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.”
Discoveries about the molecular ups and downs of fruit flies’ daily lives have won Jeffrey C. Hall, Michael Rosbash and Michael W. Young the Nobel Prize in physiology or medicine.
These three Americans were honored October 2 by the Nobel Assembly at the Karolinska Institute in Stockholm for their work in discovering important gears in the circadian clocks of animals. The trio will equally split the 9 million Swedish kronor prize — each taking home the equivalent of $367,000. The researchers did their work in fruit flies. But “an awful lot of what was subsequently found out in the fruit flies turns out also to be true and of huge relevance to humans,” says John O’Neill, a circadian cell biologist at the MRC Laboratory of Molecular Biology in Cambridge, England. Mammals, humans included, have circadian clocks that work with the same logic and many of the same gears found in fruit flies, say Jennifer Loros and Jay Dunlap, geneticists at the Geisel School of Medicine at Dartmouth College. Circadian clocks are networks of genes and proteins that govern daily rhythms and cycles such as sleep, the release of hormones, the rise and fall of body temperature and blood pressure, as well as other body processes. Circadian rhythms help organisms, including humans, anticipate and adapt to cyclic changes of light, dark and temperature caused by Earth’s rotation. When circadian rhythms are thrown out of whack, jet lag results. Shift workers and people with chronic sleep deprivation experience long-term jet lag that has been linked to serious health consequences including cancer, diabetes, heart disease, obesity and depression. Before the laureates did their work, other scientists had established that plants and animals have circadian rhythms. In 1971, Seymour Benzer and Ronald Konopka (both now deceased and ineligible for the Nobel Prize) found that fruit flies with mutations in a single gene called period had disrupted circadian rhythms, which caused the flies to move around at different times of day than normal.
“But then people got stuck,” says chronobiologist Erik Herzog of Washington University in St. Louis. “We couldn’t figure out what that gene was or how that gene worked.” At Brandeis University in Waltham, Mass., Hall, a geneticist, teamed up with molecular biologist Rosbash to identify the period gene at the molecular level in 1984. Young of the Rockefeller University in New York City simultaneously deciphered the gene’s DNA makeup. “In the beginning, we didn’t even know the other group was working on it, until we all showed up at a conference together and discovered we were working on the same thing,” says Young. “We said, ‘Well, let’s forge ahead. Best of luck.’” It wasn’t immediately apparent how the gene regulated fruit fly activity. In 1990, Hall and Rosbash determined that levels of period’s messenger RNA — an intermediate step between DNA and protein — fell as levels of period’s protein, called PER, rose. That finding indicated that PER protein shuts down its own gene’s activity.
A clock, however, isn’t composed of just one gear, Young says. He discovered in 1994 another gene called timeless. That gene’s protein, called TIM, works with PER to drive the clock. Young also discovered other circadian clockworks, including doubletime and its protein DBT, which set the clock’s pace. Rosbash and Hall discovered yet more gears and the two groups competed and collaborated with each other. “This whole thing would not have turned out nearly as nicely if we’d been the only ones working on it, or they had,” Young says.
Since those discoveries, researchers have found that nearly every cell in the body contains a circadian clock, and almost every gene follows circadian rhythms in at least one type of cell. Some genes may have rhythm in the liver, but not the skin cells, for instance. “It’s normal to oscillate,” Herzog says. Trouble arises when those clocks get out of sync with each other, says neuroscientist Joseph Takahashi at the University of Texas Southwestern Medical Center in Dallas. For instance, genes such as cMyc and p53 help control cell growth and division. Scientists now know they are governed, in part, by the circadian clock. Disrupting the circadian clock’s smooth running could lead to cancer-promoting mistakes.
But while bad timing might lead to diseases, there’s also a potential upside. Scientists have also realized that giving drugs at the right time might make them more effective, Herzog says.
Rosbash joked during a news conference that his own circadian rhythms had been disrupted by the Nobel committee’s early morning phone call. When he heard the news that he’d won the prize, “I was shocked, breathless really. Literally. My wife said, ‘Start breathing,’” he told an interviewer from the Nobel committee.
Young’s sleep was untroubled by the call from Sweden. His home phone is the kitchen, and he didn’t hear it ring, so the committee was unable to reach him before making the announcement. “The rest of the world knew, but I didn’t,” he says. Rockefeller University president Richard Lifton called him on his cell phone and shared the news, throwing Young’s timing off, too. “This really did take me surprise,” Young said during a news conference. “I had trouble even putting my shoes on this morning. I’d go pick up the shoes and realize I needed the socks. And then ‘I should put my pants on first.’”
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.
NEW ORLEANS — Saturn’s mighty rings cast a long shadow on the gas giant — and not just in visible light.
Final observations from the Cassini spacecraft show that the rings block the sunlight that charges particles in Saturn’s atmosphere. The rings may even be raining charged water particles onto the planet, researchers report online December 11 in Science and at the fall meeting of the American Geophysical Union.
In the months before plunging into Saturn’s atmosphere in September (SN Online: 9/15/17), the Cassini spacecraft made a series of dives between the gas giant and its iconic rings (SN Online: 4/21/17). Some of those orbits took the spacecraft directly into Saturn’s ionosphere, a layer of charged particles in the upper atmosphere. The charged particles are mostly the result of ultraviolet radiation from the sun separating electrons from atoms. Jan-Erik Wahlund of the Swedish Institute of Space Physics in Uppsala and Ann Persoon of the University of Iowa in Iowa City and their colleagues examined data from 11 of Cassini’s dives through the rings. The researchers found a lower density of charged particles in the regions associated with the ring shadows than elsewhere in the ionosphere. That finding suggests the rings block ultraviolet light, the team concludes.
Blocked sunlight can’t explain everything surprising about the ionosphere, though. The ionosphere was more variable than the researchers expected, with its electron density sometimes changing by more than an order of magnitude from one Cassini orbit to the next.
Charged water particles chipped off of the rings could periodically splash into the ionosphere and sop up the free electrons, the researchers suggest. This idea, known as “ring rain,” was proposed in the 1980s (SN: 8/9/86, p. 84) but has still never been observed directly.
OXON HILL, Md. — Astronomers may soon know for sure if Europa is spouting off. After finding signs that Jupiter’s icy moon emits repeating plumes of water near its southern pole, astronomers using the Hubble Space Telescope hope to detect more evidence of the geysers.
“The statistical significance is starting to look pretty good,” astronomer William Sparks of the Space Telescope Science Institute in Baltimore says. He presented preliminary results on the hunt for the plumes at a meeting of the American Astronomical Society on January 9. Sparks’ team started observing Europa on January 5, hoping to catch it passing in front of Jupiter 30 times before September. Hubble can detect active plumes silhouetted against background light from Jupiter. If the plume repeats as often as it seems to, “it’s essentially a certainty we’ll see it again if it’s real,” Sparks said.
Europa probably hosts a vast saltwater ocean buried under a thick icy shell. In 2012, astronomers using Hubble spotted high concentrations of hydrogen and oxygen over Europa’s southern hemisphere — signs that Europa was spitting water into space (SN: 1/25/14, p. 6). Later efforts to find those signs using the same technique yielded nothing.
But using Jupiter as a backdrop for the plumes, Sparks and his colleagues spotted several eruptions (SN Online: 9/26/16) — once in March 2014, again in February 2016 and possibly also in March 2017, Sparks said.
Story continues below images Maps of Europa’s heat and ionosphere made by the Galileo spacecraft in the 1990s show the plumes’ location was warmer than the surrounding ice. It also had an unusually high concentration of charged particles, perhaps the result of water splitting into hydrogen and oxygen. Both observations support the idea that some ocean is escaping at that spot.
“If it’s a coincidence, it’s a hell of a coincidence,” Sparks says.
Hunter-gatherers and farming villagers don’t write parenting handbooks, much less read them. But parents in WEIRD societies — Western, educated, industrialized, rich and democratic — can still learn a few childrearing lessons from their counterparts in small-scale societies.
It’s not that Western parents and kids are somehow deficient. But we live in a culture that holds historically unprecedented expectations about how to raise children. Examples: Each child is a unique individual who must be allowed to make decisions independently; children are precious and innocent, so their needs are more important than those of adults; and kids need to be protected from themselves by constant adult supervision. When compared to family life in foraging and farming cultures, and in WEIRD societies only a few decades ago, there is nothing “normal” about parenting convictions such as these.
“Childhood, as we now know it, is a thoroughly modern invention,” says anthropologist David Lancy of Utah State University in Logan. He has studied traditional societies for more than 40 years.
In his book Raising Children: Surprising Insights from Other Cultures, Lancy examines what’s known about bringing up kids in hunter-gatherer groups and farming villages. Among the highlights:
Babies are usually regarded as nonpeople, requiring swaddling and other special procedures over months or years to become a human being. Children are typically the lowest-ranking community members. Because kids can’t feed and protect themselves, they accumulate a moral debt to their elders that takes years of hard work to repay. If that sounds harsh to WEIRD ears, withhold judgment before considering these child-rearing themes from traditional cultures.
Allow for make-believe about real life Hunter-gatherer and village kids intently observe and imitate adults (SN: 2/17/18, p. 22). Playtime often consists of youngsters of various ages acting out and even parodying adult behaviors. Virtually everything, from relations between the sexes to religious practices, is fair game. Kids scavenge for props, assign each other roles and decide what the cast of characters will say.
Western children would benefit from many more chances to play in unsupervised, mixed-age groups, Lancy says.
Let kids play collaborative games A big advantage of play groups of kids of all ages is that they become settings for games in which kids negotiate the rules. Until recently, these types of games, such as marbles, hopscotch and jump rope, were common among U.S. children.
Not anymore, at least not in neighborhoods dominated by adult-supervised play dates and sports teams. Sure, tempers can flare as village youngsters hash out rules for marbles or jacks. But negotiations rarely go off the rails. Older kids handicap themselves so that younger children can sometimes win a game. Concessions are made even for toddlers.
The point is to maintain good enough relations to keep adults from intruding. In modern societies, Lancy suspects, bullying flourishes when kids don’t learn early on how to play collaboratively.
Put young children to work In most non-WEIRD societies, miniature and cast-off tools and utensils, including knives, are the toys of choice for kids of all ages. Play represents a way to prepare for adult duties and, when possible, work alongside adults as helpers.
Western parents can find ways for preschoolers to help out around the house, but it demands flexibility and patience. Lancy suggests making allowances for a 3-year-old who mixes up socks when sorting the laundry. Maybe paper plates are needed until a kitchen helper becomes less apt to drop them.
Still, carefully selected jobs for 3- and 4-year-olds promote a sense of obligation and sympathy toward others, Lancy says. Western kids given chances to help adults early on may, like their non-WEIRD peers, willingly perform chores at later ages, he predicts.
Whether children live in city apartments or forest huts, having the freedom to explore and play with no adults around proves an antidote to boredom. Lancy recalls how boredom-busting works from his own early childhood in rural Pennsylvania during the 1950s. His family lived in a house bordering a river. Lancy would sit on the river bank for up to an hour at a time. His mother liked to tell visitors a story that, when asked what he had been doing, the boy replied “watching the ‘flections.”
