a little bit of string theory. Okay. >> A deep dive into string theory. >> Okay. The infamous uh physics theory that is supposed to solve everything. Okay. >> Yes. >> And just to briefly before we dive in, Yeah. is the is the is this the idea that it's meant to be a theory of everything or unifying classical and quantum or or No, that's

17:09like a different concept. >> No. No, no, no. This is the same theory and it's it's unifying um general relativity and quantum. >> I'm sorry. Okay. Yes. >> Yeah. Yeah. But it is that is exactly what we're talking about. This is the thing of Ed Whitten fame and um who's that guy who goes on Joe Rogan >> and talks trash all the time. [laughter] >> I know. >> Weinstein, Eric Weinstein, I believe his name is the geome geometric unity. >> Geometric unity. So So string theory is purporting to do the following, which is unify quantum mechanics. Yes. With general relativity, right? And in quantum mechanics the the best picture that my favorite picture is Fineman's picture which has to do with the particle picture but the field picture of quantum mechanics. Okay. The way we

17:51understand the interaction between particles in quantum mechanics is that they exchange virtual particles. So here what we see is an electron that's being deflected by an electron. You know electrons repel each other. That's the classical picture, right? Electrons have negative charge so they don't like to be next to each other. In the quantum picture, the reason why they don't want to be next to each other and the reason why they get deflected in opposite directions is because they're exchanging a virtual photon. Okay? And that's what you're seeing in that Fineman diagram. An electron is coming from the left and electron is coming from the right and they're exchanging a virtual photon and that's how the momentum gets transferred from one to another. >> Copy. Okay. So that's the Fineman picture, right? In string theory,

18:33there's no longer anything called particles. Instead, all of our fundamental particles, all of our fundamental fields are made out of one-dimensional strings. So, you can imagine a rubber band that's one-dimensional. It's a line. And that line oscillates in multiple dimensions. Okay? Just like a guitar string can oscillate, you know, this way or into the guitar and out of the guitar. So, that's two dimensions that it's oscillating. A string can oscillate in multiple dimensions. Sometimes 10, sometimes 12, depending on what theory is your favorite. Okay? And and the idea here is that in this diagram we see starting from matter all the way at the top as you break down to smaller scales molecule, atom, neutron, strings are

19:13smaller than that are the are the most are in this theory are the most fundamental atomic unit of physical reality. >> And they're the same. The beauty of string theory is that it's all just one string, but the way that it oscillates in all these different dimensions make it an electron or a photon or a up cork or a down quirk, things like that. So, it's this nice like unified principle, right? That actually there's no such thing as all these different fields and all these different particles. There's only one fundamental thing and that is the one-dimensional string and the way that it moves around in these 10 dimensions gives it the properties of an electron or up quark or a down quirk and all these things. So it's it's a nice

19:54like mathematical unified picture. >> That's interesting. um it just hasn't found any like realworld physics use cases because in order to prove whether these strings exist you need to go down to the fundamental scale like the plank scale of really tiny length really tiny time um the energy is massive 10 the 19 um giga electron volts which you know the LHC only has 10 the 4 gig electron volts so we're trying to do 10 the 15 on top of what the LHC at CERN is doing >> the the Large Hadron Collider for those who might not know. >> That's right. Yeah, the LHC is the large hydron collider. So, we need a we need a LHC the size of the galaxy in order to

20:34probe these limits. >> And so, the the the point being like this is very largely theoretical because as of yet there's been no way to experimentally play with these theories. >> Exactly. Yeah. Exactly. And it's been a really cool mathematics exercise on its own. If you were to just look at the mathematics, it's amazing. It's won Fields medals. Ed Whitten won the Fields Medal for his work in string theory. He's never going to win the physics Nobel Prize. >> Okay? Because to win the physics Nobel Prize, there has to be some experimental grounding for whatever thing you do. But the Fields Medal is pretty nice. [laughter] So, I don't think he's like too worried about it.

21:14>> He didn't win the Oscar, but he got the Golden Globe. Oh, no. >> Yeah. Yeah. Yeah. Exactly. Even though I mean well I think you're gonna piss off some mathematicians in the audience who are gonna bad analogy analogy do not come in my comments. Maybe like something closer is like he won finals MV MVP but not league MVP. >> Yeah. Yeah. That's maybe a better >> Yeah. Yeah. No one please stay out of my comments. [laughter] >> Yeah. But um so in string theory you know we had that original Fineman diagram which was looking at the interaction between particles. So in string theories what you can do is you can turn a Fineman diagram into something called a world sheet. Okay. So

21:56on the left we have that same Fineman diagram and on the right you have strings that are merging together and they're interacting in this world sheet which is like how they're going through spaceime and all these dimensions and then they come out. >> And that is the key here. the world sheet >> that world sheet is the key here because it turns out that that smooth merging of those 2D world sheets those 2D string world sheets because when they merge they have to obey um smoothness there can't be any kinks in this world sheet because that's unphysical and they also have to minimize the surface area in this

22:37>> world sheet in this [clears throat] in this you know space whatever they're moving in and that is topologically identical ical to the smooth merging of biological tubes in 3D space. >> Yes, >> that's the key insight. >> Okay. Yes. >> Okay. >> Yes. Yes. >> That is the key insight. And the equivalence is pretty stark. They they lay it out in their paper. >> You know, on the right hand side, they've got that fman diagram and and the world sheet. >> Yes. >> And on the left, what they're showing is if you've got these physical networks, they're going to start obeying the same physics of those world sheets. Right. That's the that's the key thing. The string cross-section is now our biological tube cross-section. >> The time evolution for strings, like how

23:19I go from past to future. For us, it's just, oh, I'm just like moving along the tube. >> Okay, so it's another spatial dimension. >> My 2D world sheet is my biological membrane now. And then the tension, the string tension, which is something that is used in string theory to figure out like you know how tense sort of the strings are in in a loose sense that becomes the metabolic cost of surface area. >> Yes. >> Oh, that's so that's really okay. I I I'm tracking because I I think what's what's what's fascinating is we've basically there's been a mathematical structure that has been very millions and millions of dollars has been spent on Yes. in string theory to

24:00really hone in on this mathematical framework and language to communicate a conceptual idea. >> Yes. that when we look at these physical manifolds in like biology, >> that math that we've spent millions of dollars on almost is a perfect and you can see from these images almost a perfect map onto our observed >> biological structures that effectively take the same shape to respond to a similar math framework. >> Exactly. Exactly. That's the idea. And what they're doing is they're they're trying to basically do the math of string theory which is this Namboo Goto action. It's an action that measures the area of the world sheet, which is the total cost, right? And the equation is

24:41insane. I think I've got a little photo of it. The the equation is it's it's minimizing this integral over all of your possibilities, right? And it's making sure this mathematics is making sure that your stuff is smooth. And it's making sure that if you minimize this giant integral that the surface area of this whole thing is going to be minimal. Okay. The point is this math looks insane. >> Looks like man, >> but >> we've worked on it. >> Yeah. >> Because all of these physicists >> have been working on string theory. So, we've worked on it. So, we don't need to start from scratch. We can just take all of these results and apply it here. Right. And it bakes in the smooth it

25:22bakes in the smoothness >> and all of these mathematical tool kicks toolkits that have been developed can now just be applied directly to the biological problem. So the the point being the biologists don't have to spend years sort of waiting through the dark of discovery around you know finding the four corners of the sandbox on the mathematics. It's a Lego block that already exists. They can just be like oh here are these glasses. Let's look at it over. Oh my god I can see. >> Yeah. >> Yeah. Yeah. It's pretty cool. It's pretty cool. So now let's see how it actually works with biology. Okay. So let's first tackle the bifurcation versus triurcation which is the idea of

26:04a single path going to two according to the old paradigm of Steiner trees. But now what we see is single paths going to three. Why would the new paradigm now that we've borrowed this math from string theory allow triurcations and make that totally chill? >> Yes. >> Okay. The reason for that is because the surface area is what matters, >> right? not the length. Not the length. Right? So the wiring economy of minimum length only matters if your width of the world sheet is really small. And basically if if the thickness of your world sheet the string like the radius of your string is really small

26:45then it becomes a line. >> Yes. >> And then you only have >> bifurcation. There's there's not enough complexity to need bifurcation. But the thicker we make the biological network, the more we might need triurcations. And that's what you see in this paper. That's what they figured out. As we go from from thinner manifolds to thicker manifolds, at some point the the two forks in the road merge to become these >> three forks in the road. >> Road be because as you get thicker, your surface area is increasing over every length of distance.

27:25>> Yeah. >> And so subsequently it's becoming inefficient uh even if you're at the same distance. >> Yes. Exactly. And they actually introduce an order parameter. They call it kai and that controls whether you're going to go for two or three. Okay. If it's really thick compared to the separation distance, that's the thing that matters. >> Ah, yeah. You see? Yeah, that makes sense. >> If it's like if it's really far away, then I could do these Steiner tree approximations. But if my branching is really close compared to my thickness, then I'm going to need to >> do these higher order >> Yes. >> bcations. >> That that makes sense. >> You know what I mean? And that's what this is showing. And you can actually see the prevalence in the biological data. So they got data from the human

28:06conneto project. They got data from fly neurons. They got data from tropical trees. They got data from blood vessels. They got data from coral. It's just a massive amount of data. And what you can see is 15% of the time you do have these triifications. And it depends on that order parameter that I was telling you about. How thin is it versus how thick is it? like how far away is the is the branching versus how thick it is and they can immediately map the biological data and show that the Steiner tree algorithm is wrong. But this new heristic that they have using the string tree using the string three using the string theory mathematics >> yes

28:46>> that's what's closer to the biological data. So the idea is if you have two nodes at some distance apart, the old Steiner model worked if the distance between those two nodes where the branching was happening was >> really far away, >> was really far away. But as those nodes close in distance, um the the the thickness of the the line between those two nodes or or the path and branch between those two nodes as it gets thicker and as it gets closer. >> Again, the the the optimal the the optimization algorithm flips from um optimizing for two bifurcations to now we need we need to start introducing three. >> We need to offload more.

29:27>> Yeah. and keep it's almost like the thicker you are, the shorter you need the distances, the more bifurcate, the more uh out outlets you need for optimal for optimizing for uh efficiency. >> Exactly. And what's really cool to me is that across all of these different bi biological networks, right, we've got neurons, we've got trees, we've got fungi, we've got coral, it's all showing the same strategy. >> There there is an under again going back to what we always talk about there. There's an there is something that is true across all these different types >> that they all are speaking the same mathematical language that the string theorists have been trying to get everyone else to think about for so long. >> And it and it kind of makes sense

30:08because like even though these guys are so evolutionarily distinct, right? You've got plants, fungi, coral, and and the human brain. Even though evolutionarily we're divided by millions, hundreds of millions of years,

Why branches like 120° (Steiner trees, soap films, and energy minimization)

30:23the fundamental resource is surface area, right? The fundamental resource is how how can I build this? And so I still want to optimize minimum surface area because I don't want the building cost to go up. >> Yes. >> Right. This the second thing that I was telling you about the Steiner tree is all of the all of the nodes happen at 120° but in normal biological networks it should hap sometimes it happens at 90°. Why is that happening? Well, if the thing that's coming out of my tube >> is really thin. >> Again, it's that same order parameter. If the thing that's coming out of my tube is really thin, then it's actually preferable for me to be 90 degrees away from the tube. On the other hand, if the

31:04branching is like thick thick thick, then I get to the 120 degrees. And that's what they're showing. They're showing that with these neurons, what what you can see is >> if you've got these like tiny little um synapses that come out of, let's say, an axon or a dendrite spine, right? >> Those those tiny little synapses are really thin compared to the thickness of the dendrite. And so what I really want to do in that situation is come out at 90°. I don't want to go at 120 >> because that's the thing that minimizes surface area. >> It's it's funny if you think about suburban neighborhoods >> and or the design like the uh the design

31:46of the road networks. >> Yeah. >> You know, this looks like a culde-sac in a subdivision. >> It kind of does. >> You know, because you have the your main Yeah. two-lane road, then you have these offshoots that are narrower >> right off the main road at 90° angles. >> Yeah. [laughter] >> So, we've also mimicked that obviously and for, you know, Yeah. It's it's the whole image of you see people with traffic going really fast in cities and then people like map the like blood vessels and there's the similarities in these structures. >> Yeah. I mean, their structure >> all the way down, you know, all the way down. And so, I just thought this this was really cool. Um, in terms of the computational methodology, I thought the way that they did it was very cool. This is the minurf netwa. It was implemented

32:28in Mathematica. This is the new code that they use to with finite element methods to solve for surface variation problems. They actually have it available on GitHub. So, you can use it. Yes. And what what they really did was they inputed the connectivity of real maps and then they said okay what is they asked the algorithm to generate the optimal surface geometry. So it makes up a bunch of meshes >> of polygonal meshes and then tries to figure out what the optimal surface geometry is such that it minimizes surface area and they could reproduce what the biological networks were doing. So there's sort of this generative model that's based on mathematics but it is it

33:09is not it is generating net new yeah >> structures >> and those net new structures that were generated based on this underlying mathematical framework when then held up next to our observed biological uh uh comparison point >> the statistics match >> it's just it's it's like it's very nice >> so I just thought this was a very cool paper right and it's it's kind of a unification of biological physics across so many different types of organisms, fungi plants vessels brains coral all of them have the same principle, right? The structure is shaped by a universal physical constraint of minimizing the amount of membrane that I

33:49need to use to create a network. >> And because it kind of makes sense, too, because if you think, you know, um the larger your surface area, the more energy you need to make it >> to make the things and sustain the things. So it's like again if you're trying to minimize that. >> Yeah. Yeah. And it's not not just the energy in making the thing, it's the material cost. You're using more carbon atoms, right? You're using more hydrogen atoms which you could use for something else, right? So you want to optimize at all times. Um there are a bunch of really cool applications that I just want to go over. One of them is 3D bioprinting. So what you can do is design scaffolds using this algorithm to create physically correct microvasculare. If you want to create an artificial

34:30tissue and you want to improve blood flow in medicine. Yes. Right. >> So now we now basically have a pathway to 3D print things that can be integrated into our system that would map structurally >> more onto the way our biological system is is exists to to optimize for the potential for the body rejecting it and all these other >> Exactly. and and the body not having to work harder than it should to push blood through this artificial system, right? Um, another one could be hardware design like if you want to use these surface optimization principles to make 3D chip architectures, right? Like neuromorphic chips, you can you can now use these

35:12principles to do that. Yes. >> Right. And finally, what I want to touch on is like kind of the philosophy of science, right? There's string theory has gotten a lot of heat and it shows in this XKCD comic string theory summarized the string theorist said I just had an awesome idea suppose all matter and energy is made of tiny vibrating strings and his friend says okay what would that imply and the string theorist says [laughter] I don't know >> it's string theory has been the butt of so many jokes in physics and mathematics in the idea of funding fundamental science and I think that's quite dangerous. Now there there's stuff to be said about perhaps the scale at which we

35:54funded string theory versus something else. But the idea of funding something like string theory should not be totally looked down upon just because oh it doesn't have any real world applications because at the end of the day if we go into fundamental research >> yes

What this means (and what it doesn’t) for string theory

36:11>> just being curious about how stuff works maybe it works this way what would the mathematics look like you don't know where it's going to end up >> 100% you know and this is a perfect example of the importance of curiosity >> just just curiositydriven research >> in frontier research Again, this is the difference between research that's being done in the corporate environment and you know in business architectures which are well funded >> but do not have the incentive to explore areas of research that don't immediately have a profit motive. >> Exactly. And there are so many things that are beneficial to mankind and all of us in our health, in our

36:51socialization, all these things that don't inherently have the profit motive but are still valuable. Again, if you're starting to create artificial hearts and heart transplant stuff or or skin grafting and replacement therapies >> based on the fact that string theorist spent all this time and money figuring out this structure and now it's going to save people and extend your life. >> That's pretty cool. That's pretty good. >> That's that's that's pretty good. And this is this is again why we always like to talk about the methods and the concepts behind things even if it's not an immediately applicable >> like finding because this is what we're going to end up utilizing as we get

37:31older, as our children get older in the world around us. It's not going away. It's only accelerating. And it's a great way to start episode 22 with with a little bit of balanced views on string theory. >> Yeah. >> You know, we have our not we >> It's still funny. [laughter] >> The the the comic is still funny. I'm going to laugh, you know.

The Rundown begins

37:54>> Uh this is this is so good. And I could there's so many things about that I could dive dive into, but we are moving on to the rundown for the week after our first deep dive >> on string theory. Again, we are sort of looking to see what else will come from that. But here's the thing that's interesting. You have four universities >> Mhm. >> involved in this story. >> Mhm. Um it takes a village >> to do these things. This is not some one person in a lab by themselves. No >> it really requires cross institutional

38:35and crossf functional work to do so.

Iron asteroids — why metal bodies can be tougher than we model

38:38>> But that being said we will move into the rundown. Uh it seems like the audience likes a little quick rapid fire. So again the concept of the rundown we can't cover all frontier science research in the level of detail we do. We would be here until the end of time. But it is still important to know briefly what's going on in the world around us. And we're going to start with our first round rundown story from January 8th. The hidden strength of iron asteroids. This is a planetary defense story. The obl of space. Why iron asteroids are harder to deflect. The by line on the story is a groundbreaking simulation reveals that ironrich asteroids possess a hidden stren

39:19strength that makes them more resilient to impacts. than previously theorized. This is out of the University of Oxford and CERN. And the implications here are using CERN's high map facility, physicists irdiated meteorite samples with high energy proton beams to see how they would deform under stress. Yeah. >> They discovered that there was this dampening, meaning that the faster the asteroid is hit, the better its internal structure dissipates energy, which is a little counterintuitive. So this just suggests that kinetic impactors like the idea of NASA's dart mission which I believe was to like intercept shoot something intercept an asteroid on on its way in.

40:00>> Yeah. So intercept an asteroid and change its trajectory. >> Yes. Yes. The idea is um that it would remain intact because it would be able to uh dissipate the the asteroid would be able to dissipate the energy on impact. >> Exactly. we wouldn't have like an Armageddon situation um where the asteroid would break up into a bunch of chunks and then now instead of worrying about one thing, we're worrying about nine things [laughter] that are coming in. Um

ReForm — a synthetic pathway that turns electrochemical CO₂/formate into acetyl-CoA

40:27basically the idea is like as you hit the asteroid if it's full of iron, it's going to like ring, >> you know, and and sort of dissipate its energy internally, maybe heat up a little bit, but it's not going to totally >> break apart. >> Yes. The the context analogy for this one is it's similar to hitting a pool of obl, which is cornstarch and water. If you poke it slowly, >> yeah, >> it's soft, but if you punch it hard, turns into a solid wall. And these asteroids essentially have this hardening response to an impact, making them tougher to move. So, if you're in planetary defense, we can't just bomb and shoot our problems away. Yeah. Uh, >> you got to get a little bit clever, but

41:08>> the asteroid could do some of the work for you. >> Uh, Armageddon was a great movie. >> It was a good movie. I like that movie. >> There was uh one of uh Ben Affleck's greatest works was the DVD commentary on uh >> I have not watched Armageddon because if you remember, it was like a bunch of guys who work on an oil rig came into NASA and they were like, "We have no one else on the planet who can do this job other than a bunch of guys from the oil rig rig." which Ben Affleck was making fun of in his DVD commentary. He's like, why would NASA not know how to drill, >> right? Oh, yeah. It's like, why [laughter] couldn't we just train astronauts >> to do oil rigs? Maybe that's easier than training oil riggers to be astronauts.

41:49>> Yeah. >> And he was h having a fun time taking the piss. Our story number two, reform an artificial metabolism for carbon. Uh this is a synthetic biology story. Uh January 5th, 2026 headline, "Synthetic biologists transform waste CO2 into the building blocks of life. Scientists have created an entirely new artificial metabolism that bypasses natural evolution to turn waste carbon dioxide into valuable chemicals. And this is out of Stanford University and Northwestern University led by Michael Jwitt." Yeah,

42:31this one's a pretty interesting one. It's using like this cellfree system. So, they remove all of the enzymes from a cell. They put it inside a test tube and then what you can do is screen thousands, I think 3,000 enzyme variants to create what they call the reform pathway. And what it's doing is it's a synthetic system. So, it's outside of a living cell. And it's taking formate, which is a chemical compound that's derived from CO2, and converting that into acetal co-enzyme A, which is a universal molecule found in all living cells. It's part of our metabolism. Mitochondria love it. So on and so forth. And it provides a path for sustainable carbon neutral manufacturing because if we want to take CO2 out of

43:11the atmosphere, we got to get clever. Maybe this is one of the ways that we do it. >> Mhm. The for those who were lost in the

Helper T cells from stem cells — a manufacturing unlock for cell therapies

43:18explanation, the analogy is imagine opening the hood of a car and using the engine to power a factory. Instead of just driving the car, researchers are using the machinery of life in a test tube to build sustainable products out of thin air. Again, that's out of Stanford University and Northwestern University. Story number three is a stem cell research story culturing the immune systems conductors. This is January 8th, 2026. Headline: UBC scientists solve the mystery of growing helper tea cells. Now, if you've watched this show in the past, you know we've talked about the

43:59immune system a lot. So, in our past episodes, we do cover this. But the log line is for the first time researchers have reliably produced helper tea cells from stem cells a major hurdle in creating offtheshelf living drugs. And this is out of the University of British Columbia led by Peter Zanstra and Megan Levings. Yeah, this one's interesting because, you know, we've been able to grow killer tea cells, which are, you know, the soldiers of our immune system, but the helper tea cells, which are kind of like the generals or like the kernels of the immune system, they're the ones that conduct all of the responses. They

44:40activate B cells to make antibodies. They trigger the macrofasages, which are the eating immune cells that'll like eat up bacteria and bad things. They conduct all of this stuff by releasing cytoines and all of these other important factors, but they're really hard to grow in a lab. And these guys precisely tuned a development signal called Notch. >> And they were able to direct stem cells to become either type, >> right? >> Either killer or helper, right? And now this allows for large scale manufacturing. Again, personalized drugs. You could take the stem cells from a patient and then make

45:21designed helper tea cells >> that are specific to like that specific patient. >> Yeah. >> Um >> it could be much more affordable. You know, you can customize patient specific therapies. It's very cool. >> So to the analogy that you brought up, we've already learned how to train soldiers to fight cancer, but we uh didn't know how to recruit the conductors who lead the orchestra on the battlefield. So this discovery finally gives us the full leadership team needed for a strong lasting immune response. Our last rundown story of the day as the UFO guy. You know we always have to some point talk about exoplanet exploration

46:04unmasking alien atmospheres. This is on January 12th. a conversation. NASA's Pandora Telescope will study stars in detail to learn about exoplanets orbiting them. Now, I'll just briefly note this is the not the last time we're going to be talking about uh exoplanet exploration. That's right. We're not going to leak any classified information at this moment in time. But for this story log line as the first mission of NASA's astrophysics pioneers program, the Pandora small sat will provide the first ever high precision tool for disentangling stellar noise from the actual atmospheric signal of distant

46:45worlds. And we did talk about this in the episode where we covered the greatest evidence yet. >> Yeah. K218b >> of life on an exoplanet. Uh, but this story is out of the University of Arizona led by Daniel Apay, NASA Gddard Space Flight Center and Ames Research Center and the Lawrence Liverour National Lab and Cornell University. Yeah, this one's a cool one because it's quite a small um satellite. The lens is only like 17 in, which is pretty small compared to like Hubble or James Web,

Pandora — NASA’s exoplanet atmosphere mission

47:19but it's very specific. What it's trying to do is basically stare at the stars that have transiting planets. And what they want to do is disentangle when we see a planet going in front of the star and the sort of light coming from the star going down versus if the star has a sunspot >> and the sunspot has come this way and now all of a sudden I'm just seeing less light because part of the star is dark. >> Right. >> Right. It's very hard to disentangle the two. But if you just like stare at something for a long enough time, you'll have an averaged out version of what that star should look like and then you'll have a much better idea of what the planet looks like when it goes in

48:01front of the star. >> Yes. >> You can disentangle those two aspects. >> Yes. >> It's only in charge of 20 planetary systems. It's got a year's worth of Oh, really? >> Yeah. It's so it's it's kind of like a pilot project in some sense but not really because you know even 20 getting that understanding for just 20 at that precision is going to be vital for interpreting data from larger missions like the James web space telescope. You can use that understanding for how stars work and then now start applying it to when the James Web looks at some something right? >> Mhm. Mhm. This goes back to our point we

Story 2 begins — jellyfish sleep and DNA repair

48:34always talk about which is all of these discoveries and these movements are cumulative. They're not happening in a vacuum. So the immediate thing I just thought of was you know Vera Rubin and the massive amount of data that's going to be there uh in combination with JWST and some of the instruments that are looking at exoplanet atmospheres like there are data sets that have answers >> that we already have in our possession. Yeah. >> Some are proprietary some are open source whatever >> but that >> once we find and unlock >> all of a sudden makes these data sets incredibly valuable. >> Uh and this is sort of maybe a scout type of mission. Yeah. to be able to create some of that better understanding to get rid of the baseline of the planet

49:15so we can better read. And so that's again the exoplanet stuff I'm always such a huge fan of. >> We are going to come back into our main story number two of three and this one is going to be a an interesting combination. It's neuroscience, >> sleep and marine biology. >> Yes. Uh this is in nature communications published on January 7th. Why do jellyfish need sleep? The ancient origins of our nightly rest. This is from Bar Ilhan University. And the hook here is imagine a creature without a brain.

49:55>> Yep. >> That sleeps just like you. And scientists have discovered that jellyfish do exactly that. >> Do exactly that. >> They have no brain, but they sleep like us. >> Yes. Um, it's a core paradox in biology to be honest. It's like why do we sleep, >> right? >> It's a very simple question, but if you think about it from an evolutionary point of view, it seems naively really dumb to do. [laughter] Okay? Because sleep is a state there's behavioral vulnerability. You're not you're not like aware of stuff. Something could come and kill you. Um, you're vulnerable to predation. It ceases foraging. You're not getting food. And also, you're not having sex,

50:36>> so you're not really procreating. Yes. Right. So, what's the point? And yet, it's conserved across 600 million years of evolution. >> We have whales doing it, humans do it, and now we've got jellyfish do it, right? And the traditional view was it's a neuroscentric view, meaning it has to do with complicated neuro mechanisms, right? this idea of this synaptic homeostasis hypothesis, which is the idea that over the day your neurons are constantly learning and your synapses are growing. And if you just let it just keep growing and growing and growing, then you're going to get this runaway interaction kind of like last episode we were talking about in deepseek, right? If you have these matrices that just

51:18start blowing up, then you're going to get a very chaotic system. So, you need some way to constrain them. And maybe sleep was one way to constrain our synapses such that the signals don't propagate in a chaotic way. But now we've got this new finding which shows that sleep is actually a fundamental cellular requirement because of DNA repair and it's happening even in these brainless basil metazzoans. So in this case jellyfish but they also did the experiments on sea anemone and they found very similar results. Okay. So let's get into some of the historical context [snorts] for sleep research. Pre-1980s there was this guy Hans Berger in 1929. He invents the EEG which is the

52:00electroinsphlogram. And what he showed was that people that are see sleeping they've got these cortical oscillations alpha waves and other types of brain waves. And he said okay sleep is basically the state when we get that >> right. >> All right. Now that's fine, but that totally excludes invertebrates because invertebrates don't I can't do an EEG on an invertebrate, right? I need some kind of centralized brain. So I can do this on mammals, but are you saying that like you know lower order organisms don't sleep? So in the 1980s there was this neuroscientist Irene Toblar at the University of Zurich. She came up with the behavioral

52:42criterion. Okay. She showed that actually insect sleep, scorpion sleep and there were three def three things that she wanted to classify something as sleep. She was a pioneer in her field. The three things are the following. First, reversible immobility. That makes sense, right? When we sleep, we're stillish, but somebody can wake us up. So, that's the reversible part. >> Okay. >> Okay. The second one is increased arousal threshold. If I poke you when you're awake, >> you can sense it. But if I poke you when you're asleep, you probably can't. I need to poke you really hard in order to wake you up. >> Okay? Like I need to like slap you >> and then you're going to wake up.

53:22>> The the amount of effort needed to uh >> elicit a behavioral response >> is higher >> is higher when you sleep. Okay. And the third thing is homeostatic regulation. This means that if I deprive you of this sleep, then the next time you sleep, you're going to sleep longer. >> We've all had this, right? Like if you're if you've had a night out, the next day you're quote hung over, but what you're really doing is homeostatic regulation on top of the fact that you don't have any electrolytes, but you know, you're sleeping way longer than you should be. >> Yes. >> So, those are the three sort of behavioral definitions that I need in order to characterize sleep. >> I don't think I've ever thought about how do you define sleep before in my

54:03life. Like, but that's, >> you know, it becomes obvious when you talk about humans. You can just ask them like, did you sleep with mammals? Again, you could hook up a EEG, but now when we're getting into something like jellyfish, >> it's like how do we define sleep for an organism that's just doing this the whole time, >> right? >> Yes. >> So, but these three criteria actually we can translate. >> Okay. So, the model organisms in this paper are Nigerians, jellyfish, and sea anemone. Okay. It's a paper that came out of nature communications and these organisms actually diverged from the bilaterals which are us the the you know bilateral meaning to the symmetric kind of thing

54:44instead of the round symmetry. >> Yes, >> they diverged from us 600 million years ago. >> Okay, >> it's even thought that maybe the nervous system of these Nigerians >> is completely separately evolved from us. There's a little bit of debate on that because if you look at the neurons of these guys, the genes that they express are very similar to the neurons the genes that our neurons express. So perhaps there's a little bit of independence, but also at the same time a little bit of the same >> genes going forward. But what's very cool is these guys don't have a brain. They have nerve nets. There's no centralized repository. >> Okay? It's a bunch of nerves that are just in a net together and they're

55:26making decisions together and it's very unclear how they're doing it. [laughter] Okay, but in any case, very different from our centralized nervous system that we have with a central hub and it branching out to create motor functions. There's a decision-m that happens in our brain and then the rest of the neurons carry it out. Here we've got a very decentralized decision-m process. Is this the idea of like there's like no executive function in these uh like because there's not very related >> there's not like a there's not an authority who decides and then everyone else has to follow a better way to put it. >> Okay. Very very much so. So the the challenge now becomes okay we've got these two organisms that we want to

56:08study. Let's try to define sleep for them right using those three criteria. So what they did was just make videos of these organisms in their natural not natural habitat but in lab conditions. They used infrared light because if you use normal light then you're just messing with their rhythm, right? It's like you you still need a night and day cycle. So instead you use high resolution infrared light. So this is infrared light. And here you see the jellyfish that are pulsating, right? This is Cassiopia Andromeda, the jellyfish. And what they could do is analyze these videos and figure out basically classify two behavioral

56:48states. >> Okay, >> based on those three rules that we had about what sleep is, they did the same thing for the starlet anemone. That's the neat neatella vectennis, right? These are they they're like hydra looking things. This is a 20 minute time lapse. They're much slower moving. So, it's actually harder to classify immobility. But what you could do is actually classify the no even even when they're awake, they have a little bit of jitter. And they could they could really hone in on that jitter and classify >> rest just 5 minutes of rest versus actual sleep. >> Sleep. Okay. >> Okay. And and that's that's that that's what was really cool about how they did

57:28this, right? They they actually figured out a way to move beyond just these arbitrary dimensions and used um threshold-based optimization algorithms that were custom made for this use case. Right? >> Yes. Yes. >> And it's a rigorous definition. So they come up came up with these rigorous definitions. And now let's go ahead and try to analyze the sleep. When you analyze the sleep, one thing that's really cool is the um Cassopia Andromeda, which is the dellifish. Yes. >> Yeah. That's the one that actually it so in the light phase it was highly active. So during the day it's very active. That's the high on the y-axis. And then during dark times it was very inactive. But what's very cool is during the day

58:08right in the middle it takes a siesta. >> It takes a midday nap. >> They're very European. I see. >> Yeah. Yeah. These jellyfish are Spanish. In fact. >> In fact. [laughter]

Sleep pressure, behavior, and what “rest” is doing biologically

58:19>> Yeah. But so these jellyfish just like us, we like taking naps. These jellyfish like taking naps. The sea anemone on the other hand, they were actually more active during dusk and dawn and then they slept mainly during morning and that could have something to do with the fact that like their feeding has to do with tides, right? Because the water is moving in and out. So perhaps you want to be more active during the dusk and dawn and then you can be less active when the sun is high in the sky and when the sun is low in the sky, >> which kind of maps onto like nocturnal versus non- nocturnal. >> Yeah. And speaking of nocturnal versus non- nocturnal, they also had the melatonin >> that was affecting them. So melatonin, you know, that's the very famous compound that we can take in order to

59:00induce sleep. Yes. They tested it with 100 microar melatonin and the melatonin promoted both in promoted sleep in both the dal jellyfish and these anemone. So, you're saying that someone's gonna make billions of dollars selling melatonin 10 milligram 100 mill whatever packages to to see like Nemo? Yeah. >> Nemo, tell your dad we're we're coming with the melatonin. >> Yeah. Exactly. [laughter] Yeah. There's a huge market there, dude. >> Yeah. So, the now we've established that they sleep and the sleep is like pretty close to how we do it. >> We saw it both in a natural context and by trying to induce it. >> Yes. Exactly. So now the big question is

59:41why are these guys sleeping so much? >> Mhm. >> Okay. If >> if they have no brains, >> if they've got no brains, >> the way we the way we conceptualize >> Yeah. the way we conceptualize brains, they don't have these like synaptic connections that they need to regulate up and down. So why are they sleeping so much? And this is the key significant contribution of this paper. Okay, they're tying the sleep not to neural activity but to cellular activity and the idea that at the molecular level all cells need to recover. >> Oh, >> okay. Because at the molecular level, what's happening with activity is activity means high entropy, right? There's high metabolic flux. The

1:00:22mitochondria are going crazy. They're creating ATP. Now, that's going to create a lot of reactive oxygen oxygen species. Okay? And these reactive oxygen species are going to cause damage to the DNA backbone. If you remember from our DNA episode, the DNA has this phosphate backbone, right? The reactive oxygen species that come as a byproduct of mitochondrial metabolism is going to start attacking this DNA backbone. And if you ever get something like >> a doublestranded break, that's really bad. >> Okay. [clears throat] And what they could actually quantify is during wakefulness the double stranded break was happening more often. Uh so literally the the the the process of

1:01:05living of being awake and active is not only impacting your body in the physical sense that we think about it today. But the reason that our body needs sleep and rest is because at the gen at the DNA >> at the DNA level it's causing damage. >> It's causing damage and and that's that's because again I always thought like from my layman's perspective like oh we sleep cuz your brain is tired. >> Yes. >> Like that was the that was the idea. It's like you need to give your brain a rest. You're going to go crazy. >> Yeah. But really what this is identifying is that the the destructive property that happens from lack of sleep is a degradation of your of underlying

1:01:47DNA. >> Yeah. Yeah. And for higher order organisms it could be that the brain is also tired and the synaptic homeostasis model works right and that could be a thing. >> Yes. >> And it most definitely is. There's a lot of papers that do that. But this is showing that even at 600 million years back we needed sleep because fundamentally the act of living as you said degrades the molecular structure of our genetics. >> Mhm. >> Right. And what they could do is show they could actually tag um phosphorolated histone and this phosphorilation happens at doublestranded breaks and they could track that actually the longer you're awake the more double stranded breaks you're having. Okay. And if you kept these organisms awake, the breaks would

1:02:29actually build up. The craziest thing to me was they could actually they could actually use that damage to drive sleep. So, you know that homeostatic regulation that I was telling you about, like, okay, if I if I deprive these animals of sleep, then they want to sleep longer. Well, I could actually artificially >> make double stranded breaks happen, you know, expose them to UV radiation. Now, I have a bunch of DNA damage. the animals would sleep more because of more UV radiation because there was more doublestranded breaks. So, it's a direct causation now. It's not just a correlation. You've established a direct causation between DNA damage and more

1:03:09sleep. >> That's fascinating. >> Very cool. >> They they they artificially triggered >> DSBs, double stranded breaks. >> Yeah. >> After having already monitored natural behavior. >> Yep. And it triggered an increase in the amount of sleep in both the jellyfish and the ceneemy when they were exposed to uh an external factor that one of the primary things it did was create a larger volume of double stranded breaks or degradation of the molecular DNA stuff. >> And they didn't comment on this, but I've always felt that like if I'm out in the sun for really long, >> I need to I need a nap. >> I need a nap. I don't know if they're I'm not saying that the authors I'm just

1:03:51saying it's like kind of >> I don't know. >> Look, it is Southern California, so we do know what it's like to be out in the sun a lot. >> I feel like when I get sunburned and I do get sunburned, okay? Like even though like I'm brown, I do get sunburned. And then when I do, it's like I do like I end up napping for like two hours and it's the best nap. >> It's the best. It's usually the best nap. It's It's usually the best. You know, maybe that's like, you know, the DNA repair being like, finally, we get to like fix all this nonsense that he's been up to. >> This this this is good. And again, this is like again a story at the intersection of spaces I didn't expect to to sort of see, right? You you have it was like I as a DNA damage, okay, it's a neuroscience story. Oh, because

1:04:32we thought that sleep was about the brain only. Again, we we've caveed this is not saying that uh sleep is not involved in neurological activity. It is just that we can see that it is not the only >> thing that it's involved in. >> Yeah. And it's like an invariant across animals that have neurons. And one of the cool things is when you think about like why would neurons specifically need sleep? >> Yeah. >> Right. Neurons are organisms that don't divide. Right. Neurons not organisms. Neurons are cells that are post mitoic. They

The core mechanism — damage signals and repair cycles

1:05:06don't do mitosis. They don't cell divide. So once you have them, you have them. So maintaining the cell state of a neuron is way more important than, let's say, skin cells. Skin cells, if you've got something that's bad, you shed it, you make new ones because those things are going through mitosis all the time. Muscle cells as well, they're replicating all the time. Neurons, on the other hand, you got what you got. And so you need to maintain it, right? which is why animals with neurons might require sleep because DNA damage in a neuron is way more important to fix than anything [clears throat] else. >> And so in order to protect your limited neurons, make sure you share the pod with a friend because what better way to stretch out and exercise those neurons

1:05:48than listening to the from first principles podcast. It's for your brain health. >> Yes. So if they ask you this podcast helps my mental health, my biological mental health >> by stretching and pulling and keeping flexy >> and then get some sleep right after because then that'll actually studies have shown that you know sleep actually consolidates that memory into long-term storage. So you might actually remember something that we're talking about. >> It's it's for your health. That's why you listen to the pod. Great story number two. We started with string theory. >> Yeah. Then we went into this sort of biology, neuroscience, sleep story, understanding a little bit more about sleep having a larger scope of value

1:06:32>> to to other sort of animals within the animal kingdom. >> And we're going to end this episode with a space story. >> Yes. >> Uh story number three is about dark matter. Yes. >> Dark Matters First Frontier and the Discovery of Cloud9. Not that Cloud9, a different Cloud N. Or is it? >> Or is it? I don't know. I don't know how it was named, honestly. We will get to that question, but astronomers may have found the first example of a primordial gas cloud trapped in a dark matter halo offering a rare glimpse into how the

1:07:13universe's first structures formed billions of years ago. This is coming in the Astrophysical Journal letters on January 7th from the Space Telescope Science Institute, the Universita Daglas Dudi de Milano Bioca, University of Victoria and another placement I think they might be third place in our stories of the University of Wisconsin. >> Wisconsin is doing well in our stories. >> They this they are performing very very highly. Um, so what makes Cloud9, the

Story 3 begins — Cloud9 and the “dark matter halo without stars” idea

1:07:50new celestial discovery, a cosmic gamecher for star formation theories? >> Yeah. So they're claiming, you know, it's been all over the news. They're claiming that this is a new type of object. Okay. It's a compact, gas-rich starless object. And it's a totally different thing in our cosmic zoo. You know, we got galaxies, we got stars, we got planets, and now perhaps a totally different animal altogether. >> Okay. >> Okay. That's very rare >> cuz we we've been staring at the night sky for a while. >> Right. Right. >> So, a new type of object is is quite interesting right? >> Yes. >> That that definition doesn't fit into

1:08:31another bucket. >> Yes. Yes. And what's really important about this new type of object is it's confirming a lot of theories about dark matter that we have. There were some holes in that theory that this is sort of filling and at the same time it's raising some new questions. Okay. >> As almost all new stuff does. Yes. Right. So let's start at the very beginning. We're going to start at the big bang and we're going to start with something called lambda CDM >> in the bening >> there. There it is. Lambda CDM is cold dark matter. That's the prevailing cosmological model that we have for all the stuff that is in our universe. Not a lot of normal matter, a lot of dark matter. So I think 85% of stuff that is

1:09:12gravitationally influenced is dark matter. Only 15% is normal matter. And then we don't have to talk about dark energy which is a whole other thing. You know that's actually even more than dark matter, right? But let's not get into that. Let's just talk about stuff that is influenced by gravity. So in the very beginning there was the big bang. The big bang expanded into it didn't expand into space. Space itself expanded. Let me catch myself when I say that. Right. >> The comments would have gone crazy with that. >> Yeah. Yeah. So, 300,000 years after the Big Bang, we formed atoms. That's when we got our cosmic microwave background. And then we had our first stars, our first galaxies, our first black holes.

1:09:53And through all of this, dark matter was also expanding with all of the berionic matter. That's the ordinary matter, stuff of atoms, quarks, electrons, things like that. And at some point the first stars began collapsing. They started blowing up. The dark the black holes started creating a mess, right? They started creating ultraviolet radiation. And all of this time what was happening was this ultraviolet radiation was heating up the intergalactic medium to a temperature of approximately 10 the 4. So 10,000 Kelvin, 10,000° C. Okay,

1:10:35>> it's pretty cool. Pretty cool. >> Pretty cool. And this was happening around 400 million years after the Big Bang. This is called the reionization epoch because what's happening now is all of the hydrogen that's in our intergalactic medium is getting bathed in this UV radiation. Okay? And this whole time dark matter is just doing its thing. Okay? So all of the hydrogen, which is the berionic matter, is getting bathed in UV radiation. Meanwhile, the UV radiation is not at all interacting with the dark matter. That's the point of dark matter. It does not interact with light, hence it's dark. Okay? So, dark matter has been collapsing this whole time. And when dark matter collapses, it forms clumps. Okay? >> Okay. These are called halos. And the

1:11:17clumps happen at all different scales. If we look out at the dark matter halo function, the mass function that we see see today, that function is something that we call scale free. So instead of the size of the dark matter halos sort of collapsing as an exponential where there's some like fundamental scale constant of that exponential, this thing is a power law which means as we get to smaller and smaller sca like scales of halos, there should be more and more of these dark matter halos. So there should be a halo for the Milky Way that is as big as the Milky Way. And at the center of this dark matter halo is the our normal Milky Way. But then around this Milky Way, there should be a bunch of

1:11:58different tiny clumps. And around those tiny clumps, there should be even smaller clumps. And around those, so on and so forth. It's it's kind of this like self referential fractally system. >> It's like the Russian dolls. >> Yes. Exactly. But the smaller the Russian doll, the more there should be. That's key. >> Okay. >> Okay. >> Yes. Yeah. >> Okay. Interesting. Okay. So, now let's think about what would happen with these halos right? The bigger the halo, there should be galaxies inside, right? We don't actually see all that many halos. >> Mhm. >> That's kind of a problem. Okay. >> Yeah. >> Now, there could be a solution about why

1:12:39we don't see a bunch of small halos. Okay. And that has to do with the nature

The 21-cm hydrogen line, telescopes, and how Cloud9 was detected

1:12:43of these halos and the normal matter that's inside of these halos. So let's talk about something called hydrostatic equilibrium. Normally we think about hydrostatic equilibrium in the sense of why are stars stable? Like why isn't every star a black hole? >> Yeah. >> Okay. Because if gravity is just like pushing everything inside, >> then everything should just collapse into a black hole. Well, the reason is there's a bunch of fusion going on and that fusion creates an external pressure outward that is counteracting the gravitational pressure, right? And that's what keeps a star >> stable. >> Yes. >> Okay. Now let's think about dark matter halos. >> Okay. >> Okay. With dark matter halos, remember I told you that during reionization, this cosmic dawn is what they call it, when

1:13:25there [clears throat] was a bunch of ultraviolet radiation, that ultraviolet radiation is going to heat up >> normal matter. Okay? So that normal matter is inside of a gravitational well of dark matter. So it's getting pushed in because of gravity, but it's also hot. >> Yes. >> And the hot is going to push out. >> And so there itself is a kind of hydrostatic equilibrium. The hotness is not coming from fusion. It's just coming from being bathed by this early ultraviolet light. >> Yes. Got it. Got it. >> Understood? Yes. >> Okay. So, there we get into a very cool thing >> which is there's actually a size limit to how big a dark matter halo can get before it starts having stars inside of

1:14:08it. Because imagine a very large dark matter halo. Okay? It's got a bunch of matter inside. the very large dark matter halo is going to start collapsing. The matter inside is big enough that it's going to start collapsing and it's going to start creating galaxies. But if it's really small, >> yeah, >> the matter inside is just going to sort of evaporate out >> because the hotness is too big. >> It's sorry, the hotness is too hot. The the particles are moving too fast for the gravitation of that small tiny little dark matter halo to keep everything inside. So there there comes this critical mass threshold >> and they calculate it. the the paper actually quotes it to be around 10^ the 9.7 times the mass of the sun. The Milky

1:14:49Way for context is around 10 12. So this is much smaller than the Milky Way. But at that critical mass threshold, the dark matter halo is big enough where it's like keeping the stuff inside. Okay? Anything bigger and the stuff that's inside is going to coales to become a galaxy and become stars. anything smaller and it's not enough and the and the gas is just going to leave. But right at that threshold, we've got this sweet spot where >> the gas is going to retain inside of my halo, >> but it's not enough to create stars and galaxies. >> You see, there's a sweet spot. It it it

1:15:29is it is this it is this like buffer line >> where it can just contain a lot of this is why like I'm thinking about this is why the cloud metaphor becomes interesting here because it doesn't it doesn't it's at a threshold where it can't materialize ma like this physical large scale matter the way we think about it and it's just these gas particles all >> yeah just moving around >> hanging out >> hanging out yeah and and so this there was a term that was coined in 2017 in monthly notices it was called the relic which is I think it stands for reionization limited H1 clouds. H1 meaning neutral hydrogen. Okay, it was [snorts] a paper out in monthly notices and what they said was there should be these halos right at the critical mass,

1:16:12right? That should be massive enough to retain neutral gas, >> neutral hydrogen gas, but not massive enough to cool down and condense and form stars. >> Yes. Okay. Okay. Yes. >> And these are called relics. So, finding one of these relics would be awesome. >> I just want to quickly pause. Yeah. >> And say back engineering. I can't remember what the name for it was. Back engineering. The acronym from what you want it to sound like. >> Relics. I mean, it's CL. >> That's a good one, dude. Scientists do this all the time. So, relics is a really good one. Reionization limited H1 clouds. >> That's pretty good. >> Okay. So, finding these relics would be really awesome. >> Yes. But finding it is very hard because

1:16:53this thing has no stars. >> Right. Right. So how do you >> The whole point like how do we see something? We we see it using light. But this thing has no stars which >> and dark matter is invisible. >> Which is Yeah. Right. Which is what makes any kind of discovery around this subject of dark matter extremely difficult. Yes. >> Because our we are only as good as the instruments we build in order to understand the world around us. And we have not yet maybe >> figured out a lot of this but maybe >> maybe maybe and what comes to the rescue is the hydrogen 21cm line. >> Yes. >> Okay. This is um our favorite >> line 1420 MHz. This is the hyperfine transition. This was used in the Pioneer plaque to tell aliens what our standard

1:17:37time and our standard unit of measurement is for all of the, you know, diagrams that we have on the Pioneer plaque. In case the aliens found the Pioneer plaque and they wanted to see where Earth was. All of the standard measurement on that pioneer plaque is made using the hyperfine transition of hydrogen, which is effectively hydrogen has a proton and an electron. If they're spinning parallel to one another versus when they're spinning opposite to one another, that difference in energy corresponds to a particle of light, a photon that is exactly the wavelength 21 cm or around 1420 MHz, right? Um, sidebar, at Princeton Physics, one of my

1:18:19favorite classes was the undergraduate advanced physics lab where we actually used the hydrogen 21 cm line to map out the Milky Way. There was a radio telescope on the roof of the physics department that we could use to point it in different directions and we could point it >> at the galactic disc of the Milky Way and you could see the 21 cm and you could actually figure out what part of the galactic disc was moving away from us versus moving towards us by pointing

Why the dark matter interpretation is plausible (and the alternatives)

1:18:49it in different directions and that 1420 MHz would be a little bit redshifted or a little bit blues shifted depending on where you were looking and what you could do. I mean, there's a lot of error, right? Because you don't know exactly where you're pointing. It's kind of a, you know, old disc that they were like, "Okay, let let the undergrads, [laughter] you know, use it for their lab." Um, so you don't know where it's pointing and the frequency resolution is not that great. But you could actually like I remember calculating the rotation curve, which is depending on how far away I am from the galactic center, um, how fast is the stuff moving. And you could actually chart out the galactic rotation curve and you could see that the galactic rotation curve

1:19:31plateaued. Now, okay, this is not as good as the data that I got. The data that I got had massive error bars [laughter] and only like eight data points, but what you could see is that it would rise and then it would plateau. And this is characteristic of dark matter. Vera Rubin the astronomer actually used these galactic rotation curves to calculate dark matter because if you think about it the farther away you get from a galaxy center the slower >> yes >> you should be moving >> right >> because Newton's laws inverse square law but >> here the farther you get away >> you're still moving at the same rate the only way this is possible is if there's a bunch of invisible matter that's actually pulling on you >> that's creating this gravitational force

1:20:12>> that's the stain that creates that plate to sustained uh uh speed because otherwise like there's no other external factor that could be >> Yeah. because we don't see any stars. And yet you're telling me that there's a bunch of new mass that's pulling on you >> because you're farther and farther away and your speed has not changed. Yeah. >> Even though it should now be slower because you're farther away from the gravitational center. >> Exactly. >> So that gravitational force that must come from has come from something. >> And that's where sort of Okay. Yeah. Dark matter. It makes sense. There's a lot of other avenues of research that actually point to dark matter, but this is one of the really cool ways of showing. Yeah, it's got there's got to be something there that we're not seeing. >> And it's also rel like even for someone who's not as deep in this, it can be relatively intuitive.

1:20:53>> Yeah. Yeah. And it's something that an undergrad can figure out, you know, using um a shitty radio dish [laughter] on the roof of Jadwin Hall. Um that that was one of my favorite like two weeks was actually doing this experiment and then writing the report. Okay, so now let's get back to the story. How do we find these? Well, we look at the 121 cm line, right? And the fast radio telescope, which is the 500 meter aperture spherical telescope. In 2023, it found a region of space that looked kind of weird. This is the radio telescope. It's out in Guijou, China, just north of Vietnam. It looks kind of like Aerosibo, which was out in Puerto Rico. It got damaged by the hurricane

1:21:34and is no longer in operation. But now China has something that's even better. >> That pronunciation was a pretty pretty good. And where did you learn that from? >> I I looked it up. [laughter] I I looked it up earlier. >> Yeah. So, um, they've got this telescope, the fast radio telescope. In 2023, it spots this object. Okay. >> Yes. >> And the this object has a bunch of 21 cm 1420 hertz coming through. >> And from all of the previous surveys, there's nothing starry there. >> Right. >> Okay. Okay. So, it's it's a patch of sky where we're getting a bunch of >> hydrogen emission, but there's no real stars there. It's right next to another

1:22:14galaxy, the M94 group. And the recession velocity of this gas. So, if you were to look at the red shift of this gas, it's very similar to the recession velocity of 304 km/s >> of the galaxy. So, it's associated to that galaxy because they're both moving away from us at the same speed and they're both right next to each other. So they've got to be sort of tied together, right? Okay. So that's the first observational signature, right? >> The second observational signature comes from the VA, the very large array. >> Yes, >> this is very recent. I think it came out last year. It shows that the broadening of that Halpha, that hydrogen line

1:22:55is very close to what we would expect if that hydrogen was thermalized to that UV radiation that I was talking about in the reionization epoch. Because imagine, right, I've got a cloud of hydrogen. It's hot and the temperature is right about at 10 4 Kelvin, which is what we calculate the UV radiation to be back then. And if the temperature is around that, then there's going to be some gas particles that are moving away from us, some gas particle moving towards us. And the spread of that thermal radiation is going to be right around 12 km/s if you calculate what the thermal radiation should be. And that's exactly what we see. The VA looks at that thing and sees that the spread is about 12 km/s. >> Mhm.

1:23:35>> So again, okay, this is thermalized radiation from that reunization epoch. >> Okay, very good. >> Very good. >> Very good. Also, if you look at rotating dwarf galaxies, those dwarf galaxies have a thermal radiation of like 30 to 50 km/s, much faster than what we see. And this is where we get to the astrophysical journal letters paper that we're going to talk about. The main story is this paper that came out very recently, the first relic question mark. They're asking, >> is this Cloud9 that we've been looking at for the past 2 or 3 years? Is this finally the thing that we had hypothesized in 2017? And actually, some of the authors of this paper

1:24:16>> were the authors on that original paper that hypothesized the relic. So, this guy's been chasing after a relic for like 3 or 4 years now, and and he's finally trying to figure out, is this it? And what he's done is use the Hubble Space Telescope to point it at that thing and say, there are no stars here. >> You're right. >> Okay. >> Unequivocally, cuz before we have like surveys and they might have missed something, but here we've got a Hubble Space Telescope mission that is looking at this thing for a very long time. Okay, almost 5 hours total. So, it had two 9,000 second exposures at that spot in the sky, and

1:24:56it's looking at it with two different filters. And those filters are optimized so that it looks for red giant branch stars because if there's a dwarf galaxy in there, that dwarf galaxy is probably going to be old and metal poor. So, the brightest stars in there are going to be red giants. Okay? And the amount of exposure time that these guys used was enough to see stars that are much fainter than those red giants. So, we're not going to miss it. >> Right? >> That's the point. If there's any there, we're not going to miss it. >> The threshold was high enough that it would have captured it if it's like, "Oh, well, you didn't turn it." >> No, no, no. We stared at this thing for long enough. If there was something there, we would have seen it. And they only observed three sources that they detected in that region. And then they

1:25:37said, "Okay, is three a lot? Maybe those three stars are you know what I can can I say that is that a lot is that a little well this this is something that's very clever and something that people do in science all the time is they try to measure something and then they say what could I see just by chance >> what is my background okay so they looked and they measured the density of sources in blank regions of that image that are far away from cloud9 and what they found was the average background is about 3.7 for this for a similar region so even just by chance. >> Yes. >> The amount of stuff that I would see in something that's not there >> is about 3.7. So, the fact that I'm seeing three is kind of just a roll of the dice. >> You know, there's it's not out of

1:26:20>> it it it it it doesn't it's not out of

The big question: why no stars? (reionization limits + missing halos)

1:26:23the ordinary. The fact that there's three there is what all other background space looks like. It's about three things there. >> Yeah. Yeah. Exactly. So, if I were to just like look at a random patch in the sky, I'd see three things in that in that region. But if there's a dwarf galaxy there, I should see a hell of a lot more than just three. Yes. Right. They also simulated what it would look like if there was a galaxy there. And what they found was, you know, on the bottom you you see, okay, what if there's like this many? What if there's a dwarf galaxy that's this big? And they went all the way down to really small dwarf galaxy. >> Yes. >> And they said you would still be able to see something. Yes. >> Okay. If I do the simulation, I'd be able to see something. But I'm not. only

1:27:04>> we're seeing three in real life. >> If we simulate being very bright and very dim, >> we would see 25. >> Yeah, we'd see a lot more, >> but >> but we're seeing three. [laughter] >> So, it's like there is nothing there, guys. >> Yes. Yes. Yes. Yes. >> There's nothing there. And and a lot of this is it's trying to like remove it's trying to one provide a repeatable path to say you can look at the same patch of sky and you can do the same math calculations and it's not a red dwarf this that and the third because we've done it and you can repeat it and you would get to the same >> it'd be hard to get time on the Hubble Space Telescope again to look at the same patch of sky but I mean there's groundbased telescope and things like that so you could do it and actually one

1:27:45of the one of the future things that you could do is maybe use the James Web Space Telescope and look for that same spatch patch of sky and get into even deeper stellar mass limit, right? Maybe maybe we're we're not seeing like brown dwarfs or like really cool stars, right? >> Yes. >> That's something that the James Web telescope could do. Okay, so the next thing they did was derive the halo mass >> of this thing. And this is actually kind of cool because usually when we want to derive the halo mass, the dark matter halo that's in a galaxy, what we look at is stars. We say, "Okay, how fast is the star moving around?" based on that I can calculate how much mass is inside the stellar orbit and then from that then I can extrapolate

1:28:25and say okay if there's much mass in the inside the stellar orbit and the mass function of a of a galactic halo kind of falls off like this then I can integrate over that entire volume and then I could say okay the total mass of the of the halo is something like this here I've got no stars >> yeah so how do you >> so how do I what what am I going to do right so so what they did was they went back to that hydrostatic equilibrium analogy and they said, "Okay, if I've got a gas temperature that's 10 4 and I and I and I do it, I can calculate the mass and the mass is somewhere close to 5 * 10 9 solar masses, which is very close to the 10 9.7 that they were showing earlier. Okay, so they calculate

1:29:08this critical mass and it's right at that threshold, which is again very good because that's the whole point of the right of of that theory of like there's this critical mass. Now we've actually found this critical mass and it matches up. >> Yes. >> And just and just to clarify that the point being it's this critical mass where it's it is um >> not going to end up with stars forming because things collapse inwardly and it's not going to end up with dissipation because things are exiting because it's too hot. It's just balanced and we're observationally seeing something that is at that line where there should be that balance >> which and and that's inevitably like it kind of is all pointing to this could be the relic >> because it's in it's in the the the

1:29:49Goldilocks zone for for this dark matter halo concept with no matter inside but still able to maintain structure without dissipating or or expanding. >> Exactly. >> Okay. Exactly. >> Okay. >> And so now the question is how unique is this thing? >> Right. Right. Right. How unique because you're saying this is a new celestial object. That's a tall order. Okay. So here what they've plotted is on the x-axis the stellar mass of the thing. >> Yes. >> And then on the y-axis is the hydrogen mass. How much hydrogen does it have? >> Most of the stuff like the dwarf galaxies, normal galaxies are way to the right. >> The top to the right. They're they're very big with a lot of hydrogen. >> Yeah. Because the point is, if you've got a lot of hydrogen, then usually

1:30:31you're going to have a bunch of stars. >> Mhm. [clears throat] >> Okay. If you've got a lot of intergalactic hydrogen, usually you got a bunch of stars. Here you don't have a lot of stars, >> but you got a bunch of hydrogen. That's why it's all the way to the left. And there's nothing like it. It is. It is. And for people who are listening, and I we really do encourage you if you have the opportunity to to watch the show when you can or even segments because the visuals really illustrate it. Th this is like an order of magnitude >> separated. >> Yeah. No, it's actually two orders of magnitude because the the the the axes are on a log scale. >> Ah, okay. >> Yeah. >> It's it is like so far away from anything else to to the point of is it unique? And it's like, well, it doesn't look at the look at look

1:31:12>> look, dude. It's it's like there's nothing around it, >> right? This is a new type of object and it could be this relic that they've been trying to look for. >> Yes. Yes. >> So, future observations, what are you going to do? James Webb Space Telescope obviously try to find even cooler stars and really rule out that there's no stars there. Yes. Um the other one that I thought was really cool interesting was the deep Halpha imaging, right? This is the idea of really honing in on that hydrogen line because if there's a bunch of hydrogen there >> and it's fluoresing, right? Because it's still interacting with all of the radiation that's in the universe. Then on the skin of that cloud,

1:31:52>> you should be able to see Halpha emission that's more prominent than let's say on the interior. So you should see like a ring around that cloud if you really stare at it for long enough, right? And from that you could maybe figure out the substructure of that cloud and how that hydrogen is like >> moving around let's say or like really clumped together >> because you would map the outside and because you then have you know ideas of how motion and things move in space and then you can sort of project in in simulations. Yeah. >> How would you end up with this? Yeah. >> Based on some of the composition. >> Exactly. Based on all the stuff that's around and you can you can figure out the ionization rate of that hydrogen and the gas density profile. Um, the other cool thing is there's a new array coming

1:32:34up, the Square Kilometer array, okay? It's going to be in Australia and it's going to be in South Africa. It's set to come online in 2027. This is an artist rendition. They haven't made these yet. Okay. But it's going to be massive. There's going to be a bunch. It's going to be VA on steroids. >> This is like way bigger than VA. >> Yeah. Yeah. It's going to be the very large array, but very, very, very [laughter] large array. The square kilometer array. and it's expected to detect thousands of these relics. So, if we can really hone in on how to find a relic using this one that we found, then we could transform the study of these into a statistical science rather than it's a one-off deal. >> This kind of maps conceptually onto the

1:33:15idea of the our previous story where the string theorist created the framework that then was very easily able to be applied to biology in a new category with new data. This is same same. Yeah, this is not different. Different. Yeah. But the idea that past work is helping to lower the barrier. >> Yeah. And that's always something that science does, right? Past work is always good for >> the future. >> We we we love to see Compounding Valley. This this was this was great. >> I thought this was very cool. >> This was very cool. And we are going to have our call out for those who have

Wrap-up — what to remember from all three stories

1:33:44listened to what has been one of our most tightly run pods. Uh especially with our new season two setup. This was great for episode two. If you've listened this far and you've stayed, it's helpful for us to know. So, the comment of those for those who stayed is what do you think the reason is why this was named Cloud9 or any other Cloud9 related puns? >> Yeah, cuz I don't know why it was named Cloud9. >> And actually, one thing I do want to shout out is um friend of the pod, Daniel Gilman. I actually asked him about this story and he connected me with Julio who's one of the authors of this paper and we had a really quick

1:34:25email exchange where I had some questions about some of the stuff that was said on this. So, you know, thank you guys for actually indulging and like giving me a little bit of your time because I know you guys are busy trying to figure out what dark matter is and all this stuff. But very cool paper, Julio. >> Yes. Yes. and thank you for the work. And hopefully if you take a listen to it, we did it justice. >> Yes. And >> let us know. >> If not, always send over a correction for us. Which brings up >> our next section, which is going to be corrections from last episode. Yeah. These aren't that egregious. But, you know, as always, we try to stay true to who we are. So, if we do make mistakes, we do want to shout them out. Okay. First one, not a science mistake, more

1:35:07of a literature mistake. I said that I was quoting um Julius Caesar the famous Shakespeare play and I said that the friends Romans countrymen lend me your ears and also you know but Brutus was an honorable man and they are all honorable men. I said that was Augustus aka Octavius. No, that was a speech by Mark Anthony. >> Ah >> at Caesar's funeral. >> Yes. And it was it's an amazing speech because that's the speech that finally convinces all of Romans that Brutus is actually a bad guy and maybe killing Caesar was not a good thing. Um the the second, this is more of not really a correction. This is kind of an interesting tidbit. I was talking to my friend Alan Southworth who works in

1:35:48energy in California and he was actually telling me that energy or markets might be an exception to the square root law because energy markets don't really work the same way that commodities markets or stock markets or you know crypto markets work because >> the demand is like not elastic at all like the price and demand stuff is not elastic at all because imagine right let's say that like there's a demand for electricity at a certain rate. Like we're offering we're offering to buy electricity at a certain rate. At that rate, it might only be feasible for certain electricity producers to meet that demand because it could be,

1:36:29you know, wind or solar or things like that because turning on additional wind turbines is not really that hard. >> Right. Right. Right. On the other hand, turning on like nuclear or turning on natural gas, you got to buy a bunch of natural gas to turn something on, right? So the it's it's not so trivial to just be like, "Oh, you need this much amount of electricity. Let me just go turn on my, you know, gas burner." >> Yeah. Yeah. >> So there that square root law might not actually work. >> So it might not be truly universal. >> Universal. And and that's something that the Tokyo Stock Exchange might not actually account for, right? which is which is what that paper was. That paper was very specifically for the Tokyo

1:37:09Stock Exchange, >> right? >> Perhaps it's different for these other types of markets. >> Uh great great insight and note from Friend of the Pod. >> Yeah. And then the final one was I did say that the there was a Japanese satellite that took that photo of being in between the sun and the earth, taking that photo of the moon coming in between and crashing it. That's not actually true. That's from the Deep Space Climate Observatory, which is a NASA satellite. So, we want to give NASA its due credit. That was NASA, not the Japanese. >> The Americans. >> Yeah, we're doing it. >> Whenever something good, really good, or really bad is happening, a reasonable response is the Americans. [laughter] >> Yep. >> Um, >> so those are the three corrections.

1:37:49>> Three corrections. And And just as a quick reminder, uh, we love comments. We love seeing the back and forth and discussion. Keep commenting. Keep sharing. If you work in a lab, bring it into your next lab meeting. Just play them a snippet. people will love it, that you're gonna sound great and amazing. So, we appreciate the sharing. We're trying to fight the billionaire algorithms as best that we can. And we're trying to provide some mental health benefits to the people of the world by stretching out some of those neurons, getting them exercised. Your feeds are full of enough random nonsense. Uh so, those who tune in and listen to the full thing, we really appreciate you again. Great episode

1:38:29today. We started off string theory applying to biological networks. That was a nice crossover episode. It's like when Power Rangers went on whatever thingy you learn the Power Rangers from space hooked up with the Power Rangers from Mighty Morphin and >> um great first story. We did our rundown which had some fascinating tidbits. >> Jellyfish sleep even though they don't have a brain >> and it's because it damages your DNA. And we may have found a genuinely new celestial object in cloud9 more soon. We shall see. >> We shall see. >> Uh a great spectrum of institutions with papers in nature communications, nature

1:39:10and the astrophysical journals. I am your host Lester Nar joined as always by my co-host and our resident PhD who is offcenter in this broadcast. So, we'll make sure that he's more >> I'm sure I'm sure someone's going to say some >> Yeah. >> Oh, why he's leaning into the camera? Can't we get the camera angle centered? >> Yeah, you can't [laughter] do anything right. >> Uh, we really appreciate you all so much as always. This is from First

Closing

1:39:37Principles. [music]

1:39:52>> [music] [bell]