Science Friday - Bad Data, CRISPR Therapies, Wildfire Impact, Oilbirds. August 6, 2021, Part 2

Episode Date: August 6, 2021

How Imperfect Data Leads Us Astray Datasets are increasingly shaping important decisions, from where companies target their advertising, to how governments allocate resources. But what happens when the data they rely on is wrong or incomplete? Ira talks to technologist Kasia Chmielinski, as they test drive an algorithm that predicts a person’s race or ethnicity based on just a few details, like their name and zip code, the Bayseian Improved Surname Geocoding algorithm (BISG). You can check out one of the models they used here. The BISG is frequently used by government agencies and corporations alike to fill in missing race and ethnicity data—except it often guesses wrong, with potentially far-reaching effects.     CRISPR Stops Rare Genetic Disease In New Human Trial When the gene-editing technique CRISPR first came on the scene in 2012, researchers were excited by the potential the technology offered for editing out defects in genetic code, and curing genetic diseases. The researchers behind the technique, Jennifer Doudna and Emmanuelle Charpentier, won a 2020 Nobel Prize.  In one of the first clinical applications of the technique, last month researchers reported in the New England Journal of Medicine that CRISPR had stopped a genetic disease called amyloidosis, which occurs when an abnormal protein accumulates in your organs. They’re not the only group moving toward using CRISPR on humans; recently, the FDA approved a human clinical trial that will use the technique to edit genes responsible for sickle cell disease.  Fyodor Urnov, a professor in the department of molecular and cell biology at the University of California at Berkeley and the director of the Innovative Genomics Institute, joins Ira to discuss the clinical trials, as well as what other therapeutic targets for CRISPR-based gene editing lie on the horizon.     Latinos In The West Are Twice As Likely To Be Affected By Wildfires  A housing crisis, mixed with the location of farmwork and frontline jobs that attract Latino residents, particularly migrant workers, have put the community at greater risk of being impacted by wildfires, California activists and experts say. According to reporting by Politico, which analyzed data from risQ, “The Latino population makes up about 18 percent of the U.S. but represents 37 percent of the people who live in the areas that risQ identified as facing the most extreme wildfire risks.” José Trinidad Castañeda, a climate activist in Orange County who serves as the Beautification and Environmental Commissioner for the city of Buena Park, says that in order to address the wildfire issue, California must address its housing crisis.  “Climate does not discriminate, but our housing crisis has,” said Trinidad Castañeda. Read the full story and listen to a conversation with Abbie Veitch, editor in chief at Currently.      Consider The Nocturnal, Whiskered Oilbird At first glance, the oilbird doesn’t seem so strange. It’s a chestnut-colored, hawk-like bird that lives in South America. But with a closer look, its strange qualities start to stack up. Oilbirds are nocturnal creatures that roost in caves in huge colonies. Sure, some other birds, like nightjays, do the same. But oilbirds also have a triple threat for navigating the darkness: They’re one of the few birds that use echolocation, they have incredible eyesight and sense of smell, and they have whiskers on their faces. Unlike bats, their ecolocating peers, oilbirds exclusively live off a fruit diet, confounding researchers looking into why they evolved so many specialized traits.  They also have an incredible screech—when deployed in large numbers, it’s easy to understand why local populations have given them a name that translates to “little devils." “It’s wrong in every way, as far as birds go,” says researcher Mike Rutherford, curator of zoology and anatomy at the Hunterian museum at the University of Glasgow in Scotland. Rutherford studied oilbirds in Trinidadian caves to learn more about their population sizes. “A lot of people say every species is unique, but some are more than others, and the oilbird is one of those.” Rutherford joins Ira and SciFri producer Kathleen Davis to make the argument that the oilbird deserves to be labeled a charismatic creature, and join the ranks of the Charismatic Creature Corner.   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 Irafledo. Many of you have probably run into this issue. A survey asks you for your race or ethnicity, but none of the options quite fit, right? It's frustrating when you're filling it out, but it's about more than that. It means the data the survey's collecting isn't really accurate. And depending on how it's used, you can imagine this can have serious implications. My next guest is interested in all the ways in perfect data can lead us astray. Kasha Shimolinsky is a technologist and affiliate at the Berkman Client Center at Harvard University,

researching data ethics with a group called the Data Nutrition Project. Welcome, Kasha. Hi, Ira. It's great to be here. Okay, so let's get right into this. In perfect data, what does that mean? Why should we pay attention to it? Right.

Well, I spent a lot of time thinking about data and its impact. It turns out that no data set is perfect. So how a data set was collected, who collected it, when it was collected, all of that can affect the quality of the data and what it can be used for. And especially when we aren't aware of these things, we can end up misusing that data in really important ways. Interesting. All right.

Give us an example of where we would come across, let's say, an imperfect data set. Yeah. So like many people, I recently signed up for a COVID vaccine. The state form asked for my name, which is Kasha Shimalinski, and my zip code. That's fine. I know those. It also asked for my race and my ethnicity. and I could choose one of each.

But here's a trick. There are only a few choices. I can choose one of white, black, Asian, Hispanic, etc. But the problem here is that I'm mixed race. I'm half Asian. I'm half white. And I can only choose one answer. So what do I do?

Well, I can choose an inaccurate answer, right? Or I could not provide an answer. And if I leave it blank, I actually risk someone guessing for me later on when they input the data. So none of these options are really ideal. I see what you're talking about. we're already seeing some problems at the data collection stage. Okay, how can this cause problems? It can cause a lot of problems, right?

The first is my frustration at the doctor's office. But more importantly, at a population level, this means that we have race and ethnicity data that's incomplete or it's inaccurate. And when you've got something as serious as a pandemic, you know, especially given systemic issues around race in this country, we really need to be able to answer questions like, you know, is one race or ethnicity getting tested more than another, who's falling ill, who's receiving the vaccine, where do we need to target funding, right? And for all these questions, we actually, we need really accurate demographic data. You know, that reminds me of the old

computer phrase, guy go, garbage in, garbage out. If you don't have accurate data going in, you're not going to have accurate results. That's exactly right. And the COVID data is actually very incomplete. So in early 2020, race and ethnicity was missing from three quarters of the data about coronavirus cases. It has improved since then, but we're still missing about half of that data. So imperfect data sets have been around for as long as data sets have, and we've come up with a lot of clever mathematical ways to get around things like missing data. We call this imputing data. Okay, so what are these clever ways? So these are basically mathematical formulas or algorithms that take in a little bit of information

about someone, and it spits out a prediction for the person's race and ethnicity. So there are a few free versions online. So I set up the models on my computer. This is using openly available code on the internet. And I figured we just give him a go. Oh, cool. Can you throw my name in there? Yeah. Okay. Let's do it. Let's go step by step. This is a, this is in like a shared notebook between me and my colleagues here. So it's just filled with spaghetti code is what we call it. So I can put your name in here, last name. So let's see, that first version here believes that you are, oh, it's interesting. Okay. So you come up as 87% probability that you're, white and a 12% probability that you're black.

Cool. Yeah, it's really interesting. Now, this next model is actually a model called Wiki model, and there's just a ton of these on the internet. It's actually really scary, how many there are. So this one here takes your first and your last name. And if I scroll over here, you've got 14% potentially that you're British, you've got 8% Eastern European, 62% greater European Jewish.

It's interesting. Well, it got some key data right about me. I don't know if it was the IRA that tipped off the Jewish or my last name. I don't know. And this is the tricky thing about these models as well, as they don't tell you why this is happening. Okay. All right. We got my name in there. Kasha, let's go with your name. Okay. So if I start with the first model here, I put my very Polish last name in. And it comes out with a 98% chance that I'm fully white, which is not true. I actually identify as mixed race, but it's missing the Chinese. part of my identity because it's looking at my last name, which is my dad's name, and he's white. Now, if I move on to the next version of this, which takes a little bit more information, so now it's also requiring my first name. I put in my first and my last, and it comes out with just a 50% chance that I'm white, and suddenly there's a 10% chance that I'm mixed race,

which is actually how identify. And then the third version here requires the most information of them all. And that's my first, my last, and a zip code. So I put all of that information in there. And suddenly now the model is drawing a blank, most likely because my name, my surname of the combination are just too infrequent for the model to have really seen that before. And so it comes back with no information whatsoever on what probability my race could be. You know, that is amazing the way you talk about this. I would have thought that these were very sophisticated tools and the more data you put in, you know, the better the results should be. Why is it so inaccurate with your information? So the model that's the model that's the way.

thought there was a 10% chance of me being mixed race is actually quite a famous algorithm. It's called a Bayesian Improved Surnamed Geocoding Algorithm, or BISG. And it's very widely used. BISG was created in 2008 by the RAND Corporation. And it's been extremely influential in imputing race and ethnicity data where that information is missing or was never collected at all. But it's not so much that the model is inaccurate in this case. The math itself is fairly neutral. What we have to do is look at how the model was trained, and in particular, any weirdness with the underlying data that it was trained with. So every model requires this training data. And if there are issues with the data, the model is also going to come up with

bad results. So when we think about BISG, we need to then turn our attention to the data set that it was built on, which is one you might have heard of. It's called the census. Oh, yeah. I've heard of that. What's wrong with the census? Well, in this case, you know, the census was really meant for districting purposes. It was created and it's deployed every 10 years for that reason. And it's really not meant to be used by all these other tools such as BISG. And that can have unintended consequences. So first of all, BISG is a subset of the 2000 and the 2010 census data. So it includes only folks who are in the U.S. and answered the census 11 and 21 years ago, which means that we're leaving out newer immigrants and those marginalized

communities that are less likely to answer the census at all. Because a lot has happened since 10 years ago, hasn't? Yeah, especially 21 years. So imagine all the migration patterns and the folks who've come in and out of the country, then none of that's going to be captured in the census data from 20 years ago. Another thing here is the data set only captures a person's current surname. And many people, mostly women, have changed their last name like my mom, who took my dad's name when they married. And finally, the data includes a surname only if more than 100 people have the surname. So there are four men.

million last names, and that covers 30 million people who are not included because their surnames are just too uncommon. So does this mean that the tool is more accurate for, let's say, John Smith, but less accurate for somebody like you, Kasha Shemolensky? Yes, that's exactly right. So there's just more data on John Smiths because there are more John Smiths, so the model has more to go on when it's trained. In fact, the model performs best with males and people 65 years or older, and for those who identify as white, because those are the best represented demographics.

But the model is extremely insensitive. In some cases, it's basically useless on particular communities like American Indian, Alaska Native, and multiracial communities. Okay, you said the model is used for many purposes. What kind of impacts, then can we expect these limitations to have? Yeah, that's a great question. So, for example, the BISG algorithm is actually used by several federal agencies, including the Consumer Financial Protection Bureau. One of the things the Bureau does is it fines lenders if they violate fairness rules. So, for example, the Bureau ordered Ally Bank to pay $98 million in damages to minority borrowers in 2013.

They used BISG on auto lender datasets that did not have race or ethnicity data to determine that African American, Hispanic, and AAPI borrowers paid two. to $300 more in interest than their non-Hispanic white counterparts in the same geography. So it's actually really good that BISG could detect that bias. But in the actual payout, some white Americans received checks and some non-white borrowers had to apply. So the algorithm didn't get it totally right at the individual level. More recently, BISG has also been used extensively to fill in the missing COVID-related data that I talked about in the beginning. And there's a lot at stake here because a majority of the recent funding from the CDC is actually earmarked for

program that increased vaccination equity. And how are you supposed to determine what's equitable if you don't have that data? So inaccurate data at this level could mean millions or even billions of dollars going to approximate rather than actual areas of need. You know, I'm thinking about my household appliance when it's broken. Is it better to fix it or throw it out and just buy a whole new one? Can we fix this model? Or is it better not to use? use these tools at all? Yeah, I think that's the first time anyone's ever compared BISG to a household appliance, but I like it. And in this case, I'd say, you know, keep the refrigerator, right? I don't think that the answer is not to use the tool, right? It's just to use it thoughtfully. So BASG is actually

very important. And it's pretty good at what it was meant to do, which is to predict race and ethnicity for an entire population, especially majority populations. The key here is the model is really only as good is the data it was trained on. So if you want to mitigate harms, this is the place to start. It's not to throw away the entire refrigerator, right? It's to focus on the ways that it's broken, and then in this case, identify better data or improve the data, and then work up from there. Okay, so as we wrap up here, what are the takehomes that we should be paying attention to? Yeah, that's a good question. You know, I think with data science, especially data science, when you apply it to people, there often isn't a single right answer or perfect data set.

and we have to keep that in mind. Every data set we build is going to have inherent bias, not to mention whatever bias it picks up from society. And our goal isn't to remove the bias entirely because it's not possible. Rather, we have to understand it so that we can mitigate those issues. But despite all those challenges, it's also very important that we continue to find ways and innovate to make our data sets more complete. And that means filling in the gaps with tools like BISG

so that we can track and address potential discriminatory harms and get closer to a better solution. So the result of this is always going to be an approximation of reality, and we need to be constantly monitoring and improving our datasets and our models to assess just how far from the truth we believe we are. Terrific report. Thank you for taking time to be with us today. Thanks.

Kasha Shumelinski, a technologist and affiliate at the Berkman-Klein Center at Harvard University researching data ethics. After the break, you know we've heard a lot about some of the possibilities for gene editing technique CRISPR, but now the first case of a CRISPR success in stopping an illness. Details after the break, stay with us. This is Science Friday. I'm Iroflato. When the gene editing technique CRISPR first came on the scene, researchers were excited by the

potential CRISPR offered for editing out defects in our genetic code and therefore curing genetic diseases. The researchers behind the technique, Jennifer Dowdna and Immanuel Scherner, Charpontier won a 2020 Nobel Prize, but the promise has been slow to yield results until now. Last month, researchers reported in the New England Journal of Medicine that CRISPR had stopped a genetic disease amyloidosis in its tracks. And recently, the FDA approved a clinical trial that would use the technique to edit genes responsible for a sickle cell disease. Joining me now to talk about those and other therapeutic uses of CRISPR,

on the horizon is Fyodor Ernov. He's a professor in the Department of Molecular and Cell Biology at UC Berkeley and director of the Innovative Genomics Institute there. Welcome to Science Friday. Thank you for having me. Did I get that study correct, published in the journal? You did, except I would have added emoji of excitement and, you know, champagne bottles. Well, tell us about what happened, how it was done, and why people are so excited. It is literally a dream come true. We've known that diseases can be genetic for well over a century. We've known about sickle cell disease and its genetic basis for 70 years plus. We first read human genes in the 1970s and we first read all of human genes in the DNA of just a few

people in the early 2000s. CRISPR is the equivalent of flying to the stars if we were a strapped. We're no longer now limited to cataloging the stars slash genes. We can fly to them. We can touch them and we can change them. All of this was a promise. And as you mentioned, Jennifer and Emmanuel's landmark 2012 Nobel Prize winning paper, it's remarkable.

It's only been 10 years. Progress has been so fast. Proposed the notion that we would use CRISPR to change DNA to treat disease. And a large community of folks, let's just be clear, this, this is a is a proverbial village raising this child, scientists, physicians, regulators, ethicists working all over the globe. But there we have it. We have our first poster humans who have been gene edited. They walk our planet and fortunately they seem to be well. And so nothing bad happened. You know, the first rule of medicine do no harm. And even more fortunately, they are thriving. And we have

every reason to believe that having gene edited them will help them. Tell me about the experiment. Walk me through what actually happened. It may surprise you that what actually happened in the most recent work with this amyloidosis was very similar to what happens when you get vaccinated for SARS-CoV-2. And folks who have had the vaccine made by Pfizer or Moderna have now learned rather convoluted things like lipid nanoparticle or messenger RNA. It's kind of of amazing to me that 2020 has gotten us to a point where most folks know what those acronyms mean. With CRISPR, that same messenger RNA encodes a protein which does not have a poetic enough name.

Its name is very bland. It's called Cass 9. It's Mother Nature's device, which of course people have repurposed as guided by Jennifer's and Emmanuel's discovery, that can seek out a stretch of genetic code and tell Mother Nature to repair it. So in the case of the most recent experiment with TTR amyloidosis, messenger RNA encoding this wonder protein, Cas9, along with a wonderfully named guide, a separate short snippet of RNA that tells Cas9 which gene to change was injected into six people. It's not just naked RNA, just like with the SARS-CoV-2 vaccine. They're encompassed in a protective layer called a lipid nanoparticle, and it routes itself through the bloodstream into the liver, and then the messenger RNA makes Cas9 protein. Cas9 wakes up inside the cell of the

liver, takes this little guide that it has, goes into the nucleus of the cell, finds the gene, which causes the disease, and gets rid of it. Now, that happens at the cellular level. And the reason we're all, frankly, waking up with a smile on our face and a sense of strong motivation every morning is, I mean, it's all well and good to do this in the lab or in mice or monkeys, but humans are a different matter. Full credit to the biotechnology company, Intellia, which did this work. Great, great job. They did this on six human beings. In technical terms, they're known as subjects. The subjects are doing well. There's nothing adverse to happen to them as best as we can tell. and critically, when physicians who actually did the injection measured some critical biological features of these humans, they're doing well at the molecular level.

And what that means is we have strong hope that the severe disease there succumbing to which damages their nerves, damages their heart, etc., is actually going to be reversed. Now, I hear, and I understand, and, you know, your enthusiasm, your joy is palpable. but it is a very small, with N equals six or something, a very small sample. Is it not? I mean, are we a little too excited too soon or not? You raise a key point. These are experimental treatments and the central goal of our entire community is to do everything we can to focus on safety first.

Now, a number of groups around the world, both, biotechnology, and I mentioned Intellia, there are other companies, and a number of academic groups, you know, I'll just mention a representative example, Children's Boston, St. Jude in Memphis. And here at UC Berkeley, the Innovative Genomics Institute, which Jennifer Dowden found it, in partnership with the University of California, San Francisco, and UCLA, we are developing CRISPR-based methods to treat other diseases, including sickle cell disease. Our number one concern is that we will cause harm. That risk is non-zero. We will never know it's completely safe until we actually treat human beings. So safety first, we're taking it slow. So for example,

the clinical trial that we're honored to have been supported by the California Institute for Regenerative Medicine to do, this is a joint effort between UC Berkeley, IGI, UCS, UCS, UCS and UCLA. The vision is to treat nine people, but do that over three years. And so why so slow? Well, you treat one person and you see how it goes. And if they're doing well, then you treat another person. So this staged dosing, here's a technical term, stage dosing, which means one person at a time and walk before you run, precisely addresses the question you raised about, you know, not getting too excited too soon.

It's early days. And tell us about your time frame for that sickle cell study. Because people are going to, they're going to hear this, they're going to hear this, they're going say, I want this, you know, you know how this works. People get very hopeful. Here's, in practical terms, what's going to happen. Assuming things track to plan, UCSF, which is where the clinical trial will happen, Mark Walters is our principal investigator, we're hopeful to treat the first patient this year. But I want to paint a slightly bigger picture, which is the IGI is not alone, and that's actually really important. We are not the patient's only sort of

hope. A number of biotechnology companies, and I might just actually, actually, going to list them all because I think it's important that your audience is aware of who else is doing this. Sangamo, CRISPR therapeutics, editus medicine, intelia therapeutics, and graphite bio, all have open clinical efforts for sickle cell disease in addition to us at the IGI and UCSF and UCLA. All in, we are expecting that over the next three to five years, this combined effort, assuming things, go to plan and nothing goes awry, which we always wake up with both equal parts excitement and concern about safety. Assuming things go to plan, we are hopeful as a community to treat, I'm going to go give a relatively low number, but bear with me, maybe 100, 200 folks with

sickle in the United States over the next couple of years. But the really important point, Ira, that I think I want to communicate is experience shows that when a treatment such as what we're seeing with CRISPR has an effect as powerful as it appears to have, then in contrast to other diseases, for example, cardiovascular disease or neurodegenerative disease, where the Food and Drug Administration sets a standard that you have to study thousands of individuals over many years, experience shows that to do a clinical trial that convinces the Food and Drug Administration, that this is a medicine that should be approved, Those trials are relatively small in size and could be as few as 20, 30, 40 individuals per individual approach.

Let's just be clear. And that means that assuming things track to plan within a few relatively short years, we should expect, again, fingers crossed, multiple approved genomic therapies such as CRISPR for sickle cell disease in the United States. And at that point, that means that physicians in America will be able to quite literally prescribe a CRISPR gene edit for a human being with sickle. Now, I would like to take an important detour into the issue of health justice. These genomic therapies, when they are approved, cost millions of dollars per person. There is a genomic therapy for spinal atrophied, it's $2 million. There's a therapy for blindness.

It's $850,000. We as a nation have a responsibility to ensure equitable care for our fellow Americans. A good fraction of folks with Sickle and the United States are of African American ancestry. So this is a community that oftentimes is socioeconomically disadvantaged. Here in California, I have 10,000 of my fellow Californians with Sickle and, you know, less than 20% have private health insurance. They can't afford a $2 million per person cure. So now switching to the realm of academic medicine,

A vision for the Innovative Genomics Institute is health justice and health equity. It's deeply moving to me to work with Jennifer Dowden because she set a mandate for our institute. We have to develop crispureers that would be equitable. And I don't want to position this as a zero-sum game, you know, academia versus industry. But I do want to say that we as a community, as a nation, have a responsibility to make sure that these remarkable technological innovations are equitably administering. And I cannot think of a better example than to showcase how good we are as a nation, is that in building a health-just distribution of CRISPR and other genomic therapies for our fellow Americans, many of whom are socioeconomically disadvantaged.

Talking CRISPR gene editing with Fyodor Ernov, he's a professor in the Department of Molecular and Cell Biology at the University of California at Berkeley, Director of the Innovative Genomics Institute there. This is Science Friday from WNYC Studios. Are there other sorts of conditions or diseases that would yield itself to CRISPR therapy? I'm sure there are plenty, correct? You know, I'm wondering if we can schedule science Saturday and Science Sunday. So I could do full justice.

So let me first let me be clear. There is no low-hanging fruit. These are experimental therapies and these are hard. I'm going to showcase the things that sort of are the low hanging of the high hanging fruit. The first one is other genetic diseases of the blood. And this is because we can take blood stem cells out of a person, crisper them, quality control them, and put them back in. And a major need are the so-called rare genetic diseases of the blood,

like immune deficiencies or disorders of the immune system. I put quotation marks around the world where they're rare individually. You know, there's maybe, let's say, 50 people in America with disease number four. But in aggregate, they're actually quite abundant. And here, health justice is yet again an issue, because it's one thing for a biotechnology company to pursue sickle cell disease. They have 100,000 patients ready to receive their medicine if they succeed. But who's going to spend time and money building a medicine for which there's 20 patients? So again, I'm honored to be partnered with UCSF and the Gladstone as part of the Innovative Genomics Institute

to address that question. How do we develop CRISPR technologies where we can equitably and affordably CRISPR treat somebody who is the proverbial n equals one? There's only one person with that rare genetic disease or maybe five. How do we innovate in the CRISPR space where we can treat that person?

So that's area number one. Genetic diseases of the blood, immune system, absolutely goal sort of firmly in our sights. Now, stepping away from blood disorders, I think an area of everyone's sort of deep scientific passion are disorders of neurodegeneration. We are actively working in partnership with UCSF here at the IGI to build a CRISPR approaches for neurodegenerative disease. And I want to be very clear to not give folks false hope.

But I just want to say that we as an institute in partnership with UCSF are actively working on this as are many others. Another area which I can actually give you a specific in terms of hope coming in focus, actually is heart disease. You know, cardiovascular disease is a major killer and continues to be. And, you know, as tasty as butter is, it's on balance not very good for you. Olive oil is better, but, you know, we can't change people's habits overnight. So what if we could crisper someone in a way where they would be genetically protected against

heart disease? And that's actually not science fiction. I want to highlight work from a biotech company called V-E-R-V-E. And they're doing precisely that. They're working on a next-generation form of CRISPR, something called base editing, which was invented by David Liu at the Broad. And they are working to put a CRISPR base editor into a person

so that, just like Intellia did for amyloidosis, it would go to the liver, but instead of going after a gene that causes that disease, it would tweak a different gene that we know from all sorts of experimentation, would give a person, we hope, a lifetime of protection from heart attack. This is actual late-stage pre-clinical reality. We are all as a field hopeful that Verve will go into the clinic next year. And again, folks will start sending emails going, And where do I sign up for some CRISPR protection against cardiovascular disease?

These things take time. I would anticipate that these clinical trials will take four to five years to play out. But there's solid scientific foundation for this. There's clear proof of concept, for example, from the work of Intellion. And, you know, enthusiasm is, as discussed earlier, healthily married with concern about safety. But frankly, Ira, let me just say this. We have irreversibly stepped into an age of genetically engineering human beings to treat their disease. There are crispered humans among us.

Their numbers will only rise. And there will be crisper for blood disease, for rare disease, for neurodegeneration, cardiovascular. I also want to highlight cancer, some beautiful work from the University of Pennsylvania, led by Carl June and many others, including biotech companies. Watch this space, please. The next five years will be quite a right. Dr. Ernov, fascinating stuff. We have run out of time. I want to thank you for taking time to be with us today. Ira, what an honor, frankly, to speak with you. I'm a lifetime fan, and I can't believe I'm pinching myself. I found myself speaking with you on air. Oh, thank you. You're too kind. I'd like to thank you for the kind of work you're doing, and good luck. We'll be, we'll be keeping

close attention, paying close attention to it. I appreciate it. Fyodor Earnoff is a professor in the Department of Molecular and Cell Biology, UC Berkeley, director of the Innovative Genomics Institute there, and we will be watching, as I say, the future of CRISPR technology. We're going to take a break, and when we come back, a look at why some Latino communities in California and the West are being disproportionately affected by wildfires.

Stay with us. This is Science Friday. I'm Iroflato. The western half of the country has been experiencing record-breaking wildfire seasons. Last year in California, nearly 10,000 fires burned over 4 million acres in the state, destroying land and wildlife, but also homes and entire communities. And certain communities are disproportionately affected. According to the climate service firm, Risk Q, Latinos make up 18% of the U.S. population, but 37% of the population that face extreme wildfires. In California, according to California activists and experts, a housing crisis mixed with the location of farmwork and frontline jobs that attract Latino residents, especially migrant workers, has put the community at greater risk.

A lot of folks described a situation where they're sort of in this cycle. That's Abby Veach, editor-in-chief of currently a weather and climate newsletter. She's covering this story. Housing is too expensive, so they move somewhere with accessible jobs and affordable housing. But those affordable homes are in these more wildfire-prone areas. They're more likely to lose their homes and have poor health effects. And I think a lot of people are frustrated. But they're also just worn down from being stuck in this cycle.

Farm workers and other residents affected by the housing crisis, can feel like they have no place to go. Climate does not discriminate, but our housing crisis has. That's Jose Trinidad Castaneda. He's a climate activist in Orange County, and he's seen this housing cycle happen in his own family. When I hear my family members talk about how it's very unaffordable to live in Orange County or even Riverside, and they're moving out towards high desert regions,

like Apple Valley, then I'm not surprised that the further out they move and closer to some of the rural areas of California and the West, the more impacted they are by fires and climate impacts like drought. Solving the housing crisis would be the ideal solution, but during a wildfire emergency, more immediate personal safety issues need to be at the forefront. And that can be a challenge. in aiding an isolated Latino community. Angie Sanchez has worked with Latino community organizations in Sonoma and has worked on emergency wildfire response in the area. I think the number one thing is the outreach, the engaged in the community.

You should not wait for individuals to come to us when the disaster is already happening. We need to be more preventive. And letting the community know of the resources, where are the evacuation centers, where are the shelters and just really being there and available. Communicating this information is vital, but language can be a barrier, and that means the government needs to disseminate information in Spanish and indigenous languages through trusted sources in the community, according to organizers. Also, police presence at evacuation centers, especially for undocumented people,

can be a huge impediment, causing people to turn away. from resources that do exist. So organizations are working to ready communities before a wildfire disaster strikes. Here's Abby. They're working on training community members on how to help their neighbors and families to prepare for crisis.

They're also working to ensure folks know about how to access resources like renters insurance and disaster preparedness kits prior to crisis, so they're not scrambling when the fire is already at their doorstep. You can read Abby V.C. his entire Currently article, it's up on our website at ScienceFriety.com. And you can sign up for more currently stories at Currentlyhq.com. Well, it's the end of another week. So what better way to

celebrate than with one of our favorite segments. It's time for another charismatic creature corner. And joining me as always is our charismatic creature correspondent Kathleen Davis. Hi, Kathleen. I'm glad to be back, Ira. So, which charismatic creature, potential charismatic creature, have you brought us this time? So this week's charismatic creature candidate I'd like to highlight is one that doesn't seem that weird from first glance, but the more you learn about it, the stranger it becomes. Are you ready? Yeah, hit me.

So this week, we are talking about the oil bird. Do you know anything about this bird? You know, it sounds like a logo for a fossil fuel company. No, I never heard of it, as you can tell. Well, it is unlike any bird I have ever researched. So it lives in South American caves. It uses echolocation and it has whiskers just to name a few things about it. Whoa, whiskers, a bird, really?

Yeah, really. But as always, I am no connoisseur of this creature, just a fan from afar. So I have recruited an expert to help explain just how charismatic this creature is. So help me welcome our guest. Mike Rutherford is curator of zoology and anatomy at the Hunterian, a museum at the University of Glasgow in Scotland. He used to live in Trinidad in Tobago, where he studied oil birds in the wild. Welcome to Science Friday. Thank you very much for having me.

Nice to meet you, Mike. No problem. Nice to meet you too, Ira. All right. So Kathleen gave us a little preview of what makes these birds special, but Mike, can we start with the basics? What does an oil bird look like? Imagine, if you will, maybe a super streamlined owl, because they're a night bird, they're a nocturnal bird, but more like a cross between an owl and a hawk of some sort. I don't know people, your listeners are familiar with night jars. They're probably most similar to those. But to give you a basic description. They're kind of a rufous brown colour, chestnut brown. They are about 45 centimetres long,

some sort of tip of the tail to the nose for your American listeners, so that's something like 18 inches or foot and a half. And the wingspan is about three and a half feet, you know, just over a meter. So they're quite a big bird. And covering the body as well as there's all that nice brown chestnut background, they've got white spots all over, long tail feathers, long end wing feathers. A very curved, hooked beak, which makes them reminiscent of a hawk, not that they eat anything like the same sort of diet as hawks. And as Kathleen mentioned, they also have these what called rictal bristles or whiskers around the mouth part. Yeah, that's what intrigues me. Why would a bird have whiskers?

Well, this is a big question. No one's really sure of the exact purpose of a rictal bristles, possibly to do with sensing air movements as they're so feeding, as they're. flying around. There's been lots of experiments of the studies over the years, but no one's really come up with a satisfactory answer just yet. And they are found in different birds which have different diets and different lifestyles. So there's no real hard and fast kind of answer for that one, I'm afraid. And the name oil bird, where does that come from? Yeah, so that's a bit of a gruesome background. You kind of mentioned sort of this, a logo for a fossil fuel company. But before we had oil being pumped out the ground, people actually harvested oil birds.

and use the oily fat that came from them for purposes such as lighting, lamps, and for cooking. The young are not very attractive at all. They are like big grey blobs. So when you see one sitting on a nest, it's kind of a big fatty blob. And actually, the young can weigh more than an adult because they get fed on this very rich diet of various palm tree seeds. And they just get fatter and fatter and fatter, and eventually when they grow their proper flight feathers, then they sort of toned down a bit. So the oil bird comes from the purpose

they're used for. But it's just one of the names been given to the birds over the years. One old old name that came across was Trinidad Goat Sucker, but that name wasn't useful. Too long. I can imagine. Local name in various parts of South America where they come from

was a Guacharo. That translates roughly as ones who cry and lament. And this also leads to another local name in Trinidad, where they're found called the Diabloeteen or Little Devil. And both of these names refer to the ghastly screeching noises that the birds make when they're disturbed when you go into their cave habitats. Well, speaking of what they sound like, I have to play a clip for Ira and our listeners so that they truly understand why they're called these little devils. And sorry in advance to everyone who's about to. to hear this for the first time.

Whoa, that just blew me away. That's probably several hundred birds all flying around at once inside the cave. And as someone else described it, when you're in the cave, it sounds like you're standing or flying along in an open-topped airplane. It's so noisy. You can't have a proper conversation. Wow, that really was noisy. You know, we've got to talk about the echolocation.

Yeah. Speaking of noise, that was mentioned earlier. Do they use it like bats do to find food? They use it for a variety of purposes. So they nest inside caverns and caves. So being able to navigate inside these long dark spaces, the echolocation is very useful. But they also, when they go out searching through the tropical forest at night, they possibly use the echolocation to navigate through trees as well

because they tend to swoop down on palm trees and other types of trees that produce big, juicy seeds. and pluck off the seeds in flight, and they're doing this in the darkness. So although they've got very good night vision as well, echolocation probably does help with some of that close-up maneuvering. And they're nocturnal too, as you said. I mean, what would be the benefit for this bird to be, you know, going out and getting fruit at night as opposed to during the day? Well, not 100% sure, but I guess it would be a useful way of, you know,

finding your own niche. They're not having to compete against other fruity. during the day, where at nighttime there's not that same competition possibly. And also because they live in caves, which a nice safe place for nesting, moving in and out of caves under cover of darkness, gives even less clue to predators as where they're coming from. So, you know, there's lots of potential reasons for being a nighttime feeder. Huh. So they live in caves, which makes me think that their eyesight is not too good because they're in the dark all the time. Now, they have fantastic eyesight. There's a very big

eyes. Really? Look at big black, limpid pools. They live out in the forest when they're foraging. They only returning to the caves relief or back to the nest. They do spend time out in the open as it were. And not just to mention, the third really good sense

is they have great sense of smell, which is quite unusual for birds. Not many birds use their sense of smell much, but it's thought that the oil birds can help find ripened fruit when they're flying through the woods because a lot of the very ripe palm trees will have a distinctive odor. Reminding our listeners that this is Science Friday from WNYC Studios.

We're talking to oil bird enthusiast Mike Rutherford. So one of the big things that usually convinces people that a creature is charismatic or not is their social structure. What is the oil bird social structure like? Well, they're very gregarious. They like roosting in large numbers. And the noise you heard, you know, the screeching. in the caverns. That's not what it was like all the time, fortunately. Otherwise, I think the birds would drive each other insane. That only happens when you've got a disturbance, like a researcher,

such as myself, wandering into their cave. But if you go in, switch off your torches, sit down, and just relax, then within about, you know, five, ten minutes, the birds are all quietened down, and there's just these kind of occasional cliques and chirps. And they obviously, you know, they're sometimes nesting several of them on ones off a short ledge on the side of the cave. they're quite comfortable with each other. They definitely seem to get on well enough in close quarters. But the foraging, I think from what some of our studies shows, the attaching data transmitters, GPS transmissions the birds.

When they go out of the caves, we think they tend to spread out into forests. They don't go out en masse. But there's not much to know about that. Trying to spot birds in a tropical rainforest in the middle of the night is a pretty tricky job. So that's why most of the research has been done. on them has focused on their sort of life in the caves. Mike, what do you like most about this bird?

It's wrong in every way as far as the birds go. It's just different in every way. That's what I love. They're just completely individual. A lot of people say, you know, every species is unique, but some are more unique than others. And the oil bird is one of those. The echolocation that's roosting in caves,

yeah, some other animals do that. But, okay, thrown as well, being a fruit eater, that just makes it even weird. And then just looking so spectacularly beautiful as well, you know, they're a beautiful shape, except when they're young, they're kind of ugly. And just having all these crazy names, the crazy behavior. It really is just a whole package deal for me. They just tick all the boxes for what makes an animal interesting. Well, we are out of time.

So, Ira, I have to ask you, do you think that the oil bird is charismatic? You know, this is a tough one because I was leaning toward no. I was thinking it really was not charismatic. It has those crazy bird calls and it's all that noise. But when I asked Mike what he likes most about the bird and he says it's wrong in every way, I can relate to that. So I think I'm going to have to say it is charismatic, but just by a whisker. And Mike, I think I know your answer.

but do you think that the oil bird is charismatic? Oh, yes, 100%. Oh, there you have it. Well, I want to, that's terrific. I want to thank Mike for joining us. Thank you for joining us and enlightening us about the oilberg. My pleasure. Mike Rutherford, curator of zoology and anatomy at the Hontarian.

That's a museum at the University of Glasgow in Scotland. And Kathleen, I hear something interesting is happening in charismatic creature corner land. Tell us about that. Yes, so this fall we are going to hold the first ever. charismatic creature carnival. Whoa. And we need the help of our listeners to pull this off. Okay, so what do our listeners need to do?

So all you wonderful people listening at home need to send us your suggestions for charismatic creatures you would like Science Friday to talk about. So give us your weirdest, funkiest, most charismatic creatures. It doesn't matter if it lives on land, in water, if it's super big or microscopic. It can even be extinct. but we need to know what charismatic creatures you would like us to talk about. So if you are listening and you've got a great charismatic creature up your sleeve, you have been waiting for us to talk about, let us know about it.

And you can do that a few different ways. So the first way, you can send us a voice memo on our SciFri Vox Pop app. The second way, you can also tweet at us, tag us at SciFri on Twitter, and tell us your suggestion. And make sure to hashtag MyFriVoxpop app. My charismatic creature. That is very important so we can find it. Hashtag my charismatic creature, all one word.

Okay, great. And what about good old-fashioned email? Yeah, you can also email us your suggestions. That address is SciFri at ScienceFriday.com. And make sure in the subject line to put My Charismatic Creature so we can pick it out easily. So again, my charismatic creature is the hashtag on Twitter and the subject line on email. Yep. And this fall, we will hold our first charismatic creature carnival where we're going to talk about a bunch of the listener suggested creatures. And eventually we'll have our listeners

vote for their favorite creature. So at the end of all that, we will have our very first true inductee of the charismatic creature corner Hall of Fame. You know, it all sounds great, Kathleen. We are excited to kick off the carnival a little bit later this year. It'll be great. SciFrat producer Kathleen Davis. Thank you for joining us again. Thanks so much for having me. And that's about all the time we have for this week. If you missed any part of the program or you'd like to hear it again, yeah, subscribe to our podcasts or ask your smart speaker to play Science Friday. Have a great weekend. We'll see you next week. I'm Ira Plato.