Carbon nanotubes may be the key to shrinking down transistors and squeezing more computer power into less space.
Historically, the number of transistors that can be crammed onto a computer chip has doubled every two years or so, a trend known as Moore’s law. But that rule seems to be nearing its limit: Today’s silicon transistors can’t get much smaller than they already are.
Carbon nanotubes may offer a sizable solution. In the June 30 Science, IBM researchers report a carbon-nanotube transistor with an overall width of 40 nanometers — the smallest ever. It’s about half the size of typical silicon transistors.
Researchers have created carbon-nanotube transistors with certain supersmall components before, but the whole package was still bulky, says study coauthor Qing Cao of IBM’s Thomas J. Watson Research Center in Yorktown Heights, N.Y. The new study confirms that, in terms of size, carbon-nanotube transistors can beat out silicon — and that’s no small feat.
Quantum particles can burrow through barriers that should be impenetrable — but they don’t do it instantaneously, a new experiment suggests.
The process, known as quantum tunneling, takes place extremely quickly, making it difficult to confirm whether it takes any time at all. Now, in a study of electrons escaping from their atoms, scientists have pinpointed how long the particles take to tunnel out: around 100 attoseconds, or 100 billionths of a billionth of a second, researchers report July 14 in Physical Review Letters. In quantum tunneling, a particle passes through a barrier despite not having enough energy to cross it. It’s as if someone rolled a ball up a hill but didn’t give it a hard enough push to reach the top, and yet somehow the ball tunneled through to the other side.
Although scientists knew that particles could tunnel, until now, “it was not really clear how that happens, or what, precisely, the particle does,” says physicist Christoph Keitel of the Max Planck Institute for Nuclear Physics in Heidelberg, Germany. Theoretical physicists have long debated between two possible options. In one model, the particle appears immediately on the other side of the barrier, with no initial momentum. In the other, the particle takes time to pass through, and it exits the tunnel with some momentum already built up.
Keitel and colleagues tested quantum tunneling by blasting argon and krypton gas with laser pulses. Normally, the pull of an atom’s positively charged nucleus keeps electrons tightly bound, creating an electromagnetic barrier to their escape. But, given a jolt from a laser, electrons can break free. That jolt weakens the electromagnetic barrier just enough that electrons can leave, but only by tunneling.
Although the scientists weren’t able to measure the tunneling time directly, they set up their experiment so that the angle at which the electrons flew away from the atom would reveal which of the two theories was correct. The laser’s light was circularly polarized — its electromagnetic waves rotated in time, changing the direction of the waves’ wiggles. If the electron escaped immediately, the laser would push it in one particular direction. But if tunneling took time, the laser’s direction would have rotated by the time the electron escaped, so the particle would be pushed in a different direction.
Comparing argon and krypton let the scientists cancel out experimental errors, leading to a more sensitive measurement that was able to distinguish between the two theories. The data matched predictions based on the theory that tunneling takes time. The conclusion jibes with some physicists’ expectations. “I’m pretty sure that the tunneling time cannot be instantaneous, because at the end, in physics, nothing can be instantaneous,” says physicist Ursula Keller of ETH Zurich. The result, she says, agrees with an earlier experiment carried out by her team.
Other scientists still think instantaneous tunneling is possible. Physicist Olga Smirnova of the Max Born Institute in Berlin notes that Keitel and colleagues’ conclusions contradict previous research. In theoretical calculations of tunneling in very simple systems, Smirnova and colleagues found no evidence of tunneling time. The complexity of the atoms studied in the new experiment may have led to the discrepancy, Smirnova says. Still, the experiment is “very accurate and done with great care.”
Although quantum tunneling may seem an esoteric concept, scientists have harnessed it for practical purposes. Scanning tunneling microscopes, for instance, use tunneling electrons to image individual atoms. For such an important fundamental process, Keller says, physicists really have to be certain they understand it. “I don’t think we can close the chapter on the discussion yet,” she says.
Humans inhabited rainforests on the Indonesian island of Sumatra between 73,000 and 63,000 years ago — shortly before a massive eruption of the island’s Mount Toba volcano covered South Asia in ash, researchers say.
Two teeth previously unearthed in Sumatra’s Lida Ajer cave and assigned to the human genus, Homo, display features typical of Homo sapiens, report geoscientist Kira Westaway of Macquarie University in Sydney and her colleagues. By dating Lida Ajer sediment and formations, the scientists came up with age estimates for the human teeth and associated fossils of various rainforest animals excavated in the late 1800s, including orangutans.
Ancient DNA studies had already suggested that humans from Africa reached Southeast Asian islands before 60,000 years ago.
Humans migrating out of Africa 100,000 years ago or more may have followed coastlines to Southeast Asia and eaten plentiful seafood along the way (SN: 5/19/12, p. 14). But the Sumatran evidence shows that some of the earliest people to depart from Africa figured out how to survive in rainforests, where detailed planning and appropriate tools are needed to gather seasonal plants and hunt scarce, fat-rich prey animals, Westaway and colleagues report online August 9 in Nature.
The sun can’t keep its hands to itself. A constant flow of charged particles streams away from the sun at hundreds of kilometers per second, battering vulnerable planets in its path.
This barrage is called the solar wind, and it has had a direct role in shaping life in the solar system. It’s thought to have stripped away much of Mars’ atmosphere (SN: 4/29/17, p. 20). Earth is protected from a similar fate only by its strong magnetic field, which guides the solar wind around the planet. But scientists don’t understand some key details of how the wind works. It originates in an area where the sun’s surface meets its atmosphere. Like winds on Earth, the solar wind is gusty — it travels at different speeds in different areas. It’s fastest in regions where the sun’s atmosphere, the corona, is dark. Winds whip past these coronal holes at 800 kilometers per second. But the wind whooshes at only around 300 kilometers per second over extended, pointy wisps called coronal streamers, which give the corona its crownlike appearance. No one knows why the wind is fickle. The Aug. 21 solar eclipse gives astronomers an ideal opportunity to catch the solar wind in action in the inner corona. One group, Nat Gopalswamy of NASA’s Goddard Spaceflight Center in Greenbelt, Md., and his colleagues, will test a new version of an instrument called a polarimeter, built to measure the temperature and speed of electrons leaving the sun. Measurements will start close to the sun’s surface and extend out to around 5.6 million kilometers, or eight times the radius of the sun.
“We should be able to detect the baby solar wind,” Gopalswamy says.
Set up at a high school in Madras, Ore., the polarimeter will separate out light that has been polarized, or had its electric field organized in one direction, from light whose electric field oscillates in all sorts of directions. Because electrons scatter polarized light more than non-polarized light, that observation will give the scientists a bead on what the electrons are doing, and by extension, what the solar wind is doing — how fast it flows, how hot it is and even where it comes from. Gopalswamy and colleagues will also take images in four different wavelengths of light, as another measurement of speed and temperature. Mapping the fast and slow solar winds close to the surface of the sun can give clues to how they are accelerated. The team tried out an earlier version of this instrument during an eclipse in 1999 in Turkey. But that instrument required the researchers to flip through three different polarization filters to capture all the information that they wanted. Cycling through the filters using a hand-turned wheel was slow and clunky — a problem when totality, the period when the moon completely blocks the sun, only lasts about two minutes. The team’s upgraded polarimeter is designed so it can simultaneously gather data through all three filters and in four wavelengths of light. “The main requirement is that we have to take these images as close in time as possible, so the corona doesn’t change from one period to the next,” Gopalswamy says. One exposure will take 2 to 4 seconds, plus a 6-second wait between filters. That will give the team about 36 images total.
Gopalswamy and his team first tested this instrument in Indonesia for the March 2016 solar eclipse. “That experiment failed because of noncooperation from nature,” Gopalswamy says. “Ten minutes before the eclipse, the rain started pouring down.”
This year, they chose Madras because, historically, it’s the least cloud-covered place on the eclipse path. But they’re still crossing their fingers for clear skies.
A genetic “crystal ball” can predict whether certain people will respond effectively to the flu vaccine.
Nine genes are associated with a strong immune response to the flu vaccine in those aged 35 and under, a new study finds. If these genes were highly active before vaccination, an individual would generate a high level of antibodies after vaccination, no matter the flu strain in the vaccine, researchers report online August 25 in Science Immunology. This response can help a person avoid getting the flu.
The research team also tried to find a predictive set of genes in people aged 60 and above — a group that includes those more likely to develop serious flu-related complications, such as pneumonia — but failed. Even so, the study is “a step in the right direction,” says Elias Haddad, an immunologist at Drexel University College of Medicine in Philadelphia, who did not participate in the research. “It could have implications in terms of identifying responders versus nonresponders by doing a simple test before a vaccination.”
The U.S. Centers for Disease Control and Prevention estimates that vaccination prevented 5.1 million flu illnesses in the 2015‒2016 season. Getting a flu shot is the best way to stay healthy, but “the problem is, we don’t know what makes a successful vaccination,” says Purvesh Khatri, a computational immunologist at Stanford University School of Medicine. “The immune system is very personal.” Khatri and colleagues wondered if there was a certain immune state one needed to be in to respond effectively to the flu vaccine. So the researchers looked for a common genetic signal in blood samples from 175 people with different genetic backgrounds, from different locations in the United States, and who received the flu vaccine in different seasons. After identifying the set of predictive genes, the team used another collection of 82 samples to confirm that the crystal ball accurately predicted a strong flu response. Using such a variety of samples makes it more likely that the crystal ball will work for many different people in the real world, Khatri says.
The nine genes make proteins that have various jobs, including directing the movement of other proteins and providing structure to cells. Previous research on these genes has tied some of them to the immune system, but not others. Khatri expects the study will spur investigations into how the genes promote a successful vaccine response. And figuring out how to boost the genes may help those who don’t respond strongly to flu vaccine, he says.
As for finding a genetic crystal ball for older adults, “there’s still hope that we’ll be able to,” says team member Raphael Gottardo, a computational biologist at the Fred Hutchinson Cancer Research Center in Seattle. Older people are even more diverse in how they respond to the flu vaccine than younger people, he says, so it may take a larger group of samples to find a common genetic thread.
More research is also needed to learn whether the identified genes will predict an effective response for all vaccines, or just the flu, Haddad says. “There is a long way to go here.”
You’ve probably encountered at least one machine-learning algorithm today. These clever computer codes sort search engine results, weed spam e-mails from inboxes and optimize navigation routes in real time. People entrust these programs with increasingly complex — and sometimes life-changing — decisions, such as diagnosing diseases and predicting criminal activity.
Machine-learning algorithms can make these sophisticated calls because they don’t simply follow a series of programmed instructions the way traditional algorithms do. Instead, these souped-up programs study past examples of how to complete a task, discern patterns from the examples and use that information to make decisions on a case-by-case basis. Unfortunately, letting machines with this artificial intelligence, or AI, figure things out for themselves doesn’t just make them good critical “thinkers,” it also gives them a chance to pick up biases.
Investigations in recent years have uncovered several ways algorithms exhibit discrimination. In 2015, researchers reported that Google’s ad service preferentially displayed postings related to high-paying jobs to men. A 2016 ProPublica investigation found that COMPAS, a tool used by many courtrooms to predict whether a criminal will break the law again, wrongly predicted that black defendants would reoffend nearly twice as often as it made that wrong prediction for whites. The Human Rights Data Analysis Group also showed that the crime prediction tool PredPol could lead police to unfairly target low-income, minority neighborhoods (SN Online: 3/8/17). Clearly, algorithms’ seemingly humanlike intelligence can come with humanlike prejudices.
“This is a very common issue with machine learning,” says computer scientist Moritz Hardt of the University of California, Berkeley. Even if a programmer designs an algorithm without prejudicial intent, “you’re very likely to end up in a situation that will have fairness issues,” Hardt says. “This is more the default than the exception.” Developers may not even realize a program has taught itself certain prejudices. This problem gets down to what is known as a black box issue: How exactly is an algorithm reaching its conclusions? Since no one tells a machine-learning algorithm exactly how to do its job, it’s often unclear — even to the algorithm’s creator — how or why it ends up using data the way it does to make decisions. Several socially conscious computer and data scientists have recently started wrestling with the problem of machine bias. Some have come up with ways to add fairness requirements into machine-learning systems. Others have found ways to illuminate the sources of algorithms’ biased behavior. But the very nature of machine-learning algorithms as self-taught systems means there’s no easy fix to make them play fair.
Learning by example In most cases, machine learning is a game of algorithm see, algorithm do. The programmer assigns an algorithm a goal — say, predicting whether people will default on loans. But the machine gets no explicit instructions on how to achieve that goal. Instead, the programmer gives the algorithm a dataset to learn from, such as a cache of past loan applications labeled with whether the applicant defaulted.
The algorithm then tests various ways to combine loan application attributes to predict who will default. The program works through all of the applications in the dataset, fine-tuning its decision-making procedure along the way. Once fully trained, the algorithm should ideally be able to take any new loan application and accurately determine whether that person will default.
The trouble arises when training data are riddled with biases that an algorithm may incorporate into its decisions. For instance, if a human resources department’s hiring algorithm is trained on historical employment data from a time when men were favored over women, it may recommend hiring men more often than women. Or, if there were fewer female applicants in the past, then the algorithm has fewer examples of those applications to learn from, and it may not be as accurate at judging women’s applications. At first glance, the answer seems obvious: Remove any sensitive features, such as race or sex, from the training data. The problem is, there are many ostensibly nonsensitive aspects of a dataset that could play proxy for some sensitive feature. Zip code may be strongly related to race, college major to sex, health to socioeconomic status.
And it may be impossible to tell how different pieces of data — sensitive or otherwise — factor into an algorithm’s verdicts. Many machine-learning algorithms develop deliberative processes that involve so many thousands of complex steps that they’re impossible for people to review.
Creators of machine-learning systems “used to be able to look at the source code of our programs and understand how they work, but that era is long gone,” says Simon DeDeo, a cognitive scientist at Carnegie Mellon University in Pittsburgh. In many cases, neither an algorithm’s authors nor its users care how it works, as long as it works, he adds. “It’s like, ‘I don’t care how you made the food; it tastes good.’ ”
But in other cases, the inner workings of an algorithm could make the difference between someone getting parole, an executive position, a mortgage or even a scholarship. So computer and data scientists are coming up with creative ways to work around the black box status of machine-learning algorithms.
Setting algorithms straight Some researchers have suggested that training data could be edited before given to machine-learning programs so that the data are less likely to imbue algorithms with bias. In 2015, one group proposed testing data for potential bias by building a computer program that uses people’s nonsensitive features to predict their sensitive ones, like race or sex. If the program could do this with reasonable accuracy, the dataset’s sensitive and nonsensitive attributes were tightly connected, the researchers concluded. That tight connection was liable to train discriminatory machine-learning algorithms.
To fix bias-prone datasets, the scientists proposed altering the values of whatever nonsensitive elements their computer program had used to predict sensitive features. For instance, if their program had relied heavily on zip code to predict race, the researchers could assign fake values to more and more digits of people’s zip codes until they were no longer a useful predictor for race. The data could be used to train an algorithm clear of that bias — though there might be a tradeoff with accuracy.
On the flip side, other research groups have proposed de-biasing the outputs of already-trained machine-learning algorithms. In 2016 at the Conference on Neural Information Processing Systems in Barcelona, Hardt and colleagues recommended comparing a machine-learning algorithm’s past predictions with real-world outcomes to see if the algorithm was making mistakes equally for different demographics. This was meant to prevent situations like the one created by COMPAS, which made wrong predictions about black and white defendants at different rates. Among defendants who didn’t go on to commit more crimes, blacks were flagged by COMPAS as future criminals more often than whites. Among those who did break the law again, whites were more often mislabeled as low-risk for future criminal activity.
For a machine-learning algorithm that exhibits this kind of discrimination, Hardt’s team suggested switching some of the program’s past decisions until each demographic gets erroneous outputs at the same rate. Then, that amount of output muddling, a sort of correction, could be applied to future verdicts to ensure continued even-handedness. One limitation, Hardt points out, is that it may take a while to collect a sufficient stockpile of actual outcomes to compare with the algorithm’s predictions. A third camp of researchers has written fairness guidelines into the machine-learning algorithms themselves. The idea is that when people let an algorithm loose on a training dataset, they don’t just give the software the goal of making accurate decisions. The programmers also tell the algorithm that its outputs must meet some certain standard of fairness, so it should design its decision-making procedure accordingly.
In April, computer scientist Bilal Zafar of the Max Planck Institute for Software Systems in Kaiserslautern, Germany, and colleagues proposed that developers add instructions to machine-learning algorithms to ensure they dole out errors to different demographics at equal rates — the same type of requirement Hardt’s team set. This technique, presented in Perth, Australia, at the International World Wide Web Conference, requires that the training data have information about whether the examples in the dataset were actually good or bad decisions. For something like stop-and-frisk data, where it’s known whether a frisked person actually had a weapon, the approach works. Developers could add code to their program that tells it to account for past wrongful stops.
Zafar and colleagues tested their technique by designing a crime-predicting machine-learning algorithm with specific nondiscrimination instructions. The researchers trained their algorithm on a dataset containing criminal profiles and whether those people actually reoffended. By forcing their algorithm to be a more equal opportunity error-maker, the researchers were able to reduce the difference between how often blacks and whites who didn’t recommit were wrongly classified as being likely to do so: The fraction of people that COMPAS mislabeled as future criminals was about 45 percent for blacks and 23 percent for whites. In the researchers’ new algorithm, misclassification of blacks dropped to 26 percent and held at 23 percent for whites.
These are just a few recent additions to a small, but expanding, toolbox of techniques for forcing fairness on machine-learning systems. But how these algorithmic fix-its stack up against one another is an open question since many of them use different standards of fairness. Some require algorithms to give members of different populations certain results at about the same rate. Others tell an algorithm to accurately classify or misclassify different groups at the same rate. Still others work with definitions of individual fairness that require algorithms to treat people who are similar barring one sensitive feature similarly. To complicate matters, recent research has shown that, in some cases, meeting more than one fairness criterion at once can be impossible.
“We have to think about forms of unfairness that we may want to eliminate, rather than hoping for a system that is absolutely fair in every possible dimension,” says Anupam Datta, a computer scientist at Carnegie Mellon.
Still, those who don’t want to commit to one standard of fairness can perform de-biasing procedures after the fact to see whether outputs change, Hardt says, which could be a warning sign of algorithmic bias.
Show your work But even if someone discovered that an algorithm fell short of some fairness standard, that wouldn’t necessarily mean the program needed to be changed, Datta says. He imagines a scenario in which a credit-classifying algorithm might give favorable results to some races more than others. If the algorithm based its decisions on race or some race-related variable like zip code that shouldn’t affect credit scoring, that would be a problem. But what if the algorithm’s scores relied heavily on debt-to-income ratio, which may also be associated with race? “We may want to allow that,” Datta says, since debt-to-income ratio is a feature directly relevant to credit.
Of course, users can’t easily judge an algorithm’s fairness on these finer points when its reasoning is a total black box. So computer scientists have to find indirect ways to discern what machine-learning systems are up to.
One technique for interrogating algorithms, proposed by Datta and colleagues in 2016 in San Jose, Calif., at the IEEE Symposium on Security and Privacy, involves altering the inputs of an algorithm and observing how that affects the outputs. “Let’s say I’m interested in understanding the influence of my age on this decision, or my gender on this decision,” Datta says. “Then I might be interested in asking, ‘What if I had a clone that was identical to me, but the gender was flipped? Would the outcome be different or not?’ ” In this way, the researchers could determine how much individual features or groups of features affect an algorithm’s judgments. Users performing this kind of auditing could decide for themselves whether the algorithm’s use of data was cause for concern. Of course, if the code’s behavior is deemed unacceptable, there’s still the question of what to do about it. There’s no “So your algorithm is biased, now what?” instruction manual. The effort to curb machine bias is still in its nascent stages. “I’m not aware of any system either identifying or resolving discrimination that’s actively deployed in any application,” says Nathan Srebro, a computer scientist at the University of Chicago. “Right now, it’s mostly trying to figure things out.”
Computer scientist Suresh Venkatasubramanian agrees. “Every research area has to go through this exploration phase,” he says, “where we may have only very preliminary and half-baked answers, but the questions are interesting.”
Still, Venkatasubramanian, of the University of Utah in Salt Lake City, is optimistic about the future of this important corner of computer and data science. “For a couple of years now … the cadence of the debate has gone something like this: ‘Algorithms are awesome, we should use them everywhere. Oh no, algorithms are not awesome, here are their problems,’ ” he says. But now, at least, people have started proposing solutions, and weighing the various benefits and limitations of those ideas. So, he says, “we’re not freaking out as much.”
In the scientific version of her obituary, Dolly the Sheep was reported to have suffered from severe arthritis in her knees. The finding and Dolly’s early death from an infection led many researchers to think that cloning might cause animals to age prematurely.
But new X-rays of Dolly’s skeleton and those of other cloned sheep and Dolly’s naturally conceived daughter Bonnie indicate that the world’s first cloned mammal had the joints of normal sheep of her age. Just like other sheep, Dolly had a little bit of arthritis in her hips, knees and elbows, developmental biologist Kevin Sinclair of the University of Nottingham in England and colleagues report November 23 in Scientific Reports. The researchers decided to reexamine Dolly’s remains after finding that her cloned “sisters” have aged normally and didn’t have massive arthritis (SN: 8/20/16, p. 6). No formal records of Dolly’s original arthritis exams were kept, so Sinclair and colleagues got Dolly’s and Bonnie’s skeletons and those of two other cloned sheep, Megan and Morag, from the National Museums Scotland in Edinburgh. Megan and Bonnie were both older than Dolly at the time of their deaths and had more bone damage than Dolly did. Morag died younger and had less damage. Dolly’s arthritis levels were similar to those of naturally conceived sheep her age, indicating that cloning wasn’t to blame. “If there were a direct link with cloning and osteoarthritis, we would have expected to find a lot worse, and it would be more extensive and have a different distribution than what we’re finding in ordinary sheep,” says study coauthor Sandra Corr, a veterinary orthopedic specialist at the University of Glasgow in Scotland. Dolly’s slightly creaky joints may have stemmed from giving birth to six lambs, including Bonnie. Pregnancy is a risk factor for arthritis in sheep.
For decades, the name “virus” meant small and simple. Not anymore. Meet the giants.
Today, scientists are finding ever bigger viruses that pack impressive amounts of genetic material. The era of the giant virus began in 2003 with the discovery of the first Mimivirus (SN: 5/23/09, p. 9). The viral titan is about 750 nanometers across with a genetic pantry boasting around 1.2 million base pairs of DNA, the information-toting bits often represented with A, T, C and G. Influenza A, for example, is roughly 100 nanometers across with only about 13,500 base pairs of genetic material.
In 2009, another giant virus called Marseillevirus was identified. It is different enough from mimiviruses to earn its own family. Since 2013, mega-sized viruses falling into another eight potential virus families have been found, showcasing a long-unexplored viral diversity, researchers reported last year in Annual Review of Virology and in January in Frontiers in Microbiology.
Giant viruses mostly come in two shapes: polyhedral capsules and egglike ovals. But one, Mollivirus, skews more spherical. Pacmanvirus was named for the broken appearance of its outer shell. Both represent potential families. Two newly discovered members of the mimivirus family, both called tupanviruses and both with tails, have the most complete set of genes related to assembling proteins yet seen in viruses (SN Online: 2/27/18). Once unheard of, giant viruses may be common in water and soils worldwide. Only time — and more discoveries — will tell. Virus length and genome size for a representative from each of two recognized giant virus families (mimivirus and marseillevirus families) and eight potential families are shown. Circles are scaled to genome size and shaded by size range, with influenza A and E. coli bacterium included for comparison. Years indicate when the first viruses were described.
Graphic: C. Chang; Sources: P. Colson, B. La Scola and D. Raoult/Annual Review of Virology 2017; J. Andreani et al/Frontiers in Microbiology 2018
A strand of spaghetti snaps easily, but an exotic substance known as nuclear pasta is an entirely different story.
Predicted to exist in ultradense dead stars called neutron stars, nuclear pasta may be the strongest material in the universe. Breaking the stuff requires 10 billion times the force needed to crack steel, for example, researchers report in a study accepted in Physical Review Letters.
“This is a crazy-big figure, but the material is also very, very dense, so that helps make it stronger,” says study coauthor and physicist Charles Horowitz of Indiana University Bloomington. Neutron stars form when a dying star explodes, leaving behind a neutron-rich remnant that is squished to extreme pressures by powerful gravitational forces, resulting in materials with bizarre properties (SN: 12/23/17, p. 7).
About a kilometer below the surface of a neutron star, atomic nuclei are squeezed together so close that they merge into clumps of nuclear matter, a dense mixture of neutrons and protons. These as-yet theoretical clumps are thought to be shaped like blobs, tubes or sheets, and are named after their noodle look-alikes, including gnocchi, spaghetti and lasagna. Even deeper in the neutron star, the nuclear matter fully takes over. The burnt-out star’s entire core is nuclear matter, like one giant atomic nucleus.
Nuclear pasta is incredibly dense, about 100 trillion times the density of water. It’s impossible to study such an extreme material in the laboratory, says physicist Constança Providência of the University of Coimbra in Portugal who was not involved with the research. Instead, the researchers used computer simulations to stretch nuclear lasagna sheets and explore how the material responded. Immense pressures were required to deform the material, and the pressure required to snap the pasta was greater than for any other known material.
Earlier simulations had revealed that the outer crust of a neutron star was likewise vastly stronger than steel. But the inner crust, where nuclear pasta lurks, was unexplored territory. “Now, what [the researchers] see is that the inner crust is even stronger,” Providência says.
Physicists are still aiming to find real-world evidence of nuclear pasta. The new results may provide a glimmer of hope. Neutron stars tend to spin very rapidly, and, as a result, might emit ripples in spacetime called gravitational waves, which scientists could detect at facilities like the Advanced Laser Interferometer Gravitational-wave Observatory, or LIGO. But the spacetime ripples will occur only if a neutron star’s crust is lumpy — meaning that it has “mountains,” or mounds of dense material either on the surface or within the crust.
“The tricky part is, you need a big mountain,” says physicist Edward Brown of Michigan State University in East Lansing. A stiffer, stronger crust would support larger mountains, which could produce more powerful gravitational waves. But “large” is a relative term. Due to the intense gravity of neutron stars, their mountains would be a far cry from Mount Everest, rising centimeters tall, not kilometers. Previously, scientists didn’t know how large a mountain nuclear pasta could support.
“That’s where these simulations come in,” Brown says. The results suggest that nuclear pasta could support mountains tens of centimeters tall — big enough that LIGO could spot neutron stars’ gravitational waves. If LIGO caught such signals, scientists could estimate the mountains’ size, and confirm that neutron stars have superstrong materials in their crusts.
When you’re stressed and anxious, you might feel your heart race. Is your heart racing because you’re afraid? Or does your speeding heart itself contribute to your anxiety? Both could be true, a new study in mice suggests.
By artificially increasing the heart rates of mice, scientists were able to increase anxiety-like behaviors — ones that the team then calmed by turning off a particular part of the brain. The study, published in the March 9 Nature, shows that in high-risk contexts, a racing heart could go to your head and increase anxiety. The findings could offer a new angle for studying and, potentially, treating anxiety disorders. The idea that body sensations might contribute to emotions in the brain goes back at least to one of the founders of psychology, William James, says Karl Deisseroth, a neuroscientist at Stanford University. In James’ 1890 book The Principles of Psychology, he put forward the idea that emotion follows what the body experiences. “We feel sorry because we cry, angry because we strike, afraid because we tremble,” James wrote.
The brain certainly can sense internal body signals, a phenomenon called interoception. But whether those sensations — like a racing heart — can contribute to emotion is difficult to prove, says Anna Beyeler, a neuroscientist at the French National Institute of Health and Medical Research in Bordeaux. She studies brain circuitry related to emotion and wrote a commentary on the new study but was not involved in the research. “I’m sure a lot of people have thought of doing these experiments, but no one really had the tools,” she says.
Deisseroth has spent his career developing those tools. He is one of the scientists who developed optogenetics — a technique that uses viruses to modify the genes of specific cells to respond to bursts of light (SN: 6/18/21; SN: 1/15/10). Scientists can use the flip of a light switch to activate or suppress the activity of those cells. In the new study, Deisseroth and his colleagues used a light attached to a tiny vest over a mouse’s genetically engineered heart to change the animal’s heart rate. When the light was off, a mouse’s heart pumped at about 600 beats per minute. But when the team turned on a light that flashed at 900 beats per minutes, the mouse’s heartbeat followed suit. “It’s a nice reasonable acceleration, [one a mouse] would encounter in a time of stress or fear,” Deisseroth explains.
When the mice felt their hearts racing, they showed anxiety-like behavior. In risky scenarios — like open areas where a little mouse might be someone’s lunch — the rodents slunk along the walls and lurked in darker corners. When pressing a lever for water that could sometimes be coupled with a mild shock, mice with normal heart rates still pressed without hesitation. But mice with racing hearts decided they’d rather go thirsty.
“Everybody was expecting that, but it’s the first time that it has been clearly demonstrated,” Beyeler says. The researchers also scanned the animals’ brains to find areas that might be processing the increased heart rate. One of the biggest signals, Deisseroth says, came from the posterior insula (SN: 4/25/16). “The insula was interesting because it’s highly connected with interoceptive circuitry,” he explains. “When we saw that signal, [our] interest was definitely piqued.”
Using more optogenetics, the team reduced activity in the posterior insula, which decreased the mice’s anxiety-like behaviors. The animals’ hearts still raced, but they behaved more normally, spending some time in open areas of mazes and pressing levers for water without fear. A lot of people are very excited about the work, says Wen Chen, the branch chief of basic medicine research for complementary and integrative health at the National Center for Complementary and Integrative Health in Bethesda, Md. “No matter what kind of meetings I go into, in the last two days, everybody brought up this paper,” says Chen, who wasn’t involved in the research.
The next step, Deisseroth says, is to look at other parts of the body that might affect anxiety. “We can feel it in our gut sometimes, or we can feel it in our neck or shoulders,” he says. Using optogenetics to tense a mouse’s muscles, or give them tummy butterflies, might reveal other pathways that produce fearful or anxiety-like behaviors.
Understanding the link between heart and head could eventually factor into how doctors treat panic and anxiety, Beyeler says. But the path between the lab and the clinic, she notes, is much more convoluted than that of the heart to the head.
