A Qualcomm já confirmou que todos os seus produtos, incluindo o futuro Snapdragon 8 Elite Gen 6 Pro, vão ficar mais caros a partir do próximo mês. A subida abre uma janela de oportunidade à MediaTek, que, segundo rumores da cadeia de fornecimento, poderá vender o Dimensity 9600 Pro por bem menos dinheiro aos fabricantes de smartphones.
The spots on a leopard, the stripes on a zebra, and the mask on a raccoon are all types of coloration in mammal fur, referred to as pelage. Scientists have struggled for a long time to understand how these colorations evolved. A team of researchers recently discovered how to reconstruct the colors of ancient mammal fur using cell structures that contain the chemical pigment melanin, known as melanosomes. They hope to use this understanding of ancient mammal pelage to determine how they lived and what their environments were like.
To decipher the color of ancient mammal fur, the researchers constructed a statistical computer model to predict their fur colors based on the shape of their melanosomes. First, they needed to understand how the shapes of melanosomes control fur color in modern mammals. To measure the length and width of the modern melanosomes, the scientists used a beam of electrons like a flashlight to map them in great detail, a technique called scanning electron microscopy.
They measured the length and width of 2,615 melanosomes from the fur of 116 different living mammal species, including a Red Panda, a German Shepard, and one or more species of most types in the mammalian classification. Then they recorded the wavelengths of light reflected off each melanosome using a microspectrophotometer. These wavelengths represented the exact color of hair from each melanosome they measured. This means they now had a quantitative measurement of the color of each hair to compare with its corresponding melanosome length and width.
When they examined their data, the researchers found that spherical melanosomes produced brighter fur colors, like reds and oranges, while elongated melanosomes produced darker fur colors, like browns and grays. The researchers plotted the microspectrophotometric measurements for each melanosome as a graph showing how much light it absorbed across each possible wavelength, creating reflectance curves. Based on these data, the researchers used computer software to create a statistical model that could predict reflectance curves, and therefore color, from melanosome shape alone.
Once the researchers created a model, they needed to find fossilized mammal melanosomes to predict their fur color. Mammal fossils are rare, and pieces of cells are only preserved in the most immaculate fossil deposits. In fact, these researchers only found 6 fossils of this caliber, from Mesozoic rocks deposited around 250 million years ago, soon after mammals evolved.
The fossils came from 2 world-renowned deposits in Northeastern China, the Yanliao and Jehol biotas. They selected 5 previously studied fossils from these locations, including 1 specimen of Megaconus mammaliaformis, 1 specimen of Vilevolodon diplomylos, 2 specimens from the group Docodonta, and 1 specimen from the group Eutheria. All of these animals were small, rodent-like mammals. The researchers also discovered a new species of tree-dwelling mammal, Arboroharamiya fuscus.
The scientists used scanning electron microscopy to measure a total of 2,159 fossilized melanosomes to refine their color predictions. The fossilized melanosomes were all slightly elongated spheres and had far fewer differences in shape than in modern mammals. The researchers used their model to predict reflectance curves based on the shapes of the fossil melanosomes to determine the ancient mammals’ colors. They found that all the extinct mammals they examined likely had dark brown fur without any color patterns.
Based on these findings, the researchers suggested that most, if not all, early mammals were nocturnal, like many dark-colored mammals today. They suggested that this nocturnal lifestyle probably helped them survive the meteorite impact that wiped out the dinosaurs 100 million years later. While dinosaurs were unable to survive in the post-impact wasteland, these smaller, dark-colored mammals were more resilient and thrived in the aftermath. As mammals filled the ecological roles left by the dinosaurs, they evolved new color patterns, some based solely on camouflage, and others on new factors, like sexual selection and visual communication.
The researchers concluded that their model can predict fossilized mammal colors and help determine how these ancient animals lived. Notably, their data suggest that mammals likely stayed small and nocturnal throughout the Mesozoic until the dinosaurs were gone. Future researchers could use similar methods to uncover exactly how mammals evolved the colors we see today. For example, studying mammals closer to the impact event could reveal if it took a long time to develop diverse fur patterns, or if they began before the dinosaurs’ demise. Ultimately, similar studies could help determine whether the extinction of dinosaurs was necessary for the rise of mammals.
Qualcomm reported fiscal Q3 results which beat Wall Street’s revenue expectations but missed on profit, as the chipmaker continued to grapple with the impact of a memory supply crunch weighing on the smartphone sector.
The company posted revenue of $9.9 billion for the quarter ended 28 June, down 4 per cent from $10.4 billion a year earlier but ahead of the roughly $9.7 billion analysts had expected. Net income during the period was $2 billion, down 25% year-on-year.
Handset chip sales fell 20% to $5.1 billion, which Qualcomm CEO Cristiano Amon (pictured) stated reflected a bottoming out in the China market after OEMs pulled back on orders amid memory supply uncertainty.
“We actually don’t expect fundamental changes in the premium tier and the higher tier volume,” Amon said on the company’s investor call.
Going forward, the executive expects Apple-related revenue to decline sharply and faster than previously anticipated, after Qualcomm’s share of components in the upcoming iPhone range came in well below an earlier estimate of around 20%.
Amon said the company is offsetting the loss with growth elsewhere, including its emerging data centre business, which it expects to generate $5 billion in revenue by fiscal 2027.
“We expect Android to grow and significantly offset the reduction in Apple, and then data centre revenue that starts to ramp in the fourth quarter,” CFO Akash Palkhiwala said.
Amon said its Q3 results reflected “solid execution of our growth strategy” despite the challenging environment.
He noted Qualcomm is taking steps to protect margins going forward, including raising prices across its chip portfolio from 1 September, as component costs continue to climb.
Automotive remained a bright spot, with revenue reaching $1.59 billion which marked a 61% increase and the 23rd consecutive quarter of double-digit year-on-year growth for the segment. Qualcomm reiterated a target of $10 billion in annual automotive revenue by fiscal 2029.
IoT revenue was $1.83 billion, representing a 9% increase. Combined automotive and IoT revenue climbed 28%, underscoring the company’s push to diversify beyond its traditional handset business as it also builds out a data centre and custom silicon strategy with a leading hyperscaler.
Qualcomm forecast fourth-quarter profit below Wall Street estimates on Wednesday and said revenue from Apple products would decline faster than expected as supply constraints and rising costs weighed on its business.
In an interview with Reuters, CEO Cristiano Amon said costs had risen not just for memory chips, but across the supply chain. Qualcomm plans to raise prices starting September 1 in an effort to return its margins to historical levels, he said.“We’re just passing through big cost increases that we have,” Amon said, noting that Qualcomm will have to negotiate with each customer. “The temporary disconnect between cost and pricing causes a slight decline temporarily in gross margin.”
Qualcomm also said it expects revenue from Apple products to decline more quickly starting in the fourth quarter as supply constraints reduce its share of components used in the next iPhone launch to well below its earlier estimate of 20%.
“It’s availability of supply,” Amon told Reuters of the Apple share change.For fiscal 2027, the company expects the majority of its chip sales will come from categories other than smartphones.
“We kind of replaced Apple with the data center,” Amon said.Qualcomm expects adjusted profit to be between $2.05 and $2.25 per share in the fourth quarter, below analysts’ average estimate of $2.36, according to data compiled by LSEG. It forecast revenue of between $9.7 billion and $10.5 billion during the period, compared with estimates of $10.02 billion.
Revenue in its chip segment is expected to be between $8.4 billion and $9 billion, while analysts were expecting $8.49 billion.
Qualcomm reiterated that revenue from Chinese phone makers bottomed out in the third quarter as customers worked through excess inventory. Xiaomi, Oppo, and Vivo posted the steepest shipment declines among the top five global smartphone vendors in the June quarter, according to Counterpoint Research.
While unit sales of Android phones began to recover, Amon said handset makers were forced to raise prices, which in turn prompted buyers to look toward the bottom end of the premium phone segment, or even at older models, both of which hurt Qualcomm margins.“There was a mix change versus what we expected,” Amon said.
Qualcomm’s broad exposure to devices such as smartphones makes its results a closely watched indicator of demand trends across consumer electronics.
MacDailyNews Note: Its third-quarter revenue fell 4% to $9.95 billion, beating estimates of $9.67 billion. Adjusted profit came in at $2.21 per share, compared with estimates of $2.23.
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BMW Group handed Qualcomm Technologies a long-term deal to be the lead supplier of chips for its digital cockpit and next generation automated and assisted driving systems.
The chip company revealed it had been appointed the automotive giant’s lead compute silicon provider for the business areas over the next decade, with products being used from its Snapdragon Digital Chassis portfolio.
It noted the latest deal follows years of technical collaboration between the pair, pointing to a commercial launch of an automated driving system in the BMW iX3 vehicle in November 2025.
Qualcomm EVP and group GM, Automotive, Industrial and Embedded IoT and Robotics Nakul Duggal said “as agentic and physical AI drive a new generation of intelligent vehicles, this collaboration enables both companies to define the future of mobility”.
The company added the deal “establishes the hardware basis for BMW Group to deliver the next generation AI-powered experience”.
Qualcomm has been actively pursuing the automotive sector in recent years as part of a diversification strategy to spread its revenue away from being handset-heavy.
Accelerating ambitions Last month, the company upped financial targets for revenue from automotive and general non-smartphone earnings.
At its investor day in June, it set out an ambition to make $10 billion in revenue from the automotive segment in its fiscal 2029 (ending late September 2029), forming part of a wider target of $40 billion in non-handset revenue that year.
It expects revenues from handsets to make up a third of its top line figure in fiscal 2029.
The previous target for fiscal 2029 was $8 billion from automotive out of a non-handset figure of $22 billion.
In its fiscal 2025, automotive revenue generated almost $4 billion of its total $38.4 billion. Handsets contributed $27.8 billion of its top line.
Have you ever wondered how researchers use artificial intelligence (AI) in environmental science? Certain AI computer models can learn from scientific data and operate without manual programming, in a process called machine learning. Machine learning models can identify patterns in environmental datasets and help scientists assess public health risks linked to environmental conditions.
One well-known public health risk is the rise in antibiotic-resistant bacteria. These bacteria carry genes called antibiotic resistance genes that protect them from antibiotic medicines. As the number of these genes increases, the medicines become less effective, and diseases spread faster.
Agricultural soils are the largest reservoirs of antibiotic resistance genes, because manure from livestock like cows and pigs often contains leftover antibiotics fed to them. When used as fertilizer, this manure transfers antibiotics into the agricultural soil, where bacteria develop antibiotic resistance genes.
To better understand how antibiotic-resistant bacteria will affect global health in the future, scientists want to develop faster ways to detect antibiotic resistance genes. However, current methods of detection are slow and fail to predict future trends. To address this problem, a group of scientists from Algeria used machine learning models to predict the prevalence of antibiotic resistance genes in soil microbes under future climate scenarios, and to identify the key environmental drivers and high-risk areas.
They integrated the microbial dataset with climate data, including global temperature and precipitation measurements from WorldClim, and land use data, including crop types, irrigation systems, and livestock density from the European Space Agency Climate Change Initiative Land Cover maps. The researchers also included data on future climate projections for 2050 and 2070 from the World Climate Research Programme based on low, medium, and high global greenhouse gas emission scenarios.
After preparing the datasets, the team set up 6 machine learning models that differ in terms of their data size, model complexity, analytical speed, and customization. These models included Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), and Logistic Regression (LR).
They ran each model using different combinations of variables from their datasets, including soil properties, microbial community metrics, climate variables, and land use characteristics. To validate the models, the team separated the full dataset into 10 random subsets. They trained the models on 9 of the subsets, then recorded the result of the 10th subset. Each model repeated this process 10 times in a different order so that each subset was evaluated once in the results, using a technique known as stratified 10-fold cross-validation. The researchers scored each model on 5 performance metrics, including precision, sensitivity, and predictive power. Based on these results, they determined that LightGBM was the most accurate model.
They found that the LightGBM model identified soil temperature as the strongest environmental predictor of antibiotic resistance genes. At soil temperatures above 18°C (about 64°F), the number of antibiotic resistance genes increased dramatically. The model also identified soil acidity, organic carbon content, moisture, and annual precipitation as influential variables, accounting for 71% of the increase in antibiotic resistance genes under future climate scenarios. Likewise, the LightGBM model predicted that the number of high-risk areas would increase by 35% under the high-emissions scenario, with South Asia, Sub-Saharan Africa, and Mediterranean Europe as the most vulnerable regions.
Based on the LightGBM model output, the team concluded that soil conditions will change to favor antibiotic resistance genes under all future climate scenarios. They also suggested that LightGBM is a fast and effective model for environmental predictions that could be used to assess other climate change risks such as natural disasters. They recommended that future researchers explore how to apply machine learning methods to early warning systems and utilize machine learning results in environmental management decisions.
The Morning Show Season 5 will officially conclude one of the longest-running and most recognizable dramas on Apple TV when it premieres in 2027.
Apple confirmed that the fifth season will be the last, allowing Jennifer Aniston, Reese Witherspoon and the creative team to complete the stories of Alex Levy, Bradley Jackson and the troubled journalists surrounding their fictional television network.
Aniston and Witherspoon will return as stars and executive producers. Both indicated that the ending was planned while the season was being developed, giving the writers an opportunity to create a deliberate conclusion rather than leaving the characters suspended after an unexpected cancellation.
Apple has not announced a specific premiere date or episode schedule. The company has released first-look images featuring Aniston and Witherspoon together, confirming that production on the final chapter is moving forward for a 2027 arrival.
Image Credit: Apple Inc.
The Morning Show Season 5 Brings a Planned Ending
The Morning Show Season 5 was originally announced in September 2025, shortly before the fourth season premiered. At the time, Apple presented the renewal as another chapter for an established global hit without indicating that it would end the series.
The decision to identify it as the final season changes the expectations surrounding every storyline. Rather than creating another crisis that could lead into a sixth season, the writers can determine where the central characters belong after years of professional upheaval, corporate conflict and personal compromise.
Showrunner Charlotte Stoudt suggested that the final episodes will examine what the journalists ultimately choose for themselves. That direction fits a series in which success has rarely provided its characters with stability or satisfaction.
Alex and Bradley began the drama from sharply different positions. Alex was an established television personality attempting to protect her career during a public scandal, while Bradley entered the network as an unpredictable outsider. Their relationship developed through rivalry, loyalty, separation and repeated confrontations with the institutions employing them.
The final season can now treat those ambitions as completed journeys rather than temporary stages. Alex must decide what remains worth defending after years of negotiating with executives, billionaires and political interests. Bradley must determine whether pursuing the story at any personal cost remains sustainable.
Season four moved the drama into a media environment shaped by deepfakes, conspiracy theories, corporate influence and uncertainty over what audiences can trust. Its story continued examining how journalism operates when the network reporting the news is also affected by commercial priorities and internal power struggles.
Apple has not disclosed the central subject of Season 5. The first-look images concentrate on Aniston and Witherspoon, placing their complicated partnership back at the center after a fourth season that separated the characters through an international crisis.
A Flagship Series That Grew With Apple TV
The Morning Show debuted on November 1, 2019, the same day Apple launched its streaming service.
Its position in the original lineup made the series more than another drama acquisition. Apple needed recognizable performers and an ambitious production capable of signaling that the new platform intended to compete for major television audiences and awards.
Aniston and Witherspoon provided immediate visibility, while the newsroom setting allowed the series to respond to current debates involving workplace misconduct, corporate accountability, political polarization and the influence of television journalism.
The first season began after allegations against morning-show anchor Mitch Kessler, played by Steve Carell, forced the network to restructure its leading program. Alex attempted to retain control while Bradley was selected as an unconventional replacement capable of changing the tone of the broadcast.
Later seasons expanded far beyond the original scandal. The drama incorporated the COVID-19 pandemic, a technology billionaire attempting to acquire the network, the merger of UBA and NBN, artificial intelligence, misinformation and international political pressure.
The willingness to move quickly into recognizable real-world subjects became part of the identity of The Morning Show. Its fictional network frequently operated only a short distance from controversies affecting actual media companies, public figures and technology platforms.
The approach sometimes pushed the series toward heightened melodrama, but that excess also distinguished it from a conventional workplace production. Executive decisions, personal betrayals and breaking-news events could carry the intensity of a political thriller.
Billy Crudup became one of its most acclaimed performers as Cory Ellison, the charismatic executive whose enthusiasm for disruption regularly concealed more complicated motives. Crudup won two Emmy Awards for the role and will return for the final season.
Apple executives have described the production as a defining part of the streaming service. Ending it after five seasons closes a direct connection to the original Apple TV launch lineup while allowing the platform to preserve the series as a completed story.
Image Credit: Apple Inc.
The Final Cast Adds More High-Profile Names
The final season will bring back Aniston, Witherspoon, Crudup, Mark Duplass, Nicole Beharie, Nestor Carbonell, Karen Pittman and Jon Hamm.
Hamm joined the series as Paul Marks, a technology billionaire whose business ambitions and relationship with Alex reshaped the third season. His continued presence indicates that the consequences of his involvement with the network have not been fully resolved.
Several new performers will enter the drama for its last run. Apple has assembled additions including Jeff Daniels, Reneé Rapp, Jesse Williams, Sean Hayes and Lizzy Caplan, although complete character details remain limited.
Daniels brings a particularly appropriate history to the production after starring as news anchor Will McAvoy in The Newsroom. His presence creates an immediate association with another television drama concerned with journalism, newsroom ethics and the conflict between ratings and public responsibility.
Rapp returns to a major television role after building her music career and appearing in projects including The Sex Lives of College Girls and Mean Girls. Williams previously spent more than a decade on Grey’s Anatomy, while Hayes is best known for Will & Grace and has expanded into dramatic work.
Caplan has moved between comedy, drama and thrillers through projects including Masters of Sex, Fleishman Is in Trouble and Fatal Attraction. The scale of the additions suggests that the final season will introduce another major institutional or professional conflict rather than concentrating exclusively on farewell scenes.
Stoudt remains showrunner and executive producer. Mimi Leder continues as director and executive producer, while Media Res, Echo Films and Hello Sunshine remain behind the production.
Aniston described the opportunity to finish the story knowingly as a way to honor the characters with intention. Witherspoon emphasized that the series was never only about the personalities appearing on television, but also about the role of journalism and the people responsible for protecting the press.
That theme gives the final season a defined destination. The Morning Show began by asking whether a television institution could survive the misconduct it had protected. Its last episodes can decide whether the journalists inside that institution changed it, escaped it or became part of the machinery they once believed they could challenge.
Around 4.6 billion years ago, an immense cloud of gas and dust in space collapsed in on itself. As it collapsed, gravity pulled most of the material toward several small points, one of which would eventually become our Sun. The other points formed other stars in what scientists call an open cluster.
During this early stage of the Sun’s life, it was surrounded by a swirling disk of leftover material aligned with its equator, known as a protoplanetary disk. Eventually, this disk condensed to form the planets that make up our solar system. This process normally results in a system with a star at its center, and the planets orbiting the plane of the star’s equator.
However, scientists have found that this scenario doesn’t quite hold for our solar system. The planets in our solar system orbit the Sun in a single plane offset from the Sun’s equator by approximately 6°. The fact that the planets are in the same orbital plane suggests that the protoplanetary disk model of solar system formation is correct, since the odds of the Sun capturing 8 rogue planets in this arrangement are astronomically low. So, what threw the planets in our solar system off-kilter?
The plane in which the solar system’s planets orbit the Sun is offset from the Sun’s equator by approximately 6°. Li simulated how the solar system tilted based on its 4 largest planets, from left to right: Neptune, Saturn, Jupiter, and Uranus. Illustration by Andrew Bizal.
To determine what caused the solar system to tilt, Daohai Li at Queen Mary University of London recently used the computer program NBODY6++ to model different versions of the open cluster from which the Sun came. In these simulations, he varied the number of stars in the cluster, from 500 to 2,000 to 8,000. He also varied the radius of the cluster, from 0.25 to 1 to 4 parsecs (8 to 30 to 100 trillion kilometers or 5 to 20 to 70 trillion miles). Finally, he varied the percentage of stars with a binary companion from 10% to 50% to 90%. Li assigned each star a randomly generated initial mass, starting position, and velocity, and simulated their interactions for 400 million years.
First, Li examined the simulations to identify stars with masses approximately equal to the Sun’s. From this group of solar-massed stars, Li found ones that left the cluster after 400 million years as a single star, after either forming alone or having lost a former companion star. He proposed that these stars were the most similar to the Sun and could thus host a simulated solar system.
Li then simulated the planets and their orbits as they would have formed on these potential host stars. To simplify this process, Li only simulated the 4 giant planets, Jupiter, Saturn, Uranus, and Neptune, which dominate planetary behavior in our solar system. He modeled these solar systems independently for 200 million years to ensure they would remain intact and used those that did for statistical analysis of the solar system’s past.
Overall, Li discovered 2 scenarios that could explain the solar system’s tilt. In the first, the primordial solar system was jostled by many distant neighboring stars in its cluster. This jostling tilts the planets’ orbits, but it does so randomly. Therefore, this scenario would make it unlikely for the planets to remain in the same orbital plane. It would also cause them to orbit the Sun faster and more chaotically than in our solar system.
In the second scenario, the Sun had a temporary companion star that it either formed with or briefly encountered before separating. When this occurred, the simulated planets’ orbits all tilted together. Stars that formed with a binary companion had a 10% chance of tilting their planets’ orbits by 6° or more, while stars that formed alone had a less than 0.1% chance.
Li concluded that the solar system could have tilted as a result of interactions between the Sun and a temporary companion star within its parent cluster. However, he acknowledged that the model strongly depends on how closely a star passes by the Sun, since the closer a star passes, the more it disturbs the solar system. Despite this uncertainty, it’s possible that somewhere in space is the Sun’s twin or former neighbor that we haven’t seen in eons, and that’s what made our solar system crooked!
If you’ve ever swum in the ocean and been touched by seaweed, there’s a good chance it was Sargassum! This free-floating seaweed can have population explosions in coastal waters, degrading water quality, ecosystems, fisheries, and tourism. However, despite its drawbacks, scientists think that Sargassum could be useful for extracting valuable metals, known as rare earth elements (REEs).
REEs are classified by their average masses and atomic weights. Light REEs include cerium, praseodymium, and neodymium, middle REEs include gadolinium, terbium, and dysprosium, and heavy REEs include erbium, thulium, and ytterbium. These metals are used in magnets, electronic motors, wind turbines, spacecraft, and more. Our current methods of extracting REEs, open-pit mining and chemical processing, are invasive, causing habitat loss and producing toxic waste. Additionally, REEs are difficult to import due to restricted access worldwide. But what if these metals could be extracted using natural methods?
Scientists from Woods Hole and UCLA recently tested whether Sargassum could accumulate REEs, creating a sustainable solution to these problems. They harvested Sargassum at Waterfront Park in Woods Hole, rinsed the wet samples, and stored them in sterilized, filtered seawater, which they aerated with 12-hour cycles of light and dark to keep the Sargassum alive. They dried another set of samples in the sun under controlled outdoor conditions, stored them in sealed containers, and later rehydrated them in seawater for 3 hours before the experiments.
For each experiment, they added 5 grams (about 0.2 ounces) of Sargassum to solutions containing a mixture of 9 REEs – cerium, praseodymium, neodymium, gadolinium, terbium, dysprosium, erbium, thulium, and ytterbium – in concentrations totalling 0.1 micromolar (μM) REEs, 11μM REEs, 100 to 110μM REEs, and 575 to 600μM REEs. They performed each experiment 4 times: twice with fresh seaweed and twice with rehydrated seaweed.
The researchers removed 1 gram (about 0.04 ounces) of seaweed from each solution after 1, 3, and 7 days, and dissolved it in acid to release the REEs it had absorbed. They analyzed the concentrations of REEs in each seaweed sample and the leftover solutions using 2 different plasma-based methods. They used these REE concentrations to calculate the ratio of REEs in the Sargassum versus in the solution, known as the bioconcentration factor, to determine how effectively the seaweed absorbed REEs.
They found that after 7 days at lower REE concentrations (0.1μM), the fresh seaweed had bioconcentration factors of over 1,000. At higher REE concentrations (11μM), their bioconcentration factors ranged from about 400 to 700. However, these levels of REEs were eventually toxic to the fresh Sargassum, so they died after 2 weeks. In addition, the higher REE concentrations of 100 to 600μM were immediately toxic to the fresh Sargassum, which died within 1 day in solution.
The researchers determined that once the seaweed died, it could no longer actively take up REEs into its tissues but could only accumulate them on its surface or adsorb them. Adsorption can be thought of as a powdered donut – when the surface is covered with powdered sugar, no more powder fits on the donut. Similarly, there was a limit to how many REEs could stick to the Sargassum. Due to this limit, the fresh seaweed samples had bioconcentration factors below 250 in the highest REE concentrations. In contrast, the rehydrated Sargassum consumed less REEs at lower concentrations, but didn’t experience toxicity and continued to accumulate REEs at higher concentrations.
Finally, the team compared Sargassum to a common industrial method of accumulating REEs, known as activated carbon. REEs bond easily to activated carbon because of its abundant surface area and high porosity. Both materials accumulated a similar amount of REEs in solutions of 0.1μM REEs. However, at 575 to 600μM REEs, Sargassum accumulated about twice as many REEs as activated carbon, especially the heavier REEs.
The researchers concluded that Sargassum is a natural hyper-accumulator, meaning it successfully concentrates REEs. However, they recommended that future researchers test whether Sargassum performs the same in the natural environment as it does in the lab. If so, Sargassum could be used to accumulate REEs from wastewater, like mine drainage, or other water sources with higher concentrations of REEs. Regardless, they suggested that using Sargassum to obtain REEs could simultaneously expand global access to these metals and mitigate toxic blooms.
O retorno da produção, que conta com Reese Witherspoon e Jennifer Aniston como protagonistas e produtoras executivas, está marcado para o ano que vem, com boa parte do elenco já conhecido e algumas adições.
Ao longo de suas quatro temporadas, “The Morning Show” já foi indicada a diversas premiações (vencendo algumas delas) e debateu temas como a pandemia de COVID-19 e assédio, tendo como pano de fundo os bastidores do telejornalismo.
O Apple TV está disponível no aplicativo Apple TV em mais de 100 países e regiões, seja em iPhones, iPads, Apple TVs, Macs, dispositivos Android, smart TVs ou online — além de também estar em aparelhos como Roku, Amazon Fire TV, Chromecast com Google TV, consoles PlayStation e Xbox. O serviço custa R$29,90 por mês, com um período de teste gratuito de 7 dias. Por tempo limitado, quem comprar e ativar um novo iPhone, iPad, Apple TV ou Mac ganha 3 meses do Apple TV. Ele também faz parte do pacote de assinaturas da empresa, o Apple One.
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I’m excited to share that Qualcomm is acquiring Modular: this will accelerate our path to unifying accelerated compute with an open platform. This will also mark a new era in open software development for Qualcomm.
[…]
This will accelerate our progress and path, and their vision is expansive: [spanning] edge to cloud, CPU, GPU, NPU, and custom ASICs and perhaps more.
As AI scales, efficiency, not capability, becomes a constraint. Performance-per-watt drives the cost of inference, and cost determines what scales. Meeting this demand requires more than hardware. Developers need software that connects system-level optimization with heterogeneous, disaggregated compute, turning silicon performance into reliable and efficient AI services across accelerators, environments, and use cases.
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Micron Technology secured strategic supply agreements with major automotive partners including Qualcomm and Harman to guarantee long-term chip access for software-defined vehicles.
The deals come during an aggressive industry-wide race to expand manufacturing capacity to meet a massive surge in memory chip demand driven by rapid AI adoption.
As the only US-based manufacturer of high-bandwidth memory chips used in Nvidia’s AI processors, Micron occupies a unique market position alongside South Korean rivals Samsung and SK Hynix, allowing it to command premium prices while solidifying long-term commitments during ongoing hardware shortages.
Micron also struck deals with Visteon, Joynext, Denso, Astemo, and Hyundai Mobis. It stated the agreements would improve visibility and help optimise production planning. It expects greater stability in pricing and supply chains for increasingly sophisticated AI-enabled vehicle platforms.
Demand for high-performance DRAM and NAND flash memory has grown as the automotive sector transitions toward highly connected, autonomous and intelligent designs.
Modern vehicle architectures require robust computing hardware to support advanced driver assistance systems, infotainment services and high-speed connectivity.
Sanjay Mehrotra, chair, president and CEO of Micron, emphasised the next phase of automotive innovation hinges on the strength of the ecosystem behind it. He noted “memory and storage serve are critical enablers” for the sophisticated digital experiences consumers expect.
Qualcomm CEO Cristiano Amon highlighted the collaborative effort required to deliver advanced digital cockpit and driver assistance technologies. He stated working with Micron provides a strong hardware foundation as vehicles become more intelligent and connected.
The automotive sector is a key area for increasing Qualcomm’s revenue beyond mobile chips. In its April Q2 earnings, it reported Automotive sales rose 38 per cent to $1.3 billion.
Humans have been around for only about 0.01% of Earth’s history, yet we’ve altered natural landscapes and processes that have remained stable for millions of years. A team of researchers from the University of Glasgow recently studied a formerly sandy beach in the UK that highlights how fast these changes can occur.
Industrial steel and iron furnaces operated extensively in the UK from 1856 until the 1980s. During these decades, workers dumped roughly 335 million cubic meters (90 billion gallons) of molten furnace waste, called slag, across the country. Of that, roughly 27 million cubic meters (6 million gallons) now sits on the coast of Derwent Howe in a massive 30-meter (100-foot), solidified cliff.
The researchers analyzed this region to understand how man-made, or anthropogenic, materials influence the way rocks form. Rocks naturally form and change into different types of rocks over million-year timescales, through a continuous process called the rock cycle. When the team uncovered a soda can tab manufactured in 1989 within the hardened grains, or clasts, of slag, they concluded that the beach had solidified within the last 35 years. Based on this discovery, they proposed a new term for rocks formed from anthropogenic sediments: the anthropoclastic rock cycle.
The team collected 1,300 grains of sediment from 13 sites in the study area and found that 83% were slag. Natural beach sediments, like quartz sand, comprised only about 17% of the grains. As wind and waves broke down the cliff, slag clasts fell off and became rounded by friction as tides transported them from the cliff to the beach.
At the boundary where the slag touched the beach, the researchers also saw evidence that the slag had seeped into the underlying wet sediment. The rocks there resembled a type of rock called peperite, which forms when lava flows over wet, loose sediments. They concluded that this peperite-like rock formed as the molten slag flowed over the still sandy beach. Slag is much thicker than wet sand, so the resulting rock looked like blobs of slag encased in sandy rock.
The researchers then visually observed how the slag grains were arranged in the solidified rock to reconstruct the tidewater speeds at different points in the coast’s history. The different ways sediments are arranged within a rock are called sedimentary features, and they reflect the processes that formed the rock. For example, as water flows over sand, it forms small, dune-like ripples. When the sand beds harden into rock, the ripples are preserved as features known as trough cross-bedding.
Faster water erases any ripple formations and instead forms horizontal layers in the sediment, known as planar lamination. The researchers observed both trough cross-bedding and planar laminations preserved in the slag beach rocks. These sedimentary features indicate that the beach at Derwent Howe turned from loose sediment into rock in a generation.
To investigate why this beach rapidly solidified, the researchers scanned the surfaces of the beach rock with electron beams and X-rays using a scanning electron microscope, or SEM, equipped with an energy dispersive X-ray, or EDX. Every chemical element emits a unique set of X-rays with different wavelengths when hit with EDX, so the researchers could compare the X-rays from their samples to databases of known mineral X-ray signatures. They identified the primary binding agents as calcium- and iron-rich minerals like calcite, goethite, and brucite. These minerals are called cements because they bind grains of sediment together.
The researchers expected the slag to produce these cement compositions, since extracting metal ores from rock enriches the resultant slag in calcium, magnesium, manganese, and iron, which readily dissolve in water. When water flowing between closely packed slag grains can no longer hold additional dissolved elements, those elements precipitate as solid mineral cements. Since cements only form when the water has very high concentrations of dissolved elements, the introduction of massive amounts of slag caused the cement to form faster than it would in waters with lower concentrations of elements.
The researchers suspected that the anthropoclastic rock cycle they observed at Derwent Howe is not uncommon. Since slag contains all the elements necessary to form rocks when in contact with seawater and air, slag deposits all over the world could be rapidly influencing their local environments. With this in mind, the team suggested that future researchers update geological models to account for human contributions to the natural rock cycle.
Nitrogen is an essential building block of proteins, amino acids, and DNA. Without nitrogen, cells can’t function or reproduce. Nitrogen is abundant in the atmosphere as dinitrogen gas, but plants and animals can’t access atmospheric nitrogen. Instead, we rely on biologically available forms like ammonia and nitrate.
Specialized microbes can transform atmospheric nitrogen into ammonia via a process called biological nitrogen fixation. Nitrogen-fixing soil bacteria have a unique protein complex, the nitrogenase complex, that facilitates this nitrogen transformation. The nitrogenase complex is activated by a group of genes known as nif genes.
Legume plants like peanuts, peas, and beans have nitrogen-fixing bacteria in the soil zone around their roots, called the rhizosphere. The symbiotic relationship between nitrogen-fixing soil bacteria and legume plants supplies most of the biologically available nitrogen to life on Earth.
Bacteria attached to plant roots “feed” nitrogen to plants through the soil, and in turn receive organic acids for energy from plants performing photosynthesis. Source: Wikimedia Commons
However, one piece of the puzzle remains unexplored: the role of soil viruses in these nitrogen transformations, referred to as the nitrogen cycle. Viruses are microscopic particles made up of genetic material and proteins, but they can’t reproduce without a host cell. Scientists consider viruses to be biological entities that are halfway between living and non-living.
To reproduce, viruses contact a host cell and inject their genetic material into it. The virus’s genetic material takes over the host cell’s genome and forces the cell to produce proteins that assemble into new viruses. As new viruses are created, they can incorporate genes from the host cell into their own genetic material. Genes that are transferred from a host cell to a virus are known as auxiliary metabolic genes (AMGs). Viruses can carry AMGs and pass them to other hosts or use them to increase their own ecological fitness.
Scientists from China, Spain, and the Czech Republic set out to investigate whether viruses from legume roots could increase local soil nitrogen fixation by expressing nif AMGs or by transferring nif genes to other bacteria. First, they determined the global distribution of viral nitrogen-fixing genes by analyzing about 8.6 million viral genomes from the Integrated Microbial Genomes/Virus database. They looked for the DNA sequences of known nitrogen-fixing genes and the locations where the viruses were sampled. They found that only 0.003% of the viruses in the database had at least 1 nitrogen-fixing gene, but they clustered in the same locations as nitrogen-fixing bacteria.
The team also found 3 types of viruses that most commonly carried nitrogen-fixing genes: Kyanoviridae, Nudiviridae, and Bronfenbrennervirinae. Kyanoviridae viruses carried a nif gene called nifU. The researchers used a computer program called DRAM-v to confirm that nifU was a fully functional gene and a real tool that viruses could use.
Next, the team collected soil samples from a cowpea field in Nanjing, China, to test whether the cowpea roots influenced the abundance of viruses using nitrogen-fixing genes. They collected soil from the cowpea rhizospheres and soil away from the plants. They analyzed both viral and bacterial RNA in the soil samples to identify active genes, using a sequencing process called metatranscriptomics. They found that the viral nifU gene was actively expressed in the cowpea rhizosphere samples more than in the non-crop soil. Although about 96% of nifU genes were being expressed by bacteria, viruses contributed about 4%.
The researchers then assessed whether rhizospheric viruses changed the amount of nitrogen being fixed in these soils. They set up small, enclosed containers of sterile soil and added either bacteria or a mixture of bacteria and viruses extracted from the cowpea rhizospheres. They found that soil with viruses had higher total nitrogen (36 milligrams per kilogram, or mg/kg) than soil without viruses (17 mg/kg).
Finally, they adjusted the air in each container to have a different form of dinitrogen gas. Atoms of the same element that have different atomic masses are known as isotopes. Nitrogen has 2 stable isotopes: lighter nitrogen-14 and heavier nitrogen-15. Bacteria or viruses that fix nitrogen at natural isotope abundances will take up nitrogen-14. But scientists can track who in the population fixes nitrogen by adding nitrogen-15 to the air, a process called nitrogen-15 stable isotope probing. Nitrogen-fixers will incorporate the heavy nitrogen-15 into their biomass over time and increase their weight, allowing researchers to separate and identify them.
The team grew the bacteria and viruses with nitrogen-15 for 35 days, then used stable isotope probing to separate the nitrogen fixers. They detected viral nifU AMGs among the heavier biomass, confirming that rhizospheric viruses were fixing nitrogen.
The team concluded that rhizospheric viruses and the viral nifU AMG can increase nitrogen fixation in cowpea soils. The team suggested that although viruses with nitrogen-fixing genes are rare, they can influence the soil nitrogen cycle. They recommended that future researchers further test the contribution of viral nifU genes to plant nitrogen fixation with controlled infection experiments.
In March 2021, the carcasses of at least 260 marine organisms from 81 species, including large animals called megafauna, washed ashore in South Africa. Scientists questioned whether this event was the result of an extreme temperature drop that can happen when deep, cold water rises to the surface along coastlines. Scientists refer to these rapid cooling events as coastal upwelling.
Climate change is making oceans warmer, but it’s also making coastal upwelling stronger, with unknown consequences for marine megafauna. Scientists recently studied 2 boundary currents that are rapidly warming and creating strong upwelling near the edges of sharks’ geographic ranges: the Agulhas Current in southern Africa and the East Australian Current in eastern Australia. They wanted to determine whether climate-driven changes in upwelling zones are making cold-shock events more frequent and intense, and how these events are pushing sharks beyond their temperature limits.
First, the researchers analyzed sea surface temperature data from satellites to see if rapid cooling from upwelling caused the mass marine die-off in 2021. They also obtained hourly temperature recordings from temperature loggers to measure subsurface temperature changes during the event. They found that during the March 2021 event, sea surface temperatures along the coast dropped by up to 7°C (12°F) in less than 48 hours, from 24°C (75°F) to below 17°C (63°F). Subsurface temperatures dropped faster, by more than 9°C (16°F) in 24 hours, from 21°C (70°F) to 12°C (54°F).
Bullsharks experience physiological stress at temperatures below 19°C (66°F), which is near their lower thermal limit, when migrating through rapid cooling zones. Therefore, the researchers deduced that during the 2021 cold-shock event, the sharks were exposed to temperatures several degrees below their thermal limit, possibly more than 5°C (or 10°F) colder.
The researchers also tagged 66 bull sharks with acoustic and satellite transmitters – including 41 at the Agulhas Current in South Africa and 25 at the East Australian Current in eastern Australia – to determine how they responded near their lower thermal limits in these zones. The acoustic tags record when sharks pass underwater receivers, providing information on their movements, locations, and water depths at those specific locations. In contrast, satellite tags continuously record the water temperatures and depths the sharks experience over time.
The researchers used these data to compare sharks living in warm subtropical waters with those living in cooler waters at the edge of their temperature range. The researchers found that low temperatures that persisted for long periods prevented the sharks and other megafauna from physiologically recovering. They saw that some sharks try to swim to shallower depths and look for warmer water, but cooling sometimes happened faster than they could respond.
The researchers also obtained wind records from the South African Weather Service to determine if upwelling-favorable winds happened before the 2021 event. They reported strong easterly winds pushing cold water on the surface along the coastline immediately before the event occurred. Because the cooling, strong winds, and animal deaths happened at the same time and place, the researchers inferred that upwelling was the main reason for the die-off.
Finally, the researchers examined 41 years of sea surface temperature data and used mathematical models to compute changes in the frequency and intensity of these events. Their analysis showed that rapid cooling events linked to upwelling can last a lot longer than the usual 1 to 3 days. For example, the March 2021 event lasted for 7 days.
They found that these events have increased in both frequency and intensity in the past decades, and produce more extreme temperature drops than typically happen within 24 hours. Their results suggested that the 2021 die-off was part of an increasing trend, where warming oceans allow species to move into different areas, but intense upwelling can suddenly expose them to fatal cold events. This pattern is what scientists call a climate bait-and-switch.
The researchers concluded that climate change is making coastal upwelling stronger, which can push marine megafauna beyond their thermal limits, even when initial warming appears to be beneficial. Their findings highlight an understudied aspect of climate change in the context of how temperature and upwelling cause shifts in species survival. They concluded their findings could help predict how marine ecosystems respond to future changing ocean conditions.
When you think of an asteroid impact, you’re probably imagining something like the cataclysmic event that nearly wiped out the dinosaurs. In an astonishingly rare event, a rock the size of the island of Manhattan could come hurtling from deep space towards Earth and destroy the world.
On the contrary, asteroids are generally leftover debris from the primordial solar system that didn’t become part of a larger body, like a planet or moon, and they’re not particularly rare. Scientists estimate that there are about 1 billion asteroids and comets classified as near-Earth objects, or NEOs. NEOs are generally much smaller than the asteroid that killed the dinosaurs. They range from weakly held-together piles of rubble 200 meters (700 feet) across, to the minuscule 2024 BX1, approximately 44 centimeters (17 inches) across.
For several decades, astronomical surveys of the sky have looked for these asteroids. NASA’s Center for Near-Earth Object Studies (CNEOS) Fireball and Bolide Database has records of over 1,000 incidents of objects colliding with our planet since the mid-1990s. Among these is a set of 11 objects that scientists detected before they hit Earth, known as imminent impactors. Finding imminent impactors allows scientists unique opportunities to study the smallest building blocks of the solar system, enabling them to learn more about asteroid composition, structure, and history.
A team of scientists recently conducted a study on how the newly built Vera Rubin Observatory could detect more imminent impactors before they strike Earth. They examined the CNEOS Fireball and Bolide Database impact records spanning February 1, 1994, to January 1, 2026, for records of the asteroids’ coordinates, velocities, and the energy they released at the time of collision.
They found that 343 NEOs met this criterion, including 5 known imminent impactors. Using these recorded data, they reconstructed the objects’ pre-impact characteristics. The team predicted the asteroids’ orbital paths from the recorded coordinates and velocity. Assuming the objects were spherical, they estimated the size from velocity and impact energy. From this information about the asteroids’ state before impact, the team could then simulate whether the Rubin Observatory would have detected them.
When small asteroids hit Earth, they mostly burn up in the atmosphere, producing a bright flash that is relatively easy to spot compared to when they are in space. The Rubin Observatory can see objects up to 10 billion times dimmer than the brightest star in the night sky, Sirius. To predict if the Rubin Observatory can detect dim NEOs, the team used the solar system simulator SORCHA. SORCHA takes an observatory’s specifications and the physical properties of the NEOs and uses these to determine whether the observatory could detect them. It also estimates the measured sizes and speeds of the NEOs, and predicts when the observatory would have seen them.
At the Rubin Observatory, an imminent impactor is considered discovered if it’s observed at least 3 separate times over 15 days before hitting Earth. So this team applied this same criterion to their simulation. They found that of their 343 impactors, SORCHA estimated that the Rubin Observatory would have discovered 14 of them before impact.
They also found that SORCHA predicted 4 additional impactors that could have been observed at least once, potentially allowing scientists to search other telescope data for evidence of their existence in pre-collision images. Based on these results, the team estimated that the Rubin Observatory could find 4% of all objects longer than 1 meter (3 feet) before they hit Earth. This percentage of discovery translates to 1 to 2 imminent impactors detected every year, at least double their current discovery rate.
The ability to more consistently find imminent impactors would allow researchers to study the smallest pieces of the solar system in detail and to draw general conclusions about the properties of objects that could potentially collide with Earth. This would also allow scientists to more accurately calculate the orbits of imminent impactors, determine where more meteorites land to recover them, and keep tabs on the extremely rare but more hazardous large impactors for planetary defense initiatives. So, humanity can learn more about the ancient solar system, and hopefully avoid going the way of the dinosaurs while doing so!
Schistosoma mansoni is a parasitic worm that infects millions of people worldwide, entering the body through the skin. Unlike bacterial infections that often cause pain and inflammation, S. mansoni usually slips in unnoticed, rarely causing discomfort. Scientists have long wondered why our skin’s defenses don’t react more strongly to this invader.
Researchers from the University of Pennsylvania and Tulane University explored whether a specialized group of pain-sensing neurons, called TRPV1+, plays a role in detecting and fighting off S. mansoni during its invasion. TRPV1+ neurons are nerve cells in the skin that respond to heat, spicy foods, and certain bacteria and fungi. When activated, they can trigger pain and release molecules that alert the immune system. Previous researchers have shown that these neurons help defend against bacterial and fungal skin infections, but their role in fighting off parasites was unclear.
To examine how S. mansoni infects its hosts, the team conducted experiments using groups of 4 to 8 mice, matched for age and sex. They repeated each experiment 2 to 3 times to ensure their data was reproducible. They anesthetized the mice, exposed their ears to a solution containing 100 to 150 live S. mansoni larvae, and covered the area with vaseline to keep the solution in place. After 20 to 30 minutes, they counted the number of larvae remaining in the mice’s ears to assess how many had successfully penetrated their skin.
Next, the team measured pain sensitivity in the mice exposed to the parasite by placing them in chambers and applying an infrared heat source to their paws. They recorded how long it took each mouse to withdraw its paw as a sign of pain. They repeated this test 3 times per paw on each mouse, waited at least 5 minutes between each measurement, and calculated an average withdrawal time. They found that mice infected with S. mansoni were approximately half as sensitive to heat-induced pain as an uninfected control group, suggesting that the parasite could be suppressing the mice’s TRPV1+ neurons.
To investigate this further, the team isolated nerve cells that transmit sensory signals from the mice’s skin, known as dorsal root ganglia, and grew them in the lab. They stimulated these neurons by adding a spicy compound in chili peppers, known as capsaicin. Then they measured calcium influx, a sign the neurons were active, using a special dye that glows differently under a microscope depending on calcium levels. This allowed them to see how active the neurons were and how many molecules they released in response to infection and stimulation. They found that 68% of neurons from uninfected mice responded to capsaicin, but only 26% of neurons from infected mice did so, confirming that the parasite dampened their sensory neuron responses.
To test whether restoring the mice’s neuron activity could help their bodies resist parasite invasion, the researchers genetically engineered the mice to produce a light-sensitive protein in their TRPV1+ neurons, called channelrhodopsin. They “turned on” these neurons by stimulating them with blue light for 30 minutes every day for 5 days before infecting the mice with parasites. Six days later, they assessed the mice’s immune response by using a laser to count immune cells in samples of ear tissue. They also determined how far the parasites had spread by counting larvae in lung samples. They saw that mice with the artificially activated TRPV1+ neurons had nearly double the immune response, blocked roughly twice as many larvae at the skin, and had about 20% fewer parasites in their lungs.
Next, the researchers selectively destroyed TRPV1+ neurons in a new set of mice using a compound known as resiniferatoxin. Three weeks later, they confirmed the treatment had worked by testing the mice’s pain sensitivity using the same heat assay as before. After confirming the mice had lost their pain sensitivity, the team exposed the mice to the parasites. They found that the reinfected mice had 1% to 3% weaker skin immune responses and approximately 25% to 30% more parasites in their lungs than a control group, further confirming that TRPV1+ neurons play a protective role during early infection.
The team concluded that S. mansoni has evolved ways to silence pain-sensing neurons, like TRPV1+. Their discovery suggests that future scientists could boost skin immunity against parasites by targeting sensory neurons. For example, they could develop topical creams or therapies that activate these neurons to help our bodies fight off S. mansoni and similar parasitic invaders.
Nearly 170 billion tons of ice melt off the Greenland Ice Sheet every year, but scientists don’t fully understand the process by which this melting occurs. Researchers in the past suggested that liquid water forms at the glacier’s surface, where warm air and sunlight melt the ice. This meltwater travels down through cracks in the glacier and along the bedrock beneath it and flows out to sea.
However, some scientists view this model as too simple because it assumes water only flows downward and ignores how temperature affects water movement. They argue that an oversimplified melting model is unreliable for predicting how future ice sheets will behave.
One group of researchers tested this by investigating how water movement across Greenland is controlled by lakes beneath the ice sheet, called subglacial lakes. They monitored a newly discovered subglacial lake using high-resolution satellite images taken from 2012 to 2019 and 3D surface maps generated from the images. They focused on ice sheet changes from July 22 to August 1, 2014, during a suspected subglacial lake drainage event. A drainage event is like a water balloon that’s too full, bursting from excess pressure.
They examined images of the Greenland ice sheet taken from Landsat-8, 3D surface maps from the Polar Geospatial Center, and data from ICESat and ICESat-2 taken in the summer of 2014. Based on these data, the researchers identified a 2-square-kilometer (0.8-square-mile) region of ice rising 10 to 15 meters (about 30 to 50 feet), forming a dome on the ice sheet’s surface. They suggested that this dome formed when a large, meltwater-fed lake developed on the bedrock beneath the ice sheet, pushing the ice upward.
They also saw that on July 22, 2014, the dome began to collapse. Over the next 10 days, it dropped by 85 meters (about 280 feet), forming a basin. Using the dome’s dimensions, the researchers estimated that 90 million cubic meters, or about 3 billion cubic feet, of water drained from the lake at an average rate of 100 cubic meters, or about 3500 cubic feet, per second over the 10 days. This is roughly equivalent to 36,000 Olympic swimming pools draining at a rate of 1 pool every 25 seconds!
The researchers also found that during the collapse, 40-meter-high (130-foot) ice blocks were uprooted about 1 kilometer (0.6 mile) downstream from the basin, followed by 6 square kilometers (about 2 square miles) of smooth ice. The researchers hypothesized that these features formed when water burst through the ice, flowed along its surface, and then reentered the ice sheet.
Next, they used data from Landsat 5, Landsat 9, the National Snow and Ice Data Center, and the U.S. Geological Survey to demonstrate that the drainage event also affected the surrounding environment. When the water reentered the ice sheet, it flowed beneath the Harding Glacier downstream. This influx of fast-moving water reduced pressure at the glacier’s base, slowing its movement. Simultaneously, the increased drainage beneath the Harding Glacier caused 500 to 600 meters (about 1,600 to 2,000 feet) of ice to break off its end.
In response to these changes, the researchers hypothesized that subglacial waters rose to the surface rather than sinking to the bedrock because the ice sheet was frozen to it. To test this, they simulated temperatures at the base of the ice sheet using computer-generated thermal models. They input a range of likely bedrock temperatures and ice thicknesses to simulate possible conditions under the ice sheet. They found that all the simulations predicted the glacier’s base would remain below -5°C (23°F). At such low temperatures, the ice would be frozen to the bedrock before subglacial water could drain out. Since water couldn’t flow under the glacier, it was forced to travel upward.
The researchers used these findings to develop a new conceptual model of how glacial meltwater moves. First, surface ice melts and flows into a subglacial lake. As meltwater accumulates, pressure builds at the base of the ice sheet, forming a dome on the surface. A drainage event causes the dome to collapse. As water moves downward through the glacier, it is blocked from reaching the base because the ice is frozen to the bedrock. The water then moves upward, bursts through the surface, travels across it, and eventually reenters the glacier before draining to the ocean.
The team concluded that water movement above, through, and below glaciers is interconnected. Destructive upward flow of meltwater, followed by downward flow beneath glaciers, can weaken ice sheet structure and alter downstream glacier dynamics. Their work highlights the need for more scientists to consider the processes driving glacial ice loss.
Many volcano scientists spend their time monitoring volcanoes to predict if, or when, they’ll next erupt. The challenge for these scientists is to forecast the exact probability of an eruption and the moment it will occur.
To address this problem, a team of researchers developed an early-detection system to determine when to issue alerts about volcanic eruptions. Their motivation was that previous alert systems weren’t accurate enough because they couldn’t detect underground volcanic processes that occur right before an eruption.
From 2014 to 2023, the researchers conducted a series of tests to accurately detect temporary, low-frequency vibrations using an instrument called a seismometer. The researchers focused on these transient signals because they come from earthquakes and other ground phenomena, which scientists often rely on to predict volcanic eruptions. These phenomena include ground tilting caused by fracturing from the movement of magma and volcanic gases. The scientists named these signals the “Jerk” signals because they occur due to sudden movements.
The team set up their experiment at Piton de la Fournaise, a volcano at La Réunion Island off the east coast of Madagascar. They discovered the Jerk signals by detecting and analyzing mysterious short-term signals from existing and experimental seismic data. During real-time experiments, they detected signals 8 kilometers (about 5 miles) from the volcano at the Rivière de l’Est seismic station. These signals resulted from changes in ground elevation as magma moved, and they preceded eruptions by minutes and sometimes hours.
The researchers measured the Jerk signals in units of newton-meters per second, which conveys how fast physical changes occurred in or around the volcano. In addition to real-time experiments, the team checked the Jerk signals’ accuracy by analyzing the station’s past data to determine when these signals would have occurred before an eruption.
To avoid false alarms during their experiments, the researchers needed to distinguish between the signal amplitudes of ocean tides and the transient Jerk signals. So, they used computational software to predict the signals from ocean tides. This was necessary because the instruments that record ground phenomena are also sensitive to tidal movements.
The Jerk system sent its first automatic alarm in June 2014, 1 hour before the first volcanic tremor, signalling that magma was at the Earth’s surface. The team recorded the last signal in their study on July 2, 2023, 40 minutes before the eruption, at a slow 1.5 newton-meters per second. From 2014 to 2023, other Jerk detections occurred between a few minutes and up to 8 hours before an eruption.
To further assess the strength of the Jerk signals, the team examined previous eruption records at Piton de la Fournaise. They found that for the 24 eruptions between 1998 and 2010, a Jerk alarm would have gone off 83% of the time. Of the 48 eruptions that occurred between 1998 and 2023, including the eruptions during their real-time tests, about 42 Jerk alarms would have occurred. However, they found that the Jerk signals were more accurate during their real-time analysis than they would have been for the eruptions 20 years earlier.
Although the researchers successfully detected pre-eruption signals, they noted that the Jerk alarms occasionally arrived too late due to the data processing time, which takes at least 10 to 15 minutes. They reported 2 late alarms: one on June 11, 2019, and the other on February 10, 2020. Therefore, after 10 years of tracking the Jerk signals, the team evaluated their success rates at 22 successful alarms out of 24 eruptions, for a 92% positive rate.
Before using the Jerk signals, many volcanologists struggled to predict the exact moment a volcano erupts. The team concluded that, in addition to the usual methods for detecting pre-eruption earthquakes, observatory scientists could now use Jerk signals to confidently notify authorities in advance of volcanic eruptions, so they can alert people living near volcanoes worldwide.
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