Led by Natashya Falcone and co-first authors Tess Grett Mathes and Mahsa Monirizad, the research team delved into the realm of self-assembling peptide-based hydrogels, renowned for their versatility in mimicking extracellular matrices (ECMs) of diverse microenvironments.

AD presents an intricate challenge in neurodegenerative research. Traditional two-dimensional (2D) models have limitations in capturing the complexity of the disease. Through their innovative approach, the team developed a multi-component hydrogel scaffold, named Col-HAMA-FF, designed to mimic the amyloid-beta (β) containing microenvironment associated with AD.

The study’s findings, published in a recent issue of Acta Biomaterialia, illuminate the formation of β-sheet structures within the hydrogel matrix, mimicking the nanostructures of amyloid-β proteins. By culturing healthy neuronal progenitor cells (NPCs) within this amyloid-mimicking environment and comparing results to those in a natural-mimicking matrix, the researchers observed elevated levels of neuroinflammation and apoptosis markers. This suggests a significant impact of amyloid-like structures on NPC phenotypes and behaviors.

Dr. Ali Khademhosseini, the study’s corresponding author, expressed excitement about the implications of their findings: “This foundational work provides a promising scaffold for future investigations into AD mechanisms and drug testing. By bridging the gap between 3D hydrogel models and the complex reality of AD pathological nanostructures, we aim to understand this interaction on healthy neuronal cells so that we can accelerate the development of effective therapeutic strategies.”

The study represents a crucial step towards unraveling the mysteries of the b-amyloid-like environment which can be found in AD and marks a milestone in the quest for innovative solutions to combat neurodegenerative disorders.

“Since President Biden and Vice President Harris unveiled the first image from the James Webb Space Telescope two years ago, Webb has continued to unlock the mysteries of the universe,” said NASA Administrator Bill Nelson. “With remarkable images from the corners of the cosmos, going back nearly to the beginning of time, Webb’s capabilities are shedding new light on our celestial surroundings and inspiring future generations of scientists, astronomers, and explorers.”

Also take a moment to scan the background. Webb’s image is overflowing with distant galaxies. Some take spiral and oval shapes, like those threaded throughout the Penguin’s “tail feathers,” while others scattered throughout are shapeless dots. This is a testament to the sensitivity and resolution of the telescope’s infrared instruments. (Compare Webb’s view to the 2018 observation that combines infrared light from NASA’s retired Spitzer Space Telescope and near-infrared and visible light from NASA’s Hubble Space Telescope.) Even though these observations only took a few hours, Webb revealed far more distant, redder, and dustier galaxies than previous telescopes — one more reason to expect Webb to continue to expand our understanding of everything in the universe.

Want more? Take a tour to the image, “fly through” it in a visualization, and compare Webb’s image to the Hubble Space Telescope’s.

Arp 142 lies 326 million light-years from Earth in the constellation Hydra.

“In just two years, Webb has transformed our view of the universe, enabling the kind of world-class science that drove NASA to make this mission a reality,” said Mark Clampin, director of the Astrophysics Division at NASA Headquarters in Washington. “Webb is providing insights into longstanding mysteries about the early universe and ushering in a new era of studying distant worlds, while returning images that inspire people around the world and posing exciting new questions to answer. It has never been more possible to explore every facet of the universe.”

The telescope’s specialization in capturing infrared light — which is beyond what our own eyes can detect — shows these galaxies, collectively known as Arp 142, locked in a slow cosmic dance. Webb’s observations, which combine near- and mid-infrared light from Webb’s NIRCam (Near-Infrared Camera) and MIRI (Mid-Infrared Instrument), respectively, clearly show that they are joined by a haze represented in blue that is a mix of stars and gas, a result of their mingling.

Their ongoing interaction was set in motion between 25 and 75 million years ago, when the Penguin (individually cataloged as NGC 2936) and the Egg (NGC 2937) completed their first pass. They will go on to shimmy and sway, completing several additional loops before merging into a single galaxy hundreds of millions of years from now.

Let’s Dance!

Before their first approach, the Penguin held the shape of a spiral. Today, its galactic center gleams like an eye, its unwound arms now shaping a beak, head, backbone, and fanned-out tail.

Like all spiral galaxies, the Penguin is still very rich in gas and dust. The galaxies’ “dance” gravitationally pulled on the Penguin’s thinner areas of gas and dust, causing them to crash in waves and form stars. Look for those areas in two places: what looks like a fish in its “beak” and the “feathers” in its “tail.”

Surrounding these newer stars is smoke-like material that includes carbon-containing molecules, known as polycyclic aromatic hydrocarbons, which Webb is exceptional at detecting. Dust, seen as fainter, deeper orange arcs also swoops from its beak to tail feathers.

In contrast, the Egg’s compact shape remains largely unchanged. As an elliptical galaxy, it is filled with aging stars, and has a lot less gas and dust that can be pulled away to form new stars. If both were spiral galaxies, each would end the first “twist” with new star formation and twirling curls, known as tidal tails.

Another reason for the Egg’s undisturbed appearance: These galaxies have approximately the same mass or heft, which is why the smaller-looking elliptical wasn’t consumed or distorted by the Penguin.

It is estimated that the Penguin and the Egg are about 100,000 light-years apart — quite close in astronomical terms. For context, the Milky Way galaxy and our nearest neighbor, the Andromeda Galaxy, are about 2.5 million light-years apart. They too will interact, but not for about 4 billion years.

Now, look to the top right to spot a galaxy that is not at this party. This edge-on galaxy, cataloged PGC 1237172, is 100 million light-years closer to Earth. It’s also quite young, teeming with new, blue stars.

Want one more party trick? Switch to Webb’s mid-infrared-only image to see PGC 1237172 practically disappear. Mid-infrared light largely captures cooler, older stars and an incredible amount of dust. Since the galaxy’s stellar population is so young, it “vanishes” in mid-infrared light.

But a model’s uncertainty quantifications are only useful if they are accurate. If a model says it is 49% confident that a medical image shows a pleural effusion, then 49% of the time, the model should be right.

MIT researchers have introduced a new approach that can improve uncertainty estimates in machine-learning models. Their method not only generates more accurate uncertainty estimates than other techniques, but does so more efficiently.

In addition, because the technique is scalable, it can be applied to huge deep-learning models that are increasingly being deployed in health care and other safety-critical situations.

This technique could give end users, many of whom lack machine-learning expertise, better information they can use to determine whether to trust a model’s predictions or if the model should be deployed for a particular task.

“It is easy to see these models perform really well in scenarios where they are very good, and then assume they will be just as good in other scenarios. This makes it especially important to push this kind of work that seeks to better calibrate the uncertainty of these models to make sure they align with human notions of uncertainty,” says lead author Nathan Ng, a graduate student at the University of Toronto who is a visiting student at MIT.

Ng wrote the paper with Roger Grosse, an assistant professor of computer science at the University of Toronto; and senior author Marzyeh Ghassemi, an associate professor in the Department of Electrical Engineering and Computer Science and a member of the Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems. The research will be presented at the International Conference on Machine Learning.

Quantifying uncertainty

Uncertainty quantification methods often require complex statistical calculations that don’t scale well to machine-learning models with millions of parameters. These methods also require users to make assumptions about the model and data used to train it.

The MIT researchers took a different approach. They use what is known as the minimum description length principle (MDL), which does not require the assumptions that can hamper the accuracy of other methods. MDL is used to better quantify and calibrate uncertainty for test points the model has been asked to label.

The technique the researchers developed, known as IF-COMP, makes MDL fast enough to use with the kinds of large deep-learning models deployed in many real-world settings.

MDL involves considering all possible labels a model could give a test point. If there are many alternative labels for this point that fit well, its confidence in the label it chose should decrease accordingly.

“One way to understand how confident a model is would be to tell it some counterfactual information and see how likely it is to believe you,” Ng says.

For example, consider a model that says a medical image shows a pleural effusion. If the researchers tell the model this image shows an edema, and it is willing to update its belief, then the model should be less confident in its original decision.

With MDL, if a model is confident when it labels a datapoint, it should use a very short code to describe that point. If it is uncertain about its decision because the point could have many other labels, it uses a longer code to capture these possibilities.

The amount of code used to label a datapoint is known as stochastic data complexity. If the researchers ask the model how willing it is to update its belief about a datapoint given contrary evidence, the stochastic data complexity should decrease if the model is confident.

But testing each datapoint using MDL would require an enormous amount of computation.

Speeding up the process

With IF-COMP, the researchers developed an approximation technique that can accurately estimate stochastic data complexity using a special function, known as an influence function. They also employed a statistical technique called temperature-scaling, which improves the calibration of the model’s outputs. This combination of influence functions and temperature-scaling enables high-quality approximations of the stochastic data complexity.

In the end, IF-COMP can efficiently produce well-calibrated uncertainty quantifications that reflect a model’s true confidence. The technique can also determine whether the model has mislabeled certain data points or reveal which data points are outliers.

The researchers tested their system on these three tasks and found that it was faster and more accurate than other methods.

“It is really important to have some certainty that a model is well-calibrated, and there is a growing need to detect when a specific prediction doesn’t look quite right. Auditing tools are becoming more necessary in machine-learning problems as we use large amounts of unexamined data to make models that will be applied to human-facing problems,” Ghassemi says.

IF-COMP is model-agnostic, so it can provide accurate uncertainty quantifications for many types of machine-learning models. This could enable it to be deployed in a wider range of real-world settings, ultimately helping more practitioners make better decisions.

“People need to understand that these systems are very fallible and can make things up as they go. A model may look like it is highly confident, but there are a ton of different things it is willing to believe given evidence to the contrary,” Ng says.

In the future, the researchers are interested in applying their approach to large language models and studying other potential use cases for the minimum description length principle.

The study, published in Nature Ecology and Evolution, helps answer the question of how normally tame rabbits — which have many natural predators — can become a force of ecological destruction when purposefully or accidentally reintroduced to the wild.

Here Comes Peter Cottontail

Every gardener knows how much of a nuisance rabbits can be, but many people may not realize the magnitude of ecological destruction that rabbits are capable of.

“The classic example is Australia, which was colonized by rabbits to the point that it caused one of the largest environmental disasters in history,” said Dr. Leif Andersson, a professor in the VMBS’ Department of Veterinary Integrative Biosciences and a professor at Uppsala University in Sweden. “In 1859, an Englishman named Thomas Austin released 24 European rabbits onto his estate as game animals, but the population of rabbits exploded, causing an infestation that continues to cause millions of dollars’ worth of crop damage each year.

“What is interesting is that rabbits had already been introduced to Australia in 1788. Why did Austin’s rabbit release cause such a population explosion and not the earlier release?” he said.

Thanks to the recent study, scientists now believe that they have the answer.

“After sequencing the genomes of nearly 300 rabbits from Europe, South America, and Oceania, we found that all of them had a mix of feral and domestic DNA,” Andersson said. “This was not what we had expected to find — we expected that feral rabbits were domestic rabbits that have somehow relearned how to live in the wild. But our findings show us that these rabbits already had a portion of wild DNA helping them survive in nature.”

Andersson’s discovery explains why the 24 rabbits introduced to the Australian landscape in 1859 were so quick to adapt to living in the wild — they already possessed the right genetic traits that would help them thrive.

Rewilding Domestic Rabbits

But returning a species to the wild after centuries of domestication isn’t a simple process. For example, domestic rabbits have been bred by humans to be more docile and trusting than their wild counterparts. They are also often bred to have certain coat colors that humans find attractive — like all-black or all-white coats — that would make them easier for predators to spot in the wild.

“During the rewilding process, natural selection removes many of these domestic traits because they are maladaptive — or unhelpful for survival — in the wild,” Andersson explained. “But it’s not just coat colors that change. We also observed that many of the genetic variants removed during natural selection are related to behavior, like tameness. This brings back the wild flight instinct that is important for eluding predators.”

The entire process appears to depend on whether the rabbits already have wild genes in their DNA as a sort of foundation for the rewilding process.

“We hope that this study will help lawmakers understand the importance of preventing domestic animals from being released into the wild,” Andersson said. “This project has helped us understand not only how rabbits become feral but also how other species like pigs and cats can become feral nuisances.”

The study is a collaboration with the Research Center in Biodiversity and Genetic Resources (CIBIO), a Portuguese research organization.

“Recent advances in surgical neuromodulation have enabled long-term continuous monitoring of brain activity in OCD patients during their everyday lives,” said Dr. Nicole Provenza, an assistant professor at Baylor College of Medicine and McNair Scholar. “We used this novel opportunity to identify key neural signatures that can act as predictors of clinical state in twelve individuals with treatment-resistant OCD who were receiving DBS therapy.”

DBS is emerging as an effective treatment for severe, treatment-resistant OCD

OCD is a common and debilitating mental health condition that affects 2-3% of the population worldwide. About two million people in the US suffer from OCD. In severe cases, patients spend an extraordinary amount of time performing repetitive, seemingly senseless compulsions and perseverating on intrusive thoughts. OCD has a huge toll on the well-being and quality of life of patients and their caregivers and can interfere with the ability to maintain employment and relationships. While psychotherapy and medications are effective in a majority of the affected individuals, approximately 20-40% of individuals with severe OCD are resistant to these conventional treatments.

Since the early 2000s, DBS therapy has been used to modulate neural activity in specific regions of the brain linked to OCD symptoms. Many patients who qualify for this therapy have not received sufficient benefit from conventional therapies. In this treatment-resistant population, roughly two-thirds of patients show significant improvement in OCD symptoms after DBS.

Much like how pacemaker devices regulate electrical activity in the heart, DBS devices regulate electrical activity in the brain. DBS devices carry electrical impulses from the generator, typically implanted in the upper chest, via a pair of thin leads (wires) to specific target regions in the brain. Precise tuning of the stimulation parameters allows the electrical pulses to restore a dysfunctional brain circuit to a healthy state.

DBS is an FDA-approved procedure commonly used to treat movement disorders such as essential tremors and Parkinson’s disease and is increasingly being used to treat severe OCD.

“We have seen remarkable progress in the field of DBS research, a technology that has been used for decades to treat movement disorders,” said Dr. John Ngai, Director of the Brain Research Through Advancing Innovative Neurotechnologies® Initiative (The BRAIN Initiative®) at the National Institutes of Health, which provided partial funding for this study. “The advance reported here represents just one on a growing list of success stories where the BRAIN Initiative has helped develop a new generation of DBS technologies, bringing treatments for conditions like OCD closer to the clinic.”

Need for a clinical biomarker to monitor OCD patient’s response to DBS

Defining the correct dose is oftentimes more difficult for psychiatric disorders like OCD than for movement disorders. “In patients with movement disorders, it is more obvious when stimulation delivery and tuning is correct because abnormal movements such as tremors or stiffness decrease right away,” said Dr. Sheth, professor and Vice Chair of Research in the Department of Neurosurgery at Baylor College of Medicine, director of the Gordon and Mary Cain Pediatric Neurology Research Foundation Laboratories, and investigator at the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital. “However, it is much more difficult to achieve this level of precise DBS programming for OCD and other psychiatric disorders because there is a long delay between stimulation initiation and symptom improvement. It is difficult to know what particular adjustment led to a particular change months later. Our goal in undertaking this study was therefore to find a reliable neural biomarker to guide us during DBS management, and to remotely monitor changes in our patients’ symptoms. This is particularly important because several of our patients travel long distances from around the country or world to get DBS treatment, which for OCD is currently offered only in very few specialized centers.”

Targeting the root of the OCD problem

To identify an optimal target for developing a biomarker, the team focused on one of the most characteristic behaviors in OCD — the tendency for pathological avoidance. Individuals with OCD often suffer from difficult-to-control avoidance of potential harm or distress. In trying to avoid such perceived threats in daily life, they are often plagued by intrusive internal thoughts and irrational fears (obsessions), which lead to rigid routines and repetitive behaviors (compulsions).

The team’s goal was to understand how low-frequency brain oscillations in the theta (4-8 Hz) to alpha (8-12 Hz) range, which have been found by a large body of scientific literature to play a prominent role in cognitive processes, were altered in individuals with severe, treatment-resistant OCD. To do so, they took advantage of a novel feature of modern DBS devices — the ability to not only deliver stimulation but also record brain activity.

Usually, studies that monitor brain activity patterns are designed to be brief episodes that are conducted as participants perform a specific cognitive task. However, this study is unique because the researchers were able to use the DBS system to continuously monitor brain activity patterns in the background of everyday activities. This feature of the study brought the research into the natural lives of the study participants rather than confining it to unnatural laboratory settings.

Recordings started upon implantation of the DBS system. Because stimulation is typically initiated days to weeks later, the team was able to measure neural activity patterns in the severely symptomatic state. Interestingly, they found that 9 Hz (theta-alpha border) ventral striatum neural activity demonstrated a prominent circadian rhythm that fluctuated over the 24-hour cycle.

“Before DBS, we saw an extremely predictable and periodic neural activity pattern in all participants,” said Dr. Goodman, professor and D. C. and Irene Ellwood Chair in Psychiatry in theMenninger Department of Psychiatry and Behavioral Sciences at Baylor College of Medicine. “However, after DBS activation, as individuals began responding and improving symptomatically, we saw a breakdown in this predictable pattern. This is a very interesting phenomenon and we have a theory to explain it. Individuals with OCD have a limited repertoire of responses to any given situation. They often perform the same rituals repeatedly and seldom vary their routines or engage in new activities, which may result in high predictability of activity in this brain region. However, after DBS activation, their behavioral repertoire is expanded; they might respond more flexibly to situations and not be just driven by a strong desire to avoid OCD triggers. This expanded repertoire may be a reflection of the more diverse brain activity pattern. Thus, we think this loss of a highly predictable neural activity indicates that the participants engaged in fewer repetitive and compulsive OCD behaviors.”

“In summary, we have identified a neurophysiological biomarker that can serve as a reliable indicator of improvements in mood and behaviors in OCD patients after DBS treatment. We anticipate these findings to transform how patients are monitored throughout DBS therapy,” added Dr. Sheth, who is also a McNair Scholar and Cullen Foundation Endowed Chair at Baylor College of Medicine.

“Incorporating this information into a clinician-facing dashboard, for example, could help guide therapy delivery, thus demystifying the process of DBS programming for OCD and making the therapy more accessible to a greater number of clinicians and patients. Moreover, we are excited by the potential possibility that such similar neural activity signatures may underlie other neuropsychiatric disorders and could serve as biomarkers to diagnose, predict, and monitor those conditions,” Dr. Provenza concluded.

Californians are familiar with landslides that occur around storms, when saturated soil and rock loses its grip and slips from its perch on the substrate. These types of landslides can be triggered by intense rainfall, and incoming storms can be a warning that neighborhoods need to evacuate.

Landslides that happen during the hot, dry summers, though, tend to take people by surprise. In July 2023, for example, a landslide seemed to come out of nowhere to devastate a neighborhood in Rolling Hills Estates, located on the northern side of the Palos Verdes Peninsula in Los Angeles County.

Now, landslide researchers at UCLA and NASA’s Jet Propulsion Laboratory, or JPL, have published a paper in Geophysical Research Letters that shows that the 2023 Rolling Hills Estates event was a slow-moving, progressive landslide that began the winter before, when unusually heavy rainfall infiltrated into the slope and reduced its strength. The researchers used satellite data to measure minute shifts in the surface of the affected area before, during and after the slide and concluded that this method could be used to detect future landslides before they become catastrophic.

“Movement on the Palos Verdes Peninsula’s Portuguese Bend Landslide has been recorded since the late 1950s,” said paper co-author Alexander Handwerger, a research scientist at UCLA’s Joint Institute for Regional Earth System Science & Engineering and JPL. “But there was no discernible movement in this region of the nearby Rolling Hills Estates before 2023. People began reporting movement, as indicated by cracks in houses, in April 2023, which matches our observations. There was initial slow movement that accelerated progressively, culminating in complete collapse several months later.”

The study, led by UCLA postdoctoral researcher Xiang Li, used satellite radar and optical data taken over Los Angeles every few weeks to measure ground motion over time. The satellite radar data for Rolling Hills Estates from 2016 to July 2023 revealed that after very slight movement during the 2019 rainy season, the ground remained stable until heavy winter rainfall, starting in December 2022, kickstarted movement in February. By June, the area had moved 0.04 meters, or about 1.6 inches, and on July 8 — a sunny, dry day preceded by 40 dry days — around 10 meters, or 33 feet, of horizontal motion occurred, destroying 12 homes.

The likely reason for the delay between initial movement in February and complete failure in July is that it took time for increased instability to develop. The researchers hypothesize that as water seeped through the ground, a sliding surface formed, causing the landslide body, including the ground surface, to slide progressively until the entire landslide moved rapidly all at once.

“Formation of the sliding surface will induce some movement, while the collapse will only occur when the sliding surface is fully developed,” Li said. “The progression can happen over hours, months or years.”

The researchers then attempted to determine if the Rolling Hills Estates landslide could have been predicted. By computing the displacement over time, they arrived at a predicted failure date on July 11, three days after the real landslide on July 8. They note that although their results are encouraging, predicting landslides using satellite remote sensing data needs further refinement, and landslides in areas without good historical satellite data might not be possible to predict in this way.

Li said that one of the challenges in forecasting landslides is the time period over which the progression takes place. Accurate forecasting requires continuous historical and ongoing satellite radar or in-situ measurements.

Handwerger is a core member of a project at JPL that is building an analysis-ready surface displacement database from satellite radar data for the entire United States, U.S. territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border up to and including Panama. The project, called Observational Products for End-Users from Remote Sensing Analysis, or OPERA, will contain analysis-ready data for near real-time monitoring and, possibly, landslide prediction.

“These motions can be quite subtle before they begin to move fast,” Li said. “Cracks in structures are what people tend to notice first. In fact, local residents in Rolling Hills Estates first reported cracks in their houses starting in April 2023. Signs of active movement require caution and monitoring because they could signal a progressive failure in the future.”

The study, in fruit flies and mice, also identified ways to block this process, which could have implications for treating or preventing the muscle wasting sometimes associated with inflammatory diseases, including bacterial infections, Alzheimer’s disease and long COVID.

The study is published July 12 in the journal Science Immunology.

“We are interested in understanding the very deep muscle fatigue that is associated with some common illnesses,” said senior author Aaron Johnson, PhD, an associate professor of developmental biology. “Our study suggests that when we get sick, messenger proteins from the brain travel through the bloodstream and reduce energy levels in skeletal muscle. This is more than a lack of motivation to move because we don’t feel well. These processes reduce energy levels in skeletal muscle, decreasing the capacity to move and function normally.”

To investigate the effects of brain inflammation on muscle function, the researchers modeled three different types of diseases — an E. coli bacterial infection, a SARS-CoV-2 viral infection and Alzheimer’s. When the brain is exposed to inflammatory proteins characteristic of these diseases, damaging chemicals called reactive oxygen species build up. The reactive oxygen species cause brain cells to produce an immune-related molecule called interleukin-6 (IL-6), which travels throughout the body via the bloodstream. The researchers found that IL-6 in mice — and the corresponding protein in fruit flies — reduced energy production in muscles’ mitochondria, the energy factories of cells.

“Flies and mice that had COVID-associated proteins in the brain showed reduced motor function — the flies didn’t climb as well as they should have, and the mice didn’t run as well or as much as control mice,” Johnson said. “We saw similar effects on muscle function when the brain was exposed to bacterial-associated proteins and the Alzheimer’s protein amyloid beta. We also see evidence that this effect can become chronic. Even if an infection is cleared quickly, the reduced muscle performance remains many days longer in our experiments.”

Johnson, along with collaborators at the University of Florida and first author Shuo Yang, PhD — who did this work as a postdoctoral researcher in Johnson’s lab — make the case that the same processes are likely relevant in people. The bacterial brain infection meningitis is known to increase IL-6 levels and can be associated with muscle issues in some patients, for instance. Among COVID-19 patients, inflammatory SARS-CoV-2 proteins have been found in the brain during autopsy, and many long COVID patients report extreme fatigue and muscle weakness even long after the initial infection has cleared. Patients with Alzheimer’s disease also show increased levels of IL-6 in the blood as well as muscle weakness.

The study pinpoints potential targets for preventing or treating muscle weakness related to brain inflammation. The researchers found that IL-6 activates what is called the JAK-STAT pathway in muscle, and this is what causes the reduced energy production of mitochondria. Several therapeutics already approved by the Food and Drug Administration for other diseases can block this pathway. JAK inhibitors as well as several monoclonal antibodies against IL-6 are approved to treat various types of arthritis and manage other inflammatory conditions.

“We’re not sure why the brain produces a protein signal that is so damaging to muscle function across so many different disease categories,” Johnson said. “If we want to speculate about possible reasons this process has stayed with us over the course of human evolution, despite the damage it does, it could be a way for the brain to reallocate resources to itself as it fights off disease. We need more research to better understand this process and its consequences throughout the body.

“In the meantime, we hope our study encourages more clinical research into this pathway and whether existing treatments that block various parts of it can help the many patients who experience this type of debilitating muscle fatigue,” he said.

The ozone layer, a crucial shield protecting life on Earth from harmful ultraviolet (UV) radiation, has been on a path to recovery thanks to the Montreal Protocol. This landmark international treaty, adopted in 1987, successfully led to phasing out the production of numerous substances responsible for ozone depletion. Over the past decades, the ozone layer has shown significant signs of healing, a testament to global cooperation and environmental policy.

However, the stability of this vital atmospheric layer is now facing a new and unexpected challenge. During the 2019/20 Australian wildfires, researchers observed a dramatic increase in stratospheric aerosols — tiny particles that can influence climate, health, and atmospheric chemistry.

Smoke-charged vortex transports aerosol up to 35 kilometers

Utilizing new satellite data and numerical modeling, the research team successfully demonstrated the impact of wildfires through a novel phenomenon: the smoke-charged vortex (SCV).

“The SCV is a powerful, smoke-laden whirlpool that transports wildfire smoke into the middle stratosphere, reaching altitudes of up to 35 kilometers,” explained Prof. Hang Su from the Institute of Atmospheric Physics at the Chinese Academy of Sciences, one of the corresponding authors of the study. “This process led to at least a doubling of the aerosol burden in the southern hemisphere’s middle stratosphere. Once reaching such high altitudes, these aerosols initiated a series of chemical reactions at their surface that impacted ozone concentrations.”

The international team discovered that these wildfire-induced aerosols facilitated heterogeneous chemical reactions in the stratosphere, which paradoxically led to both ozone depletion and ozone increase at different atmospheric layers.

While the lower stratosphere experienced significant ozone depletion, the new study shows that the increase of smoke aerosol particles in the middle stratosphere enhances the heterogeneous uptake and hydrolysis of N₂O₅, which leads to a decrease of reactive nitrogen gases, e.g., NOx, and an increase of ozone. In Southern Mid-Latitudes, the complex interplay managed to buffer approximately 40% (up to 70%) of the ozone depletion observed in the lower stratosphere in the following months of the mega-bushfire events.

So why does this matter?

“Our study uncovers an unexpected and crucial mechanism by which the absorbing aerosols in wildfire smoke, such as black carbon, can induce and sustain enormous smoke-charged vortices spanning thousands of kilometers, fundamentally changing the stratospheric circulation. The vortices can persist for months, carrying aerosols deeply into the stratosphere and affecting the ozone layer in distinct ways at different altitudes. This highlights the need for continued vigilance and research as climate change progresses,” said Prof. Yafang Cheng, another leading author from the Max Planck Institute for Chemistry.

“We’ve made a significant step forward in simulating the SCV as a new effective pathway for wildfires to modify stratospheric dynamics and chemistry, especially the ozone layer. I love this study because it once again demonstrates how closely different parts of the Earth system are connected. Smoke from a forest fire can significantly change the wind and circulation tens of kilometers above the ground, which allows the smoke to modify the ozone layer, influencing life on our planet,” said Dr. Chaoqun Ma, the first author of the study and postdoc researcher in Cheng’s team at the MPIC.

The ozone layer’s role in filtering UV radiation is crucial for protecting all life forms on Earth. The Montreal Protocol’s success in reducing ozone-depleting substances was a monumental achievement. Still, the new findings highlight that natural events, exacerbated by climate change, pose additional risks to this fragile atmospheric layer. With the increasing frequency and intensity of wildfires driven by global warming, the formation of SCVs and their impact on the stratosphere could become more common, posing a threat to the ozone layer.