scientificamerican--2020-11-12--Early COVID Vaccine Results Bode Well for an Approval This Year

Editor ’ s Note ( 11/16/20 ) : A week after this story was published , the company Moderna announced that , based on an early analysis of its data , its COVID-19 vaccine was 94.5 percent effective and appeared to prevent severe disease .
Both the Pfizer and Moderna vaccines are based on mRNA and must be stored at cold temperatures .
But Moderna said its vaccine was stable for 30 days in a refrigerator and 12 hours at room temperature , according to the New York Times .
After a week during which the U.S. had nearly a @ @ @ @ @ @ @ Pfizer and its German partner BioNTech announced on Monday that a preliminary analysis suggests their experimental vaccine is more than 90 percent effective at preventing the disease .
The welcome news signifies that efforts to slow the worst pandemic in more than a century might finally be bearing fruit .
The news was reported following an early look at data on 94 cases from Pfizer ’ s phase III clinical trial , which has so far enrolled nearly 44,000 people .
Questions remain about how long protection lasts and whether the vaccine prevents @ @ @ @ @ @ @ vulnerable populations .
Still , the preliminary results—announced in a press release— “ are extremely encouraging , ” says Paul Offit , a pediatrician and director of the Vaccine Education Center at the Children ’ s Hospital of Philadelphia .
“ It means that the vaccine is likely to be very effective as we look at more participants in the trial , moving forward.
” Developing a new vaccine ordinarily takes years .
But Pfizer ’ s candidate began clinical testing only months after scientists published the genome sequence for SARS-CoV-2 , the @ @ @ @ @ @ @ two doses three weeks apart , the vaccine contains a genetic blueprint , known as mRNA , for the virus ’ s “ spike ” protein .
The infusion of viral mRNA triggers human cells to make and display the spike , prompting an immune reaction against future infection .
Although some laboratories have created such gene-based vaccines for other human illnesses , none has been commercialized in the U.S. Pfizer is one of four U.S. companies with vaccines against COVID that are now in phase III clinical trials in the nation ; @ @ @ @ @ @ @ to start its phase III trial later this month .
Three firms—Moderna , Johnson & Johnson and AstraZeneca—have received funding to develop their interventions from Operation Warp Speed , a federal program aiming to begin mass inoculations against COVID in the U.S. by early next year .
( Operation Warp Speed has promised $ 1.95 billion for manufacturing and distributing Pfizer ’ s vaccine , but it has not supported its development . )
The three companies ’ vaccines also deliver spike protein genetic material , albeit using different strategies .
And Pfizer @ @ @ @ @ @ @ reason to be optimistic for all of them , ” says Larry Corey , a virologist at the COVID-19 Prevention Network , which is helping to coordinate Operation Warp Speed ’ s clinical trials .
Offit concurs , adding that while the genetics-based approach appeared promising in animals , Pfizer ’ s efficacy data provide the first solid and eagerly awaited evidence that it can also succeed in people .
The company had initially planned to look at the data when the combined number of COVID cases in the placebo ( control ) @ @ @ @ @ @ @ , at that point , the vaccine was 80 percent protective or better—meaning that there were five times more sick people among the control group than there were in the vaccinated one—then an independent panel of reviewers could declare it successful .
Pfizer and the Food and Drug Administration later changed course and scheduled a data readout once the total number of cases reached 62 .
But with the pandemic raging , there were 94 cases to assess by the time the review actually took place .
The vaccine appears to be well @ @ @ @ @ @ @ with FDA requirements , trial investigators still need to monitor side effects for an average of two months after participants get a second dose .
Bill Gruber , Pfizer ’ s senior vice president of vaccine clinical research and development , predicts the company will wrap up that safety evaluation by next week .
If all goes well , the company will apply for an emergency use authorization ( EUA ) to start making the intervention publicly available to a limited group—most likely including first responders and people at high risk of severe @ @ @ @ @ @ @ date “ is probably the earliest it would be granted , ” he adds .
Pfizer plans to continue the trial until a final analysis is conducted at 164 cases—which Gruber predicts will be reached by the end of the month—so that investigators can assess additional end points .
The company ’ s recent announcement concerns the level of protection achieved seven days after the second dose ( and 28 days after the first ) .
Trial investigators will now extend their review to look for protection two weeks after the second dose @ @ @ @ @ @ @ more time to become symptomatic .
Even if the FDA grants an EUA , Pfizer will follow subjects for two years from the start of the trial to assess whether the vaccine has any rare side effects , how long its protection lasts and whether it protects against severe disease .
Gruber acknowledges that the company would “ feel an ethical responsibility ” to vaccinate the control subjects , even though doing so would compromise the trial ’ s ability to monitor efficacy .
“ My hope is that participants will see the @ @ @ @ @ @ @ ” he says .
“ We have to navigate with regulatory authorities to navigate that path.
” One approach under discussion would be to give everyone in the control group the vaccine and give the vaccinated people a placebo .
“ That way , everyone is protected , and you can follow them for a year or two .
And if the vaccine is safe and durable for an extra year , there would be little [ risk of infection ] in the second year for those who got it a long time @ @ @ @ @ @ @ That ’ s a strategy we ’ ve been thinking about , and we hope that people in the trial will consider it.
” For now , experts eager to see Pfizer 's clinical trial data will have to wait until they are published .
Company representatives claim that up to 42 percent of the trial participants are Asian , Black , Latinx , or Native American—and that up to 45 percent of all the participants are between 56 and 85 years old .
“ But what I want to know is : @ @ @ @ @ @ @ over 70 years old ?
” Offit says .
“ A 56-year-old person is not nearly as likely to die of COVID as a someone over 70 , and that ’ s an example of why we need to see the data .
This is basically science by press release at this point , which is always a little frustrating.
” Pfizer ’ s plant in Kalamazoo , Mich. , is now ramping up manufacturing toward a goal of supplying 50 million vaccine doses—enough for 25 million people—in 2020 and up to 1.3 @ @ @ @ @ @ @ that the first COVID vaccines out of the gate may not be the best , however .
And Pfizer ’ s mRNA candidate comes with several key disadvantages : it requires two doses , and it has to be stored at temperatures below –70 degrees Celsius .
The cold-temperature restriction could be problematic , especially in low- and middle-income countries .
By contrast , Johnson & Johnson ’ s vaccine requires only one dose and does not need subzero storage .
A phase III clinical trial for that intervention is currently underway .
@ @ @ @ @ @ @ ’ s vaccine “ increases our optimism that the others will work as well , ” says Dan Barouch , director of the Center for Virology and Vaccine Research at Beth Israel Deaconess Medical Center in Boston and a co-developer of the Johnson & Johnson vaccine .
“ No single vaccine can supply the global market .
We are going to need several to put this pandemic behind us.
” Read more about the coronavirus outbreak from Scientific American here .
And read coverage from our international network of magazines here .

Editor’s Note (11/18/20): After this story was published, Pfizer announced results from its final analysis of 170 confirmed cases, showing its vaccine is 95 percent effective. A Pfizer press release claimed the vaccine’s “efficacy was consistent across age, gender, race and ethnicity demographics” and that it exceeded 94 percent in adults older than 65. Earlier this week, the company Moderna announced that, based on an early analysis of its data, its COVID-19 vaccine was 94.5 percent effective and appeared to prevent severe disease. Both the Pfizer and Moderna vaccines are based on mRNA and must be stored at cold temperatures. But Moderna said its vaccine was stable for 30 days in a refrigerator and 12 hours at room temperature, according to the New York Times.
After a week during which the U.S. had nearly a million new COVID cases, pharmaceutical company Pfizer and its German partner BioNTech announced on Monday that a preliminary analysis suggests their experimental vaccine is more than 90 percent effective at preventing the disease. The welcome news signifies that efforts to slow the worst pandemic in more than a century might finally be bearing fruit.
The news was reported following an early look at data on 94 cases from Pfizer’s phase III clinical trial, which has so far enrolled nearly 44,000 people. Questions remain about how long protection lasts and whether the vaccine prevents severe disease in the elderly and other vulnerable populations. Still, the preliminary results—announced in a press release—“are extremely encouraging,” says Paul Offit, a pediatrician and director of the Vaccine Education Center at the Children’s Hospital of Philadelphia. “It means that the vaccine is likely to be very effective as we look at more participants in the trial, moving forward.”
Developing a new vaccine ordinarily takes years. But Pfizer’s candidate began clinical testing only months after scientists published the genome sequence for SARS-CoV-2, the coronavirus that causes COVID. Given in two doses three weeks apart, the vaccine contains a genetic blueprint, known as mRNA, for the virus’s “spike” protein. The infusion of viral mRNA triggers human cells to make and display the spike, prompting an immune reaction against future infection. Although some laboratories have created such gene-based vaccines for other human illnesses, none has been commercialized in the U.S.
Pfizer is one of four U.S. companies with vaccines against COVID that are now in phase III clinical trials in the nation; a fifth company, Novavax, plans to start its phase III trial later this month. Three firms—Moderna, Johnson & Johnson and AstraZeneca—have received funding to develop their interventions from Operation Warp Speed, a federal program aiming to begin mass inoculations against COVID in the U.S. by early next year. (Operation Warp Speed has promised $1.95 billion for manufacturing and distributing Pfizer’s vaccine, but it has not supported its development.) The three companies’ vaccines also deliver spike protein genetic material, albeit using different strategies. And Pfizer’s new results “give us reason to be optimistic for all of them,” says Larry Corey, a virologist at the COVID-19 Prevention Network, which is helping to coordinate Operation Warp Speed’s clinical trials.
Offit concurs, adding that while the genetics-based approach appeared promising in animals, Pfizer’s efficacy data provide the first solid and eagerly awaited evidence that it can also succeed in people.
The company had initially planned to look at the data when the combined number of COVID cases in the placebo (control) and vaccinated groups reached 32. If, at that point, the vaccine was 80 percent protective or better—meaning that there were five times more sick people among the control group than there were in the vaccinated one—then an independent panel of reviewers could declare it successful. Pfizer and the Food and Drug Administration later changed course and scheduled a data readout once the total number of cases reached 62. But with the pandemic raging, there were 94 cases to assess by the time the review actually took place.
The vaccine appears to be well tolerated so far. But to comply with FDA requirements, trial investigators still need to monitor side effects for an average of two months after participants get a second dose. Bill Gruber, Pfizer’s senior vice president of vaccine clinical research and development, predicts the company will wrap up that safety evaluation by next week. If all goes well, the company will apply for an emergency use authorization (EUA) to start making the intervention publicly available to a limited group—most likely including first responders and people at high risk of severe disease—by mid-December, Gruber says. That date “is probably the earliest it would be granted,” he adds.
Pfizer plans to continue the trial until a final analysis is conducted at 164 cases—which Gruber predicts will be reached by the end of the month—so that investigators can assess additional end points. The company’s recent announcement concerns the level of protection achieved seven days after the second dose (and 28 days after the first). Trial investigators will now extend their review to look for protection two weeks after the second dose is administered, thus giving infected individuals more time to become symptomatic.
Even if the FDA grants an EUA, Pfizer will follow subjects for two years from the start of the trial to assess whether the vaccine has any rare side effects, how long its protection lasts and whether it protects against severe disease. Gruber acknowledges that the company would “feel an ethical responsibility” to vaccinate the control subjects, even though doing so would compromise the trial’s ability to monitor efficacy. “My hope is that participants will see the advantage of remaining in the trial,” he says. “We have to navigate with regulatory authorities to navigate that path.” One approach under discussion would be to give everyone in the control group the vaccine and give the vaccinated people a placebo. “That way, everyone is protected, and you can follow them for a year or two. And if the vaccine is safe and durable for an extra year, there would be little [risk of infection] in the second year for those who got it a long time ago,” Corey says. “That’s a strategy we’ve been thinking about, and we hope that people in the trial will consider it.”
For now, experts eager to see Pfizer's clinical trial data will have to wait until they are published. Company representatives claim that up to 42 percent of the trial participants are Asian, Black, Latinx, or Native American—and that up to 45 percent of all the participants are between 56 and 85 years old. “But what I want to know is: How many cases are over 65—and especially over 70 years old?” Offit says. “A 56-year-old person is not nearly as likely to die of COVID as a someone over 70, and that’s an example of why we need to see the data. This is basically science by press release at this point, which is always a little frustrating.”
Pfizer’s plant in Kalamazoo, Mich., is now ramping up manufacturing toward a goal of supplying 50 million vaccine doses—enough for 25 million people—in 2020 and up to 1.3 billion doses in 2021. Corey acknowledges that the first COVID vaccines out of the gate may not be the best, however. And Pfizer’s mRNA candidate comes with several key disadvantages: it requires two doses, and it has to be stored at temperatures below –70 degrees Celsius. The cold-temperature restriction could be problematic, especially in low- and middle-income countries. By contrast, Johnson & Johnson’s vaccine requires only one dose and does not need subzero storage. A phase III clinical trial for that intervention is currently underway.
Still, the apparent success of Pfizer’s vaccine “increases our optimism that the others will work as well,” says Dan Barouch, director of the Center for Virology and Vaccine Research at Beth Israel Deaconess Medical Center in Boston and a co-developer of the Johnson & Johnson vaccine. “No single vaccine can supply the global market. We are going to need several to put this pandemic behind us.”
Read more about the coronavirus outbreak from Scientific American here. And read coverage from our international network of magazines here.


scientificamerican--2020-11-17--Strange Supernovae Upend Expectations

Strange Supernovae Upend Expectations

On September 9 , 2018 , a robotic telescope on its routine patrol of the night sky detected what looked like a new star .
Over the next few hours , the “ star ” grew 10 times brighter , triggering a flag by software I had written to identify unusual celestial events .
It was nighttime in California , and I was asleep , but my colleagues on the other side of the world reacted quickly to the alert .
Twelve hours later we had obtained enough additional data from telescopes on Earth and in space to confirm that @ @ @ @ @ @ @ supernova—in a distant galaxy .
But this was no ordinary supernova .
Tying together the evidence from different telescopes , we concluded that after shining for millions of years , the star did something surprising and mysterious : it abruptly cast off layers of gas from its surface , forming a cocoon around itself .
A few days or a week later the star exploded .
The debris from the blast collided with the cocoon , producing an unusually bright and short-lived flash of light .
Because the explosion took place in a @ @ @ @ @ @ @ billion years to reach Earth—it was too dim to be seen with the naked eye but bright enough for our observatories .
Through a retrospective search of telescope data , we were even able to detect the star in the act of shedding two weeks before it exploded , when it was one one-hundredth as bright as the explosion itself .
This was just one of several recent discoveries that have shown us that stars die in surprisingly diverse ways .
Sometimes , for example , the remnant of a star 's core @ @ @ @ @ @ @ remains active after the star has collapsed—it can launch a jet of material moving at hyperrelativistic speeds , and the jet itself can destroy the star with more energy than a normal supernova .
Sometimes , in the final days to years of its life , a star blows away a significant fraction of its gas in a series of violent eruptions .
These extreme deaths appear to be rare , but the fact that they happen at all tells us there is much we still do not understand about the basics of @ @ @ @ @ @ @ my colleagues and I are amassing a collection of unusual stellar endings that challenge our traditional assumptions .
We are beginning to be able to ask and answer fundamental questions : Which factors determine how a star dies ?
Why do some stars end their lives with eruptions or violent jets , while others simply explode ?
The story of stellar birth , life and death is a tale of competing forces .
Stars are formed in interstellar clouds of hydrogen gas when the force of gravity pulls part of the cloud inward @ @ @ @ @ @ @ of magnetic fields and gas particles traveling at high speeds .
As the cloud fragment collapses , it becomes 20 orders of magnitude denser and heats up by millions of degrees—temperatures high enough for the hydrogen atoms to collide and stick together to form helium .
Fusion has begun , and a new star is born .
Like a cloud , a star is itself a battleground , with gravity pushing in and pressure from nuclear fusion pushing out .
The evolution of a star depends on its temperature , which in turn @ @ @ @ @ @ @ the star , the heavier the elements it can forge , and the faster it burns through its fuel .
The lightest stars fuse hydrogen to helium and stop there—the sun is more than four billion years old and is still burning its hydrogen .
Heavier stars live much shorter lives , only 10 million years or so , yet manufacture a much longer chain of elements : oxygen , carbon , neon , nitrogen , magnesium , silicon and even iron .
A star 's mass also determines how it will die @ @ @ @ @ @ @ around eight times the mass of the sun—die relatively peacefully .
After exhausting their supplies of nuclear fuel , the outer layers of these stars blow out into space , forming beautiful planetary nebulae and leaving the stars ' cores exposed as white dwarfs—hot , dense objects with about half the mass of the sun that are only slightly larger than Earth .
Heavier stars , however , meet a violent end because of the enormous temperatures and pressures in their cores .
Around the time they reach iron in the nuclear burning @ @ @ @ @ @ @ things fall apart—iron atoms can start breaking into smaller pieces .
The chain of fusion is cut off , and the star loses its internal pressure .
Gravity takes over , and the core collapses until its constituent atoms are so close together that another opposing force steps in : the strong nuclear force .
Now the core has become a neutron star , an exotic and dense state of matter made mostly of neutrons .
If the star is massive enough—say , more than 20 times the mass of the sun—gravity overcomes @ @ @ @ @ @ @ the neutron star collapses further into a black hole .
Either way , some of the energy released when the core collapses pushes the outer layers of the star into space , creating an explosion so bright that for a few days it outshines the rest of the stars in the galaxy combined .
Human beings have spotted supernovae by eye for thousands of years .
In 1572 a Danish astronomer named Tycho Brahe noticed a new star in the constellation Cassiopeia .
It was as bright as Venus and stayed that bright @ @ @ @ @ @ @ wrote that he was so shocked that he doubted his own eyes .
Today the aftermath of the explosion—the debris—is still visible and is known as Tycho 's Supernova Remnant .
For a supernova to be bright enough to be seen by the unaided eye , it must be in the Milky Way , as Tycho 's supernova was , or in one of its satellite galaxies , and this is rare .
I might not see a supernova without the help of a telescope in my lifetime , although I can hope @ @ @ @ @ @ @ using telescopes to find supernovae beyond the Milky Way by taking repeated observations of the same set of galaxies and looking for changes , called transients .
Our telescopes are now roboticized and outfitted with modern cameras , enabling us to discover thousands of supernovae every year .
An early sign that some stars die in extreme ways was the 1960s discovery of gamma-ray bursts ( GRBs ) , so named because of the bright blasts of gamma-ray light they emit .
We believe we see them when a massive star collapses into @ @ @ @ @ @ @ , the newborn compact object launches a narrow jet of matter , that jet successfully tunnels from the core through what remains of the star , and the jet just happens to be pointing at Earth .
What might create such a jet ?
The basic idea is the following .
When a normal star runs out of fuel and dies , its core collapses into a neutron star or a black hole , and that is the end of that .
In a gamma-ray burst , however , the corpse stays active @ @ @ @ @ @ @ absorbing mass from a disk of material around it , releasing energy in the process .
Or maybe the newly created neutron star is rotating quickly , and a powerful magnetic field acts as a brake , releasing energy as the star slows down .
Either way , this “ central engine ” pumps out energy that gets funneled into a jet of extremely hot plasma that tunnels from the center of the star out through the infalling material , glowing in gamma rays .
The passage of the jet through the star @ @ @ @ @ @ @ supernova dubbed “ Type Ic-BL , ” which is 10 times more energetic than ordinary supernovae .
As the jet plows into the surrounding gas and dust , it produces light all across the electromagnetic spectrum , called an afterglow .
Afterglows are difficult to find because although they are 1,000 times brighter than typical supernovae , they are 100 times more fleeting , appearing and disappearing in just a few hours .
The best hope for finding an afterglow is to wait for a gamma-ray burst to be discovered by a satellite @ @ @ @ @ @ @ the reported location of the burst .
By waiting for a satellite to discover a burst , though , you limit the kinds of phenomena you can discover .
A lot of things have to go right for a GRB to be produced : the jet has to be launched , make it through the star , and be pointing at you .
In fact , it seems extremely unlikely for GRBs to occur : the gamma-ray photons emitted by the jet should get trapped unless the jet is moving at 99.995 percent @ @ @ @ @ @ @ to reach such speeds , the jet would need to somehow make it through the star without dragging along the star 's matter with it .
What if most jets actually do get slowed down by the star , and we see only the small fraction that make it through unscathed ?
In other words , perhaps gamma-ray bursts represent the rare occasions that jets escape their stars and do n't slow down too much .
If that were true , there would be a huge number of extreme stellar deaths out there @ @ @ @ @ @ @ .
For my thesis , I set out to find afterglows without relying on a trigger from a satellite .
My plan was to use the Zwicky Transient Facility , a robotic telescope at the Palomar Observatory in California , to patrol the sky for unusually fleeting , unusually bright points of light—and then react quickly .
When I presented my thesis proposal in May 2018 , my faculty advisers warned me that I might not find what I was looking for .
They urged me to keep an open mind because new @ @ @ @ @ @ @ month later that is exactly what happened .
And two years later when I graduated , my thesis looked very different from what I had expected .
When I began my work , I wrote a program to find celestial phenomena that were changing in brightness more rapidly than ordinary supernovae .
On a normal day I examined 10 to 100 different candidates and concluded that none of them were what I was looking for .
On some days , though , I encountered something that gave me pause .
In June 2018 @ @ @ @ @ @ @ telescope facility called ATLAS , reporting a strange event dubbed AT2018cow .
“ AT ” stood for “ astronomical transient , ” the prefix automatically given to all new transients , “ 2018 ” for the year of discovery , and “ cow ” was a unique string of letters .
In the next couple of days there were reports of similarities between this event and gamma-ray bursts , yet there had been no detected show of gamma rays .
“ Aha , ” I thought , “ this is it !
” @ @ @ @ @ @ @ nearby , there was intense worldwide interest in this object , and astronomers observed it all across the electromagnetic spectrum .
I immediately made plans to observe AT2018cow using a radio telescope in Hawaii called the Submillimeter Array .
AT2018cow stunned just about everyone .
It unfolded completely differently than any cosmic explosion seen before .
We were like the people in a classic parable who are trying to identify an elephant in the dark .
One person feels its trunk and says it is a waterspout , whereas another feels the ear @ @ @ @ @ @ @ , and a third feels the leg and says it is a tree .
Similarly , AT2018cow shared characteristics with several different classes of phenomena , but it has been difficult to put a complete picture together .
My collaborators and I spent long days and nights going over our data repeatedly , trying to figure out how to interpret them .
Some of those moments—calculating the properties of the shock wave together on a chalkboard , a team member running down the hallway waving a piece of paper with new results , @ @ @ @ @ @ @ shock when a beautiful new measurement came in—remain my most treasured memories from graduate school .
In the end , we concluded that there were two important components to AT2018cow .
The first was a central engine , as in a gamma-ray burst , but lasting for much longer—weeks rather than the typical days ; x-rays shining from the heart of the explosion stayed bright for much longer than expected .
The second was that for some reason , when the star burst apart , it was surrounded by a cocoon of gas @ @ @ @ @ @ @ mass of the sun .
Our evidence for the cocoon is indirect : when the star exploded , we saw a flash of optical light and radio waves that seemed to indicate debris hitting a mass surrounding the star .
Such cocoons have been seen in other types of explosions , but we do not know how they get there—it may be that the material is shed by the star shortly before exploding .
If this theory is correct , it would be the first time astronomers have directly witnessed the birth of @ @ @ @ @ @ @ or a black hole ; most of the time the corpse is completely shrouded by what remains of the star .
In the case of AT2018cow , we think we could actually see down to the compact object that produced all of this amazingly variable and bright x-ray emission .
Still , we are left with many questions .
What kind of star exploded ?
Was the central engine a neutron star or a black hole ?
Why did the star shed mass shortly before exploding ?
To make progress , we needed @ @ @ @ @ @ @ colleagues and I set out to find another AT2018cow using the Zwicky Transient Facility .
Three months later I thought we found one—the bright , fast-rising explosion of September 9 , 2018 .
Initially it looked very similar to AT2018cow .
Within a week , however , it became clear that this event was a Type Ic-BL supernova—the kind associated with gamma-ray bursts .
Its name was SN2018gep .
I was excited .
Sure , it was not another AT2018cow , but we finally had something that looked like a gamma-ray burst .
@ @ @ @ @ @ @ observations all across the electromagnetic spectrum .
We searched the data for evidence of a jet—but we found none .
Instead , yet again , my collaborators and I concluded that we were seeing bright , fast-evolving optical emission from the collision of explosion debris with a cocoon of material .
This was a surprise .
Although cocoons have been seen surrounding other types of stars , they are not commonly observed in the types of supernovae associated with gamma-ray bursts .
Our discovery implies that more stars shed gas at the end @ @ @ @ @ @ @ We know the gas was lost in the final moments of the star 's life because it was so close to the star at the time of the explosion ; if it had been cast off earlier , it would have had time to get farther away .
That means the star lost a significant chunk of its outer atmosphere in the final days to weeks of its life , after shining for millions to tens of millions of years .
It seems , then , that this shedding heralds the death of @ @ @ @ @ @ @ were left with questions .
How prevalent are these death omens in different types of stars ?
What is the physical mechanism that produces them ?
I realized that I had a new direction to my research now—not just gamma-ray bursts and jets but also the warning signs of soon-to-explode massive stars .
And perhaps these different phenomena were even connected .
It was not until the final six months of my Ph.D. program that I finally found a gamma-ray burst afterglow .
On January 28 , 2020 , I did my usual @ @ @ @ @ @ @ looked promising .
I knew better than to get excited—there had been many , many false starts over the years .
I immediately requested additional observations with a telescope in La Palma in the Canary Islands , and they confirmed that this source was fading away quickly , as would be expected for an afterglow .
That night I requested urgent observations on the 200-inch Hale Telescope at the Palomar Observatory that showed the source was still fading .
The next night I obtained observations with the Swift X-ray space telescope and detected @ @ @ @ @ @ @ confirming this was truly a GRB afterglow .
The night after that I got a brief window of time on the Keck Telescope on Mauna Kea in Hawaii , with the hope of measuring how far away the explosion was .
I slept in a sleeping bag in the remote observing room at my university , the California Institute of Technology , and set an alarm for 4 A.M .
When the time came , I felt panicked—I was squeezing in this observation right at the end of the night , the sky @ @ @ @ @ @ @ was very faint , and I was terrified of being too late .
I did the best that I could .
When it was too bright to observe any longer , I called my colleague Dan Perley of Liverpool John Moores University in England on Skype , and we looked at the data together .
I was lucky .
The source was faint , but there was a big , booming , obvious feature in the light from the event that enabled us to measure the distance , which was vast : a @ @ @ @ @ @ @ light had significantly reddened during its journey through the cosmos .
When this star exploded , the universe was only 2.3 billion years old .
The photons from the blast took 11.4 billion years to reach Earth .
Today the physical location of the burst is 21 billion light-years away—the explosion happened so long ago that the universe has expanded significantly since then .
This was the real deal .
A few months after finding our first afterglow , we found a second .
To put that in perspective , prior to the @ @ @ @ @ @ @ had ever been found without a gamma-ray burst first occurring and telling astronomers where to look , and we found two in just a few months .
Now that we have our search strategy ironed out and working , I hope we can find these routinely .
Still , even with two afterglows in hand , I can not definitively answer the questions I originally set out to answer .
It is difficult to tell whether any given afterglow is something new or just a normal gamma-ray burst that high-energy satellites happened to @ @ @ @ @ @ @ more events before we can tell if we are witnessing truly different phenomena .
Since the discovery of an unexpected new type of engine-driven explosion in AT2018cow , my search has uncovered a variety of unusual stellar displays .
There was the weird Ic-BL supernova ( the kind associated with GRBs ) crashing into a cocoon of material but showing no evidence for a powerful jet ( the hallmark of a GRB ) .
Then there was another event similar to AT2018cow .
There were also two Ic-BL supernova that probably had jets @ @ @ @ @ @ @ wider than those in traditional gamma-ray bursts .
And finally , right at the end of graduate school , two actual cosmological afterglows , one of which turned out to have an associated gamma-ray burst .
So far we astronomers have been like zoologists , going out into relatively uncharted territory and characterizing all the different creatures ( in this case , explosions ) that we see .
The next stage will be to look for patterns .
What are the relative rates of each type of blast ?
Do they seem to @ @ @ @ @ @ @ not another ?
Are these different categories actually different “ species ” or just different manifestations of the same phenomenon ?
To answer these questions , we will need a much larger catalog .
Beginning in a few years , the Vera C. Rubin Observatory , currently under construction in Chile , will use the largest digital camera ever constructed ( three billion pixels ) to spot 10 million potential transients every night—10 times more than the Zwicky Transient Facility does now .
With more data , I would like to investigate which @ @ @ @ @ @ @ before they die and how often .
I want to study how we can tell if there was a jet that got choked inside a star and how to recognize the kind of faint emission emitted during a star 's death throes to predict where and when a star will explode .
Ultimately I would like to probe questions about the factors that lead to these unusual deaths—perhaps it is something about a star 's rate of spin or its history of interactions with other stars that causes it to die in such @ @ @ @ @ @

On September 9, 2018, a robotic telescope on its routine patrol of the night sky detected what looked like a new star. Over the next few hours, the “star” grew 10 times brighter, triggering a flag by software I had written to identify unusual celestial events. It was nighttime in California, and I was asleep, but my colleagues on the other side of the world reacted quickly to the alert. Twelve hours later we had obtained enough additional data from telescopes on Earth and in space to confirm that this was the explosion of a star—a supernova—in a distant galaxy. But this was no ordinary supernova.
Tying together the evidence from different telescopes, we concluded that after shining for millions of years, the star did something surprising and mysterious: it abruptly cast off layers of gas from its surface, forming a cocoon around itself. A few days or a week later the star exploded. The debris from the blast collided with the cocoon, producing an unusually bright and short-lived flash of light. Because the explosion took place in a galaxy far away—the light took almost a billion years to reach Earth—it was too dim to be seen with the naked eye but bright enough for our observatories. Through a retrospective search of telescope data, we were even able to detect the star in the act of shedding two weeks before it exploded, when it was one one-hundredth as bright as the explosion itself.
This was just one of several recent discoveries that have shown us that stars die in surprisingly diverse ways. Sometimes, for example, the remnant of a star's core that is left over after a supernova remains active after the star has collapsed—it can launch a jet of material moving at hyperrelativistic speeds, and the jet itself can destroy the star with more energy than a normal supernova. Sometimes, in the final days to years of its life, a star blows away a significant fraction of its gas in a series of violent eruptions. These extreme deaths appear to be rare, but the fact that they happen at all tells us there is much we still do not understand about the basics of how stars live and die.
Now my colleagues and I are amassing a collection of unusual stellar endings that challenge our traditional assumptions. We are beginning to be able to ask and answer fundamental questions: Which factors determine how a star dies? Why do some stars end their lives with eruptions or violent jets, while others simply explode?
A New Star
The story of stellar birth, life and death is a tale of competing forces. Stars are formed in interstellar clouds of hydrogen gas when the force of gravity pulls part of the cloud inward strongly enough to overcome the outward push of magnetic fields and gas particles traveling at high speeds. As the cloud fragment collapses, it becomes 20 orders of magnitude denser and heats up by millions of degrees—temperatures high enough for the hydrogen atoms to collide and stick together to form helium. Fusion has begun, and a new star is born.
Like a cloud, a star is itself a battleground, with gravity pushing in and pressure from nuclear fusion pushing out. The evolution of a star depends on its temperature, which in turn depends on its mass. The heavier the star, the heavier the elements it can forge, and the faster it burns through its fuel. The lightest stars fuse hydrogen to helium and stop there—the sun is more than four billion years old and is still burning its hydrogen. Heavier stars live much shorter lives, only 10 million years or so, yet manufacture a much longer chain of elements: oxygen, carbon, neon, nitrogen, magnesium, silicon and even iron.
A star's mass also determines how it will die. Lightweight stars—those that weigh less than around eight times the mass of the sun—die relatively peacefully. After exhausting their supplies of nuclear fuel, the outer layers of these stars blow out into space, forming beautiful planetary nebulae and leaving the stars' cores exposed as white dwarfs—hot, dense objects with about half the mass of the sun that are only slightly larger than Earth.
Heavier stars, however, meet a violent end because of the enormous temperatures and pressures in their cores. Around the time they reach iron in the nuclear burning chain, conditions are so hot that things fall apart—iron atoms can start breaking into smaller pieces. The chain of fusion is cut off, and the star loses its internal pressure. Gravity takes over, and the core collapses until its constituent atoms are so close together that another opposing force steps in: the strong nuclear force. Now the core has become a neutron star, an exotic and dense state of matter made mostly of neutrons. If the star is massive enough—say, more than 20 times the mass of the sun—gravity overcomes even the strong nuclear force, and the neutron star collapses further into a black hole. Either way, some of the energy released when the core collapses pushes the outer layers of the star into space, creating an explosion so bright that for a few days it outshines the rest of the stars in the galaxy combined.
Human beings have spotted supernovae by eye for thousands of years. In 1572 a Danish astronomer named Tycho Brahe noticed a new star in the constellation Cassiopeia. It was as bright as Venus and stayed that bright for months before fading away. He wrote that he was so shocked that he doubted his own eyes. Today the aftermath of the explosion—the debris—is still visible and is known as Tycho's Supernova Remnant.
For a supernova to be bright enough to be seen by the unaided eye, it must be in the Milky Way, as Tycho's supernova was, or in one of its satellite galaxies, and this is rare. I might not see a supernova without the help of a telescope in my lifetime, although I can hope. In the past century astronomers began using telescopes to find supernovae beyond the Milky Way by taking repeated observations of the same set of galaxies and looking for changes, called transients. Our telescopes are now roboticized and outfitted with modern cameras, enabling us to discover thousands of supernovae every year.
An early sign that some stars die in extreme ways was the 1960s discovery of gamma-ray bursts (GRBs), so named because of the bright blasts of gamma-ray light they emit. We believe we see them when a massive star collapses into a neutron star or a black hole, the newborn compact object launches a narrow jet of matter, that jet successfully tunnels from the core through what remains of the star, and the jet just happens to be pointing at Earth.
What might create such a jet? The basic idea is the following. When a normal star runs out of fuel and dies, its core collapses into a neutron star or a black hole, and that is the end of that. In a gamma-ray burst, however, the corpse stays active. Perhaps the nascent black hole is absorbing mass from a disk of material around it, releasing energy in the process. Or maybe the newly created neutron star is rotating quickly, and a powerful magnetic field acts as a brake, releasing energy as the star slows down. Either way, this “central engine” pumps out energy that gets funneled into a jet of extremely hot plasma that tunnels from the center of the star out through the infalling material, glowing in gamma rays.
The passage of the jet through the star causes it to explode in a special supernova dubbed “Type Ic-BL,” which is 10 times more energetic than ordinary supernovae. As the jet plows into the surrounding gas and dust, it produces light all across the electromagnetic spectrum, called an afterglow. Afterglows are difficult to find because although they are 1,000 times brighter than typical supernovae, they are 100 times more fleeting, appearing and disappearing in just a few hours. The best hope for finding an afterglow is to wait for a gamma-ray burst to be discovered by a satellite and then immediately point your telescope to the reported location of the burst.
By waiting for a satellite to discover a burst, though, you limit the kinds of phenomena you can discover. A lot of things have to go right for a GRB to be produced: the jet has to be launched, make it through the star, and be pointing at you. In fact, it seems extremely unlikely for GRBs to occur: the gamma-ray photons emitted by the jet should get trapped unless the jet is moving at 99.995 percent of the speed of light. But to reach such speeds, the jet would need to somehow make it through the star without dragging along the star's matter with it. What if most jets actually do get slowed down by the star, and we see only the small fraction that make it through unscathed? In other words, perhaps gamma-ray bursts represent the rare occasions that jets escape their stars and don't slow down too much. If that were true, there would be a huge number of extreme stellar deaths out there that are totally invisible to gamma-ray satellites.
For my thesis, I set out to find afterglows without relying on a trigger from a satellite. My plan was to use the Zwicky Transient Facility, a robotic telescope at the Palomar Observatory in California, to patrol the sky for unusually fleeting, unusually bright points of light—and then react quickly. When I presented my thesis proposal in May 2018, my faculty advisers warned me that I might not find what I was looking for. They urged me to keep an open mind because new avenues of inquiry might arise. One month later that is exactly what happened. And two years later when I graduated, my thesis looked very different from what I had expected.
Credit: Ron Miller
Holy Cow
When I began my work, I wrote a program to find celestial phenomena that were changing in brightness more rapidly than ordinary supernovae. On a normal day I examined 10 to 100 different candidates and concluded that none of them were what I was looking for. On some days, though, I encountered something that gave me pause.
In June 2018 I saw a report from a robotic telescope facility called ATLAS, reporting a strange event dubbed AT2018cow. “AT” stood for “astronomical transient,” the prefix automatically given to all new transients, “2018” for the year of discovery, and “cow” was a unique string of letters. In the next couple of days there were reports of similarities between this event and gamma-ray bursts, yet there had been no detected show of gamma rays. “Aha,” I thought, “this is it!” Because AT2018cow was so bright and so nearby, there was intense worldwide interest in this object, and astronomers observed it all across the electromagnetic spectrum. I immediately made plans to observe AT2018cow using a radio telescope in Hawaii called the Submillimeter Array.
AT2018cow stunned just about everyone. It unfolded completely differently than any cosmic explosion seen before. We were like the people in a classic parable who are trying to identify an elephant in the dark. One person feels its trunk and says it is a waterspout, whereas another feels the ear and thinks it must be a fan, and a third feels the leg and says it is a tree. Similarly, AT2018cow shared characteristics with several different classes of phenomena, but it has been difficult to put a complete picture together.
My collaborators and I spent long days and nights going over our data repeatedly, trying to figure out how to interpret them. Some of those moments—calculating the properties of the shock wave together on a chalkboard, a team member running down the hallway waving a piece of paper with new results, and meeting a colleague's eyes in shock when a beautiful new measurement came in—remain my most treasured memories from graduate school. In the end, we concluded that there were two important components to AT2018cow. The first was a central engine, as in a gamma-ray burst, but lasting for much longer—weeks rather than the typical days; x-rays shining from the heart of the explosion stayed bright for much longer than expected. The second was that for some reason, when the star burst apart, it was surrounded by a cocoon of gas and dust with about one one-thousandth the mass of the sun. Our evidence for the cocoon is indirect: when the star exploded, we saw a flash of optical light and radio waves that seemed to indicate debris hitting a mass surrounding the star. Such cocoons have been seen in other types of explosions, but we do not know how they get there—it may be that the material is shed by the star shortly before exploding.
If this theory is correct, it would be the first time astronomers have directly witnessed the birth of a compact object like a neutron star or a black hole; most of the time the corpse is completely shrouded by what remains of the star. In the case of AT2018cow, we think we could actually see down to the compact object that produced all of this amazingly variable and bright x-ray emission. Still, we are left with many questions. What kind of star exploded? Was the central engine a neutron star or a black hole? Why did the star shed mass shortly before exploding? To make progress, we needed to find similar events, so my colleagues and I set out to find another AT2018cow using the Zwicky Transient Facility.
Three months later I thought we found one—the bright, fast-rising explosion of September 9, 2018. Initially it looked very similar to AT2018cow. Within a week, however, it became clear that this event was a Type Ic-BL supernova—the kind associated with gamma-ray bursts. Its name was SN2018gep. I was excited. Sure, it was not another AT2018cow, but we finally had something that looked like a gamma-ray burst. Within five days we had collected detailed observations all across the electromagnetic spectrum. We searched the data for evidence of a jet—but we found none. Instead, yet again, my collaborators and I concluded that we were seeing bright, fast-evolving optical emission from the collision of explosion debris with a cocoon of material.
This was a surprise. Although cocoons have been seen surrounding other types of stars, they are not commonly observed in the types of supernovae associated with gamma-ray bursts. Our discovery implies that more stars shed gas at the end of their lives than we thought. We know the gas was lost in the final moments of the star's life because it was so close to the star at the time of the explosion; if it had been cast off earlier, it would have had time to get farther away. That means the star lost a significant chunk of its outer atmosphere in the final days to weeks of its life, after shining for millions to tens of millions of years. It seems, then, that this shedding heralds the death of the star.
Once again, we were left with questions. How prevalent are these death omens in different types of stars? What is the physical mechanism that produces them? I realized that I had a new direction to my research now—not just gamma-ray bursts and jets but also the warning signs of soon-to-explode massive stars. And perhaps these different phenomena were even connected.
It was not until the final six months of my Ph.D. program that I finally found a gamma-ray burst afterglow. On January 28, 2020, I did my usual candidate review when I saw something that looked promising. I knew better than to get excited—there had been many, many false starts over the years. I immediately requested additional observations with a telescope in La Palma in the Canary Islands, and they confirmed that this source was fading away quickly, as would be expected for an afterglow. That night I requested urgent observations on the 200-inch Hale Telescope at the Palomar Observatory that showed the source was still fading. The next night I obtained observations with the Swift X-ray space telescope and detected x-rays from the event, all but confirming this was truly a GRB afterglow. The night after that I got a brief window of time on the Keck Telescope on Mauna Kea in Hawaii, with the hope of measuring how far away the explosion was.
I slept in a sleeping bag in the remote observing room at my university, the California Institute of Technology, and set an alarm for 4 A.M. When the time came, I felt panicked—I was squeezing in this observation right at the end of the night, the sky was getting brighter quickly, the source was very faint, and I was terrified of being too late. I did the best that I could. When it was too bright to observe any longer, I called my colleague Dan Perley of Liverpool John Moores University in England on Skype, and we looked at the data together. I was lucky. The source was faint, but there was a big, booming, obvious feature in the light from the event that enabled us to measure the distance, which was vast: a redshift of 2.9, which means its light had significantly reddened during its journey through the cosmos. When this star exploded, the universe was only 2.3 billion years old. The photons from the blast took 11.4 billion years to reach Earth. Today the physical location of the burst is 21 billion light-years away—the explosion happened so long ago that the universe has expanded significantly since then. This was the real deal.
A few months after finding our first afterglow, we found a second. To put that in perspective, prior to the Zwicky Transient Facility, only three afterglows had ever been found without a gamma-ray burst first occurring and telling astronomers where to look, and we found two in just a few months. Now that we have our search strategy ironed out and working, I hope we can find these routinely. Still, even with two afterglows in hand, I cannot definitively answer the questions I originally set out to answer. It is difficult to tell whether any given afterglow is something new or just a normal gamma-ray burst that high-energy satellites happened to miss. We will need to find more events before we can tell if we are witnessing truly different phenomena.
Expanding the Catalog
Since the discovery of an unexpected new type of engine-driven explosion in AT2018cow, my search has uncovered a variety of unusual stellar displays. There was the weird Ic-BL supernova (the kind associated with GRBs) crashing into a cocoon of material but showing no evidence for a powerful jet (the hallmark of a GRB). Then there was another event similar to AT2018cow. There were also two Ic-BL supernova that probably had jets, but these were less energetic and wider than those in traditional gamma-ray bursts. And finally, right at the end of graduate school, two actual cosmological afterglows, one of which turned out to have an associated gamma-ray burst.
So far we astronomers have been like zoologists, going out into relatively uncharted territory and characterizing all the different creatures (in this case, explosions) that we see. The next stage will be to look for patterns. What are the relative rates of each type of blast? Do they seem to occur in one type of galaxy but not another? Are these different categories actually different “species” or just different manifestations of the same phenomenon?
To answer these questions, we will need a much larger catalog. Beginning in a few years, the Vera C. Rubin Observatory, currently under construction in Chile, will use the largest digital camera ever constructed (three billion pixels) to spot 10 million potential transients every night—10 times more than the Zwicky Transient Facility does now. With more data, I would like to investigate which stars lose some of their mass right before they die and how often. I want to study how we can tell if there was a jet that got choked inside a star and how to recognize the kind of faint emission emitted during a star's death throes to predict where and when a star will explode. Ultimately I would like to probe questions about the factors that lead to these unusual deaths—perhaps it is something about a star's rate of spin or its history of interactions with other stars that causes it to die in such a spectacular and rare way.


scientificamerican--2020-11-17--Sharing the Human Side of Science

Sharing the Human Side of Science

One of the joys of being a science journalist is that it 's your job to talk with people who are doing mind-bending and world-changing research and to ask them goofy questions .
We ask them serious questions , too , of course , but we also encourage scientists to share the funny , tense , disappointing , surprising , human sides of their work .
The goal is not to make an expert seem ridiculous but to demonstrate that we 're all just people trying to figure out how to make sense of the world .
This month 's @ @ @ @ @ @ @ stars explode and die is an exciting look at a rapidly growing field that is studying phenomena at awesome time and size scales .
But it 's also a human drama about how Anna Y. Q. Ho had to sleep in a sleeping bag in a remote observing lab , wake up at 4 A.M. and race the dawn to get a reading on an exploding star 21 billion light-years away .
See Explosions at the Edge for more about her pursuit of strange star endings .
One reason we urge scientists to @ @ @ @ @ @ @ is that we hope it demystifies what they do .
Increasingly , we 're seeing the danger of people rejecting scientific findings and claiming that certain fields are all a hoax or a conspiracy .
It 's distressing but mostly harmless when people fall for fake documentaries claiming the earth is flat .
It 's life-threatening when they fall for misinformation about the COVID-19 pandemic .
Starting here , Filippo Menczer and Thomas Hills detail the ways conspiracy theories spread—including a disinformation campaign targeted at their own research group .
The delicate surgery @ @ @ @ @ @ @ the start of the process ; the recipient must then relearn how to use it .
The brain reroutes neural signals in many different areas , showing how nimble and adaptable it can be .
Scott H. Frey describes how his early interest in neuroscience was inspired by his mother 's multiple sclerosis and her loss of motor control .
He shares this research starting here .
The human body is actually a superorganism teeming with bacteria , fungi and hundreds of trillions of viruses .
The study of the human virome is @ @ @ @ @ @ @ the research is accelerating as scientists respond to SARS-CoV-2 , the virus that causes COVID .
These viruses are n't all bad .
Some are harmless , and some might help treat diseases or fight antibiotic resistance .
Turn here for David Pride 's fascinating discoveries about the viruses that live in and among us .
The idea of an international collaboration to build a fusion reactor that could produce clean energy came out of a Superpower Summit in Geneva in 1985 featuring Ronald Reagan and Mikhail Gorbachev .
Now the International Thermonuclear @ @ @ @ @ @ @ project feels like a series of marathons , the ITER director tells senior editor Clara Moskowitz .
Parts have been made all over the world , and beginning here , you can see the stunning facility coming together .
We 're delighted to have editor in chief emerita Mariette DiChristina back in our pages this issue .
Once again , she and the World Economic Forum teamed up with a steering committee of experts in a wide range of fields to highlight 10 emerging technologies .
It 's an inspiring package and a @ @ @ @ @ @ @ potential to save and improve our lives .

One of the joys of being a science journalist is that it's your job to talk with people who are doing mind-bending and world-changing research and to ask them goofy questions. We ask them serious questions, too, of course, but we also encourage scientists to share the funny, tense, disappointing, surprising, human sides of their work. The goal is not to make an expert seem ridiculous but to demonstrate that we're all just people trying to figure out how to make sense of the world.
This month's cover story on new discoveries about how stars explode and die is an exciting look at a rapidly growing field that is studying phenomena at awesome time and size scales. But it's also a human drama about how Anna Y. Q. Ho had to sleep in a sleeping bag in a remote observing lab, wake up at 4 A.M. and race the dawn to get a reading on an exploding star 21 billion light-years away. See Explosions at the Edge for more about her pursuit of strange star endings.
One reason we urge scientists to show us the personal side of research is that we hope it demystifies what they do. Increasingly, we're seeing the danger of people rejecting scientific findings and claiming that certain fields are all a hoax or a conspiracy. It's distressing but mostly harmless when people fall for fake documentaries claiming the earth is flat. It's life-threatening when they fall for misinformation about the COVID-19 pandemic. Starting here, Filippo Menczer and Thomas Hills detail the ways conspiracy theories spread—including a disinformation campaign targeted at their own research group.
The delicate surgery required to transplant a hand is just the start of the process; the recipient must then relearn how to use it. The brain reroutes neural signals in many different areas, showing how nimble and adaptable it can be. Scott H. Frey describes how his early interest in neuroscience was inspired by his mother's multiple sclerosis and her loss of motor control. He shares this research starting here.
The human body is actually a superorganism teeming with bacteria, fungi and hundreds of trillions of viruses. The study of the human virome is only about a decade old, and the research is accelerating as scientists respond to SARS-CoV-2, the virus that causes COVID. These viruses aren't all bad. Some are harmless, and some might help treat diseases or fight antibiotic resistance. Turn here for David Pride's fascinating discoveries about the viruses that live in and among us.
The idea of an international collaboration to build a fusion reactor that could produce clean energy came out of a Superpower Summit in Geneva in 1985 featuring Ronald Reagan and Mikhail Gorbachev. Now the International Thermonuclear Experimental Reactor is being built. The project feels like a series of marathons, the ITER director tells senior editor Clara Moskowitz. Parts have been made all over the world, and beginning here, you can see the stunning facility coming together.
We're delighted to have editor in chief emerita Mariette DiChristina back in our pages this issue. Once again, she and the World Economic Forum teamed up with a steering committee of experts in a wide range of fields to highlight 10 emerging technologies. It's an inspiring package and a reminder that research and innovation have the potential to save and improve our lives.


scientificamerican--2020-11-17--Four Years after Science Took a Hit, There's Hope

The 2020 U.S. election could not have come at a more tumultuous time—amid a global pandemic , widespread unemployment , demands for racial justice , all amplified by blatant disdain for science , evidence and human rights .
From shepherding and normalizing hatred and bigotry as domestic policy to slowing down meaningful progress on climate to eroding the pillars of our democracy , the impacts of the 2016 U.S. election will continue to ripple through the world for decades to come .
The last four years have shown us just how deeply white supremacy , patriarchy and oppressive societal norms @ @ @ @ @ @ @ society .
We know these problems didn ’ t start with the 2016 election and they won ’ t end come 2021 .
Today , we breathe a collective sigh of relief and celebrate a victory , but let us be clear : it should never have been this close .
Over 71 million Americans cast their ballots either in outright celebration of bigotry , hatred and lies—or with a callous indifference to their effects .
And lest we forget , a large portion of those ballots were cast by college-educated white women @ @ @ @ @ @ @ the rhetoric of the last four years .
Progress towards an equitable and just society doesn ’ t hinge on one election , and the 2020 U.S. election has proven that we have our work cut out for us .
As an organization , we of 500 Women Scientists are a long way from where we started in November 2016 , as we watched a vastly unqualified and unprepared man get the most powerful job in the world over a supremely qualified woman—an experience intimately familiar to most women in STEM .
That @ @ @ @ @ @ @ of us .
We have spent the last four years learning the ropes of grassroots activism , building a powerful network and calling women scientists to action , all with the goal of dismantling systemic barriers that hold women back in science and launching women into positions of power .
Since 2016 , we have grown to thousands of members and almost 500 pods ( local chapters ) worldwide .
We created a platform to abolish manels and make it easy to find a woman scientist with expertise in any discipline ( 13,000 @ @ @ @ @ @ @ thousands of Wikipedia pages to make sure women ’ s contributions to science are acknowledged , launched a fellowship for women of color , and grew a network of thousands of women scientists and supporters who are working on making science open , inclusive and accessible .
WHAT THE 2020 ELECTION MEANS TO US Watching President-elect Biden and Vice President-elect Harris ’ s remarks on November 7 was emotional ; the despair and trauma of the last four years released into tears of relief , pride and joy .
The Biden-Harris victory is @ @ @ @ @ @ @ is the first woman to occupy the post of vice president , the first Black person , the first Indian and Asian American , a child of immigrants , and the daughter of a scientist !
The new Biden-Harris administration has expressed a commitment to embedding science and equity into all aspects of their work , from launching new federal programs to combat the climate and water crises to specific mandates for investments in lower income and historically marginalized communities .
Our goals now more closely align with an administration that understands and @ @ @ @ @ @ @ and innovation in solving some of our most pressing challenges .
However , in the midst of relief and celebration , we would be remiss not to acknowledge that President-elect Biden and Vice President-elect Harris have also harmed marginalized and vulnerable communities , particularly Black and Indigenous communities , in the past .
Representation alone has never been , and will never be , enough ; we need tangible , material benefits from Biden , Harris and our government .
Therefore , we remain committed to holding our institutions accountable in support of @ @ @ @ @ @ @ marginalized communities in policy development , and a commitment to improving public health , environmental quality , and gender and racial equity .
We stand ready to help meet our collective goals and , when needed , hold the new administration accountable for following through .
We will continue to push for a progressive science-based agenda , including the Green New Deal , pay equity , accountability for harassment and discrimination , equitable access to health care , defunding the police , and investing in our communities to ensure everyone has the opportunity @ @ @ @ @ @ @ administration takes charge on January 20 , 2021 , our priorities for the new administration include ensuring : • Scientists , specifically women and BIPOC , in leadership positions in the administration .
• Immediate steps to rebuild and reinvigorate the federal STEM workforce that has been depleted during the Trump administration .
• Aggressive and ambitious steps to address climate change in an equitable and just way , beyond re-entering the Paris Agreement on climate .
• Comprehensive family-leave policies that support parents and ensure women ’ s careers are not negatively @ @ @ @ @ @ @ to level the playing field for parents and kids from all racial and economic backgrounds .
• Removal of barriers to voting , especially in historically disenfranchised communities .
• Support for international science collaboration to solve our most pressing global challenges including : COVID-19 , cancer , environmental degradation and climate change .
We will no longer have to spend as much of our energy on defense , resisting policies that turn back the clock on human rights and that endanger lives .
We are hopeful that we can now make rapid @ @ @ @ @ @ @ to prioritize equity , justice and science .
However , our work is far from done ; the U.S. and the world still face an uphill battle and we must not get complacent—rooting out racism and misogyny doesn ’ t happen overnight .
With the election of Biden and Harris , we recommit to our mission to make science and society more equitable and just .
We call on scientists and supporters across the world to join us .
We seek transformation of our societal and institutional norms , and it will take @ @ @ @ @ @ @ we joined a lifelong fight for justice and our vision remains the same : • That social justice , diversity , equity and inclusion are foundational in a thriving society and in science .
• That our movement must center the most marginalized among us .
• That gender and sex are not binary .
• That science needs Dreamers and no person is illegal .
• That borders are antithetical to our shared humanity and the spirit of progress .
• That those who harass and bully have no place in society @ @ @ @ @ @ @ can not address climate change without addressing racial and other forms of injustice .
• That evidence-based policy is central to tackling issues ranging from climate change to gun violence .
• That we must act on climate .
• That all people have a fundamental right to decide if , when and how to have children , and that parenthood should not limit women ’ s careers .
• That women should set the agenda and have the resources to succeed in science and society .
We are members of racial , @ @ @ @ @ @ @ We are immigrants .
We are people with disabilities .
We are LGBTQIA .
We are scientists .
We are women .
Join us .

The 2020 U.S. election could not have come at a more tumultuous time—amid a global pandemic, widespread unemployment, demands for racial justice, all amplified by blatant disdain for science, evidence and human rights. From shepherding and normalizing hatred and bigotry as domestic policy to slowing down meaningful progress on climate to eroding the pillars of our democracy, the impacts of the 2016 U.S. election will continue to ripple through the world for decades to come. The last four years have shown us just how deeply white supremacy, patriarchy and oppressive societal norms are embedded into every fiber of our society. We know these problems didn’t start with the 2016 election and they won’t end come 2021.
Today, we breathe a collective sigh of relief and celebrate a victory, but let us be clear: it should never have been this close. Over 71 million Americans cast their ballots either in outright celebration of bigotry, hatred and lies—or with a callous indifference to their effects. And lest we forget, a large portion of those ballots were cast by college-educated white women who have either excused or delighted in the rhetoric of the last four years. Progress towards an equitable and just society doesn’t hinge on one election, and the 2020 U.S. election has proven that we have our work cut out for us.
As an organization, we of 500 Women Scientists are a long way from where we started in November 2016, as we watched a vastly unqualified and unprepared man get the most powerful job in the world over a supremely qualified woman—an experience intimately familiar to most women in STEM. That election was a wake-up call for many of us. We have spent the last four years learning the ropes of grassroots activism, building a powerful network and calling women scientists to action, all with the goal of dismantling systemic barriers that hold women back in science and launching women into positions of power.
Since 2016, we have grown to thousands of members and almost 500 pods (local chapters) worldwide. We created a platform to abolish manels and make it easy to find a woman scientist with expertise in any discipline (13,000 experts and counting!), edited thousands of Wikipedia pages to make sure women’s contributions to science are acknowledged, launched a fellowship for women of color, and grew a network of thousands of women scientists and supporters who are working on making science open, inclusive and accessible.
WHAT THE 2020 ELECTION MEANS TO US
Watching President-elect Biden and Vice President-elect Harris’s remarks on November 7 was emotional; the despair and trauma of the last four years released into tears of relief, pride and joy. The Biden-Harris victory is historic for many reasons. Kamala Harris is the first woman to occupy the post of vice president, the first Black person, the first Indian and Asian American, a child of immigrants, and the daughter of a scientist! The new Biden-Harris administration has expressed a commitment to embedding science and equity into all aspects of their work, from launching new federal programs to combat the climate and water crises to specific mandates for investments in lower income and historically marginalized communities.
Our goals now more closely align with an administration that understands and supports the role of science, research and innovation in solving some of our most pressing challenges. However, in the midst of relief and celebration, we would be remiss not to acknowledge that President-elect Biden and Vice President-elect Harris have also harmed marginalized and vulnerable communities, particularly Black and Indigenous communities, in the past. Representation alone has never been, and will never be, enough; we need tangible, material benefits from Biden, Harris and our government. Therefore, we remain committed to holding our institutions accountable in support of broadening participation in STEM fields, centering marginalized communities in policy development, and a commitment to improving public health, environmental quality, and gender and racial equity.
We stand ready to help meet our collective goals and, when needed, hold the new administration accountable for following through. We will continue to push for a progressive science-based agenda, including the Green New Deal, pay equity, accountability for harassment and discrimination, equitable access to health care, defunding the police, and investing in our communities to ensure everyone has the opportunity to thrive.
“I will name a group of leading scientists and experts to lead the transition.” — @JoeBiden
SCIEEEEEEENNNNNCCCEEE — Dr. Ayana Elizabeth Johnson ���� (@ayanaeliza) November 8, 2020
As the new Biden-Harris administration takes charge on January 20, 2021, our priorities for the new administration include ensuring:
Scientists, specifically women and BIPOC, in leadership positions in the administration.
Immediate steps to rebuild and reinvigorate the federal STEM workforce that has been depleted during the Trump administration.
Aggressive and ambitious steps to address climate change in an equitable and just way, beyond re-entering the Paris Agreement on climate.
Comprehensive family-leave policies that support parents and ensure women’s careers are not negatively affected by having children.
Gender pay equity.
Steps to level the playing field for parents and kids from all racial and economic backgrounds.
Universal health care.
Removal of barriers to voting, especially in historically disenfranchised communities.
Support for international science collaboration to solve our most pressing global challenges including: COVID-19, cancer, environmental degradation and climate change.
LOOKING AHEAD
We will no longer have to spend as much of our energy on defense, resisting policies that turn back the clock on human rights and that endanger lives. We are hopeful that we can now make rapid progress with an administration that appears ready to prioritize equity, justice and science. However, our work is far from done; the U.S. and the world still face an uphill battle and we must not get complacent—rooting out racism and misogyny doesn’t happen overnight. With the election of Biden and Harris, we recommit to our mission to make science and society more equitable and just.
We call on scientists and supporters across the world to join us. We seek transformation of our societal and institutional norms, and it will take time and collective effort—four years ago, we joined a lifelong fight for justice and our vision remains the same:
We are members of racial, ethnic, and religious minority groups. We are immigrants. We are people with disabilities. We are LGBTQIA. We are scientists. We are women. Join us.


scientificamerican--2020-11-17--Amid Research into Vaping, a Whiff of a Familiar Tactic Emerges

The on-screen type appears in boldface and capital letters .
“ WHAT CIGARETTE DO YOU SMOKE , DOCTOR ?
” it asks .
The answer has that certain 1950s-radio-announcer quality : “ The brand named most was Camel , ” an off-screen voice says as a man in a doctor ’ s white coat lights up a cigarette and a nurse bustles about .
In a subsequent scene , he bends forward to light his patient ’ s smoke as they sit puffing in his office couch .
“ Yes folks , ” another voice says , “ the pleasing @ @ @ @ @ @ @ enjoyable to a doctor as it is to you or me .
This mid-20th-century television advertisement would seem shocking if it ran today .
But some public health experts say the strategy behind such ads—using science as a legitimizer—is very much alive , thanks to its reincarnation by the vaping industry .
“ When we look at what ’ s going on with e-cigarettes , with the development of the science , and look at that interrelationship to the marketing , you can ’ t understand it unless you go back and look @ @ @ @ @ @ @ says David Michaels , an epidemiologist at George Washington University .
“ That ’ s gotten us to where we are today.
” In this short documentary , Retro Report partners with Scientific American to dig deep into that history .
What becomes clear is that all the way back in the 1950s , big tobacco started to build a kind of denialist playbook about the health risks of smoking .
Part of that strategy involved co-opting research to serve the cause of uncertainty .
Now , as science races to understand the @ @ @ @ @ @ @ some say plays from that book appear to be resurfacing .
“ The concern is that exactly the same kinds of illegitimate debate that delayed tobacco regulation could very well be happening again , ” says Samir Soneji , an associate professor of health behavior at the University of North Carolina at Chapel Hill .
This could not come at a more troubled time for respiratory health in the U.S .
Flu season hasn ’ t gotten into full swing yet , but its peak is coming .
And COVID cases are rising @ @ @ @ @ @ @ that as temperatures drop and winter descends on the Northern Hemisphere , the battle over the science of e-cigarettes will just be heating up .

The on-screen type appears in boldface and capital letters. “WHAT CIGARETTE DO YOU SMOKE, DOCTOR?” it asks. The answer has that certain 1950s-radio-announcer quality: “The brand named most was Camel,” an off-screen voice says as a man in a doctor’s white coat lights up a cigarette and a nurse bustles about. In a subsequent scene, he bends forward to light his patient’s smoke as they sit puffing in his office couch. “Yes folks,” another voice says, “the pleasing mildness of a Camel is just as enjoyable to a doctor as it is to you or me.
This mid-20th-century television advertisement would seem shocking if it ran today. But some public health experts say the strategy behind such ads—using science as a legitimizer—is very much alive, thanks to its reincarnation by the vaping industry.
“When we look at what’s going on with e-cigarettes, with the development of the science, and look at that interrelationship to the marketing, you can’t understand it unless you go back and look at the history of tobacco,” says David Michaels, an epidemiologist at George Washington University. “That’s gotten us to where we are today.”
In this short documentary, Retro Report partners with Scientific American to dig deep into that history. What becomes clear is that all the way back in the 1950s, big tobacco started to build a kind of denialist playbook about the health risks of smoking. Part of that strategy involved co-opting research to serve the cause of uncertainty.
Now, as science races to understand the effects vaping has on the body, some say plays from that book appear to be resurfacing. “The concern is that exactly the same kinds of illegitimate debate that delayed tobacco regulation could very well be happening again,” says Samir Soneji, an associate professor of health behavior at the University of North Carolina at Chapel Hill.
This could not come at a more troubled time for respiratory health in the U.S. Flu season hasn’t gotten into full swing yet, but its peak is coming. And COVID cases are rising dramatically around the country.
It seems that as temperatures drop and winter descends on the Northern Hemisphere, the battle over the science of e-cigarettes will just be heating up.


scientificamerican--2020-11-17--Health Care AI Systems Are Biased

Health Care AI Systems Are Biased

Thanks to advances in artificial intelligence ( AI ) and machine learning , computer systems can now diagnose skin cancer like a dermatologist would , pick out a stroke on a CT scan like a radiologist , and even detect potential cancers on a colonoscopy like a gastroenterologist .
These new expert digital diagnosticians promise to put our caregivers on technology ’ s curve of bigger , better , faster , cheaper .
But what if they make medicine more biased too ?
At a time when the country is grappling with systemic bias in core societal institutions , we @ @ @ @ @ @ @ not exacerbate them .
We ’ ve long known that AI algorithms that were trained with data that do not represent the whole population often perform worse for underrepresented groups .
For example , algorithms trained with gender-imbalanced data do worse at reading chest x-rays for an underrepresented gender , and researchers are already concerned that skin-cancer detection algorithms , many of which are trained primarily on light-skinned individuals , do worse at detecting skin cancer affecting darker skin .
Given the consequences of an incorrect decision , high-stakes medical AI algorithms need @ @ @ @ @ @ @ from diverse populations .
Yet , this diverse training is not happening .
In a recent study published in JAMA ( the Journal of the American Medical Association ) , we reviewed over 70 publications that compared the diagnostic prowess of doctors against digital doppelgangers across several areas of clinical medicine .
Most of the data used to train those AI algorithms came from just three states : California , New York and Massachusetts .
Whether by race , gender or geography , medical AI has a data diversity problem : researchers can @ @ @ @ @ @ @ medical data sets—and that can lead to biased algorithms .
Why aren ’ t better data available ?
One of our patients , a veteran , once remarked in frustration after trying to obtain his prior medical records : , “ Doc , why is it that we can see a specific car in a moving convoy on the other side of the world , but we can ’ t see my CT scan from the hospital across the street ?
” Sharing data in medicine is hard enough for a single patient @ @ @ @ @ @ @ of cases needed to reliably train machine learning algorithms .
Whether in treating patients or building AI tools , data in medicine are locked in little silos everywhere .
Medical data sharing should be more commonplace .
But the sanctity of medical data and the strength of relevant privacy laws provide strong incentives to protect data , and severe consequences for any error in data sharing .
Data are sometimes sequestered for economic reasons ; one study found hospitals that shared data were more likely to lose patients to local competitors .
And @ @ @ @ @ @ @ exists , lack of interoperability between medical records systems remains a formidable technical barrier .
The backlash from big tech ’ s use of personal data over the past two decades has also cast a long shadow over medical data sharing .
The public has become deeply skeptical of any attempt to aggregate personal data , even for a worthy purpose .
This is not the first time that medical data have lacked diversity .
Since the early days of clinical trials , women and minority groups have been underrepresented as study participants @ @ @ @ @ @ @ fewer benefits and more side effects from approved medications .
Addressing this imbalance ultimately required a joint effort from the NIH , FDA , researchers and industry , and an act of Congress in 1993 ; it remains a work in progress to this day .
One of the companies racing toward a COVID vaccine recently announced a delay to recruit more diverse participants ; it ’ s that important .
It ’ s not just medicine ; AI has begun to play the role of trained expert in other high-stakes domains .
@ @ @ @ @ @ @ , redirect the focus of law enforcement , and suggest to bank officers whether to approve a loan application .
Before algorithms become an integral part of high-stakes decisions that can enhance or derail the lives of everyday citizens , we must understand and mitigate embedded biases .
Bias in AI is a complex issue ; simply providing diverse training data does not guarantee elimination of bias .
Several other concerns have been raised—for example , lack of diversity among developers and funders of AI tools ; framing of problems from the perspective @ @ @ @ @ @ @ about data ; and use of outputs of AI tools to perpetuate biases , either inadvertently or explicitly .
Because obtaining high-quality data is challenging , researchers are building algorithms that try to do more with less .
From these innovations may emerge new ways to decrease AI ’ s need for huge data sets .
But for now , ensuring diversity of data used to train algorithms is central to our ability to understand and mitigate biases of AI .
To ensure that the algorithms of tomorrow are not just powerful but @ @ @ @ @ @ @ technical , regulatory , economic and privacy infrastructure to deliver the large and diverse data required to train these algorithms .
We can no longer move forward blindly , building and deploying tools with whatever data happen to be available , dazzled by a veneer of digital gloss and promises of progress , and then lament the “ unforeseeable consequences.
” The consequences are foreseeable .
But they don ’ t have to be inevitable .

Thanks to advances in artificial intelligence (AI) and machine learning, computer systems can now diagnose skin cancer like a dermatologist would, pick out a stroke on a CT scan like a radiologist, and even detect potential cancers on a colonoscopy like a gastroenterologist. These new expert digital diagnosticians promise to put our caregivers on technology’s curve of bigger, better, faster, cheaper. But what if they make medicine more biased too?
At a time when the country is grappling with systemic bias in core societal institutions, we need technology to reduce health disparities, not exacerbate them. We’ve long known that AI algorithms that were trained with data that do not represent the whole population often perform worse for underrepresented groups. For example, algorithms trained with gender-imbalanced data do worse at reading chest x-rays for an underrepresented gender, and researchers are already concerned that skin-cancer detection algorithms, many of which are trained primarily on light-skinned individuals, do worse at detecting skin cancer affecting darker skin.
Given the consequences of an incorrect decision, high-stakes medical AI algorithms need to be trained with data sets drawn from diverse populations. Yet, this diverse training is not happening. In a recent study published in JAMA (the Journal of the American Medical Association), we reviewed over 70 publications that compared the diagnostic prowess of doctors against digital doppelgangers across several areas of clinical medicine. Most of the data used to train those AI algorithms came from just three states: California, New York and Massachusetts.
Whether by race, gender or geography, medical AI has a data diversity problem: researchers can’t easily obtain large, diverse medical data sets—and that can lead to biased algorithms.
Why aren’t better data available? One of our patients, a veteran, once remarked in frustration after trying to obtain his prior medical records:, “Doc, why is it that we can see a specific car in a moving convoy on the other side of the world, but we can’t see my CT scan from the hospital across the street?” Sharing data in medicine is hard enough for a single patient, never mind the hundreds or thousands of cases needed to reliably train machine learning algorithms. Whether in treating patients or building AI tools, data in medicine are locked in little silos everywhere.
Medical data sharing should be more commonplace. But the sanctity of medical data and the strength of relevant privacy laws provide strong incentives to protect data, and severe consequences for any error in data sharing. Data are sometimes sequestered for economic reasons; one study found hospitals that shared data were more likely to lose patients to local competitors. And even when the will to share data exists, lack of interoperability between medical records systems remains a formidable technical barrier. The backlash from big tech’s use of personal data over the past two decades has also cast a long shadow over medical data sharing. The public has become deeply skeptical of any attempt to aggregate personal data, even for a worthy purpose.
This is not the first time that medical data have lacked diversity. Since the early days of clinical trials, women and minority groups have been underrepresented as study participants; evidence mounted that these groups experienced fewer benefits and more side effects from approved medications. Addressing this imbalance ultimately required a joint effort from the NIH, FDA, researchers and industry, and an act of Congress in 1993; it remains a work in progress to this day. One of the companies racing toward a COVID vaccine recently announced a delay to recruit more diverse participants; it’s that important.
It’s not just medicine; AI has begun to play the role of trained expert in other high-stakes domains. AI tools help judges with sentencing decisions, redirect the focus of law enforcement, and suggest to bank officers whether to approve a loan application. Before algorithms become an integral part of high-stakes decisions that can enhance or derail the lives of everyday citizens, we must understand and mitigate embedded biases.
Bias in AI is a complex issue; simply providing diverse training data does not guarantee elimination of bias. Several other concerns have been raised—for example, lack of diversity among developers and funders of AI tools; framing of problems from the perspective of majority groups; implicitly biased assumptions about data; and use of outputs of AI tools to perpetuate biases, either inadvertently or explicitly. Because obtaining high-quality data is challenging, researchers are building algorithms that try to do more with less. From these innovations may emerge new ways to decrease AI’s need for huge data sets. But for now, ensuring diversity of data used to train algorithms is central to our ability to understand and mitigate biases of AI.
To ensure that the algorithms of tomorrow are not just powerful but also fair, we must build the technical, regulatory, economic and privacy infrastructure to deliver the large and diverse data required to train these algorithms. We can no longer move forward blindly, building and deploying tools with whatever data happen to be available, dazzled by a veneer of digital gloss and promises of progress, and then lament the “unforeseeable consequences.” The consequences are foreseeable. But they don’t have to be inevitable.