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'Weird bat-winged' dinosaurs glided through treetops in attempt at flight: study – CTV News



A new study investigating the flight capabilities of two tiny dinosaurs with thin, bat-like wings is shedding light on the evolution of avian flight itself — an evolution that, it turns out, had a lot of dead ends and false starts along the way.

Published in the journal iScience earlier this week, the study looked at Yi and Ambopteryx, two dinosaurs who lived around 160 million years ago in the Late Jurassic era of China. Both were believed to have the potential for flight due to the thin membranes stretched between their arms and their bodies.

However, when researchers applied mathematical modelling to these prehistoric creatures, they found that they were nowhere near capable of propelling themselves through the air like birds, and instead would’ve used their small wings only to glide.

“We know some dinosaurs could fly before they evolved into birds,” Hans Larsson, a professor at McGill University and Director of McGill’s Redpath Museum, said in a press release. “What this shows us is that at least one lineage of dinosaurs experimented with a completely different mode of aerial locomotion.”


Researchers scanned fossils of Yi and Ambopteryx with lasers to pick out where the soft tissue would fall on their wings, details that couldn’t be seen under regular light.

Then they reconstructed the dinosaurs’ morphology with computer modelling to see whether they could power themselves to flight, whether by leaping from trees or from the ground. They also changed important variables like wingspan and body weight to assess different scenarios on how they might have flown.

In order to flap their wings with enough force to support their own body, the dinosaurs would’ve needed strong pectoral muscles, which were absent. Ambopteryx could only take off in flight at the lowest estimated body size and highest estimated power level, and Yi could not obtain any lift-off except at body weights researchers said were likely too small to be accurate. In almost all scenarios, the dinosaurs could not get off the ground under their own power.

Even with a running start to help them, the minimum take off speed for Yi would be between 1.1 and three times the maximum possible speed Yi would have been capable of. For Ambopteryx, the minimum take-off speed was even more out of reach, needing to be at least 2.3 to four times their top sprinting speed.

The two dinosaurs were capable of gliding if they leaped from trees — but not well. The research found that compared to other dinosaurs capable of gliding or flying, these two “show poorly developed gliding abilities.”

Both Yi and Ambopteryx would have to launch from higher points in trees at higher speeds than other creatures that glide, and they would be less precise when they landed.

They are thought to have spent most of their lives in trees, eating insects, seeds and plants.


Many modern creatures can glide, but only pterosaurs, bats and birds developed the structures necessary to fly by flapping their wings.

It’s well known that modern day birds are descendants of dinosaurs, but this new research adds a complication to the predominant theory of how avian flight came about.

The majority of dinosaurs with flying capabilities — called avialans — have had very similar characteristics and body types, and different families of dinosaurs who have evolved towards flight have started as ground-dwelling creatures and gone through similar body changes — such as a reduction in body size, getting an increased shoulder mobility and developing feathers on their four limbs — before gaining the ability to fly.

This has told a reasonably streamlined tale about the evolution of flight from dinosaurs to birds for the most part, the study explained.

But Yi and Ambopteryx are outliers, showing that dinosaur flight went through some bumps on the road.

Both are therapods, a categorization of carnivorous dinosaurs with hollow bones that includes the T-rex and birds, but they’re also part of a little-understood group called Scansoriopterygidae, which are climbing and gliding dinosaurs.

It’s been posited before that scansoriopterygids could represent an interim stage before avialans, an early model of bird flight that then evolved to support more powered flying. But researchers say this was far more likely an independent attempt at flight, a “failed experimental lineage of early arboreal gliders” unconnected to the evolution of avialan flight.

“Given the large number of independent occurrences of gliding flight within crown mammals, this should perhaps be unsurprising, but it does create a more complex picture of the aerial ecosystem,” the study stated.

“We used to think of birds evolving as a linear trend from their ground-dwelling dinosaur ancestry,” Larsson said in the release.

“We can [now] revise this textbook scenario to one that had an explosive diversity of experimentation, with dinosaurs evolving powered flight several times independently from birds, many having fully feathered wings but with bodies too heavy or wings too small to have gotten off the ground, and now, a weird bat-winged group of dinosaurs that were not only the first arboreal dinosaurs, but ones that glided.”

He added that he feels like researchers are “still just scratching the surface,” of dinosaur biodiversity.

Yi, Ambopteryx and others scansoriopterygids had a short-lived existence, unable to compete with the mammalian gliders and avialan fliers that were evolving around them.

Both dinosaurs went extinct after only a few million years, according to the press release.

“Once birds got into the air, these two species were so poorly capable of being in the air that they just got squeezed out,” lead author Thomas Dececchi, an assistant professor of biology at Mount Marty University, said in the release. “Maybe you can survive a few million years underperforming, but you have predators from the top, competition from the bottom, and even some small mammals adding into that, squeezing them out until they disappeared.”

Gliding isn’t an efficient way to get around, as you have to climb to a great height first to travel any sort of distance, he explained.

“It’s not efficient, but it can be used as an escape hatch. It’s not a great thing to do, but sometimes it’s a choice between losing a bit of energy and being eaten. Once they were put under pressure, they just lost their space.

“They couldn’t win on the ground,” he said. “They couldn’t win in the air. They were done.”

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DeepMind's latest AI breakthrough can accurately predict the way proteins fold – Engadget



Alphabet-owned DeepMind may be best known for building the AI that beat a world-class Go player, but the company announced another, perhaps more vital breakthrough this morning. As part of its work for the 14th Critical Assessment of Protein Structure Prediction, or CASP, DeepMind’s AlphaFold 2 AI has shown it can guess how certain proteins will fold themselves with surprising accuracy. In some cases, the results were perceived to be “competitive” with actual, experimental data.

“We have been stuck on this one problem – how do proteins fold up – for nearly 50 years,” said Professor John Moult, CASP chair and co-founder, in a DeepMind blog post. “To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment.”

Researchers and enthusiasts across the internet have met the news enthusiastically, with some proclaiming that AlphaFold has solved the “protein solving problem.” But what does that mean, exactly? And how do we stand to benefit from it?

To start answering these questions, we need to take a closer look at the proteins themselves. As your biology teacher might have said, proteins are the building blocks of life, responsible for countless functions inside and outside the human body. Each one starts as a series of amino acids strung together into a chain, but it doesn’t take long — sometimes just milliseconds  — before things start to get complicated. Some parts of the amino acid chain twist into helixes. Others fold back onto themselves as “sheets”. Before long, these helixes and sheets coalesce and contort into a protein’s final structure, and that’s what gives a protein the ability to perform specific tasks, like ferrying oxygen through your body or strengthening the structure of your bones. 

In other words, shape is everything, and researchers have spent decades trying to find a way to determine a protein’s final, folded structure based solely on the amino acids that make up its backbone. That’s where CASP comes in — since 1994, the program has served as a focal point of sorts for teams around the world working to crack the protein solving problem with computational ingenuity. The rules are fairly simple: Every other year, organizers select a series of target proteins from a bevy of submissions whose structures have been determined experimentally, but haven’t been published yet. Researchers then get a few months to tune their systems and make their predictions, which are then judged by experts in the field for about a month after submissions are closed. 

While CASP has been running for 26 years, it’s been in the past few that the scientific community has been able to bring quantum leaps in compute power and machine learning to bear on the challenge. In DeepMind’s case, that involved training AlphaFold 2’s prediction model on about 170,000 known protein structures, along with a vast number of protein sequences whose 3D structures haven’t yet been determined. This testing data, the team admits, is fairly similar to what it used in 2018, when the original AlphaFold system achieved top marks during CASP 13. (At the time, organizers hailed DeepMind’s “unprecedented progress in the ability of computational methods to predict protein structure.”) 

That said, the team made some notable changes to its machine learning approach — they haven’t published a full paper yet, but the CASP 14 abstract book highlights some of their modifications. And beyond that, DeepMind also relied on about 128 of Google’s cloud-based TPUv3 cores, which ultimately gave AlphaFold 2 the ability to accurately determine a protein’s structure within just days, if not sooner — the New York Times notes that, in some cases, predictions can be generated in a matter of hours. 


This all sounds impressive — and it is, certainly — but there’s still plenty of work to be done. On the whole, AlphaFold’s results represented a dramatic improvement in accuracy compared to past years, and as mentioned, some of DeepMind’s predictions were accurate enough to rival experimental results at an atomic level. Others, however, fell short of that threshold. The company notes that “for the very hardest protein targets, those in the most challenging free-modelling category, AlphaFold achieves a median score of 87.0 GDT” — that’s just shy of the 90 GDT metric CASP co-founder Moult uses as the barrier for calling results “competitive” with real data. Put another way, DeepMind hasn’t fully solved the protein solving problem, but it’s getting closer than many had thought possible. 

As DeepMind’s work continues, we’ll start to see the full extent of accurate protein prediction take shape — for now, the jury still seems out on what practical benefits we could expect to see in the short term. The company points to potential advances in sustainability and drug design as a result of its protein folding research, though it didn’t elaborate on specifics. Meanwhile, Janet Thornton, a structural biologist at the European Molecular Biology Laboratory-European Bioinformatics Institute, told Nature that she hopes this leap in accuracy could shed light on the functions of “thousands” of unsolved proteins at work in the human body. If nothing else, though, researchers could be looking at a glut of new protein structure data to investigate, test against, and work backward from — that’s worth celebrating, even if we don’t know how it’ll be used yet.

All products recommended by Engadget are selected by our editorial team, independent of our parent company. Some of our stories include affiliate links. If you buy something through one of these links, we may earn an affiliate commission.

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How iron hydroxide forms on quartz – Futurity: Research News





New research reveals the full details of how iron hydroxides form on a quartz substrate.

From the red hues in the Grand Canyon to the mundane rust attacking a neglected bicycle, iron hydroxides are all around us. They are even as common as quartz, the most widely distributed mineral on the planet.

Scientists know that iron hydroxides can capture heavy metals and other toxic materials, and that iron oxides also can be natural semiconductors. While these properties suggest many applications, the full details of how iron hydroxides form on a quartz substrate have been hidden in a “black box” of sorts—until now.

Young-Shin Jun, a professor of energy, environmental, and chemical engineering in the McKelvey School of Engineering at Washington University in St. Louis, has devised a way to open that box and observe the moment iron hydroxide forms on quartz. Her research appears in the journal Environmental Science & Technology.

Iron hydroxide formation

“This is telling the story of the birth of iron hydroxide,” Jun says.

When people speak of “formation,” typically they are talking about a substance growing. Before growth, however, there needs to be something to grow. Where does that first bit of iron hydroxide come from?

First, sufficient precursor elements need to be in place. Then the components can come together to form a stable nucleus that will go on to become a tiny solid particle of iron hydroxide, called a nanoscale particulate. The process is called solid nucleation.

Science has a firm grip on the sum of these two processes—nucleation and growth, together known as “precipitation”—and their sum has been used to predict iron hydroxide’s formation behavior. But these predictions have largely omitted separate consideration of nucleation. The results “weren’t accurate enough,” Jun says. “Our work provides an empirical, quantitative description of nucleation, not a computation, so we can provide scientific evidence about this missing link.”

This contribution opens many important possibilities. We can better understand water quality at acid mine drainage sites, reduce membrane fouling and pipeline scale formation, and develop more environmentally friendly superconductor materials.

X-rays and nucleation

Jun was able to look inside of the black box of precipitation by using X-rays and a novel experimental cell she developed to study environmentally relevant complex systems with plenty of water, ions, and substrate material, observing nucleation in real time.

Working at the Advanced Photon Source at Argonne National Laboratory in Lemont, Illinois, Jun employed an X-ray scattering technique called “grazing incidence small angle X-ray scattering.” By shining X-rays onto a substrate with a very shallow angle, close to the critical angle that allows total reflection of light, this technique can detect the first appearance of nanometer size particles on a surface.

The approach is so novel, Jun says, that when she discusses her lab’s work on nucleation, “People think we are doing computer modeling. But no, we are experimentally examining it at the moment it happens,” she says. “We are experimental observers. I can measure the initial point of nucleation.”

Her empirical method reveals that the general estimates scientists have been using overstate the amount of energy needed for nucleation.

“Iron hydroxide forms much more easily on mineral surfaces than scientists thought, because less energy is needed for nucleation of highly hydrated solids on surfaces,” Jun says.

Furthermore, having a precise value will also help improve reactive transport models—the study of the movement of materials through an environment. For instance, certain materials can sequester toxic metals, keeping them from entering waterways. An updated reactive transport model with more accurate nucleation information will have significant implications for water quality researchers working to better predict and control sources of pollution.

“Iron hydroxide is the main sequestration repository for these contaminants,” Jun says, “and knowing their origin is critical to predicting their fate.”

For high-tech manufacturing facilities, having a more precise understanding of how iron oxides or hydroxides form will allow for the more efficient—less wasteful—production of iron-based superconductors.

Source: Washington University in St. Louis

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DeepMind's AlphaFold Crosses Threshold in Solving Protein Riddle – BNN



(Bloomberg) — DeepMind Technologies Ltd.’s protein analysis project swept a major international competition, in a step toward making the tool more widely available to scientists studying new drugs and diseases.

AlphaFold, which uses artificial intelligence to predict the structure of proteins, beat other researchers in the latest Critical Assessment of Structure Prediction competition. The event started in 1994 and is held every two years to accelerate research on the topic.

DeepMind became a subsidiary of Google after a 2014 acquisition and is best known for its gamer AI, teaching itself to beat Atari video games and defeating world-renowned Go players like Lee Sedol. The company’s ambition has been to develop AI that can be applied to broader problems, and it’s so far created systems to make Google’s data centers more energy efficient, identify eye disease from scans and generate human-sounding speech.

The task of determining the structures of proteins has been described as a project akin to mapping the genome. Different folds in a protein determine how it will interact with other molecules, and understanding them has implications for discovering how new diseases like Covid-19 invade our cells, designing enzymes to break down pollutants and improving crop yields.

“These algorithms are now becoming strong enough and powerful enough to be applicable to scientific problems,” DeepMind Chief Executive Officer Demis Hassabis said in a call with reporters. After four years of development “we have a system that’s accurate enough to actually have biological significance and relevance for biological researchers.”

DeepMind is now looking into ways of offering scientists access to the AlphaFold system in a “scalable way,” Hassabis said.

“Citizen Science”

CASP scientists analyzed the shape of amino acid sequences for a set of about 100 proteins. Competitors were given the sequences, and charged with predicting their shape. AlphaFold’s assessment lined up almost perfectly with the CASP analysis for two-thirds of the proteins, compared to about 10% from the other teams, and better than what DeepMind’s tool achieved two years ago

Hassabis said his inspiration for AlphaFold came from “citizen science” attempts to find unknown protein structures, like Foldit, which presented amateur volunteers with the problem in the form of a puzzle. In its first two years, the human gamers proved to be surprisingly good at solving the riddles, and ended up discovering a structure that had baffled scientists and designing a new enzyme that was later confirmed in the lab.

“Determining a single protein structure often required years of experimental effort,” said Janet Thornton, director emeritus of the European Bioinformatics Institute and one of the pioneers of using computational approaches to understanding protein structure. “A better understanding of protein structures and the ability to predict them using a computer means a better understanding of life, evolution and, of course, human health and disease.”

©2020 Bloomberg L.P.

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