Science Friday - New Mask Rules, Pain Algorithm, Assorted Nuts, Muldrow Glacier. May 14, 2021, Part 1

Episode Date: May 14, 2021

Fully Vaccinated Can Unmask Often, CDC Says As the number of vaccinated Americans continues to rise and evidence mounts that the vaccines may reduce viral transmission in addition to lessening disease severity, the CDC announced Thursday that fully-vaccinated people may be able to go mask-free except in specific crowded indoor situations. The announcement caused celebration in some circles and anxiety in others, with people wondering how the new guidelines fit into their personal risk assessments. Sarah Zhang, staff writer at The Atlantic, joins Ira to talk about the latest news in the pandemic and beyond, including a WHO committee report discussing the early days of the outbreak, the latest on the Colonial gas pipeline shutdown, research into cats’ love of sitting in boxes, and more.   Can An Algorithm Explain Your Knee Pain? In an ideal world, every visit to the doctor would go something like this: You’d explain what brought you in that day, like some unexplained knee pain. Your physician would listen carefully, run some tests, and voila—the cause of the issue would be revealed, and appropriate treatment prescribed. Unfortunately, that’s not always the result. Maybe a doctor doesn’t listen closely to your concerns, or you don’t quite know how to describe your pain. Or, despite feeling certain that something is wrong with your knee, tests turn up nothing. A new algorithm shows promise in reducing these types of frustrating interactions. In a new paper published in Nature, researchers trained an algorithm to identify factors often missed by x-ray technicians and doctors. They suggest it could lead to more satisfying diagnoses for patients of color. Dr. Ziad Obermeyer, associate professor of Health Policy and Management at the University of California, Berkley joins Ira to describe how the algorithm works, and to explain the research being done at the intersection of machine learning and healthcare.   Ever Wonder Why Big Cereal Chunks Are Always On Top? You may not have heard of it, but you’ve probably seen the “brazil nut effect” in action—it’s the name for the phenomenon that brings larger nuts or cereal chunks to the top of a container, leaving tinier portions at the bottom of the mix. But the process by which granular materials mix is weirdly hard to study, because it’s difficult to see what’s going on away from the visible surfaces of a container. In recent work published in the journal Scientific Reports, researchers turn the power of three-dimensional time-lapse x-ray computer tomography onto the problem. By using a series of CT scans on a mixed box of nuts as it sorted itself by size, the researchers were able to capture a movie of the process—finally showing how the large Brazil nuts turn as they are forced up to the top of the mix by smaller peanuts percolating downwards. Parmesh Gajjar, a research associate in the Henry Moseley X-ray Imaging Facility at the University of Manchester, talks with SciFri’s Charles Bergquist about the imaging study, and the importance of size segregation in mixing of materials—with applications from the formation of avalanches to designing drug delivery systems.   This Alaskan Glacier Is Moving 100 Times Faster Than Usual One of the glaciers on Alaska’s Denali mountain has started to “surge.” The Muldrow Glacier is moving 10-100 times faster than usual, which is about three feet per hour. About 1% of glaciers “surge,” which are short periods where glaciers advance quickly. Geologist Chad Hults has been on the glacier to study it during this surge period. He talks about how the glacier’s geometry and hydrology contribute to this surge period.           Subscribe to this podcast. Plus, to stay updated on all things science, sign up for Science Friday's newsletters.

This is Science Friday. I'm Ira Flato. A bit later in the hour, we'll be talking about an AI algorithm studying pain, and a story that may drive you to nuts. But first, last week, we anticipated the impending green light for use of the Pfizer-COVID vaccine in adolescence. That approval came through this week, and people aged 12 to 15 can now get the vaccine. This week, the CDC changed its guidance on the coronavirus somewhat, officially acknowledging what many people have been taking for granted. The airborne nature of the virus is spread. And the CDC said that fully vaccinated people might be able to go mask-free in many situations. In fact, CDC head Rochelle Wollenski spoke at a White House briefing yesterday. Anyone who is fully vaccinated can participate in indoor and outdoor activities, large or small, without wearing a mask or physical. physical distancing. If you are fully vaccinated, you can start doing the things that you had stopped

doing because of the pandemic. Here to talk about that and other short subjects in science is Sarah Zhang, staff writer for the Atlantic. Hi, Sarah. Hi, Ira. Thanks for having me. So the big COVID news this week for many people is probably this guidance that masks might not be necessary for fully vaccinated people. Help us unpack that a bit. So the CDC says that if you're vaccinated, you don't have to wear a mask indoors. Though there are a couple exceptions. The couple exceptions are doctors' offices and public transportation, because these are places where your risk of exposure might just be higher.

From the biology itself, this makes a lot of sense. We know right now that if you are vaccinated, you're very unlikely to get sick. If you do get sick, you're very unlikely to get seriously sick. So it is quite clear that if you're vaccinated, the risk to yourself is just very very low at this point. I think where the remaining questions are and where people might, you know, still have some hesitations, is that a lot of parents, their kids might still not be vaccinated or if they're young, they might not be vaccinated for a long time. So I think one thing to think about this is the floor, right? Like cities and local governments and even individual stores might have still have

mask guidances and, you know, we'll still have to follow this. I guess there might be a lot of trust involved here because the challenge is that now you won't know if someone is in fact, or just refusing to wear a mask? Yeah, that's right. And I think that's what people who, you know, still have unvaccinated people in their lives are worried about, right? The risk is certainly not zero just because even in very small cases, you can be vaccinated and still perhaps get it asymptomatically.

But it does still reduce the risk a lot. And, of course, if you're still worried, feel free to keep wearing a mask. Of course. And in other COVID news, a committee of the World Health Organization issued a report on what went wrong last year in the early stages of a global response to the disease. And it called February 2020, quote, a lost month. Tell us why that is. Yeah, yeah. Well, if you remember back to February 2020, I think those of us in the U.S., we were seeing all these countries in Asia lockdown. We had a lot of questions about how are these going to affect our supply chains and no one

here was really thinking that much, oh, this is going to happen to us. So this report for It's an independent panel set up by the WHO to kind of assess how we did. And looking back over the year, I think we can all agree that we could have done a lot better. The panel has a bunch of specific recommendations for what the WHO could have done. But I think the big takeaway for me, the big conceptual takeaway, is the precautionary principle, which is that we should act even before we are perfectly certain of something. And so remember a year ago, we had all these questions, is this virus spreading from person to person? Is it spread in asymptomatically? Is it airborne?

And it took some time for the evidence to really accumulate 100%. And so when you're acting on a pandemic, you sometimes need to act before you know it's 100%. Maybe it's starting to look likely that is asymptomatic. Okay, we need to start making sure that people are wearing masks and shutting down even before we're 100% sure of that. Yeah, don't assume anything. Take precautions in advance.

In other news, let's move on. It appears that the colonial pipeline has paid that ransom to get its pipeline back online? Yes, that's right. Is there a gas shortage or is there not? It sort of seems it depends on where you live. It does seem to depend on where you live. So the colonial pipeline carries about half the fuel on the East Coast. So it was shut down for about a week. I think they just turned it back on on Wednesday. So that did cause a bit of a supply crunch. But the other thing that was happening is that people were hearing about the hack, people were going out and filling up their cars and then there were lines at the gas station.

Then people were saying, oh, there are lines of the gas station. The gas stations are running out. I have to go to the gas station. So suddenly you had this huge spike in demand as well. The problem was not that there was not enough gas on the East Coast necessarily, but just that it was not in the right pieces what people wanted it at this moment.

Yeah, because there were some photos of people filling up just about any plastic container they could get their hands on. Yes, which you should not do. No. Did this not sort of remind you of the great toilet paper crisis? from last March? Yeah, yeah.

You know, in the same way, is that it wasn't like that there was necessarily a lack of toilet paper in the world. It was just about getting the right toilet paper to the right people who wanted it at this exact time.

Suddenly everyone wanted it at the same time and then you had a problem. Did they actually figure out what was behind this pipeline problem? The pipeline itself was actually not hacked. What happened was that the company that runs the pipeline was hacked.

It's called a ransomware hack. They basically stole their data and said if you, unless you pay, us this money, you can't do anything. The reporting suggests that it actually took down their billing system. And of course, the company was worried that they were able to run anything, so they kind of just shut everything down for several days. The kind of ironic thing is that I think they actually paid the ransom, but kind of unlocking their data took so long that it was faster

for them to just go back and restore through their backups. It's kind of an ironic little twist. Yeah, this is sort of portends bad news for any other infrastructure. Yeah, you know, all of our, you know, our electrical plants, you know, these are all really vulnerable to hacks like this. Yeah, not only that, but our financial institutions, banks, maybe the stock market. Yeah, these ransomware hacks have actually been happening, usually to smaller institutions, but they've been happening at hospitals, local institutions, at local city governments. And a lot of times, you know, if you're a small, I think in this case, the government got involved in Kloyal Pipeline's a pretty big company. But if you're a small company, it's sometimes you just have to pay up because that seems like the easiest way to deal with it.

Like the hospitals. Let's move on to other news. There's important old seed news this week. Old seeds. Old seeds have sprouted. So the reason this is important is because this is actually part of a 140-year experiment to see how long seeds can last. So back in 1879, a botanist by the name of William James Beal had actually hidden thousands of seeds around Michigan, he literally buried them in a secret spot. And the idea is that he and his future colleagues every 20 years will go out and dig up these seeds under the cover of dark and then plant them and the sea if they will still sprout. And so this latest excavation happened this past April, and some of these seeds have sprouted about a dozen of them so far. Wow. What kinds of seeds are we talking about here? Yeah. Well, the funny thing is that it seems like all of the seeds have sprouted

have all belong to one species, that's something called a mothamolin, which is like a flowering herb. The slightly bad news is that they are an invasive species. So now that we know that they can stay on the ground for 140 years and still germinate, that's not great. No, no, well, that might explain why they're so invasive, too. Exactly. You have a story about an aquarium favorite, the betafish, the Siamese fighting fish, you know, those brightly colored fish kept in little round bowls. Tell us about that. Yeah, so these fish were initially domesticated in Southeast Asia, where they're native as you said fighting fish, right?

They're kind of like an aquatic version of cockfights that people like to watch and embed on. And so scientists were actually really interested in studying the genetics of aggression in these fish because they're easy to keep in the lab, especially compared to something like a bull or a chicken that might be bred for aggression. A better fish is a lot easier. And so they went and looked at the DNA of these domesticated betterfish. and I actually found something unrelated that was surprising,

which is that they have a sex gene that is not found in the same way in wild bed of fish. So this might seem like, you know, they have a sex gene that's pretty normal. But what's unusual about fish is that the way they become male or female is just so varied and so diverse and far we think of as humans. Sometimes it's based on temperature. Sometimes if it's all males, one of the males will turn into a female. So we don't really know what's going on in wild beta fish. but in the process of breeding better fish that are more beautiful, maybe better fighters,

we seem to have also accidentally given them a sex gene. Wow. Who knew? I guess now we know, right? Now we know. Yeah. No one set out looking for it, but there it was. So they don't know what accounts for the sex and the wild benefish? No, it might be more than one factor. You know, sometimes breeders have talked about maybe temperature affecting whether it's going to be a mostly female. or mostly male brood. But it might be multifactorial.

There are other, not better fish, but there are other species of fish out there that have kind of similar chromosomes to humans and X, Y. Others have similar chromosomes to birds. Some have both at the same time. It's just there are dozens and dozens in ways in which sex is determined in fish. Really interesting. Sticking with our potential pet theme this morning, there's a cat psychology news. The whole cats in boxes effect.

Tell us about that. Yeah, so we probably all know that cats love boxes and shopping bags and suitcases. And the theory is that these boxes just feel safe, right? They're kind of like swaddled and like sheltered from everything else in the world. But what's also interesting is that cats also love to sit on flat objects, like 2D objects. Like if I leave a sweater on my bed, my cat will always sit on the sweater, which is annoying because then sometimes I want to wear the sweater. or if there are videos where if you take a scarf and you make a circle on the ground, the cat will go and sit inside that circle.

Or even if you just tape a box on the ground, the cats would go and sit inside that box. So the latest wrinkle to this, scientists actually did a study, where what happens if you don't even have a box? You just have an illusion of a box. This is an optical illusion where you take a circle, kind of cut out some wedges and arrange them. So it looks like there's a box. box, but it's not actually enclosed. And it seems like even then some cats will go sit in this

illusory box. 500 cats were participated. Only 30 made it through, as you can imagine, being cats. But it does seem like that they're fooled even by this optical illusion. Wow. So we've now given all our listeners something to do this weekend with their cats. Make that illusion. Exactly. Thank you very much, Sarah. Thank you. Sarah's acting staff writer for the Atlantic. One way to come back. how computers pay closer attention to what patients say about knee pain than doctors do. And so the algorithm is expanding our repertoire for understanding the kinds of things that cause pain in the knee by listening to the patient. Stay with us. This is Science Friday. I'm Ira Flato.

Much of medical diagnosis now depends on artificial intelligence intended to help the doctor find out what is wrong with you. For example, research shows that computers can spot potentially deadly skin cancers better than doctors. And now a new computer algorithm is able to help diagnose knee problems that doctors may have missed, especially in people of color. Researchers trained an algorithm to see something that x-ray technicians and doctors are not seeing, and it's leading to more satisfying diagnoses for those patients. Here to explain is Dr. Ziyad Obermeier, Associate Professor of Health Policy and Management at the University of California, Berkeley, whose research was published in nature. Dr. Obermeier, welcome to Science Friday.

Thank you. It's great to be here. So nice to have you. Tell us, why did you decide to train an algorithm to read knee x-rays? Pain is such a huge problem in our society, as we all know, from the ravages of the opiate epidemic. And so it seemed like a really interesting use for algorithms was to try to get them to make headway on what might be causing pain and what might be accounting for it so that we could try to develop better solutions for it. So imagine a patient coming in to your office as a doctor with knee pain. What you do with that patient and how you think about them is going to really depend on whether or not their knee pain is rooted in causes of pain in the knee or might be, for example, the manifestation of stress or anxiety or depression. And so

The fundamental question is, when I do an x-ray of the knee, do I find something in the knee that I need to manage by sending them to an orthopedic surgeon or to a physical therapist, or do I pursue other lines of reasoning? So the x-ray really helps the doctor hone in on causes of pain inside of the knee. Now, the problem is that if we look at the patients who are coming in with knee pain, it's more likely if that patient is black for the x-ray to come back looking normal. and for the doctor to then think, well, there's nothing in the knee, I'm going to pursue some other options for helping this patient with their pain. What our algorithm show it is that some fraction of those patients and a larger fraction of patients, if they're black, actually have some definable cause of pain in the knee that the doctors aren't seeing.

And as a result, they're pursuing other directions rather than focusing in on the knee. And what does the algorithm actually do? What does it look for? How does it work? The algorithm learns from the patient's experience of pain, not necessarily the doctor's medical knowledge. So our knowledge about the particular problem we studied, which is arthritis in the knee, comes from studies that were done in the 1950s on coal miners in Lancashire, England. And so that was the source of our knowledge about arthritis.

And so if the doctor's medical knowledge is built on certain populations who are largely white and male and only learns about the causes of pain that affect those populations, they're not going to apply to patients who are not white and male and living in Lancashire in 1950. And so the algorithm is expanding our repertoire for understanding the kinds of things that cause pain in the knee by listening to the patient and correlating the patient's pain report to features of the X-ray. That's amazing.

So that that truly is a very narrow population, is it not? It's a very narrow population on which this huge foundation of medical knowledge is built up. And I think that's just the way we have historically produced medical knowledge. We need doctors to look at individual patients, look at their x-rays, try to figure out what's going on. So I think that's the, it's a very characteristic of the human way to produce medical knowledge. And one of the things that I'm most excited about is deploying algorithms to do that exact same thing, but at much larger scale and hopefully in a much more equitable way. When you say equitable, what do you mean by that? One of the key strengths of our study, which was led by my co-author Emma Pearson, who's a professor at Cornell,

is that we learned from a very diverse population of people. So the algorithm didn't just look at one study population in one hospital. We had the benefit of a huge study that was sponsored by the National Institutes of Health that enrolled of really diverse, large population of patients from across the U.S. And so the algorithm was able to learn from the experience and the x-rays of a really, really diverse set of patients. And that was the secret to the algorithm seeing things that radiologists had missed in these earlier studies. And I understand that you call this a tool for justice. Why do you say that?

I think a lot of times when we build up medical knowledge or even when we see patients, we only pay attention to certain things. And in this case, I think it's not necessarily the doctor's fault that medical knowledge was built up in this very specific way from this very specific population of patients. And so my hope from this algorithm, but from a lot of other places where we're starting to see algorithms being used is that they can learn from that huge cross-section of society and listen to the experiences and the pain of very many different groups of people. And that's the first step to, you know, making that pain and that experience visible is the first step to helping those people.

We can't help people whose experience we don't see or pay attention to. And I think that algorithms can be really helpful in highlighting exactly those experiences so that we can help. When you say people that the experiences don't pay attention to, you're talking about people of color, I imagine, because those people were probably not included in the original dataset. Yeah, I think that's exactly right. And I think I can just walk through maybe a concrete example, which is that say a patient comes into your office and you're the doctor with knee pain, you might examine the patient and then send them for an x-ray. And if that patient is black, that x-ray is more likely to come back as looking normal, even though that patient is in severe pain. But that's in part because what we consider normal doesn't capture the experience of people of color, of socioeconomically and education. less privileged people. And so the algorithm by seeing the causes of pain in those groups can

actually help the doctors see, oh, no, there really is a problem in this person's knee. And maybe they need to go see an orthopedist, not go see a therapist or some other modality of treatment. So the algorithm can point to something in the knee, but it can't tell you what the problem is. Yeah. So the algorithm can point to the parts of the x-ray that look like they connect to the patient's pain, but that's it. It's just going to tell you, look there. And that's why I think a doctor or a researcher will see that and take the next step of saying, well, what is there? How can I poke at that further and start to understand it better? So what are those next steps? How do we get to that next step, knowing what is causing the pain that we can't see? Well, as they always say,

further research is needed. But let me try to tell you a little bit about the kind of research that I imagine and that I know some colleagues are starting to do. When we see two x-rays that a radiologist would look at in the same way and say these two knees basically look the same, we can find pairs of x-rays where the algorithm disagrees and says, no, x-ray on patient B actually looks like it's going to hurt a lot more. So then we can look at that patient's MRI and try to get a sense of, well, how is that different? What are these things that we can see if we take a closer look that might be linked to that pain. We can also ask the doctor, here are two images that you see the same way, but the algorithm disagrees and thinks patient B is much higher and just ask the doctor to

say, what's different about these two knees? And how does that correlate to what you learned about in medical school, about the knee and the structure? How does it correlate to what ends up happening to that patient in terms of their long-term outcome? And so plugging that algorithm into the normal process of scientific discovery seems like a really promising avenue for future research. Do you see this algorithm as doing the doctors work for them or is it a tool? I see it more like a tool. I think that, you know, as I mentioned, the usual way that we train these algorithms when we're doing work in artificial intelligence is to train the algorithm to replicate what the doctor is saying. So there the implicit target is replacing the

doctor, of course. But I don't think that's what we want to do because we know that doctors and medical knowledge in general have their limits and we want to do better. So I think that by training algorithms to listen to patients, to learn from nature, to see what goes on to happen to patients in terms of real outcomes that we care about, we're developing a new tool that can plug into medical knowledge and add to it, not just replace the doctor. So is this something that if doctors are in medical school, they would then be introduced to the training then instead of later on in life? I really hope so, because I think that one of the things that everyone agrees on about the future of medicine is that it's going to be all about data.

And yet, currently, medical schools are not producing graduates or even selecting people to come into medical school on the basis of any kind of data science or statistical knowledge. So given how foundational, I think these tools are going to be for the future, we really have a pipeline problem of people who are going to be building and using and interpreting these tools. And I hope that medical schools, and when they select their pre-med students, will start to catch up. Your profile says you work at the intersection of machine learning and health care. That sounds like an intersection we should be investing heavily in, and it sounds like you're in favor of that. Absolutely.

I think that a lot of really exciting new fields come from the intersection of two other fields. But I think it's not as simple as just putting two people together, one from each field. I think both of those people have to share a common language and be, in a sense, bilingual. So I trained as a doctor, but I spent an enormous amount of time trying to teach myself and learn from other people about the technical side of how to build and use algorithms. And I think that people from that technical side also need to spend a lot of time in healthcare understanding what the important questions are, where the data comes from, how doctors make their decisions. But I think that once we have that cadre of bilingual researchers, those people will be a really, really powerful force for making medicine better in the future. You know, I remember talking a while back with doctors about using algorithms.

I remember a study where melanoma doctors, skin cancer doctors, were asked to create an algorithm or to tell an algorithm what were the diagnoses criteria that they used. for diagnosing skin cancer. And then they gave the doctors and the algorithm the same set of slides to look at. And the algorithm did better than the doctors because the doctors sort of ignored their own advice. But the algorithm went right ahead with what it was supposed to do. It's a very deep point because often we don't know how we do what we do.

I think this is a very robust finding from decades of research and psychology is that we have this intuitive, tacit knowledge about, you know, anything from walking down the street, which we can't fully describe how we do, to interpreting a complex medical signal, like a picture of a melanoma or an x-ray. And so one of the miracles of human intelligence is that we're able to do so many of these things without knowing how, just by virtue of having learned from repetition. And so it doesn't surprise me at all that an algorithm can can beat the doctor at her own game simply by consistently applying these rules in a standardized way. Well, because we know humans are fallible, right?

Absolutely. On the other hand, there are doctors who are great diagnosticians, and maybe AI hasn't reached their level. So we're not saying let's get rid of the doctors and go all AI because we want some kind of combination of both. Absolutely. I mean, to get rid of the doctors, you'd need to be able to write down in a rule exactly what the doctors are doing. And as your example just illustrates, we can't do that. We can't say,

okay, algorithm, go find all of the patients with a heart attack because we can't actually even write down in our data set what is a heart attack. And so that's why I view these tools as very powerful. When we have a target like the patient's report of pain to train them on, the algorithms can add a huge amount of value. When we can show them, okay, this patient 10 years later went on to have a heart attack. What can we see in their electrocardiogram today? That can be very powerful. But simply trying to replace the doctor at what she's currently doing today is not a great target for the algorithm because we can't write down do what the doctor does. That's the magic of seven years of medical training. This is Science Friday from WNYC Studios. Are these algorithms and these

tools cheap enough so that they can be used widespread into all areas and also, socioeconomic levels? Well, it depends on how much value you assign to an hour of my and my co-authors' times. But over in the grand scheme of things, building these algorithms is very, very cheap. I think that there are some fixed costs to hospitals getting their data online and in a form that can be integrated into those algorithms. So it's a little bit like all of these big improvements in, you know, for example, the digital revolution in the 80s and 90s or electricity at around the turn of

the century, there are these big fixed costs that institutions need to pay. But once they've paid them, actually running the algorithms is dirt cheap and really has the potential to add a lot of value for very little cost. And of course, one of the questions about algorithms is how do you keep them from being biased? Because they are made by people. Absolutely. You know, the algorithms learn from data that are biased and the data are biased because they come from a health system that is also very biased and that denies access to certain people and that treats certain people very differently. And so it's a huge problem when we're building these algorithms. That said, in a lot of our own work, we found that even though there's widespread use of biased algorithms, a lot of those

algorithms can actually be corrected and improved and turned from tools that are fundamentally unjust into tools that get resources and attention to people who need them. And it all depends on these little technical choices that we make when we're training the algorithm. So in our case, the knees, do we train the algorithm to listen to the doctor and potentially replicate decades of bias built into medical knowledge? Or do we train it to listen to the patient and represent underserved patients' experience of pain accurately and make their pain visible? So now that you know this much about your algorithm, where do you go from here? How do you make it better? I think a really important part of making any algorithm better is getting better data. So we

were very, very lucky to have this study that was done by the National Institutes of Health that made the data public. That's what let us, as researchers, access the data and build this algorithm to begin with. But unfortunately, those kinds of data sets are very rare. And so what we're doing now in collaboration with some of my co-authors and part of a nonprofit we started called Nightingale Open Science is working with a number of health systems in the U.S. and around the world to build up exactly these kinds of data sets, images matched with not just what a doctor said about the image, but what happened to the patient, what the patient says about their experience. By building up those data and making them public and available to researchers free of charge to do

nonprofit research, I think we're going to start building up more and better algorithms that do exactly these kinds of things. I think a key phrase that you said there is collecting the data from around the world, which means more diversity in the data. Absolutely, both around the world and also within the United States. There was an article a few months ago that showed that of all of the algorithms that are being trained, the vast majority come from these small niche academic medical centers that have the data resources to actually feed into algorithms. So a big part of our mission in this venture is going to under-resourced county health systems and building up their data infrastructure and going outside of the U.S. and building up data infrastructure in lots of different places. exactly as you said, because we need that diversity to train better and more just algorithms. Well, we're happy that you took time to be with us today, doctor. It was such a pleasure, and as a longstanding fan, a real honor to be on the show.

Thank you very much. Dr. Zayad Obermeier, Associate Professor of Health Policy and Management, University of California at Berkeley. After the break, what does a bag of mixed nuts have in common with an avalanche? Find out. Stay with us. This is Science Friday. I'm Ira Flato. Okay. Where are those little nuts? Always on the bottom somewhere. Hey, Ira.

Charles Merkowitz, hi, Charles. You want a nut? Oh, thanks. Have you ever noticed how in a box of nuts like this, the nuts aren't mixed thoroughly? Really? Always seems like the nuts are in the layers. Here, like the big stuff is on the top, and the smaller pieces are,

Way down on the bottom here. Right, it's actually something called the Brazil nut effect after the really big nuts that tend to clump up at the top of the mix. And there's new research explaining why that happens. People actually study that. They do indeed. And I asked Dr. Parmish Gudgeur of the X-ray imaging facility at the University of Manchester,

just why he was spending brainpower and instrument time looking at boxes of mixed nuts. It's known as the Brazil nut effect. But actually, it's a much wider phenomenon known as the phenomenon of size segregation. So that is, large size objects gather at the top of a pile. So if they're shaken, if they're shared, agitated, any of those types of motion cause the large particles to come to the top and separate from smaller particles, which are closer to the bottom.

And this has huge implications across many industries, many environmental processes. For example, in industry, if you're filling a silo full of material, as you fill the silo, large separates from small, and then you empty the silo, you're not going to get an even mixture. And in the environment around us, say avalanches, a snow avalanche or a rock avalanche, the large particles come to the top of the avalanche, and then get to the front of the avalanche and increase the power of the avalanche. So it has an effect on the way the avalanche travels and hence it's devastation. So haven't we known about this for years? I feel like this is something that we've all seen. So actually during my PhD, because my PhD was on this process of side segregation,

my mom used to laugh all the time and say the process that you're studying, this process by which large particles rise to the top and small go to the bottom is something that. that people in the kitchen have known for years. And it's true. We've known about this for years. But how does it really happen? That's the key question here. You're using something called

Time Lapse 3D X-ray computer tomography to find the answer to this question. Why can't you just look at the box of nuts? The problem with all granular materials is a problem of how do we understand what's happening in 3D away from the surface or away from things that we can see.

So if I just take an example, so here's a cereal box and we want to know what's happening inside of the serial box. Now, if you look closely, Charles, can you see the window that's there? I see it. So we could shake this

and we could monitor what's happening at the window, but that doesn't tell us what's happening on the inside of the box, because we can't see on the inside of the box. And that's the difficulty with granular materials. We can only kind of see one layer, and we can't actually understand what's happening in 3D on the inside of materials. So to kind of take a step forward, people started to do things with spheres. Spheres are great. we can understand a lot. We can see the process of large particles coming to the top with spheres, but most of the things, most of the materials that we have in the real world are not spheres. So I work in an x-ray imaging facility. So this is one of the largest x-ray imaging facilities in the world.

And we scan material science applications. So we put anything basically that's non-human or non-clinical inside of our machines and we get a 3D image of them. And so what we could do in this way is we can build up a 3D picture of what's happening to the nuts over time. We take a series of CT scans and we build up a time picture. Walk me through when you're using this series of CT scans to see inside the material inside the box. What did you see happening? What we could see by examining each of the individual nuts was a really unique motion. The Brazil nuts that rose to the top initially started horizontal in the pile

and then slowly as the box was shaken, these nuts started to go from a horizontal more towards a vertical position. Then when they became vertical, more vertically aligned, they then started to rise upwards, And then when they reached the top, at the surface, they then started to fall back to being horizontal again. We had this changing orientation of the Brazil nuts in different parts of its motion. So should I think of this as the Brazil nuts wiggling their way up, or is everything else sort of shoving its way down? That's actually a really, really good question. So as the box is shaken, the small particles kind of fall. their way through the gaps. It's called a process of kinetic sieving or percolation. And as they

find their way down, they gather towards the bottom. But the mass has to be preserved. If small particles are coming down, something has to go up. And so that kind of levers the large particles upwards. It's given the phrase squeeze expulsion, but a Essentially, you have the small particles kind of just percolating down through gaps, and that in turn causes a mass balance of the mass, some large particles to move upwards, and that in this case is the Brazil nuts. Does this finding apply to other particles in general as well? Can I use this to predict the chunks of granola in my box of granola in addition to these uniquely shaped nuts?

The nuts are quite unique because of their shape. But I think this opens massive doors. Up to now, nobody has really been able to examine the process by which irregular shaped articles segregate. So our box of Musley, nothing in our box of Musley is really that spherical, right? So I think the technique that we've unlocked here really opens a lot of doors. In fact, I'd really love to get Musley, put Musley in my experimental box and see how that segregate. So if I give the box of nuts a shake, does that just randomize everything again, or does it speed up the sorting process? Actually, shaking will kind of speed up the sorting process. This is one of the problems that they have in industry.

When you do any kind of shaking, any kind of blending, it actually causes the anti-mixing which you're trying to avoid in the first place. Does what you're learning by looking at your box of nuts still apply if you scale it way down to say particles in a pharmaceutical mixture? Pharmaceuticals have a range of different sizes. When you're down at the very small range, breathable medicines that we use for, say, asthma, respiratory medicines that we use for asthma, need particles that are very small, down to smaller than five microns to reach into the the deep part of the lungs. At this kind of size range, there are a lot of other effects. The inter-particulate effects between powders

cause them to be very cohesive, stick together. And so at that size range, this kind of segregation becomes almost secondary. But for more tableting materials, for larger, say, granules, yeah, the process of size segregation certainly has a big effect. And it's one of the problems that they have, when producing tablets. I imagine there must be other important factors like the smoothness of the particle or the density and things like that.

How do those interact in this problem? Yeah, actually, that's a really, really good question. So size is an almost kind of unintuitive way of sorting particles out. We don't imagine that the large ones necessarily come to the top until we recall our Rizelnut experience. Density is one that we are more familiar with. Dense objects are likely to sink to the bottom. And as you said, the smoothness, the surface friction, shape, all of these factors have an effect as well.

So we just take an example, large dense objects and small light objects. Now, do the large ones rise or do the large ones sink? It's actually a really interesting question, and that actually depends on the ratio between the density and the size in this case. And they're actually cut off where we can predict where the large ones rise up or actually the large ones sink because they're denser. How did you come to be interested in this? What made you start studying this? When I was looking around for a PhD project, I think it was the sheer simplicity of it. such a simple problem, like large particles coming to the top, small, coming to the bottom.

I think that caught my imagination that how we could apply math theories to understand and model that. So actually my PhD thesis was on modeling this phenomenon, seeing if we could put mathematical theories to understand and predict how large particles rise, how small particles come, what kind of after what kind of time range does that happen, what kind of height they rise to. These are the questions that we were trying to predict with mathematical theories.

And actually the mathematical theories for this are really good. The difficulty is on the experimental side, and that's what we've managed to uncover in the last few years using X-ray-computed tomography. So how well does the real world match up with the theories that you came up with before, the models? I think that the models that we were able to apply actually fit really well. There's still areas where the model can be improved,

and I think that's where we need really good experimental data to really help us in that regard. Interesting. You're listening to Science Friday from WNYC Studios. In case you're just joining us, I'm Charles Bergquist, talking with Parmish Gudgeur about a problem known as the Brazil nut problem. So in the practical world, if I wanted to use this knowledge to create an even distribution of something, is the answer just don't have weird things in your mix that are

shaped like Brazil nuts or make everything the same shape? Or is there something more fine grain that I should be thinking about? Making everything the same shape is certainly, I think, helpful, but not always practical. I mean, Musley would be pretty uninteresting if everything was the same shape. I think the knowledge of what's happening in the real world is vital to the way we design processes that help us mix something together. Just a challenge to try at home. Take a box musli say, or take a jar and fill a jar full of, let's say, peanuts in

Brazil's or a simpler example if you have, like, lent. split lentils and whole lentils. Fill it up and then try and get it well mixed. You could shake it, you could tumble the jar, you could rotate it back and forth. You'll actually find it, I think, quite difficult to get an even mix of the two. And this is what in industry they're trying to do all the time. I think there's a lot more work to be done. This is really the first insight into how things are moving in 3D, but this opens doors. If we can really understand how the process is happening, we can then put in steps to mitigate it, to stop it happening in the first place. That could be potentially designing our mixing cell in such a way that it causes an even number.

mix. And we can even then test it, look at it in 3D to make sure that what we're trying to do is actually happening in the real world. Thank you so much for taking time to talk to me about this. Thank you. It's been a real pleasure to talk to you and to just discuss this work with you. Dr. Parmash Gudger is a research associate in the Henry Mosley X-ray imaging facility at the University of Manchester in Manchester, UK. I'm Charles Bergquist. Fun fact, it turns out Dr. Gudger allergic to nuts, so he had to get someone else to fill up his experimental box of nuts. Oh, one last thing. Up on Alaska, one of the glaciers on Donali Mountain has started to surge. And I mean, really move. The Muldrow Glacier is slip sliding away 10 to 100 times faster than usual,

about three feet per hour. I can't necessarily like see it move, but you can see evidence of it moving. Chad Holtz is the regional geologist for Alaska National Parks. Pushing up dirt mound. You can hear the sounds of ice crashing almost confidently. So you should be able to see it if you sat there and had lunch. He flew out with his team and landed a helicopter on top of the cracked surface to study the glacier. And this glacier, you know, in its normal state, looks very boring.

It's all covered with debris. It's got a lot of melt ponds and channel flow. on top of it, but then now it's just like woke up the dragon, and the thing is just raging down the mountain. About 1% of glaciers surge. Here's what happens. Ice can dam up for long periods of time. The last time Muldro surged was 64 years ago, so that's nearly seven decades of ice buildup. And because you build up basically a hydrologic head in the glacier, it effectively floats the glacier with that water buildup because you have so much potential energy. in the upper reaches of the glacier, it just finally reaches a threshold and releases.

All the water starts going to the base. It just basically lubricates the bottom of the glacier and just completely free flows until it releases that water in the end of the surge. Chad and his team use radar satellite data to figure out this surge started back in September. Chad calls this a once-in-a-lifetime, once-in-a-career event. He's placing seismometers to capture the sounds of Muldrow to study how water is flowing through the glacier. These surges are a natural phenomenon and not due to climate change, says Haltz. But because these events are so rare, the role climate change might have is unknown. And yes, if you are in Alaska, you can view Muldrow on the move from a safe distance.

If you can't make it out there, you can see photos of Moldro and hear sounds of the glacier from these seismometers, all on our website at ScienceFriday.com slash glacier. And one last thing before we go. If you recall last month for Citizen Science Month, we shared all sorts of crowdsourced science projects. And now we're doing a little research project of our own. We want to hear about your citizen science experience to make citizen science more inclusive and fun. Visit Science Friday.com slash citizen science.com.com and science to help in our research study. And that's all the time we have this hour. Charles Berkwist is our director. Our producers are Christy Taylor, Katie Feather, and Kathleen

Davis, senior producer Alexa Lim, contributing editor John Dan Koski. Have a great weekend. I'm Ira Flato.