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Nobel Prize 2026: The Science Gets Stranger · From First Principles

• 0:00
  Researchers switched on neurons that were linked to a mouse's cold experience, and its oxygen consumption went up without actually experiencing the cold.
• 0:11
  And still, there are neutrinos going through my fingernail because they're going through the earth and then going through my fingernail.
• 0:17
  Like, that's how little they interact, right?
• 0:19
  The Royal Swedish Academy of Sciences, they actually responded to our...
• 0:26
  video on Instagram.
• 0:28
  Hello, Internet.
• 0:29
  This is your captain speaking, Lester Nare, joined as always by my co-host and our resident PhD, Krishna Chowdhury.
• 0:37
  We are back for a follow-up on the Nobel Prizes.
• 0:41
  The last few episodes we have spent going through the winners of this year's Nobel Prizes in physics, chemistry, and medicine.
• 0:51
  And today we're going to take
• 0:52
  A little bit of a further look at what researchers are doing with the science of our award winners.
• 0:58
  Now we've got four papers that we're going to talk about restoring aspects of vision in people who've lost it.
• 1:04
  How activating memory can change metabolism.
• 1:08
  A way scientists are using neutrinos to understand the universe around us as well as a little bit about what's going on here at home worldwide.
• 1:15
  And then molecular handedness and the search for life, one of my favorite topics.
• 1:21
  But before we get into those, there's some interesting comments over the last couple of weeks.
• 1:25
  And I wanted to get into a little bit of how the what are the Nobel Prizes and how do they actually get vaccinated?
• 1:31
  nominated and so the prizes came out of alfred nobel's will in 1895 and the first one was awarded in 1901 i believe his initial money that was given was actually 31 million swedish kroner at the time uh not inflation adjusted um so this has been going on for more than 120 years yeah not a short period of time uh alfred nobel was the swedish inventor entrepreneur scientist he was a chemist
• 1:59
  and businessman who also happened to write both poetry and drama.
• 2:03
  So that gives an interesting reference point to his respect for literature, famously known as the inventor of dynamite and holder of 355 patents.
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  The idea was to recognize work that brought the greatest benefit to humanity.
• 2:19
  One interesting note that came up in the comments was who gets to decide the winners?
• 2:23
  Yeah.
• 2:24
  And notably, there isn't just one committee for the Nobel Prizes that decides all of these.
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  There's first the Royal Swedish Academy of Sciences, which awards the ones we focus on.
• 2:35
  Some of the ones we focus on physics, chemistry and the economics prize.
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  which was added later and first awarded in 1969.
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  Medicine is decided by the Nobel Assembly at the Karolinska Institute.
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  Literature is the Swedish Academy.
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  And finally, the Peace Prize is the Norwegian Nobel Committee, which is composed of five members that are appointed by the Storting, which is the Norwegian Parliament.
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  Interestingly, in Nobel's will, he did not give a reasoning why the Peace Prize –
• 3:07
  Was randomly Norway?
• 3:08
  Yeah.
• 3:09
  There's not really an understanding of where that was coming from.
• 3:15
  I wonder if his wife was Norwegian or something.
• 3:18
  I have no idea.
• 3:19
  It seems kind of random, but yeah.
• 3:21
  So different prizes have different committees.
• 3:23
  And so if you're unhappy with one prize and how they decide to give it out, it's not necessarily across all of them.
• 3:30
  So the way – the other thing that came up is these things are old.
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  Why are they just getting a prize now?
• 3:37
  And I think the way to think about the science prizes in particular is more akin to or similar to the Hall of Fame in sports for all the sports ball fans out there.
• 3:46
  So you're recognizing a particular discovery or contribution.
• 3:49
  But the work may have happened decades earlier allowing you to sort of see how that contribution has grown over time and how researchers have built on top of it to see where it leads.
• 4:00
  Yeah.
• 4:01
  This allows us to be able to trace the connection between the original question that was asked and how it's now being implemented, which is part of what makes these follow ups that we do now for the second year running so interesting.
• 4:11
  So as always, we're going to talk about the science from the ground up today, because this is from First Principles.
• 4:32
  Krishna, let's start with a quick recap of medicine and optogenetics.
• 4:39
  Optogenetics won the medicine prize.
• 4:42
  Specifically, they recognized Carl Diceroth, Peter Hegemann, and Georg Nagel for discoveries concerning light-gated ion channels and optogenetics.
• 4:51
  Those are the three with their artwork.
• 4:54
  This is a technology that allows neuroscientists to turn on and off specific neurons in the brain using light, and this allows scientists to establish causal links to
• 5:07
  between stimuli, neural firing, like the activity of neurons in the brain, and behavior of the animal at large.
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  This is one of the first times that we can really do this kind of science.
• 5:19
  The notion of causality here.
• 5:21
  The notion of causality is what's important.
• 5:24
  So how does it work?
• 5:25
  Well...
• 5:27
  Very briefly, you can think of the neuron as a type of capacitor, okay?
• 5:31
  It stores a voltage across a membrane using charges on either side.
• 5:35
  These charges, instead of being the electrons of a conventional capacitor in your electrical grid, these are ions like calcium ions, sodium ions, potassium ions.
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  There's ions on either side of this membrane, on the inside and the outside, and that stores a voltage.
• 5:51
  Now, to activate the neuron, all you have to do is introduce a short in the capacitor.
• 5:56
  When you introduce a short, the ions can freely flow from the inside of the neuron to the outside or vice versa, and you can sort of activate this thing, and the neuron starts firing.
• 6:07
  It can then influence other neurons to fire, right?
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  What optogenetics lets us do is...
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  Open up the short circuit on command.
• 6:18
  That's the point.
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  So there's a protein called channel rhodopsin.
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  It's a type of protein, really.
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  It's a class of proteins.
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  And what it does is absorb light and use that energy from the light to open up and close
• 6:32
  channels on the membrane such that ions can move from the outside to the inside in this case but sometimes from the inside to the outside it doesn't really right now we've gotten so good at this thing that we've just generalized it as a tool for whatever type of ion transport you want so depending on the channel and the electrical conditions it's going to change the cell's voltage and we can command now turning on neurons we can command turning off neurons and we can see what
• 6:59
  That's quite nice if we're trying to understand how the brain works.
• 7:03
  Exactly.
• 7:04
  Because you can literally poke at the cellular level a neuron in a living animal, in a living brain, and see what happens, right?
• 7:14
  So, you know, the brain is still a black box, but at least we can poke at stuff in the black box.
• 7:20
  For the AI people in the audience, this is the thing they talk about.
• 7:23
  Interpretability.
• 7:25
  Yeah.
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  And it's a vector for us to be able to interpret and make some understanding of what's actually causal links.
• 7:32
  Yeah, 100%.
• 7:33
  And actually, I mean, you make a good point about like how a lot of times with interpretability in AI, some of the experiments you do are very similar to these optogenetic experiments where you go into a single layer of a neural network and you like silence them.
• 7:49
  Or you silence particular neurons and you try to see what kind of output the giant neural network gives out.
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  Or you try to see what types of inputs are activating specific neurons in the middle latent spaces of these models.
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  So...
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  Pretty similar to the kinds of optogenetic experiments we're doing, but it's obviously harder in a biological animal, right, than just like code where you can just go, okay, like if in this layer, you know, flip a coin and turn off all these neurons, right?
• 8:21
  So it's pretty important.
• 8:23
  This is why it won the prize.
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  The important combination here is the genetic selection plus the optical timing.
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  Because what you can do now is you can express this channelrhodopsin-type protein in specific circuits in the brain, right?
• 8:37
  You can be like, I only want layer 5 pyramidal neurons.
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  The way you do that is you figure out a kind of genetic cocktail that only...
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  Layer 5 pyramidal neurons will respond to.
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  And in that cocktail, you add the genetic instructions for channel rhodopsin 2.
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  So the layer 5 pyramidal neurons, they're going to recognize the cocktail.
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  They're going to be like, oh, I'm going to take this amount of genes in.
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  Part of the genes that they take in are also the instructions to make that channel rhodopsin.
• 9:08
  That goes into the cell membrane, and all of a sudden now we can control using light.
• 9:12
  So this shows, you know, different types of neurons.
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  On the bottom there, that's a pyramidal neuron.
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  On the top, those are different types of neurons.
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  And each one you can tag with different types of channelrhodopsins.
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  And now you can even do multiplexing.
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  You can be like blue light activates this population.
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  Green light activates this population.
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  Red light activates this population.
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  You can do like strobe-like neurons.
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  Control of single neurons even sometimes, mostly populations of neurons, but it's an incredibly powerful tool.
• 9:47
  Neuronal Burning Man.
• 9:49
  And it's sort of akin to this keeping the analogy going.
• 9:53
  With AI, we have the code and we can manipulate it directly.
• 9:56
  We're giving the code to the neuron cells with the genetic delivery, which then we now know we have the tools to be able to say turn off or on.
• 10:04
  But it first needs to be able to do so in a way where we understand that when we turn the lights blue, green, etc., the actual cells themselves have the instructions to respond the way that we want them to.
• 10:18
  Exactly, yeah.
• 10:19
  And so –
• 10:20
  So there's two papers that I want to talk about as a follow-up to that Nobel Prize.
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  The first one is going to be a direct medical application.
• 10:26
  And the second one is going to be more kind of fundamental science-y and really the reason why these three authors won.
• 10:32
  But the first one, you know, I think it's still pretty cool that you can directly apply it to a medical therapy.
• 10:38
  So this has to do with a vision treatment.
• 10:41
  This vision treatment asks the eye's output cells to take over the job
• 10:46
  of broken light detectors effectively.
• 10:48
  Like there's a, you know, there's a disease called advanced retinitis pigmentosa where the retinal pigments that capture light from the surroundings, they get deteriorated.
• 11:00
  This is an attempt to restore vision among those patients.
• 11:03
  Okay.
• 11:03
  It's a paper from the University of Pittsburgh and actually really a whole consortium out in the New England Journal of Medicine.
• 11:11
  Again, I had to get my bootleg version from my friend who's a doctor.
• 11:15
  But this was out in October 2026.
• 11:17
  So like this month, right?
• 11:20
  Very, very recent.
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  And it examines a optogenetic therapy in 10 people with blindness due to advanced retinitis pigmentosa.
• 11:31
  So to understand this approach, let's first actually just review what the retina is as a neural circuit.
• 11:36
  So this is what the retina looks like.
• 11:38
  The retina is one of my favorite neural circuits, not only because of its simplicity, but also because in undergrad at Princeton,
• 11:45
  My senior thesis was on physically modeling the retina as like an electrical circuit in some sense.
• 11:52
  It's a very simple circuit, okay?
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  In the back of the retina are the rods and cones.
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  This is the stuff that's in the back, like, towards the back of your head.
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  So the eye, the light is coming from the center, from in front of you.
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  The sensors are actually in the back, which is weird.
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  You would think that the sensors, like in a traditional CCD, it goes CCD and then the wires come out from behind it, right?
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  Here, it's the opposite.
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  Like, the CCD is kind of in the back and the wires come out forward and then have to go through a hole to get to the brain.
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  The reason for that is because our detectors, the rods and cones, they deteriorate very quickly because they respond to so much light.
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  The proteins are always changing shape.
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  That's going to lead to degradation.
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  And so you need to be close to blood vessels to trash all of the proteins and get new proteins or get enough energy to get new proteins.
• 12:48
  You know what I mean?
• 12:48
  Yeah, 100%.
• 12:49
  So you need to be close to the trash chute.
• 12:51
  The structural shape is related to how it's actually used functionally and proximity to basically the recycle mechanism.
• 12:58
  Yeah, yeah.
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  It matters.
• 12:59
  Yeah, exactly.
• 13:00
  Like, if the rods were actually in the same design of cameras, then you would just get a buildup of, like, your rhodopsins that have been used up in the front and you'd very quickly...
• 13:10
  not be able to see.
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  See.
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  Right?
• 13:12
  So, okay, that's why the rods and cones are in the back.
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  And then you have a bunch of cells in the middle that are kind of like processing cells.
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  There's bipolar cells, amocrine cells.
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  And at the very end, you have these things called retinal ganglion cells.
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  These are single cells that have their cell body in our eye, and they've got an axon that goes through the hole that I was talking about from the retina because they're in the front, so you've got to topologically go through the hole.
• 13:37
  There's a hole that goes through the eye to your brain.
• 13:41
  And it crisscrosses right in the middle, and that's where the center for the circadian rhythm is, all that other kind of stuff.
• 13:49
  But in any case, the actual sensors that go all the way to your brain are in the very front.
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  Those are the retinal ganglion cells, and the cell bodies are in the very front.
• 13:59
  There's a little bit of processing that's happening.
• 14:02
  I want to be clear here.
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  This is not just simply whatever I see in this pixel is what I'm sending.
• 14:07
  Right?
• 14:07
  there's a little bit of data compression that happens within our eye because you can't send all of the raw data, right?
• 14:13
  It's a thin wire.
• 14:15
  You better not fry it.
• 14:16
  Right.
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  And you better not heat it up.
• 14:18
  So you have to sort of compress the data in order to send it to the brain in an efficient manner.
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  And the retina is doing that.
• 14:25
  Our eyes are Pied Piper for data compression.
• 14:27
  Yeah, dude.
• 14:28
  They're creating zip files in some sense.
• 14:31
  The retina itself is creating a zip file to send to the brain.
• 14:35
  Yeah.
• 14:35
  It's pretty remarkable.
• 14:36
  And I love this network for that reason.
• 14:39
  It's very simple.
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  There's only like five or six different types of neurons involved.
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  And yet you can have this rich machinery for a lot of very cool computation.
• 14:50
  And everyone can understand the benefits of having eyes because it's a part of our core five senses and our lived experience in terms of how we interact with the world around us.
• 14:59
  And so to have something that's so visceral to everyone's lived experience and the mechanics, the functional mechanics being complex, but in a way that is complex.
• 15:10
  simple enough compared to other biological systems is actually quite interesting.
• 15:15
  Exactly, yeah.
• 15:16
  And the problem that these patients have with this retinitis pigmentosa is that the photoreceptors, the original photoreceptors, get damaged.
• 15:27
  Okay?
• 15:28
  So...
• 15:30
  How do you do this?
• 15:30
  Well, the strategy is to give retinal ganglion cells a microbial light-sensitive channel.
• 15:36
  You basically do optogenetics on the cells in the very front, the ganglion cells that are sending messages to the brain.
• 15:45
  So you're like, okay, I'm not going to care about the photoreceptors and things like that.
• 15:48
  I'm not even going to care about the middle circuitry.
• 15:50
  I'm just going to go to the output.
• 15:52
  I'm going to express my optogenetics in that, and then
• 15:57
  You know, when the things light up, the light is going to go in, but it's not going to hit the rods and cones because the rods and cones are kind of degraded, maybe even dead.
• 16:05
  The light is going to go hit the channel rhodopsin.
• 16:08
  That's going to cause these ganglion cells to fire, which is then going to send the signal to the brain.
• 16:12
  So I see what you're saying.
• 16:13
  So the middle and back layers, right, are not – there's a direct connection between what's happening in the ganglion cells in the front that sends the actual signal to the brain that does the interpretation.
• 16:22
  And so even if your middle and back machinery is degraded, which is what's happening in this particular disease –
• 16:29
  We're just overriding that by saying we're going to take the direct route.
• 16:32
  Yeah.
• 16:32
  We're going to take the express lane and activate what we need to in the actual sensors that are taking in the light and doing this zip file type compression.
• 16:41
  Yeah.
• 16:41
  To the source.
• 16:42
  Exactly.
• 16:43
  That's exactly the point.
• 16:44
  Exactly.
• 16:44
  And so how do you actually administer this thing?
• 16:46
  Well, in the 2026 trial that we're talking about here, each participant received an injection into the worst-seeing eye.
• 16:52
  So they had like two eyes.
• 16:53
  One of them had this retinitis.
• 16:55
  The other one didn't.
• 16:56
  So in the worst eye, you inject this kind of capsid, which is like a viral capsid with...
• 17:02
  this particular gene.
• 17:04
  There's a genome for crimson R, which is the channel rhodopsin type thing.
• 17:11
  Yes.
• 17:11
  And then you've got a promoter that kind of tags the retinal ganglion cells.
• 17:15
  And once those cells see this piece of DNA, they can express the protein on their membranes, and then they are now light sensitive.
• 17:23
  Before they weren't.
• 17:24
  This is the delivery device.
• 17:25
  Yeah.
• 17:26
  This is the delivery device, exactly.
• 17:28
  And so...
• 17:30
  One key point to make here is that we're going not to the photoreceptors, but to the ganglion cells, right?
• 17:36
  And I told you that there's some compression that's happening, right?
• 17:40
  So you can't just deliver light like it's normal eyes, okay?
• 17:45
  You have to use specific types.
• 17:47
  You have to do a little bit of the data crunching before you send it in, right?
• 17:53
  Because the ganglion cells are expecting a data crunched,
• 17:57
  set of numbers, right?
• 17:59
  A set of stimuli.
• 18:01
  They're not expecting the raw light.
• 18:03
  They're expecting all of the...
• 18:05
  They're expecting zip files.
• 18:07
  Right, right.
• 18:07
  Right?
• 18:08
  And so you have to give them kind of zip files.
• 18:11
  We have to pre-process.
• 18:12
  Yeah, we have to pre-process.
• 18:13
  And that's why we need goggles.
• 18:15
  Okay?
• 18:15
  So there are goggles that the patients wear...
• 18:18
  that's not going to actually give you the raw image.
• 18:20
  So on the lower right, you're seeing what the projected image is from the goggles.
• 18:26
  In this case, what they're doing is kind of an edge detection.
• 18:29
  The left is just a hand, black and white, but the edge detection is detecting movement and changing pixels, and it's only highlighting those pixels.
• 18:39
  Now, one thing that the authors are careful about is that this is not the only type
• 18:43
  of pre-processing that the retina does, right?
• 18:46
  The circuit is extremely complex.
• 18:48
  There are on off cells, there's movement cells, there's all sorts of like different types of, um, processing that happens.
• 18:55
  And this is just one of them.
• 18:57
  Um,
• 18:58
  And furthermore, in the ganglion cells population, there's specialized ganglion cells that do one thing or the other, right?
• 19:05
  It's not like all of them respond to edge detection.
• 19:08
  In a specific slice, there's going to be some edge detection cells, but there's also going to be some on-off cells and so on and so forth, right?
• 19:15
  So this is a crude way of kind of approximating what the retina does, but it's not even close, really, to what the retina does.
• 19:21
  The point of this study was to establish safety, right?
• 19:24
  OK, so that they can go on and do other studies.
• 19:27
  Right.
• 19:27
  Because you're doing a lot here.
• 19:29
  You're administering some genetics into a patient's eye.
• 19:34
  You're trying to see whether it responds to light.
• 19:35
  You're trying to make sure that it doesn't give them like epileptic seizures in the worst case scenario.
• 19:39
  You know, there's all sorts of things that can go wrong in biology.
• 19:42
  So the first and foremost primary purpose of this 2026 study was safety.
• 19:48
  The secondary one was to measure, okay, like, does the full-field stimulus, like, change the threshold of the eye?
• 19:55
  Can the eye, like, increase threshold sensitivity?
• 19:57
  This is why they actually did patients with one eye with retinitis and the other one normal, because then now you can compare, like, how...
• 20:04
  How much better is the left eye compared to your control, which is the normal eye?
• 20:08
  It makes total sense.
• 20:09
  Yeah, it's a well-done study.
• 20:11
  There were 10 patients.
• 20:12
  That's really clever, actually.
• 20:13
  Yeah, yeah.
• 20:14
  And it was done pretty well.
• 20:15
  The 10-person study, open label.
• 20:17
  So that means, I mean, obviously the people know that they're getting the viral vector, right?
• 20:21
  There's also, if you wanted to do a real study, you'd have to do a fully blind one where people are getting saline, right?
• 20:27
  And then, I don't know, what if the mind-body connection— I see better now.
• 20:32
  Things are crazy, dude.
• 20:33
  The mind is crazy.
• 20:34
  Right.
• 20:34
  And, like, that's totally an outcome that could happen.
• 20:37
  So this is the first in multiple, but it's a pretty cool scientific payoff, right?
• 20:46
  It's a new route into surviving neural circuitry.
• 20:51
  One of the things about this story that – because when we talked about the prize originally, it was just looking at sort of the past and how it built up to now this being again an October study that came out literally like this month.
• 21:03
  Right.
• 21:04
  Just not that long ago.
• 21:06
  And how we've taken – an original question which was totally unrelated to giving partial visual restoration –
• 21:16
  And we can say like in the clinical context, we can say, okay, well, there's only partial vision repair.
• 21:21
  And we talked about this edge detection idea.
• 21:24
  Like, okay, well, that's – you're not giving people their sight back.
• 21:27
  And it made me think about this idea of, you know, for people like us that currently – maybe not into the future, currently – have our full ability to see in our sight, it can be hard to understand –
• 21:39
  the impact of even this first incremental step in someone who was a part of the study and is experiencing it directly.
• 21:48
  We can look at the measurements.
• 21:50
  We can talk about the calculations.
• 21:52
  But there is a real lived experience that is getting fundamentally changed, even if not perfectly back to their original state, by these early steps, which I find it's important to always remember that when we talk about these things ending up in patients –
• 22:10
  These are people who didn't even think there was an opportunity for them to get their sight back.
• 22:14
  And I think like some of the things now it's like, yes, maybe they can't make a person's face.
• 22:19
  Maybe they can't, you know, read perfectly, but they can start doing things.
• 22:25
  They were removed from their lived experience that I think is really, really unbelievable.
• 22:32
  Again, we started this by looking at like how it was working algae.
• 22:37
  Yes, channel rhodopsin was first found in algae by Peter Hegeman.
• 22:41
  And then it became, oh, this is a way for us to be able to create and drive action within neurons.
• 22:49
  And then now this is being applied by clinical researchers for...
• 22:54
  restoration of eyesight and when we you were looking at this idea in algae you were not thinking that oh this is going to lead to restoration in eyesight maybe once you got to the transitional step of understanding you can control neurons obviously the that's pandora's box yeah but um
• 23:10
  some of these things can take one of the things that I think is incredible about this discovery, especially is sometimes when you're these researchers, you don't get to live to see the impact on people's real lived experience of the work that you do.
• 23:24
  Yeah.
• 23:25
  A lot of these researchers have been able, are still around and are able to actually see it come to fruition and
• 23:31
  everyday lived experience, which sometimes basic research and lived experience, there's a large delta between these two things.
• 23:37
  I think that's just such a cool part of this story.
• 23:39
  Yeah, and it's only going to get better, right?
• 23:42
  Yeah.
• 23:42
  Like, this is the first few things.
• 23:45
  I mean, this isn't even the first...
• 23:48
  paper that we covered that was trying to restore eyesight we had the prima paper yes earlier but and that one that one was actually much more involved because that involves like a literal chip going in the back of your eye that is responding to infrared great episode that's a crazy episode too yeah so i mean you know we live in a we live in a day and age where like medicine is the frontier right it's it's it's pretty cool the things that we can do
• 24:15
  and there are more than one ways to solve the problem and this just happens to be one.
• 24:19
  But I think the thing you brought up that's important is safety was the key aspect of the study, but they now have a foundational platform around the, those was it ganglion cells on the front, knowing that there's multiple functional pieces to it.
• 24:38
  Okay.
• 24:38
  Now we can start to take this,
• 24:41
  uh success with doing one to now making it a a multiplex kind of solution and seeing if we can now not just do egg detect edge detection can we get movement can we get these other things it's going to be fascinating to see and a lot of people in the comments were like i'm studying in optogenetics we do it we're doing this and we're doing that so it's an area clearly of of robust interest in research
• 25:03
  Yeah, I mean, as I said, every single university has an optogenetics lab, right?
• 25:09
  The main justification really for the optogenetics prize is not its application in these niche sort of medical disciplines, which, of course, it's amazing that it's happening.
• 25:20
  But the real bread and butter for why optogenetics has transformed neuroscience is because of what I talked about earlier, which is,
• 25:27
  this ability to establish causation between neural circuits and behavior, neural circuits and aggregate bodily functions in some sense, right?
• 25:38
  It's become the hammer for neuroscience fundamental research.
• 25:43
  Especially this is true in simple organisms.
• 25:46
  And I wanted to showcase C. elegans, which is the nematode, because I remember actually when I was doing my PhD in physics,
• 25:57
  I went to a summer school like in Okinawa, Japan.
• 26:01
  It's amazing, right?
• 26:03
  That I get to go to like.
• 26:04
  I'm going there in 10 days.
• 26:05
  Oh, really?
• 26:05
  That's where, anyway.
• 26:07
  Okinawa.
• 26:07
  Yeah, yeah.
• 26:07
  That's awesome.
• 26:08
  Yeah, no, they have the Okinawa Institute for Science and Technology, OIST, which is on a hill.
• 26:14
  Beautiful.
• 26:14
  Beautiful architecture over there.
• 26:16
  They have like a summer school for computational neuroscience, which I had the pleasure of attending.
• 26:22
  I forget who the PI was who gave this talk.
• 26:25
  Comment when you see this.
• 26:26
  Yeah, but they were talking about their use of optogenetics in C. elegans to understand motor movement, okay?
• 26:33
  This particular video is from the Gen Lab at the Research Institute in Toronto, the Tannenbaum Research Institute in Toronto.
• 26:42
  Here's what's happening, okay?
• 26:44
  You're optogenetically activating the inhibitory motor neurons when the light turns on.
• 26:49
  And watch the behavior.
• 26:51
  It arrests the behavior, right?
• 26:53
  This is a pretty easy causal link, right?
• 26:55
  I mean, we already had the causal link.
• 26:57
  Obviously, if you inhibit the motor neurons, you're going to stop the movement.
• 27:01
  of the of the worm but here it's just so obvious right that when the light turns on the you can imagine right there's little proteins in the neurons of that nematode that are as soon as the light turns on they're letting in ions which are depolarizing the cell or hyperpolarizing the cell in this case because it's inhibitory and it's stopping the activity of the muscles we're hitting the e-brake on the nematode
• 27:25
  It's pretty insane, right?
• 27:26
  And it happens almost immediately.
• 27:27
  Yeah.
• 27:28
  And we talked about the time as being a key part of this and the immediacy.
• 27:32
  And again, this is just a great visual to express that.
• 27:36
  And so this is just like stopping movement.
• 27:39
  Now, can you control movement?
• 27:40
  Mm-hmm.
• 27:41
  This next video is from the Alchema Lab at UMass Chan Medical School.
• 27:46
  Here, the animals express a light-gated channelrhodopsin in a pair of motor neurons called RIV.
• 27:54
  These neurons innervate the ventral neck muscle, and they're important for the omega turn.
• 28:00
  in C. elegans.
• 28:01
  And now you'll notice whenever the light turns on, I mean, it shows, I don't know if you can see, there's a little blue thing that says blue light on.
• 28:10
  Whenever the blue light turns on, the thing is going to turn left, right?
• 28:14
  You are now controlling the motor movement.
• 28:17
  You're controlling like the movement of this worm using light by turning lights on and on, on and off.
• 28:24
  This is clearly American because it said it can only turn left like NASCAR.
• 28:30
  Yeah, yeah, effectively.
• 28:32
  But I think the bigger point here is it's not just there is a granularity of control.
• 28:39
  And that's the big step here.
• 28:40
  It's not just on and off, but like we can actually, that on and off from a behavior perspective can be very specific.
• 28:46
  Exactly.
• 28:47
  And now using these types of optogenetic tools, we can map out the entire circuit to be like, what is the if-else thing?
• 28:56
  code for motor movement in this worm.
• 29:00
  See, Elegance is really nice because there's only like 300 neurons, so you can get that kind of granularity.
• 29:05
  But now with the advent of large data, with the advent of
• 29:10
  artificial intelligence, and with the honing of these types of tools, we could get granularity to much larger neurons, much larger species, mammals perhaps.
• 29:21
  And that is the second paper that I want to talk about.
• 29:24
  This is In Nature 2025 from Trinity College, Dublin.
• 29:31
  And it asks a different kind of question.
• 29:33
  This is in mammals.
• 29:34
  Yeah.
• 29:35
  And here you can do crazier things because mammals don't just move around.
• 29:39
  See, elegans, like the worms, you know, they're nice.
• 29:42
  They're a nice model organism because they really have very prescribed, typical types of movement.
• 29:47
  They eat, they poop, they reproduce, right?
• 29:51
  Right.
• 29:52
  It's about the spectrum of their behavior.
• 29:55
  There's going to be some C. elegans neuroscientists out there who are going to be pissed off.
• 29:59
  But like, I'm sorry.
• 30:00
  I'm not saying it's a bad thing, right?
• 30:02
  For certain questions, it's really good to have very simple model organisms, right?
• 30:07
  Several Nobel Prizes have been won by C. elegans scientists, okay?
• 30:11
  So don't come at me.
• 30:12
  But mammals can do even crazier things.
• 30:14
  And in this case, here's what they're asking.
• 30:16
  Can retrieving an experience, like reliving a memory by...
• 30:21
  activating the neurons that are associated with that memory, can doing that recruit the body's responses to the experience?
• 30:30
  Yep.
• 30:30
  Okay?
• 30:31
  Can, like, the body feel the memory being reactivated?
• 30:35
  And because we have the remote control now, it gets weird.
• 30:41
  It gets weird.
• 30:41
  Right?
• 30:42
  I mean, it's a real... Let's just talk about the science, okay?
• 30:45
  First, let's just talk about the science.
• 30:46
  So, the idea is...
• 30:50
  The researchers, in a nutshell, the researchers switched on neurons that were linked to a mouse's cold experience and its oxygen consumption went up without actually experiencing the cold.
• 31:03
  Okay?
• 31:03
  So the neurons that thought...
• 31:07
  that made it think that it was cold, started creating a bodily effect, where it started consuming more oxygen, the metabolism increased, so on and so forth, right?
• 31:17
  We already understand that a mammal exposed to the cold can increase heat production.
• 31:22
  This is why Joe Rogan is always in his ice baths.
• 31:26
  What are these called?
• 31:28
  Cold plunges?
• 31:28
  Cold plunges, the new brand name.
• 31:31
  He's in there with all of his friends in Austin, Texas.
• 31:34
  The idea is that you go into this cold plunge,
• 31:37
  It, like, momentarily increases your metabolism.
• 31:40
  It increases your oxygen intake because your body is like, oh, my God, I'm really cold.
• 31:44
  I need to, like, regenerate heat on my own, right?
• 31:47
  And this has, like, you know, benefits purported.
• 31:51
  Some of it's actually, you know, true and documented in scientific literature.
• 31:56
  In any case...
• 31:58
  The experimental logic now for this paper is the following.
• 32:02
  What we're going to do is we're going to pair an environment with the cold.
• 32:06
  So when the mouse is in a certain type of room with certain types of stimuli, we're going to pair that with cold.
• 32:13
  So now he's going to associate the stimuli with the experience of cold.
• 32:19
  Now, later on, we want to see if the mouse has earned, sorry, has learned that association.
• 32:26
  So we're going to put the mouse in that environment again, but no cold, and see if it responds as if it is cold.
• 32:33
  And the mice do.
• 32:35
  So that means that they have learned that association.
• 32:38
  Okay?
• 32:39
  Now what we do is we ask, what are the neurons that were activated in this experience?
• 32:44
  Okay.
• 32:46
  Okay.
• 32:47
  So first we need to make sure that we understand that when the mouse has a baseline, without doing any of the optogenetics magic yet.
• 32:55
  We got to make sure that it even, there's some construct in its brain that's like, oh, this is cold, I am cold.
• 33:02
  Yes.
• 33:03
  Right?
• 33:03
  Right.
• 33:04
  Even without the cold.
• 33:05
  Right.
• 33:06
  Right.
• 33:06
  Right.
• 33:07
  So now we've got an association where there's certain neurons that are activating in the brain
• 33:13
  That are associated with this cold experience, right?
• 33:15
  100%.
• 33:16
  And that are eliciting a cold response of elevated oxygen.
• 33:21
  This physiological response.
• 33:22
  Yeah, more metabolism.
• 33:23
  In the body.
• 33:24
  Elevated heart rate, that kind of stuff, right?
• 33:27
  And now we ask, what are the neurons that are involved?
• 33:29
  And can we optogenetically enhance those neurons, right?
• 33:34
  That's exactly what they do.
• 33:36
  So here, this is figure four of their paper.
• 33:40
  This is, again, this is not a deep dive.
• 33:43
  This is a rundown.
• 33:43
  So we're skipping directly to figure four.
• 33:45
  Usually we go one, two, three, right?
• 33:48
  Anyways, so on the left-hand side, the green is where the channel rhodopsin is.
• 33:52
  Okay?
• 33:53
  So the green is the optogenetically tagged neurons.
• 33:56
  Those specific neurons are the ones that were activated directly.
• 34:00
  during that cold experience which we are able to see because we have these tools to be able to scan yeah yeah and know what was activated before so we're just trying to artificially activate what we've already seen previously yeah yeah and on the right hand side we've got the on on the on the y axis is the volume of oxygen that is consumed like per minute or whatever and then x axis is time and
• 34:25
  The blue regions are where we've illuminated those neurons.
• 34:30
  So we've activated those neurons.
• 34:32
  The oxygen intake goes up.
• 34:34
  Nothing is happening to the mouse.
• 34:36
  The mouse is not in the environment that it's associated with the cold.
• 34:40
  And it's not in the cold.
• 34:41
  It's just in a normal...
• 34:42
  It's just chilling.
• 34:43
  Yes.
• 34:44
  And then you activate those neurons.
• 34:46
  And all of a sudden, the mouse thinks it's cold, I guess.
• 34:50
  We don't know, right?
• 34:50
  I don't want to say that.
• 34:51
  Sorry, I don't want to say that.
• 34:54
  The neurons that are associated with the cold experience are activated, and that causes a physiological response.
• 35:00
  We can't say subjectively what the mouse is feeling or thinking, right?
• 35:04
  I shouldn't say that.
• 35:05
  It's very easy to, and I always make this mistake, even as a trained neuroscientist or whatever, but yeah, it's important to have that distinction.
• 35:14
  I think what's so important about the construction we have here is we've now removed the environmental stimuli that was driving this response.
• 35:22
  And we're now just artificially, with light, trying to trigger the same thing that the environmental stimuli was doing before in a completely different, unassociated environment.
• 35:35
  And we are seeing that as driving the same physiological response.
• 35:39
  Exactly.
• 35:40
  So we've effectively taken...
• 35:41
  The memory of a lived environment in these mice and are able to artificially create, to artificially trigger something that results in a similar physiological response without necessarily making this connection that what the mouse's subjective experience is, is necessarily true.
• 36:04
  what it was like when it was in that environment.
• 36:06
  But it could be, it could not be.
• 36:08
  But it almost doesn't matter because the body is responding.
• 36:10
  Yeah, the body is responding, right?
• 36:11
  And so we have established a causal link.
• 36:14
  And this, in this case, this is in the dentate gyrus, which is a region of the hippocampus of the mouse.
• 36:20
  Dente gyrus, very famously, is one of the few regions in the brain where active neurogenesis happens.
• 36:26
  There's like new neurons being created in the dentate gyrus.
• 36:29
  The other one is the olfactory bulb.
• 36:31
  But it's very rare for that to happen, right?
• 36:34
  Um, so...
• 36:36
  One question that you might ask is like, well, how do you actually tag the cells that are active, right?
• 36:41
  And I just wanted to briefly mention, I'm not going to go into too much detail, but this alone, I think, is something that deserves a Nobel Prize in neuroscience, in like physiology and medicine, is the ability to actually even do that, yeah?
• 36:56
  It's to use, it's a general strategy that uses activity-dependent gene expression.
• 37:02
  The idea is that like when neurons become active,
• 37:05
  They start expressing specific genes.
• 37:07
  They start transcribing those genes.
• 37:09
  And you can have like a tracer that tags exactly those neurons that were active during that experience.
• 37:16
  That's really interesting.
• 37:16
  And then tag that tracer or like tie that tracer to then when you inject your optogenetic cocktail, right?
• 37:23
  Right.
• 37:23
  Only the neurons that were active during that time are going to then respond to the optogenetics and express channelrhodopsin.
• 37:31
  That's actually a huge... Let us know in the comments if you want us to cover that because there's actually...
• 37:35
  I have so many questions about... Because that's a non-trivial component.
• 37:38
  It's super non-trivial.
• 37:39
  Yeah.
• 37:40
  Yeah.
• 37:40
  It's super non-trivial.
• 37:42
  It's a thing called TRAP2, Targeted Recombination in Active Populations of...
• 37:48
  version two.
• 37:50
  It's, it's absolutely incredible that we can even do these things, but, but there's two conditions here, right?
• 37:54
  You have the activity and then you have an open labeling window, which is like right after, right after the experience of cold and everything that you have the activity, you label those neurons and,
• 38:06
  And then if the cells meet both of those conditions, they receive some kind of genetic change.
• 38:11
  And then only those things that have the genetic change are the ones that are going to express the optogenetics.
• 38:16
  And that way we can only turn on those cells that were important for that particular experience.
• 38:22
  So when we talk about that delivery device that we mentioned earlier, this is figuring out what locations do we need to deliver that delivery device to in order for it to generate the expression so when we hit it with the channel adoption, so when we hit it with the light we want to.
• 38:38
  what we've labeled and associated with the experience of whatever experiment we're doing.
• 38:45
  Like, that's how we know what the map is.
• 38:46
  Yeah, yeah, exactly.
• 38:48
  And it's a pretty general strategy.
• 38:49
  Which means... That's crazy.
• 38:51
  That's crazy.
• 38:52
  Yo, it's crazy.
• 38:53
  That's crazy.
• 38:53
  Okay, so the other causal...
• 38:55
  Whenever we talk about causal tests in biology, you always want the negative version of it.
• 38:59
  Like, you've shown the positive version, which is you activate this thing, the animal responds as if it is cold.
• 39:04
  What if you...
• 39:06
  put it in the experience of cold, but you deactivate the neurons, right?
• 39:10
  Does the animal respond as if it is not cold?
• 39:13
  And that's exactly what they did in figure five, right?
• 39:16
  So they show the other way around.
• 39:17
  This is not optogenetics.
• 39:19
  Here they use actually pharmacology.
• 39:20
  They use a drug that's activating a receptor in those targeted cells that inhibits those cells.
• 39:27
  And then you can deactivate this population, and that's going to trigger the response.
• 39:33
  And it's going to suppress the usual learned response of, I am cold, I need to, you know, the body at least needs to increase its metabolism.
• 39:44
  So you can show this both ways.
• 39:46
  It's incredible paper out in nature.
• 39:48
  Yeah, yeah, yeah.
• 39:50
  So I want to come back to this idea that we started with this idea of memory.
• 39:55
  And.
• 39:57
  you know, we talked a lot about the methods and the details, but one of the big ideas here is for memory is something that everyone can relate to.
• 40:05
  Uh, whether it's through our own personal lived experience, positive, negative memory, you know, you hear a song come on on the radio and it reminds you of being in high school or you smell the smell of rain in the summer.
• 40:17
  And it reminds you of like summer camp back in the day or like, so we, like we kind of have this thought of like memory being backwards looking.
• 40:23
  Um,
• 40:24
  And I think one of the things that this paper really shifted my thinking around is it's actually a two-way street where –
• 40:35
  This idea of memory, and I understand what these caveats of, we don't know what the subjective experience is of these subjects in this study.
• 40:41
  Yeah.
• 40:42
  But just to generalize it a little bit, your experience, which generates this activation in your brain, which in some cases will relate to how we recall in memory, actually has this aspect where when you are under similar conditions environmentally, you're…
• 41:02
  it will prepare your body for that experience.
• 41:05
  I mean, the immediate thing I think about is like PTSD.
• 41:07
  Yeah.
• 41:08
  As an example.
• 41:09
  Exactly.
• 41:10
  Just in terms of like this connection between memory and then a bodily, physical, physiological response to some future stimulus that's not from the past.
• 41:18
  Yeah, that's not really real, right?
• 41:19
  But there's some like weird stimulus that then triggers the same neurons that then causes the response that we call PTSD.
• 41:29
  I...
• 41:32
  In my head, this gets dark very quickly.
• 41:33
  Yeah, it gets dark very quickly.
• 41:35
  It also, I mean, if we linger on the PTSD aspect a little bit, right...
• 41:39
  If you can identify the neural population that creates the response like PTSD, like trauma and things like that, then you can find ways to perhaps delete those neurons, right?
• 41:50
  I mean, that's the most crude way of thinking about it.
• 41:53
  But there could be ways to restructure that neural network so that those triggers do not trigger anymore, right?
• 41:59
  That could be a possible therapy in some future.
• 42:03
  Seems pretty cool.
• 42:04
  Hugely beneficial for trauma victims of a wide variety of types.
• 42:09
  At the same time.
• 42:10
  Right.
• 42:11
  You know, the immediate.
• 42:12
  You know.
• 42:13
  Yeah.
• 42:13
  Like it, you know.
• 42:16
  It's like Total Recall.
• 42:17
  You know that movie?
• 42:18
  Yeah.
• 42:19
  Like the, yeah.
• 42:20
  It's literally like you could do Total Recall.
• 42:23
  And the, our ability to now understand these biological systems better is,
• 42:29
  Like this is the tension that always happens, right?
• 42:32
  Which is that, yes, science is an incredible way for us to understand the world around us.
• 42:37
  And the discoveries in many cases, understandably, some people's reactions like, oh, some billionaire is going to use this for evil.
• 42:45
  Oh, right.
• 42:47
  And so I want to make a quick distinction – shout out to my SpongeBob fans out there – between like where – so where we are right now is that we're doing these in model organisms, in this case mice.
• 43:00
  This is not necessarily something that has risen to the level of doing so in humans.
• 43:07
  And so that delta is not small.
• 43:08
  Yeah, yeah.
• 43:09
  So this is not like an overnight tomorrow the surveillance state is going to be like manipulating your memories.
• 43:18
  No.
• 43:18
  But there are always these ethical challenges that arise with these things.
• 43:23
  I mean I've seen a lot of like –
• 43:27
  science fiction short stories in the in the age of like neural link and these types of brain computer interfaces that that talk about you know a world where having a bci having a brain computer interface is like a mandatory thing right but like just how like nowadays you know everyone has these like medical tests that everyone has to do
• 43:48
  like this isn't a weird dystopian world.
• 43:52
  There's that Black Mirror episode, right?
• 43:53
  Where everyone has that, like the implant in their retina and their brain that like downloads the memories and like you can like relive memories.
• 44:00
  This is kind of that, you know?
• 44:02
  Yeah.
• 44:04
  Black Mirror continues to have been very prescient in many of these intersections.
• 44:08
  And Black Mirror is very, very far away, I think, from the technology of today.
• 44:12
  Right.
• 44:12
  But at the same time, you know, it's incredible just how much we're,
• 44:19
  commanding the human body and the human brain as we move forward, right?
• 44:24
  And there's still so much left to know, right?
• 44:26
  The brain is still very much black boxy.
• 44:29
  100%.
• 44:30
  But we're chipping at it.
• 44:31
  I think these conversations will continue to become more relevant because we are getting to a place where...
• 44:37
  the powers that we are discovering are increasingly powerful to make an ineloquent statement.
• 44:46
  And so it's never too early to just ask the question.
• 44:52
  And it just makes me feel like this idea of memory really became much more interesting than,
• 44:57
  After looking at this paper, cause I never thought about it in this.
• 45:01
  It's not a one way street.
• 45:02
  Yeah.
• 45:02
  Yeah.
• 45:03
  Now we're getting a, it's two way exchange.
• 45:05
  It's, it's, which is again, a totally different way to think about it.
• 45:10
  Um, and we're going to have to decide what kinds of interventions we're comfortable with allowing.
• 45:15
  Cause obviously we want the benefit, but you know, um, who gets to make that decision?
• 45:20
  Yeah.
• 45:21
  Uh, but not to end on, uh, in that segment, a, uh,
• 45:27
  Awkward place, but we're going to pause to do a little bit of housekeeping.
• 45:31
  Again, we're trying to be careful because we want to make a distinction between where we are now and where these things can go.
• 45:37
  But where these things can go, we know very much in our history.
• 45:41
  When we don't have these conversations early enough.
• 45:43
  We will go.
• 45:45
  Correct.
• 45:47
  For those who are joining us recently, whether it's through our Nobel Prize coverage, Golden Goose, all of our AI math stuff recently, welcome to FFP Nation with Krishna and myself, Lester.
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• 47:41
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• 47:50
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• 47:53
  We want to continue to build these learning tools for us.
• 47:57
  Whether you're a graduate student, a PhD, you're a PI at a lab, or you're an undergrad, you're in high school, you're just starting to get interested in these subjects, that is what we are here for.
• 48:08
  With that, now let's get back to the science with the next subject for the recap, which is physics.
• 48:17
  The Physics Prize this year recognized Francis Halsey's contributions to IceCube and the discovery of high-energy neutrinos from astrophysical sources.
• 48:27
  Now, this instrument, IceCube, is...
• 48:31
  absolutely wild love it's in the south pole on the antarctic ice sheet the antarctic ice sheet is you know two kilometers plus of sheer ice and what they've done is drill down into the ice sheet and create a giant neutrino detector the only one of its kind in the world funded primarily by nsf and managed primarily by the university of wisconsin where francis halzen is on the faculty um
• 48:59
  The idea is to use the ice as a medium in which these rare particle interactions, the ones that neutrinos take part in, become visible.
• 49:09
  Because neutrinos are notoriously hard to interact with.
• 49:13
  They're called the ghost particle in the standard model.
• 49:17
  They whiz right through.
• 49:18
  There's about trillions of them going through my fingernail at this very moment per second, even though the sun is on the other side of the earth, right?
• 49:28
  It's not even daytime right now in Los Angeles.
• 49:31
  And still there are neutrinos going through my fingernail because they're going through the earth and then going through my fingernail.
• 49:36
  Like that's how little they interact, right?
• 49:38
  So you need a large volume to basically, you know,
• 49:46
  hedge the bet in your direction, right?
• 49:48
  You're going to have this incident capture.
• 49:50
  Yeah.
• 49:50
  It's like the probability is so low, but if you flip a coin that's like super low probability a million times, at some point you're going to get heads, right?
• 50:00
  Like, you know, it's the gambler's fallacy except in physics.
• 50:06
  Right?
• 50:06
  So, so what's a neutrino?
• 50:07
  Well, a neutrino is a particle that has no negative charge.
• 50:12
  And it's got interactions with ordinary matter that is quite infrequent.
• 50:16
  But sometimes when it does interact with ordinary matter, it knocks out a charged particle, something like an electron or a muon.
• 50:23
  We'll get into more of those details later on.
• 50:25
  But effectively, the way it works is those electrons or muons that are going to go through the ice and they're going to travel faster than the speed of light in ice.
• 50:33
  In ice.
• 50:34
  Yeah.
• 50:35
  exactly because the speed of light in ice is about 20% lower than in a vacuum and the speed of light in vacuum is the universal speed limit so you can still technically go a little bit faster than you know that if you get a little bit closer to the speed of light but just faster than the 20% deficit then you're technically going faster than light in that medium and you'll initiate a shock wave in the electromagnetic field essentially and if we can record that shock wave we can figure out
• 51:03
  what the direction was of the neutrino that it came, what was the initial momentum, things like that.
• 51:08
  And from that, we can understand a little bit more about where that neutrino came from.
• 51:13
  So that's how Ice Cube works, right?
• 51:15
  And Ice Cube, at the very top, there's a tiny dot.
• 51:18
  That's the building that we were just looking at.
• 51:20
  Otherwise, this whole thing is a cubic kilometer of ice.
• 51:24
  It's a small dot for audio listeners.
• 51:27
  It's a very small dot.
• 51:27
  It's a very small dot.
• 51:28
  This thing is absolutely massive, right?
• 51:31
  So what's special about Ice Cube is because it's so big, it can capture high-energy neutrinos.
• 51:40
  High-energy neutrinos are neutrinos that come from these sources in the universe that are the greatest, largest, and most powerful particle accelerators ever.
• 51:52
  Right?
• 51:52
  They make CERN look...
• 51:54
  Like tiny, tiny little, I need another thing.
• 51:57
  I said Hula Hoot last time.
• 51:59
  I should have come with something else.
• 52:01
  Some other, you know, those racetracks, the Hot Wheels racetracks?
• 52:05
  Yeah, that's what it makes CERN look like.
• 52:07
  You know, this thing is massive.
• 52:09
  It's the size of galaxies, and it's flinging these charged particles, accelerating them to incredible energies, several orders of magnitude higher than what CERN can do.
• 52:21
  If I recall correctly, were we talking about how they're approaching the speed of light in the vacuum?
• 52:28
  Not quite.
• 52:29
  Yeah.
• 52:29
  Right?
• 52:30
  Yeah, but it's like 99.99999, and I think it's like 29s.
• 52:33
  Right.
• 52:34
  It's quite close.
• 52:35
  It's like as close as anything has ever gotten, right?
• 52:38
  That is massive.
• 52:39
  Obviously, light goes at the speed of light, but this is something that is going as close as it can to the speed of light.
• 52:45
  and still have a little bit of mass.
• 52:47
  So there's a little bit of mass energy in that term.
• 52:51
  It can't go truly at the speed of light, but it can get really, really close.
• 52:57
  We want to understand how these things work and what is the nature of these things.
• 53:04
  The problem is that these giant particle accelerators, they fling out...
• 53:11
  Cosmic rays, which is what we've seen before, these are charged particles that move at that speed.
• 53:19
  They also fling out gamma rays, and they fling out neutrinos.
• 53:23
  There's three types of things that come out of these particle colliders, okay?
• 53:28
  The cosmic rays are usually charged particles.
• 53:30
  They're like protons or heavy atomic nuclei, which means that they're going to be perturbed by magnetic fields in the extragalactic medium.
• 53:38
  And that means that if I saw a cosmic ray come from here, I'm not sure if it actually came from here or if it curved from over there and came from here, right?
• 53:47
  So I can't actually localize a source.
• 53:49
  Gamma rays, on the other hand, have a similar kind of problem.
• 53:52
  They're more straight.
• 53:53
  Gamma rays are just photons at the end of the day.
• 53:56
  But...
• 53:58
  These gamma rays interact with the intergalactic medium, so they can get bent somehow, and they can also lose energy by interaction within the intergalactic medium.
• 54:07
  On the other hand, neutrinos just go straight, okay?
• 54:11
  Their cross-section with respect to, like, how much they interact with stuff is, like,
• 54:15
  tiny tiny tiny so they're just going straight from source all the way to us here on earth right and so if we happen to catch one in antarctica and we can localize where it's coming from we can be pretty sure that it actually is a straight line to the source okay and that's the idea but at the same time they're very rare these high energy neutrinos are extremely rare which
• 54:39
  of the size of ice cube.
• 54:41
  The rarity of these events, you can calculate, it's something like one per cubic kilometer per year.
• 54:49
  So if you had a cubic kilometer detector and you saw this thing, then you'd see one every year.
• 54:56
  That's so crazy.
• 54:57
  And...
• 54:58
  That's why we need a cubic kilometer detector.
• 55:01
  It's also – and to come back to like this idea, I think if I recall correctly, part of what we're trying – this is a mapping endeavor because why – like we're trying to understand where it came from.
• 55:14
  And so we've now looked at all the options to do this mapping endeavor and we figured out that this is the best thing to – this is the best –
• 55:24
  secondary or outcome of a process that's happening very far away for us to try to trace back the source of that process and the scale of that process.
• 55:34
  And so fundamentally, you know, the purpose is we're trying to understand astrophysical phenomena that are these massive, quote, particle accelerators that are some of the most violently high energy phenomena.
• 55:51
  things in existence in the universe yeah exactly and the key thing here and this kind of follows up to the paper that we're going to talk about is that by studying these astrophysical sources we can also glean insight into the fundamental physics of particles in general and like the standard model we can push the standard model to its limit in terms of you know trying to understand how neutrinos work
• 56:18
  They're a very enigmatic particle, right?
• 56:20
  And catching them is extremely rare.
• 56:22
  The higher the energy, the more kind of esoteric physics you can get into.
• 56:27
  And that's the point.
• 56:28
  So this next paper, it's the Ice Cube paper from the Ice Cube collaboration published in the Physical Review Letters in August 2026.
• 56:38
  So again, very recent.
• 56:39
  Very recent.
• 56:39
  Very recent.
• 56:40
  This is like, you know, two months before Ice Cube won the Nobel Prize.
• 56:45
  And it measures...
• 56:46
  The three-flavor composition of cosmic neutrinos.
• 56:51
  The cosmopolitan ice cube of cosmic neutrinos.
• 56:54
  Yeah, that's actually, that should have been the title, honestly.
• 56:58
  What are you guys doing at Ice Cube?
• 56:59
  Come on.
• 57:00
  This branding opportunity.
• 57:01
  Yeah, yeah.
• 57:02
  So let's review the standard model real quick.
• 57:05
  Here's the standard model.
• 57:08
  Beautiful, beautiful standard model of particle physics.
• 57:12
  Everything that is anything except gravity and maybe dark matter and dark energy, I guess, if those things are real.
• 57:21
  So you're saying the majority of the universe?
• 57:24
  Yeah, fair enough.
• 57:27
  Yeah, things that are normal.
• 57:29
  Things that are normal.
• 57:31
  It's in here.
• 57:31
  Yes.
• 57:31
  Okay?
• 57:32
  At the center and the four that are flanking the center, those are the bosons.
• 57:37
  At the very center is the Higgs boson.
• 57:39
  And the four on the side, those are the gluon, the photon, and the W and Z bosons.
• 57:46
  The gluon mediates the strong nuclear force.
• 57:48
  This is the thing that keeps the nucleus of the atom together.
• 57:53
  The photon mediates the electromagnetic force, like, you know, electricity, magnetism, light, and,
• 58:00
  And the W and Z bosons mediate
• 58:02
  the weak nuclear force.
• 58:04
  This has to do with beta decay.
• 58:06
  And it turns out that, you know, the photon, the W and Z bosons, they're kind of part of the same sort of gang, the electroweak force.
• 58:13
  The weak gang.
• 58:15
  And the Higgs boson is just sort of this enigmatic particle that gives the W and Z bosons mass.
• 58:21
  Okay?
• 58:23
  Finally, on the outer ring, those are the stuff that have mass.
• 58:28
  Okay?
• 58:29
  Those are the particles that...
• 58:31
  create matter in some sense.
• 58:34
  The top six are the quirks.
• 58:36
  You've got the up and the down, the charm and the strange, and the top and the bottom.
• 58:43
  Those come in those pairs.
• 58:45
  There's three pairs.
• 58:46
  And then on the bottom, you have the leptons, which are the electron and the electron neutrino, the muon and the muon neutrino, and the tau and the tau neutrino.
• 58:56
  Crucially, those neutrinos come in three different flavors for the three generations of matter.
• 59:02
  And this is the standard model.
• 59:04
  Each of these things actually represents quantum fields.
• 59:07
  And the particles are excitations in the quantum field.
• 59:11
  But the thing that is fundamental is really the field of the thing.
• 59:16
  But quantum field theory is very weird, and I need a few more semesters understanding it before I start talking about it in a bit more detail.
• 59:25
  We love QFT.
• 59:26
  In any case, three types of neutrinos, three different flavors of neutrinos.
• 59:34
  The question that we want to ask here...
• 59:37
  is...
• 59:39
  The flavors of those neutrinos, right, that are observed in IceCube, can we infer something about the physics of the sources that made those neutrinos by looking at the distribution of the flavors of the neutrinos?
• 59:54
  Because IceCube is going to see this number of electron neutrinos, this number of tau neutrinos, this number of muon neutrinos.
• 1:00:01
  Using that numbering scheme, the statistics of that, can I tell something about how...
• 1:00:07
  the blazars and those weird particle accelerators at the edge of the universe, how those work.
• 1:00:12
  I have strawberry, I have vanilla, I have chocolate.
• 1:00:16
  If I have more strawberry and then vanilla is the next biggest and then chocolate is the next biggest, it can tell me the source of what produced my cosmopolitan ice cream.
• 1:00:28
  What's wrong with the ice cream machine?
• 1:00:29
  Right, right.
• 1:00:31
  But again, being annoying about the analogy, but this idea that the proportion of the ingredients –
• 1:00:39
  If we take the flavors of ice cream, tell something about the McFlurry machine that made the ice cream.
• 1:00:45
  The In-N-Out.
• 1:00:46
  The In-N-Out.
• 1:00:48
  We're from California here.
• 1:00:49
  We are fans of In-N-Out Burger.
• 1:00:51
  No sponsorship yet, but In-N-Out Burger would be a great sponsor.
• 1:00:56
  We're hungry.
• 1:00:57
  You see how bony we are over here?
• 1:01:00
  We need a little caloric intake anyway.
• 1:01:02
  Yeah, so...
• 1:01:03
  That's the idea, right?
• 1:01:05
  We're trying to measure the distribution of the types of neutrinos that we see in IceCube.
• 1:01:10
  Now, it's useful to separate three different things, okay?
• 1:01:13
  There's the distribution that comes out of the particle accelerator at the edge of the universe.
• 1:01:18
  Then there's all of the stuff that happens in between.
• 1:01:21
  And then there's the statistics of the IceCube detector itself, right?
• 1:01:26
  We're going to separate those three parts.
• 1:01:28
  Let's start with the production, okay?
• 1:01:29
  The production of these neutrinos
• 1:01:31
  happens in probably this way.
• 1:01:34
  This is how we're guessing.
• 1:01:35
  There's a proton that's coming in from these jets, super high energy.
• 1:01:40
  It interacts with a gamma particle, which is a really high beam of high photon of light, high energy photon of light.
• 1:01:48
  And when it interacts with that, it's going to create pions.
• 1:01:51
  Those pions are going to then decay into muons and gamma particles and electrons and so on and so forth.
• 1:01:57
  Let's just look at the end of the diagram, the end of the trees, where the branches are, okay?
• 1:02:03
  You've got the positron at the top.
• 1:02:04
  Yes.
• 1:02:04
  Then you've got an electron neutrino.
• 1:02:06
  That's the new E, the V-looking thing with the E. That's an electron neutrino.
• 1:02:11
  Then you've got an anti-muon neutrino.
• 1:02:14
  That's the bar.
• 1:02:15
  And then a muon neutrino.
• 1:02:16
  And then the rest are electron, positron, gamma, gamma.
• 1:02:19
  At the bottom.
• 1:02:19
  Okay?
• 1:02:19
  Okay.
• 1:02:21
  Out of the neutrinos,
• 1:02:23
  We have two mu neutrinos, two muons, two muon neutrinos, and one electron neutrino, right?
• 1:02:30
  So it's a two to one sort of ratio, okay?
• 1:02:34
  Let's not worry about the fact that one of them is an anti-neutrino and the other one's a neutrino.
• 1:02:39
  There's this weird theory that the neutrino is its own antiparticle, but we don't know because, again, we don't know a lot about neutrinos.
• 1:02:46
  But in any case, the signature in our detector would be very similar given if it's a...
• 1:02:53
  muon neutrino or an anti-muon neutrino.
• 1:02:55
  What's really important is the flavor.
• 1:02:57
  And in this case, the flavor is a two-to-one ratio.
• 1:02:59
  Two muon neutrinos, one electron neutrino.
• 1:03:02
  Okay?
• 1:03:03
  That's what we're guessing is the starting distribution.
• 1:03:07
  What's happening at the source.
• 1:03:08
  What's happening at the source.
• 1:03:09
  Okay?
• 1:03:11
  Crucially, no tau flavors.
• 1:03:13
  Yeah, I was going to say, where's my umami?
• 1:03:15
  Yeah, no tau flavors.
• 1:03:16
  Okay?
• 1:03:18
  Now...
• 1:03:19
  From the source, it's going to come through billions of light years to the Earth.
• 1:03:25
  And during that time, it is going to change the mixture because neutrinos mix.
• 1:03:30
  They've got these things called mass eigenstates, which you can imagine are like three directions in your Hilbert space of possible neutrino flavors.
• 1:03:41
  Okay.
• 1:03:43
  They're not really even... You wouldn't even call them like...
• 1:03:48
  What's interesting about neutrinos is like the three relevant directions that you can express what the neutrino is.
• 1:03:55
  aren't even like electron muon tau.
• 1:03:58
  It's like mass one, mass two, mass three.
• 1:04:02
  I don't like it.
• 1:04:03
  Yeah, it's weird.
• 1:04:04
  And the three flavors are expressed as combinations of these three different directions in some sense.
• 1:04:10
  So you can imagine it's kind of like, you know, if we go to the analogy of the ice cream, it's like imagine there's like three ingredients that make up chocolate, vanilla and strawberry, but they come in different like sort of...
• 1:04:22
  proportions for each of them for each of the individual for each of the individual ones but they're like a mystery like box so you could get the dark chocolate chocolate or the milk chocolate yeah yeah and but like but like somehow if the milk chocolate and the dark chocolate combine it gives you strawberry you know neutrinos are weird right but in any case these three mass eigenstates are mixing all the time yeah and so sometimes the electrons
• 1:04:46
  the electron neutrinos will transition to tau neutrinos, will transition to muon neutrinos.
• 1:04:51
  And one of the famous experiments done by the Nobel Prize winners that discovered the neutrinos, Raymond Davis...
• 1:05:01
  he discovered that there's only a third of the neutrinos that we see from the sun showing up in my detector.
• 1:05:07
  And it's like, where are the other two-thirds?
• 1:05:08
  And everyone thought, we went over this in the episode of America 250.
• 1:05:14
  You know, John Bacall at Princeton was going, well, you should be seeing this many events.
• 1:05:19
  And he's like, no, I'm seeing exactly a third of what you're seeing.
• 1:05:23
  So what's going on?
• 1:05:24
  Turns out there's three flavors, and the neutrinos were changing flavor on their way from the sun to the earth.
• 1:05:29
  Okay?
• 1:05:31
  That's the idea.
• 1:05:32
  On the way here, that ratio is going to change.
• 1:05:35
  Yes.
• 1:05:36
  And finally, when we get to our detector, the detector has a different probability of measuring the three different types.
• 1:05:46
  Ah, okay.
• 1:05:46
  Right?
• 1:05:46
  Because it's not like all of these three interactions are created equal.
• 1:05:50
  Right?
• 1:05:50
  Right.
• 1:05:51
  If the tau came in, that has a lower percentage of interacting with the ice than, let's say, the electron neutrino.
• 1:05:58
  So good thing there's not a lot of taus coming from the source.
• 1:06:00
  Yeah.
• 1:06:00
  Well, but we don't know.
• 1:06:02
  Oh, that's true.
• 1:06:03
  That's the theory.
• 1:06:04
  It might transition on the way.
• 1:06:07
  On the way.
• 1:06:08
  So that's the theory.
• 1:06:09
  But it might be the case that it's there, but we just don't.
• 1:06:11
  On the observational level, we actually are not.
• 1:06:13
  You're right.
• 1:06:13
  So you've got to be careful.
• 1:06:14
  Yeah, yeah, yeah.
• 1:06:15
  Right?
• 1:06:15
  I like that.
• 1:06:15
  Now, there's three different signatures for the three different flavors.
• 1:06:18
  Okay.
• 1:06:19
  Okay.
• 1:06:19
  The electron one just looks like kind of like a single track.
• 1:06:25
  The electron one is in the center.
• 1:06:27
  It's not even a track, actually.
• 1:06:28
  It's more of just like an explosion that happens in the center of the detector.
• 1:06:32
  The track, actually, is the muon-looking.
• 1:06:35
  So the muon neutrino looks like a track that goes through the detector.
• 1:06:40
  The electron one is kind of like a central explosion.
• 1:06:42
  You can't really tell where it came from, but you've got a really good estimate of the energy because all of the...
• 1:06:48
  stuff is inside the detector with the muon it's like it's coming through it could have lost energy outside it could have lost energy on its way out like but you know where it's coming from because it's a really nice sort of linear thing and the the tau one has this like double bang idea where like bang bang yeah it goes through one and then it transitions in the middle of the detector it transitions into another
• 1:07:11
  particle and then does another um track i so it dumps a bunch of energy and then it has a little bit of energy left over and then does i just want you to know that i just gave a very very famous sports meme that you have no idea i have no idea what you're talking about and people in the comments will know what i'm talking about
• 1:07:32
  I'm not going to tell you.
• 1:07:33
  I didn't mean to – I just couldn't stop thinking about the double bang because double bang is in the lexicon.
• 1:07:39
  It's a very specific thing.
• 1:07:40
  Really?
• 1:07:40
  And it's not talking about the tau neutrino.
• 1:07:42
  No, dude.
• 1:07:44
  In physics, when people say double bang, everyone knows you're talking about the tau neutrino.
• 1:07:49
  And you're talking about this sort of double explosion that happens because a decay happens in the middle of the track.
• 1:07:55
  Everyone knows that's what you're talking about.
• 1:07:57
  I'm going to now call – and I'm not going to tell you, but what I'm referencing, I'm now going to call it the Tau Neutrino and make it a thing.
• 1:08:04
  For the physics who are also sports fan people, let's make it happen.
• 1:08:08
  But not to take away from your point here, but there are signatures on the observational level that are very unique and have different – they express themselves differently.
• 1:08:17
  They're very distinct.
• 1:08:18
  So we know –
• 1:08:20
  what flavor we're getting.
• 1:08:21
  Exactly.
• 1:08:22
  And we are getting some Tao because we know that it's the double bang.
• 1:08:24
  Yeah, yeah.
• 1:08:25
  But those things are quite rare, right?
• 1:08:27
  But you have to... You have to...
• 1:08:33
  scaffold the actual data that you see with the probability that you would see that thing, right?
• 1:08:39
  Because like, it could be that the thing is actually rare, or it could be that like, there's a lot of it, but it interacting with the ice in my ice cube detector is rare, right?
• 1:08:50
  Which one is it?
• 1:08:50
  And so you have to do a lot of forward modeling to actually figure out what is the incoming mixture from what you see.
• 1:08:56
  There's like a distribution of what you see, and from that you build like, what is it that is actually coming into the earth?
• 1:09:02
  from the source.
• 1:09:03
  The actuality and the possibility are different things.
• 1:09:06
  Very much so.
• 1:09:07
  And so this is what this new analysis did in the physical review letters.
• 1:09:11
  After 11.4 years of IceCube data, they figured out the best fit cosmic flavor fractions, and it's roughly at about 30% electron, 37% muon, 33% tau.
• 1:09:20
  Oh, interesting.
• 1:09:25
  So it's about a third, a third, a third.
• 1:09:27
  And the error is quite big because there's not that many events.
• 1:09:31
  It hasn't been online for that long.
• 1:09:33
  And again, we only have a cubic kilometer, right?
• 1:09:36
  You've got a bigger thing.
• 1:09:37
  You've got more data.
• 1:09:39
  And you can constrain this thing a little bit more.
• 1:09:42
  But for all intents and purposes, we can use at least this data to create some kind of inference about what's happening at the source.
• 1:09:49
  And we can tell that we're probably correct.
• 1:09:52
  About the fact that it's a proton that's interacting with the gamma to create a two-to-one ratio muon electron neutrinos.
• 1:09:59
  Yeah, yeah.
• 1:10:00
  This is...
• 1:10:02
  But this is cool because we're gleaning insight into those particle reactions.
• 1:10:08
  At the edge of our universe.
• 1:10:10
  This is...
• 1:10:13
  I have so many thoughts about this.
• 1:10:14
  It gets back to... And we'll talk about it in a moment.
• 1:10:17
  But why are we doing this?
• 1:10:20
  And so I'll leave that for a moment.
• 1:10:21
  But one of the things that we didn't...
• 1:10:23
  This is just one aspect, again.
• 1:10:24
  Paper that came out recently of how we're using this new instrument.
• 1:10:29
  The Ice Cube Neutrino Observatory.
• 1:10:32
  To look at things that are very far away.
• 1:10:34
  A paper that we...
• 1:10:36
  did not cover which is another aspect of this is this sort of earth tomography um yeah concept which is similar to the muon tomography that we've actually talked about on the pod which was related to um research in like uh volcanology and mapping the pyramids which is basically the hidden chambers in the yeah of the cool
• 1:10:55
  The Khufu Pyramid.
• 1:10:56
  Yes, yes.
• 1:10:56
  In Giza.
• 1:10:57
  Great, great past episodes for you to look at.
• 1:11:00
  But it's giving us another way to – the actual instrument of Ice Cube in the Earth Tomography paper was looking at how it passes through the Earth.
• 1:11:07
  Yeah.
• 1:11:08
  Which lets us then understand the structure and mapping of the insides of our own planet.
• 1:11:14
  That's right.
• 1:11:14
  So this is not just a thing that's looking outward.
• 1:11:16
  It is also giving us the ability to understand more about our home here on Earth.
• 1:11:21
  And it goes to this idea that these tools, these instruments that are very expensive are not necessarily single-use.
• 1:11:30
  And also, you might build them as single-use initially and discover that there are these tools.
• 1:11:37
  ancillary use cases, these other use cases after the fact that you might not have initially thought was going to be the case, but they're so powerful that we can do a lot of things with them.
• 1:11:49
  So I think Ice Cube is so cool because I'm a space nerd.
• 1:11:54
  Yeah.
• 1:11:54
  And so the space stuff is super interesting to me.
• 1:11:57
  But the Earth tomography paper, I think, may be worth a follow-up in the future because there's some interesting aspects to that.
• 1:12:04
  But that's totally fair.
• 1:12:06
  And like, well, before we move on to the chemistry prize, I do want to linger on some of the reactions that this physics Nobel Prize caused.
• 1:12:15
  There were reactions.
• 1:12:16
  There were reactions, okay?
• 1:12:18
  There's always reactions, right?
• 1:12:21
  Let's talk about it, okay?
• 1:12:23
  So there's been a lot of support.
• 1:12:25
  From the experimental astrophysical community, from the astroparticle community, and from the cosmology community, it's been enormous support for Francis Halsey.
• 1:12:37
  They think this is a no-brainer.
• 1:12:38
  They've been calling Ice Cube as something that wins the Nobel Prize for a long time.
• 1:12:43
  And Sean Carroll, a very famous scientist and physicist and science communicator, I guess, in some sense, he said, Francis Halsey is a great person and, quote, let's turn the Antarctic ice sheet into a neutrino detector.
• 1:13:01
  is exactly the kind of idea that deserves a prize if you can pull it off.
• 1:13:05
  Such a good Nobel Prize.
• 1:13:07
  And if I recall correctly, it's been a while since an astrophysics or cosmology-related topic won the Physics Nobel?
• 1:13:17
  Yeah, well, I mean, I think the last one was 2020.
• 1:13:19
  Okay, okay.
• 1:13:22
  That was Andre Gez.
• 1:13:23
  That's right.
• 1:13:24
  And cohort for the Sagittarius A-star Milky Way.
• 1:13:28
  So not that long ago.
• 1:13:29
  Yeah.
• 1:13:29
  Not that long ago, but I mean, it's been happening, right?
• 1:13:35
  I think the one before that was probably the Dark Energy one by Reese, and...
• 1:13:44
  I shouldn't call it dark energy because they said the accelerated expansion of the universe.
• 1:13:48
  The Nobel Committee is very particular because we don't know if it's dark energy or cosmological constant.
• 1:13:54
  But certainly the accelerated expansion is a thing.
• 1:13:57
  So for the experimental discovery, they won the Nobel Prize.
• 1:14:02
  But still, it's...
• 1:14:05
  So it's in the grand history of Nobel Prizes, astrophysics is certainly rare.
• 1:14:10
  Fair enough.
• 1:14:11
  Fair enough.
• 1:14:12
  It's nice to see astrophysics get some kind of credit, you know.
• 1:14:17
  So the vast majority of the community was very supportive.
• 1:14:20
  Some have not been so kind.
• 1:14:22
  Sabina Hosenfelder, who has a very entertaining show on YouTube.
• 1:14:29
  Usually I quite enjoy her sass for physics, but every once in a while, I guess in this particular one, I did not enjoy it.
• 1:14:37
  She had a video titled Benefit to Humankind?
• 1:14:40
  Yeah.
• 1:14:41
  Nobel Prize in Physics, you will hate me for saying this.
• 1:14:44
  Well, hate is a strong word, but I think I will certainly disagree.
• 1:14:49
  So here's what she said in the video.
• 1:14:51
  She said, what have we learned from it?
• 1:14:53
  Well, we have learned that neutrinos exist, which no one doubted.
• 1:14:58
  It does teach us something about neutrino sources outside our galaxy, but that's pretty much it.
• 1:15:03
  This Nobel Prize just documents how irrelevant the foundations of physics have become.
• 1:15:11
  Okay?
• 1:15:12
  Those are some pretty strong words.
• 1:15:13
  Those are some pretty strong words.
• 1:15:15
  Well, I would like to contend step by step, okay?
• 1:15:18
  So she says, what have we learned from it?
• 1:15:19
  Well, we've learned that neutrinos exist.
• 1:15:22
  She says, which no one doubted, right?
• 1:15:25
  No one doubted that these neutrinos existed.
• 1:15:27
  And so the fact that we saw them doesn't really change anything.
• 1:15:32
  Well, there are several Nobel Prizes in our history that do exactly that.
• 1:15:37
  One of our favorite ones that we've talked about on this show is the Lamb shift from 1947.
• 1:15:43
  Willis Lamb at the University of Columbia determined that two specific energy levels of the hydrogen atom had distinct energies.
• 1:15:56
  And there was a tiny, tiny difference in that energy, which he measured using microwave radar technology.
• 1:16:05
  This was in 1947.
• 1:16:06
  So post-war, there was a bunch of radar technology that the U.S. government had developed.
• 1:16:11
  He used that to find that shift.
• 1:16:13
  Now, in the 1930s, people had been measuring the spectrum of hydrogen using visible light, and they already knew something was up.
• 1:16:23
  okay?
• 1:16:23
  Dirac had came up with his equation for the electron in the late 1920s, and from Dirac's
• 1:16:32
  equation from Dirac's theories, you can sort of theorize and you can sort of deduce what the energy spacing should be.
• 1:16:40
  But one of those energies, two of those energy levels should be degenerate, meaning they should have the same energy level.
• 1:16:48
  And pretty soon, even in the 1930s, using visible light, people had figured out something was up, right?
• 1:16:55
  It took Willis Lamb to actually measure that thing.
• 1:16:57
  And there's a very famous, there's a very famous
• 1:17:01
  interview by Hans Bethe.
• 1:17:03
  I think it's either by Hans... No, yeah.
• 1:17:05
  It's a very famous interview by Hans Bethe who said everyone knew that it was there, but until Lam measured it, we were just kind of like...
• 1:17:13
  It was a coffee conversation.
• 1:17:16
  It was like, oh, that's kind of interesting.
• 1:17:18
  And then Lam put a number on it.
• 1:17:21
  He's like, this is the number.
• 1:17:22
  This is the shift between those two energy levels.
• 1:17:25
  And all of a sudden it became a real thing that the theorists were like, okay...
• 1:17:29
  So now there's a number that I can chase, right, to try and understand what's going on.
• 1:17:35
  We already knew that no one was surprised that the lamp shift existed, but measuring it was important, right?
• 1:17:43
  Because it falsifies several theories and it gives room to...
• 1:17:47
  for theorists to actually propose new theories.
• 1:17:50
  Same thing happened with the Higgs.
• 1:17:52
  The Higgs is something that we knew to exist for a very long time, 40 or 50 years since Peter Higgs wrote that paper.
• 1:17:59
  Measuring its mass is something else.
• 1:18:02
  We still built CERN.
• 1:18:03
  Yeah, we still built CERN to measure the mass.
• 1:18:05
  And I'm sure Sabina Hossenvelder is going to say that CERN is a waste of time too.
• 1:18:09
  So she's got her opinions, but I would like to say that
• 1:18:14
  LAM did it with microwaves, even though the visible spectrum people had said that it might not exist.
• 1:18:21
  I mean, sorry, that it definitely exists.
• 1:18:22
  And that led to the revolution of QED and quantum field theory.
• 1:18:26
  That's how we have atomic clocks.
• 1:18:28
  That's how we have GPS.
• 1:18:30
  I mean, come on.
• 1:18:31
  It's pretty nice, right?
• 1:18:32
  So similarly, in the case of IceCube, observing neutrinos...
• 1:18:39
  And finding the distribution of the neutrinos, the power distribution, right?
• 1:18:44
  Of like, not only, yeah, in the case that we just reviewed, which is the flavors of the neutrinos that are coming through, but also the power distribution.
• 1:18:52
  Yeah.
• 1:18:52
  Like, how often do I see a neutrino of a certain power?
• 1:18:55
  Right.
• 1:18:55
  That is going to constrain the...
• 1:18:58
  physics, that is going to constrain the physics of the sources and also constrain the physics of the particles themselves.
• 1:19:05
  That's going to tell us more about neutrinos in general.
• 1:19:09
  And who knows, at some point in the vast future, it might lead to...
• 1:19:15
  Things that we can't even dream about.
• 1:19:17
  This is what we just talked about with optogenetics.
• 1:19:19
  Optogenetics started with someone looking at how light and algae.
• 1:19:23
  And now we're solving vision.
• 1:19:25
  Yeah.
• 1:19:26
  And understand how to control, to activate neurons in the brain.
• 1:19:30
  Yeah.
• 1:19:30
  And so you can't always know where this goes.
• 1:19:33
  Yeah.
• 1:19:34
  And I think physics has been, you know, physics has had the luxury over the past century or two centuries of,
• 1:19:39
  of almost having like an immediate effect on economics right it's like we discover the nucleus on the like 20 years later we build a bomb and it's like dope like yo and so and so hold on hold on i don't want to be careful there's a difference between the scientific process of the bomb
• 1:19:59
  And the lived experience, just to be clear.
• 1:20:01
  Yeah, just to be clear.
• 1:20:01
  Just to be clear.
• 1:20:02
  Yeah, we split the atom.
• 1:20:03
  Now we have nuclear fission technology, right?
• 1:20:06
  We have unlimited power in some sense, right?
• 1:20:09
  We have radioactive carbon dating.
• 1:20:12
  We have radiology for cancer.
• 1:20:14
  We have all of these technologies, right?
• 1:20:16
  With the quantum mechanics stuff, like 1920s, we had quantum mechanics.
• 1:20:20
  Like 20 or 30 years later, we're inventing the transistor and superconductivity and all this other stuff.
• 1:20:25
  And we have phones and supercomputers, all this stuff.
• 1:20:26
  Right?
• 1:20:26
  It's like very quick.
• 1:20:28
  But that's because we've lived in an energy regime where the physics is right there for us.
• 1:20:35
  It's right there.
• 1:20:35
  Just touch it.
• 1:20:36
  You could just touch it.
• 1:20:37
  You could just tune up the voltage and all of a sudden new physics is happening in front of me, right?
• 1:20:44
  We've kind of spent...
• 1:20:46
  our tokens yeah okay and now now we need we need the big models you know in some sense like and so yeah like the progress is going to be a little bit slower because the questions we're starting to ask now require a lot higher energy and a lot more time and a lot more work but that doesn't mean that it's not worth it
• 1:21:09
  You know, I want to ask a stupid question under that same construction.
• 1:21:17
  That's the bean is put forth.
• 1:21:18
  Yeah.
• 1:21:19
  I want to talk about string theorists.
• 1:21:21
  Okay.
• 1:21:22
  She hates string theory.
• 1:21:23
  You know, they're like, oh, we the thing works.
• 1:21:26
  It makes predictions.
• 1:21:27
  Yeah.
• 1:21:27
  But there would obviously be, but it can't be experimentally tested.
• 1:21:30
  Yeah.
• 1:21:31
  No, it's not yet.
• 1:21:32
  Not yet.
• 1:21:32
  And so, you know, wouldn't it be fundamentally different between where the string theorists are now, where it can make predictions, versus some experimental...
• 1:21:43
  detection yeah i think i think i think that's a that's a very good and relevant question as the as a similar kind of analogy because it's basically saying you're saying oh the string theorists already figured it out no you just measured it and it's like well that's a very big difference right it is a very big difference i mean so there there's some there's something to be said about maybe the over exposure right
• 1:22:06
  of the theoretical physics granting initiatives.
• 1:22:11
  Yes.
• 1:22:12
  And let's say string theory.
• 1:22:14
  Which, yes.
• 1:22:15
  Maybe that's true.
• 1:22:16
  I mean, I'm sure that, I think string theory should get funded.
• 1:22:19
  And actually in the advent of AI solving math problems, like string theory should in some sense get cheaper because you can just like ask, because it's like pure math in some sense.
• 1:22:30
  And you can just ask AI to solve some of the string theory problems.
• 1:22:33
  It shouldn't take all the oxygen in the room, which is what a lot of people in the physics community complain about.
• 1:22:36
  It's become the – it just absorbs.
• 1:22:40
  It's just like this black hole, a funding black hole.
• 1:22:43
  And it's like, okay.
• 1:22:44
  Yeah, but also I've been looking more into it.
• 1:22:47
  Oh, ooh.
• 1:22:48
  And it seems that like a lot of the noise against string theory are by –
• 1:22:54
  The types that don't actually have an in on the physics community at large in the first place.
• 1:23:00
  That's fair.
• 1:23:00
  Right?
• 1:23:01
  Like, there is a truth to it that, like, the string theory community has maybe an outsized role in...
• 1:23:08
  The cabal.
• 1:23:09
  Yeah, in the cabal.
• 1:23:10
  But it's not nearly the extent that, like, is...
• 1:23:14
  reported in the popular zeitgeist, right?
• 1:23:18
  It's not like... Because, like, there are quantum gravity...
• 1:23:20
  There's, like, the loop quantum gravity.
• 1:23:22
  That's a big initiative that, like, a lot of people are getting money for to explore.
• 1:23:27
  There's other ways to do grand unified things, right?
• 1:23:32
  So, yeah, it's...
• 1:23:36
  I see where you're coming from, and I do think that there's a healthy tension that should happen between the theoretical physics community and the experimental physics community in terms of like you've got to give us stuff to test.
• 1:23:47
  And the experimental community also has to find experiments that will push the theorists.
• 1:23:53
  And Ice Cube is one of those.
• 1:23:55
  It's a perfect example.
• 1:23:56
  And I think the point I'm trying to land the plane on here is like –
• 1:24:02
  The experimentalists have a role to play, even if the theorists have arrived at a conclusion prior to the experimentalists being able to show it in lived experience.
• 1:24:12
  Yeah, yeah.
• 1:24:12
  And the experimentalists, like experimental physics has a role.
• 1:24:20
  So what am I trying to say?
• 1:24:21
  So experimental physicists, they've got...
• 1:24:26
  An obligation in some sense to just push the frontier at all times.
• 1:24:30
  Okay.
• 1:24:31
  When the muon was discovered, there was no theorist that had that on the radar.
• 1:24:37
  Aha.
• 1:24:38
  Okay.
• 1:24:39
  When the muon was discovered, I think it was Wolf.
• 1:24:41
  No, it was Robbie.
• 1:24:43
  Oh, yeah.
• 1:24:44
  It was Robbie who was like, who ordered that?
• 1:24:46
  Because it didn't make any sense.
• 1:24:48
  Right.
• 1:24:48
  We're sitting very nicely.
• 1:24:50
  We've got the quark.
• 1:24:51
  We've got the up and the down.
• 1:24:52
  We've got the electron.
• 1:24:53
  We've got this nice generation of stuff.
• 1:24:55
  We even have the neutrino from Pauli being like, you know, let's just do the bookkeeping and make sure the energy is conserved.
• 1:25:02
  All of a sudden the muon shows up and it's a parallel universe.
• 1:25:06
  And then the tau shows up and it's another parallel universe.
• 1:25:10
  We still don't have an answer to those three generations of matter, right?
• 1:25:13
  But that happened because experimentalists were just pushing the envelope on the types of particles that they could see, right?
• 1:25:19
  The theorists didn't even have it on their radar that something like this could exist.
• 1:25:23
  Mm-hmm.
• 1:25:24
  So experimentalists have an obligation to just keep pushing the envelope on the more and more sensitive the experiments can get, the more and more the parameter sweeps that we can see.
• 1:25:34
  It's always going to be good for humanity at large.
• 1:25:38
  My favorite – I think my favorite physical physics experiment continues to be LIGO.
• 1:25:43
  But that is a previous episode.
• 1:25:45
  So if you want to know about LIGO, gravitational wave detector.
• 1:25:50
  And was it the graduate students or postdocs that were driving?
• 1:25:54
  Yeah.
• 1:25:54
  Yeah.
• 1:25:55
  They were driving at the same time as the detection.
• 1:25:59
  And great story.
• 1:26:00
  No, like an hour before.
• 1:26:02
  The PIs were frantically calling them.
• 1:26:06
  Unbelievable story.
• 1:26:08
  In any event, that concludes, that was a large digression, but I think an important kind.
• 1:26:12
  This is a discussion.
• 1:26:13
  And it's fun and it's healthy.
• 1:26:15
  One of the things I think I love about our community and FFP nation, we have the smartest comments section on the internet.
• 1:26:21
  Yes, we really do.
• 1:26:23
  And I don't think it's close.
• 1:26:24
  If you think you have a better one, please try.
• 1:26:27
  Every time we talk about something, there's always like at least five people who are like, oh, yeah, that's my PhD.
• 1:26:32
  It's unbelievable.
• 1:26:34
  And shout out to all the folks who are hard at work here in the comments sharing because it makes us feel very nice.
• 1:26:43
  Very nice.
• 1:26:43
  Yeah, it really does.
• 1:26:44
  We will end with our chemistry recap and breeze through this.
• 1:26:51
  Again, this is a recap episode, so we're trying to have a little bit of fun here.
• 1:26:54
  Yeah.
• 1:26:54
  Not going too deep, but let's start with – let's end with, I should say, chemistry.
• 1:26:59
  The chemistry prize recognizes Henri Kagan and Kenzo Soai for nonlinear effects and autocatalysis in asymmetric organic synthesis.
• 1:27:09
  Asymmetric in the sense that chemistry has a handedness and it cares about the handedness.
• 1:27:15
  It cares about mirror images.
• 1:27:17
  The theme was how reactions can produce an excess of one mirror image form.
• 1:27:23
  And in suitable systems, it can amplify that small handedness imbalance.
• 1:27:29
  The central property that we're talking about here is chirality.
• 1:27:33
  Chirality is a geometric property of 3D molecules.
• 1:27:39
  Two mirror images, they're different in the sense that the bonds are exactly the same.
• 1:27:46
  The atoms are exactly the same.
• 1:27:48
  But the arrangements of them in 3D are exactly mirror flipped in one direction.
• 1:27:55
  Okay, and these are called enantiomers.
• 1:27:58
  Here we're looking at two examples of L-alanine and D-alanine.
• 1:28:02
  These are two enantiomers of the amino acid alanine.
• 1:28:07
  Same exact molecular formula, but the 3D structures are flipped.
• 1:28:11
  Now, for unbiased synthesis in a symmetric environment, there's no obvious reason to prefer one over the other.
• 1:28:18
  But life, for some reason, biology, strikingly,
• 1:28:22
  uses a consistent handedness for every single molecular species.
• 1:28:27
  All of the amino acids that could be chiral are always in their left-handed versions in life.
• 1:28:34
  All of the sugars that could be chiral are in their right-handed version.
• 1:28:38
  And the prize-winning work,
• 1:28:40
  this year demonstrates that there's chemical mechanisms through which you can establish an imbalance, and then that imbalance can become a lot larger than it was previously.
• 1:28:50
  And perhaps it is a way in which you can achieve homochirality from an initially
• 1:28:57
  you know, sort of balanced environment.
• 1:29:00
  Like an unbiased initial condition.
• 1:29:02
  Yeah, exactly.
• 1:29:04
  Now, before we talk about biological homochirality and the historical origin and all that... You know, light conversation.
• 1:29:09
  Yeah.
• 1:29:10
  Before we talk about all that, I do want to do some very light conversation and recap some of the whirlwind...
• 1:29:18
  The whirlwind of events that happened the morning of Wednesday.
• 1:29:21
  I just want to preface this for those who might be podcast listeners or audio and video listeners, but don't follow us on socials.
• 1:29:27
  Yeah, this was crazy.
• 1:29:29
  This purely happened in the social media.
• 1:29:32
  On Instagram, really.
• 1:29:33
  It was all Instagram.
• 1:29:34
  And so there's a whole universe.
• 1:29:36
  of FFP that happens on the socials, particularly Instagram.
• 1:29:40
  So if you don't follow there, this is a very good reason to follow.
• 1:29:44
  Sorry, please.
• 1:29:45
  This is crazy.
• 1:29:46
  So usually with our Nobel coverage, we wake up 15 minutes before the prize and we're watching it live.
• 1:29:54
  the press conference, 2.45 a.m., the Nobel Committee announces the prize.
• 1:29:59
  And as soon as the prize is announced, we're like, okay, my predictions didn't work, but oh well.
• 1:30:05
  I get to devouring their popular information and their scientific background.
• 1:30:09
  So the Nobel Committee puts out this PDF of popular information and for scientific background, presumably for journalists like me and Lester to sort of understand and create content out of.
• 1:30:23
  Okay.
• 1:30:23
  Okay.
• 1:30:24
  I noticed that in one of the SOI reaction visuals in the popular information, there might have been a mistake.
• 1:30:30
  Specifically, they had two versions, the two enantiomers of a particular intermediate in the reaction.
• 1:30:37
  The left version had a dotted link to a zinc-oxygen pair.
• 1:30:43
  And the right version had the whole thing flipped, but the thing wasn't dotted.
• 1:30:47
  It was a solid line.
• 1:30:49
  And I thought, you know, that thing should be dotted because you've already flipped it, so it should probably be dotted.
• 1:30:54
  I asked ChatGBT, actually, and it said it was fine.
• 1:30:56
  And then I went through the logic, my own logic, of, like, it should be dotted because it's, like, flipped or whatever.
• 1:31:01
  And then it, like, spent some time and it was like, oh, yeah, you're right.
• 1:31:04
  But then I was like...
• 1:31:05
  You know, I'm still doubting it because it's like, okay, is ChatGPT just telling me I'm right because I am right?
• 1:31:10
  It's like being sycophantic.
• 1:31:11
  Right.
• 1:31:12
  Right?
• 1:31:12
  So we get to the podcast recording.
• 1:31:16
  Right.
• 1:31:17
  And I'm still, like, wondering if I'm right or wrong.
• 1:31:20
  But I go off on a limb and say, I think I made a mistake.
• 1:31:23
  We post it on Instagram.
• 1:31:25
  Mind you, this is five hours after it was announced.
• 1:31:28
  Yeah, this is five hours after it's announced.
• 1:31:31
  I mean, I should have really called up some of my chemistry friends.
• 1:31:34
  But, you know, I'm not going to wake them up at, like, 6 a.m. Be like, hey, can you check this enantiomer for me?
• 1:31:41
  And...
• 1:31:43
  They'll be like, I'm deleting you from my contacts.
• 1:31:46
  So we talk about it on the podcast.
• 1:31:51
  We post it on Instagram.
• 1:31:53
  It gets a lot of traction.
• 1:31:55
  There's a lot of chemists in the audience saying that I am actually right.
• 1:31:58
  And they notice another mistake, which is that the flipped version was actually...
• 1:32:06
  it was connected to the isopropyl group in the right version.
• 1:32:09
  On the left, it's connected to the oxygen.
• 1:32:11
  It should be connected to the oxygen because the oxygen has two bonds, one to the carbon ring, one to the rest of the zinc isopropyl thing, right?
• 1:32:18
  But the isopropyl doesn't have any bonds left to go to the carbon.
• 1:32:21
  It doesn't make any sense.
• 1:32:22
  So the chemist, the actual organic chemist in the audience, actually noticed that.
• 1:32:28
  There are several organic chemist...
• 1:32:30
  teachers in the audience who are just, you know, they fulfill the stereotypical organic chemistry teacher of just being like masochists.
• 1:32:41
  And they're like, this is a great test problem.
• 1:32:45
  This is a great homework problem.
• 1:32:47
  To all the undergrads who are going to get this as a problem in the future, apologies in advance.
• 1:32:52
  Yeah, I pity your students like I pitied myself when I was in Orgo.
• 1:32:56
  God, that...
• 1:32:58
  Yeah, you guys are all the same.
• 1:33:00
  All of the Orgo teachers are all the same.
• 1:33:03
  In any case, so the Royal Swedish Academy of Sciences, they actually responded.
• 1:33:12
  Yes.
• 1:33:12
  To our...
• 1:33:13
  Yes.
• 1:33:13
  Yes.
• 1:33:15
  Video.
• 1:33:16
  On Instagram, they posted a photo of the Futurama scientist, and they said, dear FFP pod, we are very glad that you like the material and find it useful.
• 1:33:27
  We certainly do like the material.
• 1:33:29
  The popular information and the scientific background is essential for getting me the...
• 1:33:35
  the background that I need to like do this episode in five hours.
• 1:33:40
  Right.
• 1:33:40
  So yeah, they said, thank you for reading it so carefully.
• 1:33:43
  Good catch.
• 1:33:44
  You are certainly correct about the error in figure six, which has been now corrected.
• 1:33:48
  We have posted the updated version on our website.
• 1:33:52
  And the updated version has not only the dotted line, which I noticed, but also the oxygen getting flipped, which means that they actually read our comments.
• 1:34:01
  So FFP Nation, you have impact on what is being decided in terms of the public information, the popular information.
• 1:34:10
  from the Nobel Prize Committee.
• 1:34:12
  Yeah, absolutely insane.
• 1:34:13
  By the way, this is already, like, something that I never could have imagined.
• 1:34:18
  This is ridiculous.
• 1:34:18
  This is ridiculous that, like, the Royal Academy of Sciences in Sweden, the guys who were in charge of giving the physics and the chemistry Nobel Prize
• 1:34:28
  and disseminating the information about them are actually responding to us and like we caught an error and they're like they're gracious enough and true scientists exactly like be like yeah you know what that's a mistake we're gonna correct it and so yeah i'm freaking out you're freaking out we're both like okay what do we do what do we do we like wake up the next day or whatever it's like what's happening
• 1:34:47
  Yeah, so we're like, okay, well, and then I'm like, okay, I'll make a response video in my bedroom, right?
• 1:34:54
  So, because we didn't have a plan.
• 1:34:57
  Right.
• 1:34:58
  It was like, once chemistry is done, it's like, I want to take the week off.
• 1:35:02
  Yeah, right.
• 1:35:02
  We were supposed to be on vacation until today.
• 1:35:04
  And then this happened, so I'm like, okay, I'll make a video at my desk.
• 1:35:08
  Yeah.
• 1:35:09
  We make a quick reply video, and at the end of the whole thing, I do a cheeky, like, you know, if you guys are interested in a podcast covering the Nobel Prizes, like, we're... We're available.
• 1:35:21
  We're available.
• 1:35:22
  And the Royal Academy of Sciences in Sweden, they respond.
• 1:35:28
  They say, dear Krishna and Lester, you are both very welcome to Stockholm in 2027.
• 1:35:33
  I've passed your email addresses on to our press officer, who will send you a reminder when press accreditation...
• 1:35:39
  accreditation when press accreditation opens in early september hope to see you then yes uh yes
• 1:35:47
  How about yes?
• 1:35:49
  And my comment with our podcast account is literally, wait, I can't believe that worked.
• 1:35:55
  It's so – there's so many things that are so incredible about this because for those of you who have been with us for a long time, the original construction for the show was this idea of we're trying to build an ESPN for science.
• 1:36:06
  And so many comments on our Nobel coverage independent of this were like –
• 1:36:11
  It would be incredible if you guys could be there like it's the NFL and do reporting the way ESPN does it.
• 1:36:20
  And it's literally now manifested itself in real time.
• 1:36:23
  I want to make one last reference to this because, again, it's incredibly humbling and we're incredibly gracious to the Royal Swedish Academy of Sciences for –
• 1:36:35
  Even engaging with it.
• 1:36:36
  Yeah, yeah.
• 1:36:37
  Like even – You know, like that's – And it's a testament to what the scientific community is about, which is it's a collaborative search for the truth and the right answer without ego, which we can all participate in, including you in the comments.
• 1:36:54
  A funny added tidbit was the American Chemical Society, which is like the trade organization –
• 1:37:02
  or chemists, has a publication called Chemical and Engineering News.
• 1:37:08
  And this was now a few days after this.
• 1:37:11
  It came up in our Instagram feed where the title of the article, which unfortunately we don't have an overlay for, was something to the effect of, you know, we think there was a mistake in the chemistry Nobel.
• 1:37:24
  And it looks like some – oh, go ahead.
• 1:37:26
  It was editorial.
• 1:37:27
  How many chemists does it take to check a stereo center?
• 1:37:31
  The 2026 Nobel announcement sparked a frenzy of chirality fact-checking.
• 1:37:36
  Turns out nailing stereochemistry is tough for everyone.
• 1:37:39
  Yeah, and I sent you the screenshot, and I'm like, is this movie about us?
• 1:37:44
  And then midway through the article, they had been looking at this similarly to how you were in the early morning.
• 1:37:49
  And then someone on their team frequents Instagram and was like, look, guys, we're not the only one.
• 1:37:55
  And by that time, it had gone super viral already.
• 1:37:58
  And I actually read the article.
• 1:38:00
  They noticed discrepancy in the stereochemistry illustrations in the press conference.
• 1:38:06
  Because the press conference, they have material in the back.
• 1:38:09
  And some of the versions of the molecules there had, because stereochemistry is very difficult.
• 1:38:13
  And so then they mentioned that actually even in the popular information, we had noticed that there was a discrepancy.
• 1:38:20
  So it's just shout out to all the chemists that chimed in.
• 1:38:26
  Y'all are the goats.
• 1:38:27
  This was a team victory.
• 1:38:29
  It takes a village and you were part of that village.
• 1:38:32
  And so when we're reporting live from Stockholm next year, we will be sure to have you all with us in our spirits.
• 1:38:41
  And we're going to try to come up with some clever ideas to spice up what is otherwise a very understandably dry and academic endeavor.
• 1:38:52
  So we'll try to find clever ways to make it exciting.
• 1:38:55
  Anyway.
• 1:38:56
  I'm excited about it.
• 1:38:57
  I'm so excited.
• 1:38:58
  Yeah.
• 1:38:58
  I've already blocked out my calendar for that week.
• 1:39:01
  I've only ever been to Sweden.
• 1:39:02
  I've been to Sweden a few times, but it was to play football at the Gatia Cup in Gothenburg.
• 1:39:06
  Wow.
• 1:39:07
  Which is not Stockholm.
• 1:39:08
  So I've never been.
• 1:39:09
  And I was very young then.
• 1:39:11
  And so I'm very excited to go as an adult.
• 1:39:13
  Oh, dude.
• 1:39:13
  I'm so excited.
• 1:39:14
  And get some of that sort of Scandinavian humor that I hear so much about.
• 1:39:18
  Yeah.
• 1:39:18
  Yeah.
• 1:39:19
  Anyway.
• 1:39:20
  It's going to be good.
• 1:39:21
  Not excited for the food, though.
• 1:39:22
  But, you know.
• 1:39:24
  Maybe I'll be wrong, but I haven't heard good things.
• 1:39:27
  I'm going to be honest.
• 1:39:28
  Surprise us.
• 1:39:29
  Surprise us.
• 1:39:29
  Put us on game, guys.
• 1:39:31
  All right.
• 1:39:31
  So the follow-up that I want to do for the chemistry Nobel Prize.
• 1:39:37
  asks if we can measure the handedness that we were talking about, right, with stereochemistry, the idea that you have these mirror molecules and the idea that life is homochirality.
• 1:39:47
  It's a single-handedness.
• 1:39:49
  Well, can we measure chirality in the first place in very limited samples?
• 1:39:55
  Why is that important?
• 1:39:56
  Well, for the origin of life stuff, it's important because it's possible that life arose elsewhere and came to Earth on a meteorite.
• 1:40:06
  And if the life on that meteorite, you know, if the sample size there, it's like a bottleneck effect.
• 1:40:11
  If the sample size there is very, very small and the skew is one way versus the other, that could introduce the initial asymmetry that causes the asymmetry that we see today.
• 1:40:23
  This is a very strong candidate for panspermia.
• 1:40:26
  Exactly.
• 1:40:26
  So panspermia is the idea.
• 1:40:28
  How did life become homochiral?
• 1:40:29
  Well, the idea is that a meteorite comes in from outer space and,
• 1:40:35
  You have this racemic primordial soup, racemic meaning like both left and right-handed, doesn't matter which.
• 1:40:43
  It's sort of an equal...
• 1:40:46
  It's an equal proportion.
• 1:40:49
  And then finally, you have mechanisms that create homochirality.
• 1:40:53
  For example, ultraviolet radiation selects for one over the other, or a meteorite comes in and introduces a little bit of imbalance.
• 1:41:02
  That causes homochirogenesis, which is what this Nobel Prize is about.
• 1:41:06
  The idea of going from a racemic mixture of 50%, 50%, plus 0.0001%,
• 1:41:13
  And then that's sort of taking over and becoming the homochirality landscape that we see today, right?
• 1:41:19
  So a meteorite can contain amino acids without ever having contained life.
• 1:41:26
  And their molecular handedness...
• 1:41:28
  adds a clue to maybe how life on Earth arose and why it became homocryl.
• 1:41:33
  That's the idea.
• 1:41:34
  This October 2026 paper, again, just this year.
• 1:41:37
  It's happening right now.
• 1:41:38
  Just this year, just this month in Nature Communications on the 5th of October.
• 1:41:43
  So like two days before the Chemistry Nobel Prize in chirality.
• 1:41:49
  This is from Osaka University in Georgia Tech.
• 1:41:52
  It examines an electrical discrimination technique for amino acid and anti-humors.
• 1:41:58
  It's trying to say, if you were to give me a meteorite sample that has a very small number of amino acids, can I still give you a sense of what the anti-humor landscape is?
• 1:42:11
  What the left versus right-handedness is on that meteorite.
• 1:42:17
  Meteorite.
• 1:42:17
  Now, why is this difficult?
• 1:42:19
  Well, if the sample size is really, really small, it's going to be really hard to use conventional strategies, because conventional strategies involve, like, effectively doing a chemical reaction, some kind of mass spectrometry, and, like...
• 1:42:36
  you do kind of a mass spectrometry liquid chromatography, which is the idea of taking a bunch of these samples and separating them by mass.
• 1:42:47
  And then with liquid chromatography, you have them go through liquids
• 1:42:53
  And the different types of enantiomers, the red and the left-handed, are going to go through that differently based on their little chemistry.
• 1:42:59
  And that's going to tell you the mixture of left versus right.
• 1:43:02
  But you need a lot of source material to pull that off.
• 1:43:06
  Yeah, you need a lot of source material.
• 1:43:07
  Which you can't do here.
• 1:43:08
  Right.
• 1:43:09
  So the researchers here, they're introducing this mechanically controllable break junction technique.
• 1:43:14
  Here's the idea.
• 1:43:15
  You've got a narrow goal junction.
• 1:43:18
  that you break to create a kind of tunnel, okay?
• 1:43:21
  And the separation of the two gold plates can be sort of adjusted.
• 1:43:27
  You hook that up to electrodes, and the gap, you make kind of a molecular scale gap.
• 1:43:34
  And then you let the samples go through that molecular scale gap.
• 1:43:39
  This is so ridiculous.
• 1:43:40
  Okay?
• 1:43:41
  And what you're relying on are quantum tunneling effects.
• 1:43:43
  Yes.
• 1:43:44
  Okay?
• 1:43:44
  Okay.
• 1:43:46
  It's pretty insane.
• 1:43:47
  The solution involves quantum tunneling.
• 1:43:49
  Because here's what's happening.
• 1:43:50
  You've got that gold gap.
• 1:43:52
  The molecules go through.
• 1:43:53
  And depending on whether they're left or right-handed, they're going to cause a current to go through that gap.
• 1:44:00
  Because the current is going to quantum tunnel through.
• 1:44:04
  It's not a classical effect here.
• 1:44:05
  So how does quantum tunneling actually work?
• 1:44:07
  Well, here's how it actually works.
• 1:44:09
  So...
• 1:44:11
  You've got these electrodes on two sides.
• 1:44:13
  Extremely small gap.
• 1:44:14
  You need an incredibly small gap.
• 1:44:18
  And when whatever substrate goes through, whatever left-handed or right-handed enantiomer, if it's right in the middle and if it's left-handed, it's going to cause a current.
• 1:44:27
  Mm-hmm.
• 1:44:28
  And if it's not, then it's going to not cause a current.
• 1:44:31
  And depending on the shape of that current, you should be able to tell if it's left or right-handed.
• 1:44:36
  That's the idea.
• 1:44:37
  The tunneling current is the signal with which we can detect the handedness when we look at quantum tunneling.
• 1:44:46
  And it's so funny that macroscopic quantum tunneling won last year as the Nobel.
• 1:44:50
  Yeah, in physics.
• 1:44:51
  In physics.
• 1:44:52
  It's just these things, again, the connections are just...
• 1:44:55
  Exactly.
• 1:44:56
  That's a good point, actually.
• 1:44:57
  Yeah.
• 1:44:58
  Anyway.
• 1:44:59
  And so, all right.
• 1:45:00
  So how do you actually do this in practice?
• 1:45:01
  Well, they got a bunch of samples together.
• 1:45:04
  They like built samples out of like, you know, make sure that they already knew.
• 1:45:08
  They're like, okay, I'm going to put this amount of L alanine and this amount of D something.
• 1:45:12
  Right.
• 1:45:13
  And what they want to do is train machine learning model to discern between one type of an ant tumor and another.
• 1:45:21
  Right.
• 1:45:22
  This is where we're controlling now what's going to go through this gold apparatus.
• 1:45:25
  Yeah.
• 1:45:26
  Knowing what the hand-in-ness is.
• 1:45:27
  Knowing what the ground truth is.
• 1:45:29
  Before we put it in, because we want to test to see if this architecture and the hypothesis that the quantum tunneling and the tunneling current will trigger based on the hand-in-ness actually happens when we are controlling the input.
• 1:45:42
  Yes, exactly.
• 1:45:43
  Like, we want to make sure that, like, even in a controlled scenario, this actually works before testing it on...
• 1:45:48
  Like actual meteorite stuff, right?
• 1:45:50
  Yes.
• 1:45:50
  And so this is how it actually works.
• 1:45:52
  You make your mixture that's very prescribed, and you know what the signals look like, and then you train on your data.
• 1:46:01
  And on the right-hand side, you see the actual versus the predicted.
• 1:46:05
  The actual is 20, 20, 20.
• 1:46:06
  There's five different versions of these amino acids.
• 1:46:10
  It's quite close.
• 1:46:11
  And it's pretty close, right?
• 1:46:12
  It's getting you to 22, 15.
• 1:46:14
  Like, it's not something that's crazy.
• 1:46:17
  But again, this is the first iteration, and it's getting pretty close.
• 1:46:20
  Right?
• 1:46:21
  So the predicted versions, at least it's not missing anything.
• 1:46:24
  It's not like, oh, there's none of this, when actually there's 20%.
• 1:46:28
  I mean, only one of the five bands is a difference of five.
• 1:46:33
  The other are effectively a difference of two.
• 1:46:34
  Yeah, it's getting close.
• 1:46:36
  And this is the first time that we're doing something like this, right?
• 1:46:39
  Now, how does this do on real world samples?
• 1:46:41
  Well, they got samples from two different meteorites.
• 1:46:44
  There's one meteorite from the Diego de Almagoro in the Atacama Desert in Chile.
• 1:46:52
  And then there's another one that's the Merchinson meteorite extract from Victoria, Australia.
• 1:46:58
  These are two meteorites that had amino acids on them.
• 1:47:01
  They took those samples and they tried to look at if it would actually work.
• 1:47:07
  Didn't do so well.
• 1:47:07
  Okay.
• 1:47:08
  Okay.
• 1:47:09
  On the x-axis is the stuff that we get from liquid chromatography.
• 1:47:13
  Okay.
• 1:47:15
  On the y-axis is the stuff that we get from this new method.
• 1:47:18
  And if it's perfect agreement, everything should lie on the green dotted line in the diagonal, right?
• 1:47:27
  But obviously it's not, right?
• 1:47:29
  One thing that we do know for sure that's happening is that if there is an imbalance, the method tells you that there is an imbalance.
• 1:47:36
  It's just doing an imbalance in the other direction almost.
• 1:47:39
  It's like uncorrelated.
• 1:47:40
  So you might say that it's kind of...
• 1:47:44
  It's kind of surprising that this got into nature communications, given that it's a negative result.
• 1:47:49
  But I still think it's kind of nice because it's the first time that we're doing something like this.
• 1:47:53
  And I think that's what the editors really saw in this paper is like this is a challenging problem.
• 1:47:59
  perhaps it requires more data to do the machine learning.
• 1:48:02
  Perhaps it requires more careful analysis, so on and so forth.
• 1:48:07
  But really these researchers, they're trying to distinguish mirror image molecules.
• 1:48:13
  Yeah.
• 1:48:13
  Right.
• 1:48:14
  Yeah.
• 1:48:14
  Which means it's again, the same atoms, the same mass, the same geometry, almost just flipped in one direction to another.
• 1:48:23
  So,
• 1:48:24
  The quantum tunneling effect is going to be very small.
• 1:48:27
  Yeah.
• 1:48:28
  The difference is going to be very small between a left-handed and a right-handed version.
• 1:48:32
  But there's a chance.
• 1:48:33
  You can clearly see that in the control experiment.
• 1:48:36
  And now all we have to do is tweak the knobs.
• 1:48:40
  It's a translation problem.
• 1:48:41
  It's a translation problem to get to the actual kind of distributions that we would see on an actual meteorite.
• 1:48:47
  This is going to get very important because...
• 1:48:50
  Moving forward.
• 1:48:51
  And there's a reason why Japan and America are collaborating on this, because we've covered the space missions that they are going to be pursuing in the near future, where they're trying to get samples of asteroids from Phobos on Mars.
• 1:49:05
  Yes.
• 1:49:05
  And from like these random asteroids in the asteroid belt.
• 1:49:09
  And they're bringing back the samples, right?
• 1:49:12
  Even these meteorites that they're talking about, the Merchinson and the Diego de Almogoro meteorite, those are the ones that have been contaminated by Earth.
• 1:49:19
  You don't know if those amino acids are from the Earth somehow creeping in, right?
• 1:49:24
  This is the Martian moons explorations, MMX, which we covered a few episodes back, which the target launch date is actually approaching.
• 1:49:37
  in a few days.
• 1:49:38
  Oh, very nice.
• 1:49:39
  And it's, you know, it's arrival is scheduled for late 2027 on Mars.
• 1:49:44
  And then the retrieval targeted for 2031.
• 1:49:47
  We've already done this with an asteroid.
• 1:49:50
  Yeah.
• 1:49:50
  And returned the sample.
• 1:49:52
  And so this is the scale up of that same idea.
• 1:49:54
  The point being, we're going to be able, we now have the methodology to start to retrieve more of these things.
• 1:50:00
  We know that meteorites can carry amino acids.
• 1:50:02
  We might see other things as we get more of these from celestial bodies, both ones that are local to our solar system and ones that are coming in or traveling throughout our solar system.
• 1:50:15
  Or if it's like a muamua that are interstellar objects, which is more difficult and there's challenges and timing and blah, blah, blah.
• 1:50:23
  But next time we're going to be ready.
• 1:50:24
  We're going to be ready.
• 1:50:25
  We're going to be ready.
• 1:50:27
  And it's so cool because I think the point about negative results getting published, I actually think is a good side note just to briefly touch on, which is I think they do matter a lot.
• 1:50:38
  And my understanding is there's currently a skew where negative results proportionally are obviously not published as much as like I found the thing.
• 1:50:49
  But there is, I think, benefit to encouraging people to take the effort to publish negative results because it is helping draw the path.
• 1:51:02
  Um, and you know, especially for complex problems like this, we need to celebrate people taking the time and spending the money to do so, even if they didn't get the thing.
• 1:51:12
  Yeah.
• 1:51:12
  Um, and so I do think to your point, it is interesting that it did get published, but I think it matters.
• 1:51:18
  Yeah, I think it does matter.
• 1:51:19
  And I mean, it's the first step to an instrument that could tell us the enantiomer ratio, the left and right handed ratio on these meteorites, right?
• 1:51:30
  And it could even like lead to a space instrument.
• 1:51:33
  Yes.
• 1:51:33
  Like on the International Space Station.
• 1:51:35
  Yes.
• 1:51:35
  Or like on these...
• 1:51:37
  asteroid probes themselves right which can do it you know without having to bring it back yeah which would be uh fantastic i i think this brings us back to there was this sort of thought earlier around ice cube that i was kind of thinking about which is this whole question of why does this stuff matter how does it impact us and like with ice cube
• 1:52:00
  The thing that it made me think about is, you know, two of like the most important questions that many people have, you know, particularly across cultures and across religious practices is, you know, where did we come from and what is our kind of purpose in life?
• 1:52:18
  Like what is the purpose of life?
• 1:52:19
  What is our place in the universe?
• 1:52:21
  These are questions that are so important to billions of people, deeply important to billions of people on the planet.
• 1:52:28
  And the through line both with Ice Cube and this like meteorite amino acid handedness detection concept kind of poke at the exact same question but in a way that's not through theology or philosophy.
• 1:52:43
  And it's also secular.
• 1:52:44
  It's not necessarily people who are religious that all ask these questions because we're looking at these things at these grand scales of the universe around us.
• 1:52:53
  Yeah.
• 1:52:54
  And they are poking at that same curiosity, that same internal thing that's within us, just in a different way.
• 1:53:01
  Yeah.
• 1:53:02
  And so, you know, people practice...
• 1:53:08
  in a religious context, if you're secular, it's fine, or a philosophical context, asking these exact same questions.
• 1:53:14
  And it's not looked at in the same way as when we ask these questions from a scientific perspective.
• 1:53:19
  It's the same thing.
• 1:53:21
  It's the same thing.
• 1:53:22
  To me, it's the same thing, right?
• 1:53:23
  Like, who am I, who are you, and what the hell is all this?
• 1:53:28
  It's always that same question.
• 1:53:29
  The interesting thing about Ice Cube is that it has both the –
• 1:53:35
  Potential to help us understand some of the physical aspects of that question very, very far away.
• 1:53:40
  But also some aspects of the questions close at home.
• 1:53:43
  And this handedness, this chirality question is another two-way street similar to our memory story because it's both about origins of life.
• 1:53:58
  Ultimately, the Nobel Prize was given in part to this idea that you can have a small imbalance in left and right-handedness that becomes a runaway reaction that then creates effectively like a 95, 99 percent proportion of one or the other handness even though your original balance –
• 1:54:16
  Yeah, was effectively 50-50.
• 1:54:18
  Was effectively 50-50.
• 1:54:19
  That's very interesting.
• 1:54:20
  Yeah.
• 1:54:21
  And then when you talk about origin of life, it is a question that you have to then ask, which is, okay, well, if our basis is homochiral and you look at this idea of panspermia, if you have this sort of unbiased soup as the starting point.
• 1:54:38
  And a small bias came in.
• 1:54:39
  Yeah.
• 1:54:40
  There seems to be a very easy to jump to conclusion through line between those two things.
• 1:54:45
  I'm not saying that that's the case.
• 1:54:47
  Yeah, but it's there.
• 1:54:49
  It's cool to think about.
• 1:54:51
  It's very cool to think about.
• 1:54:52
  And then the reverse of that is we're now in a place as a human species where –
• 1:54:57
  We're going to Europa.
• 1:54:59
  Yeah.
• 1:54:59
  Where we think there's going to be maybe something underneath these ice sheets that are life.
• 1:55:07
  But similar to a meteorite's landing –
• 1:55:12
  And having impact here in terms of how life happens, both from a origin of life and the mirror life problem, which is a potential threat, which many people had opinions about.
• 1:55:24
  And like it's complicated and we have solutions.
• 1:55:27
  Yeah.
• 1:55:27
  But it's, you know, not ideal.
• 1:55:29
  Yeah, it's not ideal.
• 1:55:30
  When we go somewhere else, it's like, you know, colonial times.
• 1:55:34
  where we introduce things that this environment hasn't seen.
• 1:55:36
  Like this tiny bacteria.
• 1:55:37
  And then all of a sudden, you know, and we should be, as we can continue to increase our power to probe the universe, we both need to be careful about how we do that to ourselves, which links to our first story, and how we can now activate the physiological reaction from memory, how we explore our local solar system.
• 1:55:58
  I mean, these are such incredibly awesome things
• 1:56:01
  conversations to be having because we're now getting so good at this stuff yeah and i just i i love this time of year for science because i think the nobel prizes like give us an opportunity just like step back you know we're usually on this podcast we're always talking about the current event totally and things like that right the nobel prize always like
• 1:56:22
  is an opportunity to step back and be like, yeah, that was sick, right?
• 1:56:25
  Like 20 years ago when Peter Hegeman, actually no, 30 years ago when Peter Hegeman figured out this algae has a one millisecond time response to light.
• 1:56:34
  That's sick.
• 1:56:35
  Unbelievable.
• 1:56:36
  That's cool.
• 1:56:37
  Right.
• 1:56:37
  That like he did that, right?
• 1:56:38
  And then Soai, doing the Soai reaction like 20 years ago.
• 1:56:46
  Francis Halsey trying to figure out how to do Antarctic neutrino astronomy again 20-30 years ago.
• 1:56:54
  And spent years doing it in the ocean and being like ah.
• 1:56:57
  Yeah it doesn't work.
• 1:56:58
  Doesn't work that well.
• 1:56:59
  There's all this bioluminescence that's annoying.
• 1:57:02
  It's I just I love this time of year.
• 1:57:06
  And we now are the official show.
• 1:57:10
  covering the golden goose awards and next year uh the nobel prizes so shout out to triple as shout out to the royal swedish academy of sciences uh details tbd yeah um but uh we again find our way to be able to make two hours of just when we're having a relaxing follow-up this was pretty relaxing it was it was the last couple weeks i've been intense guys yeah uh
• 1:57:36
  Very intense on the material, on the schedule, especially because we had to do daily drops and travel.
• 1:57:43
  We really appreciate you all so much for joining us.
• 1:57:46
  If you've made it to the end, what should be our comment for the comments?
• 1:57:51
  Give us an idea on what to do when we're in Sweden.
• 1:57:54
  It can be from – if you've been to Sweden or if it's just random like stuff that – me, me, Sweden, Scandinavian stuff.
• 1:58:03
  Yeah.
• 1:58:04
  Don't just say eat Swedish meatballs.
• 1:58:05
  That might not be a thing.
• 1:58:07
  But something – give us a deep cut.
• 1:58:09
  Yeah.
• 1:58:11
  And maybe Zlatan will be there and we can hang out with Zlatan.
• 1:58:14
  My name is Lester Nari, joined as always by my co-host and our resident PhD, Krishna Chowdhury.
• 1:58:19
  Thank you for joining us for our Nobel follow-up.
• 1:58:22
  We will be having a couple of interview episodes coming up over the next several weeks as we get into spooky season and as the weather cools down, although there was just a wildfire on Mulholland.
• 1:58:33
  like earlier today.
• 1:58:34
  LA, baby.
• 1:58:35
  The Santana winds are kicking up.
• 1:58:36
  And we're having that.
• 1:58:38
  Santana winds.
• 1:58:39
  We've had three hurricanes off the coast.
• 1:58:42
  The Kelvin wave.
• 1:58:43
  The beach is gone.
• 1:58:45
  Homes are gone.
• 1:58:46
  There's a great white, a 20-foot great white that's swimming around, and so you can't swim.
• 1:58:52
  No surfing.
• 1:58:54
  We're going to need help.
• 1:58:55
  We will see you all next week.