This striking satellite snap shows the scorching land and sea surface temperatures that hit Southern Europe during one of the continent's record-breaking heat waves this summer.
The image was created using data from the Sea and Land Surface Temperature Radiometer (SLSTR) on board the European Space Agency's (ESA) Sentinel-3B satellite. It was selected for this week's edition of Earth from space by Simonetta Cheli, director of Earth observation programs at ESA and head of the European Space Research Institute (ESRIN) in Frascati, Italy.
In the thermal image, dark reds highlight terrestrial temperatures in excess of 95 degrees Fahrenheit (35 degrees Celsius) across most of Spain and parts of France and Italy, while dark purple spots show highs above 104 F (40 C) across patches of Spain and North Africa. The image also reveals sea surface temperatures spiking across the Mediterranean Sea. (The colors in the image do not represent the same temperatures on land and sea. For example, orange indicates temperatures of around 86 F (30 C) on land but around 73 F, or 23 C, at sea.)
Cheli said that assessing how terrestrial and marine environments interact during extreme weather events is really important and that including both in the same image is the only way to appreciate the true scale of this heat wave: "You see the red in the sea, and you see the red on the land, and I think it gives the feeling of the heat wave that we've all been living through this summer," she said.
While many people may find such images related to human-caused climate change disheartening, Cheli sees it as a sign of hope that orbiting spacecraft can monitor otherwise-invisible changes to our planet's surface in such detail.
"It makes me feel that we can do a lot about it," she said. "It makes me feel that we have tools and certainly information capabilities to support, help and also raise awareness about this topic."
Cheli told Live Science that ESA's ability to monitor extreme weather events like this will only improve as the agency launches new-and-improved spacecraft into orbit. In September, ESA successfully launched Sentinel-3C, the successor to the spacecraft that snapped this image, which has already captured its first photos of Earth and will help study future heat waves in greater detail.
Between late May and mid-August, parts of Europe were hit by at least five separate heat waves, meaning temperatures stayed unusually high throughout the season. "We had, I would say, the hottest summer ever," Cheli said.
The peak heat occurred in June, when the average temperature for that month reached 69 F (20.7 C) — its highest in Western Europe's recorded history, according to the World Meteorological Organization. (This average includes nighttime temperatures, which is why it seems relatively low compared to other temperatures mentioned so far.)
Eight countries — Austria, Belarus, Czechia, Denmark, Germany, Hungary, Poland and Slovakia — recorded their highest land surface temperature of all time at various points throughout this summer, and several others had record temperatures for a specific month or region.
The extreme weather caused widespread droughts across the continent, some of which are still ongoing, and helped trigger several wildfires, including a series of particularly large blazes in France. Experts think the high temperatures resulted in at least 35,000 excess deaths across Europe, although the true number is expected to be much higher, according to The Guardian.
The heat waves were caused by "heat domes," which are pockets of high pressure in the atmosphere that trap warm air beneath them, like placing a lid on top of a boiling pot. These are also linked to higher sea surface temperatures.
'> 'It makes me feel that we can do a lot about it': ESA official shares striking satellite snap of Europe's record-breaking summer heat waves
The Orionid meteor shower will peak overnight Wednesday (Oct. 21) through Thursday (Oct. 22), potentially producing around 20 meteors per hour under a dark sky, according to Time and Date. The Orionids are caused when Earth passes through the debris left by Halley's Comet, which last passed through the inner solar system in 1986 and will return in 2061.
Halley's Comet is presently beyond the orbit of Neptune, according to The Planets Today, but it doesn't need to be close to Earth to cause "shooting stars." Every time the comet travels through the inner solar system, heat from the sun causes it to shed dust and rocky particles along its orbital path. Earth's own orbit takes the comet through streams of that ancient debris several times a year, with the particles vaporizing as they slam into our planet's atmosphere.
The Orionids are particularly fast meteors, striking Earth's atmosphere at around 41 miles per second (66 kilometers per second). They can produce bright streaks, occasional fireballs (extremely bright, sometimes colorful meteors) and "persistent trains" — glowing trails that remain visible briefly after a meteor has disappeared. The best way to view them is with the naked eye, although a nice pair of stargazing binoculars or a small backyard telescope will help you enjoy more sights during peak skywatching season this fall.
Halley's Comet produces not one but two annual meteor showers. Its orbit comes close to Earth's at two different points, so our planet encounters the comet's debris twice each year — once during the Orionids in late October, and again during the Eta Aquariid meteor shower in early May.
There's a slight complication with the Orionids this year. A 71%-illuminated waxing gibbous moon will brighten much of the night, making it harder to spot meteors. However, the moon will set early Thursday morning, creating a valuable window of darkness before dawn. That's also when the constellation Orion will be high in the sky, making the predawn hours the best time to watch.
You can trace the paths of Orionid meteors back to their radiant: the famous red supergiant star system Betelgeuse in the constellation Orion. (That's why the shower is called the Orionids.) However, there's no need to look directly at Orion. Meteors seen farther from the radiant can produce longer streaks across the sky; each one comes from a tiny piece of Halley's Comet completing a journey that began many centuries ago.
'> See pieces of Halley's Comet fall to Earth this week as the Orionid meteor shower peaks . "Most likely, it was a bread shovel, which was used to bake bread in an oven. On the reverse side, however, three squares have been carved, one inside the other."
The "bread shovel" — also known to contemporary home bakers as a wooden bread peel, a pizza paddle or a pizza peel — was found at the archaeological site of Pyatnitsky in the center of Staraya Russa, a medieval town between Moscow and St. Petersburg where last month, archaeologists reported their discovery of a "snake amulet."
The preserved portion of the bread peel measures about 12 by 16 inches (30 by 40 centimeters), and it was likely crafted from oak. While one side is flat, likely for baking tasty treats, the other side had been carved with a board for the ancient game known as merels, merrelles, mills, nine-men's morris or cowboy checkers.
A typical merels board has three nested squares connected by horizontal and vertical lines. Each player has nine pieces of the same color. The goal is to arrange the pieces into columns of three along the lines. When a player forms a line, they can remove any one of their opponent's pieces. In this way, merels is a mixture of tic-tac-toe and checkers. A player wins when their opponent has only two pieces left or is completely blocked. (You can play an online version here.)
"Perhaps special boards for playing merels existed in antiquity," Toropov said, "but more often, one finds playing boards scratched or carved into the flat surfaces of household objects."
The archaeologists are not sure whether the wooden bread peel served two functions or whether it was repurposed as a game board after it broke and was no longer useful.
"At any moment, one could mark out a board on a flat surface, find suitable pieces — for example, small stones lying underfoot, and while away some time," Toropov said.
Merel boards and playing pieces have been recovered from ninth-century archaeological sites in the kingdom of the Rus', whose ancestors were Vikings who arrived in what is now Russia in the eighth century, according to the statement, but "a find as well-preserved as the one from Staraya Russa is practically unique."
'> 11th-century 'pizza peel' discovered in Russia doubled as a medieval game board
Historically, scientists studying Alzheimer's disease have focused on two proteins that accumulate in patients' brains: amyloid-beta and tau. However, there's a "third thing that people didn't pay attention to, until recently," said study co-author Li-Huei Tsai, a neuroscientist at MIT. That third thing is the accumulation of droplets of fat, or lipids, in the brain, she told Live Science.
The APOE gene makes proteins responsible for transporting fat around cells throughout the body and brain. A version of the gene called APOE4, which increases the risk of late-onset Alzheimer's three- to 12-fold, makes proteins that are less efficient at this function, Tsai said. But how APOE4 affects different brain cells was not clearly understood.
Tsai's team has been working to close that gap for about a decade.
"APOE4 can influence many different cell types," she emphasized. "It's not just one cell type."
Zeroing in on the clues
In past laboratory research, Tsai's team found that APOE4 causes a buildup of cholesterol in astrocytes, star-shaped brain cells that support the function of neurons. Genetic analyses suggested that the gene variant may mess with how these cells process fat.
Another study zoomed in on oligodendrocytes, cells that help insulate neurons' outgoing wires with a fatty material called myelin. This material helps neurons send messages more efficiently. APOE4 caused the oligodendrocytes to accumulate cholesterol. So, in lab mice, the team used a drug to help the cells get rid of that extra cholesterol, and it improved the cells' ability to make myelin and the mice's performance on cognitive tests.
In their latest research, published Thursday (Oct. 15) in the journal Cell Stem Cell, Tsai's team dug into the question of why APOE4 causes fat to accumulate in the first place.
They used stem cells to grow APOE4-carrying oligodendrocytes and then turned specific genes on or off, to see if they increased or decreased the formation of fat droplets. This screening pointed to a major group of genes involved in cell-to-cell communication, called the Wnt signaling pathway. This included a key gene called DKK1, which acts as a brake on this signaling system.
Disabling DKK1 — essentially lifting the brake — reduced the concentration of fat droplets in the cells, the team found.
In brain tissue samples from organ donors with APOE4, the team also found signs of low Wnt signaling. This finding "establishes a very strong link" between the signaling pathway and formation of fat droplets, said Rosie Jackson, a cell biologist at the University of Dundee who was not involved in this study.
Myelin (orange) is a fatty material that coats the outgoing wires of many neurons, helping the cells send signals more efficiently. (Image credit: koto_feja via Getty Images)APOE4's effects across the brain
The researchers then investigated exactly how Wnt signaling was disrupted in oligodendrocytes.
APOE4 seemed to decrease the cells' supply of a protein that helps relay Wnt signals. That helpful protein was being broken down by an enzyme whose levels had been boosted by the APOE4. In mice, the team used a drug to block that extra enzyme, and the treatment ultimately restored normal Wnt signaling, reduced fat buildup, and helped the cells make myelin properly.
This could change how scientists think about treating Alzheimer's disease, Jackson said. It "opens up a new wave of potential therapeutics" that could target the accumulated fats, rather than going after only amyloid-beta and tau, she said.
"I think that this is becoming a really common idea in the field — that we're going to need multiple medications that target slightly different elements," she told Live Science. "And targeting lipids will be a really important part of that."
That said, the findings have not been confirmed in humans yet.
"It's very encouraging that facilitating lipid clearance and lipid movement is a potential therapeutic avenue," said study co-author Joel Blanchard, a neuroscientist at the Icahn School of Medicine at Mount Sinai. The question is how we can do that in a safe and effective manner in humans, he said.
Blanchard has led other research that underscores APOE4's effects beyond fats and myelin. The gene variant may affect cells that support blood vessels in the brain by driving scarring and contributing to amyloid-beta buildup. It may also mess with the "trash collectors" inside astrocytes, again leading to a buildup of harmful proteins.
While cells with other versions of the APOE gene can put a lot of energy toward breaking down the proteins, APOE4-carrying cells put all their energy toward fat storage, Blanchard said.
But more research is needed to understand how to fix the problem, he said. Hopefully, targeting fat storage "could rescue not just one thing but multiple things that go wrong in the course of Alzheimer's disease," he told Live Science.
This article is for informational purposes only and is not meant to offer medical advice.
'> A gene variant triples the risk of getting Alzheimer's disease — fat buildup in brain cells may be to blame, study finds A drone powered by artificial intelligence (AI) has completed a championship race course faster than some of the world's best human drone pilots.
In a study published Oct. 14 in the journal Science Robotics, scientists created an AI system capable of pushing a quadcopter through a demanding course at over 60 mph (100 km/h), using little more than a single forward-facing camera, an inertial measurement unit, and a tiny onboard computer.
The system, called MonoRace, even won the 2025 Abu Dhabi Autonomous Racing League drone racing competition, outperforming every rival autonomous team and posting faster times than three former human world-champion drone pilots.
MonoRace accomplished this without the aid of GPS, external motion-capture systems, stereo cameras, or a preloaded map of the course. The system is an onboard autonomous flight model created by researchers at the Delft University of Technology's Micro Air Vehicles Lab in the Netherlands. Its name reflects its central design feature; "mono" refers to its monocular vision. The drone relies on a single rolling shutter camera and monitors its own motion with an internal measurement unit (IMU) that gauges acceleration and rotation.
Flying at breakneck speeds
The limited sensor package feeds a neural network, a machine-learning model that uses interconnected artificial neurons to learn patterns from data. This neural network identifies racing gates and estimates the drone's position and motion to generate motor commands. Then, it sends the commands to the four propellers at a rate of 500 times per second through the drone's flight controller.
The system fits a drone that weighs a mere 966 grams (2.13 pounds) and measures approximately 11.8 x 11 x 3.9 inches (30 x 28 x 10 centimeters). Because the MonoRace hardware was so compact, lightweight and computationally efficient, the team was able to operate it entirely aboard the drone.
On the Abu Dhabi course, MonoRace completed two laps around 11 gates in 16.56 seconds, hitting a top speed of approximately 63 mph (102 km/h).
A recently released video tracks MonoRace's development and performance in the competition, with real flight sequences demonstrating how the drone banks sharply, accelerates through gates, and navigates a course without human assistance.
However, some of the sequences are computer-generated. Study co-author Guido de Croon, a professor at the Delft University of Technology, said those are intentional visual effects. The only fully simulated imagery appears at about the two-minute mark, during the segment showing the training process.
"Here we show the gates in a visual simulator for the video but the real training happened in a stripped-down, pure physics simulator that ran really fast," de Croon told Live Science in an email. "Simulating vision was not done during training, as the control neural network receives an estimated state as input, which comes from a different perception and state estimation process."
The rest of the footage is real, although some shots include overlays showing how the drone interprets its camera feed. A convolutional neural network segments the white gates, sometimes displaying them as red in the normal camera image. Small circles mark detected gate corners, red lines indicate gate edges, and a blue overlay shows the portion of the image where the drone expects to find the next gate.
How the AI learned to race
Scientists trained the MonoRace model with reinforcement learning, a machine learning method in which an agent repeatedly attempts a task and receives feedback based on its success or failure.
"The reinforcement learning ran in simulation, because it needs lots of trial and error — like millions of tries," de Croon said. "It actually learns to fly from scratch."
The network learned to translate the drone's position on the track, velocity and orientation directly into motor commands. According to de Croon, early training focused on the basics, like avoiding crashes and reaching the first gate. As the training progressed, the system learned to advance farther along the course and fly at higher speeds.
The researchers didn't train the system on a single aircraft. Instead, they varied dimensions such as weight, voltage, drag, sensor noise and propeller condition so the AI model could cope with real-world variations and imperfections.
"That ensures that the trained network can control different drones," de Croon said.
MonoRace could become a training tool for human drone pilots, "a bit like powerful chess programs have also inspired humans playing the game," de Croon said.
Its broader significance lies in demonstrating advancements in physical AI, especially how a robot can perceive, decide and act at high speeds despite limited sensing and computing power. The control network runs on a microcontroller-class chip, meaning that sophisticated control doesn't require a large computer.
De Croon noted that a similarly sized network could optimize for minimal energy use rather than minimum lap time. Because most machines rely on small processors and conventional controllers, energy-optimal neural control could eventually reduce power consumption in everyday robotics, he said.
'> World's fastest AI-powered racing drone outflies the best human pilots