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Reptile fossil provides clues to origin of snakes and lizards

While palaeontologists know quite a lot about the early evolution of the archosaurs (crocodiles, avian and non-avian dinosaurs), they know very little about lepidosaur evolution (lizards, snakes and tuataras) because the fossil record is very patchy.

Now, a 231-million-year-old fossil found in Argentina is helping fill the gaps.

Fossil skull on white background, giving clues to lepidosaur evolution
Credit: Ricardo Martinez

“It is the most complete fossil representing the early stages of lepidosaur evolution that we have so far,” says researcher Gabriela Sobral from the Stuttgart State Museum of Natural History, Germany.

“The almost perfectly preserved Taytalura skull shows us details of how a very successful group of animals, including more than 10,000 species of snakes, lizards, and tuataras, originated,” adds Ricardo Martínez, a palaeontologist at the National University of San Juan in Argentina, and lead author of the study published in Nature.

The fossil shares several unique features with the modern tuatara, showing that these features must have evolved very early on.

Plus, the fossil was the first lepidosaur found in Argentina (almost all others were found in Europe), suggesting these ancient creatures had a much wider geographical range than thought.

Gold medal for fastest-orbiting asteroid in the Solar System

This artist’s rendering shows the asteroid (above) and the planet Mercury (below). Credit: CTIO/NOIRLab/NSF/AURA/J. da Silva (Spaceengine)

Astronomers have just discovered an asteroid that zips around the Sun in 113 days – the shortest known orbital period for any asteroid.

The only thing in the entire Solar System with a shorter orbit is the planet Mercury (88 days).

The asteroid, called 2021 PH27, is one kilometre in diameter and travels in an unstable orbit that intersects with that of Mercury and Venus – which means that within a few million years, it’ll likely be destroyed by a collision or kicked out of its orbit.

“Most likely 2021 PH27 was dislodged from the Main Asteroid Belt between Jupiter and Mars and the gravity of the inner planets shaped its orbit into its current configuration,” says Scott S. Sheppard from Carnegie University, who discovered the asteroid.

Dotted lines showing orbital paths of the asteroid and several planets
The illustration shows the locations of the planets and asteroid on the discovery night of 13 August 2021, as they would be seen from a vantage point above the Solar System (north). Credit: CTIO/NOIRLab/NSF/AURA/J. da Silva (Spaceengine)

“Although, based on its large angle of inclination of 32 degrees, it is possible that 2021 PH27 is an extinct comet from the outer Solar System that ventured too close to one of the planets as the path of its voyage brought it into proximity with the inner Solar System.”

Sheppard discovered the asteroid when examining evening twilight images taken by the Dark Energy Camera (DECam) in Chile.

This research was reported to the Minor Planet Center.

What gives coffee its distinctive “mouthfeel”?

A smooth, rich brew is much more enjoyable to drink than a watery one, but it’s not just the milk or sugar that gives coffee its “mouthfeel” – new research has found several chemical compounds that contribute.

“We’ve known that coffee itself can impact textural sensations, and it was traditionally thought to be because of sugars and lipids,” says Christopher Simons, co-investigator in this new research project.

“But our team finds that this feeling may actually be driven by small molecules, which is kind of unique.”

To isolate the compounds responsible, the team used a panel of eight skilled coffee tasters who tested different samples and rated them based on four different tactile attributes: chalkiness, mouthcoating, astringency and thickness. Over many tests, the team pinpointed exact compounds responsible for each attribute.

This knowledge may help producers and growers make the best coffee by fine-tuning processing and roasting conditions.

The results are being presented at the autumn meeting of the American Chemical Society (ACS).

Tsunami in the tree rings

Tree rings taken from 38 old-growth Douglas firs on the Oregon coast of the US show evidence of a tsunami more than 300 years ago, according to a new paper in the journal Natural Hazards and Earth System Sciences.

In the year 1700, a 9.0 earthquake along the Cascadia subduction zone triggered a tsunami that struck both the west coast of North America and the coast of Japan. Previous research has revealed “ghost forests” of red cedar trees in Oregon and Washington, which have outermost growth rings that formed in 1699.

This new research adds extra evidence of this event, revealing that the Douglas fir trees didn’t grow as much in the year following the earthquake and tsunami.

“The tsunami appears to be the event that most affected the trees’ growth that year,” says Robert Dziak from the National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory.

Dziak and colleagues also ran a tsunami model, showing that the area could have been inundated with up to 10 metres of water following the event.

“Getting these little bits of the picture helps us understand what we might expect when the next ‘big one’ hits,” Dziak says.

AI can predict Arctic sea ice loss

Artificial intelligence algorithms are changing the game for many scientific disciplines, from medicine to astronomy. Now, researchers have used it to forecast sea ice conditions in the Arctic.

“The Arctic is a region on the frontline of climate change and has seen substantial warming over the last 40 years,” says Tom Andersson, data scientist at the British Antarctic Survey (BAS) AI Lab.

Andersson is the lead author of a new paper in Nature Communications describing IceNet, an AI predictive tool that can predict sea ice coverage up to two months in advance, with almost 95% accuracy.

“IceNet has the potential to fill an urgent gap in forecasting sea ice for Arctic sustainability efforts and runs thousands of times faster than traditional methods,” he says.

Three modelled images showing the change in sea ice cover in june, july and august 2018, decreasing in each month.
IceNet figure. Credit: British Antarctic Survey

The tool uses “deep learning” to draw on thousands of years of climate simulation data as well as observational data.

“Now we’ve demonstrated that AI can accurately forecast sea ice, our next goal is to develop a daily version of the model and have it running publicly in real-time, just like weather forecasts,” Andersson says.

“This could operate as an early warning system for risks associated with rapid sea ice loss.”

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