At 7:27 pm on Sunday, SpaceX launched a crew of four aboard a Crew Dragon module, carried into orbit by a Falcon 9 rocket. The vehicle, laden with three NASA astronauts as well as a colleague from the Japanese space agency, is now en route to the International Space Station as part of the Crew-1 mission.
“Not even gravity contains humanity as we explore for all,” said Mission Control as the craft soared above Florida.
This isn’t the first time SpaceX has launched astronauts aboard the Crew Dragon. Back in May, NASA astronauts Bob Behnken and Doug Hurley hitched a ride aboard the craft to the space station — though technically, that was a demonstration mission, whereas today’s is the first of what are anticipated to be regular launches to the space station that NASA is contracting to SpaceX.
If all goes according to plan, the astronauts will dock with the ISS early this week, where they’ll stay for approximately six months. You can watch NASA’s live feed of the mission here:
READ MORE: SpaceX NASA Live Updates: Watch Astronauts Launch to Orbit [The New York Times]
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.
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.
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.
London A.I. Lab Claims Breakthrough That Could Accelerate Drug Discovery – The New York Times
Some scientists spend their lives trying to pinpoint the shape of tiny proteins in the human body.
Proteins are the microscopic mechanisms that drive the behavior of viruses, bacteria, the human body and all living things. They begin as strings of chemical compounds, before twisting and folding into three-dimensional shapes that define what they can do — and what they cannot.
For biologists, identifying the precise shape of a protein often requires months, years or even decades of experimentation. It requires skill, intelligence and more than a little elbow grease. Sometimes they never succeed.
Now, an artificial intelligence lab in London has built a computer system that can do the job in a few hours — perhaps even a few minutes.
DeepMind, a lab owned by the same parent company as Google, said on Monday that its system, called AlphaFold, had solved what is known as “the protein folding problem.” Given the string of amino acids that make up a protein, the system can rapidly and reliably predict its three-dimensional shape.
This long-sought breakthrough could accelerate the ability to understand diseases, develop new medicines and unlock mysteries of the human body.
Computer scientists have struggled to build such a system for more than 50 years. For the last 25, they have measured and compared their efforts through a global competition called the Critical Assessment of Structure Prediction, or C.A.S.P. Until now, no contestant had even come close to solving the problem.
DeepMind solved the problem with a wide range of proteins, reaching an accuracy level that rivaled physical experiments. Many scientists had assumed that moment was still years, if not decades, away.
“I always hoped I would live to see this day,” said John Moult, a professor at the University of Maryland who helped create C.A.S.P. in 1994 and continues to oversee the biennial contest. “But it wasn’t always obvious I was going to make it.”
As part of this year’s C.A.S.P., DeepMind’s technology was reviewed by Dr. Moult and other researchers who oversee the contest.
If DeepMind’s methods can be refined, he and other researchers said, they could speed the development of new drugs as well as efforts to apply existing medications to new viruses and diseases.
The breakthrough arrives too late to make a significant impact on the coronavirus. But researchers believe DeepMind’s methods could accelerate the response to future pandemics. Some believe it could also help scientists gain a better understanding of genetic diseases along the lines of Alzheimer’s or cystic fibrosis.
Still, experts cautioned that this technology would affect only a small part of the long process by which scientists identify new medicines and analyze disease. It was also unclear when or how DeepMind would share its technology with other researchers.
DeepMind is one of the key players in a sweeping change that has spread across academia, the tech industry and the medical community over the past 10 years. Thanks to an artificial intelligence technology called a neural network, machines can now learn to perform many tasks that were once beyond their reach — and sometimes beyond the reach of humans.
A neural network is a mathematical system loosely modeled on the network of neurons in the human brain. It learns skills by analyzing vast amounts of data. By pinpointing patterns in thousands of cat photos, for instance, it can learn to recognize a cat.
This is the technology that recognizes faces in the photos you post to Facebook, identifies the commands you bark into your smartphone and translates one language into another on Skype and other services. DeepMind is using this technology to predict the shape of proteins.
If scientists can predict the shape of a protein in the human body, they can determine how other molecules will bind or physically attach to it. This is one way drugs are developed: A drug binds to particular proteins in your body and alters their behavior.
By analyzing thousands of known proteins and their physical shapes, a neural network can learn to predict the shapes of others. In 2018, using this method, DeepMind entered the C.A.S.P. contest for the first time and its system outperformed all other competitors, signaling a significant shift. But its team of biologists, physicists and computer scientists, led by a researcher named John Jumper, were nowhere close to solving the ultimate problem.
In the two years since, Dr. Jumper and his team designed an entirely new kind of neural network specifically for protein folding, and this drove an enormous leap in accuracy. Their latest version provides a powerful, if imperfect, solution to the protein folding problem, said the DeepMind research scientist Kathryn Tunyasuvunakool.
The system can accurately predict the shape of a protein about two-thirds of the time, according to the results of the C.A.S.P. contest. And its mistakes with these proteins are smaller than the width of an atom — an error rate that rivals physical experiments.
“Most atoms are within an atom diameter of where they are in the experimental structure,” said Dr. Moult, the contest organizer. “And with those that aren’t, there are other possible explanations of the differences.”
Andrei Lupas, director of the department of protein evolution at the Max Planck Institute for Developmental Biology in Germany, is among those who worked with AlphaFold. He is part of a team that spent a decade trying to determine the physical shape of a particular protein in a tiny bacteria-like organism called an archaeon.
This protein straddles the membrane of individual cells — part is inside the cell, part is outside — and that makes it difficult for scientists like Dr. Lupas to determine the shape of the protein in the lab. Even after a decade, he could not pinpoint the shape.
With AlphaFold, he cracked the problem in half an hour.
If these methods continue to improve, he said, they could be a particularly useful way of determining whether a new virus could be treated with a cocktail of existing drugs.
“We could start screening every compound that is licensed for use in humans,” Dr. Lupas said. “We could face the next pandemic with the drugs we already have.”
During the current pandemic, a simpler form of artificial intelligence proved helpful in some cases. A system built by another London company, BenevolentAI, helped pinpoint an existing drug, baricitinib, that could be used to treat seriously ill Covid-19 patients. Researchers have now completed a clinical trial, though the results have not yet been released.
As researchers continue to improve the technology, AlphaFold could further accelerate this kind of drug repurposing, as well as the development of entirely new vaccines, especially if we encounter a virus that is even less understood than Covid-19.
David Baker, the director of the Institute for Protein Design at the University of Washington, who has been using similar computer technology to design anti-coronavirus drugs, said DeepMind’s methods could accelerate that work.
“We were able to design coronavirus-neutralizing proteins in several months,” he said. “But our goal is to do this kind of thing in a couple of weeks.”
Still, development speed must contend with other issues, like massive clinical trials, said Dr. Vincent Marconi, a researcher at Emory University in Atlanta who helped lead the baricitinib trial. “That takes time,” he said.
But DeepMind’s methods could be a way of determining whether a clinical trial will fail because of toxic reactions or other problems, at least in some cases.
Demis Hassabis, DeepMind’s chief executive and co-founder, said the company planned to publish details describing its work, but that was unlikely to happen until sometime next year. He also said the company was exploring ways of sharing the technology itself with other scientists.
DeepMind is a research lab. It does not sell products directly to other labs or businesses. But it could work with other companies to share access to its technology over the internet.
The lab’s biggest breakthroughs in the past have involved games. It built systems that surpassed human performance on the ancient strategy game Go and the popular video game StarCraft — enormously technical achievements with no practical application. Now, the DeepMind team are eager to push their artificial intelligence technology into the real world.
“We don’t want to be a leader board company,” Dr. Jumper said. “We want real biological relevance.”
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