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The scientists who say the lab-leak hypothesis for SARS-CoV-2 shouldn’t be ruled out

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David Relman


It could have been career suicide for scientists to voice suspicions about a possible lab leak, says Metzl, especially when there was already a long history of viral disease outbreaks spilling over from nature. Alina Chan, a postdoctoral fellow specializing in gene therapy and cell engineering at the Broad Institute in Cambridge, Massachusetts, echoes that view. Chan says the risk of challenging the orthodoxy that SARS-CoV-2 has natural origins—an entirely plausible hypothesis, she maintains—is greatest for established scientists in infectious disease with supervisory roles and staffs to support. She herself has spent much of the last year calling for more scrutiny of a potential lab leak, claiming that as a postdoc, she has less to lose.

The vitriol also obscures a broader imperative, Relman says, which is that uncovering the virus’s origins is crucial to stopping the next pandemic. Threats from both lab accidents and natural spillovers are growing simultaneously as humans move steadily into wild places and new biosafety labs grow in number around the world. “This is why the origins question is so important,” Relman says.

“We need a much better sense about where to place our resources and effort,” he adds. And if a lab release for SARS-CoV-2 looks plausible, Relman says, “then it absolutely deserves a whole lot more attention.”


If SARS-CoV-2 did spill over into humans from the wild, how and where did that happen? A year into the pandemic, these remain open questions. Scientists still speculate about whether the virus passed directly into humans from infected bats (known reservoirs for hundreds of different coronaviruses) or through an intermediary animal species. The Huanan Seafood Wholesale Market in Wuhan was initially thought to be the originating site of a potential spillover, since that’s where the first cluster of covid-19—the disease caused by the virus—was detected. But newer evidence suggests that animal or human infections may have been circulating elsewhere for months beforehand, and the focus has since broadened to other markets in the city, wildlife farms in southern China, and other possible scenarios, such as consuming virally contaminated frozen meat originating in other provinces.

Importantly, the virus’s immediate ancestors have yet to be identified. The closest known relative, a coronavirus dubbed RaTG13, is genetically 96% similar to SARS-CoV-2.

A lab-escaped virus, meanwhile, would have been introduced to the world by a researcher or technician who became infected with it. These sorts of lab leaks have happened before, and were implicated in several cases of community transmission during SARS outbreaks in the early 2000s. In 2017, the Wuhan Institute of Virology became the first lab in mainland China to receive a Biosafety Level 4 (BSL-4) designation, the highest security status for a research space. But the institute also has a history of questionable safety practices. The lab’s scientists reported a lack of appropriately trained technicians and investigators at the facility, prompting US diplomatic scientists who visited in 2017 and 2018 to alert the State Department. At the same time, many scientists have pointed out, particularly in the aftermath of a recent, and for some, contentious, examination of the lab-leak hypothesis in New York magazine, that coronaviruses have typically been handled at BSL-2 or BSL-3—lower security levels.

Such caveats aside, a prevailing theory among lab-leak proponents has been that SARS-CoV-2 was not simply brought into the Wuhan lab but was somehow engineered there, given that many of its scientists routinely perform genetic research on coronaviruses and may also have “collaborated on publications and secret projects with China’s military,” according to a US State Department fact sheet released during the last week of the Trump administration. On March 9, a Washington Post columnist, citing an unnamed State Department official, suggested that the Biden administration—while stopping well short of endorsing any particular theory regarding the origin of the virus—did not dispute many of the points made in that fact sheet.

Still, skeptics who doubt the lab-leak hypothesis say SARS-CoV-2 doesn’t look anything like an engineered virus. Instead of appearing in discrete chunks, as would be expected with a genetically engineered microbe, the differences with RaTg13 are distributed randomly throughout the viral genome. In an email to Undark, University of Chicago emeritus virology professor Bernard Roizman wrote that “we are many, many years away from a complete understanding of viral gene functions and regulation—the key elements critical for construction of lethal viruses.”

The virus does have an inexplicable feature: a so-called “furin cleavage site” in the spike protein that helps SARS-CoV-2 pry its way into human cells. While such sites are present in some coronaviruses, they haven’t been found in any of SARS-CoV-2’s closest known relatives. “We don’t know where the furin site came from,” says Susan Weiss, a microbiologist who co-directs the Penn Center for Research on Coronaviruses and Other Emerging Pathogens at the University of Pennsylvania’s Perelman School of Medicine. “It’s a mystery.” Although Weiss says SARS-CoV-2 is unlikely to have been engineered, she adds that the possibility that it escaped from a lab can’t be ruled out.

Stanford microbiologist David Relman believes the lab-leak hypothesis was never given a fair hearing.

ALBERTO E. RODRIGUEZ/GETTY IMAGES

Relman says it’s also possible that scientists working with undisclosed and even more closely related coronaviruses—perhaps one with a furin cleavage site and another with the SARS-CoV-2 gene backbone—may have been tempted to create a recombinant virus so they could study its properties. Indeed, researchers at the Wuhan Institute of Virology initially failed to disclose that eight other SARS-like coronaviruses had been detected in samples collected from the same mine cave where RaTG13 was found. Workers who cleaned bat feces in that cave, located in Yunnan Province near the border with Laos, went on to develop severe respiratory disease, and one of them died.

Petrovsky leans towards another potential scenario, namely that SARS-CoV-2 might be evolved from coronaviruses that snuck into lab cultures. Related viruses in the same culture, he explains, such as one optimized for human ACE2 binding and another not, can swap genetic material to create new strains. “We’ve had this sort of thing happen in our own lab,” he says. “One day, you’re culturing flu, and then one day you sequence it, and you go, ‘Holy shit, where did this other virus come from in our culture?’ Viruses are evolving the whole time, and it’s easy for a virus to get into your culture without you knowing it.” Petrovsky and several coauthors speculated in a paper published as a non-peer-reviewed preprint in May of last year as to whether the virus was “completely natural” or whether it originated with “a recombination event that occurred inadvertently or intentionally in a laboratory handling coronaviruses.” The team wasn’t “saying this is a lab virus,” Petrovsky emphasizes, but rather “just presenting our data.”

But in late April 2020, as Petrovsky’s group was thinking about where to publish their work, “Trump blurted out” that he had reason to believe the virus came out of a Chinese lab, Petrovsky says. And at that point, he adds, much of “the left-wing media” decided “they were going to paint the whole lab thing as a conspiracy theory to bring down Trump.” When Petrovsky approached administrators of the preprint server bioRxiv, the paper was refused. BioRxiv staff replied that it would be more appropriately distributed after peer review, “which stunned us,” Petrovksy says. “We thought the whole point of preprint was to get important information out quickly.”

The paper was subsequently posted on a different preprint server called arXiv.org, based out of Cornell University. Soon reporters came calling, but most were from right-wing news outlets representing what Petrovsky calls “the Murdoch press.” Petrovsky says he had to work at stopping some tendentious reporters from distorting his paper’s findings to shape a narrative that SARS-CoV-2 had unequivocally been manufactured. And at the same time, he says, other media tried “to make a mockery of the whole possibility of the lab thing.”



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How AI is reinventing what computers are

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How AI is reinventing what computers are


Fall 2021: the season of pumpkins, pecan pies, and peachy new phones. Every year, right on cue, Apple, Samsung, Google, and others drop their latest releases. These fixtures in the consumer tech calendar no longer inspire the surprise and wonder of those heady early days. But behind all the marketing glitz, there’s something remarkable going on. 

Google’s latest offering, the Pixel 6, is the first phone to have a separate chip dedicated to AI that sits alongside its standard processor. And the chip that runs the iPhone has for the last couple of years contained what Apple calls a “neural engine,” also dedicated to AI. Both chips are better suited to the types of computations involved in training and running machine-learning models on our devices, such as the AI that powers your camera. Almost without our noticing, AI has become part of our day-to-day lives. And it’s changing how we think about computing.

What does that mean? Well, computers haven’t changed much in 40 or 50 years. They’re smaller and faster, but they’re still boxes with processors that run instructions from humans. AI changes that on at least three fronts: how computers are made, how they’re programmed, and how they’re used. Ultimately, it will change what they are for. 

“The core of computing is changing from number-crunching to decision-­making,” says Pradeep Dubey, director of the parallel computing lab at Intel. Or, as MIT CSAIL director Daniela Rus puts it, AI is freeing computers from their boxes. 

More haste, less speed

The first change concerns how computers—and the chips that control them—are made. Traditional computing gains came as machines got faster at carrying out one calculation after another. For decades the world benefited from chip speed-ups that came with metronomic regularity as chipmakers kept up with Moore’s Law. 

But the deep-learning models that make current AI applications work require a different approach: they need vast numbers of less precise calculations to be carried out all at the same time. That means a new type of chip is required: one that can move data around as quickly as possible, making sure it’s available when and where it’s needed. When deep learning exploded onto the scene a decade or so ago, there were already specialty computer chips available that were pretty good at this: graphics processing units, or GPUs, which were designed to display an entire screenful of pixels dozens of times a second. 

Anything can become a computer. Indeed, most household objects, from toothbrushes to light switches to doorbells, already come in a smart version.

Now chipmakers like Intel and Arm and Nvidia, which supplied many of the first GPUs, are pivoting to make hardware tailored specifically for AI. Google and Facebook are also forcing their way into this industry for the first time, in a race to find an AI edge through hardware. 

For example, the chip inside the Pixel 6 is a new mobile version of Google’s tensor processing unit, or TPU. Unlike traditional chips, which are geared toward ultrafast, precise calculations, TPUs are designed for the high-volume but low-­precision calculations required by neural networks. Google has used these chips in-house since 2015: they process people’s photos and natural-­language search queries. Google’s sister company DeepMind uses them to train its AIs. 

In the last couple of years, Google has made TPUs available to other companies, and these chips—as well as similar ones being developed by others—are becoming the default inside the world’s data centers. 

AI is even helping to design its own computing infrastructure. In 2020, Google used a reinforcement-­learning algorithm—a type of AI that learns how to solve a task through trial and error—to design the layout of a new TPU. The AI eventually came up with strange new designs that no human would think of—but they worked. This kind of AI could one day develop better, more efficient chips. 

Show, don’t tell

The second change concerns how computers are told what to do. For the past 40 years we have been programming computers; for the next 40 we will be training them, says Chris Bishop, head of Microsoft Research in the UK. 

Traditionally, to get a computer to do something like recognize speech or identify objects in an image, programmers first had to come up with rules for the computer.

With machine learning, programmers no longer write rules. Instead, they create a neural network that learns those rules for itself. It’s a fundamentally different way of thinking. 

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Decarbonizing industries with connectivity and 5G

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Decarbonizing industries with connectivity and 5G


The United Nations Intergovernmental Panel on Climate Change’s sixth climate change report—an aggregated assessment of scientific research prepared by some 300 scientists across 66 countries—has served as the loudest and clearest wake-up call to date on the global warming crisis. The panel unequivocally attributes the increase in the earth’s temperature—it has risen by 1.1 °C since the Industrial Revolution—to human activity. Without substantial and immediate reductions in carbon dioxide and other greenhouse gas emissions, temperatures will rise between 1.5 °C and 2 °C before the end of the century. That, the panel posits, will lead all of humanity to a “greater risk of passing through ‘tipping points,’ thresholds beyond which certain impacts can no longer be avoided even if temperatures are brought back down later on.”

Corporations and industries must therefore redouble their greenhouse gas emissions reduction and removal efforts with speed and precision—but to do this, they must also commit to deep operational and organizational transformation. Cellular infrastructure, particularly 5G, is one of the many digital tools and technology-enabled processes organizations have at their disposal to accelerate decarbonization efforts.  

5G and other cellular technology can enable increasingly interconnected supply chains and networks, improve data sharing, optimize systems, and increase operational efficiency. These capabilities could soon contribute to an exponential acceleration of global efforts to reduce carbon emissions.

Industries such as energy, manufacturing, and transportation could have the biggest impact on decarbonization efforts through the use of 5G, as they are some of the biggest greenhouse-gas-emitting industries, and all rely on connectivity to link to one another through communications network infrastructure.

The higher performance and improved efficiency of 5G—which delivers higher multi-gigabit peak data speeds, ultra-low latency, increased reliability, and increased network capacity—could help businesses and public infrastructure providers focus on business transformation and reduction of harmful emissions. This requires effective digital management and monitoring of distributed operations with resilience and analytic insight. 5G will help factories, logistics networks, power companies, and others operate more efficiently, more consciously, and more purposely in line with their explicit sustainability objectives through better insight and more powerful network configurations.

This report, “Decarbonizing industries with connectivity & 5G,” argues that the capabilities enabled by broadband cellular connectivity primarily, though not exclusively, through 5G network infrastructure are a unique, powerful, and immediate enabler of carbon reduction efforts. They have the potential to create a transformational acceleration of decarbonization efforts, as increasingly interconnected supply chains, transportation, and energy networks share data to increase efficiency and productivity, hence optimizing systems for lower carbon emissions.

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Surgeons have successfully tested a pig’s kidney in a human patient

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Surgeons have successfully tested a pig’s kidney in a human patient


The reception: The research was conducted last month and is yet to be peer reviewed or published in a journal, but external experts say it represents a major advance. “There is no doubt that this is a highly significant breakthrough,” says Darren K. Griffin, a professor of genetics at the University of Kent, UK. “The research team were cautious, using a patient who had suffered brain death, attaching the kidney to the outside of the body, and closely monitoring for only a limited amount of time. There is thus a long way to go and much to discover,” he added. 

“This is a huge breakthrough. It’s a big, big deal,” Dorry Segev, a professor of transplant surgery at Johns Hopkins School of Medicine who was not involved in the research, told the New York Times. However, he added, “we need to know more about the longevity of the organ.”

The background: In recent years, research has increasingly zeroed in on pigs as the most promising avenue to help address the shortage of organs for transplant, but it has faced a number of obstacles, most prominently the fact that a sugar in pig cells triggers an aggressive rejection response in humans.

The researchers got around this by genetically altering the donor pig to knock out the gene encoding the sugar molecule that causes the rejection response. The pig was genetically engineered by Revivicor, one of several biotech companies working to develop pig organs to transplant into humans. 

The big prize: There is a dire need for more kidneys. More than 100,000 people in the US are currently waiting for a kidney transplant, and 13 die of them every day, according to the National Kidney Foundation. Genetically engineered pigs could offer a crucial lifeline for these people, if the approach tested at NYU Langone can work for much longer periods.

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