New system captures seizures in real time

A new study is among the first to provide a high-resolution 3D video of a seizure event.

A seizure is a sudden, overwhelming wave of electrical energy passing through the brain in a matter of seconds—so fast, in fact, that it’s been nearly impossible to fully capture where these electrical bursts originate, how they move and where they stop.

That’s why University of Georgia researchers developed a new high-resolution light-sheet imaging system that is fast enough to document seizures in 3D in real-time.

Using zebrafish larvae, the go-to animal model for neuroscience research, the researchers captured images of a seizure making its way through the brain.

The images show that the seizure began toward the back of the brain and moved forward toward the part of the midbrain that processes visual information known as the optic tecta. This region controls eye movement and manages responses to what the animal is seeing. The electrical activity gradually subsides over several seconds.

The study is among the first to provide a high-resolution 3D video of a seizure event from start to finish.

“The brain is obviously three dimensional, so when you have 2D imaging, not everything is going to be visible on that single 2D plane,” says Peter Kner, corresponding author of the study and a professor in UGA’s College of Engineering.

“Seeing where something is going or where something happens, if you’re looking at a 2D plane, you start to wonder, ‘Did I actually capture the whole thing?'”

Examining those images in 3D could offer new insights into how seizures form and how they propagate. Seizure propagation is how seizure activity starts in one part of the brain and moves through other parts of the brain. This, in turn, helps researchers understand how the brain operates.

A better understanding of how the brain operates could inform new treatments of brain diseases and disorders, Kner says.

Light sheet microscopy uses a thin sheet of light to illuminate a single slice of a sample at one time. It works well on living organisms because it provides clear images at high-speed with low background, enabling fast tracking of complex processes like brain activity.

The new microscope also relies on adaptive optics, a technology originally developed for use in astronomy.

“When you look at the stars in the night sky, they sort of twinkle because the atmosphere is making the image wobble around,” Kner explains.

“It looks nice, but it’s not great for astronomers because they don’t get a good, sharp image. Adaptive optics technology corrects that.”

A similar problem is present in brain imaging caused by the tissues the light travels through. As the light travels through the tissue, its path gets bent, and the images get blurred. Using adaptive optics enables researchers to get a sharper image.

“You always want the sharpest image you can get,” Kner says. “The whole field of imaging is really exciting right now. Microscopes have been around since roughly 1650, so you think what could possibly be new?

“But there are a lot of places left for the field to go.”

The research appears in Biomedical Optics Express.

The study was funded by a grant from the National Institutes of Health.

Source: University of Georgia

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Prolonged exposure to wildfire smoke raises heart health risks

Repeated and cumulative exposure to wildfire smoke is linked to a higher risk of being hospitalized for cardiovascular disease in older adults in the US, a new study finds.

As the climate warms, wildfires are becoming larger, more frequent, and more intense. The smoke now routinely travels hundreds of miles, as was seen when Canadian wildfires this month sent heavy smoke across the Midwest and Northeastern US, triggering dangerous air quality across the region.

But the health risks of wildfire smoke goes beyond the short-term threat to your lungs.

In the new study, Yale researchers found that repeated and cumulative exposure to the fine particles in wildfire smoke is linked to a higher risk of being hospitalized for cardiovascular disease in older adults in the United States.

Looking at more than 65 million Medicare beneficiaries across the continental US,the researchers saw that exposure to even relatively modest levels of smoke, built up over a few years, was associated with more hospitalizations for conditions like ischemic heart disease, irregular heart rhythms, stroke, and heart failure.

“In other words, the cardiovascular toll of wildfire smoke reaches well beyond the fire lines and well beyond the day the sky turns orange,” says Kai Chen, associate professor of epidemiology (environmental health sciences) at the Yale School of Public Health and corresponding author of the study.

The study appears in the Journal of the American College of Cardiology.

So far, much of the research into wildfire-related smoke has focused on the immediate effects of breathing in smoke over a few days or weeks, Chen says. And it has mostly looked at the respiratory system, including the lungs. On smoky days, there are more emergency department (ED) visits and hospitalizations, especially for breathing problems like asthma, studies have shown.

In the longer term, a small but growing number of studies have also started to connect prolonged wildfire smoke exposure to cardiovascular death and, more recently, to new cases of heart failure and stroke.

But those earlier studies largely didn’t include incidents from more recent years, which is when wildfire activity and smoke exposure has really intensified. And they didn’t look across the full range of heart and blood-vessel conditions, the authors say. So, the long-term cardiovascular picture—especially for specific subtypes like ischemic heart disease and arrhythmias, and for the older adults who are most at risk—was still very much an open question.

Because heart disease is the leading cause of death in this country, the researchers decided to look into what that repeated, long-term exposure does to cardiovascular health—and whether some groups of people carry more of that burden than others.

To do that, they built a large population-based study using Medicare records for everyone aged 65 and older in the contiguous US from 2017 through 2022—about 65 million people. Then, for the exposure side, they used a validated model that estimates fine particulate matter related to smoke—what they call “smoke PM2.5″—on a 10-by-10-kilometer grid across the country, using satellite imagery, smoke-plume tracking, and ground monitors.

They then assigned those smoke levels to each person based on the ZIP code where they lived. Because the researchers were more concerned with long-term exposure than exposure during a single bad day, they calculated each person’s cumulative average smoke level over the prior three years, deliberately leaving out the year of hospitalization so they were capturing chronic buildup, not a short-term spike, Chen says.

On the health side, they tracked for relevant individuals the first hospitalization for overall cardiovascular disease and for specific subtypes: ischemic heart disease, stroke and other cerebrovascular disease, heart failure, and arrhythmias.

Finally, they used statistical models to estimate how risk of hospitalization changed across increasing levels of smoke exposure, carefully accounting for other factors that affect heart health like other kinds of air pollution, temperature, humidity, and a wide range of community-level social and economic factors. They also checked whether risk differed by age, sex, race, and socioeconomic status, and they ran several sensitivity and falsification tests to make sure the findings held up.

Through this process, the researchers found that long-term wildfire smoke exposure was clearly associated with a higher risk of cardiovascular hospitalization. Heart failure showed the strongest association, with risk more than 20% higher at higher exposure levels. And for stroke, particularly ischemic stroke, the risk just kept rising as exposure went up.

Two additional findings stood out to the researchers, Chen says. First, they saw elevated long-term risk even at fairly modest smoke levels, which means communities that are only lightly touched by smoke—not just those near the fires—may still face real cardiovascular risk. Second, the burden wasn’t shared equally: people of lower socioeconomic status were potentially more vulnerable, which points to a real equity problem in how this exposure plays out, they found.

“Wildfire smoke should be treated as more than a temporary respiratory nuisance,” says Harlan Krumholz, a professor of medicine at Yale School of Medicine (YSM) and coauthor of the study.

“This study finds that cumulative exposure is linked to a higher risk of cardiovascular hospitalization in older adults. Clinicians should help high-risk patients prepare for smoke events, and public health leaders should expand timely alerts, clean-air spaces, and access to effective filtration, especially for communities with fewer resources.”

These findings suggest that “the same steps that protect your lungs also help protect your heart,” says Yuan Lu, associate professor of medicine at YSM and co-senior author of the study. That includes checking local air quality, staying indoors when pollution levels are high, running air purifiers or creating what have become known as “clean-air rooms” (a designated space in any building where air quality is maintained), limiting strenuous outdoor activities, and using a well-fitting N95 or KN95 mask when outdoors.

Source: Yale

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Why do your pupils dilate when you’re surprised?

New research digs into why people’s pupils dilate when they’re surprised.

When people encounter new information that challenges their expectations, their pupils are likely to dilate. This physiological response is a sign that something is happening in the brain to help the person adapt to the new situation, according to a new study.

“Pupil dilation signals a spike in arousal, and our findings support the idea that these rapid fluctuations in arousal are doing something useful in the brain,” says study author Matt Nassar, an associate professor of neuroscience and of cognitive and psychological sciences at Brown University.

“They’re enabling us to deal with a world that often changes from one context to another context.”

Nassar is part of a research team at Brown’s Carney Institute for Brain Science that studied the function and purpose of spikes in physical alertness. Their findings in Nature Human Behaviour show that the spikes are a signal of the brain’s transition into a new mode, instantly changing how the person perceives and learns from what is happening around them.

Surprising events elicit activity in the part of the brain known as the locus coeruleus, which is the primary source of norepinephrine, the chemical messenger that drives the body’s flight-or-flight stress response. This activity is correlated with a change in pupil diameter as well as with specific brain waves measured by electroencephalography (EEG). Despite many studies showing spikes in norepinephrine and other markers of arousal in response to surprising events, the function of these physical signals and how they might shape behavior has been unclear, Nassar says.

The team designed an experiment, involving colored squares on a screen, during which participants repeatedly make predictions about what they are about to see. Then participants are shown a new set of squares, report what they see and make new predictions about what they’d see next. During this time, the researchers collect physiological data including changes in pupil diameter as well as EEG signals.

“We wanted to capture the phenomenon associated with two overarching ideas about how the arousal system affects behavior,” says study coauthor Harrison Marble, who earned a bachelor’s degree in neuroscience from Brown in 2023 and is now a research assistant and manager in Nassar’s lab.

“One of them is related to learning, and the other is related to perceptual bias.”

The experiment included 63 participants, which resulted in 57 EEG datasets and 60 pupil measurement datasets.

The researchers found that surprising colors—those that didn’t match predictions—elicited pupil dilation and amplified certain brain waves. They also found that these measurements related to reductions in bias and adjustment in learning: When images looked like what the participants thought they were going to see, participants were biased toward their expectations. Yet pupil and brain measurements showed that participants were also able to learn from unexpected results and adjust their subsequent prediction accordingly.

In surprising situations, the researchers found, the norepinephrine spike is almost like a refresh mechanism—it’s a sign the brain is adjusting to new information that changes expectations.

“The brain holds on to some mental context, and then when it recognizes that you’re in a new situation, you replace that context,” Nassar says.

“Changes in pupil diameter, as well as specific EEG readings, are external markers of the brain that shows it’s loading that new context. This both limits the effect that the previous context had on perception and also provides a clean slate, unencumbered by previous expectations, thereby allowing one to learn faster.”

Funding for was provided by the National Institute of Mental Health.

Source: Brown University

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Team uncovers environmental cues behind bear hibernation

New research provides fresh insight into how environmental conditions influence black bear hibernation.

The findings have implications for wildlife management in today’s environment.

The study, led by Brogan Holcombe, a doctoral student in the Department of Fish and Wildlife Conservation in the Virginia Tech College of Natural Resources and Environment, and coauthored by Professor Marcella Kelly and Bernardo Mesa-Cruz, examines how ambient temperature and day length interact to shape hibernation behavior in American black bears.

“As temperatures rise, bears may become more active during times when they would traditionally remain dormant,” Holcombe wrote in the study.

This increased activity could lead to mismatches between bear behavior and seasonal food availability, potentially driving bears to seek out human-associated resources.

Using more than 22,000 hours of continuous video footage collected at Virginia Tech’s Black Bear Research Center during Mesa-Cruz’s original study, Holcombe tracked the activity of four wild, pregnant female bears across multiple stages of the hibernation cycle. The analysis categorized 45 distinct behaviors and linked them to environmental cues such as temperature and photoperiod—the seasonal pattern of daylight—as well as physiological changes during hibernation.

The team found that both temperature and day length play important and interacting roles in driving bear activity, particularly during critical periods such as pre-hibernation feeding and den emergence. While temperature alone influenced behavior during the onset of hibernation, the combined effects of temperature and photoperiod were key drivers of activity in other stages.

These findings challenge the long-standing assumption that hibernation timing in black bears is driven primarily by temperature. Instead, the results suggest a more complex relationship between environmental signals, one that could be disrupted as climate patterns shift.

Because photoperiod remains constant while temperatures fluctuate, the researchers highlight the risk that climate change could decouple these environmental cues. Such mismatches may alter hibernation timing, with cascading effects for ecosystems and increases in human-wildlife interactions.

The study in the Proceedings of the Royal Society B: Biological Sciences, represents one of the first to directly examine the combined influence of temperature and photoperiod in bear hibernation ecology, offering new data to inform conservation strategies.

By improving understanding of how black bears respond to environmental change, the research provides wildlife managers with better tools to anticipate behavioral shifts and plan for future conditions.

Source: Virginia Tech

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Your ideas about antibiotics are probably outdated

“Always finish your antibiotics” is no longer considered medical best practice for all conditions.

Surprised? You’re not alone.

A new study has found that almost 90% of Americans believe that it’s always best to take the full course of antibiotics, even when you feel better—consistent with long-running but now outdated health campaigns.

The reality is more complicated. Sometimes shorter courses are safer, and sometimes longer courses are best.

The survey demonstrates a need for better communication between doctors and patients about what’s healthiest.

“Historically, there was very strong guidance by major health organizations and clinicians that you must always finish the course,” says Alistair Thorpe, research assistant professor of population health sciences at University of Utah Health and first author on the study.

“Now, we’re seeing a growing body of evidence saying that that is not always the case. And oftentimes, shorter durations of antibiotics are as effective and safe as longer alternatives.”

The results appear in Open Forum Infectious Diseases.

The research team surveyed 1,475 people across the country on their attitudes about antibiotic course length. They asked participants whether they’d feel more comfortable taking a three- to five-day course of antibiotics if they had pneumonia, as is recommended by current guidelines, or if they’d prefer an antibiotic course of a week or more, which is recommended by outdated guidelines. While shorter courses of antibiotics are as effective and safer for pneumonia than longer ones, about 60% of people says they’d rather take the longer course.

One of the main reasons people gave for preferring longer antibiotic courses was that they had been told to “always finish their antibiotic course”—88% of respondents had heard of, and agreed with, this common mantra. Most had been told this by their clinician, and many had also heard it via a public health campaign.

A strong body of scientific evidence shows that, for many common infections, shorter courses of antibiotics work as well as longer courses and are less likely to cause side effects. Still, there are some cases, like tuberculosis, where longer courses are most effective.

The study authors suggest that doctors and public health campaigns use several evidence-informed strategies to better communicate the complex reality of antibiotic course length—for instance, avoiding overly simplistic claims that either shorter or longer courses are universally better, and acknowledging that as scientific evidence accumulates over time, health recommendations can change.

To patients who have been prescribed antibiotics, Thorpe recommends having an open conversation with your doctor about the appropriate course length and adherence plan to get health advice that’s specific to your situation.

“Discuss with your clinician what the right duration is for you and when the right time is to stop your course,” Thorpe says.

“Getting advice directly from a clinician on a one-to-one basis about what is most appropriate for you in that situation is the right way to go.”

Thorpe emphasizes that the changing recommendations are a positive outcome of increasing knowledge.

“Evidence is growing and guidance is evolving on antibiotic use, which is a normal process and a good sign that we are working to improve how we provide care,” he says.

“Our knowledge about how best to use antibiotics has changed, but it has changed because we’re learning more, and it’s important that we make sure we are communicating this well to the public.”

The work was supported by the Jon M. Huntsman Presidential Endowed Chair of the population health sciences department at the University of Utah, internal research funds from the internal medicine department at the University of Utah, and from the medicine department at the University of Alabama at Birmingham, and the American Heart Association.

Content is solely the responsibility of the authors and does not necessarily represent the official views of the funders.

Source: University of Utah

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Foreign tariffs made US whiskey cheaper in some places

A new study finds that one side effect of Trump-era trade wars has been cheaper whiskey in much of the United States.

But if you live in whiskey hubs Kentucky or Tennessee? Your prices actually went up.

In 2018, the Trump administration imposed a series of tariffs, kicking off a trade war with many prominent trade partners. In response, Mexico, the European Union, Canada, and China imposed substantial tariffs on whiskey produced in the US.

“Distilled spirits are an interesting sector because consumers have significant preferences, which can influence pricing on a market-to-market basis,” says Carly Burd, coauthor of a paper on the work and an assistant professor of accounting at North Carolina State University’s Poole College of Management.

“Whiskey constituted the vast majority of US liquor exports prior to 2018, and we wanted to examine how US whiskey producers responded to a sudden decrease in foreign sales.”

For this study, the researchers collected sales data on 8,674 stores over the course of the 2018 calendar year. The researchers looked at the cost of 2,514 unique whiskey products—all of which measured 750 milliliters in volume. Ultimately, the researchers had data on 11.4 million sales of whiskey products.

The researchers looked at how much the cost of US whiskeys changed before and after the introduction of export tariffs and compared it to the cost of imported whiskeys over the same time period. The imported whiskeys served as a control group, since they were not subject to US tariffs in 2018.

“Overall, US whiskey producers responded to export tariffs by decreasing the cost of whiskey in order to increase domestic sales,” says Burd. “However, there were significant exceptions.

“For example, producers actually increased the price of locally-produced whiskeys in Kentucky and Tennessee. Those two states produce the vast majority of whiskey sold in the US, and our theory is that consumers purchasing whiskey in those states were willing to pay a premium for locally-produced products.”

On average, whiskey prices either didn’t change or went up slightly in states where there was already significant demand for whiskey products, and went down everywhere else.

“One important factor here is that whiskey has to be aged, so producers are unable to rapidly increase or decrease supply,” says Burd.

That means whiskey producers couldn’t respond to decreased exports in 2018 by quickly scaling back production. This is likely a big reason producers opted to pursue a dynamic pricing model. And continued trade policy uncertainty has likely played a role in continuing to discourage supply chain responses; pricing responses are faster and more flexible.

“Whiskey is a good case study for understanding the ways in which political tensions, trade disputes, and tax changes can pose significant challenges for domestic producers—and how producers adapt those challenges,” says Burd.

“It also illustrates how the impact of trade policy on consumers can vary significantly in different parts of the country.”

The paper appears in the journal The Accounting Review.

The paper was coauthored by Duke Ferguson, an assistant professor of business and economics at the University of Kentucky.

Source: North Carolina State University

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Why do you have reread some sentences and not others?

New research digs into why we breeze through some sentences in a book or article but have to reread others to comprehend their meaning.

A team of linguists and data scientists has found a partial answer in AI—some of this processing parallels that of neural-network-based large language models (LLMs).

However, other aspects of why we read this way cannot be explained by these technologies, revealing where human and AI language processing diverge and maintaining the mystery of some stages of the reading process.

The new study by researchers from New York University and the University of Massachusetts Amherst shows that humans and AI process language in similar ways during the earliest moments of reading: both rely on next-word predictions. However, as reading continues and passages become more complex—often requiring rereading—humans’ processing differs from that of AI, which is entirely built on next-word prediction and therefore cannot account for how we navigate these types of passages.

“Language models develop their remarkable language understanding capabilities by being trained to predict the next word in a sentence, which led us to ask whether the same predictive processes that drive these AI systems could also explain how humans comprehend sentences,” explains William Timkey, a linguistics doctoral student at NYU and the lead author of the paper, which appears in the journal Proceedings of the National Academy of Sciences (PNAS).

“We found that LLMs can explain how long it takes people to recognize words when their eyes move smoothly forward through a text, but they fail to capture the cases where people have difficulty integrating a word into the larger context of a sentence, which is often accompanied by rereading.”

The authors note that despite the remaining uncertainty on how humans read—notably the rereading of passages—the findings nonetheless offer a potential roadmap for both improving language learning and addressing reading-related afflictions.

“We now know a little bit better how humans and models are different,” says Brian Dillon, a professor of linguistics at the UMass Amherst and the paper’s senior author.

“That is the first step in understanding how we can close that gap, which we want to do because that could have enormous advantages down the road.”

“Our work shows that AI can be very valuable for cognitive science, but it is not enough,” adds Tal Linzen, an associate professor of linguistics and data science at NYU and one of the paper’s authors.

“The human mind does not always work like standard AI systems—for instance, 20% of our eye movements when reading are backward, and AI models cannot explain when we decide to do that. We now have our work cut out for us to create computational models that more closely match the human mind and that can help us understand in detail how it operates.”

When we see words on a page, we go through mental processes of taking the visual information of the letters, accessing the meaning of the word, and then integrating that with the rest of a sentence. While much of reading is driven by word prediction, less clear are its limits—a question the researchers explored in the PNAS study.

To do so, they deployed LLMs because their predictive-text feature aligns with some theories of how the brain works: the prediction process drives our ability to comprehend sentences.

“LLMs seem to capture some of the properties of language as we understand it—they can generate text fluently and they appear to react in a way that suggests they have some understanding of what’s going on,” explains Dillon, who directs the Computational Sentence Processing Lab in the linguistics department at UMass Amherst.

“We build a mental representation of what we think a sentence means based on the words on the page, then use that representation to make predictions about the next words, and then update our mental representation when those predictions are wrong,” adds Timkey.

In the study, the researchers used eye-tracking technology to analyze 368 adult readers, focusing on how long participants spent reading—and rereading—each word of carefully designed sentences. These included a diverse set of syntactically challenging sentences, known as garden- path sentences—grammatically correct sentences that start in such a way that a reader’s initial interpretation will likely be incorrect. For instance, take the sentence “The old man the boat.” Readers may initially think the sentence is about an old man, but instead, the sentence means that old people are manning a boat. Such sentences, the authors note, are good candidates for understanding how we process complex passages.

The researchers then compared those eye movements with predictions generated by more than 400 AI language models.

The results showed that AI models’ next word predictions can explain the first step of processing each word of a sentence: identifying the word from a sequence of letters. However, they can’t explain the next step of integrating that word into the larger meaning of the sentence—a process that is particularly difficult for humans in garden-path sentences, and one which still remains poorly understood.

“The predictability of a word really doesn’t even come close to explaining just how much time we spend on difficult words and garden-path sentences,” says Timkey. “LLMs were drastically underpredicting the type of difficulty that we experience when reading.”

“It’s in that second stage of processing—recognizing a word and then integrating it with other words in passages—where we find big gaps between what word predictability can explain and what we need cognitive models to explain,” adds Linzen.

The research was supported by grants from the National Science Foundation.

Source: New York University

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