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NASA inches closer to printing artificial organs in space

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3D printed tissue


In America, at least 17 people a day die waiting for an organ transplant. But instead of waiting for a donor to die, what if we could someday grow our own organs?

Last week, six years after NASA announced its Vascular Tissue Challenge, a competition designed to accelerate research that could someday lead to artificial organs, the agency named two winning teams. The challenge required teams to create thick, vascularized human organ tissue that could survive for 30 days. 

The two teams, named Winston and WFIRM, both from the Wake Forest Institute for Regenerative Medicine, used different 3D-printing techniques to create lab-grown liver tissue that would satisfy all of NASA’s requirements and maintain their function.

“We did take two different approaches because when you look at tissues and vascularity, you look at the body doing two main things,” says Anthony Atala, team leader for WFIRM and director of the institute. 

The two approaches differ in the way vascularization—how blood vessels form inside the body—is achieved. One used tubular structures and the other spongy tissue structures to help deliver cell nutrients and remove waste. According to Atala, the challenge represented a hallmark for bioengineering because the liver, the largest internal organ in the body, is one of the most complex tissues to replicate due to the high number of functions it performs.

Liver tissue created by team Winston for NASA’s Vascular Tissue Challenge.

WAKE FOREST INSTITUTE FOR REGENERATIVE MEDICINE

“When the competition came out six years ago, we knew we had been trying to solve this problem on our own,” says Atala.

Along with advancing the field of regenerative medicine and making it easier to create artificial organs for humans who need transplants, the project could someday help astronauts on future deep-space missions.

The concept of tissue engineering has been around for more than 20 years, says Laura Niklason, a professor of anesthesia and biomedical engineering at Yale, but the growing interest in space-based experimentation is starting to transform the field. “Especially as the world is now looking at private and commercial space travel, the biological impacts of low gravity are going to become more and more important, and this is a great tool for helping to understand that.”

But the winning teams must still overcome one of the biggest hurdles in tissue engineering: “Getting things to survive and maintain their function over an extended period is really challenging,” says Andrea O’Connor, head of biomedical engineering at the University of Melbourne, who calls this project, and others like it ambitious.

Equipped with a $300,000 cash prize, the first-place team—Winston—will soon have a chance to send its research to the International Space Station, where similar organ research has already taken place.

In 2019, astronaut Christina Koch activated the BioFabrication Facility (BFF), which was created by the Greenville, Indiana-based aerospace research company Techshot to print organic tissues in microgravity.

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Meet the people who warn the world about new covid variants

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Meet the people who warn the world about new covid variants


In March 2020, when the WHO declared a pandemic, the public sequence database GISAID held 524 covid sequences. Over the next month scientists uploaded 6,000 more. By the end of May, the total was over 35,000. (In contrast, global scientists added 40,000 flu sequences to GISAID in all of 2019.)

“Without a name, forget about it—we cannot understand what other people are saying,” says Anderson Brito, a postdoc in genomic epidemiology at the Yale School of Public Health, who contributes to the Pango effort. 

As the number of covid sequences spiraled, researchers trying to study them were forced to create entirely new infrastructure and standards on the fly. A universal naming system has been one of the most important elements of this effort: without it, scientists would struggle to talk to each other about how the virus’s descendants are traveling and changing—either to flag up a question or, even more critically, to sound the alarm.

Where Pango came from

In April 2020, a handful of prominent virologists in the UK and Australia proposed a system of letters and numbers for naming lineages, or new branches, of the covid family. It had a logic, and a hierarchy, even though the names it generated—like B.1.1.7—were a bit of a mouthful.

One of the authors on the paper was Áine O’Toole, a PhD candidate at the University of Edinburgh. Soon she’d become the primary person actually doing that sorting and classifying, eventually combing through hundreds of thousands of sequences by hand.

She says: “Very early on, it was just who was available to curate the sequences. That ended up being my job for a good bit. I guess I never understood quite the scale we were going to get to.”

She quickly set about building software to assign new genomes to the right lineages. Not long after that, another researcher, postdoc Emily Scher, built a machine-learning algorithm to speed things up even more. 

“Without a name, forget about it—we cannot understand what other people are saying.”

Anderson Brito, Yale School of Public Health

They named the software Pangolin, a tongue-in-cheek reference to a debate about the animal origin of covid. (The whole system is now simply known as Pango.)

The naming system, along with the software to implement it, quickly became a global essential. Although the WHO has recently started using Greek letters for variants that seem especially concerning, like delta, those nicknames are for the public and the media. Delta actually refers to a growing family of variants, which scientists  call by their more precise Pango names: B.1.617.2, AY.1, AY.2, and AY.3.

“When alpha emerged in the UK, Pango made it very easy for us to look for those mutations in our genomes to see if we had that lineage in our country too,” says Jolly. “Ever since then, Pango has been used as the baseline for reporting and surveillance of variants in India.”

Because Pango offers a rational, orderly approach to what would otherwise be chaos, it may forever change the way scientists name viral strains—allowing experts from all over the world to work together with a shared vocabulary. Brito says: “Most likely, this will be a format we’ll use for tracking any other new virus.”

Many of the foundational tools for tracking covid genomes have been developed and maintained by early-career scientists like O’Toole and Scher over the last year and a half. As the need for worldwide covid collaboration exploded, scientists rushed to support it with ad hoc infrastructure like Pango. Much of that work fell to tech-savvy young researchers in their 20s and 30s. They used informal networks and tools that were open source—meaning they were free to use, and anyone could volunteer to add tweaks and improvements. 

“The people on the cutting edge of new technologies tend to be grad students and postdocs,” says Angie Hinrichs, a bioinformatician at UC Santa Cruz who joined the project earlier this year. For example, O’Toole and Scher work in the lab of Andrew Rambaut, a genomic epidemiologist who posted the first public covid sequences online after receiving them from Chinese scientists. “They just happened to be perfectly placed to provide these tools that became absolutely critical,” Hinrichs says.

Building fast

It hasn’t been easy. For most of 2020, O’Toole took on the bulk of the responsibility for identifying and naming new lineages by herself. The university was shuttered, but she and another of Rambaut’s PhD students, Verity Hill, got permission to come into the office. Her commute, walking 40 minutes to school from the apartment where she lived alone, gave her some sense of normalcy.

Every few weeks, O’Toole would download the entire covid repository from the GISAID database, which had grown exponentially each time. Then she would hunt around for groups of genomes with mutations that looked similar, or things that looked odd and might have been mislabeled. 

When she got particularly stuck, Hill, Rambaut, and other members of the lab would pitch in to discuss the designations. But the grunt work fell on her. 

“Imagine going through 20,000 sequences from 100 different places in the world. I saw sequences from places I’d never even heard of.”

Áine O’Toole, University of Edinburgh

Deciding when descendants of the virus deserve a new family name can be as much art as science. It was a painstaking process, sifting through an unheard-of number of genomes and asking time and again: Is this a new variant of covid or not? 

“It was pretty tedious,” she says. “But it was always really humbling. Imagine going through 20,000 sequences from 100 different places in the world. I saw sequences from places I’d never even heard of.”

As time went on, O’Toole struggled to keep up with the volume of new genomes to sort and name.

In June 2020, there were over 57,000 sequences stored in the GISAID database, and O’Toole had sorted them into 39 variants. By November 2020, a month after she was supposed to turn in her thesis, O’Toole took her last solo run through the data. It took her 10 days to go through all the sequences, which by then numbered 200,000. (Although covid has overshadowed her research on other viruses, she’s putting a chapter on Pango in her thesis.) 

Fortunately, the Pango software is built to be collaborative, and others have stepped up. An online community—the one that Jolly turned to when she noticed the variant sweeping across India—sprouted and grew. This year, O’Toole’s work has been much more hands-off. New lineages are now designated mostly when epidemiologists around the world contact O’Toole and the rest of the team through Twitter, email, or GitHub— her preferred method. 

“Now it’s more reactionary,” says O’Toole. “If a group of researchers somewhere in the world is working on some data and they believe they’ve identified a new lineage, they can put in a request.”

The deluge of data has continued. This past spring, the team held a “pangothon,” a sort of hackathon in which they sorted 800,000 sequences into around 1,200 lineages. 

“We gave ourselves three solid days,” says O’Toole. “It took two weeks.”

Since then, the Pango team has recruited a few more volunteers, like UCSC researcher Hindriks and Yale researcher Brito, who both got involved initially by adding their two cents on Twitter and the GitHub page. A postdoc at the University of Cambridge, Chris Ruis, has turned his attention to helping O’Toole clear out the backlog of GitHub requests. 

O’Toole recently asked them to formally join the organization as part of the newly created Pango Network Lineage Designation Committee, which discusses and makes decisions about variant names. Another committee, which includes lab leader Rambaut, makes higher-level decisions.

“We’ve got a website, and an email that’s not just my email,” O’Toole says. “It’s become a lot more formalized, and I think that will really help it scale.” 

The future

A few cracks around the edges have started to show as the data has grown. As of today, there are nearly 2.5 million covid sequences in GISAID, which the Pango team has split into 1,300 branches. Each branch corresponds to a variant. Of those, eight are ones to watch, according to the WHO.

With so much to process, the software is starting to buckle. Things are getting mislabeled. Many strains look similar, because the virus evolves the most advantageous mutations over and over again. 

As a stopgap measure, the team has built new software that uses a different sorting method and can catch things that Pango may miss. 

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Disability rights advocates are worried about discrimination in AI hiring tools

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Disability rights advocates are worried about discrimination in AI hiring tools


Making hiring technology accessible means ensuring both that a candidate can use the technology and that the skills it measures don’t unfairly exclude candidates with disabilities, says Alexandra Givens, the CEO of the Center for Democracy and Technology, an organization focused on civil rights in the digital age.

AI-powered hiring tools often fail to include people with disabilities when generating their training data, she says. Such people have long been excluded from the workforce, so algorithms modeled after a company’s previous hires won’t reflect their potential.

Even if the models could account for outliers, the way a disability presents itself varies widely from person to person. Two people with autism, for example, could have very different strengths and challenges.

“As we automate these systems, and employers push to what’s fastest and most efficient, they’re losing the chance for people to actually show their qualifications and their ability to do the job,” Givens says. “And that is a huge loss.”

A hands-off approach

Government regulators are finding it difficult to monitor AI hiring tools. In December 2020, 11 senators wrote a letter to the US Equal Employment Opportunity Commission expressing concerns about the use of hiring technologies after the covid-19 pandemic. The letter inquired about the agency’s authority to investigate whether these tools discriminate, particularly against those with disabilities.

The EEOC responded with a letter in January that was leaked to MIT Technology Review. In the letter, the commission indicated that it cannot investigate AI hiring tools without a specific claim of discrimination. The letter also outlined concerns about the industry’s hesitance to share data and said that variation between different companies’ software would prevent the EEOC from instituting any broad policies.

“I was surprised and disappointed when I saw the response,” says Roland Behm, a lawyer and advocate for people with behavioral health issues. “The whole tenor of that letter seemed to make the EEOC seem like more of a passive bystander rather than an enforcement agency.”

The agency typically starts an investigation once an individual files a claim of discrimination. With AI hiring technology, though, most candidates don’t know why they were rejected for the job. “I believe a reason that we haven’t seen more enforcement action or private litigation in this area is due to the fact that candidates don’t know that they’re being graded or assessed by a computer,” says Keith Sonderling, an EEOC commissioner.

Sonderling says he believes that artificial intelligence will improve the hiring process, and he hopes the agency will issue guidance for employers on how best to implement it. He says he welcomes oversight from Congress.

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We just got our best-ever look at the inside of Mars

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We just got our best-ever look at the inside of Mars


NASA’s InSight robotic lander has just given us our first look deep inside a planet other than Earth. 

More than two years after its launch, seismic data that InSight collected has given researchers hints into how Mars was formed, how it has evolved over 4.6 billion years, and how it differs from Earth. A set of three new studies, published in Science this week, suggests that Mars has a thicker crust than expected, as well as a molten liquid core that is bigger than we thought.  

In the early days of the solar system, Mars and Earth were pretty much alike, each with a blanket of ocean covering the surface. But over the following 4 billion years, Earth became temperate and perfect for life, while Mars lost its atmosphere and water and became the barren wasteland we know today. Finding out more about what Mars is like inside might help us work out why the two planets had such very different fates. 

“By going from [a] cartoon understanding of what the inside of Mars looks like to putting real numbers on it,” said Mark Panning, project scientist for the InSight mission, during a NASA press conference, “we are able to really expand the family tree of understanding how these rocky planets form and how they’re similar and how they’re different.” 

Since InSight landed on Mars in 2018, its seismometer, which sits on the surface of the planet, has picked up more than a thousand distinct quakes. Most are so small they would be unnoticeable to someone standing on Mars’s surface. But a few were big enough to help the team get the first true glimpse of what’s happening underneath. 

NASA/JPL-CALTECH

Marsquakes create seismic waves that the seismometer detects. Researchers created a 3D map of Mars using data from two different kinds of seismic waves: shear and pressure waves. Shear waves, which can only pass through solids, are reflected off the planet’s surface.  

Pressure waves are faster and can pass through solids, liquids, and gases. Measuring the differences between the times that these waves arrived allowed the researchers to locate quakes and gave clues to the interior’s composition.  

One team, led by Simon Stähler, a seismologist at ETH Zurich, used data generated by 11 bigger quakes to study the planet’s core. From the way the seismic waves reflected off the core, they concluded it’s made from liquid nickel-iron, and that it’s far larger than had been previously estimated (between 2,230 and 2320 miles wide) and probably less dense. 

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