Dream Engineering, the Proton Radius Puzzle, and an ALS Breakthrough (EP. 27)

Hello, Internet. This is your captain speaking, Lester Nare, joined as always by my co-host and our resident PhD, Krishna Chowdhury. We have an action-packed full episode this week with three main stories. We're going to start off with the neuroscience of dreams. We're going to follow that up with the physics of protons. And then we're going to wrap up with some of the latest advancements in the fight against ALS. We are going to learn about science from the ground up today because as you know this is from first principles for our first story we are going to get a little bit into what feels like science fiction here With interactive dream engineering as the subject, apparently we now have the ability to hack into people's dreams, influence, and interact with them and help them solve problems later. This is from Northwestern University in the Neuroscience of Consciousness. And this story is fascinating to me. Yeah, this one's like pretty, pretty crazy, okay? I mean, we've all heard of the idea that, you know, if you're trying to solve a problem and you get stuck, sleep on it, right? That's the advice that we get a lot of times. There's been a lot of problem solving that's happened during sleep. Inception is in the public zeitgeist about, like, you know, influencing dreams. And it turns out there's more truth to that than previously thought. That's so crazy. Like we're getting into the science fiction realm where we are able to now influence with dreams, interact with them, and then help the people who we interacted with in those dreams solve problems that they previously didn't solve before. It's unbelievable. It's pretty insane, dude. And the scientific evidence for like a lot of this, like dreams influencing your reality the next day, it's been elusive. And it's been challenging because, you know, we can't really systematically manipulate dreams until now. That's the idea. There's this new study by neuroscientists at Northwestern University. And what it does is validate the possibility of influencing dreams. and it's really supporting this theory that like REM sleep which is when dreams occur that stage of sleep when dreams occur it might actually be especially conducive to helping individuals come up with creative solutions to problems this is so fascinating i mean even the idea of saying the phrase dream engineering yeah no that seems like something out of a chris nolan movie yeah but now it's like actually real exactly there's a science paper that's out right so as always let's start with some of the history yes dreams have always been viewed as sacred as this kind of psychological gateway into the subconscious mind the egyptians literally had sleep temples for sleep and incubation rituals and they used to practice these in the ancient temples with the egyptian deity emotep do you remember the mummy emotep yeah yeah exactly and then i mean it's been it's been a thing of research for psychoanalysis for quite a while. Sidney Freud was really into it. Carl Jung was really into it. But the modern birth really came in 1953 when Aserinsky and Kleitman discovered something called rapid eye movement sleep. Okay. And they linked that sleep stage to vivid dreams. What was really the breakthrough here is we didn't have a subjective telling of, oh, I dreamed. And that was the stage that I dreamt. Because what they could do is they could hook up electrodes onto the skull, and they could actually see signatures of dreaming that were not based on subjective reporting, right? You don't have to wake up and be like, hey, I was dreaming. You could look at the eye movement during sleep. There's a certain part of sleep called REM sleep, rapid eye movement sleep, where your eyes move very rapidly. during that part the electrophysiological signature from the eeg electrodes is actually very distinct compared to other parts of sleep so it was this real actual stage in the sleep cycle it happens about 25 of total sleep happens about 90 minutes after you fall asleep and then during rem what we realized over the years of you know doing research on it the brain is highly active it actually resembles the waking state. If you look at the ephys signature, like the electrode signature of what these little electrodes on our skull picks up, and then you look at other stages of sleep versus when we're awake and we're like looking at stuff and thinking about stuff, the electrophysiological signature during REM sleep is more similar to wakefulness than it is to other stages of sleep. Right? So when we're dreaming, it's almost like the brain is kind of awake, but obviously we're still asleep. And it's been this really cool enigma to try to figure out. So the starting point was you had the subjective experience from people saying, while I was sleeping, I saw stuff. Yeah. But we did not at the time have a way to quantitatively track with instruments that state that people were describing. that they were having until in this historical context, we used electrodes and tracking the brain activity to see and mapping it with this rapid eye movement sleep. Yeah. That there is a state while you are asleep where the brain activity looks very akin to awake state. And that maps to when people are now happy. You can now map the data with the subjective reporting from those sleeping and kind of map those two together. Exactly. And then let's go even further, right? So now we figured out that there is definitely such a thing as REM sleep. Yes. And that's probably when you're dreaming. Yes. Now, what if we wanted to talk about dream recall and how that affects how you solve problems the next day or day to day, right? Right. That's still correlational still, right? Because the subject has to recall the dream. And then there's some correlation between him recalling certain dreams and the creativity that he has the next day. There's some correlation between sleep deprivation and insight. Like there's a decorrelation really. Because like the more you're sleep deprived, the less you're really going to do novel stuff the next day. You need your sleep in order to actually fully function. But these are all correlational, right? You can't do a causal mapping. Meaning like this caused that. Exactly. Yeah. It's always just observational studies. And this breakthrough, which was in the neuroscience of consciousness, this is a causation study between your dreams and your ability to solve problems. So before we were just saying these two things are happening at the same time, and so we think they're related. Yeah. But we didn't really see the bridge between the two things. Yeah, yeah, yeah. We weren't able to literally, like, influence the dreams, and now this is what's happening. That's fascinating. Okay. Yeah. Okay. Yeah, this is getting good. It's pretty insane. Yes. So let's talk a little bit about cognitive psychology, okay? It's divided into two domains. When it comes to creative problem solving, there's really like two ways of thinking about it. There's the convergent thinking, which is the idea that it's like super brute force and logical. If I want to solve a problem, I start with all of my parameters, and then I sort of logically deduce how to get to the solution. then there's divergent thinking which is i start with some nexus of a problem and then i just like look at relationships and try to like creatively figure out how to solve a problem right now with the divergent thinking there's something called a spreading activation model the idea is that your memory is represented like a giant graph of like a graph like you know the internet is a graph we've talked about this before on the podcast, the idea that like on the internet, each webpage is linked to other webpages. So you have webpages and you have links between them. That's what defines a graph. Stuff and then links between that stuff. Now with the spreading activation model, our semantic memory can be thought of as a complex graph because there's nodes, which are the concepts like for example, dog. Dog can be furry. It can be friend. It can be foe. If you're out hiking, and you see a wolf, like there's all these different relationships that you can go from dog all the way to, is it an enemy or is it a friend? Is it my own dog? And then from there, you can start connecting nodes. So you can imagine you start with a dog, and then you have that idea of a dog. And from a dog, you can now go to friend. You can go to does he want to play? Like all the stuff that I would think about when a dog comes up in my memory, right? There's all these connections. And the idea of the spreading activation model is that you start with some connection, and then you, if you want to solve a problem around that connection, you start exploring all the stuff that's related to that, and you try to figure out what's the best path to get to your solution. So as an example, let's say I'm afraid of dogs, which I'm not because I have three of them. Yeah. But let's say in this example I'm afraid of dogs. So when I then see a dog, you know, I'm immediately now making nearby connections, which in this context might be fear. I need to run how to like defensive like MMA. Yes. Like how to kick. Yeah. Again, only because in my head, in this specific context, I'm afraid of dogs. And so I will be thinking of these associated concepts in my own head that are related to a dog and then most adjacently fear and then all the things that come from that. And all the stuff that comes around that, right? Exactly. And so now let's try to take this network architecture and apply it to problem solving, right? When you're in the waking state, what ends up happening is suppose you want to solve a problem. based on the brain state what ends up happening is you start with the problem statement and you try to get to the solution during the waking state my activation of all these different concepts is tightly regulated okay because there's a lot of inhibit inhibition that goes on there's these inhibitory neurons that are fighting a lot during my wake state and so only strong obvious connections are actually the ones that are going to pop up. And so I might get stuck in like a fixation where I'm trying to like solve a problem and I get stuck while trying to find the solution. And it's really hard for me to get out of that. And I've had this personally in my research. Like when I'm trying to solve something during my work or during my research, it's like I work on it for like two hours and I'm stuck and I don't find a path forward. And that's what a lot of people say. Why don't you just sleep on it? The idea is you have a problem. Imagine it like traffic. Or you're trying to drive through Los Angeles. Yeah. But 50% of the roads are closed. Yeah. In your waking state. Yeah. And so you can't take the 405. You can't take the 101. That's a really good analogy. And so it's going to – you can't really – it's hard to get to where you need to go. You try to go the back roads. And you end up somewhere in El Segundo. Yeah. Okay. Exactly. So – Okay. Okay. Maybe dreaming could fix something like this, right? Yes. And the analogy that I want to make is actually a physics analogy, something called annealing, okay? Whenever we have a material that is in a certain state and we want to get it to a new thermodynamic equilibrium, what you can do is you can raise the temperature. Okay. When you raise the temperature, you're raising the noise, right? And so in a physics thing, suppose I've got a potential landscape, right, which is hills and valleys. my state is stuck in a local valley but it's not like the global minimum yes right it's stuck in some local solution and i want to get to the global minimum which is the actual solution yes if the temperature is too small my state is only going to really jiggle around that local solution but if i raise the temperature then my state is able to explore a lot of different avenues because the jiggle has increased. That's the idea of annealing, right? And what people have started to figure out is perhaps the biological function of sleep and specifically dreaming is to raise the parameter of your neural network such that you start exploring different spaces in your brain. Yes. Okay? This happens a lot of times in like artificial neural networks. Yes. Like when you're trying to code up an LLM or like some kind of AI, a lot of times it's going to get stuck in a local minima. And what you kind of do is you like increase – there's like various tricks that you can do to make it not get stuck in that local minima. One of the ways is to increase the temperature. So you increase the jump size in your gradient descent. The other thing you can do is introduce momentum so that if it's going in one direction, even if that's a local minima, It's going to have enough to like jump over that energy barrier. And so these the same things is sort of. A theory that's happening in brains. Now, is there like a neurological idea? Like, is there a mechanistic philosophy in our brain that we can actually point to here? Right. Yep. It turns out there is. There's a lot of research on how this annealing phenomenon, this raising of temperature is actually happening in our brain. So the REM sleep initiation. And just briefly, and just because I want to make sure I'm tracking, the way you described it earlier was when you're in the waking state, there are these tight parameters around where to explore. Exactly. The temperature is low. The temperature is low. And so we're basically trying to understand if, you know, does dreaming loosen those parameters? The process we made, we made the analogy that's akin to annealing in physics. but the simple way of saying it is is turning off the guardrails a little bit to expand the ability to explore into other areas yes that's exactly right okay yeah and that's that's effectively what's happening and there is actually neurological studies that show that this is happening at a cellular level okay at like the biochemistry level okay so REM sleep initiation is driven by um the activation of these REM on cells in our brain okay and they're located in like the LDT and the PPT, that's the lateral dorsal and tegmental nuclei in the brainstem. Okay, the brainstem is the part that connects our brain to the spinal cord and make sure our brain can talk to the rest of the body. Now, these cells, what they do is they flood the cortex and the hippocampus with massive amounts of acetylcholine. And the norepinephrine goes to zero. Norepinephrine is kind of an inhibition acetylcholine is an activation okay during wakefulness there's high amounts of norepinephrine so you have a lot of inhibition that's those guardrails that you were talking about but during dreaming that's gone so now without that constraint now these cortical networks have a really high temperature they have a lot of noise they're able to explore all the different possibilities right and then when we think about like dream phenomenology right All those like bizarre hallucinatory nature of the dreams, like when you're like flying or like random crap happens during dreaming. Yes. That might be a subjective experience of this noise, of all of the brain sort of like getting really activated. it so we're sort of seeing there's like a the chemistry balance of what's happening in the brain changes yeah in between waking and sleeping state with this acetylcholine and uh what was the second one it was norepinephrine yeah the norepinephrine is the is the the guardrails yeah that's the guardrails the guardrails and the acetylcholine is what allows you to kind of explore a little bit more crazy yeah yeah and because that balance is off during REM sleep that's then it's like wow what's over here yeah okay very oh i'm flying oh that's a monster like crazy stuff and they during your dream and it tracks because it makes sense for anyone who's had vivid dreams before right this idea that um you know they do kind of exist in this weird realm a little bit beyond what is realistic yeah but still feel grounded in realism exactly yeah because it's like synthesizing all the memories that you previously have but now it's just on overdrive It's like trying to connect to all this other kind of stuff, right? Makes total sense. It's pretty cool that we can go all the way down to the biochemistry level and, like, still see the subjective effects all the way up. Neuroscience is like crazy, and there's just so much more that we don't know about the brain, right? And the fact that we're getting this far is already, I think, really, really cool. So now let's talk a little bit about information theory and the brain, okay? because the brain is an information chugging machine. There's this idea of entropy, which is the same kind of idea that we have in physics, the idea of randomness, how structured is the data in our brain. If we look at the ECG signals, which is the signal, the electrical signal that we would get if we were to put electrodes on our scalp, right? You can see that the wakefulness and the REM sleep look very, very similar. This is what I was alluding to before. Okay, got it. low wave sleep which is deep sleep yes that's the part where we're not dreaming that's like super deep nothing there's no subjective experience so far as we can tell of that and light sleep are look very different compared to REM and wakefulness REM is almost like we are wakeful but our body is completely paralyzed and the way that our body does that is our brain our brain stem, which is the part that connects our brain to the spinal cord, completely blocks all the signal. Oh, okay. That's why we don't act out our dreams. Well, I mean, some people do, and they've got a little problem. We're acting out our dreams right now. I think we are actually on this show. We've talked about this for a very long time. Yeah, but you know what I mean, right? Like when we're dreaming and like we're doing all the crazy stuff, our brainstem is actually paralyzing the rest of our body. Okay. But the dream state itself looks very much like the wakeful state. That's something that I really want you to like imagine. And I think that's kind of crazy to think about. Yeah, it is. That like during dreaming, the brain is basically simulating reality. Right. Of being awake. It's just we're not acting it out because the brain stem, which is the part in the back of our brain connecting our spinal cord to the brain, is just like, no, none of that signal is getting through. It's sort of like the brain is putting on a biological VR headset, utilizing your memory as the context by which it then projects imagery into your mind's eye while paralyzing your body movement. So you don't have to worry about running into your TV. Yeah, that's exactly right. I mean, it's exactly what it's doing. It's like a little VR that's in our head that we're making up as we go along. So given all these constraints, right? Yes. Now let's talk about how do we do sleep engineering. Right. Because now we've set the basis of why we can actually look at dreaming from a quantitative perspective and understand the numbers and see it and track it. Yeah. But now the question is how can we actually be more targeted, not just read. How do we read and write? Yes. Yes. How do we now write into the sleep itself, right? We want a deliberate manipulation of our sleep content, right? And it's funny because in the waking state, we have a variety of things that manipulate our conscious waking experience. Yeah, yeah. So theoretically, I mean, just like loosely speaking. Yeah. If there's such a matchy-matchy. Yeah. Then it should be possible, right? That's where we get into something called targeted memory reactivation. Okay. Okay. This is something that's used not just in dream research, but just sleep research in general. Okay. Here's the idea. Okay. So we pair learning. Let's say we're awake and we're learning something. we pair that learning with some kind of sensory signal. Usually it's a sound. So I'm reading something. I'm playing the piano. There's a sound that's playing. Okay. Now, when we go and sleep, that same sound is going to play while we sleep. Okay. And then later on, when we wake up, because that sound is associated with the thing that we were learning, and now we heard it while we slept, it's going to sort of give that extra oomph to consolidate that into the greater brain machinery. Does that make sense? It does. It's a signal that sort of triggers hardening of the concept from your waking state in your sleeping state. Exactly, yeah. There's this external stimulus that's associated with the thing that you're trying to learn, either the task that you're trying to learn or the memory that you're trying to remember. And then when you trigger it during your sleep, it like doubles down. The neurons double down because they're getting a coincidence signal. You know? I wish I knew this when we were in school. Dude, I mean, there's a lot of cool research now that's doing this exactly right. Now, historically, this TMR, targeted memory reactivation, has focused on non-REM sleep. Okay, non-REM sleep is the slow-wave sleep that we get. That's the deep sleep, and that is usually involved in memory consolidation. That's the idea of whatever short-term memory that I'm getting today, whatever is salient and I need to store into long-term memory, that's what happens during this deep sleep. And so if I want to store certain things into long-term memory, what I can do is – or if I want to, like, have a skill that I really want to store into long-term memory, I can have this targeted memory reactivation, and it's actually highly effective. In 90-plus studies, this particular one by Rash and other authors have shown that this is, like, very much works. Okay. Like those who learn piano, for example, with an auditory stimulus, and then you reactivate the auditory stimulus during sleep, they're going to remember that piano sequence better. Like their motor neurons literally are going to be better the next day. So for all the coaches who used to say don't play music while we were training for soccer, have it be known that had you let us play our rap music and then we played it during our sleep. During the sleep, we would have won more games. Yeah, yeah, yeah. That's the big one, right? I'm being facetious a little bit here. Yeah. And so here in this particular study, what they're trying to do is break that barrier and not just go into non-REM sleep. They're trying to do targeted memory reactivation during dreaming. Okay? It gets a little tricky. Okay. Okay? When we get into dreaming, right? Because what we want to do is we want to prove that the targeted memory reactivation during dreaming can bias dream content. Oh, okay. Right? So that's one thing. Yes. Where, like, you're going to dream of certain things. And then the second thing is when you dream of those certain things, you're actually better at the task later on. Interesting. So we're not just trying to harden it so that it can be reactivated when there's external stimulus. That was just a grounding. What we're saying now is can we actually influence what someone sees and feels when they are actively dreaming? Yeah. And then not only that, can we then also have it so that the thing we're influencing them to see and feel while they are dreaming is functionally beneficial to like a reactivation or some functional process. Yes. For when they're now returning to the waking state. Yes, exactly. And in order to really make this foolproof, they actually selected their participants to be 20 subjects with high lucid dreaming. Okay. Propensity. Yeah, yeah. Do you know about lucid dreaming? I am from – we do live in L.A. Yeah. So there's a lot of people who love to talk about the woo-woo. Yeah. And lucid dreaming is very – it's the idea basically that you can have agency. Like you have awareness. During dreams. During dreams. And then you can make decisions. Yeah. Not just be on a roller coaster ride through it. Yeah. Yeah. I mean I think it's one of the coolest things that we can do as human beings is be conscious of the fact that we are in our brains simulation. Yep. Right? and then have agency and control over that dream, right? This is central to the plot point of Inception and so on and so forth, right? So here's what they did. They got 20 subjects with really high lucid dream propensity, okay, 39 overnight sessions, and what these participants did was they tried to solve tasks, like little puzzles. This is like a matchstick puzzle, which is like you want to move exactly five matches such that this random scale that's made out of matches is exactly balanced. And these are all insight-driven puzzles. That's the key. They're not brute force puzzles. They're not like math algebra problems where there's a systematic algorithm that you can use to solve it. They're really like a creativity type of puzzle. Okay? And participants usually used to have some amount of puzzles that were not solved. Here's the key. Here's what they're doing. While they're solving the puzzles during the normal day, there is an auditory cue for each puzzle. Each puzzle has a unique sound associated with it. So as they're solving it, there's a unique sound that's being played in the background. So they're associating subconsciously that sound to this particular puzzle. Okay. Now we get into their sleeping. Okay. Now they're sleeping. We have something called targeted lucidity reactivation. Okay. They're able to target lucid dreaming to happen in these patients. No way. Yeah. This is what's crazy. So at 4 a.m., they wake up, okay, and they spend 20 minutes training their mind to associate another set of auditory clues with a critical state of mind that will provoke lucidity. So as they're falling asleep, so they wake up at 4 a.m., and as they're falling asleep, they get played these three-tone beeps that are in ascending frequency. It's like beep, beep, beep, beep, beep, beep. You hear that pitch, guys? You hear that pitch perfect? over here but i mean that's what they're doing no no no it's kind of crazy and and what they're doing is as they're lightly falling asleep they're aware of that right and they're like oh that's the sound that's the sound and they're trying to keep that lucidity so they're trying to keep the conscious awareness of the sound yes while they're falling asleep the thing the reason i'm kind of like giving all these facial expressions is have you ever heard of this concept of like binaural beats no so there's this whole community like you know monroe institute and a variety of these entities have done this research into this concept of binaural beats and there are two aspects to it one is that it helps you the way the way the frequencies are tuned it helps you like relax but in particular they people use it for lucid dreaming as like the thing to fall asleep to and And then like, oh, yeah, that's that's very much it puts you in the right kind of in line and state that enables or makes it more likely that you lucid dream. It's just interesting that there is an overlap because it's very much sort of in the, you know, people who are quantitative self self-improvement kind of universe. So it's fascinating that there's a direct overlap. Yeah. I mean, it's pretty crazy. Yes. That, like, dude, this stuff is just, yeah. I mean, so here, okay, so getting back to this, right? So they have this, like, ascending three-tone beep that then sort of, like, makes them sort of aware and try to lucidly dream more. And then here's the track. Here's the key, right? They still have those TMR cues, the same targeted memory reactivation cue of the puzzle. Yes. The puzzle sound is different from this lucid sound, okay? If they become lucid, right, they're going to – and we can monitor their brain state because we have electrodes on them. So we can see when they enter REM sleep. Right. And when they enter REM sleep, I can start playing now the sound from the puzzle. And if they're lucid and they play the sound from the puzzle and they hear the sound from the puzzle, they're going to seek out that puzzle in their hallucinated dream state. So imagine like you're dreaming and you're like out in like Yosemite or something. And like now you hear the you hear the dun dun dun, which is like, oh, I'm lucid. And now whatever sound was associated with a certain puzzle that they couldn't solve, they're going to try to hallucinate that puzzle onto the granite walls of Yosemite. This is unbelievable. I'm so I like we need to do a follow up on this because I'm so excited about this. It's insane. So just to say it back to you from the beginning, the idea is. And I haven't even gotten to the best part, by the way, but we start. I don't know if I can take which one. Yeah. We basically have an audio cue when the participants are trying to solve a puzzle. That's an insight-based puzzle. Yeah. And the idea is to connect the puzzle to the sound for the participants. Yep. The participants then go to sleep. Yeah. While they're sleeping, separately from that set of sounds, we have a different set of sounds. They wake up at 4 a.m., and we're trying to induce lucidity while dreaming. Yeah. And the second set of sounds helps to facilitate that inducement of lucidity while dreaming. That lucid state while dreaming that we've now induced with our second set of sounds means we're now incepting the first set of sounds into the dream. Yeah. Literally like the movie incepts. Literally, yeah. Literally. Where they could feel the vibrations of the car when they were crashing in the dream. In the dream. But because the sound was previously associated with the puzzle, it will basically now instigate the brain to think about the puzzle. The point being, because we've now gotten rid of those guardrails we've talked about before, the brain has more space to explore and is potentially more likely to come up with the novel solution. That is one of the craziest, most unbelievable things that we've talked about on this pod. I think this is absolutely insane. So here's the part that I thought was crazy. We need a cherry on top. Okay. This is the cherry on top. Okay. There was – so we've gotten to the part where it's like interactive dreaming, right? Right. But at the end of the day, I still want to be sure that I am making this happen. Right. Okay. I don't want subjective reporting after the patient wakes up. I want during the dream to know that this is happening. Yes. Okay. But there's a caveat, right? This is going to be kind of difficult because, as I told you, the brainstem mediated paralysis is paralyzing the entire body. Yes. But it spares the eye muscles because it's REM sleep, right? So the eye is definitely moving. That's why it's called rapid eye movement. And it's also sparing the diaphragm because you're still breathing. Oh, right. Yeah. So these subjects were trained, if they were lucid dreaming, they would move their eye around in the dream, and they would breathe, they would huff and puff during the dream. Like in their dream, right? So I'm in Yosemite, and I'm like in my dreamscape. I start huffing and puffing in my dreamscape. Well that going to transfer into the physical body huffing and puffing And I could monitor that This is so crazy So I have the EEG signal Yes I have the signal from my breathing stuff. I have the signal from the eyes. And I can now get a signal of the subject reporting back to me during dreaming that he is actually doing this. Y'all, I want you to understand something. I want you to understand something. What you're saying is we have now experimentally shown that a patient can communicate while in a dream state, particularly when they're lucid dreaming, back to the researchers that are monitoring them across at least two modalities, eye movement and breathing. And breathing, yeah. While we're trying to incept an idea to help them problem. On top of all of that. Mate, I'm at a loss for words because that is so – it's kind of weird because literally the movie Inception. It's kind of happening. It's kind of happening. Yeah, it's insane. There's no sharing of dreams, fine. Sure. But like a lot of the stuff is there, right? And the key to this two-way communication is that there's now real-time irrefutable proof that the cue was heard. Right. It was integrated into the dream. Right. And there is a conscious response that came back. Right. Because in theory, you could make the conscious response different things. Yeah. You could say breathe like this or breathe like that or move your eyes like this or move. In order to then track over multiple different iteration types to be like, yeah, no, this is definitely happening. It's like, oh, it's the same single type of eye movement or breathing. Anyway. Yeah. Okay. So now let's see if there's actually an outcome. Were they able to solve these problems better? So for one, 75% of participants reported the cued puzzle elements in their dreams. Okay. Okay. Which is way greater than control. Meaning, like, I played the sound from the puzzle and the puzzle was in their dream. They were, like, solving it or doing whatever. Right. Right. The targeted dream efficacy, right, the subjects reported 40% more success on the Q puzzles versus the not Q puzzles. That's so crazy. So people who got the inception to solve the puzzle, 40% of the time were able to do so after the fact, greater than those who did not have the inception. Yeah. So if you didn't get incepted, you didn't do well. You did a little bit better because natural sleep will get you a little bit farther. But this is like engineered dreaming is making you superhuman at solving these puzzles. This is, this is, this is, I can't, I just. It's insane, dude. For those who are listening on audio, my mouth has been mostly open, slack god, for a lot of the story. Dude, yeah, it's pretty. This is quite nice. This is quite nice. This is quite nice. And these studies are not in a vacuum, right? There's actually a lot of broad effort to do dream engineering and sleep engineering in general. It reminded me of this study from MIT called Dormio. They were actually targeting non-REM sleep. So this is like the edge of sleep, N1, which is called hypnagogia. It's like right when you're falling asleep. It's this transitional state between wakefulness and sleep. And they had a sensor. They had a glove that people would put on with sensors. and then it would give a cue, like think of a tree right when you were going to sleep. Okay? This thing was targeting divergent creativity, which is that I – you know, sorry, this thing was actually targeting convergent creativity, which is like the – Going down the rabbit hole, right? Yes. And so we're doing like separate different things, and this is part of that. So I'm just saying there's like a huge modality of scientific research that is going into specifically sleep engineering. And this is like one of the coolest examples that I've seen of like, yeah. No, this is very good stuff. I mean, have you ever had a lucid dream before? I've had it once. Yeah, but I haven't been able to like control it, you know? I want to say twice, but it was only awareness. It was not. Yeah, I wasn't able to like literally be like, I'm going to fly now. Yeah. You know, I wish I could do that. If you've ever had a lucid dream before, it is one of the coolest things. It can be obviously very scary, like depending on whatever is in your subconscious and stuff like that. But it is such a fascinating, mind-bending thing because it's so bizarre, obviously. but you're there it's like movies like Inception are always being like well like you know what is like do you have an opposite life an opposite world in your dream that is consistent because it just kind of mirrors your own life and has different parameters and relationships that transcend time and stuff like that but you just don't remember obviously we will find out soon because now we can incept our dreams I think this is opening up doors for all kinds of really cool research and there is clinical potential for this right makes sense for example if you can do like nighttime neuroprosthetics that's a new and emerging field people who have ptsd right ptsd can be modeled as a pathological local minimum in some landscape you it's like a fear network yeah and you're trapped in this local minima right and you can't get out well if you do targeted memory reactivation during REM sleep and you can cue stuff You could theoretically restructure traumatic memories. That's a huge deal. To not be traumatic anymore. Yeah, that's a huge deal. Right? That's a huge deal. Yeah. I mean, for veterans, for victims of abuse, I mean, there's a whole swath of people who could majorly benefit from that. Yeah. And, of course, with all of this comes the ethical concern. Of course. Right. Because you're you're you're beating this sensory gate to get into dreams. Now, imagine if researchers can cue puzzle solutions. What if like commercial or malicious actors attempt to embed like branded content, you know, alter preferences? This is I get remembered of Tom Hardy's role in Inception as like the defense guy. He was like dreaming a little bigger, and he like came up with a giant gun. Yep. It's like so is dream sovereignty going to now be the premier neuroethical challenge? I mean this might – I didn't even think about this nexus, but while this might sound like, oh, this is early stage research and there's a ways to go, which is true. If you just look at in the U.S. the recent disclosures around what has been colloquially called Havana syndrome, but is what is also known as anomalous health incidents. There is real legitimate congressional hearings and restitution being provided to veterans who've been victims of what are being called microwave weapon attacks from some foreign actor. There are these diplomats who started in Havana, Cuba, which is why it's called Havana syndrome, where what the theory of the case is that there's this sort of small form factor microwave weapon. You point it at the target, and it creates all kinds of nausea and neurological issues. Allegedly, the director of national intelligence and some of their people just went to review one of these Havana syndrome weapons recently in order to bring it into the U.S. inventory. It's been an issue that the government is very hesitant to discuss or address I think because it touches on the same neurological sovereignty question That this issue brings up Which is not an area we've really spent a lot of time thinking about But these technologies are getting us to a place where We can have impact on individuals External to their brain but impacts their brain. And that gets tricky. That gets very dicey. Yeah, it gets very, very tricky. It's a frontier in neuroscience. I think it's going to be incredibly exciting and a little bit weird. I just want to also briefly note that we talk about this as a science show, so we talk about the research and what the implications are and what people are doing. Just because that's the focus of the show does not mean that we don't identify with and understand the risks and challenges that are related to these in a whole number of different ways and situations. There are a lot of people who spend a lot of time thinking about that area and do content around that. We just try to talk about the science from first principles to give everyone a frame of reference to understand where is technology going and how is it going to impact our everyday life. Yeah, and how does it work? And how does it work? Because we need to know how these things work to know to even have the discussion about neurological sovereignty, which sounds so ridiculous. Yeah. But once you know how it works, you get why that's important to discuss. Yes. But we just don't yet always know how it works. Exactly. Yeah, and I just wanted to end this story on a lighter note. Yes. There's a movie, a little indie film that I was involved in. I was the scientific advisor on this film called Goldmine from a mutual friend of ours. He was the producer, director, and writer of the film. It is being distributed on Amazon and other digital platforms starting February 24, 2026. I was the scientific advisor on the film. Lester was involved in some of the promotion of it. It's a really nice story about father-daughter relationship. It involves virtual reality, getting into the neural landscape of virtual reality, maybe some potential therapeutic advantages of virtual reality in the future. I think you guys should go check it out. It's available on Amazon starting pretty soon. I think this week. Yes. Please go watch Goldmine. Big shout out to writer-director Matt Marder, who was one of the original co-hosts on one of your past podcast endeavors, Dark Matters. Yeah. So friend of the pod. Friend of the pod. Really, really helpful. Anyone is interested, it's fantastic. We went to the premiere. It's definitely worth a watch, so definitely check that out. One of our first friends to make it to streaming. Yeah. Fantastic. great story number one neuroscience story dream engineering again you're not going to get these kind of stories anywhere else folks the best of the best sir and with that we will move into our rundown again we cannot cover every breaking science research story every week in the level of detail of our main stories so we use the rundown as a way to talk about other stories we found fascinating that we want to share with you all. You can go in and dig in on your own, but we'll just cover them topically a little bit at first. We will not go as long as last week's rundown. We will keep it tight. We got a little bit excited about the AI story. But before we get there, just a couple of housekeeping notes. If you're listening to us on audio, we are a video podcast, both on Spotify and on YouTube. You can watch full episodes. We have a lot of graphics and diagrams. We usually have anywhere from 40 to 60 in an episode. And if you're having an issue trying to grok the subjects we're talking about, check out the video podcast version. It's a much more robust way to learn these concepts and have them sink in so we can get it reactivated when we start manipulating our dreams later. And when you're on said Spotify and YouTube, we are in our fight for our lives in the billionaire algorithm. so a five star on either is super helpful for us to get this show out to more people leave a comment if you want to say you have a different idea or you want to cover something anything put it in the comments like random words just anything it's super helpful for us uh share it with a friend if you're at a research lab bring it up during your lunch i know you guys have coffees and lunch and learns we talked about this is the best show for the science research community trying to get out to more people. And if you would like to support the show, we have a new donation portal at ffppod.com backslash donate become a monthly patron. It helps us run this show, which again is just the two of us. I'm going to get better at singing so I can sing that better. Last note, we do have a new update to the website. We now have chaptering on all of our videos as well as all of the past both research papers and news articles related to each episode with a little bit of a more robust research papers page. We are now set up for some of our more fun features we're looking to do in the future. So please go check that out at FFPPod.com. Our first story in the rundown is a possible pulsar at the Milky Way Center. This is an astrophysics story. There's a supermassive black hole at the center of our galaxy. you what's going on here yes what they're looking for with the supermassive black hole which is a very famous supermassive black hole it's the closest supermassive black hole to earth it's at the center of our milky way galaxy and we finally found a pulsar or we think at least that we found a pulsar near that supermassive black hole okay a pulsar is a neutron star that extremely extremely dense it's about you know neutron stars are like the size of the sun sorry no i should say this clearly it is a neutron star is about the mass of the sun if not bigger but it's about the size of a city okay okay extremely dense star where it's called a neutron star because the pressure from gravity is so high that the electrons and the protons fuse together and it's a giant ball of neutrons like not even atoms can exist at this amount of gravitational pressure okay what's cool about pulsars are they are specific types of neutron stars that have a really high spin and they shoot out radio waves and the radio waves are like a lighthouse as they're spinning and pulsars are such that the lighthouse beam is exactly aligned with earth okay okay there's plenty of neutron stars where the lighthouse beam is not aligned with Earth. But for pulsars, we just happen to be lucky enough that the lighthouse beam is aligned with Earth. So when we point our radio dish at that pulsar, we're going to get beep, beep, beep, beep, beep, beep, metronomic radio pulses from that point in the sky. Okay, this particular pulsar that they think they found is about eight milliseconds. This is a millisecond pulsar. So every eight milliseconds, this thing is turning around. Imagine that something the size of the sun, if not bigger, about the size of a city. The mass of the sun and the size of a city. Yeah, sorry. No, no, no. The mass of the sun but the size of a city is rotating around, right? And given the conservation of angular momentum, this thing is not slowing down, okay? This thing is going to keep going at 8 milliseconds. That makes this a really nice clock. Right. Okay? And if we find such a really nice clock near the galactic center where there's a giant black hole that is warping space and time, we can test Einstein's general relativity to unprecedented degree. That's why everyone's excited about this. Is the idea that the pulsar becomes an instrument in and of itself? Yes, that's exactly right. Because a lot of things can happen. The gravitational field of the black hole itself can start influencing the pulsar. Right. Right. And maybe if it wobbles a little bit less or if the timing is delayed, we can test Einstein's prediction that way. On the other hand, the radio signal that's coming from the pulsar is light and light is itself. You know, affected by space and time. Right. And if there's a lot of warping by that black hole, that light is going to get affected. Yes. So monitoring this thing over years is going to give us extremely nice tests. for Einstein's general theory of relativity. That's why everyone's excited. It's not entirely foolproof, the analysis. And so, you know, there's still more stuff that needs to be done to confirm it. Yes. But it's tantalizingly close. This was out of Columbia University and the Breakthrough Listen project, which I don't know if you know, that's the, like, new SETI. Yeah, yeah, yeah. Right? And they have this giant, like, Breakthrough Listen Galactic Center survey where they conducted like the most sensitive radio search for pulsars near the galactic center. It was out in the Astrophysical Journal. It's something a lot of astrophysicists are very excited about. That is very exciting. Again, it's early, as is almost every story we talk about on this podcast. So it goes without saying that we're waiting for continued further analysis, replication, etc. And I think it's going to be soon. So, you know, when we get a confirmation, maybe we'll do a deep dive into it. This is very exciting. That's actually a huge deal. That was our story number one, Columbia to Astrophysical Journey. For number two, decoding an ancient Roman board game, an archaeology story about how AI has now helped us figure out the rules for this unknown board game from the Roman period. Yeah. This one was pretty cool, okay? So there's a 2,000-year-old limestone slab that was found in the Netherlands, and it was identified as an ancient Roman board game. It was sitting in a museum and some people who are really into board games from antiquity and from like human civilization saw it and wanted to analyze it. The rules are lost to history, obviously. There's no like, you know, the manual that comes with the board game about what the rules are. Okay, there's nothing that we can do that. But what we can do is look at that slab of limestone and figure out where the wear and tear is. And then we can use AI to play thousands of simulated games with various rules. Right. Right? So an AI plays with itself. This is kind of like how Google DeepMind trained AlphaGo. Yep. You know? Yep. So we've got all of these AIs that are playing thousands of different rules. and from those thousands of different games tens of thousands of different games we can figure out which games required the movement of pieces right such that it would match the wear and tear on the limestone slab and they figured out about like i think there were like four um four to nine different games there's and they're all of a particular type that are still kind of played in Scandinavia, and they match the wear and tear of this thing. And it turns out it's like an asymmetric blockade game. You can actually go and play it. They made an online version of it. It's called Ludus Coriovali. It's played with four hounds trying to trap two hares. That's what they called it. Okay. But it's very cool because it's like a use of AI to something that I literally never thought about. Yeah, yeah, yeah, yeah. And it's a clever use case because you have some kind of physical indicator, which is the wear and tear. And ultimately, there's a finite number of options for what the rule set was for the game that would result in, especially on a limestone board, that kind of wear and tear. So it makes logical sense, but I never would have thought of that. I never would have thought of that. And, I mean, it's really cool because this discovery pushes the known history of these types of board games back several centuries. you know fantastic that's very cool uh we we love our archaeology we've touched on rome a couple times and this is out of lighten university in the journal antiquity yes this is good good good we are going to keep it hot with our story number three this one is a genetics and human evolution story how we became human might be related to unsurprisingly our fingers but what about this one is particularly interesting. Yeah, this one was really cool because usually when we think about human evolution, we always think about brain size. Yes. Bigger brain size means we're smarter, means we're more human-like to modern-day humans. This new research points to the fact that prenatal hormones that we are subject to when we are in our mother's womb, that's actually a surprising driving factor to brain size and finger size. The clue lies in 2D to 4D finger ratio. Okay. That's the ratio between your ring finger and your index finger. Okay. And if you've got a longer index finger, that usually means that you've had higher prenatal estrogen exposure during the first trimester. Okay. And that's also related to larger brain size. Boo. Oh, for boys and not girls, too. Oh, no. I have a small brain. Now, I think I think this is an overarching evolution story rather than individual. Fair, fair, fair, fair. You know, because because I think I think what they're saying is the effect is something where these two genes are correlated. OK. Right. OK. And it's in line with a lot of recent research that shows that there's like this thing called the feminization of the skeleton. And this is not like anything political or anything like that. It's not a culture war, I think. No, this is not a culture war at all. It's relevant because in human evolution, increases in brain size are found alongside something called the feminization of the skeleton. For example, high values of this 2D to 4D in males, the ring finger to the index finger, they've been found with elevated rates of heart problems, poor sperm counts, predisposition to schizophrenia in males. I see. Okay. And it seems that this stuff is coming from prenatal hormones, from the fact that we were subjected to estrogen in our first trimester. That led to heightened brain size But it also led to all of these problems And in the course of evolution I guess the human species decided I'll go with the large brain size Even though it comes with all of these problems That's fascinating No, that isn't Let me not, I'll leave it This is out of the Swansea University And Istanbul University In the journal Early Human Development I thought it was quite interesting because, you know, we always think about how genes are related to each other when they go through evolution. So this is something that's very important. I mean you can also map this onto the site. You know, when people talk about quote-unquote masculinity and the change in body, the body stature of like men generally over time, if there's a correlation of higher brain size equals to the skeletal structure of us being slightly different, that is an interesting thing. That is an interesting angle that we kind of see manifesting, unfortunately, in the culture war, but has sort of this genetic basis for it. Fascinating story number three. Our last story of the rundown combines AI with bioengineering. So no risk here at all. A new machine learning pipeline that is streamlining protein engineering. We've talked about AlphaFold God knows how many times. Yes. We've gotten comments. Yes, we understand it's not perfect. Nothing to talk about on the podcast is perfect. No, this is science. We get that. That being said. That being said, alpha fold is not everything. Right. Right. Alpha fold means you give me a protein sequence, which is a bunch of amino acids in a line. I'll probably be able to tell you what it looks like in 3D. Okay. And how it changes shape. Okay. What if we wanted to design proteins? If we want to design proteins, the search space is incredibly big because if I have 100 amino acids that I want to fit into a protein, there's 20 choices for each amino acid. That's 20 to the hundredth power. There's more atoms in the universe. So I'm not going to like – I need ways to like hone down if I want to design a new protein, right, how to actually reduce that search space. Right. Okay? Right. So nowadays what we can do with various techniques is get down to like tens of thousands of protein candidates. If we get down to tens of thousands of protein candidates, the bottleneck then becomes the lab. Right. Because I can only really efficiently build and test around 100 different variants. Right. Of my proteins. So what's the best way to choose which 100 I want to actually test and make in the lab and then figure out? that is what these guys at the university of california at berkeley and the arc institute are doing with this particular story that came out in science they created something called multi-evolve the idea is they've created an ensemble of protein language models and what these protein language models do is not just care about the fitness landscape of like what is the stuff that evolution constrains me for these models also care about stuff like is the enzyme going to catalyze faster is the antibody going to bind tighter is the crisper tool going to edit better these are things that aren't really captured by evolution but this particular multi-evolve paradigm is actually going to do and what what's very cool about this is now we've put the lab and the ai right in the loop right okay like it's one big loop yes of discovery where the ai is interacting with people in the lab telling you what to what to do in the lab the lab is going back to the ai and you can have this iteration happen even faster right and the ai framework was able to accurately predict how proteins will function even when several of their amino acids get mutated. Because what you want to do is if you have an amino acid, you want to figure out, oh, if I replace this amino acid with a different one, is it going to make it better or is it going to make it worse? What's more is if I replace these two, is that going to make it better? Because if I just replace this one and it's good and I replace this one and it's good, that doesn't mean if I replace both of them, it's good. There's some synergistic landscape. Maybe they're antagonists. And so all of these relationships are now something that I can parameterized with this multi-evolved model right and bioengineers can now develop this new machine learning framework that condenses that problem of protein engineering into a single round of testing and in their test the model was successfully able to find combinations of mutations that outperformed the original proteins yep showing that that having it in the loop like that actually has fundamental um improvement value yeah yeah and it's very cool because the arc institute which is in silicon valley the whole point of it is to have like frontier ai capabilities and experimental biologists under the same physical roof that's how they operate and closing that loop between computation and the wet lab yep this is like their first iteration it's very exciting to see what they're going to come up with next this is fascinating because this is something similar that you know my mom works in clinical trials and there there's this similar desire to find ways to incorporate tools like AI into the process to create that feedback, that virtuous cycle and feedback loop. But you have to start somewhere. And so it's interesting to hear in a variety of these spaces across both fundamental research and application, for example, in pharmaceuticals, that people are finding ways to integrate it. That is changing outcomes. Yeah. That is influencing outcomes. comes for the positive. I get that everyone wants to say it's wrong because they tried it one time and that means that all of it is wrong, but that's just not how it works. It's not perfect, but it is filling in gaps that are significantly meaningful. Great rundown this week. I mean, it's going to be the first story, the rundown. These are some fantastic stories. We're going to move on to our main story number two this one is a fundamental physics story it's about trying to measure one of the smallest things in the universe we talked about it a lot on this pod the proton it's one of the fundamental particles inside the atomic nucleus and we've done it to super high accuracy yes extremely high accuracy this is from the max plank institute for quantum optics and it was published in nature i'm excited for this one let's dig in yeah we'll start with the standard model yes the standard model of particle physics is the most successful model that we have to date about our universe okay it's the model that the working constituents are the fundamental particles or i should really say the fundamental quantum fields the higgs boson comes out of it the electron The proton is actually not in the standard model. You have up and down quarks. Those are the constituents of the protons and the neutrons. And it's incredible. It is incredible, but it is incomplete. There's no dark matter. There's no dark energy. And there's no gravity. So we've always been in the search for new physics. How do we break the standard model so we can find out what is missing? And what is the path forward to find out what is missing? This is the idea of like unification. Yeah. A grand unified theory. Yeah. Yeah. Not just reunification, but also these new particles like is dark matter a particle? Oh, I understood. That's like not even has anything to do with unifying with gravity. It's like there's just a whole set of other particles that we don't know anything about. Most of the universe, we have no idea what it is. Yeah. Yeah. Yeah. Exactly. So there's got to be a way for us to find out, right? Right. And usually when we think about probing that frontier of particle physics, we think about particle accelerators, right? The most famous one is the one at CERN. The Large Hadron Collider is, you know, tens of kilometers long. They want to build like a giant future collider that's hundreds of kilometers long. They want a lot of money because they want to build these large, large colliders. Higher energy means we can probe the depths of these particles in these fields. But there's another frontier that physicists talk about when they're looking to test the standard model, and that is the precision frontier. And it might offer an equally vital search for new physics. What you can do is instead of building giant particle colliders, you build extremely precise experiments in your lab, in a room, in a physics department. and you're able to now measure stuff so precisely that perhaps you can discern between what is known physics and what is new. This particular photograph is from the Max Planck Institute, and I saw this actually on LinkedIn from the author of the study that we're going to talk about, Lothar Meissenbacher. He posted this photo of his lab that actually made this measurement of the size of the proton. And what it reminds me of is a colloquium that I had attended at UCLA when I was a PhD student there. It was a colloquium given by Dave DeMille who now at University of Chicago He used to be at Yale He was one of these precision physicists AMO atomic molecular optical physicists And he was showing you know the lab setup and he was trying to measure the dipole moment of the electron. Effectively, how round is the electron? We think the electron is the roundest object in the universe, okay? But there's a possibility that it's like got a tiny bump on the North Pole, right? And the and a not tiny bump on the south pole so there there might be a little bit of not roundedness in the electron and he wanted to measure that and what he was showing was um he showed a photo of his lab and he would you know the lab the path of the laser light is you know a few meters things like that but he would put that as 0.001 kilometers and he would do a dig at the particle accelerator guys and be like i'm gonna put this um in a unit of length that you know these particle accelerator guys can understand so this is about 0.001 kilometers um the size of my lens is about 0.0 and it was just so funny dude because you know it's just like he's just like pulling their leg that's quite nice i think it was so funny look it's okay to take the piss every once in a while Yeah, yeah, yeah. He was a really funny guy. Really great speaker as well. So what these guys are trying to do at the Max Flank Institute, when they came out with this landmark nature paper in February 2026, so just this month, is try to solve the proton radius puzzle. Okay? There's been a decade-long discrepancy in how big the proton is. Right. Okay? And this particular paper in Nature is trying to put that to rest. Yes. Okay. Sub part per trillion test of the standard model with atomic hydrogen. What a great name. You know, they're testing the standard model. Yes. With just a normal hydrogen atom. Okay. Okay. This is out of the Max Planck Institute for Quantum Optics. Yes. And the precision is 0.7 parts per trillion. That's pretty small. Yeah, that's pretty small. And I want to give you a sense of just how small that is and why that's, like, ridiculous. Okay. Okay? We're going to start with the hydrogen atom. Okay. This is our favorite atom, every physicist's favorite atom. If you don't understand the hydrogen atom, there's no hope in understanding anything else. And it's something that we do on our first semester of quantum mechanics. You do the Schrodinger equation and you solve the hydrogen atom because it is the only atom that you can like perfectly solve for. Okay. The idea with quantum mechanics is the following, right? We can never observe the electron. This is something that Heisenberg said when he actually came up with quantum mechanics 100 years ago in 1925. We can never observe the electron moving around a hydrogen atom. But what we can observe is electrons jumping from one energy state to another energy state because that's when light comes out. When we make hydrogen atoms glow, certain colors are emitted by that hydrogen atom. Different atoms emit different colors. And by observing those colors of light, we can discern the structure of the atom inside. That's always the game when it comes to quantum optics. Okay? Now, these shells are not just like simple Bohr shells. They're actually very complicated. The hydrogen wave function looks like very cool clouds of electrons around the central proton. This looks like some sick app icons right here. Yeah, but that's actually what the electrons are doing around a hydrogen atom. If you were to put them in certain orbitals, in certain states of angular momentum and energy, they would occupy those clouds. Those are the probability clouds that the electrons occupy. Okay? Got it. And so what we want to do when we want to probe something like what is the size of the proton is we want to probe what the colors of light are and how those colors of light shift when the proton is yay big versus yay big. Right, okay. Because if the proton is some size or some other size, what that is going to do is change the electron orbitals, which will then change the light that comes out when electrons transition from one orbital to the other. Yes, yes. Is that perfectly clear? Yeah, so different sizes of the proton. the way in which we can look at the way in which we understand the probabilities of where the electron will be, which is this electron cloud concept, is going to be fundamentally different. Yes. And we use, like, the color spectra, the energy and how it appears as a means by which to identify what those cloud configurations will look like. Yes. If the proton is a certain size, the cloud will look a tiny bit different, which means that the light that comes out when the electron transitions from one cloud, one energy state to another, is going to be a slightly different frequency. Yes. And if we can measure that very precisely, then we can tell, back calculate what the size of the proton is. I see what you're saying. And so, okay. Yes. Makes sense. Okay. What we're getting at is we're not measuring the proton. Yeah, you're not taking a ruler. Right, and looking like, hey, here's the – it's three angstrom. What we're saying is there is an emission, which is this electron changing energy states. That emission, if we measure that emission with a level of precision, we can derive the size of the proton from that – I'm using emission as a loose term here. Exactly. And if we look at photo number eight, right? Yes. The electrons are in these different clouds. Yes. There's different orbitals. For those in chemistry, you guys will remember something called 1s, 1s2, 2s2, 2p6, 3s2, 3p6. It's like a rap game that we used to have to memorize about all the electron configurations of each of the elements on the periodic table. Each of these orbitals are shapes of the electron clouds. The 1s and the 2s are spherical. Okay, there's no net angular momentum. But like the two Ps, those are when electrons are lobed in particular axes. Like there's the x-axis, the y-axis, and the z-axis, right? And that's that second energy level. The first energy level, there's just the sphere. Yes. The second energy level, the electron can be in the sphere, or it can be in these like dungbell shapes along the three axes, right? Yes. Now notice, the 1s and the 2s, those are spheres. Yes. Those will actually interact with the proton if there's a proton in the middle of that sphere. Okay. Okay? The 2S and the 2P, most of the time the electron is hanging out on the outside. It's not hanging out near the nucleus of the atom. Which in this context is where the XYZ intersects. Yeah, the center. Yes. Right? Yes. And so by looking at transitions between these different energies, we can figure out what is the contribution of the proton that's in the center and what is not. because we're looking at basically the difference. Yes. Yes. Because the difference in these energies tells us what the frequency of the light that will come out. Higher frequency means higher energy difference. Okay? But to give you a sense of the scale of what we're talking about, right, because let's not lose sight of the fact that we're talking about a hydrogen atom, and on top of that we're talking about a proton at the center of the hydrogen atom. Right. The proton at the center of the hydrogen atom is measured in femtometers, which is 10 to the minus 15 meters. Okay. The hydrogen atom itself is about an angstrom, which is 10 to the minus 10. Okay, so there's a 100,000-fold difference between the size of a hydrogen atom and the size of a proton at the center of the hydrogen atom. And to visualize this unimaginable scale, I wanted to consider an analogy, okay? Okay. If a single hydrogen atom is expanded to the size of a professional sports stadium, then the proton that's at its center is like the size of a pea at the 50-yard line. Oh, my God. That's not the worst part, though. Okay? In order to discern the size of the pea, we are looking at an electron that is in the grandstand. Oh, my God. You see what I'm saying? Yeah, no, that's ridiculous. Because the electron is hanging out all the way out here. Over there. We're looking at the behavior of these electrons that are hanging out at the grandstands to figure out how big is the P on the 50-yard line. I just – I have no – I have nothing. I just don't even – I don't even know what to say. It is insane that we can do these kinds of calculations. That's what I'm saying. Like that's – yeah, because I'm trying to even think about how you would do that at macro scale. Yeah, yeah, yeah. If I had a P. Yeah, yeah. I can't have a P. What kind of telephoto lens would I need? at the grandstand to be able to... Yeah. Okay. This is something that is so infinitesimally small. And meaning the way in which we do all... The engineering we do to even be able to do this stuff has to be able to operate at these impossibly small scales. Yeah, and at impossibly small error. Right. That's the main part. Right. You're chasing down like 12 decimal places. Right. Okay. Right. That's a really important point. Yeah. Because it's not just like, oh, we got notes. It's like to a level of specificity where 12 decibels is crazy. Right. Which is insane. It's like, no, it's not that. It's actually this. Yeah. Okay. Oh, my God. My brain. Yeah. It's absolutely nuts. Okay. So here's how we're going to do it. Okay. Okay. We are going to calculate the transition frequency between different orbitals. Okay. Okay. When we do that, this is the equation of what an electron sees when it's on the outside. This is the binding energy of atomic hydrogen. So what is the energy of an electron that's bound to the proton in the center? What I love about this is this is the first line of their paper. The binding energy of atomic hydrogen can be expressed as a giant equation. For those listening, there's like, I don't know, 23 different characters. Yeah. It's just – it is – And it depends on – crucially, it depends on two things, okay? It depends on something called the Rydberg constant, which is like a fundamental energy scale. Okay. And it depends on the proton charge radius, okay? Now, those are two different unknowns, right? And in order to figure out what those two unknowns are, you need two different observations. You can't just rely on one. So you can't just rely on a single atomic energy spacing. You need to observe two different transition frequencies. I see. You need to observe two different colors that are coming out of my atomic hydrogen. Okay. Now, the first one is the anchor measurement. That's already been done before. Okay. This is the ultra-precise 1s to 2s transition. What they're doing in this paper is the 6p to 2s transition. The 6p orbital looks like that. It's dumbbells on top of – it's like a Russian nesting doll of dumbbells. Okay. What they're looking for is an electron in this particular energy state going down to the 2s state. Okay? What they want to do here is observe that transition frequency. And the 2s state was the larger spherical state that we talked about earlier. Yes, that we had talked about earlier. This is even bigger than that. Okay. This is like way out in the grandstands to, let's say, like on the sidelines. Yep. Right? The electron is going from the grandstand all the way to like the really nice seat. Yeah, yeah, yeah. And that transition is what they're trying to look for. Got it. Got it. Okay. Yep. Yep. Let's talk about why this is even an issue. Right. Okay. Okay. Yeah. Yeah. Proton size. Right. Because part of what I said earlier, which may or may not be true, it's like, you know, why can't we just measure it directly? Yeah. Well, I mean, how would you measure something that is a femtometer? Right. Right. But so like for someone who might not understand the scale of the problem set, it's like, well, why don't you just look at it? Yeah. And it's like, well, the problem is it's what are we going to use to do that? Exactly. I mean, you could think, right, like what if we just like set light? Okay. Okay. What if we just had like light at a femtometer? How – I think that kind of light is extremely high frequency. Okay. Okay? Like a light at a wavelength of a femtometer, anything larger than the obstacle, if I were to shine like visible light, which is like 400 nanometers on something that is a femtometer, 400 nanometers is 10 to the minus 7 meters. This thing is 10 to the minus 15. Imagine a giant ocean wave, and then there's a pebble in the way. Is the ocean wave going to care? No. No. No. The way we image things is that the wavelength of light needs to be way smaller than the thing we're measuring because then the light bends and refracts and bounces off. This is the point you're not. But if it's like – you know. The object we're trying to measure is literally half the size of the wavelength of light we would send at it to even measure it. Yeah, not even half. No, it's a hundred thousandth. A hundred thousandth. Or a millionth the size. So it literally is. It's a pebble. With a tsunami. Like, no, a grain of sand versus, like, the Nazare, you know, the Portugal wave. Yeah, yeah, yeah, yeah. The 100-foot wave in Nazare, Portugal. Yeah, yeah, yeah. In Nazare, Portugal. Yeah, yeah, yeah. Do you think the Nazare, Portugal wave cares about a tiny bit of grain of sand misplaced one way or the other? Right, right. No, it really doesn't. Right. So we've got to get really clever with this kind of stuff. So we can't just image it the way we normally do optical imaging. No. It's just the scale is not – it doesn't even make sense. It doesn't even make sense. Yeah, exactly. Okay. And so people have done it before. People have measured the size of a proton before. Yes. And it came out to about 0.8758, according to this giant consortium, where they had, like, electrons that would scatter off nuclei. Yeah, yeah. And then they would try to figure out, okay, like, what is the size of the proton in there? They also had something called electronic hydrogen spectroscopy. It's a traditional laser measurement. Okay. Kind of similar, but not as insane as the one that we're going to talk about. Okay. And they got it to about 0.8758 femtometers. Right. Then in 2010, there was a bombshell. Right. Because some of the same guys in this current paper made an exotic hydrogen atom. They made a hydrogen atom out of muons. Okay. And they published a paper in Nature called The Size of the Proton. This was almost 16 years ago. 16 years ago, these guys published a hydrogen atom where instead of an electron moving around it, they've got a muon that moves around it. A muon is the close cousin of the electron, but it's 200 times more massive. And because it's 200 times more massive, the atom that you create with a proton and a muon is going to be 200 times smaller. And we've got a little sort of like thing to show for that, right? Like the atom is going to be – the muonic hydrogen is going to be 200 times smaller. If it's 200 times smaller, that muon – effectively what we've done is take that giant stadium and turn it into like a high school stadium. Now the relationship between my muon and the proton is going to be a lot closer, and my error bar is going to be smaller. The measurement we're trying to make is the distance between the electron in the grandstands and the proton at the 50-yard line. Yeah, effectively we're trying to get, like, the light that comes out of this atom. The smaller the atom is, like, the more we can sort of nail down exactly what that frequency is. Right. The delta for error is much smaller. Yeah, because we've effectively made the atom smaller. Right. Right. And the key idea here was the muon as the orbiting, as opposed to a standard electron, It was much more massive, which meant the proton necessarily needed to be smaller, which is what created it. The atom needs to be smaller. It should be. The atom needs to be smaller, which is what collapsed the surface area of the measurement to be this more deal-withable size. Yes. Yes. And we got a whole new number for the size of the proton there, 0.84 instead of 0.87. Okay. Already on the second significant figure, we're off. Okay. That is unheard of for physicists. who are doing precision measurement, they're like, this is absolutely awful. Okay. That's like almost 5% difference. Yeah, but that's unacceptable. That's unacceptable in every sense of the word. There's two possibilities. Okay. Either our theory is wrong, meaning leptons are not universal. We used to think that the muon and the electron, the only thing that's different between them is the mass. But what if there's new physics? And the muon is actually behaving differently around my proton, which is why I'm getting this different measurement. Okay. Yeah. Either that or there's some undetected systematic error in the earlier measurement of the electron hydrogen. Basically, the first time we did it, something was wrong. Something was wrong. No one caught it at the time. Yeah. But now we get it. Or there's literal new physics. Right. It's one of the two. It's one of the two. And now you can see why this is such a big deal. Yeah. We need to really figure out if that first measurement was actually wrong. Correct. Because that's actually a – okay, yes. Right? That would be a big deal. That would be a big deal. Because if the first measurement was correct, then this is a really big deal. Because then that means there is actually new physics. The muon and the electron are actually different somehow. Yes. And there's like – now the theorists are super happy. Right. Right? Right. Or the experimentalists are not happy. Yeah, yeah. And these new experimentalists are happy because they – like their muonic hydrogen measurement was actually correct. Yes. And it's like everybody else – Theorists, go back to the chalkboard. Yeah, yeah. It's not a field day for you guys, right? And so that's where this current experiment comes in. Oh, this is fascinating. They replicated that muon experiment from 2010. From 2010, they've been working on it for 10 years to try and nail down this measurement using just normal store-grade hydrogen. Okay. Okay? And it's incredibly difficult. This is going back to our bigger stadium instead of our muon. Yeah, now we're trying to do the same thing, but with our bigger stadium. And with a bigger stadium, there's a lot more problems. Yes. Okay. Okay. And it's just incredible some of the stuff that they were dealing with, dude. It's actually insane. Okay. The transition that they were trying to figure out was the 6P orbital to 2S. So all the way up there to like down here, there's that transition. That transition releases violet light at 410 nanometers. Okay. That's the first issue. Okay. Violet light at 410 nanometers, that's really short wavelength. We've got lasers at, like, blue and green and red. We don't have lasers in violet. Okay. You can't just buy that off the shelf. You've got to make a laser at 410 nanometers. Okay. Okay? Okay. So that's something that they had to do. They actually took like a titanium sapphire laser, which operates at the infrared at 820 nanometers. And they effectively did like a trick where they stuck that inside a nonlinear optical crystal. And what this crystal did was, you know how when you like play guitar and you like strum on one of the strings, but then if you like clamp down in the middle, then you're going to get the octave higher. Yes. Right. That's what this crystal is doing. Okay. It takes in a laser light at 820 nanometers, but then effectively squishes the wavelength by half. So the frequency goes up by two, and then I get a 410 nanometer. For music producers who use keyboards, it's the jog wheel on the left. It's the same idea when you jog the wheel up. Yeah, the octave just goes up. This is just like it's going from, you know, a C to the C that's the dodo. Yeah, yeah. It's just the octave is now up. Yes. Okay. Makes sense. So now we've got a laser. Right. Now we've got to cool our hydrogen atoms down to 5 Kelvin. Trivial, you know. Trivial. Honestly, at this rate, this is pretty trivial. Yeah, yeah, yeah. Okay, cooling them down to 5 Kelvin. But even at 5 Kelvin, these hydrogen atoms in your cloud of hydrogen atoms that you're trying to, like, probe, those hydrogen atoms are moving at hundreds of meters per second, which means there's going to be some moving towards you, some moving away from you. So there's going to be Doppler shifting, right? Yeah. And so if you're trying to measure the frequency of light, well, the guys that are coming towards you, they're going to be sensitive to shorter wavelengths. The guys that are going away from you are going to be sensitive to the longer wavelengths. And so you're going to have a Doppler broadening of your transition line. And the whole point is I really need to measure what frequency this transition line is at. Yes. Okay? So that's going to be a huge problem. We don't want a range. We want an explicit. No. Yeah. I want, like, this is the frequency. Yes. Right? Yes. And so here's what they – very cool. What they did was Doppler-free one-photon spectroscopy. Effectively, the idea is they custom-built, like, these active fiber-based retro reflectors, okay? And what it's going to do is it's going to fire a laser at the atoms. The atoms are going to capture it. And then it's going to – and then the atoms come back. It's going to reflect that perfectly back into the atoms, okay? The other thing that you want is you want this beam to be perfectly straight. the beam of the lasers it can't be like spreading out as it goes into my apparatus and the usual optics that's built for 486 nanometer wavelengths which is blue green you can buy that like kind of off the shelf okay there's like specialized science companies sure we can buy that stuff sure if it works for 486 it's not going to work for 410 okay at that at that like frequency like It's the opposite of diminishing returns. Every nanometer is a headache. So you need to build custom optics such that my beam is completely straight. My new purple laser that we also had to custom make, we have to keep it in line. And so we need these custom, literally like optics glass lenses that keep it in a line. And because it's a frequency where most optics are not built for it, that purple laser, it has to be fully cut. Yeah, so the whole thing is like a custom apparatus, right? We love it. And then on top of that, there's like the quantum hurdle, which is you're putting in light in here, right? And the light is like bouncing back because of the earlier thing that I said. Well, if the light is going there and bouncing back, now you've created a standing wave. Same thing with the guitar. When you like pluck a string, it's fastened at both ends so the wave is going to go back and forth. It's going to create a standing wave, right? There's going to be nodes and anti-nodes. Yes. Nodes are going to be where the light piles up. Yep. Anti nodes are going to be where there's nothing. Yes. Now, usually you don't care. Yes. But when we're doing a precision measurement where we've put hydrogen into the six P state, the electron orbital is a little bit like it's not spherical. Right. Right. Right. And so because it's got these nodes, that electron is going to start caring about where the anti nodes and the nodes are, because the electric field is going to be higher here, lower here, higher here. And so the hydrogen atom that you're trying to understand is now getting perturbed by the system itself. And so they had to make so many like Monte Carlo supercomputer simulations to model what would the electron do in this space. Yes. And then correct for that. Right, right. Because there's so much headache. Yeah. This took like 10 years. That makes sense. It's effectively you have to remove the noise. Yeah, it's called the Stark shift. Okay, okay, right. And in order to do that, you kind of have to simulate first. Yeah. Because it's too expensive to do it experimentally all these times. Well, it's like the experiment had this artifact in itself. It's like when you have Google Photos, and it's the feature where you take a photo of you and your loved one in a crowd, and then they have the Google Magic Eraser and you just select someone's head behind your head and then it's gone. And you shoot up. That's exactly what they do. Okay. But they need to build it and do the Monte Carlo simulation completely in order to then be able to even have the thingy that will remove the noise. So they have to... It's nuts. People, I love the... just the drive to not be beaten by Mother Nature. Yeah. No, dude, no. I will figure out what the size of the proton is. You are not going to stop me. Okay? It might take me 10 years, but I want to know how big the proton is. That's so good. Okay, that makes sense. Yeah, that makes sense. It's absolutely nuts. And finally, after all of this, they publish their measurement. They publish their measurement. Okay. The blue little starfighter that you see there. Yeah, in the middle. In the middle? Yeah. That is their measurement. 0.84. Okay. Okay. It is in line with their muon measurement that they took 16 years ago. So the 0.87, I think it was. The 0.87? Yeah. That's all these other. Things over to the right. Yeah. Eh. Eh. That is wrong. You are the weakest link. Goodbye. Yep. Yeah. So they corroborated that measurement. The muonic hydrogen is the same as the normal hydrogen. The true size of the proton is 0.84. And there is no new physics, at least in terms of the difference between the muon and the electron. They are indeed the same up to this significance. For all intents and purposes, the only thing that is different between the muon and the electron is their mass. Is the mass. That's it. That's it. And what this also means then is that the theorists have to go back to the woodshop. Yeah. And work on it. Yeah. They're going to they need to figure out what else to try. Right. Right. Right. Right. Because they were they were I bet you there were tons of theorists that were really hoping that this measurement was not going to fall within that bar. Right. That the muon was going to be here. The electron was going to be here. And they'd be like, oh, I got a theory for why. turns out they're not this is okay this is actually a really great story around understanding the refinement that goes through the scientific process on something like the size of a proton like why it's incredibly difficult yeah uh like fundamentally because it's so small like we can't just image it the way we normally would yeah and so we have to go through this process of understanding like how these things interact because how they interact creates these derivative variables that we can measure exactly that electron piece that we just talked about and then you can sort of now back calculate what you're looking for because you actually have a strong understanding of these other uh aspects of physics and the relationship between these different moving parts and again this tension between theory and experiment uh and the back and forth and sort of it's sort of like a it's sort of like a this it's like a dance yeah right uh where both influence and and counter argue each other at different periods in time however the theorists did not win this one no um i'm sorry i'm sorry to say yeah uh maybe next time maybe next time probably i mean maybe this is why they're always looking at string theory because then they the experimentalists can't yeah because on screen theory they're just like oh well you i I need like 10 more decimals on your measurement. And just keep going. Just keep going. To really validate my theory or not. But like jokes aside, I mean, you know, it seems like a relatively boring thing to talk about, just the size of the proton. But the techniques and the fact that this closed that door on there being some new physics, we don't have to think about that anymore. Right, right, right. Right? Like, lepton universality is a thing. The muon and the electron are the same except for mass. Yes. Now we can worry about other stuff. Yes. Right? We can put constraints on exotic particles. Like, there's zero anomalous deviation from quantum electrodynamics rules. Right? So, like, a bunch of other stuff about dark matter. Like, oh, there's this dark sector that maybe does this. Well, if that particular way of thinking about dark matter has a different measurement, it's wrong. Right. Right. Falsifiability is everything when it comes to physics, right? That's actually – I didn't think about that. What you're saying is this experimental result can now inform research areas that are not about measurements of these subatomic particles and things like that. But there's overlap conceptually or architecturally that if you were basing your dark matter model off of the original .087 theoretical framework. Sorry. Sorry. Yeah. Gotta love science. That's a good – that is a very good, very technical story. Yeah. But really, again, the fun to me is in the journey and the process. Yeah. Not simply the results. Like you said, the result. OK, the size of the proton. Great. Yeah. But all of those new techniques, again, also are not isolated to just this specific research question. Fantastic. That was the sort of physics story on the proton size. We have this out of Max Planck Institute, and it was in quantum for quantum optics in nature. I really like that one. I'll ask you questions off air about that one, because there's a couple more I have. We are going to go ahead and move into our last story, which is a medicine story about ALS. It's been in the news recently because of the recent death of Eric Dane. Many of you have maybe saw the Netflix documentary that came out. For those who may not know, Eric Dane is quite a famous and popular actor and father and husband. He was Dr. McSteamy in Grey's Anatomy, and he, while having been diagnosed with LAS, I think it was just over 10 months ago, excuse me, when he had found out about his diagnosis, he was just wrapping up the shooting of season three of Euphoria. unfortunately accelerated quite quickly. And so we just wanted to, you know, take this opportunity to, I think, cover, you know, what is the latest in the fight against ALS, understanding it a little bit, and then we'll end with a funding piece that's related to this research area. So, you know, we know it's something that a lot of folks have been not only talking about but have been personally impacted by because of the way in which Eric communicated just his worldview, what it's like to be a father, his message to his kids, the stories that he had to tell about life, I think really resonated with a lot of people. And so with that we going to kind of sort of look at the current landscape Yes I wanted to take this opportunity to talk about ALS the disease and one particular scientific paper that came out last year that is just you know one of the latest in a series of breakthroughs. Obviously, it's still very much a crisis in medicine. Amyotrophic lateral sclerosis. Okay, it's a catastrophic motor neuron degradation that happens with motor neurons specifically these are the neurons that relay signals to our muscles in order to have them contract als in its most advanced phase gets so bad that even the contraction of the muscles like the diaphragm that allow you to breathe because everything every every bit of movement in our body is from muscular contraction, right? And like, even the act of breathing becomes something that the brain cannot sustain, because these motor neurons have degraded so much. The survival is really quite bad. 2.3 to 5 years post diagnosis is on average. One of the most famous cases is Stephen Hawking. He survived for like 50 years, but it's extremely rare to do that. You know, now he's no longer the scientific hero that he once was, but he's one of these like rare, rare cases. Annual patient care is about 250,000 plus per patient, right? And the current incidence, it's about one to two per 100,000. By 2040, there's approximately going to be a 25% increase in the global prevalence. and if we talk about like there's there's really two types of ALS there's familial ALS which comes from genetic mutations that you inherit from your family that's only about 10 percent of cases 90 percent of cases are sporadic ALS there's all these polygenetic landscapes in our genome that causes sporadic ALS and that happens just there's no there's no familial history of it You know, you didn't carry a mutation from your parents. It just happened. So let's get into like what exactly is going on. Right. Okay. In our nervous system when ALS happens. So ALS, as I described, it's motor neurons, right? Yes. Motor neurons are extreme in scale. Okay. They can be about a meter long, a needle. That's three feet. Oh, wow. A single cell is the size of like three feet. Okay. If you think about it, it kind of makes sense, right? Because think about your spinal cord, like the lumbar spinal cord. Oh, sure, sure, sure. A single cell body is going to be located in the spinal cord, but it's going to relay signal all the way down to the foot. Yep, that makes sense. Okay, that's a single motor neuron that's doing it. Okay. Okay? Now, maintaining this kind of architecture over an extremely long distance requires moving a massive amount of physical cargo from one end of the cell to the other, right? You're going to have to move proteins. You're going to have to move mRNA from your nucleus if you want to make it all the way out there. You have to move mitochondria. You have to move synaptic vesicles, which are like the acetylcholine and the neurotransmitters. That's got to move. All of that stuff has to move across a meter, a yard worth of stuff. That requires a lot of energy, and it requires a lot of motion from these molecular motors. They're called kinesin. These kinesin are like little tiny molecular motors that actually step in eight nanometer steps on our microtubules. They're incredible machines that literally take up ATP, which is like the energy currency of our cell. And each ATP they take up gets them eight nanometers across. Now, eight nanometers, that's eight times ten to the negative nine meters. If I want to traverse a meter worth of stuff for a single molecule, that's on the order of 10 to the 8 ATP molecules that I need to hydrolyze. Right? It requires a lot of energy. Okay? And when you have a lot of ATP that's being built up and something is wrong in the genetics of that neuron, you're going to get an accumulation of misfolded proteins. Specifically, one of the main ones is TDP43. It's a misfolded protein that causes physical blockage and traffic jams on these highways of transport. And when you have that traffic jam, you're going to get into an energy crisis because now your mitochondria are unhappy. They don't have the actual raw materials. They're the factories that create the powerhouse of the cell or whatever. They're the factories that actually create this ATP. You're going to not have them being happy. those guys are going to create reactive oxygen species which are just like byproducts with oxygen oxygen is just like something that wants to react with everything and so it's going to cause effectively like rust in your neurons the same way that iron rusts in atmosphere if you don't have the correct machinery to regulate the reactive oxygen that reactive oxygen is going to go and go haywire in your neurons the the other analogy for this would sort of be on that highway It's just like you're getting potholes over time that never get fixed. You don't have Department of Transportation coming through and fixing it, and then it just deteriorates over time to a point where you can no longer traverse. Exactly. It's this thermodynamic breakdown, right? And ALS is like a macroscopic manifestation of that thermodynamic breakdown. You're right. The neuron can literally not maintain its highly ordered state because the mitochondria is doing all sorts of crap. the genetics is not being good enough to keep that low entropy state alive. Right. Right. And so you're getting just massive amounts of failure everywhere for these motor neurons. And the point is this is something that's – it's like it's across your whole system. Yeah, the entire motor neuron is just like – Right. Yeah. Right? Yeah. Now the muscle is not so much, but the motor neuron is like the wire that is sending the muscle the signal. Yeah. Right? Yeah. It's like in your car when your computer goes down Turn it however much you want The engine could be fine The engine is still fine It may not be but the engine is still fine But the problem is you don't have no But like your spark plug or whatever There's no electricity going there To do anything So that's the problem The motor neurons That are really just From overuse and from degradation Are not working properly and there's been a translational chasm in drug development it's not been good okay we've been as humanity incredible at solving medical problems als is one where historically it's just been beating us yeah and it's been crazy dude like there's the example of failed drugs like there's so many failed drugs because they all show pre-clinical promise in like rat models and mouse models but then when you get to humans it's all failed these are just some of the many there's i think something like more than a hundred that fail pre-clinical that pass the pre-clinical stage in mouse models yep but then when it comes to humans it's just all failure failure failure okay and what does that say what that says is we don't have a good model in the lab to test drugs for ALS. Right, because the point here is for a variety of things, we can use these rodent, mouse, rat models. We can use them as the test bed. There's so many where it works. That's why we do research on them. It works fine, and it's fantastic. It does not work for this. It does not work for ALS. There's something fundamentally different. And what we need is in lab a model that we can actually test drugs on. Right. Right. Right. That translates post-preclinical into actual – Into actually the clinical trials. And this is where the – now we can start talking about the paper that I want to talk about. Okay. The paper has to do with IPSCs. These are pluripotent stem cells. Okay. These are induced pluripotent stem cells. In 2012, the Nobel Prize went to John Gurdon and Shinya Yamanaka for the discovery that mature cells can be reprogrammed to become stem cells. Stem cells are cells that have not differentiated into skin cells or blood cells or bone cells or whatever. They are a clean slate. They don't have a specialization. They're like you in high school. Before you went into college and then got a major and now you're stuck at whatever path of life. Yes. Right? Yes. And what induced pluripotent stem cells do is now we're able to reprogram someone who's, let's say, been through college and a PhD, pre-program their brain so that now their brain is back in high school and they can be something totally different. Just to quickly note, that's like a phenomenal – like in and of itself, which is from the 2012 Nobel Prize, That concept is really powerful because effectively what you're saying is I'm in the college, high school, college analogy. If I get to age 45 and the world has changed and my industry has been replaced by insert whatever, I can just be almost reset to being 19, 20 and retrained again. Yeah. Without like with with a with a substrate that is not my 45 year old mind, but it's my 20 year old. Yeah. Yeah. Yeah, and this is really amazing for biomedical research because, one, we don't have to rely on embryonic stem cells. Yeah, that's a good point. Right? Because embryonic stem cells are really where the stem cells are, right? Because as an embryo, you can imagine from an egg and a sperm, that egg is just like a single cell. That single cell is now becoming my nails, my lungs, my skin, my muscles, my neurons, right? So somewhere that single cell becomes a ball. Yes. And that ball has a bunch of embryonic stem cells. Each cell now defines, I'm going to go become the eye. I'm going to go become a hair cell, so on and so forth. With this technology, we don't need embryonic stem cells. We can take skin cells, graft them, put them with a genetic cocktail, and then they are going to become stem cells on their own. Right? It completely changes the landscape. And that's why they won the Nobel Prize in 2012. That makes sense. Very much deserved because it's changed biomedical research, not just for ALS, but like countless other diseases. Okay. Now, as you can imagine, 2012, this is a pretty old technology. Sure. Right. We have tried to use it for ALS. There was this consortium called Answer ALS. They got 1,000 lines from ALS patients, and they tried to make lines of stem cells. But they observed the same problem. Which is that the drugs wouldn't, like the same drugs that don't work in clinical trials, they'll work in these pluripotent stem cells. They will. They will. So they were still missing something. Right. Right. It's like these stem cells were not mimicking ALS motor neurons. They were trying, right? We got the stem cells from ALS patients that have the disease. But when we make them into stem cells, when we get the donor skin cells from the ALS patients, when we try to make them into stem cells, they don't mimic the disease that the patient has. And that's the whole point. That's the whole point. So what's going on? Oh, which is interesting because – anyway. You understand what I'm saying though, right? Yeah, because like in my head, like intuitively I'd be like, oh, if you're taking my skin – let's say I have ALS in this example. but you take my skin cells and then you try to reprogram it. Yeah, and I try to make that into a motor neuron. Right. The problem is the motor neuron is not my body's stuff, which it hasn't. Yeah, it's like a normal motor neuron. Right. Because it's been reprogrammed. But the whole point is I want to make a model of ALS in a Petri dish. Correct. That I can then mess around with. Which currently we just can't get from the boilerplate out of the box induced pluripotent stem cell process. Exactly. And that is where we get to our study in 2025. It's an in vitro model of sporadic ALS. It was in nature neuroscience, large scale drug screening. And what they did was actually figure out how to make this happen. How to make petri dish level ALS that I can mess around with in a lab and really try to understand what drugs would work and what drugs wouldn't. Right. It's out of the University of Melbourne and the University of Queensland, a lot of like Australian universities. There's quite a few institutions that were collaborating on this. That's right. So what they did different is the following. So they got 100 patients with sporadic ALS. They got 11 patients with familial ALS. They got 25 healthy controls. They got skin cells from the dermal fibroblasts. that they extracted from skin biopsies from these patients and from these 25 controls. And they used something called non-integrating epizomal vectors. So usually when we try to make stem cells, like pluripotent stem cells from whatever donor skin cells that we got, I need to change the genetics of that skin cell to now forget everything about being a skin cell and go back to being a stem cell, right? Usually how we do that is we use a retrovirus. So a virus that has a piece of RNA. The RNA goes into the cell. That becomes reverse transcribed into DNA. The DNA then goes and gets mixed in with the native DNA of the cell. With these specific non-integrating eposomal vectors, this is DNA that just goes into the nucleus and hangs out. It doesn't get inserted into the chromosome of the cell. And what that does is prevent any random, you know, like off-target stuff from happening. It's exactly what we want. Yeah, it's just it's not messing with anything that's already there because the cell is already in this sort of – it's from an ALS patient. So it already has the mutations that are causing ALS. We don't want to mess with that because we want to replicate the ALS in my petriarch. Yes. So if I want to reprogram the cell to become a motor neuron, I just get my DNA, I put it in the nucleus, and I just have you hang out. Do not go inside the house. Yeah. Just hang out. Yeah. Okay. Right? Okay. Because previously the retrovirus insertion. Yeah, it would insert. And then that changes whatever is happening inside the house. And because this is such a combinatorial problem, we have no idea. We don't know. Yeah, we don't know what happened. But now we have total control. We're trying to have total control over each of the sort of second and third order steps that happen after we introduce it into the environment. Exactly, yeah. And they did a lot of other stuff that I'm not going to totally get into. But the way that they grew the stem cells, they actually withdrew a bunch of growth factors that would prevent artificial masking of this phenotype of ALS because we want that phenotype to actually happen. They also, like, cultured it under a strict 5% oxygen, which is this hypoxic condition that happens in the spinal cord. So it's something that you want to mimic when you want to make modern neurons that are like ALS. So we removed stuff that would potentially make it so that the system would make it go away. And we also created the environment that is almost identical to the environment that exists to remove, again, And any possibility that we're missing the mark in terms of the variables that need to be present in order to generate the results we're looking for with this new lab to be able to basically replicate it in a Petri dish. Yes. We want ALS motor neurons in a Petri dish. Right. And finally, the resulting purity that they got, it was exceptionally pure. Motor neurons, 90% were co-expressing the gene that we wanted. Okay. Very low contamination. So there's not that many astrocytes. There's not that many microglia. They're all motor neurons, and they all look great. Okay, so this is a very big deal. Yes. So finally, we might have our ALS and a Petri dish. Yes. And now we can start doing preclinical trials in our Petri dish with some confidence that it might work at the clinical stage. The point here is we're saying this ALS in a Petri dish is going to increase the conversion rate of preclinical targets to those that actually can make it into clinical trials. Because we are basically saying – we're basically recreating the environment that would be an actual human in the trials, not using the rodent, mouse, rat model, which can work in a lot of other cases. Yeah. But just in this one, it does not. Exactly. Okay. Yeah. And when they created these motor neurons in the Petri dish, those motor neurons would exhibit the same kind of damage that we got in ALS. They came up with this metric called the lethal day 50%. So it's the exact day when the total length of the neurites degrades by 50%, the maximum peak length. So it's like imagine you made the motor neuron, and then you press play. And then because of degradation, it's going to get shorter and shorter. Yes. They came up with this metric. That metric was correlated, and this is kind of the nail in the coffin that we have replicated in the Petri dish. It's kind of an unfortunate metric, but at the same time, it's a lot of hope for the future because here's what they did. That metric is correlated with the actual clinical patient survival time. Oh, interesting. Do you see what I'm saying? Yes. So the patient that I got my donation from of the skin cells that I made my pluripotent stem cells from to make into a motor neuron, the survival of that culture is correlated with the patient, him or herself. The unfortunate point being here is that the real world outcome for the patient in terms of time is correlated with our ALS in a Petri dish. Yes. Meaning it is replicating the problem that the patient had to a degree that it's matching. It's matching. One to one. Exactly, yeah. And it's unfortunate, but at the same time very encouraging because now we have the model in our Petri dish. Now we can finally start getting to the bottom of how do we fix this. Yes. Right? That's actually a really, really big deal. Yeah. And we didn't have this before. Right. Right. Right. Right. We were working with rats and mice that were just awful. It just didn't work. And now we've finally got a model. We were throwing darts at the board, but the board was behind it. Yeah, exactly. But what are we doing? What are we doing? But the challenge here is, again, to your point, the model, the sort of rodent model works in so many other arenas. It's fair to have extrapolated it to this one. Yeah, yeah, yeah. Totally. But after some time. Yeah, you're just like, okay, we need something else. We need something else. Yeah. This is totally not working. Yes. Right? So here's what they did. They made a screening library, 107 commercially available drugs from phase one to three ALS trials. So this is preclinical. Yes. Right? Pretty sure, right? Phase one to three is, I think, preclinical. And then 97% of the tested drugs failed. Yeah. And this is sort of what we want. Right. Because that's what we're seeing. We're seeing them pass the preclinical, and then only when they get to clinical stage in a human being, they're failing. Now they're failing in the Petri dish. I don't have to waste time and resources like actually doing a clinical trial if it's failing in the Petri dish. That's the whole point. Just to say it back to you, we took what was working in preclinical before but failing in humans. We took our Petri dish platform. We applied the already commercially available options, and 97% of them failed in our Petri dish. when they were passing in the preclinical in the prior regime. And so the point here is, again, not only are we seeing in patients the one-to-one on survival time, we are also seeing a one-to-one on efficacy in testing in this sort of pretrial environment, in this in-lab environment, not even in lab. Yeah. Meaning we can feel we can have some level of confidence that we are actually in a new 2.0 foundation for lab lab testing. Yeah. For lab testing. Exactly. And out of the 107, three actually did pretty good. OK. Ryluzol, memantine and bar baricitinib. I think that's how you pronounce it. Okay. So let's get into what each of these things are. Rilozol is actually something that people are already testing nowadays. It was dose dependent. There was significant rescue of this lethal day 50% metric that I was telling you about. And if we look at the physical effect on these neurons, there was restored electrical coherence. So on the top row, you see just control. Yes. On the bottom row, they're treated with rilazole. Yes. The neurons are healthier. Yeah, yeah, yeah. Right? They just look better. Yep. And it inverted the disease vector. So there was a negative correlation with, like, the effect and, like, how much time the neurons actually stayed alive. Yeah, yeah, yeah. So it was all a good thing, right? Yeah, yeah, yeah. The other two, they did not confer statistically significant population-level benefit. Okay. So you think, okay, maybe they're a dud, right? Not so fast. Okay. What if we combine the drugs, the three drugs that we had a positive effect on, what if we combine them? Is it a synergistic multiplication? Okay. Yeah. Right? And that is what they found. They found that each of these columns now is individual drugs, and then you add the two, right? Like there's Riluzol and Mimantin. Yeah, yeah, yeah. There's Barycitinib and Mimantin. And then there's all three. the more you added the three were somehow working together yeah to create a much better lasting effect i mean the the i mean the the visuals here are just yeah as you go from left to right right it's just like healthier and healthier neurons yeah yeah and then the basically on the the one with all three at all levels it's it basically maintains its structure yes whereas when we were just having untreated by the time we get to that yeah by the time i mean it's like the time axis is on the on the y axis yep so it's like it's like the earliest and then as time goes on they just start dying right i mean this but at the right most with all three of them it's like they're still alive that's astonishing yeah i mean i mean the level of impact yeah the level of impact on the combinatorial piece yes and then and then if you wanted to do okay so what is the ultimate zenith, right? Is it all three? Is it just two? You can actually test. Now that we've got a petri dish, we can do AV testing, right? On all the different patient donors, like all the different profiles. Is it only for sporadic? Is it only for familial? We can do all of these different categorical tests and figure out that the triple combination is actually the best, right? Each of those cells actually lasted for 30 more days. 30 might not seem like a lot, but it's actually a 6.5 times greater than just Rilazole alone. Right. Right. Right. And it's, I mean, this is something that we can now take to clinical trials. Right. Like, why don't we just try all three at the same time? Right. Right. Right. Let alone the fact that you have the new in-lab test bed. Yeah. That's a proof of concept now. To then do whatever. Yeah. To do, even if you say, let's not use these, let's now try something totally different. Yeah. The fact that we can now try something totally different. Right. It creates a scalable, robust, and pharmacologically predictive model of ALS. That's a really big deal. Yeah, I think it's a really big deal. I thought it was a really cool paper. And the clinical advantage is clearly there, right? Like all three drugs, you do a cocktail of all three drugs. These drugs are already FDA approved for other indications with known safety profiles. So they've got known safety profiles. Now we can actively start prescribing these to patients. The next step is to rapidly move to human clinical trials. But I think it's got a potential to fundamentally alter ALS therapy and projections, right? I thought it was very cool. It's very clearly a step function change in what can be done in therapeutics for ALS and research in this area. The area we wanted to just end on here Just given Congress actually just approved new funding for this This month is related to federal funding For ALS research So for those who might not be super familiar with Who pays for fundamental science And how do we actually get the money to do a lot of this stuff There are a variety of sources But the federal government has been a huge funder of fundamental science research since the Constitution. Let me – I don't know if you know this. Did you know this? In the Constitution, the word science is used, and I think like in Article 1, which is establishing the powers of the legislature. I think it's section eight clause eight is that they Congress must, you know, basically promote the investment and, you know, work in the sciences and related arts. Wow. And like the word science is an article one of the Constitution. That's really cool. That's got to be either Benjamin Franklin or Thomas Jefferson. 100 percent. One of those two. 100 percent. Yeah. And so it is a part of the ethos of even the founding document for our nation. It's a great document, by the way. It is. They were so ahead of time. Yeah, the Constitution is an amazing document. That's quite nice. If you have not read it, I encourage you because we now need to understand it now more than ever. But on February 3rd of this year, Congress approved new ALS funding for the fiscal year of 2026. So that means for the year that we're in, fiscal year of 2026, every year all these federal agencies and projects, they need to go – they need to be appropriated new funding by Congress. This is what Congress is always fighting about, among other things, is where should money be allocated. So for fiscal year 2026, meaning next year they're going to have to – all of the folks who are supporting and doing the nonprofit work to make sure Congress does continue to appropriate money are consistently advocating for this to be true. This year was a total of $315 million, which is the highest level of funding ever for ALS research. It's a lot of money. And I just briefly want to break down what the different categories of where this is going is. So $90 million is going for accelerating access to critical therapies for ALS. It's sort of called ACT for ALS at the NIH, National Institutes of Health, which is a $15 million increase over fiscal year 2025. They're going to do $30 million for Advanced Research Projects Agency for Health. This is like ARPA-H. It's like DARPA, but for health. And so this is net new funding. This $30 million is net new that didn't exist previously. $145 million for NIH general ALS research, not specific to critical therapies, which is sort of a subsidiary under NIH. $40 million for Department of Defense ALS research through their directed medical research programs. And then $10 million for the Center for Disease Control, CDC, and Prevention of National ALS Registry. So this funding is super important to generate outcomes. It is not just private pharmaceutical companies and all these things that are kind of working in these areas, especially in areas that are not perceived to be financially lucrative. Like my dad used to work in neglected diseases. So this is a lot of things that have impact the sub-Saharan African population, et cetera. There is no market for leishmania or African-seamling sickness, et cetera, et cetera. And so it can be difficult to actually get the funding to do this work that does impact. So kudos to Congress for being able to not only continue funding ALS research but increasing it and I think applying it in ways that are going to be impactful. And again, we are seeing the results of because through, you know, NIH, et cetera, they give grants out to universities and research labs. And so this money flows through the system in order to reach the experts like the folks we just talked about in the previous story. Yeah. Yeah. Incredibly important work. The U.S. has always been at the forefront. not always but since post-war we have been at the forefront of scientific research because our government has used our incredible economy to bankroll amazing scientific research and that shouldn't stop it should not it's incredibly important we had a fantastic big chunky over two hours i think we're just over two hours episode we got a little excited we had some pretty good stories i mean we started with the dream engineering which i still want to follow up on that was out of northwestern in the neuroscience of consciousness we followed up that up with understanding measuring protons uh where we actually confirmed the previous experimental measurement this was from max plack institute for quantum optics that was in nature we ended with a sort of high level overview of als research generally um and the rundown we had a bunch of stories in there as well. We are going to go ahead and wrap this up. Again, if you are still here listening, you are one of the core FFP nation. You are so important to us. We really appreciate those of you who find this interesting and valuable in your busy day. There's so many ways you can spend your time. There's so much infinite amounts of content. You're choosing to Spend your time with us, and we are grateful to you. We must do our comment for our late show listeners. Yes. I think you said you had an idea. Did I? I'm sorry. Brain fried. No, look. It's been two hours. Hey, what would you – if you could dream engineer. Oh, yeah. Yeah. What would you want to dream engineer in your dreams? Yes, that's a good one because that's – we're going to get some interesting answers. Yeah. So let us know in the comments below. Again, if you're listening, make sure you check us on video on Spotify and YouTube. We're on all the socials where we put out all of our clips. The clips don't always have the full context, so it's always encouraged to listen to the full podcast. If you would like to support the show, you can become a patron by going to ffppod.com slash donate. Follow, like, subscribe as always. My name is Lester Nare Joined as always by my co-host And our resident PhD Krishna Chowdhury We are just so grateful for you all We will see you all Next week And I now forget what I'm supposed to say With this is from First Principles at the end of the episode Something like that, yeah Something something This is from First Principles Cue exit music Thank you.