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

Hello internet. This is your captain speaking Lester Nar joined as always by my co-host and our resident PhD Krishna Chowdery. 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 [music] [music] 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 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. [laughter] 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, but now it's like actually real. There's there's a science paper >> that's out, [laughter] 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 Imoteep. Do you remember the mummy? >> Yeah. Yeah. Yeah. [laughter] Exactly. And then I mean it's been it's been a thing of research for psychoanalysis for quite a while. Sigman Freud was really into it. Carl Jung was really into it. But the modern birth really came in 1953 when Aserinsky and Clayiteman 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 electrphysiological 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've 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 and thinking about stuff, the electrphysiological signature during REM sleep is more similar to wakefulness >> than it is to other stages of sleep. >> Mhm. >> Right. So when we're dreaming, it's almost like the brain is kind of awake. Mhm. >> But obviously we're still asleep and it's been this really cool enigma to try to figure out. So the starting point was we you had the subjective experience from [snorts] 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 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 have you can now map the 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 dayto day, right? That's still correlational still, right? Because the subject has to recall the dream. Mhm. >> And then there's 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. It's like because like the more you're sleep deprived, the less you're really going to do novel stuff the next day. You need 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. Okay. This is getting good. >> It's pretty insane. So, let's talk about a little bit about um cognitive psychology. Okay. It's divided into two domains. When 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 web page is linked to other web pages. So you have web pages 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 um 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 does he want to play does 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 which I'm not because I have three of them. But let's say in this example, I'm afraid of dogs. So when I then see dog a dog, >> you know, I'm immediately now making nearby connections, which in this context might be fear, I need to run, uh how to like defensive like MMA, yes, like how to kick again, only because in my head, in this specific context, I'm afraid of dogs. And so I will be thinking of these associated contexts concepts in my own head that are related to a dog and then most adjacently fear and then all the things that come >> all all the stuff that comes around that. Right. Exactly. And [clears throat] so now let's try to 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 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 inhibi inhibition that goes on. There's these inhibitory neurons that are firing a lot during my awake state. And so only strong obvious connections are actually the ones that are going to pop up. >> Okay? 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 I work on it for like two hours and I'm stuck and I don't find a path forward. And that's when a lot of people say, "Why don't you just sleep on it?" The the idea is you have a problem. Imagine it like traffic >> like or you're trying to drive through Los Angeles. Yeah. >> But 50% of the roads are closed. Yeah. In your waking state and so you can't take the 405, you can't take the 101. >> That's that's really good analogy. [laughter] >> 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. You wind and you end up somewhere in Elsaundo. >> Yeah. >> Uh >> 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 [clears throat] 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 a kneeling, 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. Okay, this happens a lot of times in um like artificial neural networks. Yes. >> Like when you're 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. >> Mhm. >> And what you what you kind of do is you like increase there 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 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 ide we're trying to understand if you know is does dreaming loosen >> those parameters >> the process we made we made the analogy that's akin to a kneeling in physics >> but the simple way of saying it is is turning off the guard rails a little bit to expand the ability to explore into other areas. >> Yes, that's exactly right. Okay. Yeah. [clears throat] 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. 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 brain stem. Okay? Okay, the brain stem 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. Acetyloline 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 >> yes >> nature of the dreams like when you're like flying or like you're 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. >> So we're sort of seeing there's like a the chemistry balance of what's happening in the brain >> changes in between waking and sleeping state with this acetylcholine and uh what was the second one? It was >> norepinephrine. >> Norepinephrine. >> Yeah. >> The norepinephrine is the is the the guards. Yeah. That's the guardrails. the guardrails and the acetylcholine is what allows you to kind of explore a little bit more crazy. >> Yeah. And because that balances off during REM sleep, >> that's then it's like, well, what's over here? >> Yeah. Very interesting. >> Oh, I'm flying. Oh, that's a monster. Like [laughter] crazy stuff >> and during your dreams. >> 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 to 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 all this other kind of stuff. >> Makes total sense. >> It's it's it's it's pretty cool that we can go all the way down to the biochemistry level. >> Yeah. >> And like still see the subjective effects all the way up. I >> I 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. M 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 uh 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. Slowwave sleep, which is deep sleep, 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. >> Okay. That's why we don't act out our dreams. Well, I mean, some people do, and they've got a they've got a little >> We're acting out our dreams right now. [laughter] >> I 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 brain stem 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 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 like mind's eye uh while paralyzing your body movement. So, you don't have to worry about running into your TV. >> Yeah, [laughter] dude. That's exactly right. That's I mean, it's exactly what it's doing. It's like a little VR. >> Yeah. >> That's that's in our head that we're making up as we go along. So given all this given all these constraints, right? >> Yes. >> Now let's talk about how do we do sleep engineering, >> right? Cuz now we've set the basis of why we can actually look at dreaming uh from a quantitative perspective >> and understand the numbers. Yes. >> And see it. Yeah. >> 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 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. >> So theoretic 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 it it does. It's it's a signal that sort of triggers uh 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 there's they're getting a coincidence signal. >> Mhm. >> You know, >> I wish I knew this when we were in school, >> dude. [laughter] I mean, there's 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, nonREM sleep is the slowwave 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 um 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 um Rash and other authors have shown that this is like very much >> works. Okay, >> like those 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 Yeah. >> for soccer. Uh have it be known that had you let us play our rap music and then we played it during our sleep. during our sleep during during the sleep we would have we would have won more [laughter] games. >> Yeah. Yeah. Yeah. That's the big one, right? >> I'm being facitious a little bit here. >> Yeah. Um 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 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 an 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 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, >> okay, >> to be 20 subjects with high lucid dreaming, >> okay, >> propensity. Yeah. >> Do you know about lucid dreaming? >> I I am from We do live in LA. So, you know, there's a lot of people who love to talk about the woo woo. >> Yeah. >> Uh and lucid dreaming is very >> it's the the idea basically that you can have agency like you have awareness during dreams. >> During dreams and then you can make decisions, not just be on a roller coaster ride through. >> Yeah. Yeah. I mean it's 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, um here's what they did. >> They got 20 subjects with really high lucid dreaming propensity. Okay? 39 overnight sessions. And what these participants did was they tried to solve tasks like little puzzles. This is like a match stick puzzle which is like you you you you want to move exactly five matches such that the this like random scale that's made out of matches is exactly balanced. >> Mhm. >> And these are all insight driven puzzles. That's that's the key. They're not brute force puzzles. They're not like math algebra problems where there's a systematic, you know, algorithm that you can use to solve it. They're really like a creativity type of puzzle. Okay. [laughter] And participants usually used to have some amount of puzzles that were not solved. Okay, here's the key. Here's what they're doing. While they're solving the puzzles during the normal day, >> Mhm. >> there is an auditory cue for each puzzle. >> Okay. >> 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 4:00 a.m. they wake up. Okay. and they spend 20 minutes training their mind to associate another set of a auditory clues with a critical state of mind that will provoke lucidity. So, as they're falling asleep, so they wake up at 4:00 a.m. and as they're falling asleep, they get played these three-tonone beeps that are in ascending frequency. It's like beep beep beep. >> Mhm. >> Beep beep beep. >> You hear that? You hear that pitch, guys? You hear that? It's pitch perfect over here. But I mean, that's what they're doing, dude. >> No, no, no. I get you. >> It's kind of crazy. And and and what they're doing is as they're lightly falling asleep, they're aware of that dun, 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 while they're falling asleep. >> The 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 then like >> Oh, okay. Yeah, that's that's very much >> it puts you in a the right >> kind of in line >> and state that enables or makes it more likely that you would lucid dream. It's just interesting that there is an overlap because it's it's very much sort of in the you know people who are quantitative self self-improvement kind of universe. Um so it's fascinating that there's an over direct overlap here. >> I mean it's pretty crazy. Yes. That like I mean dude this stuff is just Yeah. I mean so here Okay. So getting back to this, right? So so they have this like ascending three-tone beep. Yes. 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 the same targeted memory reactivation queue, right? >> Of the puzzle. Yes. >> The puzzle sound is different from this >> lucid sound. Okay. >> Mhm. >> If they become lucid, >> right, >> they're gonna and we can monitor their brain state because we have electrodes on them. So, we can see when they enter REM sleep, >> right? 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 yoseite or something. >> This is so crazy. >> And like now you hear the you hear the 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 Yoseite. >> This is unbeliev I'm so I'm like we need to do a follow up on this cuz I'm 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 we start I don't know if I can take much more. >> Yeah. We we we basically have an a 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. The participants then go to sleep. >> Yeah. >> While they are sleeping separately from that set of sounds, we have a different set of sounds. They wake up at 4:00 a.m. and we're trying to induce lucidity while dreaming. And the second set of sounds helps to facilitate that inducement of lucidity while dream 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 Inception. >> Literally. Yeah. >> Literally where they could like 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 guard rails 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 podcast. >> I think I think this is absolutely insane. [laughter] Here's here's the here's the part that I thought was crazy. >> We need a cherry on top. >> Okay, this is the cherry on top. [laughter] Okay, there was so 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. >> Mhm. I want during the dream to know that this is happening. >> Yes. >> Okay. >> But there's a there's a caveat, right? This is going to be this is going to be kind of difficult because as I told you, the brain stem mediated paralysis >> Yes. >> 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. >> All right. Yeah. Yeah. 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? Like so I'm in Yose and I'm I'm like in my dreamscape. I start huffing and puffing in my dreamscape. Well, that's going to transfer >> into your phys >> 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. >> Yes. >> Stuff. I have the signal for 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 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. Yeah. while we're while we're trying to incept an idea to help them problem. Like on top of [laughter] >> on top of all of that, >> mate, I I'm at a loss for words because that is so it's it's kind of weird because literally the movie Inception >> is like kind of happening. >> It's kind of happening. >> Yeah, it's it's insane. There's no sharing of dreams. Fine, but like a lot of the stuff is there, right? And and the key to this two-way communication is that there's now real time irrefutable >> proof, >> right, >> that the cue was heard, >> right? >> It was integrated into the dream 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 this >> it's not just 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 I played the sound from the puzzle and the puzzle was in their dream. They were like solving it or doing whatever. Right. >> The targeted dream efficacy, right? the the subjects reported 40% more success on the cute puzzles versus the not cute puzzles. >> That's so crazy. So people who got the the inception >> Yeah. >> 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 got you didn't do well. Yeah. If you did get >> you you did a little bit better because like sleep always like like natural sleep >> will get you you know a little bit farther. But this is like engineered dreaming is is making you like 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 my mouth has been mostly open slack jaw for a lot of the story >> dude. Yeah it's 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 a broad effort to do dream engineering and sleep engineering in general. Um it reminded me of this study from MIT called dormio. They were actually targeting nonREM sleep. So this is um like the edge of sleep N1 which is called hypnosia. >> It's like right when you're falling asleep, okay? 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 um it would give a cue like think of a tree >> right when you were going to sleep. Okay, this thing was targeting um divergent creativity which is that I the the >> you know sorry this thing was actually targeting convergent creativity which is like the the going down going down the the the rabbit hole, right? And so and so we're we're 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 this is this this is very good stuff. I mean have you ever have you ever had a lucid dream before? >> I've had it once. Yeah. But I haven't I haven't been able to like control it, you know? >> I I I hadn't I want to say twice, but it was only awareness. It was not >> Yeah. I wasn't able to like literally be like, "All right, 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 like depending on whatever is in your subconscious and stuff like that, but >> um it is such a fascinating mindbending thing >> because it's so bizarre >> obviously >> um but you're there. It it it's it's like movies like Inception always make me 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 it. >> Obviously we will find out soon because now we can incept our dreams. >> Yeah. No, no. [laughter] I think this is I think this is opening up doors for all kinds of really cool research and there is clinical potential for this, right? For example, if you can do like nighttime neuroprothetics, 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 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. Know, 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 I I get remembered of um Tom Hardy's role in Inception as like the defense guy. He was like, "Dream dream a little bigger." And he like came up with a giant gun. >> Yep. >> Um it's like it's like so is dream sovereignty >> going to now be the premier neuroethical challenge? >> I mean this and this might this what's so I didn't even think about this nexus but while this might sound like oh this is early stage research and you know there's a ways to go which is true. uh if you just look at in the US the recent uh disclosures around uh what has been colloquially called Havana syndrome but is uh 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 are It started in Havana, Cuba, which is why it's called Havana syndrome, where you know 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, you know, neurological issues. Allegedly, the uh 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 US inventory. uh it's been an issue that the government's very hesitant to discuss or address I think because it touches on the same uh neurological sovereignty question >> that this issue brings up which is not an area we've really spent a lot of time thinking about but you know these technologies are giving us getting us to a place where >> we can have impact on individuals uh external to their brain >> Yeah. but impacts their brain. And that gets >> it gets tricky. >> That gets very dicey. >> Yeah, it gets very very tricky. It's it's a it's a frontier in neuroscience. I think it's going to be it's going to be incredibly exciting and a little bit weird. >> I just want to also briefly note that it it you know we talk about this is a science show so we talk about the research and like how it you know 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 uh 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 but once you know how it works you get why that's important to discuss >> but we just don't yet always know how it works so >> exactly yeah and I just wanted to end this story on a lighter note >> um there's a movie uh little indie film that I was involved in I was the scientific adviser on this film called gold mine from a mutual friend of ours was the producer director and writer of the film it is being distribut distributed on Amazon and other digital platforms starting February 24th, 2026. I was the um scientific adviser on the film. Um Lester was involved in some of the promotion of it. It's about it. It's a really nice story about uh fatheraughter relationship. It involves virtual reality, getting into the neural landscape of virtual reality, maybe some potential um therapeutic advantages of virtual reality in the future. I think you guys should go check it out. It's available on Amazon. Um starting pretty soon, I think this this this week. >> Yes, please go watch Goldmine. Big shout out to writer, director Matt Martyr, who was one of the original co-hosts on one of your past podcast endeavors, Dark Matters. Yeah. >> Uh so friend of the pod. >> Friend of the pod. >> Uh really really uh 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. Uh great story number one. Neuroscience story. Dream Engineering. Again, you're not you're not going to get these kind of stories anywhere else, folks. It's 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. uh 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 of 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 grock 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 your dreams later. >> Okay. >> Um, 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 us to cover something, anything, put it in the comments. >> Just 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 where you talk 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 ffpod.comdonate. 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 more robust uh 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 ffpod.com. Our first story in the rundown is a possible pulsar at the Milky Way Center. Uh, this is an astrophysics story. There's a super massive black hole at the center of our galaxy. What's going on here? >> Yes. What they're looking for with the super massive black hole, which is a very famous super massive black hole. It's the closest super massive 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 super massive 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 metronomic >> radio pulses from that point in the sky. >> Okay, this particular pulsar that they think they found is about 8 milliseconds. This is a millisecond pulsar. So every 8 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. The mass of the sun but the size of a city rotating around. Right. And given the cons conservation of angular momentum, this thing is not slowing down. >> Okay. >> This thing is going to keep going >> at 8 milliseconds. >> Mhm. >> 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, >> yes, >> we can test Einstein's general relativity >> to unprecedented degree. >> Right. >> That's why everyone's excited about this >> is the idea that the pulsar becomes an instrument in and of itself. >> Yes. Yes, >> that's exactly right. Cuz a lot of things can happen. the gravitational field of the black hole itself can start influencing the pulsar right and maybe if it wobbles a little bit less or the 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 and if there's a lot of warping by that black hole that light is going to get affected so monitoring ing this thing over years is going to give us extremely nice test >> for Einstein's general theory of relativity. That's why everyone's excited. It's not entirely um >> 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 STEI. >> Yeah. Yeah. >> Right. Um, and they 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's very that 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 this is this is very exciting. That's actually a huge deal. Uh that was our story number one, Colombia Astrophysical Journey. For number two, uh, decoding an ancient Roman board game, an archaeology story about, uh, how AI has now helped us figure out the rules, uh, for this unknown board game from the Roman period. Yeah, this one was pretty cool. Okay, so there's a 2,000-y 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 >> 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. >> God, >> with various rules, >> right? >> Right. >> So, an AI plays with itself. This is kind of like how deep Google DeepMind trained um Alph Go. 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 they made an online version of it. It's called Ludus um Coriovali. It's played with four hounds trying to trap two hairs. That's what they called it. Okay. But it's it's very cool because it's like a use of AI to something that I literally never thought about. >> Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. And it's a clever it's a clever use case because >> you you have some kind of physical indicator which is the wear Yeah. >> 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 like makes logical sense but I never would have >> I never would have thought of that. Yeah. Yeah. And I mean it's really cool because this discovery pushes the known history of these types of board games back several centuries now, you know. >> Fant uh we we love our archaeology. We 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, right? >> Yes. >> Bigger brain size means we're smarter, means we're more humanlike 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. Um 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. And that's also related to larger brain size. >> Boo. >> Um, >> 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 [laughter] individual. Fair, very fair, very fair, very fair. >> You know, because because I think I think what they're saying is the effect is something where these two genes are correlated. >> Okay. >> Right. >> Okay. >> And it's in line with a lot of um recent research that shows that there's like this 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 cultural 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, um high values of this 2D to 4D in males, the the the ring finger to the um index finger, they've been found with elevated rates of heart problems, poor sperm counts, predisposition [clears throat] to schizophrenia in males. I see. >> Okay. And it seems that this stuff is coming from prenatal hormones from the fact that we are >> um 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 I'll go with the large brain size even though it comes with all of these problems. >> That's that's fascinating. No, that that that isn't the I I you know the I'll let me not I'll leave it. Yeah, I I won't let me leave it. >> This is out of the Swansia University and Istanbul University in the journal Early Human Development. I thought it was I thought it was quite interesting because you know we always think about how genes are related to each other when they when they go through evolution. So So this is something that's very important. I mean, you could you can also map this on to this ide, you know, when people talk about quote unquote masculinity and the change in body, the body stature of like men generally over time, there's this if there's a correlation higher brain size equals to the skeletal structure of us being slightly different. That is an interesting >> that is an interesting angle. Yeah. >> But that like we kind of see manifesting unfortunately in the culture war, but has sort of this uh genetic uh basis for it. Fascinating story number three. Our last story of the rundown combines AI with bioengineering. So, no no risk here at all. A new machine learning pipeline that is streamlining protein engineering. We've talked about AlphaFold uh God knows how many times. Yes, >> we've gotten comments. Yes, we understand it's not perfect. Nothing we talk about on the podcast is perfect. >> No, this is science. >> We get that. Um, 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. >> Mhm. >> Okay. What if we wanted to design proteins? If we want to design proteins, the search space is incredibly big. Because if I have a 100 amino acids that I want to fit into a protein, there's 20 choices for each amino acid. That's 20 to the 100th power, there's more atoms in the universe. So, I'm not going to like I I need ways to like hone down if I want to design a new protein. >> Mhm. >> 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 a hundred different variants, >> right, >> of my proteins. So what's the best way to choose which hundred 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 Ark Institute are doing with this particular story that came out in science. They created something called multi-evolve. >> The idea is they 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? Mhm. >> Is the antibbody 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 multie evolve paradigm is actually going to do. And what what what's very cool about this is now we've put the lab and the AI in the loop. >> Right. Right. Yeah. >> Okay. like it's one big loop 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, right? And so all of these relationships are now something that I can parameterize with this multi-evolve model, right? And bio 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 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 um that people are finding ways to integrate it that is changing outcomes that is influencing >> changing yeah >> influencing outcomes 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 um that's just not how it works. Uh it's not perfect, but it is it is filling in gaps that are significantly meaningful. >> Yeah. >> Great rundown this week. I mean, it's going to be the the first story, the rundown. These are some >> these are cool stuff. >> This is 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. Uh 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 uh 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 Higs Bzon comes out of it. The electron, the proton is actually not in the standard model. You have up and down quirks. Those are the constituents of the protons and the neutrons. >> And it's incredible. [snorts] >> It is incredible, but it is incomplete. There's no dark matter. There's [clears throat] no dark energy >> and there's no gravity. >> Mhm. 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 a grand unified theory. >> Yeah. Yeah. Not just reunification but also these new particles like is dark matter a particle? >> Oh understood. 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. [laughter] So there's got to be a way for us to find out, 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 Hydron 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. >> Okay? >> 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 um photograph is from the Maxplank Institute and I saw this actually um on LinkedIn >> from the author of the study that we're going to talk about Loar Misenbacher. >> Okay. >> 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 um a colloquium that I had attended at UCLA when I was a PhD student there. It was a colloquium given by Dave Deile who's now at University of Chicago. He used to be at Yale. >> He was one of these precision physicists um atomic molecular optical physicists. Yep. >> And he was showing you know the lab setup and he was trying to 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. >> Okay? >> 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 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 0.0001 kilometers and he would do a dig at the particle accelerator guys and be like I'm going to put this um in a unit of length that you know these particle accelerator guys can understand. So this is about [laughter] 0.001 km. Um the size of my lens is about 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 I it was so funny. Look, it's okay to take the piss every once in a while. >> Yeah. Yeah. Yeah. He's he was a really funny funny guy. Really great speaker as well. So, what these guys are trying to do at the Maxplank Institute >> when they came out with this landmark nature paper in the February 2026, so just this this month, is try to solve the proton radius puzzle. Okay. There's been a decadel long discrepancy in how big the proton is. >> Right. >> Okay. Right. And this particular paper in nature is trying to put that to rest. Yes. >> Okay. >> Subp part per trillion test >> of the standard model with atomic hydrogen. What a great name. [laughter] >> You know they're testing the standard model. >> Yes. >> With just a normal hydrogen atom. >> Okay. >> Okay. This is out of the Maxplank Institute for Quantum Optics. Yes. >> And the precision is 0.7 parts per trillion. >> That's that's that's pretty 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. Um 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. [laughter] And um it's something that we do on our first first uh semester quantum mechanics you do the Schroinger equation and you solve the hydrogen atom because because it is the only um 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 we 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 [clears throat] can discern >> the structure of the atom inside. >> That's always the game when it comes to quantum optics. Mhm. >> Okay. >> Mhm. >> Now, these shells are not just like simple bore shells. They're actually very complicated. >> The hydrogen wave function >> looks like very cool clouds. >> Yes. >> 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 what we want to do when we want to probe something like what is the size of the proton? >> Yes. 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. >> Yep. >> Which will then change the light that comes out when electrons transition from one orbital to the other. >> Yes. Yes. [snorts] >> Is that perfectly clear? >> Yeah. So at different sizes of the proton >> m >> uh the way in which we can uh look at the 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 uh like the the color spectra like the energy and how it appears on the as a means by which to identify what those cloud configurations will look like. >> Yes. If the proton is a certain side, the cloud will look a tiny bit different, which means that the >> um which means that the the light that comes out when the electron transitions from one cloud, one energy state to another is going to be slightly different frequency. And if we can measure that very precisely, then we can tell >> back calculate what the size of the proton is. >> I I I see what you're saying. And so, okay. Yes. Makes sense. Okay. So what what what we're getting at is is we're not measuring the proton. >> Yeah. You're not taking a ruler, >> right? And looking like, hey, here's the it's you know three angstrom. What we're saying is there is there is an emission >> uh 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 quote. I'm using emission as a loose. Yes. >> Term here. >> Exactly. And if we look at photo number eight, right? >> Yes. >> The electrons are in these different clouds. >> Yes. >> There's there's different orbitals. For those in chemistry, you guys will remember something called 1s, 1 s2, 2 s2, 2p6, 3s2, 3p6. It's like a rap game that we used to memorize about all the electron configurations of each of the elements on the periodic table. Um, 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 loed in particular axes. Like there's the x axis, the y ais, and the z-axis, right? And that's that second energy level. The first energy level, there's just the sphere. The second energy level, the electron can be in the sphere, or it can be in these like dung bell 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, 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 >> the center, 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 a sense of the scale of what we're talking about, right? >> [laughter] >> Because because we're let's not 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. >> The the proton at the center of the hydrogen atom is measured in phento which is 10 theus15 m. >> Okay. >> The hydrogen atom itself is about an angstrom which is 10us 10. Okay. So there's a there's a 100,000 full 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 P. >> Oh my god. >> At the 50 yard line. >> Oh my god. >> That's not That's not the worst part, though. Okay. In order to discern the size of the P, we are looking at an electron that is in the grand stance. >> Oh my god, >> this is so >> You see what I'm saying? Yeah, that's ridiculous. >> Because the electron is hanging out all the way out here. >> We're looking at the behavior of these electrons that are hanging out at the grand stands 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. insane that we can do these kinds of things. >> That's what I'm saying. That like that's Yeah. Cuz I'm trying to even think about how you would do that at macro scale. >> Yeah. Yeah. [laughter] Yeah. If I had a P. >> Yeah. Yeah. If I had a chaotic, >> what kind of telephoto? >> Oh, yeah. Right. Would I even >> lens would I need at the at the grandstand >> to be able to >> Yeah. >> Okay. So, the this is something that is so infantestimally small. Uh and meaning that the way in which we do all like the engineering we do to even be able to do this stuff has to be able to operate at these impossibly small >> Yes. >> scales. >> Yeah. And at impossibly small error, >> right? >> That's the main part. >> Right. Right. >> You're chasing down like 12 decimal places. >> Right. >> Okay. >> Right. That's a really important that's a really important point because it's not just like oh we got to know it's like >> to Yeah. to a level of specificity where 12 dB is crazy, >> right? Which it's insane. It's like, no, it's not that. It's actually >> this. >> Yeah. Okay. >> Oh my god, my brain. >> Yeah. It's it's it's absolutely nuts. Okay. So, here here's how we're going to do it. >> 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 >> and it's a giant equation. >> For those listening there's like I don't know 23 different characters and it's just it is >> and it depends on crucially it depends on two things okay it depends on something called the Ryberg 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. Yes. That are coming out of my atomic hydrogen. >> Okay. That makes sense. That makes sense. 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. M okay what they want to do here is observe that transition frequency >> and this from the the 2s state was the the larger spherical state that we >> that we had talked about earlier this is even bigger than that this is like way out in the grandstands to let's say like on the on the sidelines >> right the electron is going from the grandstand all the way to like the the really nice seats >> 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 because part of uh part of what I said earlier, which may or may not be true, it's like, you know, why do why can't we just measure it directly? >> Yeah. Well, I mean, how would you measure something that is a phentometer, >> 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? And it's like, well, the problem is you you it's it's what are we going to use to do that? >> Exactly. I mean I mean you could think, right? Like what if we just like set light? >> Okay. >> Okay. What if we just had like um light at a phentometer? >> Mhm. >> How I think I think that kind of light is extremely high frequency. >> Okay. >> Okay. Like a light at a wavelength of a phentoter. Yeah. >> Anything anything larger than the obstacle. If if I were to shine like visible light, which is like 400 nanome, >> right, >> on something that is a phento nan 400 nanome is 10 - 7 m. >> This thing is 10us 15. Imagine a giant ocean wave. >> Yeah. >> And then there's a pebble in the way. >> Okay. >> Is the ocean wave going to care? >> No. >> No. No. >> Right. The way we image things is that the the wavelength of light needs to be way smaller than the thing we're measuring because then the light bends and like refracts and like bounces off. This is important. >> But if it's like the you know >> the 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 it's it's a th 10,000th >> 100,000 >> 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 the 100 foot wave in Nazare Portugal. Yes in Nazare Portugal. Do you think the Nazare Portugal wave cares about a tiny bit of grain of sand misplaced one way or the other? No, it really doesn't. So we got to get really clever with this kind of stuff. >> So we can't just image it the way we normally do optical imaging. It's just the scale is not uh it just it doesn't even make sense. >> It doesn't even make sense. Yeah. Exactly. And so people have done it before. People have measured the the size of a proton before. Yes. And it came out to about 0.8758 >> according to this giant consortium where where they they had like um electrons that would scatter off nuclei and then they would try to figure out okay like what is the what is the size of the proton in there. They also had something called electronic hydrogen spectroscopy. It's a traditional laser measurement kind of similar but like not as like insane as the one that we're going to talk about. And they got it to about 0.8758 phentometers. >> Right. >> Then in 2010 there was a bombshell. >> Right. >> Because some of the same guys in this current paper. >> Yeah. Yeah. >> 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 this was 15 16 almost 16 years ago. >> Almost 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 time more 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? 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. >> Yeah. Yeah. >> And turn it into like, you know, a high school stadium. >> So, so >> now the 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 the the measurement we're trying to make is the distance between the electron and the grandstands and the proton at the 50 R. >> 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 the the delta for error is much smaller. >> Yeah. Because we've effectively make made the atom smaller, >> right? Right. And this the the key idea here was the muon as the orbiting uh particle as opposed to a standard electron. It was much more massive which meant the proton necessarily need to be smaller which is what creates >> the atom needs to be smaller. >> Excuse me. The atom needs to be smaller which is what collapsed the surface area of the measurement to be this >> more dealwithable. >> Yes. >> Size. >> 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 that is unheard of for physicists who are doing precision measurement. They're like, "This is this is absolutely awful." >> Okay. >> Okay. >> That's like a almost um >> that's almost 5% difference. >> Yeah. Well, that's unacceptable. >> That's unacceptable. Okay. In every sense of the word. [laughter] There's two there's two possibilities. >> Okay. >> Okay. Either our theory is wrong. Meaning lepttons are not universal. We used to think that the muon and the electron, the only thing that's different between them is the mass. >> Mhm. >> But what if there's new physics? >> Mhm. >> 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. 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. >> We need to really figure out if that first measurement was actually wrong. >> Correct. Cuz that's that's actually a Okay. Yes. >> 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 and there's like now the theorists are super happy. >> Right. >> Right. Right. or the experimentalists are not happy and these new experimentalists are happy because they like their muonic hydrogen measurement was actually correct and it's like everybody else >> oh theorist go back to the the chalkboard >> yeah yeah [laughter] it's not it's not a field day for you guys right and so that's where this current um experiment comes in >> oh this is fascinating they replicated that muon experiment >> okay from from 2010 >> from 2010 they've been working on it for 10 years >> to try and nail down this measurement using just normal storegrade hydrogen. >> Okay. >> Okay. And it's incredibly difficult. >> This is going back to our bigger stadium instead of our muonic smaller >> stadium. 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. Yes. >> And it's just incredible some of the stuff that they were dealing with. Dude, >> it's it's actually insane. Okay. the the transition that they were trying to figure out was this 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 nanome. >> 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 got to make a laser at 410 nanometers. 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 um stuck that inside a nonlinear optical crystal. And what this crystal did was, you know how when you like play guitar and um 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 8 820 nanometers, but then effectively squishes the wavelength by 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 is the same idea. When you jog the wheel up, it >> Yeah. The octave just goes up. This is just like it's going from, you know, a C to the uh the C. That's the dodo. Yeah. You know, it's just the octave is is now up. >> Yes. 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. [laughter] Honestly, at this rate, this is this is pretty trivial. 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/s, >> 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? 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 over 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. >> Yeah. >> I want like this is the frequency. >> Yes. >> Right. 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 fiberbased retroreflectors, 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. >> Mhm. >> The beam of the lasers, it can't be like spreading out as it [clears throat] 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 where you can buy that stuff. >> Sure. >> If it works for 486, it's not going to work for 410. >> Okay. At at that at that like frequency, like it's it's the opposite of diminishing returns. Like every nanometer is a headache. So, you need to build custom optics such that my beam is like completely straight. >> My new purple laser that we also had to custom make. >> Yes. >> We have to keep it in line. And so, we need these custom like literally like optics glass lenses >> that keep it in a line. >> Yeah. >> And because it's a frequency where most optics are not built for it, that purple laser, it's fully it has to be fully >> custom. Yeah. Yeah. So, the whole thing is like a custom apparatus, right? And then and then on top of that there's like the quantum hurdle which is you're you're you're putting in light in here, right? And the light is like bouncing back because of that 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. Nodes are going to be where the light piles up. >> Anti-nodes are going to be where there's nothing. Yes. >> Now, usually you don't care, >> but when we're doing a precision measurement where we've put hydrogen into the 6p state, the electron orbital is a little bit like it's not spherical, 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 the hydrogen atom that you're trying to >> understand is now getting perturbed by the system itself. Right. >> And so they had to make so many like Monte Carlo supercomputer simulations to model what would the electron do. >> Right. >> In this space. >> Yes. >> And and then correct for that. >> Right. Right. Because because it [laughter] >> there's so much headache which is why this took like 10 years. >> That that makes sense. It it's 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 you have to simulate first. Yeah. >> Because it's too expensive to do it experimentally all these times. >> No, but yeah, it's like Well, it's like the experiment has this artifact >> in it in it. So in itself, I got you. You need you need It's like um it's like when you have a 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 and then it's gone >> and you shoot it up. That's exactly what they're doing, right? Yeah, but but they need to build it and like do the Monte Carlo simulation completely >> in order to then be able to even have the thingy that will remove the noise. So they they have to >> like [laughter] >> it's nuts, >> people. I love the I love the uh the just the drive to not be beaten. >> Yeah. >> By mother nature. >> No, [laughter] dude. No, it's like it's I will figure out what the size of the proton is. You are not going to stop me. [laughter] >> Okay, it might take me 10 years, but I want to know how big the proton is. [laughter] >> That's so good. Okay, that makes sense. >> Yeah, it's it's absolutely nuts. And finally, after all of this, they publish their measurement. >> They publish their measurement. >> Okay. >> The blue little star fighter that you see there. >> Yeah. Yeah. In the middle. >> In the middle. >> Yeah. >> That is their measurement. 0.84. Okay. >> One. >> Okay. >> It is in line with their muon measurement that they took 16 years ago. >> So the 87 I think it was >> the 0.87 that's all these other >> things over to the right. >> Yeah. >> That is wrong. >> You are the weakest link. Goodbye. >> Yep. Yeah. So so they corroborated that measurement. The muonic hydrogen is the same as the normal hydrogen. The true size of the proton is 0.84. 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. >> Mhm. >> Mhm. >> 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 wood shop, woodshed, and work on it. >> Yeah. They're going to they they need to figure out what else to try. >> Right. Right. Right? Because they were they were I I bet you there were tons of theorists that were really hoping that this measurement was not going to fall within that air bar, right? That that that the muon was going to be here, the electron was going to be here, and they'd be like, "Oh, I I got a theory for why they're right." [laughter] >> 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. 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 uh and the back and forth and sort of it's it's sort of like a it's sort of like a this it's it's like a dance, right? Uh where both influence and and counterargue 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, this maybe is this why they're always looking at string theory cuz then they the experimentalists can't >> Yeah, cuz on string theory [laughter] they're just like, "Oh, well, you I need like 10 more decimals on your measure and just keep going. >> Let's keep going." >> To 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 the fact that this closed that door, >> right, >> on there being some new physics. >> Mhm. >> We don't have to think about that anymore. >> Right. Right. >> Right. Like leptton universality is a thing. The muon and the electron are the same except for mass. Yes. >> Now we can worry about other stuff. Right. You can put constraints on exotic particles like there's zero anomalous deviation from quantum electronamics 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 uh this experimental result can now inform research areas that are not about uh like 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 0087 theoretical framework. >> Sorry. >> Sorry. [laughter] Yeah. Gotta love sigh. That's a good that is a very good very technical story. >> Yeah. >> Um but really again the the the fun to me is in the journey and the process. >> Yeah. >> Um not simply the result cuz like you said the result. Okay. The size of the proton. Great. >> Um >> but all of those new techniques again also are not isolated to just this specific research question. Uh fantastic. That was uh the sort of physics story on the proton size. We have this out of Maxplank Institute and it was in uh quantum op for quantum optics in nature. Uh I just I I'll ask you like that one. >> I'll ask you questions off here about that one because there's there's a couple more I have. We are going to go ahead and move into our last story which is uh a medicine story about ALS. Um, it's been in the news recently um because of the uh recent death of Eric Dayne. Um, many of you have maybe saw the uh Netflix documentary that came out. Um, for those who may not know, uh, Eric Dayne uh is quite a famous and popular uh actor and father and husband. Uh, he was Dr. McTeami in Grey's Anatomy. uh and he uh while was while having been diagnosed with l uh I think it was just over 10 months ago uh excuse me um when he had found out about his diagnosis he was just wrapping up the shooting of season 3 of Euphoria um uh unfortunately accelerated quite quickly and so we just wanted to um you know take this opportunity to I think cover you know what is the latest in the the fight against ALS um understanding it a little bit and then we'll end with uh a funding piece that's related to this research area. Um, 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 um, Eric communicated uh, just his, you know, worldview, his 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. Um, and so with that, uh, we're 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. amotrophic 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 the 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 five 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. Um, you know, now he's no longer the scientific hero that we that he once was, but he's he's one of these like rare rare cases. >> Um, annual patient care is about 250,000 plus per patient, right? and the current incidents it's about one to two per 100 thousand. Um by 2040 there's approximately going to be a 25% increase in the global prevalence. >> Mhm. >> 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% of cases. 90% 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, [clears throat] let's get into like what exactly is going on, okay, in our nervous system when ALS happens. So, ALS as I as I described, it's motor neurons, right? >> Yes. Motor neurons are extreme in scale. >> Okay? >> They can be about a meter long. A mele that's 3 ft. >> Oh wow. >> A single cell >> is the size of like 3 ft. >> Okay. >> If you think about it, it kind of makes sense, right? Because um think about your spinal cord, like the lumbar spinal cord. >> 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. [snorts] 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 like, you know, make make it all the way out there. You have to move mitochondria. You have to you 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, >> right? >> That requires a lot of energy and it requires a lot of motion from these molecular motors. They're called chinesin. >> These kynines are like little tiny molecular motors that actually step in 8 nanometer steps. >> Oh, >> 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 require gets them 8 nanome across. >> Mhm. >> Now 8 nanometers that's 8 * 10 the 9 m. If I want to traverse a meter worth of stuff for a single molecule that's on the order of 10 the 8 >> Mhm. >> ATP molecules that I need to hydrarolyze. Y >> right. Y >> 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 [clears throat] jams >> on these highways of transport, >> right? 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, you know, the 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 go 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. So you don't have do 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 >> the genetics is not being good enough to keep that low entropy state alive. >> Right. Right. Right. And so and so you're getting >> just massive amounts of failure everywhere >> for these motor [clears throat] neurons. >> And the point is this is something that's it's un it's like it's across your your whole system. >> Yeah. The entire motor neuron like is just like >> Right. Yeah. >> Right. >> Now the muscle's not so much but the motor neuron is like the wire >> that is sending the muscle the signal. >> Yeah. Right. >> Yeah. >> It's like like in your car when your computer goes down. >> Yeah. the it you turn it however much you want. The engine could be fine. >> Fine. The engine's still fine. It may not be, but the engine's still fine. And but the problem is you don't have >> but like but like your spark plug or whatever like there's no electricity going there to do anything. >> Yeah, that makes sense. >> You know? Yeah. >> So, so that's that's the problem. It's the motor neurons that are that are that are really just >> from overuse and from degradation are not working properly. Mhm. >> 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 a more than a hundred that fail preclinical that pass the pre-clinical stage in mouse models. >> Yep. >> But then when it go 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 the point here is is for a variety of things, >> we can use these rodent, mouse, rat model. We can use them as the test bed and >> there's so many where it works. That's why we do research on >> it. It just 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. That that that translates post preclinical into actually looking >> into actually the clinical trials. >> And this is where the um >> now we can start talking about the paper that I want to talk about. The paper has to do with IPS-C's. These are pur potent stem cells. >> Okay. >> Okay. >> These are induced puro potent stem cells. In 2012, the Nobel Prize went to John Girden and Shina Yamanaka for the discovery that mature cells can be pre can be reprogrammed >> to become stem cells. Stem cells are stem so stem cells are cells that um have not differentiated into like you know 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 you know path of life, >> right? And what induced pur potent 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 like that's like a phenomenal like in and of itself this which is from the 2012 uh Nobel Prize that concept is really powerful because effectively what you're saying is >> uh 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 uh I can just be almost reset to being 1920 >> and retrained. again. >> Yeah. >> Uh without like with with a with a substrate that is not my 45year-old mind, but it's my 20-y old mind. >> Yeah. Yeah. And this is really amazing for biomedical research because one, we don't have to rely on embryionic stem cells. >> Yeah, that's a good point. >> Right. Because embryionic 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 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 embryionic 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 embryionic 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. >> Um >> 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 a thousand 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 in pluropotent stem cells >> they will >> they will so >> interesting >> they were still missing something >> right >> right it's like these stem cells were not mimicking >> ALS motor neurons M >> they were they were trying right we got 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 what's going on >> oh which which is interesting because >> anyway that you see you understand what I'm saying though right >> yeah because like in my head like intuitively I'd be like oh well If you're taking my my skin I let's say I have I have ALS in this example. You take my skin cells uh and then you try to reprogram it. >> Yeah. And I'm I try to make that into a motor neuron, >> right? The problem is the motor neuron is not my body's stuff which has >> Yeah. It's just like a normal motor neuron. >> Neuron, right? Because it's been >> reprod but the whole point is I want to make >> right >> 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 p stem cell and that is where we get to our study in 2025. It's an in vitro model of sporadic ALS. It was a nature neuroscience large scale drug screening. >> And what they did was actually figure out how to make this happen. Okay. >> How to make petri dish >> level Yes. >> 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. Mhm. Mhm. >> Um it's out of the University of Melbourne and the University of Queensland. A lot of like Australian universities. >> There's 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 um 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 episomal vectors. So usually when we try to make stem cells like pluropotent stem cells from whatever donor like skin cells that we got I need to 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 when what we how we do that is we um use a retrovirus. >> Okay. >> 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 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 offtarget stuff from happening. >> It's exactly what we want. Exactly. >> 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, >> right, >> that are causing ALS. We don't want to mess with that because we want to replicate the ALS in my petri dish. >> 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. >> Just hang out. >> Yeah. Yeah. Yeah. Okay. Okay. Okay. Because previously the retrovirus insertion >> Yeah. It would insert >> and then >> and then that changes whatever is happening inside the house. >> And because this is such a comal problem, we have no idea. We don't know. >> Yeah. We don't know what happens. >> 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 other stuff that I'm not going to totally get into, but like they the way that they um >> 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 strict 5% oxygen, which is this hypoxic condition that happens in the spinal cord. Yeah. >> So, it's something that you want to mimic when you want to make modern neurons that are um like ALS. >> So, we remove stuff that would potentially make it so that the thing would like the system would make it go away and and we also created the environment that is almost identical to the environment that exists >> to remove again any possibility >> that we're missing the mark in terms of the in like the variables that need to be present in order to generate the results we're looking for. with this new in lab they' 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 coexpressing the the gene that that we wanted. Okay. Um very low contamination. So there's not that many astroytes. There's not that many microglea. They're all motor neurons and they're all they all look great. >> This is a Okay. Okay. So this this this is a very big deal. >> Yes. So finally we might have our ALS in a petri dish. >> Yes. >> And now we can start doing preclinical trials in our [clears throat] petri dish >> with some confidence >> that it might work >> at the clinical stage. >> The point here is we're we're saying this uh 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. M 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. Yes. >> Yeah. And when they created these um 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 they came up with this metric called the lethal day 50%. So it's the exact day um when the total length of the neurites degrades by 50% the maximum peak length. So it's like imagine like you made the motor neuron and then you like press play and then you know because of degradation it's going to get shorter and shorter. >> They came up with this metric that metric was correlated and this is 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? So the patient that I got my donation from of the skin cells that I made my pur potent stem cells from to make into a motor neuron the survival of that >> culture is correlated with the patient him or herself. >> The the the you know the unfortunate point being here is that the real world uh outcome for the patient in terms of time >> is correlated with our ALS in a petri dish. >> Yes. Meaning it is it is replicating >> the problem that the patient had to to a degree that it's 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. That's that's actually a really really big deal. >> Yeah. >> Um >> and we didn't have this before. >> Yeah. Right. Right. >> Right. This this like we were working with like rats and mice that were just like awful. Right. >> It just didn't work. Right. >> Right. And now we finally got a model. >> We were throwing darts at the board, but the board was behind us. >> Yeah. Exactly. >> You know, it's it's and it's like what are we doing? >> What are we doing? But but the challenge here is again to your point, you know, >> the model the the sort of rodent model works in so many other arenas. It it's fair to have extrapolated it to this one. >> 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. >> Yes. >> So here's what they did. They they made a screening library >> 107 commercially available drugs from phase 1 to three ALS trials. So this is pre-clinical. Yes. Right. Um pretty sure, right? Phase 1 to three is is I think pre-clinical. And then 97% of the tested drugs failed. >> Yeah. >> And this is >> sort of exactly what we want, right? That's >> 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. >> So that's the whole point >> just to say it back to you. We took what was working in pre-clinical 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 pre-clinical in the prior regime. Yeah. And so the point here is is the again not only are we seeing in patients this the the the one to one on survival time we are also seeing a onetoone on efficacy in testing in this sort of pre-trial environment in in this in lab environment. Yeah. >> Not even in the 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 testing. >> For lab testing. >> Yeah. >> For lab testing. >> Exactly. And out of the 107, three actually did pretty good. >> Okay. >> Ryuzole, um, Meantene, and Barisicinib, I think that's how you pronounce it. Okay. So let's get into let's get into what these what each of these things are. Um >> rizole is actually something that >> people are already testing nowadays. It was dose dependent. There was significant rescue of this um lethal day 50% metric that I was telling you about. And [snorts] if we look at the physical effect on these neurons there was restored electrical coherence. So on the top row you see just um control on the bottom row they're treated with rizole the neur the neurons are healthier >> right they just look better y >> and it inverted the disease vector so there was a negative correlation >> with like the effect and like how how much time >> the the neurons actually stayed alive. So, it was all a good thing, right? >> The other two, >> they did not confer statistically significant population level benefit. >> 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 effect? Right? And that is what they found. They found that so each of these each of these columns now >> is individual drugs and then you add the two right like there's riole and mimantine >> there's baricetanib and meantine and then there's all three >> the more you added the three were somehow working together >> to create a much better lasting effect. >> I mean the the I mean the the visuals here are are just >> as you go from left to right it's just like healthier and healthier neurons. >> Yeah. Yeah. Yeah. And then the basically on the the one with all three at all levels, it's it basically maintains its structure. >> Yes. >> Where whereas when we were just having untreated >> by the time we get to that point. >> Yeah. By the by the time I mean it's just like the time axis is on the on the Y axis. So it's like it's like the earliest and then as time goes on they just start dying, right? But at the rightmost with all three of them it's like they're still alive. >> That's astonishing. Yeah. >> I mean I mean the the level of impact >> Yeah. The level of impact is >> 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? Um you can actually test now that we've got a petri dish. We can do AB testing. >> Right. >> Right. >> Yeah. >> 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 riole alone. >> Right. >> Right. Right. >> And it's 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, right, at the same time? >> Right. Right. Let alone the fact that you have the new inlab test bed. >> Yeah, that's the proof of concept now to then do whatever to do. Even if you say let's not use these three, let's 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 like pharmacologically predictive model. >> Yes. >> Of ALS. >> That's a really big deal. >> Yeah. I think 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 >> like prescribing these to patients. >> Um the next step is to rapidly move to human clinical trials. Um but I think it's got a potential to like fundamentally alter ALS. >> Yeah. No, that's >> therapy and projections. Right. I think I thought it was very cool. It's very clearly a a a step function change in in >> what can be done in uh therapeutics for ALS uh and research in this area. >> Um the the area we wanted to just end on here uh just given Congress actually just approved new funding for this uh this month is related to uh federal funding uh for ALS research. So for those who might not be super familiar with like how who pays for fundamental science and like how do we actually get the money to do a lot of this stuff, you know, there are variety of sources, but the federal government has been a huge funer of uh 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 the in the in article one which is establishing the powers of the legislative branch >> uh I think it's section 8 clause 8 is that uh they congress must uh you know basically promote the investment and you know uh 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. >> One of those two. >> 100%. And and so it's it is a part of the ethos of >> That's cool. >> of even the founding document for our our nation. And so >> it's a great document by the way. >> It is a they were so ahead of >> time. Yeah. The Constitution is a amazing document. >> It's quite nice. If you have not read it, I encourage you because we now need to understand it now more than ever. Yeah, >> but on February 3rd of this year, Congress approved new ALS funding for the fiscal year of 2026. So that means for the 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 uh 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, you know, all of the folks who are supporting and doing the nonprofit work to, you know, 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. Um, 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, the National Institutes of Health. Uh 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. It's like DARPA but for health. Um 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 a sort of a subsidiary under NIH. Uh $40 million for uh Department of Defense uh ALS research through their directed medical research programs. And then $10 million for uh the center for dis uh disease control, CDC and prevention of national ALS registry. Um so you know 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. My like my dad works you know used to work in neglected like neglected diseases. So this is a lot of things that have impact you know the you know subsaran African population etc. There is no market yeah for lemania or African seedling sickness etc etc and so it is 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 etc. they give grants out to universities and research labs and so this money flows through the system um in order to reach the experts like the folks we just talked about in the previous story. >> Yeah. Yeah. Incredibly important work. Um the US 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 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. the 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 Maxplank Institute for quantum optics that was in nature. We ended with a sort of highlevel 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? [laughter] >> I'm sorry. >> Brain fried. No, look. This is >> It's It's been two hours. >> Hey, what would you What would If you could dream engineer >> Oh, yeah. Yeah. >> You know, what would you what would you want to dream engineer in your dreams? >> Yes, that's a good one cuz that's We're going to get some interesting answers. So, let us know in the comments uh below. Again, if you're listening, make sure you uh 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 ffpod.com/donate. Follow, like, subscribe. As always, my name is Lester Nar joined as always by my co-host and our resident PhD Krishna Chowdery. 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. So >> something like that. Yeah, >> something something. Uh this is from first principles. Q exit music. [laughter] Heat. Heat. [music] >> [music]

Hosted by Lester Nare and Krishna Choudhary, this episode has three main stories: interactive dream engineering (yes, two-way “communication” during lucid dreaming), the proton radius puzzle finally getting resolved by a precision lab measurement, and a sobering but hopeful look at ALS—including a major breakthrough: a truly predictive “ALS-in-a-dish” model that could finally make drug screening translate to humans. Summary Dream engineering (Northwestern / Neuroscience of Consciousness): targeted cues + induced lucidity → dream content biasing + measurable performance gains the next day. Proton radius puzzle (Max Planck / Nature): a decade-long discrepancy closes—normal hydrogen agrees with muonic hydrogen, taking “new physics” off the table (for this specific anomaly). ALS breakthrough (Nature Neuroscience, 2025): an iPSC-derived motor-neuron model that correlates with patient survival + identifies a promising 3-drug synergy. Rundown: pulsar near the Milky Way center, AI decoding a Roman board game, hormones + human evolution clues, and an AI-in-the-loop protein engineering pipeline. Support the show If you want to help us keep the pod independent, you can donate here: FFPod.com/donate Follow us everywhere: @FFPod (X / Instagram / TikTok / Facebook) Chapters 00:00 Hello Internet + episode lineup 00:01:25 Story 1 begins — interactive dream engineering (lucid REM) 00:03:10 REM sleep history + why dreams were hard to study causally 00:07:25 Divergent thinking + “spreading activation” model 00:11:44 Dreaming as neural “annealing” (turning up the temperature) 00:16:05 REM neurochemistry: acetylcholine ↑, norepinephrine ↓ 00:20:48 Targeted Memory Reactivation (TMR) → Targeted Lucidity Reactivation 00:26:34 Experiment design: puzzle cues + lucid cue + REM insertion 00:32:06 Real-time proof: eye movement + breathing as dream “telemetry” 00:35:24 Results: cue incorporation + improved puzzle solving 00:38:58 Clinical potential + ethics: “dream sovereignty” 00:43:22 Quick shoutout: Gold Mine (Krishna as scientific adviser) 00:45:18 Rundown + housekeeping + donate/socials 00:47:19 Rundown 1 — pulsar near the Milky Way center (GR tests) 00:51:35 Rundown 2 — AI decodes an ancient Roman board game 00:54:34 Rundown 3 — prenatal hormones, 2D:4D ratio, and evolution 00:59:39 Rundown 4 — AI + protein engineering (“MultiEvolve”) 01:03:01 Story 2 begins — proton radius puzzle (precision frontier) 01:08:32 Hydrogen spectroscopy + what’s actually measured 01:14:10 Stadium/pea analogy + why this measurement is insane 01:18:31 Why the puzzle existed: e-H vs muonic-H discrepancy 01:25:24 How they did it: 410 nm laser, Doppler, Stark shifts, simulations 01:32:29 Result: proton radius ~0.841 fm (puzzle resolved) 01:37:42 Story 3 begins — ALS: what’s failing and why models didn’t translate 01:41:49 Motor neuron transport physics + TDP-43 traffic jams 01:47:41 iPSC breakthrough: making ALS-like motor neurons that predict reality 01:57:39 Key validation: dish “survival” correlates with patient survival 02:00:31 Drug screening: 97% fail (good sign for model fidelity) 02:02:02 Three drugs + synergy: riluzole, memantine, baricitinib 02:05:00 New federal ALS funding (FY2026) + what it supports 02:10:11 Wrap-up + audience question: what would you dream-engineer?