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Discoveries of planets around distant stars have become almost routine. But finding seven exoplanets in one go is something special. In February, a team of planet seekers announced that a small, cool star some 39 light-years away, TRAPPIST-1, hosts the most Earth-sized exoplanets yet found in one place: seven roughly Earth-sized worlds, at least three of which might host liquid water (SN: 3/18/17, p. 6).

These worlds instantly became top priorities in the search for life outside the solar system. “TRAPPIST-1 is on everybody’s wish list,” says exoplanet astronomer Lisa Kaltenegger of Cornell University. But the planets and their dim star have also stoked a raging debate about what makes a planet habitable in the first place.
Astrophysicist Michaël Gillon of the University of Liège in Belgium and colleagues found the family of worlds orbiting the ultracool dwarf star, dubbed TRAPPIST-1 for the small telescope in Chile used to discover its planets.

“I don’t think the cachet of that system is going away anytime soon,” says exoplanet expert Sara Seager of MIT.

The TRAPPIST telescope team first announced in May 2016 that the star had three temperate, rocky planets. Staring at the system with the Spitzer Space Telescope for almost three weeks straight revealed that the third planet was actually four more — all Earth-sized, and three of them are in the star’s habitable zone, the region where temperatures are right for liquid water on a planet’s surface. A seventh planet was caught crossing the star as well, though follow-up observations showed it is too cold for life as we know it (SN: 6/24/17, p. 18).
Similar but different
Planets orbiting the star TRAPPIST-1 are a lot alike in some ways and distinct in others. The slideshow below shows each planet’s specs, including how long it takes to orbit the dwarf star, distance from the star (in astronomical units), and radius and mass relative to Earth.
The number of worlds alone makes the TRAPPIST-1 system a good spot to look for life. An alien observing our solar system would think Venus, Earth and Mars all fall in the habitable zone. But only one is inhabited. The fact that TRAPPIST-1 has so many options increases the odds that the system hosts life, Seager says.

As an ultracool dwarf, TRAPPIST-1 rides the edge of what counts as a star. Such stars burn through their nuclear fuel so slowly that they can live for many billions of years, which gives any life on their planets a long time to grow and evolve. This star’s habitable zone is also incredibly close in, offering astronomers many chances to observe the planets orbiting their star.

The three planets in the habitable zone cross in front of the star every 6.10, 9.21 and 12.35 days. If two or more turn out to be habitable, then they could share life among them, either by tossing meteorites back and forth or — in the case of spacefaring civilizations — by deliberate space travel.
Future space-based observatories will be able to see starlight filtering through the planets’ atmospheres, if the planets have atmospheres. Gillon and colleagues are looking for signs of escaping hydrogen, a signal that an atmosphere might be there. “We’re already preparing,” he says.

But ultracool dwarfs are also ill-tempered. They tend to emit frequent, powerful stellar flares, which could rip away a planet’s atmosphere, threatening any potential for life. The planet-hunting Kepler space telescope recently watched TRAPPIST-1 for 80 days and saw it flare 42 times. One of those flares was as strong as Earth’s 1859 Carrington Event, among the strongest geomagnetic storms ever observed.

But there are other promising systems. Recently, a similar star, Ross 128, only 11 light-years from Earth and much calmer than TRAPPIST-1, was found to have an Earth-mass planet, making it a better place to look for life, researchers reported in November in Astronomy & Astrophysics.

Whether such stars are good or bad for life is an old and open question (SN: 6/24/17, p. 18). TRAPPIST-1’s advantage is in its numbers. “We can check it, not just with one planet but with many planets,” Kaltenegger says. “You have hotter than Earth, like Earth and colder than Earth. If you wanted Goldilocks, this is the ideal scenario.”

TRAPPIST-1 is just an opening act. A bigger, more sensitive observatory called SPECULOOS is expected to be fully operational in the Chilean desert in early 2019, Gillon says. SPECULOOS will seek planets around 1,000 ultracool dwarf stars over 10 years. “We are at the edge of maybe detecting life around another star,” he says. “It’s really a possibility.”

The holiday onslaught is upon us. For some families with children, the crush of holiday gifts — while wonderful and thoughtful in many ways — can become nearly unmanageable, cluttering both rooms and minds.

This year, I’m striving for simplicity as I pick a few key presents for my girls. I will probably fail. But it’s a good goal, and one that has some new science to back it. Toddlers play longer and more creatively with toys when there are fewer toys around, researchers report November 27 in Infant Behavior and Development.
Researchers led by occupational therapist Alexia Metz at the University of Toledo in Ohio were curious about whether the number of toys would affect how the children played, including how many toys they played with and how long they spent with each toy. The researchers also wondered about children’s creativity, such as the ability to imagine a bucket as a drum or a hat.

In the experiment, 36 children ages 18 to 30 months visited a laboratory playroom twice while cameras caught how they played. On one visit, the room held four toys. On the other visit, the room held 16 toys.

When in the playroom with 16 toys, children played with more toys and spent less time with each one over a 15-minute session, the researchers found. When the same kids were in a room with four toys, they stuck with each toy longer, exploring other toys less over the 15 minutes.

What’s more, the quality of the children’s play seemed to be better when fewer toys were available. The researchers noted more creative uses of the toys when only four were present versus 16.
Metz and colleagues noticed that initial attempts to play with a toy were often superficial and simple. But if a kid’s interest stuck, those early pokes and bangs turned into more sophisticated manners of playing. This type of sustained engagement might help children learn to focus their attention, a skill Metz likened to a “muscle that they have to exercise.” This attentional workout might not happen if kids are perpetually exposed to lots of distracting toys.

The toys used in the study didn’t include electronic devices such as tablets. Only one of the four toys and only four of the 16 toys used batteries. Noisy toys may have their own troubles. They can cut down on parent-child conversations, scientists have found. It’s possible that electronics such as televisions or tablets would have even greater allure than other toys.

Nor do the researchers know what would happen if the study had been done in kids’ houses and with their own toys. It’s possible that the novelty of the new place and the new toys influenced the toddlers’ behavior. (As everyone knows, the toys at a friend’s house are way better than the toys a kid has at home, even when they are literally the exact same toy.)

The results don’t pinpoint the optimal number of toys for optimal child development, Metz says. “It’s a little preliminary to say this is good and that is bad,” she says. But she points out that many kids are not in danger of having too few toys. In fact, the average number of toys the kids in the study had was 87. Five families didn’t even provide toy counts, instead answering “a lot.”

“Because of the sheer abundance of toys, there’s no harm in bringing out a few at a time,” Metz says.

That’s an idea that I’ve seen floating around, and I like it. I’ve already started packing some of my kids’ toys out of sight, with the idea to switch the selection every so often (or more likely, never). Another recommendation I’ve seen is to immediately hide away some of the new presents, which aren’t likely to be missed in the holiday pandemonium, and break them out months later when the kids need a thrill.

If more nerve cells mean more smarts, then dogs beat cats, paws down, a new study on carnivores shows. That harsh reality may shock some friends of felines, but scientists say the real surprises are inside the brains of less popular carnivores. Raccoon brains are packed with nerve cells, for instance, while brown bear brains are sorely lacking.

By comparing the numbers of nerve cells, or neurons, among eight species of carnivores (ferret, banded mongoose, raccoon, cat, dog, hyena, lion and brown bear), researchers now have a better understanding of how different-sized brains are built. This neural accounting, described in an upcoming Frontiers in Neuroanatomy paper, may ultimately help reveal how brain features relate to intelligence.
For now, the multispecies tally raises more questions than it answers, says zoologist Sarah Benson-Amram of the University of Wyoming in Laramie. “It shows us that there’s a lot more out there that we need to study to really be able to understand the evolution of brain size and how it relates to cognition,” she says.

Neuroscientist Suzana Herculano-Houzel of Vanderbilt University in Nashville and colleagues gathered brains from the different species of carnivores. For each animal, the researchers whipped up batches of “brain soup,” tissue dissolved in a detergent. Using a molecule that attaches selectively to neurons in this slurry, researchers could count the number of neurons in each bit of brain real estate.

For most animals, the team found the expected numbers of neurons, given a certain brain size. Those expectations came in part from work on other mammals’ brains. That research showed that with the exception of primates (which pack in lots of neurons without growing bigger brains), there’s a predictable relationship between the size of the cerebral cortex — the wrinkly outer layer of the brain that’s involved in thinking, learning and remembering — and the number of neurons contained inside it.

Story continues below interactive graphic
Feeling brainy
Comparing brain size and number of nerve cells in the cerebral cortex among several animal species revealed some surprises. Golden retrievers, for example, have many more nerve cells than cats, and brown bears have an unexpectedly low number of nerve cells given the relatively large size of their brain. Raccoons have a surprising number of nerve cells considering their small noggin. It’s too early, however, to say how neuron number relates to animal intelligence.

Tap or click the graph below for more information.

But some of the larger carnivores with correspondingly larger cortices had surprisingly few neurons. In fact, a golden retriever — with 623 million neurons packed into its doggy cortex —topped both lions and bears, the team found. (For scale, humans have roughly 16.3 billion neurons in the cortex.)

The brown bear is especially lacking. Despite being about 10 times bigger than a cat’s cortex, the bear’s cortex contained roughly the same number of neurons, about 250 million. “It’s just flat out missing 80 percent of the neurons that you would expect,” Herculano-Houzel says. She suspects that there’s a limit to how much food a big predator can catch and eat, especially one that hibernates. That caloric limit might also cap the number of energetically expensive neurons.

Another exception — but in the opposite direction — was the raccoon, which has a cat-sized brain but a doglike neuron number, a finding that fits the nocturnal mammal’s reputation as a clever problem-solver. Benson-Amram cautions that it’s not clear how these neuron numbers relate to potential intelligence. Raccoons are very dexterous, she says, and it’s possible that a beefed-up brain region that handles touch, part of the cortex, could account for the neuron number.

Herculano-Houzel expected large predators such as lions to have lots of neurons. “We went into this study with the expectation that being a predator would require smarts,” she says. But in many cases, a predator didn’t seem to have more neurons than its prey. A lion, for instance, has about 545 million neurons in its cerebral cortex, while a blesbok antelope, which has a slightly smaller cortex, has about 571 million, the researchers previously found.

It’s too early to say how neuron number relates to animal intelligence. By counting neurons, “we’ve figured out one side of that equation,” Herculano-Houzel says. Those counts still need to be linked to animals’ thinking abilities.

Some studies, including one by Benson-Amram, have found correlations between brain size, neuron number and problem-solving skills across species. But finding ways to measure intelligence across different species is challenging, she says. “I find it to be a really fun puzzle, but it’s a big challenge to think, ‘Are we asking the right questions?’”

The hardy souls who manage to push shorts season into December might feel some kinship with the thirteen-lined ground squirrel.

The critter hibernates all winter, but even when awake, it’s less sensitive to cold than its nonhibernating relatives, a new study finds. That cold tolerance is linked to changes in a specific cold-sensing protein in the sensory nerve cells of the ground squirrels and another hibernator, the Syrian hamster, researchers report in the Dec. 19 Cell Reports. The altered protein may be an adaptation that helps the animals drift into hibernation.
In experiments, mice, which don’t hibernate, strongly preferred to hang out on a hot plate that was 30° Celsius versus one that was cooler. Syrian hamsters (Mesocricetus auratus) and the ground squirrels (Ictidomys tridecemlineatus), however, didn’t seem to notice the chill until plate temperatures dipped below 10° Celsius, notes study coauthor Elena Gracheva, a neurophysiologist at Yale University.

Further work revealed that a cold-sensing protein called TRPM8 wasn’t as easily activated by cold in the squirrels and hamsters as in rats. Found in the sensory nerve cells of vertebrates, TRPM8 typically sends a sensation of cold to the brain when activated by low temperatures. It’s what makes your fingertips feel chilly when you’re holding a glass of ice water. It’s also responsible for the cooling sensation in your mouth after you chew gum made with menthol.

The researchers looked at the gene that contains the instructions to make the TRPM8 protein in ground squirrels and switched up parts of it to find regions responsible for tolerance to cold. The adaptation could be pinned on six amino acid changes in one section of the squirrel gene, the team found. Cutting-and-pasting the rat version of this gene fragment into the squirrel gene led to a protein that was once again cold-sensitive. Hamster TRPM8 proteins also lost their cold tolerance with slightly different genetic tweaks in the same region of the gene.

The fact that it’s possible to make a previously cold-resistant protein sensitive to cold by transferring in a snippet of genetic instructions from a different species is “really quite striking,” says David McKemy, a neurobiologist at the University of Southern California in Los Angeles.
As anyone who’s lain awake shivering in a subpar sleeping bag knows, falling asleep while cold is really hard. Hibernation is different than sleep, Gracheva emphasizes, but the squirrels and hamsters’ tolerance to cold may help them transition from an active, awake state to hibernation. If an animal feels chilly, its body will expend a lot of energy trying to warm up — and that’ll work against the physiological changes needed to enter hibernation. For example, while hibernating, small mammals like the ground squirrel slow their pulse and breathing and can lower their core body temperature to just a few degrees above freezing.

Modifications to TRPM8 probably aren’t the only factors that help ground squirrels ignore the cold, Gracheva says, especially as the thermometer drops even closer to freezing. “We think this is only part of the mechanism.”

Scientists also aren’t sure exactly how TRPM8 gets activated by cold in the first place. A detailed view of TRPM8’s structure, obtained using cryo-electron microscopy, was published by a different research group online December 7 in Science. “This is a big breakthrough. We were waiting for this structure for a long period of time,” Gracheva says. Going forward, she and colleagues hope that knowing the protein’s structure will help them link genetic adaptations for cold tolerance in TRPM8 with specific structural changes in the protein.

Cold weather often brings with it hot takes on so-called man flu. That’s the phenomenon in which the flu hits men harder than women — or, depending on who you ask, when men exaggerate regular cold symptoms into flu symptoms. In time for the 2017–2018 flu season, one researcher has examined the scientific evidence for and against man flu.

“The concept of man flu, as commonly defined, is potentially unjust,” Kyle Sue, a clinician at Memorial University of Newfoundland in St. John’s, Canada, writes December 11 in BMJ. Motivated by his own memorable bout of flu, he says, Sue began looking into man flu research and summarizes the work in a review article that’s part of BMJ’s Christmas issue, which traditionally features humorous takes on legitimate research.
There might be a reason men come across as wimps. In the United States, more men than women died from flu-related causes from 2007 to 2010 across several age groups, researchers reported in the American Journal of Epidemiology in 2013. An analysis of data on the 2004 to 2010 flu seasons in Hong Kong found that in children and adults, males were more likely to be hospitalized for the flu than females.

Sue isn’t the first to make a case for man flu. A prevailing explanation for men’s susceptibility says that women have higher levels of the hormone estradiol, which can boost the immune system, while men have higher levels of testosterone, which can sometimes suppress the immune system. However, these hormones interact with the immune system in other ways as well.

“There is some evidence that men make weaker immune responses to some viruses than women, but how this happens and whether it is seen across all viruses is still unclear to me,” notes John Upham, professor of respiratory medicine at Queensland University in Australia.

Sue’s review also cites evidence that women respond better to some flu shots than men do. Sex differences in immune response could have real consequences when it comes to vaccine choice, Upham says.
It’s also unclear what the evolutionary drivers for immune differences between the sexes might be. And studies of how the male and female immune systems respond differently all come with caveats, Sue notes: Such studies are often in mice rather than humans, have limited data or don’t account for health differences such as smoking habits and tendency to go to the doctor. Upham adds that studying differences in flu cases among men in Western versus non-Western societies could reveal the degree to which learned behavior plays a role in “man flu.”

As much as he’d like to help out his half of the species, Sue says, “we cannot yet conclude that this phenomenon is real, but the current evidence is suggestive that it may be.” Not surprising, his review has met just as much skepticism as previous man flu treatises.

Regardless of the possibility that men may be immunologically weaker than women, Sue says, both flu-stricken men and women alike “could benefit from resting in a safe, comfortable place with a recliner and TV.”

The computer will see you now.

Artificial intelligence algorithms may soon bring the diagnostic know-how of an eye doctor to primary care offices and walk-in clinics, speeding up the detection of health problems and the start of treatment, especially in areas where specialized doctors are scarce. The first such program — trained to spot symptoms of diabetes-related vision loss in eye images — is pending approval by the U.S. Food and Drug Administration.

While other already approved AI programs help doctors examine medical images, there’s “not a specialist looking over the shoulder of [this] algorithm,” says Michael Abràmoff, who founded and heads a company that developed the system under FDA review, dubbed IDx-DR. “It makes the clinical decision on its own.”
IDx-DR and similar AI programs, which are learning to predict everything from age-related sight loss to heart problems just by looking at eye images, don’t follow preprogrammed guidelines for how to diagnose a disease. They’re machine-learning algorithms that researchers teach to recognize symptoms of a particular condition, using example images labeled with whether or not that patient had that condition.
IDx-DR studied over 1 million eye images to learn how to recognize symptoms of diabetic retinopathy, a condition that develops when high blood sugar damages retinal blood vessels (SN Online: 6/29/10). Between 12,000 and 24,000 people in the United States lose their vision to diabetic retinopathy each year, but the condition can be treated if caught early.
Researchers compared how well IDx-DR detected diabetic retinopathy in more than 800 U.S. patients with diagnoses made by three human specialists. Of the patients identified by IDx-DR as having at least moderate diabetic retinopathy, more than 85 percent actually did. And of the patients IDx-DR ruled as having mild or no diabetic retinopathy, more than 82.5 percent actually did, researchers reported February 22 at the annual meeting of the Macula Society in Beverly Hills, Calif.

IDx-DR is on the fast-track to FDA clearance, and a decision is expected within a few months, says Abràmoff, a retinal specialist at the University of Iowa in Iowa City. If approved, it would become the first autonomous AI to be used in primary care offices and clinics.

AI algorithms to diagnose other eye diseases are in the works, too. An AI described February 22 in Cell studied over 100,000 eye images to learn the signs of several eye conditions. These included age-related macular degeneration, or AMD — a leading cause of vision loss in adults over 50 — and diabetic macular edema, a condition that develops from diabetic retinopathy.

This AI was designed to flag advanced AMD or diabetic macular edema for urgent treatment, and to refer less severe cases for routine checkups. In tests, the algorithm was 96.6 percent accurate in diagnosing eye conditions from 1,000 pictures. Six ophthalmologists made similar referrals based on the same eye images.

Researchers still need to test how this algorithm fares in the real world where the quality of images may vary from clinic to clinic, says Aaron Lee, an ophthalmologist at the University of Washington in Seattle. But this kind of AI could be especially useful in rural and developing regions where medical resources and specialists are scarce and people otherwise wouldn’t have easy access to in-person eye exams.

AI might also be able to use eye pictures to identify other kinds of health problems. One algorithm that studied retinal images from over 284,000 patients could predict cardiovascular health risk factors such as high blood pressure.

The algorithm was 71 percent accurate in distinguishing eye images between smoking and nonsmoking patients, according to a report February 19 in Nature Biomedical Engineering. And it predicted which patients would have a major cardiovascular event, such as a heart attack, within the next five years 70 percent of the time.

With AI getting more adept at screening for a growing list of conditions, “some people might be concerned that this is machines taking over” health care, says Caroline Baumal, an ophthalmologist at Tufts University in Boston. But diagnostic AI can’t replace the human touch. “Doctors will still need to be there to see patients and treat patients and talk to patients,” Baumal says. AI will just help people who need treatment get it faster.

The seeds for Martian clouds may come from the dusty tails of comets.

Charged particles, or ions, of magnesium from the cosmic dust can trigger the formation of tiny ice crystals that help form clouds, a new analysis of Mars’ atmosphere suggests.

For more than a decade, rovers and orbiters have captured images of Martian skies with wispy clouds made of carbon dioxide ice. But “it hasn’t been easy to explain where they come from,” says chemist John Plane of the University of Leeds in England. The cloud-bearing layer of the atmosphere is between –120° and –140° Celsius — too warm for carbon dioxide clouds to form on their own, which can happen at about –220° C.
Then in 2017, NASA’s MAVEN orbiter detected a layer of magnesium ions hovering about 90 kilometers above the Martian surface (SN: 4/29/17, p. 20). Scientists think the magnesium, and possibly other metals not yet detected, comes from cosmic dust left by passing comets. The dust vaporizes as it hits the atmosphere, leaving a sprinkling of metals suspended in the air. Earth has a similar layer of atmospheric metals, but none had been observed elsewhere in the solar system before.

According to the new calculations, the bits of magnesium clump with carbon dioxide gas — which makes up about 95 percent of Mars’ atmosphere — to produce magnesium carbonate molecules. These larger, charged molecules could attract the atmosphere’s sparse water, creating what Plane calls “dirty” ice crystals.

At the temperatures seen in Mars’ cloud layer, pure carbon dioxide ice crystals are too small to gather clouds around them. But clouds could form around dirty ice at temperatures as high as –123° C, Plane and colleagues report online March 6 in the Journal of Geophysical Research: Planets.