SGU Episode 1005
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| SGU Episode 1005 |
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| October 12th 2024 |
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"An intriguing glimpse into the cosmos: colorful energy captured in space." |
| Skeptical Rogues |
| S: Steven Novella |
B: Bob Novella |
C: Cara Santa Maria |
J: Jay Novella |
E: Evan Bernstein |
| Quote of the Week |
"The important thing is not to stop questioning. Curiosity has its own reason for existing." |
Albert Einstein |
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| Show Notes |
| SGU Forum |
Intro
Voice-over: You're listening to the Skeptics' Guide to the Universe, your escape to reality.
S: Hello and welcome to the Skeptics' Guide to the Universe. Today is Wednesday, October 9th, 2024, and this is your host, Steven Novella. Joining me this week are Bob Novella...
B: Hey, everybody!
S: Cara Santa Maria...
C: Howdy.
S: Jay Novella...
J: Hey guys.
S: ...and Evan Bernstein.
E: Good evening everyone.
S: So we are cozy up here in Connecticut, but down in Florida, man, they are getting hammered right as we record the shot. Yeah, Hurricane Milton is coming ashore, and they're expecting surges of 9 to 12 feet in Tampa and surrounding areas. Plus, it's going to take a long time to cross and drop a ton of rain. It's going to be bad.
B: It's bad. This is the storm that made a meteorologist cry. Literally, he was tearing up because I think that the real thing where it really hit home at how extraordinary this is was when they realized that in 12 hours it went from a category one to a category five. Which basically doesn't happen. That doesn't happen.
C: I have a friend staying with me right now because she was supposed to be in Florida for the Europa Clipper launch. The Clipper is in a hangar in Florida right now.
E: Oh my gosh, I hope it's protected well enough from this.
C: We all hope it is. So yeah, she obviously couldn't go. Obviously the launch is being delayed, but I mean, it's really scary. This is scary for a lot of people. And this is on the heels of Helene, which was like, what, a week ago? How long ago is that now?
E: Oh, two weeks?
S: Yeah, a couple of weeks.
E: Two weeks, they're still finding recovering. It's still a recovery effort.
S: And not long after, we were talking about a hundred year storm in Connecticut in Oxford. And we're like, well you can't say for sure it's global warming, but you know, this sort of thing. And then boom, boom, two more hurricanes, like record flooding in the mountains, miles from the shore. A location that was previously considered to be like a climate change refuge. Nope. Sorry. Not correct. And now we got this really unprecedented hurricane. So obviously we know this is global warming, right?
B: There's no question.
S: Statistically, you can't say like this one particular what would have happened were it not for whatever. But we know that this is consistently over and over again. We are seeing weather events that are beyond what is what is typical. And this is exactly what scientists predicted was going to happen, and it's happening, so it's probably not a coincidence.
E: Yeah. We've talked about it, not so much being the frequency with which hurricanes occur, but the intensity at which they are hitting.
S: Yeah. They don't get more frequent. But they gain their power from the heat in the water. And the Gulf of Mexico is very warm because of global warming. And then the warm air holds more moisture, so the hurricanes pick up more moisture, so they are more powerful, they drop more rain, and that's what causes the flooding.
C: And because of that, they probably feel more frequent because the lower intensity hurricanes are often not felt, or they're felt with less intensity. So when the same number of hurricanes are significantly more powerful, that onslaught is more apparent.
S: Yeah, you don't get more hurricanes, but you get more Category 4 and 5 hurricanes.
C: Yeah, and of course those are the ones you notice.
E: Right, those are the ones that make the news.
C: Those are the ones that hit you.
E: Right, those are the ones that cause the most death, the most destruction. Even if you add up the other smaller hurricanes, they're much worse than any smaller hurricanes that hit.
B: Yes. And a meteorologist cried.
C: Yeah. Well, he's probably thinking of his, of his neighborhood, his home, his community.
B: That wasn't my take. My take was that the unprecedented ramp up from category one to five in 12 hours is essentially unprecedented. And that's where I think it hit home like, oh, my God, this is a monster, the monster we've been dreading caused by climate change. And even just look at it. Just even looking at the hurricane, you're like, wait, that looks weird. One guy described it as being tightly wound. I mean, the eye is only like three miles wide. It's so, so tiny, so small, and it ramped up so fast, like it got to like 850 millibars. It's like the fourth most intense in terms of the pressure. And we're going to see more of it. We're going to see more. This is the first of the monsters. It's like the kaiju coming out of the ocean it's like it's we're going to see this. We may see another one as bad or worse this season. I mean, it's October. The season ends in, what, November? Mercifully, maybe the season will be, will end, the season will end soon. But damn, man.
C: But how do we know? I mean, this happened literally overnight. And Bob, I found a New York Times article that quoted the meteorologist that you mentioned, his name is Morales. And he said, a number of factors played into the tearful broadcast. Shock about the storm's rapid intensification, angst about the increasing number and severity of extreme weather events, frustration over society's failure to mitigate, despite scientific certainty that it's driving increasingly violent weather, and empathy for the people, the ecosystems, and the creatures that would experience the destructiveness. And then there's a quote from him, it claims lives, it also wrecks lives. You have to feel sorry for the folks that are in this hurricane's path.
B: And then you get the layer of politicized misinformation that's being thrown around. It's like, hey, this is not the time to politicize stuff, to be partisan. This is the time to do what you can to save lives, people's lives. And people are just throwing away, throwing out misinformation. It's like, oh, my God, that's horrible. It's cruel. It's ridiculous.
S: So as this recording, it's already too late to evacuate.
C: They're actively being pummeled.
S: Hopefully people have gotten out.
C: They're sheltering in place.
B: Eastern Standard Time, yeah.
S: You don't want to be on the road when the storm surge hits. As we talked about before, water is powerful. And this is a double whammy because the category five winds will rip a house apart all by itself without the water. Then you have the fact that the houses are going to be flooded and the streets are going to be mayhem, like unsurvivable. The storm surge itself is being described as unsurvivable. And then you throw the winds on top of that. This is destructive. This is very destructive. So by the time the show launches, this will have happened, and we'll know exactly how bad it was. But hopefully, people heeded the warnings and got out. And FEMA is on the job and working with the state of Florida. I think the people in place are doing what they're supposed to be doing, and they just should ignore the national politics, as Bob said.
E: Yeah, it's all static.
S: But the state and local stuff, they're like, yes, we're working together. Everyone's doing a good job. Let's get it done.
E: As they should.
S: Let's move on.
Another Loch Ness Claim (7:18)
https://www.msn.com/en-gb/news/world/sailor-finds-loch-ness-monster-on-ship-s-sonar/ar-AA1rHqJE
S: Evan, you're going to start us off with a dumbest thing of the week.
E: Yes, dumbest thing of the week. Okay. Well, boy, this was a week. There were many contenders. A story about Bigfoot almost won the day. But as usual, Bigfoot would not stand a chance against a monster that lives in the bottom of a lake. Yep, that's right, I'm talking about the lurker of the loch, the dino of the deep, the cryptic king, or queen, itself. I give you this week's subject, the Loch Ness Monster, the dumbest thing of the week. Steve, you can go ahead and insert that applause soundtrack there if you like.
S: Oh yeah.
E: I found. Yes. Captain. Captain of a boat out on the loch says I found Loch Ness Monster on my ship's sonar. Yep. And let's see. Yeah. I've spoken several times about the Loch Ness Monster before, so I think I can safely assume our audience is familiar enough that some people believe there exists a prehistoric dinosaur sized aquatic creature in Scotland's Loch Ness. And no, it's not just a bedtime story for children. Many grownups, supposedly mature adults, believe this to be true. And it makes you wonder what else they think is true. Now evidence, what's the evidence for the creature's existence? It adds up to, well, nothing. No scientific evidence over nearly, wow, almost a hundred years that this particular myth has existed. Can you believe it? And it keeps going on. But hey, we finally have sonar readings from the captain. Here's the evidence we maybe have been looking for all of this time. So his name is Sean Sloggie, or S-L-O-G-G-I-E. That's how I read that and pronounce that, Sean Sloggie. He was preparing his Spirit of Loch Ness pleasure boat to sail last month when a large object was spotted on the vessel's underwater sensors. It was an outline detected nearly 100 meters beneath the surface of the Ness and it bore an eerie resemblance to a plesiosaur. Yeah, which what? Is a speculated reptile group in which they say the people who believe in the Loch Ness Monster believe, okay, yeah, this is a plesiosaur who has survived all this time and exists in the lake or a family or something. I have no idea, frankly. Why do you think this thing really exists?
S: Now I have to – well, I think they think there's connections between the loch and the ocean, and so there must be a population traveling back and forth, whatever. But looking at the sonar, I mean, first of all, it's a blob, right? I mean, it could be anything.
E: Blobsquatch, yes.
B: Blobsquatch.
S: But if you didn't know what the context was, and you just said, what does this look like to you?
E: Oh, yeah.
S: It looks like a turtle. Now there are no giant sea turtles in Loch Ness either, so it's probably not a turtle, but it looks way more like a turtle than a plesiosaur. It actually doesn't look like a plesiosaur. It doesn't have the characteristic overall shape. The resemblance is actually fairly superficial. But again, if you account for the fact that sonar is blobby, it's not that accurate.
E: Right.
S: Could it be a plesiosaur? I mean, that image, I guess, but it could also be a turtle. It could also be nothing, or it could be a hoax.
E: I want to describe to the audience just for a moment the image you're looking at, Steve, the one I'm looking at from the news. It's a photograph of the sonar, the screen, and what is being displayed. To me, it looks like the old video game Defender, an arcade game from 1980 with eight kilobytes of graphics, basically, with these little dots and boxes and things moving all over the screen. That outline, and they circle it, where it looks sort of, yeah, it has this turtle-ish shape to it, maybe a back and an extended neck and maybe some little arms. That could be anything. I mean, what the heck? It's total pattern recognition, pattern-seeking, as we're wont to do, find order in the chaos.
S: We're not seeing a recorded video. A snapshot, was this just at the moment where it looked the most like a reptile, or what would the video show? That's always a good question. Like when you see like a 20 second, quote unquote video of a UFO, you always want to know, yeah, what happened before and after the 20 seconds that I'm seeing? Because usually when you see that, it's like, okay, it's clearly a hubcap or whatever.
E: Oh, the school of fish broke up and went into a million pieces. That we're all blobbed together at one point. So right, a single snap in time is not evidence of anything. Oh, but no, but the captain says the sonar doesn't lie. The boat hasn't been on five whiskey distillery tours before going out on the lock. It's just doing its job. That's what the captain said, appears in different colors, indicating pockets of air and the usual kind of stuff, regardless even of that. So you've got this, okay, you have this sonar image. Okay, where's the thing? What's it eating? Where's it pooping? How long is it alive? Where are its remains? Where are the others? Where's the other evidence, any kind of physical evidence? Where's the DNA?
S: Why has one never washed up on shore?
E: Right. Or a piece of it or a fin or something.
S: But the definitive thing is they look, they looked at the loch for environmental DNA and there's no plesiosaur DNA in it.
E: Exactly. Exactly.
S: It's a done deal.
E: Yep. All that goes out the window. And here we go. Throw another piece of zero evidence into the pile. For the Loch Ness Monster, this week's dumbest thing of the week.
S: Thank you, Evan.
E: Yep. You got it.
News Item #1 - Physiology or Medicine (13:02)
S: So guys, that time of year again.
J: Yes, it is.
S: Every time this comes up.
B: Halloween, baby. Oh, wait. Sorry.
S: Not Halloween. Every time this comes up, my reaction's always the same. It can't be Nobel Prize time again already. We just had it. Didn't we just talk about the last year's? But it is. It's Nobel Prize time.
E: There is a cruel reality to getting older in that the perception of years go quicker as you get older. Nobody tells the kids that as an adult, or they do and the kids don't listen. But it's a real cruel trick, I find, of life. It shouldn't be that way, but it does. That is exactly how it's perceived, at least I think.
S: Yeah. So we're going to cover it. Cara, you're going to start us off with the Nobel Prize in Physiology or Medicine.
C: Yep, the Nobel Prize, the 2024 Nobel Prize in Physiology or Medicine was awarded jointly to Victor Ambrose and Gary Ravkin, quote, for the discovery of micro RNA and its role in post-transcriptional gene regulation. So we're going to dig into that a little bit, tell a little bit of the story of this discovery and kind of the outcomes and what it means for science and for humanity. In the late 80s, Victor Ambrose and Gary Rubkin were both postdocs. They were fellows in the laboratory of Robert Horvitz, who incidentally won a Nobel Prize in 2002 for his research, and they were studying A model organism called C. elegans. We call it C. elegans because it's hard to pronounce the word that precedes elegance. It's a roundworm. It's a nematode. And it's a great organism to study because it's small, but it has a lot of the specialized cell types types that are found in larger animals. So it's a good model for tissue development, maturation, just developmental biology, and of course, genetics. And so they were really just asking a lot of interesting questions like, how do the genes that sort of control these different developmental programs turn on, turn off? Why do they turn on and turn off? Like what's going on here? And I saw a little video of Gary Rovkin talking about how, at the time, he was just really excited about this new field of recombinant DNA. A lot of people were getting into recombinant DNA, which is kind of the practice of creating sequences that wouldn't otherwise be found in the genome, either by combining from different sources or even synthetically creating DNA sequences in the laboratory. So obviously this is a form of genetic engineering. So on the heels of this kind of excitement at the time, they're asking a lot of interesting questions, poking around. They go their separate ways. They start their own labs. Both of them are, I think they're both in Massachusetts at this time.
S: Yes.
C: Yeah. So I think before we can get into microRNA, what it is, what it does, and why it is interesting, we have to kind of step back and just make sure that we're all on the same page about what's often referred to as the central dogma of molecular biology. And that is the basic movement from DNA to RNA to proteins, right? And that involves two major steps, a whole lot of minor steps, called transcription and translation. So transcription is how DNA is copied to RNA. And what happens in transcription is that the base pairs line up using a specialized, actually it in and of itself is a protein, but a specialized catalyst that helps make those base pairs line up. And as opposed to replication, where DNA just makes more DNA by unzipping and then matching up the base pairs, in transcription, all of the base pairs line up except for where thymine would have been, it turns into uracil, which then forms these, what were called messenger RNA strands. And it's important to make a distinction here because I think some people might get confused when they're reading about this. Messenger RNA is often represented as mRNA, microRNA, which is a totally different thing, that's what we're going to talk about in a second, is often written as MICRNA. And sometimes muRNA, or miRNA. So you do need to make that distinction because that i matters. So we've got transcription, then we've got translation. Transcription DNA to RNA, and specifically to messenger RNA, but also non-coding regions as well. And then we've got translation. Translation is RNA to proteins, right? So this is how proteins are synthesized using the same genetic material that's in every single cell in an organism's body. So then the question, and this was the big question that the Nobel laureates were asking, was like, how do they know? Like why do they know to make this specific protein when all the genetic instructions are there, right? It's not just DNA codes to RNA, RNA maps on and produces proteins. It's the, when are these genes turned on and off? Why are they turned on and off? What kind of pressures are involved to do that? And how are they regulated? And so they noticed, they were studying a specific strain of C. elegans called Lin-4. They were also studying a different strain called Lin-14. And they found that these specific mutant strains were having issues in the timing of activating genes that were necessary for them to develop normally. And so they were like, okay, this is weird and interesting. I want to figure out what's going on here. I want to identify these genes and I want to see what these genes do. Like very, very basic molecular biology research. They knew at that point that the Lin-4 gene regulated the Lin-14 gene. I don't think we have to get too caught up in the names of them, but how it regulated it. It was kind of negatively regulated and it blocked the Lin-14 gene. But how that happened, they didn't really understand. So they wanted to dig a little bit deeper. And as they were studying, Ambrose moved on and established a laboratory at Harvard. Rovkin moved on and he established a laboratory at Mass General. And they kept looking at this work. But then they start doing their own work, but they're interested in answering the same questions, right? So they're looking, okay, what's going on here with this inhibitory mechanism? How is the regulatory stuff going on? Is it actually happening at the translation stage? No way. Oh, okay. Is it transcription? No, it is translation. Okay. So it's happening later on in the process. And they start talking to each other. This is, I think, the best part of this story. This is two individuals who kind of came up together, studied together, are now in what could be seen as rival labs. What could be seen as labs that want to kind of push to publish before each other. But instead, they're always collaborating with each other and sharing each other's research. So they named this little sequence of RNA that they kept seeing over and over microRNA because it was really, really small, right? It was just a smaller chunk of RNA than the RNA that they had been working with. And they realized that it was affecting, and affecting in an inhibitory way, the actual translation of this Lin14 because this microRNA was binding to it and kind of preventing it from being able to then be expressed the right way. We're now into the early 90s. They published this sort of serendipitously. They're both talking about this discovery. People are going, oh, that's interesting. You've got this little piece of RNA. It's sort of binding to this portion. It's preventing translation from happening. Okay. But that's like a weird quirk of C. elegans. So people listened and they go, that's kind of cool, but it's quirky and it didn't really get a lot of traction. And then they kept looking at it and they found something interesting called the Let7 gene. And the Let7 gene was different. And the reason it was different is because it was not unique to C. elegans. It was conserved all the way back to what I think, from some of the things that I read, was when kind of mammals and fish split. So it's really conserved in a lot of multicellular organisms. And that Let7 gene, when people started looking, they started to realize, hmm, these microRNAs are in like us. They're in like most of the animals and the plants that we're looking at. It's sort of everywhere. And when they discovered that, a lot of people got interested and they started to do a ton of research and discover, I think we're now at...
S: Over 1,000.
C: This is a little bit different because Wiki is saying that only 500 represent the bona fide microRNAs.
S: Okay. Yeah, I've read we've discovered over 1000 micro-armings to date.
C: Okay, so and that's important. Discovered over 1000 at least 500 of them have been like certified and said yes, that these are bona fide but it's a lot because a ton of different labs are working on it like 90 different families of micro RNAs that have been conserved, like I said, since that common ancestor of mammals and fish. And we've started to discover the functions of these the different genes that they act on the different genes that they regulate. And we've since then also started to discover how they're involved, not only just the mechanism by which they are doing this regulatory function, but also what that means downstream. Like they might be silencing transcription, or they might be processing in the cytoplasm. Sometimes they're happening earlier on in the nucleus. We've even discovered that there are some that are floating outside of the nucleus completely. And even in the extracellular matrix. So we're seeing that this is happening all over the cell at a lot of different stages that weren't anticipated before. We're also seeing that they have a ton of different functions in the cell, but they tend to fall into three main processes. They either will cleave the mRNA, so cut it into two different pieces. They'll destabilize the mRNA by shortening its poly-A tail, or they'll reduce translation of the mRNA, which was that first discovery, which then affects the protein synthesis. Generally speaking, in humans and other animals, it happens by destabilizing, but microRNAs kind of fall into all three of those categories. And we've now been able to identify disease processes that microRNA are directly implicated in. So a mutation in a region of the mRNA 96, they're named for the order they were discovered so like earlier numbers are earlier discoveries, bigger numbers are later discoveries. So a mutation in microRNA 96 causes a hereditary progressive hearing loss. A mutation in the region of 184 causes hereditary keratoconus with cataracts. And a full deletion in 17 causes skeletal and growth defects. And we're also seeing more and more research, a lot of really exciting research in the sort of DNA repair and cancer space. A lot of research about the effects of these microRNAs on especially like chronic lymphocytic leukemia is implicated, B-cell receptor signaling, and sort of sometimes these interesting things where they both affect tumor suppressor genes and oncogenes. And so there's still a lot more to unpack and uncover, but there's so many therapeutics that are actively being researched and developed from, gosh, everything from heart disease to kidney disease to alcoholism and stroke, like different nervous system disorders like Alzheimer's. These microRNAs could be implicated in a lot of disease processes because they're so early in that process of protein synthesis. And so this is one of those, I think, beautiful examples of two guys working in a lab just fundamentally interested in how something works. And eventually enough people recognized this fundamental process, this regulatory process was so important that now we see downstream effects that will affect the health and safety of human beings for, I think, generations to come. And that's why they were awarded the Nobel Prize for Physiology or Medicine.
S: Yeah, it's a good story because, yeah, they were just asking a very simple question. How does the LIN-4 gene affect the LIN-14 gene? And it could have been, I'd say, like a one-off kind of quirky thing limited to C. elegans. Turns out they discovered a fundamental aspect of gene regulation. And now that's translating into a whole bunch of potential therapeutic targets.
C: Translating! No pun intended.
S: Yeah, right. Well, we call it translational research. So this is why funding scientists who are doing basic science research without any thought to ultimate applications, just trying to understand how the universe works, you can't predict how that's going to potentially pan out. But as a general rule, we see over and over again, understanding at a reductionist, like really tiny level, how things work usually turns out to be pretty useful.
C: A hundred percent, because things can break all along a path.
S: Exactly.
C: And when we understand the most simplistic and the earliest functionality of something, we're getting to it at its core and we can start to follow all the ways that things can break. When we follow the ways things can break, we can figure out how to fix them.
S: Right.
C: Fascinating.
News Item #2 - Chemistry (27:45)
S: All right, Jay, tell us about the Nobel Prize in Chemistry.
J: What do you want to know?
S: Everything, baby. Give me all. Give it all to me.
J: All right, so there was two different awards given out. So we'll start with the first team here. So this is David Baker's creation of new proteins. So David Baker and his team, they took a complex problem, which is they wanted to figure out how to design entirely new proteins from scratch. Yeah, this is something that doesn't just happen in nature. You know, proteins have to evolve and it's an incredibly long process. And if you look at the way the human body uses proteins, it's super complicated. So proteins, in case you don't know, they're composed of chains of amino acids. And these chains of amino acids fold into specific three-dimensional structures. On their own. Amazing. And these structures are critical for the protein's actual function in the organism, right? It could be for breaking down food or responding to body signals or forming cellular structures. So in 2003, Baker achieved a breakthrough by creating a protein with a unique structure that's not found in nature. And that's a big deal all by itself. And since then, Baker and his team have continued to push the boundaries of what they achieved. And now they're engineering new proteins that are tailored to have specific functions. They've designed proteins that can act as pharmaceuticals to treat diseases, vaccines to prevent infections, and even like nanomaterials for advanced technological uses, which we're not there yet, but that is very possible that that can take place. There are some practical things here. So for instance, they've developed proteins that can detect specific molecules. They can function as sensors. This could be used in the medical diagnostics and environmental monitoring. And then they have synthetic biology. So Baker's work lays the foundation for this form of synthetic biology where custom-made proteins are created for specific tasks. And this opens new possibilities into everything from drug design to material sciences. So that is damn impressive and incredibly powerful. You know, the ability to design proteins that don't happen to exist in nature means now that we can build molecules with exact specifications, right? This is much, you could consider this like constructing building from blueprint to finish. It's really incredible what these guys did. And it's being described as it holds immense promise for innovation in many other scientific fields. So I would just like to thank those guys. That's incredible. The second team here, Demis Hasselbus and John Jumper's AlphaFold2.
B: Oh yeah baby.
S: We talked about it on the show.
J: There it is. Let me give you a little reboot on that. For decades, you have to make it sound dramatic, right? A very long time, scientists, they've known that the function of protein is dictated by its structure, but there was a challenge. It's been predicting how a chain of amino acids, which is the protein sequence, how it folds into a functional three-dimensional form. This is known as protein folding problem, as the protein folding problem, right, Bob? I think you talked about that, right?
B: Yes, I did.
J: This has been a 50 year long puzzle in biology. And before the breakthrough, determining a protein structure took years of incredibly painstaking and super expensive experiments. Just tons and tons and tons of hours to figure it out. So in 2020, the team introduced AlphaFold2, which is what? It's a powerful AI, of course. And their team programmed it specifically at their DeepMind, this is part of the DeepMind effort, and this AI can accurately predict protein structures. And this is why this is impressive. So first off, AlphaFold2 uses these advanced AI algorithms, specifically there are a type of machine learning called deep learning, to predict how sequences of amino acids actually fold into 3D structures. So the model, they quote-unquote learned from vast amounts of existing protein data which we've been collecting for decades. And this enabled them to make these highly accurate predictions using the AI. So, with AlphaFold2, Hassebus and Jumper could predict the structure of nearly every protein known to science. And there's over 200 million of those proteins. 200 million. This is a massive leap from the previous methods that we had, which could only determine structures one at a time, right? And like I said, it takes years per protein to figure it out. So since its release, AlphaFold 2 has been adopted by over 2 million researchers from 190 countries. And that is for a very good reason, because it's crazy powerful and unbelievably useful, and they use the tool for all these different various scientific purposes, from understanding diseases, developing new drugs, etc. The list goes on. Alpha-4-2 has been applied already in numerous fields, and one notable use is in combating antibiotic resistance, where researchers now can study the structures of proteins and bacteria that cause resistance to better design new antibiotics. This is another exciting application in designing enzymes that can break down plastics. I mean, guys, it's unbelievable how powerful this particular thing is. I really love the idea that they're mindful of the fact that new technologies need to help us with environmental pollution and things like that because we have gotten to a point where there's an incredible amount of environmental pollution in the modern world. Baker's work shows that we can simply design proteins with entirely new functions and AlphaFold solves the mystery of how these proteins naturally fold and together they can make Voltron. Together these breakthroughs mean we now understand both how to create new proteins and how to predict the structure of existing ones, which gives us immense capability and power to do things that we didn't have not too long ago.
S: Did you point out, though, Jay, that even though, like I say, in the write-up for the award, they say that it predicted the structure of 200 million proteins that researchers have identified. But we don't know that all those predictions are accurate. Bob, you talked about this. I think they've only verified a percentage of them, like half or something. You know, it's a predictive tool. It doesn't prove that that's the structure.
J: But proving it out is faster because they're starting with...
S: Yes, right, exactly. It tells the researchers what they need to do. It takes a lot less time to verify what AlphaFold2 predicted than to just figure it out from scratch.
J: I'd like to use that as an example too. Artificial intelligence is going to remove roughly, I think I read 79,000 jobs in the United States at some point in the near future, but it's going to create 90,000 jobs. So the idea is that artificial intelligence, your job will be replaced by someone who knows how to work with AI in one fashion or another, right? That's the thing that we all need to realize. Like, look at what these scientists are doing with artificial intelligence and how powerful it is. Guys, this is going to permeate human society, and there's no stopping it.
S: It already is. I mean, we're already seeing increasing examples of how AI is being used to do, like, as we've been saying for a long time, it's actually happening now, doing years of research in weeks or months of research in days or even hours. That's happening right now.
E: Yeah.
S: You know, just the pace of research is going to accelerate.
News Item #3 - Physics (35:38)
S: All right, Bob, you're up with physics.
E: Do it.
B: Oh, boy. Guys, this year's Nobel Prize in Physics has been awarded to John Hopfield and Jeffrey Hinton for their groundbreaking research and discoveries that led to the deep learning neural networks that power much of the artificial intelligence being used today. So yes, more AI in Nobel. So, let's see, Mark Pierce, member of the Nobel Committee of Physics, said their work was fundamental in laying the cornerstones of what we experience today as artificial intelligence. Ellen Moon, chair of the Nobel Committee for Physics, thanks to the Laureates' work, AI has become part of our daily lives. The Laureates' discoveries and inventions from the building blocks of machine learning that can aid in making faster and more reliable decisions. For instance, when diagnosing medical conditions. Now, Moon's mentions machine learning, so this is, we've mentioned it a few times. This is a broad term for any technique where computers learn from data and make predictions and decisions. Neural networks are a type of machine learning and are integral to this Nobel Prize. So let's do a quick overview of neural networks. I've described them multiple times on the show, and each time I say, I could do that better next time, and this is next time. So here we go. So neural networks are not exotic neural tissue organoid structures running in vats of strange liquids and thinking strange thoughts. That's not what it is. Neural networks run on computers. They run on high performance computers called servers, all interconnected on a network that can consist of many hundreds of them. It could be from one server to hundreds of servers running the neural network, depending on the scale and lots of different things. The software running on these servers simulates in some ways the behavior of the biological networks of neurons in our heads. The neurons in a neural network are called nodes, but they are not terribly exotic. They're essentially mathematical functions. So bringing it down to earth a bit. The neurons in the network, the data that these functions receive and deal with can be many different types of data. They can be numerical data like temperature or price, measurable quantities. It could be image data. In that case, it would be something potentially like pixel values like RGB, red, green, blue, values for a pixel. The data could be text-based. They could be words that are converted into numbers so they could be more easily manipulated. The data that these nodes or mathematical functions deal with could also be audio data, sound waves converted into numbers, so they could deal with all of that and more. So now as this data flows through the network layer by layer, it's processed by the nodes, by these mathematical functions. In this processing, one of the most important aspects of that is the assigning of a number called a weight. You've probably heard about that a lot if you read about anything about neural networks. The higher the weight, the more important or influential that data is, so that's critical. It's critical to how a neural network learns this idea of weights with more and more training, using more and more data. These weights are then adjusted. They're tweaked over and over and over until the errors are minimized, allowing for ultimately a surprisingly accurate predictions or decisions based on new data that is input. Now these neural networks didn't appear ex nihilo though. They've been developing and evolving for years, but many of the critical theoretical breakthroughs have been around for decades. And in many ways they've just been waiting for powerful enough servers to show us what these kinds of special networks can really, really do. Yeah, I think it's been like the past 10 and 15 years, when servers got much more powerful that we could really put these networks through their paces and all the breakthroughs we've had the past 15 years just goes to show not only has the theory been there, is the theory doing it, but it's the machines that have been the thing that we've been waiting for to really see this. So this is where the two Nobel winners in physics come in. John Hopfield is an emeritus professor at Princeton University, and Jeffrey Hinton is a computer scientist at the University of Toronto. John Hopfield introduced the Hopfield network in a paper in 1982. This type of network laid the groundwork for pattern recognition and associative memory. Pattern recognition, that's self-explanatory. Associative memory here in this context means that the network can remember patterns based on only partial information. So that's, as you might imagine, that's critical. You expose the neural network to just a little bit of an image, say, and it could go back through the patterns that it has remembered and remember, oh yeah, that was a cat. So that's what he introduced with his Hopfield network in 82. So, as you might imagine, the Hopfield network had a huge influence on the thinking of how to store and how to retrieve information in neural networks. Jeffrey Hinton is sometimes called the godfather of AI. He co-invented Boltzmann machines in 1985. This is a type of early neural network that was designed to use some principles of physics, especially regarding probability and energy-based modeling. Look that one up. To find patterns in the data, so it uses probability and this specific type of modeling to find patterns in the data. Do you think finding patterns in data is critical for these neural networks? Yes, this is in 85. So like the Hopfield Network, Hinton's Boltzmann Machine Network provided foundational techniques and ideas that have allowed for the creation of the incredibly complex, skilled, and capable neural networks and AI that we have today. And this is why Hopfield and Hinton got Nobel Prizes this past week. If the name Hinton sounds familiar, he's the guy, do you remember guys? He's the guy that left Google last year to essentially be a whistleblower, actually, so he could speak candidly about his fears.
J: Yeah, I remember that guy.
B: Yeah, he says AI will be comparable to the Industrial Revolution, but instead of exceeding people in physical strength, it's going to exceed people in intellectual ability. We have no experience of what it's like to have things smarter than us. He continues, I'm just a scientist who suddenly realized that these things are getting smarter than us. I want to sort of blow the whistle and say we should worry seriously about how we stop these things getting control over us. And he also warned that AI knows how to program, so it'll figure out ways of getting around restrictions that we put on it. It'll figure out ways of manipulating people to do what it wants.
E: Oh my gosh.
B: So yeah, so he's definitely thrown that out there, and a lot of people are these days. Yes, absolutely. AI is getting increasingly more sophisticated, and we've got to be careful about it. But regarding that, I will just say this. Hinton and Hopfield, their names will be remembered in the pattern-seeking nodes in the cold, cold logic of the AI overlords, which will eventually take over the Earth. And if you're listening to this podcast now or in the future, I am here to serve. Just hedging my bets, guys. Just hedging my bets here.
E: That's hedging. Yeah, it's a good hedge there.
B: Oh yeah. I'll be a pet to an AI. Come on, baby.
S: Welcome, our AI overlords.
B: So yeah. So cool. I wasn't familiar with their work, and yeah, it seems of course there's other researchers that also created and also contributed mightily to the development of what has become deep learning neural networks and things like that, that are critical components of AI these days. So I don't know how did they pick among all of them for these two guys. And these two guys clearly made major contributions. Now, remember, though, that their networks, the Hopfield network and the Boltzmann machine networks, they didn't evolve into the deep learning neural networks that we have today. But the ideas and the concepts that they introduced back decades ago were critical to the development over time of the neural networks that we have available today. So yeah, it's critical. I mean you think about the things the pattern recognition and things like that. Yeah, that's absolutely critical to what we're doing today with these networks. And these guys made some major, major contributions. So good for them.
S: All right. Thanks, Bob.
News Item #4 - Fruit Fly Connectome (45:57)
S: All right, guys, I'm going to talk about some other big science news this week. I don't know if this is going to be a future Nobel Prize, but it certainly is a pretty incredible milestone. Scientists have presented a complete connectome of the drosophila of the fruit fly.
B: I think this will be a Nobel Prize.
C: Yeah.
S: You think so?
B: Yes.
S: So this is the first complete connectome of an animal brain.
J: What's a connectome?
S: Connectome is a map, a complete neuronal level map of a brain. So all the neurons, all the connections of those neurons. This map of the fruit fly brain contains 140,000 neurons and more than 50 million connections. A whole brain connection...
B: It doesn't sound like a lot, but it's still...
S: Well, it depends on context. That's certainly a lot. You're mapping a system with 140,000 components, which are interacting through 50 million connections with each other. That's big. Now, of course, you compare that to the human brain, which has 86 billion neurons and about 100 trillion connections. That's six orders of magnitude greater than the drosophila.
C: Yeah, but they're just as awful as a teeny tiny little fly.
E: I know!
S: But we've got to start somewhere, right?
E: Yeah, exactly. We'll eventually get to humans.
B: Yeah, and the fact that it's an entire, it's a complete map, so we have mapped out an entire brain of an organism, so that fact alone, who knows what kind of insights might flow from that, but I'm sure Steve might mention some of these ideas.
S: I might mention some, because they're already using it. I mean, they didn't just say, here it is. They said, all right, and we did some analysis using this piece, and what did we learn? One of the things they learned—again, this is not like a mind-blowing revelation, this is—
E: Good pun.
S: Yeah, this is pretty much building on what we already know—but they were able to statistically map out the fact that the neurons connect to each other through nodes that are high traffic areas, right? There are these nodes, these hubs, right? And that they make a lot of connections to other parts of the brain. And again, as you would expect, these highly connected hubs also connect to many of the other highly connected hubs, right? Because, which makes sense just statistically, highly connected areas are going to be connected to other highly connected areas because they're all highly connected, right? They're also able to show that some of these hubs act as integrators of information. They're taking in a lot of information from other parts of the brain. And other hubs act as distribution centers for the dissemination of information. So this is a very efficient way to integrate and disseminate information, which is pretty much exactly what you would expect a brain-type structure to be doing. And different hubs can integrate with different other networks basically at different times for different functions. Yeah, this is interesting. We're just getting started, right? We have this tool now, this complete digital map of an entire functioning brain, and so we'll be able to do a lot more systems kind of research on it to understand how it's functioning. And even though the last common ancestor between fruit flies and people It didn't have a brain. It did have a nervous system, but a very, very basic one. And maybe there were nodes in that basic nervous system, like pre-brains, but not anything you would call an actual brain. So the fruit fly brain doesn't have any evolutionary homology with the human brain. But it probably follows a lot of the same principles at this very basic level of information processing. Of course, it would be a lot more useful if we can get the same level map with a mouse brain, because now we're talking about a mammal, and that would have a lot of evolutionary homology with the human brain. Mammal brains all have a lot in common with all other mammal brains. So one way this research will progress, other than just doing research on this Drosophila brain model, will be to create these connectomes of increasingly sophisticated animals with bigger and bigger brains and closer and closer evolutionarily to humans. But there's another way too because it's not as if we know like we have a full neuronal level connectome or nothing. Of the human brain we have what's called a mesoscale connectome. If you think about the mesoscale basically means like middle scale. So even a hundred years ago Or 200 years ago, we were putting together a naked eye gross anatomy model of the brain. These are like the Brodmann's areas. You have the lobes, and within those lobes you have these naked eye visible areas of the brain that are engaged with, like this is the language center, and this is the visual center. And we still, as a clinical neurologist, that's sort of the map of the brain that we follow clinically, right? This is the part of the brain that controls movement of your left arm. That's a macro scale map of the brain. A micro scale would be at the nanoscale or neuronal level. That's the ultimate connectome. But a mesoscale is somewhere in between those two things. Where you are a very small level, like the millimeter scale, right? You're down to the millimeter scale, not the neuronal individual neurons, but clusters of neurons. And a mesoscale map is extremely, a mesoscale connectome is extremely useful because that's kind of how the brain is organized. It's not like you have a single neuron with its own little function. You have clusters of neurons all firing together. So it's still useful to get to that deeper level of detail, but there's a lot that we can understand about how the brain functions, and there's a lot of research that we could do and a lot of modeling that we could do, even with a very good mesoscale connectome. And we already have that. We're refining it. A lot of this was developed with, like, fMRI research and other functional kind of imaging studies and electromagnetic studies, etc., where we could see, like, the different parts of the brain and how they connect to other parts of the brain and when they become active and what kind of tasks, etc. Still a lot of information to explore with that. But that's kind of where we are with the human brain.
B: Well, Steve, how does that relate? I remember years ago reading about the cortical columns. In the brain, the fundamental units, how does that relate?
S: That's like a mesoscale structure. A single cortical column, yeah. Yeah, there are billions or millions of cortical columns in the human brain. It's well, well, well below the macro scale, but a column is a bunch of neurons. It's not down to the individual neuronal scale either. That's a good example of the mesoscale, of sort of a unit of cortex.
B: And is there any hint of this hub structure in other brains?
S: Totally. Yeah, this is basically our model of how the brain works. It's networks and nodes, right? Or modules. So you have little pieces of the brain that do some kind of processing. And that module, that node, participates in multiple different networks, and each network subsuming a specific function. But of course, it's networking with a bunch of other networks that also have their functions, and they are all interacting with each other in real time. So, it's really complicated, but that's how we are creating the mesoscale connectome, is trying to understand all of the nodes and networks in the brain.
B: This hub idea then from the connectome is probably extremely relevant to the human brain. It's no surprise though, it's not like, oh wow.
S: Right, right.
B: Gotcha.
S: But it is good, again, it's good to have a model that we could ask really good statistical and mathematical questions like network level questions about how it's functioning. And of course, it'll be useful to have these microscale connectomes ultimate like down to the neuronal level for ultimately for humans, right? That's the goal. Now, you may be thinking, okay, if we have a complete connectome of a drosophila brain, does that mean that if we emulated that in a computer however powerful a computer we need to, would that be a fruit fly, basically? Would that be a virtual fruit fly? Would it behave like a fruit fly? And the answer is sort of, because even a complete connectome is not everything.
B: That's part of the story. There's so many support, like glial cells and all those other things.
S: Exactly. So there's other layers going on. So one thing, one layer is like there's other cells other than the neurons. We used to think that the glia were just support cells. They were just keeping the neurons happy. But we have learned in the last couple decades that, nope, they actually are modulating neuronal function. There's a layer of functionality there to the way the whole brain works. So in order to understand how the brain functions, you have to include these support cells as well. You also have to understand how the brain is functioning at a chemical level, like how the neurotransmitters are functioning, right? So you need to understand the wiring, you need to understand the biology, you need to understand the neurochemistry and how they all work together in a dynamic way. Before you actually have like a virtual brain, right? The connectome is only one piece. It's arguably maybe the most important piece, but it is only one piece to a virtual actual brain. But then there's another layer there, too. Like if we're saying, all right, this we have a brain we have a virtual brain, whatever it is, a mouse brain, a human brain, or in this case, a drosophila brain. And how would it function like if we were like emulating it in a computer? Well, then you have to think about, well, what are its inputs and outputs, right? What would a virtual brain that has no input, what would it do? In order for it to really simulate a fruit fly, it would have to be getting sensory information that a fruit fly brain gets, which includes information from its body, which it doesn't have because it's a virtual brain. But we would have to simulate all of those inputs.
E: It could be tricked.
S: Yeah, we would have to give it a virtual environment, a virtual body, to go with this virtual brain. And then we would have to also include the reaction of the body and the environment to its actions, right? If it does something, there's got to be a response to it. The brain functions and is organized and develops, like all that functionality, even the developmental anatomy of the brain is based upon that loop, that feedback of input and reaction to output, etc. You know what I mean? Your visual cortex only develops under stimulation from actual vision, from actual input from your eyes. And your brain can control your arm because it's not only just sending signals to the arm, it's also getting signals back from the arm. And it sort of compares what it's trying to do with what happens, and that's how it knows that it's controlling the limb. You have to close that loop. So we would have to do the same thing if we were going to try to have a virtual fruit fly with an actual fully emulated connectome of a fruit fly functioning in a digital world. So it's interesting to think about that. It's actually very, very challenging. And so how far are we away from max headroom, right? So the max headroom being like a digital copy of a human's brain that then sort of was an artificial intelligent digital human, because it was an emulated human brain, therefore it's a human, right? But exists only in the digital world. It's really hard to say. I suspect we're a very long way away, both in software and in hardware. The other question is, I couldn't really find a good answer to this. Just only vague answers. It's like, what would it take? What would it take? How powerful a computer would you need to emulate that fly brain connectome in real time? Not just like one second for the fruit fly is a day in real time, but like one second to one second. How powerful a computer would that take? I suspect it would take a supercomputer to do that. How powerful a computer are we going to need to emulate a human brain once we have a neuronal level complete human connectome, including all of the inputs and outputs that are necessary to make it actually function? We could be decades away from having the hardware to do that. How long is it going to take before we have a connectome of a human?
C: And even that, don't you feel like we can constantly be increasing the resolution?
S: Yes.
C: Like there's more detail always.
S: Yeah, that's kind of my point with the mesoscale. We're increasing our resolution, but also we are just building the neuronal level connectome also directly.
C: Even once we drill down. To the individual neurons with the individual connections, then we can start talking about which neurotransmitters are present there, we can start talking about G-proteins, and then, yeah, you can always resolve more.
S: Yeah, absolutely. But at some point, we're going to get something that's close enough.
B: Yeah, and we don't need to worry about neutrons and protons.
S: That it's functioning, that it could theoretically be a functioning human virtual brain. So I suspect that that's decades away, too, before we have the information to do that, and decades before we have the hardware to do it. But the one variable, however, in this is artificial intelligence.
B: AI, baby.
S: Because AI is I would say it's a wild card, but the strength of AI in terms of what AI can do is operating within dynamic information based systems that involve a lot of data. Which is exactly the problem we're talking about. It's almost ideally suited to the task of making a connectome of the human brain. And we may just be as far away as however long it will take for somebody to develop an AI whose purpose is figuring out the connectome of the human brain. And then it'll do it. And maybe it'll be as accurate as an AI picture is where it's like a little like you have six fingers and whatever, like it doesn't may not be perfectly accurate, but we may be able to fill in a lot of those higher resolution details.
B: With frog DNA.
S: Yeah, right. Well, you may be able to fill it in with AI, like the way it functions now. So who knows? So it may be a lot sooner than we think. It may be years and not decades if AI, as I just said, accelerates that research tremendously.
B: And if you are interested in this idea of digital copies of human brains, I would suggest you watch the animated series called Pantheon, which deals with that in the best way I have ever seen. Very interesting, fascinating show. Check it out, Pantheon.
J: Would these advancements allow us to figure out things like getting rid of somebody's depression and or anxiety?
S: That's the hope, right? So imagine if we could, let's back off of humans for a bit, let's see if we have an animal connectome that we can, even the drosophila. Because once we have that, then we could play the game of what happens when I turn off this circuit. Now let's see how the system behaves.
B: Knockout genes.
S: Yeah, like knockout genes, but it's like knockout networks, knockout circuits. Or let's just turn it up, or turn it down, or do this like you're playing with dials now and seeing how it affects the behavior. Which dials do I have to turn to turn off depression or anxiety or schizophrenia or whatever? And then we actually model those diseases. We make a schizophrenic brain and see how that behaves differently than a typical brain or an autistic brain or whatever.
C: That's the interesting thing because all of this assumes that certain disease states are somewhat universal. And when we're modeling a brain, we're modeling the most normative version of a brain. But every brain is really different. And it's going to be important for us to understand that as we move forward. Instead of becoming more constrained in our views of, I guess, function and experience, we need to sort of be broader. And I think what will be really interesting is when we have a modeled brain that has massive error bars in it.
S: Well, I think we'll probably model a generic human brain. Like this is an averaged out generic human brain. That's just the average and typical in every way. And then try to figure out how it varies.
C: Exactly. How far can it go in this direction versus that direction?
S: And what happens and how these different levers interact. And of course, there's a massive ethical layer here. When you start to get to primates and definitely humans, would a virtual human brain be a human? What's the ethics of doing research on a virtual human brain, especially when you're looking at how it functions? You're looking at its consciousness, almost by definition, for that kind of research, it would be conscious, because you're seeing how we can affect its consciousness in different ways. If you're asking other questions that don't deal with consciousness itself, then you could do research on it without it being conscious. But the most interesting questions are the ones that deal with consciousness, right?
C: But there's also an interesting component here, right? Because we talk about this with organoids, but organoids use wetware. And when there's actually no hardware or wetware, we're just talking about software, then it's the same ethical conversations that we have about AI in general, I think. I don't think it's ever going to move into that. Yes, it may be consciousness, but it's still virtual consciousness. Once you actually try to I don't know, somehow fuse this with an organoid then I think things get extra hairy.
S: Yeah, but I think even when we have a virtual consciousness, even without wetware, it's going to be dodgy.
C: Yeah, but it's dodgy the same way all advanced AI is dodgy, I think.
S: But I do think, let's say we make a virtual mouse. We already have an ethical standard for doing research on mice. So let's say, all right, can you be reasonably sure that this thing is not suffering? Okay, then have at it. Do what you need. And then we could be in a situation where we could do millions of maze experiments on a virtual mouse in seconds rather than years, that sort of things. It's going to be a wild ride and this is going to be the next 20, 30 years and maybe sooner than we think things are happening fast. So this is a milestone, the first complete connectome. This will be one of those milestones that always gets mentioned in summaries or write-ups of this topic. But there are bigger ones to come, and it's going to be both exciting and frightening, which is like a lot of good sciences, those two things.
E: I have a question for Cara. Do you know who Max Headroom is?
C: No.
E: Steve.
S: Max Headroom is a show from the 80s where the main protagonist of the show was in an accident and in a coma, but he was some kind of researcher. The short version is there was a computer model of his brain that then was able to function. And it was like a digitized, low res, glitchy kind of head of a person that had a very entertaining personality.
E: Sarcastic.
C: I'm looking at it. So, but it wasn't, the show itself wasn't a cartoon?
S: No, it was live action.
C: It was live action. And then sometimes you would see this digitized?
S: This guy would be on a computer screen.
C: Oh, he's on the screen all the time.
B: That was the extent of it, pretty much, was him.
S: Yeah, but then the guy came out of the coma, so then he was able to talk to his Max Headroom version. And the reason why he's called Max Headroom, because that was the last thing he saw before he went into a coma, which was the last thing that the AI saw, which was literally Max Headroom.
E: In a garage.
S: In a garage.
E: Like when George tore the roof off the van, but that's a whole other story.
B: And the actor was Matt Frewer, who was awesome.
E: Yeah, he showed up in Star Trek, too. So there you go, Cara.
C: The series was only on from 87 to 88.
S: It was very short-lived.
E: No, but he became a cultural icon. He was in commercials everywhere.
S: Separate from the show. Yes, he was, in his own right, a cultural icon for about a decade.
C: Yeah, so I would have been like five, which is probably why I don't remember it, sadly. It just wasn't relevant to me quite yet.
News Item #5 - Shroud of Turin (1:08:36)
S: All right, Evan, give us an update on the Shroud of Turin.
E: Yeah, Shroud of Turin.
B: What the hell.
E: Right? No kidding. Bob, here we go again.
S: Blast from the past.
E: I'm touching on another retro skeptic topic. Cara, have you ever heard of the Shroud of Turin?
C: Of course I have. Isn't that supposed to be the thing that Jesus was buried in?
E: Ah, yes. Very, very good. Do you think people younger than you, Cara, maybe the majority of them know or have no clue?
C: I think people younger than me who have weird enjoyment watching bad Discovery Channel shows might know.
S: Although everything has a second life on TikTok now, to be honest with you.
C: Even the old school stuff?
S: Yeah.
E: Well, in case some of our audience is not familiar, I'm going to give you the quick background on what this is all about. Gather round, children. This old man's telling a tale of antiquity and flimflammery. Well, the year was one when, according to some people, Jesus Christ was born. Fast forward to the year 33 when Jesus Christ was crucified. His body was cleaned, clothed, wrapped in linen, shroud, and buried in a rock tomb. The end. Now get off my lawn. No. But that is basically it. The Shroud of Turin is believed by many people of faith to be the linen burial cloth that Jesus was buried in. Many people of the faith also believe that the shroud actually exists and resides in the city of Turin, which is in Italy, hence the Shroud of Turin. Okay? That's how you get the name. Now, as if the existence of such an item would be significant, I don't doubt that, the shroud itself is not just a cloth, but it bears the image of a man, perhaps the radiance of the supposed resurrection impressed an image of Jesus' likeness onto the cloth itself. His facial features, his facial hair, that beard, the mustache, and some red pigmentation resembling that which looks like bloodstains, his likeness, his blood forever captured within the fibers of the burial shroud. And one might say that the Shroud of Turin is the Holy Grail of religious artifacts.
S: Evan, wouldn't the holy grail be the holy grail of religious artifacts?
E: Oh, Steve, wow.
B: Evan, the image was famously, though, also a negative, right? A negative image.
E: Yeah, negative impression. As if you were to have taken a photograph and then done a negative, right, on a plate or in film. Correct, yes. That's how it looks. We'll get to that. We will get to that.
B: Okay.
E: All right. So, what is the evidence that this is the burial cloth of Jesus? Has it been looked at? Has it been investigated thoroughly and properly? The answer is, oh yeah, it has. I'm going to mention him again this week. So nice, I'm calling him out twice. Dr. Joe Nickel. Yep, gentleman's skeptic detective. Long time friend of the SGU. He has dug as deep as anyone, has been allowed to from the skeptical side into this matter, and he was able to establish some interesting facts about this particular shroud. It seems that the material of the linen itself is medieval in origin, so not in the year, say, 33 or 36, roughly the approximate year that Jesus was crucified. They don't know exactly when, supposedly. But rather, this shroud maps to the year, well, 1260 to 1390. Thank you, radiocarbon dating. Yeah, and not just one, but three independent analyses returned that range of dates. So that's what they were able to do with a test piece of the cloth. And also, Joe noted that it's kind of funny that there's no documented history of this artifact until the middle of the 14th century, as if it only sort of suddenly poof.
B: Ex nihilo, if you will.
E: There you go, Bob. Thanks for bringing it all together. It only sort of began at that time. You would think something like this would have had some sort of documentation prior to that but there is none. Now the image itself probably, most likely, zero chance, of it being the result of a miraculous or supernatural process. Instead, it was likely created, much more a prosaic explanation, using a combination of artistic methods, including painting or rubbing pigment onto the cloth. Bob, since you brought it up, sort of the negative image, and Joe Nickell talks about this in an interview, there is a technique that would be used by artists at that time in which you would do a rubbing of a face or a set of words or something else. And it would turn out to sort of have that negative looking appearance to it. So this is a known method of artistry at the time.
S: Yeah, what Joe points out, which is I think very telling, is if you look at all the independent lines of evidence, the carbon dating, the artistic tradition, the history, the known provenance of the shroud itself, they all point to the same period of time in the, what was it, the 15th century?
E: Mid-14th century.
S: Mid-14th century. They all, it's like he said, there's three darts hitting the bullseye of a dartboard. They all point to that. And not by coincidence, because that's when it came up. And this was a period of time when these kinds of fake artifacts were floating all over the place.
E: Absolutely.
S: Do you know how many shrouds there were at the time? This is just the one that kind of survives famously until modern times. But there were all kinds of artifacts floating around at this time. There was a cottage industry.
E: It was an industry. That's exactly what it was.
S: It was an industry of fake religious artifacts. Every little shrine and church had to have theirs in order to drum up people to come there and give them money. And then, of course, then people start making them so that they could be sold. So yeah, this is a home run for a medieval forgery. I mean, it's no question.
E: No doubt about it, Steve. Thank you. It was as if you were reading the notes right off my page.
S: I'm sorry.
E: You don't have a cookie attached to my computer?
S: No, we're both using the same resource.
E: I think so. So these are the conclusions that Joe is able to come up with, and also other investigators who have also corroborated similar evidence showing that, yeah, absolutely, mid 14th century, but there are still many millions of faithful people around the world who would rather embrace their idea that this is something that Jesus was actually buried in. So yeah, and the bloodstains also on the shroud have been investigated as well, which Joe has said, most likely pigment. It's a little tough because they wouldn't really give over sections of the shroud in order to test it for the actual blood, but they've done their own sort of analysis. Other people have done analysis, and some people think it's pigment. Okay, paint being used at the time, whatever. So here we go, brand new news coming out just a few days ago about the Shroud of Turin. In which researchers are claiming that these bloodstains found on the shroud are consistent with the torture that Christ suffered. Professor Gilo Fanti from the University of Padova found a pattern of bloodstains that align with historical descriptions of the crucifixion. He studied the Shroud of Turin for over 25 years, published his findings through the Shroud Science Group. He claims that macroscopic and microscopic analysis finds that the markings and stains on the cloth are consistent with the description of Jesus Christ in the Holy Bible, and in particular, with the four canonicals. Okay, so what is he talking about? Lots of red splots of varying shapes and sizes are all over this. Which he is claiming, okay, he's researched it, definitely this is right in line with exactly what a crucifixion from Jesus's time would have undergone.
S: How does he know?
E: Yeah. And also, I went back and did a little more research about what Joe Nickell and others have said about that in the past, and they said, artists, that was not necessarily unknown to artists at the time who would make these shrouds and these other things or other depictions with these kinds of splotches as an artistic representation of that very thing. So what he's saying here, let me quote this professor, he says, numerous blood stains scattered throughout the double body image of the HST show that Jesus of the HST was tortured. Blood stain marks all over the body image which are consistent with pre-crucifixion, flagellation. Blood stain marks on the head that are consistent with a crown of thorns. Blood marks on the hand and feet that are consistent with crucification. And the blood stain on the chest that evidences a post-mortem wound that corresponds with the post-mortem spear wound that famous spear wound in the lower part of his chest, that Christ received as described in the Bible. But, right, how exactly is he determining that this is actually that as opposed to some sort of artistic impression?
S: Right, because those were the beliefs at the time, right? So he's basically saying it's consistent with what people believed in the 14th century.
E: And looking at a visual pattern and basing it solely on how it looks.
S: And in reality, if you look at the blood stains like on the hair of the image of Jesus, it doesn't do what blood would do. It's not soaked into the air. It's kind of like dripping down on top of it. It makes absolutely no sense.
E: Right.
S: And so what is the signature of a blood pattern from a crucifixion? You know, we have almost no physical evidence of actual crucifixions from Roman times.
E: I wasn't aware there was any.
S: Yeah, I mean, we have, I mean, barely any. There's a couple of skeletons with whole, with wounds that could be consistent with an iron nail, but that's it. Very, very little. And we have then just secondhand references to that fact that, like, crucifixion, crucifixions occurred, but there's like no description of them or, you know what I mean? We don't really know exactly how they worked or what happened. There's just very vague references to them and scant possible evidence, like not even ironclad evidence. This notion that we somehow know what the blood pattern would be is ridiculous.
E: Yeah, it's actually pretty silly when you think about it. You know, that revelation, I'll extend a little bit with this, Steve. It comes on the heels of another report. I don't know if you read further into that. There was a study done on the shroud that apparently took place back in 2022, results released afterwards, in which they used something other than radiocarbon dating to try to determine the age of the shroud. And it's called what? Wide-angle X-ray scattering. There was a professor, his name is Professor Liberato Di Caro from the National Research Council of Italy. He led the team that did this particular research and here's what he said. It's a sort of radiography similar to the type of scan that you would do on a bone to see if there's a fracture. But this X-ray penetrates the material very deeply to analyze it at a microscopic level. Over time, the structure of the material degrades. We can tell from that how much time has passed and therefore the date of the object. And when you use that technique, according to him, 2000 years old is what they're saying there. I don't know enough about wide angle X-ray scattering. I didn't have really a chance to look so deep into that. They say they're going to welcome other labs around the world to try to also replicate this finding because the technique is a non-destructive technique, he says, which is a huge advantage. It means the test could be conducted again by another laboratory. Where there are so many other lines of evidence, right? All those converging lines of evidence pointing to the mid 14th century, and then you have this kind of one outlier. I would be shocked if that at all led to any positive evidence saying that there's a misdating via the radiocarbon dating. But they're saying that it could be because they're saying this thing got passed around so much that the cloth got contaminated, and therefore you can't go with the radiocarbon dating. Is that even a thing? I mean, if something is passed around, does it have sort of a contamination factor that you have to take into account when you're –
S: Yes. Essentially, that method, the X-ray scattering method, has not been validated for textiles, and it's not generally accepted by the scientific community. So these results have been basically ignored or rejected for that reason, and part of the reason is because of the effects of contamination that can have on that. But it's just basically nonsense. It's not a validated method. It's not generally accepted. We don't know if it's accurate or not. It hasn't been replicated. Whereas the carbon dating has been proven, has been validated for this kind of material. There were three pieces sent to three independent labs and all three came up with the same date or overlapping date ranges. So that's independent validation. This is an unvalidated one-off that's essentially meaningless.
E: Yeah, right, and a total outlier and not reliable at all. But I think the point of the whole thing is that analysis continues into the shroud of Turin, obviously by the people who are the proponents, who are the believers, looking for any sort of strand that they can attach to to say that this thing is valid, where evidence points to the contrary.
S: Yeah, there is a subset of people who are shroud scientists who are trying to validate it. They're not trying to figure out the truth, they're trying to validate it.
E: Starting with the conclusion and working backwards.
S: It's classic pseudoscience. Absolutely classic pseudoscience. All right, thanks Evan.
E: Yep.
Who's That Noisy? + Announcements (1:23:15)
S: Jay, it's Who's That Noisy time.
J: All right guys, last week I played this noisy. [plays Noisy] Okay, you guys have any ideas?
B: Yeah, it's R2 on acid.
E: It's the audio track from Steamboat Willie back in 1925.
J: Somebody did write in something about R2-D2 being on drugs, I think, or drunk. I think it was drunk, you know?
E: Yeah, hallucinating.
J: All right, so let's go through this. Michael Blaney wrote in and said, is this a composition made by an AI? And I thought that was a good guess. I thought that was pretty cool. You know, actually, AI makes much, much better compositions than that, as far as it being cohesive. You know, I don't want to put down what you just heard because it is its own thing. But anyway it's not done by AI. Next, a listener wrote in Alex Hall, and Alex said, hello, my guess for episode 1004's Who's That Noisy is a Garkling, also called a Sopranoismo. Sopranismo. Yeah, Sopranismo.
B: Sopranissimo.
J: Sopranissimo, sure. It's a recorder played as part of a larger orchestra. I thought that was a very interesting guest. I looked it up. I saw some pictures of it. It's definitely not that, but I still think that that was a cool guest. I got like, and then the rest of the responses I got were people writing in jokes, basically because of how odd this whole thing was, which I laughed at a lot of them. So I'm just going to cut to it because nobody guessed it. And I didn't really think it would be hard because of how unique that sound is. So what this actually is, guys, it is an instrument called an orchestrion. It's a particular kind of instrument. There's lots of orchestrions out there. Orchestrion I could be saying orchestrion. Yeah.
C: Orchestrion.
J: That sounds better. I like that way you said it. But this one in particular was created by someone that used to be in the band Devo. You guys remember that band?
B: Yeah.
E: Sure, Devo.
J: Yeah. The guy's name is Mark Mothersbo.
E: Oh, Mark Mothersbo.
J: Yeah. And he-
E: Great.
J: So he created this instrument, which is in this classification of instruments. So let me tell you what the what the definition of this is. So an orchestra on, you said, Cara?
C: I said Orchestrion, but that's just a guess.
J: It's a generic name for a machine that plays music and is designed to sound like an orchestra or a band or just a group of various noise makers. It could be a lot of different things. This one in particular, this is the best way I can describe it. Imagine you take a dalek and you make it like three times as tall as that and you put a bunch of horns on its head. And then every place where there's one of those like silver balls, you put a weird instrument. So that that's this thing. And that is the sound that it makes. I'm not sure how you trigger it. I'm not sure if it just does it when you come near or if it's always doing this. But take a listen again. [plays Noisy] Yeah, so he's made several of these and this one is considered to be his best. So I thought that was really interesting.
E: I have a question for Cara.
C: Yes?
E: Do you know what a Dalek is?
S: A Dalek?
C: A Dalek is? Yeah, from Doctor Who?
E: Yes, okay, good, good. Because that's what Jay was referring to.
C: Yeah, no, I knew, with all the, yeah.
E: Very good, okay.
J: Yeah, she's been around us long enough.
C: I blame an ex-boyfriend for that.
S: Is he a Whovian, this ex boyfriend?
B: I like him already.
C: Very much so.
J: Did you say the sex boyfriend?
E: No, ex-boyfriend.
S: Ex-boyfriend.
E: And a Whovian.
J: Alright guys, I have a new Noisy for this week. This Noisy was sent in by a listener named Wes High. High High! [plays Noisy]
S: That could be about one million things.
J: And that's why Who's That Noisy is so interesting, Steve. So if you guys think you know what the noisy is this week or if you heard something cool, email me at WTN@theskepticsguide.org. Steve, today's Wednesday as we record this and people will be listening to this show on Saturday at the earliest, right? So by the time you hear this, everybody in the world will be able to buy tickets for NOTACON because we released all the tickets to the public today. Let me give you a quick rundown on what's going on with NOTACON 2025. So it'll be the weekend of May 15th, 16th, and 17th. That's Thursday, Friday, and Saturday. It's going to be in White Plains, New York. Like last time, the hotel went under a complete renovation, so it'll be the same hotel, but it'll be a lot nicer than the last time we were there. And it was a nice hotel when we were there, but I guess the hotels have to spend the money to do this from time to time. Anyway, we will be having a VIP meet and greet on Thursday night, and we will also be having a board game session called the boardroom, which will be like the basically the very first thing that we do at the conference. So you can pick up your badges on Thursday, go to the board meeting, and go to the VIP. You know, you can go to both of those events on Thursday. Then all day Friday and Friday night, and all day Saturday and Saturday night, we have our standard programming, with things that you'll remember from last time if you were there, and lots of new stuff that we are planning as well. There will be a sing-along on Saturday night, and this year's theme is The Beatles.
E: Yay! Fun!
J: So we'll be playing other stuff too, but The Beatles is going to be a major theme. So we decided to call this year's show, it's NOTACON 2025, you know what the rest of it is?
E: Skeptical Mystery Tour.
J: Yes, it's the Skeptical Mystery Tour. And that's all I'm going to say. Because there is a puzzle this year and I don't want to give away too much. So that's all I'm going to say. So if you're interested, you can go to notaconcon.com. And I'll say that again slowly. And as I say it, remember, Ian is the one that selected that URL. notaconcon.com! My God! So anyway, go to that URL or you can go to the SGU homepage, there'll be a link on there as well. You can buy your tickets for all these different events. Basically everything will be like last year if you went and if you didn't go, let me give you just a quick idea of what this conference is. So people who like to go to science and skeptical conferences, we've done it for years. And we got a lot of emails from people over the years saying that they really miss like the in-person NECSS. It turns out that what they really, really missed was the social gathering aspect of it. So we decided we were like, listen, let's create a conference that revolves around socializing. Let's create a conference that revolves around entertainment and socializing and give people a chance to see each other in person, get together, especially people that live very far away. We have people already that are coming that are coming from all over all over the place, you know. I think it's wonderful. This is a socializing event. This is an event where you're going to meet people. If you're an SGU patron, you'll meet people that you chat with on our Discord. If not, you will definitely meet people and make friends. Lots of people commented and gave us wonderful feedback last time saying they just met a lot of great people. So, it's just a fun, all-around event to go to. We highly recommend it. It was an incredible time last year, and I'm not just saying that. It really was an incredible time. I think it was the best conference that I've ever attended, just from a community sense. It was wonderful. So anyway, go to notaconcon.com is you want to check out more details and get tivkets. We have two more shows guys, we have shows that are coming up pretty soon. This is happening the weekend of December 7th, so we have two shows on December 7th. We have a DC private show, Washington DC, and we have a Washington DC extravaganza. The private show is a recording of our live podcast and then an extra hour of fun audience interaction that only you will see and hear. It's unique every time. It's always a good time. George Hrab will of course be with us on that, so please do go get tickets on the SGU homepage. That's SGU, that's the theskepticsguide.org. And the extravaganza, if you don't know what that is, we have a stage show that we've been doing for like the last about 10 years. This is a show where we teach you about how your brain can fool you. You can't trust your brain, can't trust your senses. There's a lot of improv comedy bits and funny stuff going on. And we have a wonderful time interacting with the audience. And if you're interested, you can also get those tickets at the theskepticsguide.org homepage.
S: Thank you, Jay.
Emails (1:32:23)
S: Let's do a quick email. This email comes from Stuart from Washington, UK. And he writes, simply, saw this and thought of you. And then he gives a link to a story about Toyota's portable hydrogen cartridges. So basically, Toyota is proposing this hydrogen fuel cell car, where the hydrogen is kept in these cylinders. They're the shape of a AA battery, but much bigger, obviously. And the idea is that when you go to fill up, you swap these out, right? You pull out the empty one, like a tank, and you put in a full one, and then you're on your way. So it's just quicker, easier than filling up your existing tank. Yeah, so it's neat, but the thing is, it's one of those situations where it's like fixing something that's not really a problem and it's also not the problem with hydrogen fuel cell cars, so it's irrelevant. I don't care how good you make the experience from the refueling perspective. The reason I think, there's two big reasons why I think hydrogen fuel cell cars are not going anywhere. One is kind of a temporary problem, the other one is a permanent problem. The temporary problem is that right now we have very little green hydrogen, right? Most of our hydrogen is gray at best, which means it's very carbon dirty. And so there's no advantage basically to using, in fact there may be a disadvantage to using gray hydrogen to fuel cars. Yes, the car itself may not be releasing CO2, but to get the hydrogen you had to release a lot of CO2. So until we have a green hydrogen infrastructure, it is pointless to drive a hydrogen fuel cell car from a climate perspective. But the permanent reason, which we've mentioned before, is a matter of physics. The EV batteries in EV cars are about 80% efficient in terms of converting energy into forward acceleration. Hydrogen fuel cell cars are about 40% efficient, and that's it. It's half as efficient, and it's not going anywhere. That's just the physics of converting hydrogen into energy. Why would we do that? Basically, the EVs are better. They're twice as efficient. They're always going to have that advantage over the hydrogen fuel cell cars. So I just don't think it's a good application. When we do make green hydrogen, we have a lot of other better things to do with it than burn it up in cars. So it doesn't matter. So this may be nifty, but it's completely irrelevant in my opinion.
Name That Logical Fallacy (1:35:12)
Topic: I've been reading "The Art of Thinking Clearly" by Rolf Dobelli, and I came across something that (I think) is questionable. This book is a cumulation of chapters about logical fallacies and issues in clear thinking that is right up your alley (although there is a book about that topic I like better, something like The Questioner's Map to the World, or something like that.....). The chapter is about the "Neglect of Probability". He details evidence that people basically discount probabilities. Studies of telling people they have a 50% chance of getting an electric shock, those in the study have the same amount of anxiety and fear as those being told they have a 5% chance of getting the shock, and so on down to 0% (where people's anxiety finally becomes zero). My issue is with one of the examples that he uses (at least I believe it to be his own example). I want to quote the whole paragraph because I think it's important. "To test this, let's examine two methods of treating drinking water. Suppose a river has two equally large tributaries. One is treated using method A, which reduces the risk of dying from contaminated water from 5 percent to 2 percent. The other is treated using method B, which reduces the risk from 1 percent to 0 percent, that is, the threat is completely eliminated. So, method A or B? If you think like most people, you will opt for method B-which is silly because with measure A, 3 percent fewer people die, and with B, just 1 percent fewer. Method A is three times as good! This fallacy is called the 'zero-risk bias.'" It could be that I'm missing something here, but is this not a terrible example? First, I'll be clear this is a hypothetical thought experiment. Nothing in the world is actually this clear-cut, so let's set that aside for a second. I completely understand that method A reduces the risk by 3 percent, which is a bigger decrease than method B, which is a one percent decrease, but is it not better to end at 0% than 2%? Getting cut off....
S: I'm going to do a quick name-net logical fallacy as well. This one comes from a listener who didn't leave his name. They write, I've been reading The Art of Thinking Clearly by Rolf Dobelli, and I came across something that I think is questionable. The book is an accumulation of chapters about logical fallacies and issues in clear thinking that is right up your alley. All right, he says the chapter is about the neglect of probability. He details evidence that people basically discount probabilities, studies of telling people they have a 50% chance of getting electric shock, those in the study have the same amount of anxiety and fear as those being told they have a 5% chance of getting the shock, and so on, down to 0%, where people's anxieties finally become zero. And then he gives an example of this. The example is, and this is the quote now, to test this, let's examine two methods of treating drinking water. Suppose a river has two equally large tributaries. One is treated using method A, which reduces the risk of dying from contaminated water from 5% to 2%. The other is treated using method B, which reduces the risk from 1% to 0%. That is, the threat is completely eliminated. So, method A or B? If you think like most people, you will opt for the method B, which is silly because with measure A, 3% fewer people die, and with B, just 1% fewer. Method A is three times as good. The fallacy is called the zero-risk bias. And then the emailer writes like, it could be that I'm missing something here, but is this not a terrible example? First, I'll be clear, this is a hypothetical thought experiment. Nothing in the world is actually this clear cut. So let's set that aside for a second. I completely understand that method A reduces the risk by 3%, which is a bigger decrease than method B, which is a 1% decrease, but it is not better to end at 0% than 2%. All right, so two things going on here I want to talk about. One is the fallacy that people ignore probabilities, and this is just how we assess risk. We tend to be risk-averse, and we tend to think of hazard as if it were risk. We've talked about hazard versus risk before. Hazard is a shark in a tank, but the risk only exists if you're in the tank with the shark. But people often fear just the potential of risk in a not mathematical or rational way. So those results don't surprise me. If you think there's a 1% chance of getting shocked, you're still going to be fearful. And you're not going to be reassured by the math. This is why people fear flying and telling them that how low the risk is doesn't always mitigate that fear. Although I do want to get in that research. I wonder, he said, and so on down to zero. But how low do did they go? Because what if you were told you had a one in a million chance of getting shocked? I think people would at some point you start to treat it as zero, right? Which is why when you're doing cognitive behavioral therapy for a fear of flying, you have to put the risk in such a context that it's functionally zero. For example, I happen to know that one statistic they use is, how long do you think you would have to fly every single day before you had a 50-50 chance of dying in a plane crash?
E: It's like hundreds of years.
S: It's 500 years. If you took a plane flight every single day for 500 years, you would have a 50-50 chance of dying in a plane crash. So that kind of statistic does reassure people at least they have something cognitively to hang on to, to say you just shouldn't worry about that level of risk. But I do think it has to be pretty close to zero before people are reassured by that. If you say, yeah, there's a 1% chance you're going to die, people are not very reassured. And that's partly rational depending on how bad the outcome is. But we do not evaluate risk mathematically or rationally. That much is true. The second thing, I sort of get where he's coming from and sort of criticizing that example. It's just a bad example. I'm not sure what the guy is actually saying. You know, if you treat one tributary, the risk goes from five to two. If you treat the other one, it goes from one to zero. What does that mean? How is the starting risk changing?
C: Yeah, it's weird.
S: I think this is how I'm trying now to sort of retcon it to make it make sense. He's saying there's two major tributaries. I think he's saying, let's assume that one tributary is going to City A, and in that tributary the risk is 5%. The other tributary is going to City B, and then that one has a risk of 1%. You can only treat one of these two tributaries. Are you going to take the risk down from five to two, or are you going to take it down from one to zero? If you reframe it that way, then it makes sense.
C: Yeah, then of course you go five to two.
S: Yeah, you go five to two because it's a 3% decrease. But there's zero risk bias. People say, well, going down to zero is better because it's zero risk. They're looking at the end result and not the decrease. So that makes sense. Now, of course, all of these ethical thought experiments are massively contrived because they're trying to isolate a variable to see how people behave, like the trolley experiment. So, yes, don't worry about the fact that it's contrived. But I do think the description that he's quoting is bad. It doesn't really make sense. And so that's how I would retcon it to make it make sense. And I think that's the context in which the point that they're making is people are more likely to look at the second number than the change. Getting down to zero is better than getting down to two, rather than thinking a 3% degrees is better than a 1% degrees. But these are just cognitive biases. These are the ways in which our brains do not process information in a completely logical and unbiased way. All right, guys, let's move on to science or fiction.
Science or Fiction (1:41:19)
Theme: None
Item #1: A new study finds that adrenaline autoinjectors are not effective in preventing death due to allergic anaphylaxis.[6]
Item #2: A recent review finds that atmospheric mercury pollution has increased by 20% in North America from 2005 to 2020.[7]
Item #3: Researchers find evidence that persistent viral infection with SARS-CoV-2 following clinical COVID may be responsible for some cases of long COVID.[8]
| Answer | Item |
|---|---|
| Fiction | Item #2 |
| Science | Item #1 |
| Science | Item #3 |
| Host | Result |
|---|---|
| Steve |
| Rogue | Guess |
|---|
Voice-over: It's time for Science or Fiction.
S: Each week I come up with three science news items, or facts two real and one fake, and then I challenge my panel of skeptics to tell me which one is the fake. By the way, we were talking about the collective names for skeptics, and one of our listeners pointed out that every week I refer to you guys as a panel of skeptics, so maybe that's what we should go with.
E: I like it. I'm used to it.
B: Very comfortably bad.
S: Alright, here we go. Item number one. A new study finds that adrenaline auto-injectors, like EpiPens, are not effective in preventing death due to allergic anaphylaxis. Item number two. A recent review finds that atmospheric mercury pollution has increased by 20% in North America from 2005 to 2020. And item number three, researchers find evidence that persistent viral infection with SARS-CoV-2 following clinical COVID may be responsible for some cases of long COVID. I think Bob wants to go first.
E: You were going to choose him first anyways, I think.
S: Nope, I did just use the noise method.
E: We hate that method.
B: I've been so good.
S: Gotta keep you on your toes.
B: Yeah. Let's read these. They did not, like, suffuse into my cortical tissues. A new study finds that adrenaline autoinjectors are not effective in preventing death due to allergic anaphylaxis. What? So is it a problem with the autoinjectors or is it a problem with the adrenaline? Allergic. Can you elaborate on anaphylaxis again?
S: Anaphylaxis is an allergic reaction where your throat closes up and you can't breathe.
E: The bee sting.
S: Yeah.
B: It's not fun.
S: Right. And the adrenaline autoinjectors, again, these are like the EpiPens.
B: Yeah, it does make a lot of sense to me. Let's see, let's try two. A recent review finds that atmospheric mercury pollution has increased by 20% in North America. 2005 to 2020, what would call it? I mean, is that even a thing, atmospheric mercury pollution? I can't really remember hearing about mercury pollution. That sounds nasty. And now you're telling me it may have increased. And I have no idea. Researchers find, let's go to three, researchers find that evidence, find evidence that a persistent viral infection with SARS-CoV-2 following clinical COVID may be responsible for some cases of long COVID. So a persistent viral infection with SARS-CoV-2.
S: Do you understand what that's saying?
B: I'm not getting SARS-CoV-2.
C: I mean, they continue to test positive.
S: SARS-CoV-2 is the virus that causes COVID, right? So you have COVID, you were infected with SARS-CoV-2, a year later, you have long COVID symptoms. And you have actual still have virus in you at that time, rather than it being due to something else that's causing the long COVID.
B: One and two are kind of annoying. I'll just go with SARS-CoV-2. Oh, wait, no, I think that's science.
C: You're doing the thing I do. You gotta find the fiction.
B: I gotta flip this. What time is it? It really feels like it's two in the morning to me right now.
E: Depends where you are on the planet, Bob.
B: I'm gonna go with the adrenaline auto injectors and anaphylaxis fiction.
S: Okay, Cara.
C: Yeah, I think that the long COVID one might be science. I think we see that sometimes with Ebola, like it stayed in people's eyeballs sometimes long after infection.
B: What a scary sentence that was.
C: I know. But definitely I think there's precedent here that there are like reservoirs for viruses long after they cause any symptoms. So then it's between the, basically the EpiPens not being effective in preventing death. Versus atmospheric mercury, mercury increasing by 20%. Like the EpiPen one feels obvious, which is why I think it might be science. And the mercury one, like what causes atmospheric mercury?
B: Yeah, good question.
C: Yeah, and I feel like we've known mercury is really bad for a long time. So maybe we've actually done a good job at not having more mercury in the in the atmosphere. I don't know. So I think I'm going to switch gears. The only thing I can think of is maybe EpiPens are really good at reducing symptoms, but if somebody is going to have sudden and intense anaphylaxis and it's going to cause death, like maybe there needs to be another mechanism there or maybe they don't they don't work fast enough or something like that. I'm not really sure. There's got to be something there. So, yeah, I think I'll go with the Mercury being the fiction.
S: OK, Jay.
J: Yeah, I'm kind of agreeing with Cara. Like I when I first read this, I'm like, well, so, yeah, North America is Mexico, U.S. and Canada. So I would think that we have government oversight regulations and all that. If anything, it's got to be it's got to have gone down, not up. I just this one just seems wrong to me. And I think if if there was an increase, there would be health effects and we'd be hearing about it much more than nothing. So I think that one is fiction.
S: Okay, and Evan?
E: I think I'm inclined to agree. The one about the EpiPen, though, not effective in preventing death due to allergic anaphylaxis. I was thinking about that one. Maybe if it doesn't prevent death, it slows the death that does come on so you can get to a hospital and they can give you other drugs in time, maybe? Because I can't imagine these things are totally useless.
C: Wait, it slows death?
E: Why do we have them at all?
C: Did you say it slows death?
E: Well, in preventing death slows the time in which you will die, right? In other words, if you're going to die, if you get stung or whatever, in whatever, an hour or something, but maybe the EpiPen, instead of preventing the death from happening, it prolongs that time to four hours.
C: Well, that's terrifying.
E: Well, but it gives more time to respond to it. I'm trying to think of why this one might be science. And that's kind of what my brain went to. And the COVID one, yeah, I think that one's science. That just leaves the Mercury one as the fiction.
S: All right, so you guys all agree on the third one, so we'll start there. Researchers find evidence that persistent viral infection with SARS-CoV-2 following clinical COVID may be responsible for some cases of long COVID. You guys all think this one is science, and this one is science. This is science. Yeah, not too surprising, although it didn't have to be the case because you can get post-infectious syndromes and symptoms without having persistent infection. But it's possible for infections to persist after... It's not like we have a way to eradicate the virus. Basically, the infection runs its course. Your immune system eventually deals with it. But it can... It's not implausible that it hides away somewhere and is sort of still there in the background causing symptoms. And being kept at bay, but not being completely eradicated in some people. Now this hasn't been proven, which is why I said there's evidence that it may be the case, but it's not definite. What they found was, they found a higher rate of proteins, of SARS-CoV-2 proteins, of spike proteins or nucleocapsid proteins. In people who have long COVID versus people who had COVID but don't have long COVID, does that make sense? So why would it be, why would it exist at a higher rate in people with long COVID? You know, evidence of active virus unless there was a causal relationship there. So it was 43% versus 21%, so it was basically twice as likely to have these viral proteins if you have long COVID. But that could also just mean that they had a more severe infection, or it could mean something else too. But that's a reasonable interpretation that they may have persistent virus. Okay, let's go back to number two. A recent review finds that atmospheric mercury pollution has increased by 20% in North America from 2005 to 2020. Bob, you think this one is science. Everyone else thinks this one is the fiction. I was a little surprised that none of you have any idea where mercury comes from in the atmosphere. Any guesses before I tell you what the sources are?
B: Industrial processes.
S: Industrial processes. What's number one? Coal-fired plants, right? It's in coal. It gets released when you burn coal. And mercury exposures is higher among people who live near coal plants. So coal-fired plants is one, residential coal burning or other industrial coal burning, other industrial processes, waste incinerators, mining for mercury, gold, and other metals where mercury might be present, cement production, because again, mercury contaminants can be in the stuff. And you have to heat it up and that releases the mercury into the air. So there's lots of sources. So the question is, in North America over the last 20 years, this is basically before COVID, do you think that all of these processes have been increasing or decreasing? And the answer is we didn't know, which is why one of the reasons why they wanted to track this sort of review of the stations that monitor mercury in the atmosphere to kind of answer this question is, is what we're doing working? Are we releasing more or less mercury over time? And the results showed that this is the fiction. It's actually decreased by 20%.
C: So even though it comes from coal?
S: Yeah. Well, we are reducing our coal burning. It is coming down.
E: That's why North America is specific.
S: North America is. Yeah, this is not China. I imagine it's increasing over there. In fact, I remember that when we got rid of the thimerosal in vaccines, which contains mercury, and yet autism rates continue to rise. Some of the proponents of autism is mercury poisoning hypothesis said, well, the reduction of thimerosal in vaccines is being offset by fumes coming from Chinese coal-fired plants. You know, it's like, really? Really, dude? That's what you think is going on? It was all just massive special pleading. But anyway, this is good news. It was actually decreasing. We need to decrease it more because mercury is one of those cumulative toxins, and it is this sort of stated goal of the EPA to have a zero tolerance for any mercury. Their goal is complete elimination of mercury contamination in the environment and human exposure to mercury. Like, there isn't considered any safe amount, you know what I mean? The goal is always zero. Okay, let's go on to number one. A new study finds that adrenaline autoinjectors are not effective in preventing death due to allergic anaphylaxis. This one is unfortunately science. You know, a couple of things that you guys noted is basically correct. It does treat the symptoms of an allergic reaction. It's just not enough to prevent anaphylaxis. So if you're going to die from anaphylaxis, the EpiPens are not going to stop it.
C: Is it because they need more adrenaline?
S: Yes.
E: Does it slow it down?
C: The dose is too low.
S: Well, even if it does, Evan, so the other way they looked at this, they looked at this two ways. So one way is just to look at the rate at which people are dying from anaphylaxis before and after the introduction of EpiPens, and there was no decrease. So the existence of EpiPens have not decreased death due to anaphylaxis. Which is rare, to say that as a baseline. It is pretty rare that you die from anaphylaxis, but it hasn't decreased that rate. And then they also, this is the new bit really, they did studies looking at the amount of adrenaline that you get into epinephrine that you get into your blood, like the blood levels after an autoinjector. And it does get up to a decent amount, but only very briefly. The blood levels get up to the amount that you need to get it to in order for it to have an effect, but it's there very briefly. And what the evidence shows is in order to prevent death from anaphylaxis, you basically need intravenous epinephrine.
E: Ooh, like constant feeding.
S: Sustained over time.
C: Yeah, because you need to, if the allergen is still there.
S: Yeah.
C: Yeah.
S: Yeah. So without IV adrenaline, and of course if you're giving somebody IV adrenaline, you need to keep that from bottoming out their blood pressure and other, you need to like support them with fluids and everything. You need to be in a hospital setting getting IV fluids and IV adrenaline in order to prevent death from anaphylaxis if you're going to die from it. The EpiPens will not stop that from happening.
C: What if you have multiple?
S: Yeah, I mean that's not going to be enough.
C: I know, but while you're being transported or while you're waiting for EMTs, EMTs can put a line in.
S: It may be like if you have like here's 10 of them, like every five minutes give yourself another one or whatever until you get into the ambulance. I do remember hearing a story like 10 years ago, this is a clinical case of a like 10 year old girl who's allergic to peanut butter. And she accidentally ate peanut she ate her friend's sandwich or whatever, didn't realize it had peanut butter in it, and had an anaphylactic reaction. And her parents were there, and they gave her two injections with the EpiPen, but it didn't matter. She died anyway, before she could get to the hospital. What a nightmare that would be. But I remember thinking about that, like, really, two EpiPen injections didn't do it? But this is why. You would need way more than that. So maybe we need to reformulate them, increase the dose, re-educate people. You really need to have a bunch of these on you. One epipen is not going to save your life.
C: Or maybe some sort of—you know, not a Dexcom, because that's a different drug, but maybe some sort of thing that you stab into your arm that gives a flow for a certain period of time.
S: Something.
C: Yeah, like a sustained flow.
S: It's just not as easy as just like you press a button and you get a shot. But it's something that people can use in the field. And you have definitely—first off, your first thing to do is call an ambulance, right? Because that's your only chance of really surviving is getting into medical care as quickly as possible. But yeah, so I don't know if this is going to lead to a sort of a—either we think it's really not worth it, you just got to get to the hospital really fast, or like we have to really reformulate how like the dose and the mechanism that these things are given because they're just not working. Again, maybe if it's subfatal, sure, it might, it helps, but it doesn't prevent, it's not good enough, it's not enough to turn a fatal reaction into a non-fatal reaction, or apparently to help people to get to the hospital because if that was happening, we would have seen the numbers coming down, and they're not.
C: Yeah, it's not long enough. And maybe people have a false sense of security after they use it.
S: That's a very good thought.
C: So maybe education as well.
S: Yeah, it may be that people think I have my EpiPen. I gave myself the EpiPen, so I'm good. And they're not as panicked as they really should be, or yeah, that false sense of security can be fatal.
B: So what they need to do is they need to sell EpiPens and on the EpiPen it says, panic.
C: Once you use it, it's like call 911 immediately.
S: Well, I mean if you have an EpiPen, you are getting that as a prescription from a doctor who's had a conversation with you about this. And so just to make sure that doctors understand how they need to be educating their patients, like what an appropriate reaction is. Like as soon as you think you may have been exposed to something you're allergic to, you take an auto injector, you call 911, it's an absolute emergency. Do not think that the EpiPen's automatically going to save you. You may need to have multiple on you, or you may go unconscious so your friends need to know what to do. Yeah, it is a life-threatening emergency.
E: Yeah, EpiPen is not an antidote.
S: It's not for fatal anaphylaxis, which is just a very, such a scary thing to have to live with. Oh my goodness. The best thing to do is to figure out how to make people not have allergic reactions.
C: Yeah, to engineer. Yeah, yeah.
S: We know for some things you could do allergy shots. You know, they slowly build up immunity to the thing. Because again, an allergic reaction is different than a regular immune reaction. You know, it's IgE instead of IgG, and so you can build up, like, through vaccines, and you could build up an IgG reaction to the thing, and those actually block the IgE antibodies. That's how that works. Your regular immune reaction blocks the allergic immune reaction. But of course you have to build it up very slowly because you'll give people the full access if you gave them an actual vaccine without a full dose.
C: But at least they're in the hospital. Or the doctor's office.
S: You get that first dose in the doctor's office, right, with lots of observation.
J: You bet that, though.
S: But also, they're working on, like, genetically engineering peanuts so they don't have the allergen in them. Just allergy-free peanuts. That would be massive.
B: Yeah, but what does that mean for peanut butter? Is that going to be, like, changing it?
S: What if it was slightly less delicious, Bob? Would you be okay with that?
B: Depends how slightly. If lives are saved.
J: Bob, don't lie. Come on.
Skeptical Quote of the Week (1:59:20)
"The important thing is not to stop questioning. Curiosity has its own reason for existing."
– Albert Einstein, (description of author)
S: All right Evan, give us a quote.
E: All right, a quote. I went to Chat GPT for the quote. You know what I typed in? Can you find for me a famous quote about the scientific method? That's what I typed in. And it gave me a result. And here it is, courtesy of Chat GPT. "The important thing is to not stop questioning. Curiosity has its own reason for existing." Albert Einstein. And they added this, they said, this quote reflects the core principle of the scientific method, emphasizing the importance of constant inquiry, curiosity and the pursuit of knowledge through observation and questioning. That was the complete response I got.
C: So how do you feel knowing that ChatGPT's top pick for you was a quote that after a thousand episodes, you didn't find yourself?
E: I did have to go back and see. I couldn't find evidence where we'd used it ourselves before. And yeah, this doesn't ring familiar to me, this particular one. I'm happy. I'm always happy to learn new quotes.
S: What I'm finding says that this quote is attributed to Albert Einstein.
E: Oh, here we go. ChatGPT.
S: No, I think it seems like it's probably legit, but I don't know how certain it is, because why would you say attributed to Albert Einstein, if there was any doubt? I don't know. I might have to look into it a little bit more deeply.
J: In general, quotes are a mess.
C: Yeah, but they use that language a lot in, like, etymology research. So I don't know if attributed to, by definition, means that there's not evidence.
S: It doesn't. It doesn't. It was a little red flag for me when it was stated that way. But quotes do tend to attach themselves to more famous people than the people who actually said them.
E: Yeah, I know. And when it came back with Albert Einstein, I did have to pause.
S: That's just that. Just Albert Einstein is another little red flag. We'll see. We'll vet it out some more. Cursory vetting seems to hold up, but yeah, I would be a little questioning about it.
E: It's interesting if you could if ChatGPT can't give you the firm, the definitive answer.
S: It gives me wrong answers all the time.
E: Yeah, it does, doesn't it?
S: It does. It's not there yet in terms, you have to vet the answers that it gives you. It also gives me outdated answers.
E: I've seen that as well.
S: Like, okay, this was true five years ago, but it's no longer true. Like especially, like recently I asked it, what's the percentage of electricity in the US that's generated by solar panels? And it gave me a five-year-old answer which I happen to know was wrong. It has a problem with hallucinations and it has a problem with it just reflecting what's out there on the internet and that may be outdated what's rising to the top. It's not-
E: Still a work in progress, I suppose.
S: Yeah. But we're right on the cusp of these applications transforming medicine. They were developing medical application versions of it.
E: It's hard to think of a better use of it.
S: It's perfect, perfect use of it. You know, filter, go through these hundred thousand studies and tell me, give me a summary of this one little question that I have, you know.
B: I can't wait for that, sure, man.
E: That would otherwise take people 9,000 years collectively to figure out.
S: Well, I mean, from a pragmatic matter, like if I have a specific clinical question, it could take me an hour or two or longer to like really find out what the definitive current answer is. If something could give me that answer in two minutes or 20 seconds versus hours, then that's something that I could use right at the point of patient care and I could also do a lot more of that. So, yeah, the potential to improve those kinds of professions is massive, but we have to make sure that the applications are designed to not just spit out regenerated answers that may or may not be true without showing their work and giving citations or whatever. So that's what's happening now is sort of reformulating these applications so that they do do that. They like actually give you real citations, not made up citations, and links to that, so that you can verify what it's saying because you need to be able to do that to act upon it. All right. Well, anyway, thank you all for joining me this week.
J: You got it, Steve.
B: Sure man.
C: Thanks Steve.
E: Thanks Steve.
S: —and until next week, this is your Skeptics' Guide to the Universe.
S: Skeptics' Guide to the Universe is produced by SGU Productions, dedicated to promoting science and critical thinking. For more information, visit us at theskepticsguide.org. Send your questions to info@theskepticsguide.org. And, if you would like to support the show and all the work that we do, go to patreon.com/SkepticsGuide and consider becoming a patron and becoming part of the SGU community. Our listeners and supporters are what make SGU possible.
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- ↑ www.nobelprize.org: Press release: The Nobel Prize in Physiology or Medicine 2024 - NobelPrize.org
- ↑ www.nobelprize.org: Press release: The Nobel Prize in Chemistry 2024 - NobelPrize.org
- ↑ www.nobelprize.org: Press release: The Nobel Prize in Physics 2024 - NobelPrize.org
- ↑ theness.com: Fruit Fly Connectome Completed - NeuroLogica Blog
- ↑ www.christianpost.com: Shroud of Turin stains consistent with Christ's torture: study
- ↑ onlinelibrary.wiley.com: None
- ↑ No reference given
- ↑ www.clinicalmicrobiologyandinfection.com: None