Prescribed burns can help reduce fire intensity and smoke pollution: Study
These controlled blazes are much more effective outside the wildland-urban interface (WUI) — the area where homes meet wild vegetation — than within it, according to the study, published on Thursday in AGU Advances.
'Prescribed fire is often promoted as a promising tool in theory to dampen wildfire impacts, but we show clear empirical evidence that prescribed burning works in practice,' lead author Makoto Kelp, a postdoctoral fellow at Stanford University, said in a statement.
'It's not a cure-all, but it's a strategy that can reduce harm from extreme wildfires when used effectively,' Kelp added.
Experts already consider prescribed burns to be an effective strategy for curbing the threat of wildfires, the researchers acknowledged, noting that $2 billion in federal funds are allocated to such treatments.
Yet they also pointed out that the use of these controlled blazes across the U.S. West has only expanded slightly in recent years. This discrepancy, they surmised, could be due to the lack of research quantifying the practice's effectiveness and mixed public opinions on the matter.
To enumerate the benefits of the burns, the scientists used high-resolution satellite imagery, land management records and smoke emission inventories to compare outcomes of treated and untreated areas in the extreme 2020 fire season.
Specifically, they focused on places treated with controlled fire between late 2018 and spring 2020 and at adjacent untreated zones.
Their analysis ultimately showed that areas treated with prescribed fire burned less severely and generated much less smoke. That finding was particularly important to the authors, who stressed that fine particulate matter (PM 2.5) emitted by wildfires has been linked to cardiovascular and respiratory issues.
'Smoke is a silent and far-reaching hazard, and prescribed fire may be one of the few tools that actually reduces total smoke exposure,' co-author Marshall Burke, an associate professor of environmental social sciences at Stanford, said in a statement.
Meanwhile, the scientists found that controlled burns produce only about 17 percent of the PM 2.6 smoke that would result from a wildfire in the same area.
They estimated that if California achieved its goals of treating a million acres with prescribed fire annually, the Golden State could slash PM 2.5 emissions by 655,000 tons over five years. That quantity would be equivalent to 52 percent of the total smoke pollution generated during California's 2020 wildfire season, according to the study.
As far as the different effects in WUI and non-WUI zones are concerned, the authors found that prescribed burns led to an 8.5 percent drop in fire severity in the former and a 20 percent decline in the latter.
In WUI zones, they noted, agencies usually opt for mechanical thinning over prescribed burns due to smoke and safety concerns.
Although the researchers could not yet offer an explanation behind the discrepancy, they said that gaining further insight into the matter would be critical.
Senior author Noah Diffenbaugh, a professor at Stanford's Doerr School of Sustainability, noted the rapid population growth in WUI areas, where plants are 'most sensitive to climate-induced intensification of wildfire risk.'
As such, he stressed that understanding why prescribed burns are less effective in these areas 'is a key priority for effectively managing that intensifying risk.'
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Scientific American
6 hours ago
- Scientific American
AI Could Broaden the Applications of Entertaining Drone Shows
AI can allow engineers to focus on artistry over technical details for drone shows By , Jeffery DelViscio, Fonda Mwangi & Alex Sugiura Rachel Feltman: For Scientific American 's Science Quickly, I'm Rachel Feltman. This Fourth of July some of the celebrants flocking to their local parks and waterfronts won't be taking in the iconic sights and sounds of a fireworks display. In some cases, those traditional explosives could be replaced with swarms of colorful drones. Drone light shows have been popping up more and more in recent years, replacing or supplementing fireworks at the Olympics and even some Super Bowl halftime shows. They're dazzling, precise and a lot safer than explosions. Besides the obvious risks of setting off incendiary devices, fireworks shows also raise environmental concerns: studies suggest these big displays have a marked impact on local air quality in the hours that follow. On supporting science journalism If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. But swapping out fireworks for drones isn't simple: every one of those displays takes painstaking effort from a team of engineers. They have to plot the movement of every single drone, frame by frame. Today's guests recently published a paper that offers an AI-powered solution. Mac Schwager is an associate professor in the Aeronautics and Astronautics Department at Stanford University, and Eduardo Montijano is an associate professor in the Department of Computer Science and Systems Engineering at the University of Zaragoza in Spain. Thank you both so much for coming on to chat. Mac Schwager: Sure, our pleasure. Eduardo Montijano: Thank you. Feltman: Why don't we start with just a quick overview of this study: You know, how did it come to be? What got you interested in this particular aspect of drone swarms? Montijano: I've been doing research in multirobot systems for some time. Also, I've been collaborating with Mac for many years as well. And with all the development of all these new AI techniques that have been successfully applied to other problems and applications, we thought—in collaboration with mainly one student, although there are more people in this research, but here, probably, I would like to highlight Pablo Pueyo mostly—but we decided, or we discussed, how cool it would be to try to apply all these new techniques to this problem of controlling hundreds or thousands of robots for animation displays. Feltman: So speaking of those animation displays, when compared to fireworks, what problems do they solve and what problems do they raise that maybe your paper was trying to address? Schwager: I think we consider sort of animation displays with drone swarms as being much more flexible and sort of [an] artistically richer medium for entertainment. So in fireworks displays, right, there's a big bang and a big flash, but the engineer has actually very little control over exactly what the fireworks do and what they look like, right? But with drones you can program the lights and you can program the motion of the drones to display a very clear image—for a sporting event you could have somebody playing the sport floating in the air, or for the Fourth of July you could have words spelled out, you could have the American flag or whatnot. So it's much more flexible, and, you know, there's more control by the artist and the engineer as far as what they wanna convey. There's a challenge, though, which is that drone swarms, especially large drone swarms, require a lot more engineering expertise and quite a bit more infrastructure to control and to deploy, especially to do that safely. And so this was one of the targets of our research, is to basically make the planning of these large-scale drone displays much more automatic and to kind of empower people without that kind of special knowledge to create their own drone displays. Feltman: And could you kind of paint a picture for us: Currently, what does it look like to put on one of these displays? What's required in the background? Schwager: Right, so these are usually managed by large engineering companies, and there's usually a team of engineers, specialist engineers, who make sure that all the drones are properly charged and have landing stations. They would have to go out to the site where the display is gonna be performed and engineer the site to plan where all the drones would fly and where they go and to make sure that the space is clear. And really, the target of our research is that before drone display happens, there are artists and engineers that carefully chart the path of every drone. At the time of the display the drones are actually just following sort of points in space that have been preplanned by the engineers—one point at a time, one drone at a time. So you can imagine it's very much like animating an animated film: it's very painstaking, very hands-on and requires lots of expertise. So the target of Gen-Swarms was essentially to use generative AI to do that phase of planning for you ... Feltman: Hmm. Schwager: So you can type in a high-level prompt, like 'the American flag,' for example, or 'a skier skiing downhill,' and our algorithm would essentially produce these sets of waypoints, these sets of points in 3D space, for the drones to fly along to then create the illusion of this artistic display. Feltman: Mm, so basically, you enter the image you want to end up with and the AI tells the drones where to go, what colors to be, all of that stuff. Schwager: Yeah, actually, at the moment we enter just text. Feltman: Mm-hmm. Schwager: So we enter a text description of what we want to see, and then the method produces the colors, and the arrangement, and so on—although I think it wouldn't be too hard to extend our methods so that you could upload a picture or a sketch of what you wanna see. Feltman: And what are the specific challenges that arise when you're trying to control a group of drones with AI? Montijano: The way these models work, they have been popular [for] creating images, no? And at the end of the day they predict the color of each pixel when you give this prompt. So the idea here is: when you want to somehow translate this to drones, pixels [are] just a color, and they don't have any motion constraints, any collision constraint. So the idea is: when you try to translate this idea of making pixels look [how you'd] like to making drones look [how you'd] like, you need to account for [the fact] that drones cannot teleport from one location to another, so they have some dynamics—some velocity, acceleration—some constraints in the motion [such] that you cannot do any motion that you want. You need to account for those somehow in your algorithm. And also, drones have some physical properties—some mass, some size—so they can collide with each other. So there are these safety constraints that you also need to include in the planning algorithm that [uses] this generative model so that the motion of the drones, it's also safe. Feltman: Mm, and how close are we to actually being able to use the model you created with drones? Montijano: So from the research perspective I would say that our solution, in some sense, is mature enough to be applied. But then there are all these technological challenges that Mac mentioned before about all the real deployment of drones that, obviously, as academic professors, we don't have the resources to deploy 1,000 or 100 or whatever number of drones. So for that there's still a gap in terms of [going] from research to application, but it's more a matter of maybe collaborating with companies that already are deploying drones in many locations. So I think that the integration wouldn't be that difficult; it's just a matter, probably, of having the right contact within a company that has the skills for real deployment. But the algorithm, I think, it's already in shape to be deployed. Feltman: Very cool. What other applications could this have? Schwager: Yeah, so certainly, artistic displays are powerful and important, but we'd love for our robots to really help people in their day-to-day lives and also help people who are in danger. So for example, we could imagine using an algorithm like this for search and rescue. You know, if you have hikers who are stranded somewhere in the wilderness and you need some way of deploying a team of drones to go look for the lost hiker, this could be a method that could be adapted to that. We're also interested in, you know, things like exploration. Maybe in a space application, NASA might consider developing a tool like this to explore the surfaces of asteroids or planetary bodies. We're also really interested—currently, our kind of next step along this research journey is drone or other robot swarms for construction. So currently, our algorithm, you type in a prompt and the drones will organize themselves into a shape, right, that looks like what you asked for. What we're looking at now is: 'How could you type in the prompt and have the drones actually deposit material—like maybe the drones can carry little square blocks—how could they deposit the material in the right order to construct something that is useful or interesting for an artistic display?' So you could imagine drones constructing a bridge in a remote area where people maybe need to pass over some, some difficult terrain, or maybe there's an emergency scenario, maybe there's a disaster scenario, and a bridge has been washed out, and you'd like drones to automatically construct a temporary bridge—something like that. Montijano: Even when we applied this [to]a drone show because it's—the artistic component is beautiful, I would say that there are no limitations on applying this to any kind of multirobot system. So in that sense we could go for other ground robots, domestic robots, construction robots, as Mac mentioned. So the idea here is to be able to translate these high-level commands specified by text that—every person can, more or less, give these commands—and then automatically translate them into plans for teams of robots to achieve these commands. So the ambition, in that sense, I think it's—it goes way beyond the artistic display. Feltman: And what about the environmental impacts of a drone show versus a firework show? Montijano: Well, I would say that, in my opinion, drone shows are safer in the sense that fireworks are a very, you know, explosive material, and you hear [about] accidents, and you need to produce and store them. And then within my knowledge that is not very deep, I would say that, probably, the residual impact of fireworks is bigger than, probably, drone shows; that at the end of the day you can recycle or reuse these drones in multiple shows. Noisewise, probably, they are similar, even that—in the sense that drones currently are quite noisy, although it's true that when you see them from far, far away fireworks are very annoying and drone shows are not. But when you fly them in close space, let me tell you that now, having a drone flying nearby, it's more annoying than a firework [laughs]. So there I guess there could be arguments in favor or against each of them, but if I have to choose drones, I would say that this reusability and safety, in terms of explosive materials, are the two main, big advantages. Feltman: Well, and given everything that you're presenting in the paper, how do you see the world of drone shows evolving with this new tech? Montijano: Well, so I would say that [on] the artistic side of the problem the idea is that with this they are already—existing drone shows are able to develop complex and beautiful animations. The idea is that this will speed up and simplify this rather tedious and complex process; to maybe make [it possible to] scale to larger numbers of robots in an easy way; maybe also, in terms of the testing phase, deciding the appropriate number of drones to create specific figures. Well, in summary, speeding up the whole creative process and hopefully ... providing more beautiful, more complex animations and displays. Schwager: I think right now one of the most exciting research frontiers is figuring out how to use, you know, powerful, modern generative AI tools that we're all familiar with—ChatGPT, image-generation models, and so on—how to use those in ways that benefit people, you know. And myself and Eduardo being roboticists, I think we're always looking for ways to enable robots to help people, to better serve people, to make people's lives safer, and I think this is a really exciting frontier. And one of the grand challenges in robotics is: 'How do you orchestrate the activities of large groups of robots?' It's hard enough to control a single robot, and now, when you've got a large group, you know, there's this persistent problem of: 'How does one human, or a small number of humans, tell a large group of robots what they should do?' And I think this is an interesting model that we're sort of approaching: using generative AI as kind of the bridge, the interface, to allow one person, or a small number of people, to command the activities of a very large group of drones. Montijano: Another issue that I also like to point out when mixing robotics and AI would be—with the current state of the art—would be explainability. If you want to generate an image, what you care about [is] the output, but 'Why this output?' might not be as relevant as [what] you are considering about the motion of robots. So understanding and obtaining outputs that are consistent for robots, it's a very important problem that, currently, I would say that we are struggling [with] because these AI models [works] very well, but somehow they work well until they stop working well, and having some kind of understanding of when or why these things [happen] is very important from a research perspective. Feltman: Thank you both so much for coming on to chat about this. This has been great. Montijano: Thank you, Rachel. Schwager: Great, thank you, Rachel. It's our pleasure. Feltman: That's all for today's episode. We're taking Friday off for the holiday. Next week, we'll be sharing reruns of some of our favorite segments from the past year. We'll be back with a new episode on July 14. In the meantime, you can quench your thirst for fresh science news by reading Scientific American online or in print. Science Quickly is produced by me, Rachel Feltman, along with Fonda Mwangi, Kelso Harper, Naeem Amarsy and Jeff DelViscio. This episode was edited by Alex Sugiura. Shayna Posses and Aaron Shattuck fact-check our show. Our theme music was composed by Dominic Smith. Subscribe to Scientific American for more up-to-date and in-depth science news. For Scientific American, this is Rachel Feltman. Have a great weekend!
Yahoo
11 hours ago
- Yahoo
There's a Critical Thing We Can All Do to Hold Alzheimer's Symptoms at Bay
Deep sleep could forestall the declines in brain health that can eventually lead to Alzheimer's disease, the most common form of dementia. In their study of 62 older, cognitively healthy adults, researchers from the University of California (UC) Berkeley, Stanford University, and UC Irvine in the US found individuals with brain changes associated with Alzheimer's performed better on memory function tests as they got more deep sleep. This was irrespective of education and physical activity, two factors along with social connection known to contribute to cognitive resilience in older age. Those with similar Alzheimer's-linked changes who failed to get as much deep sleep didn't fare quite as well on the same tests. By comparison, sleep made little difference to those individuals with few deposits. Taken together, the results, which were published in May 2023, imply having a generous amount of solid shut-eye could help support the decline in memory that sets in as dementia begins to take hold. Watch the following video for a summary of the study: "Think of deep sleep almost like a life raft that keeps memory afloat, rather than memory getting dragged down by the weight of Alzheimer's disease pathology," said University of California (UC) Berkeley neuroscientist Matthew Walker. "This is especially exciting because we can do something about it. There are ways we can improve sleep, even in older adults." The study echoes previous research which has found a build-up of amyloid-beta proteins in the brains of people with disrupted sleep. But poor sleep is both a risk factor for and a symptom of Alzheimer's disease, making it tricky to tease apart cause and effect. Likewise, clumpy amyloid-beta proteins might only be a sign of Alzheimer's disease, not its root cause. Related: Even so, levels of amyloid-beta proteins are commonly used as a marker of Alzheimer's disease, as research suggests they – and another protein called tau – can start clogging up brain cells decades before symptoms of the disease arise. Past research from Walker's group found significant levels of amyloid-beta aggregating in the brains of older adults can disrupt deep sleep – also known as non-rapid eye movement slow wave sleep – and impair memory function. But some folk appear to stave off the decline that comes with Alzheimer's disease, even when levels of amyloid-beta proteins are relatively high. To find out why, Walker and colleagues monitored participants' brain waves as they slept, and then asked them to complete a memory test the next day. Among those whose brain scans revealed similarly high levels of beta-amyloid deposits, getting a good night's sleep seemed to make a critical difference in cognitive function. This effect was only seen when the researchers looked specifically at non-rapid eye movement slow wave sleep, and not at other sleep wave frequencies or sleep stages. Longer-term studies in older adults are needed to test whether increasing deep sleep over a number of years can actually help preserve a person's cognitive function in that time, even as levels of amyloid-beta increase. This research adds to scores of studies suggesting that sleep could be a modifiable risk factor for Alzheimer's disease, one that could potentially forestall molecular changes by giving the brain time to clean up waste products that accumulate during the day. It also points to sleep quality being important. "With a certain level of brain pathology, you're not destined for cognitive symptoms or memory issues," UC Berkeley neuroscientist and lead author Zsófia Zavecz said of the study findings in 2023. Although people may display molecular changes indicating a progression toward Alzheimer's disease, Zavecz says their findings suggest lifestyle factors can help buffer against those effects. "One of those factors is sleep and, specifically, deep sleep," she said. The study, though small, also hints at why getting good sleep naturally might be a better option than taking sleeping pills to get some shut-eye. Other research shows users of sleeping pills appear to have lower levels of amyloid proteins in their cerebrospinal fluid, which washes the brain clean at night. But these medications come with side effects; they may also lull people into shallow bouts of sleep rather than deep sleep phases. Instead, to set yourself up for a good night's sleep, Zavecz suggests cutting out coffee late in the day, doing some exercise, avoiding screen time, and taking a hot shower before bed. While you snooze, rest assured scientists are working hard to figure out the knotty problems of Alzheimer's disease, which affects millions of people worldwide. The study has been published in BMC Medicine. A version of this article was first published in May 2023. Ozempic Alternative Ditches The Needle And One Major Side Effect A Simple Change To Your Evening Routine Could Help You Exercise More 'Sky-High' Levels of Alzheimer's Protein Found in Newborns
Newsweek
a day ago
- Newsweek
Parkinson's Breakthrough Could Help Prevent Brain Cell Loss
Based on facts, either observed and verified firsthand by the reporter, or reported and verified from knowledgeable sources. Newsweek AI is in beta. Translations may contain inaccuracies—please refer to the original content. A new treatment for one type of Parkinson's disease may be on the horizon after researchers discovered a "brake" that can halt cell death. The study, led by researchers from Stanford University, California, involved a form of the neurodegenerative disorder that is caused by a single genetic mutation. This mutation causes an excess of a protein that interferes with the brain's ability to protect itself. Inhibiting this protein, the team found, can halt the damage and even allow dying neurons to recover. "These findings suggest that it might be possible to improve, not just stabilize, the condition of patients with Parkinson's disease," said paper author and Stanford biochemist professor Suzanne Pfeffer in a statement. Key, however, will be "if patients can be identified early enough," she added. While Parkinson's most recognizable symptom might be resting tremors, the earliest signs of the disease typically manifest some 15 years earlier. These first signs, Pfeffer said, include constipation, a loss of smell and REM sleep behavior disorder, a condition in which people act out their dreams while sleeping. Artist's impression of neurons in the brain. Artist's impression of neurons in the brain. FlashMovie/iStock / Getty Images Plus In the U.S, it is estimated that some 1.1 million people are living with Parkinson's disease—a figure only expected to rise in the near future, according to the Parkinson's Foundation. As Pfeffer and colleagues explain, around a quarter of all cases are caused by genetic mutations, with one of the most common being one that increases the activity of an enzyme called leucine-rich repeat kinase 2 (LRRK2). Too much LRRK2 in the brain changes the structure of cells by causing them to lose their "antenna" (technically the primary cilia) that allows them to send and receive chemical messages. In a healthy brain, communications are relayed back and forth between dopamine neurons in two regions of the brain known as the striatum and the substantia nigra. When dopamine neurons are stressed, they release a protein-based signal in the striatum called sonic hedgehog (after the video game character)—this causes neurons and support cells to produce so-called neuroprotective factors that shield other cells from dying. When LRRK2 activity crosses a certain threshold, the loss of the primary cilia in the cells of the striatum prevents them from receiving the sonic hedgehog signal; as a result, the neuroprotective factors are not produced. "Many kinds of processes necessary for cells to survive are regulated through cilia sending and receiving signals," explained Pfeffer. "The cells in the striatum that secrete neuroprotective factors in response to hedgehog signals also need hedgehog to survive. "We think that when cells have lost their cilia, they are also on the pathway to death because they need cilia to receive signals that keep them alive." A diagram shows how neurons (blue) rooted in the substantia nigra provide dopamine (dark green dots) to striatal neurons (red). A diagram shows how neurons (blue) rooted in the substantia nigra provide dopamine (dark green dots) to striatal neurons (red). Emily Moskal / Stanford Medicine It is possible to combat an excess of LRRK2 using a so-called "MLi-2 LRRK2 kinase inhibitor," a molecule that attaches to the enzyme and reduces its activity. In their study, Pfeffer and colleagues set out to test whether this inhibitor could also reverse the effects of too much LRRK2, as well as whether it was even possible for fully mature neurons and supportive glia to regrow lost cilia and regain their communication ability. At first, the results were not promising. The team gave the inhibitor for two weeks to mice that had the LRRK2 mutation (and show symptoms consistent with early Parkinson's disease)—to no effect. However, the researchers were inspired by recent studies into sleep-wake cycles, which found that the primary cilia on the mature cells involved grew and shrank every 12 hours. "The findings that other non-dividing cells grow cilia made us realize that it was theoretically possible for the inhibitor to work," said Pfeffer. Inspired by this, the team decided to try giving the mice the inhibitor for a longer time—with the results at three months being "astounding," the biochemist added. The longer treatment saw the percentage of striatal neurons and glia with primary cilia in the mice with the mutation increase to the same level as regular, healthy mice. This had the effect of restoring communication between the dopamine neurons and the striatum, leading to the normal secretion of neuroprotective factors. The researchers also found that the level of hedgehog signaling from the dopamine neurons decreased—suggesting that they were under less stress. Moreover, the density of dopamine nerve endings in the mice's striatum was found to double, suggesting that neurons which had been in the process of dying had recovered. LRRK2 inhibition decreased stress in dopamine neurons in mice models of Parkinson's (top right vs. bottom right—with healthy mice on the left for comparison.) LRRK2 inhibition decreased stress in dopamine neurons in mice models of Parkinson's (top right vs. bottom right—with healthy mice on the left for comparison.) Ebsy Jaimon & Suzanne Pfeffer With their initial study complete, the researchers say that their next step would be to determine whether other forms of Parkinson's that are not associated with the LRRK2 mutation could also benefit from the new treatment. This is possible, Pfeffer explains, because the mutation is not the only way to end up with an overactive LRRK2 enzyme. In fact, she added, the inhibitor treatment might even help with other neurodegenerative diseases. "We are so excited about these findings. They suggest this approach has great promise to help patients in terms of restoring neuronal activity in this brain circuit, said Pfeffer. She concluded: "There are multiple LRRK2 inhibitor clinical trials underway—and our hope is that these findings in mice will hold true for patients in the future." Do you have a tip on a health story that Newsweek should be covering? Do you have a question about Parkinson's disease? Let us know via health@ Reference Jaimon, E., Lin, Y.-E., Tonelli, F., Antico, O., Alessi, D. R., & Pfeffer, S. R. (2025). Restoration of striatal neuroprotective pathways by kinase inhibitor treatment of Parkinson's disease–linked LRRK2-mutant mice. Science Signaling, 18(793).
