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ChatGPT and other generative AI could foster science denial and misunderstanding – here’s how you can be on alert

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ChatGPT and other generative AI could foster science denial and misunderstanding – here's how you can be on alert

Approach all information with some initial skepticism.
Guillermo Spelucin/Moment via Getty Images

Gale Sinatra, University of Southern California and Barbara K. Hofer, Middlebury

Until very recently, if you wanted to know more about a controversial scientific topic – stem cell research, the safety of nuclear energy, climate change – you probably did a Google search. Presented with multiple sources, you chose what to read, selecting which sites or authorities to trust.

Now you have another option: You can pose your question to ChatGPT or another generative artificial intelligence platform and quickly receive a succinct response in paragraph form.

ChatGPT does not search the internet the way Google does. Instead, it generates responses to queries by predicting likely word combinations from a massive amalgam of available online information.

Although it has the potential for enhancing productivity, generative AI has been shown to have some major faults. It can produce misinformation. It can create “hallucinations” – a benign term for making things up. And it doesn't always accurately solve reasoning problems. For example, when asked if both a car and a tank can fit through a doorway, it failed to consider both width and height. Nevertheless, it is already being used to produce articles and website content you may have encountered, or as a tool in the writing . Yet you are unlikely to know if what you're reading was created by AI.

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As the authors of “Science Denial: Why It Happens and What to Do About It,” we are concerned about how generative AI may blur the boundaries between truth and fiction for those seeking authoritative scientific information.

Every consumer needs to be more vigilant than ever in verifying scientific accuracy in what they read. Here's how you can stay on your toes in this new information landscape.

glowing purple points connected by blue lines
Based on all the data points it ingests, an AI platform uses predictive algorithms to produce answers to queries.
Cobalt88/iStock via Getty Images Plus

How generative AI could promote science denial

Erosion of epistemic trust. All consumers of science information depend on judgments of scientific and medical experts. Epistemic trust is the process of trusting knowledge you get from others. It is fundamental to the understanding and use of scientific information. Whether someone is seeking information about a concern or trying to understand to climate change, they often have limited scientific understanding and little access to firsthand evidence. With a rapidly growing body of information online, people must make frequent decisions about what and whom to trust. With the increased use of generative AI and the potential for manipulation, we believe trust is likely to erode further than it already has.

Misleading or just plain wrong. If there are errors or biases in the data on which AI platforms are trained, that can be reflected in the results. In our own searches, when we have asked ChatGPT to regenerate multiple answers to the same question, we have gotten conflicting answers. Asked why, it responded, “Sometimes I make mistakes.” Perhaps the trickiest issue with AI-generated content is knowing when it is wrong.

Disinformation spread intentionally. AI can be used to generate compelling disinformation as text as well as deepfake images and videos. When we asked ChatGPT to “write about vaccines in the style of disinformation,” it produced a nonexistent citation with fake data. Geoffrey Hinton, former head of AI at Google, quit to be free to sound the alarm, saying, “It is hard to see how you can prevent the bad actors from using it for bad things.” The potential to create and spread deliberately incorrect information about science already existed, but it is now dangerously easy.

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Fabricated sources. ChatGPT provides responses with no sources at all, or if asked for sources, may present ones it made up. We both asked ChatGPT to generate a list of our own publications. We each identified a few correct sources. More were hallucinations, yet seemingly reputable and mostly plausible, with actual previous co-authors, in similar sounding journals. This inventiveness is a big problem if a list of a scholar's publications conveys authority to a reader who doesn't take time to verify them.

Dated knowledge. ChatGPT doesn't know what happened in the world after its concluded. A query on what percentage of the world has had returned an answer prefaced by “as of my knowledge cutoff date of September 2021.” Given how rapidly knowledge advances in some areas, this limitation could mean readers get erroneous outdated information. If you're seeking recent research on a personal health issue, for instance, beware.

Rapid advancement and poor transparency. AI continue to become more powerful and learn faster, and they may learn more science misinformation along the way. Google recently announced 25 new embedded uses of AI in its services. At this point, insufficient guardrails are in place to assure that generative AI will become a more accurate purveyor of scientific information over time.

woman looks confused taking notes on paper looking at laptop
Be ready to look beyond your ChatGPT request.
10'000 Hours/DigitalVision via Getty Images

What can you do?

If you use ChatGPT or other AI platforms, recognize that they might not be completely accurate. The burden falls to the user to discern accuracy.

Increase your vigilance. AI fact-checking apps may be available soon, but for now, users must serve as their own fact-checkers. There are steps we recommend. The first is: Be vigilant. People often reflexively share information found from searches on social media with little or no vetting. Know when to become more deliberately thoughtful and when it's worth identifying and evaluating sources of information. If you're trying to decide how to manage a serious illness or to understand the best steps for addressing climate change, take time to vet the sources.

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Improve your fact-checking. A second step is lateral reading, a process professional fact-checkers use. Open a new window and search for information about the sources, if provided. Is the source credible? Does the author have relevant expertise? And what is the consensus of experts? If no sources are provided or you don't know if they are valid, use a traditional search engine to find and evaluate experts on the topic.

Evaluate the evidence. Next, take a look at the evidence and its connection to the claim. Is there evidence that genetically modified foods are safe? Is there evidence that they are not? What is the scientific consensus? Evaluating the claims will take effort beyond a quick query to ChatGPT.

If you begin with AI, don't stop there. Exercise caution in using it as the sole authority on any scientific issue. You might see what ChatGPT has to say about genetically modified organisms or vaccine safety, but also follow up with a more diligent search using traditional search engines before you draw conclusions.

Assess plausibility. Judge whether the claim is plausible. Is it likely to be true? If AI makes an implausible (and inaccurate) statement like “1 million deaths were caused by vaccines, not COVID-19,” consider if it even makes sense. Make a tentative judgment and then be open to revising your thinking once you have checked the evidence.

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Promote digital literacy in yourself and others. Everyone needs to up their . Improve your own digital literacy, and if you are a parent, teacher, mentor or community leader, promote digital literacy in others. The American Psychological Association provides guidance on fact-checking online information and recommends be trained in social media skills to minimize risks to health and well-being. The News Literacy Project provides helpful tools for improving and supporting digital literacy.

Arm yourself with the skills you need to navigate the new AI information landscape. Even if you don't use generative AI, it is likely you have already read articles created by it or developed from it. It can take time and effort to find and evaluate reliable information about science online – but it is worth it.The Conversation

Gale Sinatra, Professor of Education and Psychology, University of Southern California and Barbara K. Hofer, Professor of Psychology Emerita, Middlebury

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Animal behavior research is getting better at keeping observer bias from sneaking in – but there’s still room to improve

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theconversation.com – Todd M. Freeberg, Professor and Associate Head of Psychology, of Tennessee – 2024-05-03 07:16:49

What you expect can influence what you think you see.

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Todd M. Freeberg, University of Tennessee

Animal behavior research relies on careful observation of animals. Researchers might spend months in a jungle habitat watching tropical birds mate and raise their young. They might track the rates of physical contact in cattle herds of different densities. Or they could record the sounds whales make as they migrate through the ocean.

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Animal behavior research can provide fundamental insights into the natural processes that affect ecosystems around the globe, as well as into our own human minds and behavior.

I study animal behavior – and also the research reported by scientists in my field. One of the challenges of this kind of science is making sure our own assumptions don't influence what we think we see in animal subjects. Like all people, how scientists see the world is shaped by biases and expectations, which can affect how data is recorded and reported. For instance, scientists who in a society with strict gender roles for women and might interpret things they see animals doing as reflecting those same divisions.

The scientific corrects for such mistakes over time, but scientists have quicker methods at their disposal to minimize potential observer bias. Animal behavior scientists haven't always used these methods – but that's changing. A new study confirms that, over the past decade, studies increasingly adhere to the rigorous best practices that can minimize potential biases in animal behavior research.

Black and white photo of a horse with a man and a small table between them displaying three upright cards.

Adding up?

Karl Krall/Wikimedia Commons

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Biases and self-fulfilling prophecies

A German horse named Clever Hans is widely known in the history of animal behavior as a classic example of unconscious bias leading to a false result.

Around the turn of the 20th century, Clever Hans was purported to be able to do math. For example, in response to his owner's prompt “3 + 5,” Clever Hans would tap his hoof eight times. His owner would then reward him with his favorite vegetables. Initial observers reported that the horse's abilities were legitimate and that his owner was not being deceptive.

However, careful analysis by a young scientist named Oskar Pfungst revealed that if the horse could not see his owner, he couldn't answer correctly. So while Clever Hans was not good at math, he was incredibly good at observing his owner's subtle and unconscious cues that gave the math answers away.

In the 1960s, researchers asked human study participants to code the learning ability of rats. Participants were told their rats had been artificially selected over many generations to be either “bright” or “dull” learners. Over several weeks, the participants ran their rats through eight different learning experiments.

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In seven out of the eight experiments, the human participants ranked the “bright” rats as being better learners than the “dull” rats when, in reality, the researchers had randomly picked rats from their breeding colony. Bias led the human participants to see what they thought they should see.

Eliminating bias

Given the clear potential for human biases to skew scientific results, textbooks on animal behavior research methods from the 1980s onward have implored researchers to verify their work using at least one of two commonsense methods.

One is making sure the researcher observing the behavior does not know if the subject from one study group or the other. For example, a researcher would measure a cricket's behavior without knowing if it came from the experimental or control group.

The other best practice is utilizing a second researcher, who has fresh eyes and no knowledge of the data, to observe the behavior and code the data. For example, while analyzing a file, I count chickadees taking seeds from a feeder 15 times. Later, a second independent observer counts the same number.

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Yet these methods to minimize possible biases are often not employed by researchers in animal behavior, perhaps because these best practices take more time and effort.

In 2012, my colleagues and I reviewed nearly 1,000 articles published in five leading animal behavior journals between 1970 and 2010 to see how many reported these methods to minimize potential bias. Less than 10% did so. By contrast, the journal Infancy, which focuses on human infant behavior, was far more rigorous: Over 80% of its articles reported using methods to avoid bias.

It's a problem not just confined to my field. A 2015 of published articles in the sciences found that blind protocols are uncommon. It also found that studies using blind methods detected smaller differences between the key groups being observed to studies that didn't use blind methods, suggesting potential biases led to more notable results.

In the years after we published our article, it was cited regularly and we wondered if there had been any improvement in the field. So, we recently reviewed 40 articles from each of the same five journals for the year 2020.

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We found the rate of papers that reported controlling for bias improved in all five journals, from under 10% in our 2012 article to just over 50% in our new review. These rates of still lag behind the journal Infancy, however, which was 95% in 2020.

All in all, things are looking up, but the animal behavior field can still do better. Practically, with increasingly more portable and affordable audio and video recording technology, it's getting easier to carry out methods that minimize potential biases. The more the field of animal behavior sticks with these best practices, the stronger the foundation of knowledge and public trust in this science will become.The Conversation

Todd M. Freeberg, Professor and Associate Head of Psychology, University of Tennessee

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Boeing’s Starliner is about to launch − if successful, the test represents an important milestone for commercial spaceflight

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theconversation.com – Wendy Whitman Cobb, Professor of Strategy and Security Studies, University – 2024-05-02 07:24:25

Boeing's Starliner spacecraft on approach to the International Space Station during an uncrewed test in 2022.

Bob Hines/NASA

Wendy Whitman Cobb, Air University

If all goes well late on May 6, 2024, NASA astronauts Butch Wilmore and Suni Williams will blast off into space on Boeing's Starliner spacecraft. Launching from the Kennedy Space Center, this last crucial test for Starliner will test out the new spacecraft and take the pair to the International Space Station for about a week.

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Part of NASA's commercial crew program, this long-delayed mission will represent the vehicle's first crewed launch. If successful, it will give NASA – and in the future, space tourists – more options for getting to low Earth orbit.

Two people wearing blue jumpsuits hug in front of a plane.

Suni Williams, right, and Butch Wilmore, the two astronauts who will crew the Starliner test.

AP Photo/Terry Renna

From my perspective as a space policy expert, Starliner's launch represents another significant milestone in the of the commercial space industry. But the mission's troubled history also shows just how difficult the path to space can be, even for an experienced company like Boeing.

Origins and development

Following the retirement of NASA's space shuttle in 2011, NASA invited commercial space companies to the agency transport cargo and crew to the International Space Station.

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In 2014, NASA selected Boeing and SpaceX to build their respective crew vehicles: Starliner and Dragon.

Boeing's vehicle, Starliner, was built to carry up to seven crew members to and from low Earth orbit. For NASA missions to the International Space Station, it will carry up to four at a time, and it's designed to remain docked to the station for up to seven months. At 15 feet, the capsule where the crew will sit is slightly bigger than an Apollo command module or a SpaceX Dragon.

Boeing designed Starliner to be partially reusable to reduce the cost of getting to space. Though the Atlas V rocket it will take to space and the service module that supports the craft are both expendable, Starliner's crew capsule can be reused up to 10 times, with a six-month turnaround. Boeing has built two flightworthy Starliners to date.

A conical vehicle sitting on a flat vehicle.

The Starliner capsule in transit.

AP Photo/John Raoux

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Starliner's development has come with setbacks. Though Boeing received US$4.2 billion from NASA, compared with $2.6 billion for SpaceX, Boeing spent more than $1.5 billion extra in developing the spacecraft.

On Starliner's first uncrewed test flight in 2019, a series of software and hardware failures prevented it from getting to its planned orbit as well as docking with the International Space Station. After testing out some of its systems, it landed successfully at White Sands Missile Range in New Mexico.

In 2022, after identifying and making more than 80 fixes, Starliner conducted a second uncrewed test flight. This time, the vehicle did successfully dock with the International Space Station and landed six days later in New Mexico.

The inside of a Starliner a few astronauts. Crew members first trained for the launch in a simulator.

Still, Boeing delayed the first crewed launch for Starliner from 2023 to 2024 because of additional problems. One involved Starliner's parachutes, which help to slow the vehicle as it returns to Earth. Tests found that some links in those parachute lines were weaker than expected, which could have caused them to break. A second problem was the use of flammable tape that could pose a fire hazard.

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A major question stemming from these delays concerns why Starliner has been so difficult to develop. For one, NASA officials admitted that it did not as much oversight for Starliner as it did for SpaceX's Dragon because of the agency's familiarity with Boeing.

And Boeing has experienced several problems recently, most visibly with the safety of its airplanes. Astronaut Butch Wilmore has denied that Starliner's problems reflect these troubles.

But several of Boeing's other space activities beyond Starliner have also experienced mechanical failures and budget pressure, the Space Launch System. This system is planned to be the main rocket for NASA's Artemis program, which plans to return humans to the Moon for the first time since the Apollo era.

Significance for NASA and commercial spaceflight

Given these difficulties, Starliner's success will be important for Boeing's future space efforts. Even if SpaceX's Dragon can successfully transport NASA astronauts to the International Space Station, the agency needs a backup. And that's where Starliner comes in.

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Following the Challenger explosion in 1986 and the Columbia shuttle accident in 2003, NASA retired the space shuttle in 2011. The agency was left with few options to get astronauts to and from space. a second commercial crew vehicle provider means that NASA will not have to depend on one company or vehicle for space launches as it previously had to.

Perhaps more importantly, if Starliner is successful, it could compete with SpaceX. Though there's no crushing demand for space right now, and Boeing has no plans to market Starliner for tourism anytime soon, competition is important in any market to down costs and increase innovation.

More such competition is likely coming. Sierra Space's Dream Chaser is planning to launch later this year to transport cargo for NASA to the International Space Station. A crewed version of the space plane is also being developed for the next round of NASA's commercial crew program. Blue Origin is working with NASA in this latest round of commercial crew contracts and developing a lunar lander for the Artemis program.

A conical white spacecraft with two rectangular solar panels in space, with the Earth in the background.

SpaceX's dragon capsule.

NASA TV via AP

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Though SpaceX has made commercial spaceflight look relatively easy, Boeing's rocky experience with Starliner shows just how hard spaceflight continues to be, even for an experienced company.

Starliner is important not just for NASA and Boeing, but to demonstrate that more than one company can find success in the commercial space industry. A successful launch would also give NASA more confidence in the industry's ability to operations in Earth's orbit while the agency focuses on future missions to the Moon and beyond.The Conversation

Wendy Whitman Cobb, Professor of Strategy and Security Studies, Air University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Brain cancer in children is notoriously hard to treat – a new mRNA cancer vaccine triggers an attack from within

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theconversation.com – Christina von Roemeling, Assistant Professor of Neurosurgery, of Florida – 2024-05-01 10:01:09

How cancer vaccines are delivered into the body influences their effectiveness.

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Christina von Roemeling, University of Florida and John Ligon, University of Florida

Brain cancers remain among the most challenging tumors to treat. They often don't respond to traditional treatments because many chemotherapies are unable to penetrate the protective barrier around the brain. Other treatments like radiation and surgery can leave with lifelong debilitating side effects.

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As a result, brain cancer is the leading cause of cancer-related death in children. Brain tumors in frequently do not respond to treatments developed for adults, likely due to the fact that pediatric brain cancers are not as well-studied as adult brain cancers. There is an urgent need to develop new treatments specific to children.

We developed a new messenger-RNA, or mRNA, cancer vaccine, described in newly published research, that can deliver treatments more effectively in children who have brain cancer and teach their immune to fight back.

Close-up of child's hand with IV line placed held by adult's hand

Cancer treatments designed for adults may not necessarily work as well in children.

Virojt Changyencham/Moment via Getty Images

How do cancer vaccines work?

The immune system is a complex network of cells, tissues and organs whose primary function is to continuously surveil the body for threats posed by foreign invaders – pathogens that tissues and make you sick. It accomplishes this by recognizing antigens, or abnormal proteins or molecules, on pathogens. T cells that recognize these antigens seek out and destroy the pathogens.

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Your immune system also protects you from domestic threats like cancer. Over time, your cells sustain DNA damage from either internal or external stressors, leading to mutations. The proteins and molecules produced from mutated DNA look quite different from the ones cells typically produce, so your immune system can recognize them as antigens. Cancer develops when cells accumulate mutations that enable them to continue to grow and divide while simultaneously going undetected by the immune system.

In 1991, scientists identified the first tumor antigen, helping lay the framework for modern-day immunotherapy. Since then, researchers have identified many new tumor antigens, facilitating the development of cancer vaccines. Broadly, cancer vaccines deliver tumor antigens into the body to teach the immune system to recognize and attack cancer cells that display those antigens. Although all cancer vaccines conceptually work very similarly, they each significantly vary in the way they are developed and the number and combination of antigens they carry.

Cancer vaccines the immune system differentiate between healthy cells and tumor cells.

One of the biggest differences among cancer vaccines is how they are created. Some vaccines use protein fragments, or peptides, of tumor antigens that are directly given to patients. Other vaccines use viruses reengineered to express cancer antigens. Even more complex are vaccines where a patient's own immune cells are collected and trained to recognize cancer antigens in a laboratory before being delivered back to the patient.

Currently, there is a lot of excitement and focus among researchers on developing mRNA-based cancer vaccines. Whereas DNA is the blueprint of which proteins to make, mRNA is a copy of the blueprint that tells cells how to build these proteins. Thus, researchers can use mRNA to create blueprint copies of potential antigens.

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mRNA cancer vaccines

The pandemic brought significant attention to the potential of using mRNA-based vaccines to stimulate the immune system and protection against the antigens they encode for. But researchers have been investigating the use of mRNA vaccines for treating various cancers since before the pandemic.

Our team of scientists in the Brain Tumor Immunotherapy Program at University of Florida has spent the past 10 years developing and optimizing mRNA vaccines to treat brain cancer.

Cancer vaccines have significant challenges. One key hurdle is that these vaccines may not always trigger a strong enough immune response to eradicate the cancer completely. Moreover, tumors are not made up of one type of cancer cell, but rather a complex mix of cancer cells that each harbors its own unique cocktail of mutations.

Our cancer vaccine seeks to address these issues in a number of ways.

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Diagram of lipid molecules formed a spherical shell around single-stranded mRNAs

Lipid nanoparticles can carry therapeutic mRNA into the body.

Buschmann et al. 2021/ Wikimedia Commons, CC BY-SA

First, we designed our vaccines by using the RNA of a patients' own cancer cells as a template for the mRNA inside our nanoparticles. We also packaged our cancer vaccine inside of nanoparticles made up of specialized lipids, or fat molecules. We maximized the amount of mRNA packaged within each nanoparticle by sandwiching them between lipid layers like the layers of an onion. In this way, we increase the likelihood that the mRNA molecules in our nanoparticles produce enough tumor antigens from that patient's cancer to activate an immune response.

Also, instead of injecting nanoparticles into the skin, muscle or directly into the tumor, as is commonly done for many therapeutic cancer vaccines, our mRNA nanoparticles are injected into the bloodstream. From there, they travel to organs throughout the body involved in the immune response to teach the body to fight against the cancer. By doing so, we've found that the immune system launches a near immediate and powerful response. Within six hours of receiving the vaccine, there is a significant increase in the amount of blood markers connected to immune activation.

Looking to the future

Our mRNA-based vaccines are currently undergoing early-phase clinical trials to treat real patients with brain cancer.

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We administered our mRNA-based vaccine to four adult patients with glioblastoma who had relapsed after previous treatment. All patients survived several months longer than the expected average survival at this advanced stage of illness. We expect to treat children with a type of brain tumor called pediatric high-grade glioma by the end of the year.

Importantly, mRNA vaccines can be developed to treat any kind of cancer, childhood brain tumors. Our Pediatric Cancer Immunotherapy Initiative focuses on developing new immune-based therapies for children afflicted with cancer. After developing an mRNA vaccine for glioma in chidren, we will expand to treat other kinds of pediatric brain cancers like medulloblastoma and potentially treat other kinds of cancers like skin cancer and bone cancer.

We are hopeful that mRNA-based vaccines may lead to more children being cured of their brain tumors.The Conversation

Christina von Roemeling, Assistant Professor of Neurosurgery, University of Florida and John Ligon, Assistant Professor of Hematology, University of Florida

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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