The Cassini spacecraft has snapped its penultimate pics of Saturn’s moon Titan.

This image, shot September 11 as Cassini swung past the moon at a distance of about 119,049 kilometers, shows Titan’s lake region near its north pole. “The haze has cleared remarkably as the summer solstice has approached,” Cassini Project Scientist Linda Spilker said in a news conference September 13.

Cassini performed 127 close flybys of Titan over the course of its 13-year mission, and used the moon’s gravity to adjust its trajectory each time. Those gravity assists let the team create a full global map of Titan.

Future engineers will borrow that trick to explore Jupiter’s moon Europa with the Clipper mission, which is planned to launch in the 2020s. “Cassini pioneered that whole concept,” Jim Green, head of NASA’s planetary science division director, said at the news conference.

On this final pass, Titan’s gravity had one last job. It nudged Cassini on its final trajectory: making a beeline for Saturn. Tomorrow, the probe will spend its last full day in space snapping images of its greatest hits: Saturn and the rings, Titan, a small moon forming within the rings informally dubbed “Peggy,” the moon Enceladus, ring ripples called propellers and finally, the location of its own demise. The spacecraft will disintegrate above the gas giant’s cloud tops early in the morning of September 15.

This year’s Atlantic hurricane season has already proven to be active and deadly. Powerful hurricanes such as Harvey, Irma and Maria are also providing a testing ground for new tools that scientists hope will save lives by improving forecasts in various ways, from narrowing a storm’s future path to capturing swift changes in the intensity of storm winds.

Some of the tools that debuted this year — such as the GOES-16 satellite — are already winning praise from scientists. Others, such as a new microsatellite system aiming to improve measurements of hurricane intensity and a highly anticipated new computer simulation that forecasts hurricane paths and intensities, are still in the calibration phase. As these tools get an unprecedented workout thanks to an unusually ferocious series of storms, scientists may know in a few months whether hurricane forecasting is about to undergo a sea change.

The National Oceanic and Atmospheric Administration’s GOES-16 satellite is perhaps the clearest success story of this hurricane season so far. Public perceptions of hurricane forecasts tend to focus on uncertainty and conflicting predictions. But in the big picture, hurricane models adeptly forecasted Irma’s ultimate path to the Florida Keys nearly a week before it arrived there, says Brian Tang, an atmospheric scientist at the University at Albany in New York.
“I found that remarkable,” he says. “Ten or so years ago that wouldn’t have been possible.”

One reason for this is GOES-16, which launched late last year and will become fully operational in November. The satellite offers images at four times the resolution of previous satellites. “It’s giving unparalleled details about the hurricanes,” Tang says, including data on wind speeds and water temperatures delivered every minute that are then fed into models.

GOES-16’s crystal-clear images also give forecasters a better picture of the winds swirling around a storm’s central eye. But more data from this crucial region is needed to improve predictions of just how strong a hurricane might get. Scientists continue to struggle to predict rapid changes in hurricane intensity, Tang says. He notes how Hurricane Harvey, for example, strengthened suddenly to become a Category 4 storm right before it made landfall in Texas, offering emergency managers little time to issue warnings. “That’s the sort of thing that keeps forecasters up at night,” he says.
In December, NASA launched a system of eight suitcase-sized microsatellites called the Cyclone Global Navigation Satellite System, or CYGNSS, into orbit. The satellites measure surface winds near the inner core of a hurricane, such as between the eyewall and the most intense bands of rain, at least a couple of times a day. Those regions have previously been invisible to satellites, measured only by hurricane-hunter airplanes darting through the storm.

“Improving forecasts of rapid intensification, like what occurred with Harvey on August 25, is exactly what CYGNSS is intended to do,” says Christopher Ruf, an atmospheric scientist at the University of Michigan in Ann Arbor and the lead scientist for CYGNSS. Results from CYGNSS measurements of both Harvey and Irma look very promising, he says. While the data are not being used to inform any forecasts this year, the measurements are now being calibrated and compared with hurricane-hunter flight data. The team will give the first detailed results from the hurricane season at the annual meeting of the American Geophysical Union in December.
Meanwhile, NOAA has also been testing a new hurricane forecast model this year. The U.S. forecasting community is still somewhat reeling from its embarrassing showing during 2012’s Hurricane Sandy, which the National Weather Service had predicted would go out to sea while a European meteorological center predicted, correctly, that it would squarely hit New York City. In the wake of that event, Congress authorized $48 million to improve U.S. weather forecasting, and in 2014 NOAA held a competition to select a new weather prediction tool to improve its forecasts.

The clear winner was an algorithm developed by Shian-Jiann Lin and colleagues at NOAA’s Geophysical Fluid Dynamics Laboratory in Princeton, N.J. In May, NOAA announced that it would test the new model this hurricane season, running it alongside the more established operational models to see how it stacks up. Known as FV3 (short for Finite-Volume Cubed-Sphere Dynamical Core), the model divides the atmosphere into a 3-D grid of boxes and simulates climate conditions within the boxes, which may be as large as 4 kilometers across or as small as 1 kilometer across. Unlike existing models, FV3 can also re-create vertical air currents that move between boxes, such as the updrafts that are a key element of hurricanes as well as tornadoes and thunderstorms.

But FV3’s performance so far this year hasn’t been a slam dunk. FV3 did a far better job at simulating the intensity of Harvey than the other two leading models, but it lagged behind the European model in determining the hurricane’s path, Lin says. As for Irma, the European model outperformed the others on both counts. Still, Lin says he is confident that FV3 is on the right track in terms of its improvement. That’s good because pressure to work out the kinks may ramp up rapidly. Although NOAA originally stated that FV3 would be operational in 2019, “I hear some hints that it could be next year,” he says.

Lin adds that a good model alone isn’t enough to get a successful forecast; the data that go into a model are ultimately crucial to its success. “In our discipline, we call that ‘garbage in, garbage out,’” he says. With GOES-16 and CYGNSS nearly online, scientists are looking forward to even better hurricane models thanks to even better data.

Ice in space may break out the bubbly. Zapping simulated space ice with imitation starlight makes the ice bubble like champagne. If this happens in space, this liquidlike behavior could help organic molecules form at the edges of infant planetary systems. The experiment provides a peek into the possible origins of life.

Shogo Tachibana of Hokkaido University in Sapporo, Japan, and colleagues combined water, methanol and ammonia, all found in comets and interstellar clouds where stars form, at a temperature between ‒263° Celsius and ‒258° C. The team then exposed this newly formed ice to ultraviolet radiation to mimic the light of a young star.

As the ice warmed to ‒213° C, it cracked like a brittle solid. But at just five degrees warmer, bubbles started appearing in the ice, and continued to bubble and pop until the ice reached ‒123° C. At that point, the ice returned to a solid state and formed crystals.

“We were so surprised when we first saw bubbling of ice at really low temperatures,” Tachibana says. The team reports its finding September 29 in Science Advances.

Follow-up experiments showed fewer bubbles formed in ice with less methanol and ammonia. Ice that wasn’t irradiated showed no bubbles at all.

Analyses traced spikes of hydrogen gas during irradiation. That suggests that the bubbles are made of hydrogen that the ultraviolet light split off methane and ammonia molecules, Tachibana says. “It is like bubbling in champagne,” he says — with an exception. Champagne bubbles are dissolved carbon dioxide, while ice bubbles are dissolved hydrogen.
The irradiated ice took on another liquidlike feature: Between about ‒185° C and ‒161° C, it flowed like refrigerated honey, despite being well below its melting temperature, Tachibana adds.

That liquidity could help kick-start life-building chemistry. In 2016, Cornelia Meinert of the University Nice Sophia Antipolis in France and colleagues showed that irradiated ice forms a cornucopia of molecules essential to life, including ribose, the backbone of RNA, which may have been a precursor to DNA (SN: 4/30/16, p. 18). But it was not clear how smaller molecules could have found each other and built ribose in rigid ice.

At the time, critics said complex molecules could have been contamination, says Meinert, who was not involved in the new work. “Now this is helping us argue that at this very low temperature, the small precursor molecules can actually react with each other,” she says. “This is supporting the idea that all these organic molecules can form in the ice, and might also be present in comets.”

ORLANDO, Fla. — Kissing cousins aren’t doing their children any evolutionary favors, some preliminary data suggest.

Mating with a close relative, known as inbreeding, reduces nonhuman animals’ evolutionary fitness — measured by the ability to produce offspring. Inbreeding, it turns out, also puts a hit on humans’ reproductive success, David Clark of the University of Edinburgh reported October 20 at the annual meeting of the American Society of Human Genetics.

Offspring of second cousins or closer relatives make up about 10 percent of the world population, Clark said. He and colleagues collected data on more than a million people from more than 100 culturally diverse populations and calculated the effect inbreeding has on traits related to evolutionary fitness.
Compared with outbred peers, offspring of first cousins have 1.4 fewer opposite-sex sexual partners, have sex for the first time 11 months later, have 0.11 fewer children and are 1.6 times as likely to be childless — all indicators of reduced reproductive ability. Childlessness was not because of a lack of opportunity to have kids, but rather because of fertility problems, Clark said. Children of first cousins are also 1 centimeter shorter, on average, than their peers and 0.84 kilograms lighter at birth. They also have five fewer months of education, presumably because they have less intellectual capacity than people with more distantly related parents, Clark said.

The more closely related the parents, the bigger the hit on reproductive fitness. Children of incest are 3 centimeters shorter and four times as likely to be childless than outbred peers, Clark said.

The funniest thing I’ve ever said to any botanist was, “What is a species?” Well, it certainly got the most spontaneous laugh. I don’t think Barbara Ertter, who doesn’t remember the long-ago moment, was being mean. Her laugh was more of a “where do I even start” response to an almost impossible question.

At first glance, “species” is a basic vocabulary word schoolchildren can ace on a test by reciting something close to: a group of living things that create fertile offspring when mating with each other but not when mating with outsiders. Ask scientists who devote careers to designating those species, however, and there’s no typical answer. Scientists do not agree.

“You may be stirring up a hornet’s nest,” warns evolutionary zoologist Frank E. Zachos of Austria’s Natural History Museum Vienna when I ask my “what is a species” question. “People sometimes react very emotionally when it comes to species concepts.” He should know, having cataloged 32 of them in his 2016 overview, Species Concepts in Biology.

The widespread schoolroom definition above, known as the biological species concept, is No. 2 in his catalog, which he tactfully arranges in alphabetical order. This single concept has been so pervasive that whenever Science News publishes something about species interbreeding, readers want to know if we have lost our grip on logic. Separate species, by definition, can do no such thing.
As concerned readers question our reports of hybrid species, a vast debate among specialists over how to define and identify species rolls on. The biological species concept has drawbacks, to put it gently, for coping with much of the variety and oddness of life. Alternative concepts have pros and cons, too. As specialists argue over the fine details of species concepts, I’m struck by how often the word “fuzzy” comes up.

Also striking is how at least some of the people who actually appraise species for a living have made peace with the perpetual tumult over defining just what it is they get up in the morning to study. The ambiguities seemed less jarring to me after a September conversation with the Smithsonian’s Kevin de Queiroz, deep in the maze of doors and corridors behind the scenes at the National Museum of Natural History in Washington, D.C. As a systematic biologist, he studies the evolutionary histories of reptiles, and designates species, which explains a door we passed marked “Alcohol Room.” Fire regulations require special handling for jars of animal specimens preserved in alcohol. In the cacophony of species concepts, de Queiroz sees some commonality.

Ertter, affiliated with the University of California, Berkeley and the College of Idaho in Caldwell, embraces the ambiguity. “Why do we expect that nature is nice and neat and clean? Because it’s more convenient for us,” she says. “It’s up to us to figure it out, not to demand that it’s one way or another.”
Problems with the old standard
The biological species concept has an intuitive appeal. Elephants don’t mate with oak trees to produce really big acorns. Horses can mate with donkeys, but the resulting mules are infertile. The most famous form of this species definition may be from evolutionary biologist Ernst Mayr, who wrote in 1942: “Species are groups of actually or potentially interbreeding natural populations, which are reproductively isolated from other such groups.” Famous, yes, but limited.
Modern genetics has revealed that much of the diversity of life on Earth is found in single-celled organisms that reproduce asexually by splitting in two — thus flummoxing the definition. Of course the single-celled hordes still form … somethings. There isn’t just one vast smear of microbial life where all shapes, sizes, body features and chemistry can be found in any old mix. There are clusters with shared traits, some of which cause human and agricultural diseases and some of which photosynthesize in the ocean, producing as much as 70 percent of the oxygen that we and other living things breathe. Humans need to understand the history of microbes and have names to talk about these influential organisms.

Rather than deciding that these microbes are just not species, which is one popular view, microbiome researcher Seth Bordenstein suggests “just twisting the biological species concept ever so slightly.” Genes don’t shuffle around via sex, but there’s still kidnapping of genes from other asexuals. This process might count as something like interbreeding, says Bordenstein, of Vanderbilt University in Nashville. With that interpretation, the biological species concept “could apply to microbes.” Sort of.

But one-celled microbes aren’t the only asexuals. Even vertebrates have their no-sex scandals. New Mexico whiptail lizards are a species: Aspidoscelis neomexicanus. Yet females lay eggs with no male fertilization; males don’t exist.

And plant reproduction, oy. The blends of sex and no-sex don’t fit into a tidy biological species concept. Consider a new variety of a western North American species that Ertter and botanist Alexa DiNicola of the University of Wisconsin–Madison named this year. Potentilla versicolor var. darrachii belongs to a genus that’s closely related to strawberries. Plants in the genus open little five-petaled flowers and readily form classic seeds that mix genes from pollen and ovule. On occasion, though, the genes in the seed’s embryo are only mom’s. “They basically use seeds as a form of cloning,” Ertter says. The male pollen in these cases merely jump-starts formation of the seed’s food supply.

That’s just one reason Potentilla is “one of the messiest genera you can imagine,” Ertter says. She and DiNicola hauled collectors’ gear on a backpacking trip in Oregon to sample some of the plants. The team found signs that one species was hybridizing readily with another; the species were so different that even a nonbotanist could tell them apart (leaves shaped like a feather versus an open fan). Sharing genes across species is evidently common in this genus and not at all rare among plants.

Such shenanigans have led Ertter to what she calls the “fuzzy species concept.” After looking at all the kinds of evidence she might muster for a plant, from its genes and distribution to the details of petals, leaf hairs and other parts, she sides with the preponderance of data to designate a species.

Concept zoo
There can be a lot of messiness in picking out the limits of species, but that’s OK with philosopher Matt Haber of the University of Utah in Salt Lake City. He organized three conferences this year on the complications of determining what’s a species when fire hoses of genetic information spew signs of unexpected gene mixing and tell different stories depending on the genes tracked.

“Just because boundaries are fuzzy,” Haber says, “doesn’t mean they aren’t actually boundaries.” We may not be used to thinking about species distinctions this way, but other familiar distinctions have similar “gradient boundaries,” as he calls them. “Cold and hot weather,” he says. We recognize winter weather as different from summer even though fall and spring have neither a sharp switch point nor a smooth slide. Species, too, could have zones of erratic mixing but still overall be defined as species.

There are a whole lot of species concepts, says Richard Richards, a philosopher of biology at the University of Alabama in Tuscaloosa. “We use different rules for different kinds of organisms,” he says. “For vertebrates, the interbreeding rule is useful. Not so for the many kinds of nonsexually reproducing organisms out there.”

What’s called the agamospecies concept applies to asexual organisms and cobbles together genetic or other observable similarities. The ecological species concept emphasizes adaptations to particular environmental zones. The nothospecies concept applies to plants arising when parent species hybridize. And so on. That’s not even counting “the cynical species concept,” which Zachos has heard defining a species as “whatever a taxonomist says it is.”

Land and money
Species definitions can have ramifications, financial and otherwise, for the wider world. Choosing one species concept over another can change how a creature gets classified, which could determine whether conservation laws protect it. The coastal California gnatcatcher’s status as a distinct subspecies makes it eligible for federal protection to keep the bird’s shrub-land as habitat rather than a real estate development. Critics have argued, however, that the bird isn’t distinct enough from its relatives to merit special protection.

Mammal specialists are switching over to what’s called a phylogenetic concept, Zachos says. The phylogenetic concept allows populations to upgrade to full species status if they share an ancestor and have some unique trait, such as a particular gene. Among the complex consequences of following this concept is possible “taxonomic inflation,” he warns. A 2011 rethink of the ungulate group of sheep, goats, antelope and more ballooned the species count from 143 to 279, for instance. In biology as in economics, “inflation causes devaluation,” Zachos says. “People get bored. If one of the tiger species goes extinct, they say, ‘So what? There are five more.’ ”

As individual taxonomists choose their pet concepts, “ ‘species’ are often created or dismissed arbitrarily,” argued two researchers from Australia in the June 1 Nature. The duo warned of potential “anarchy” and went as far as calling for an international organization to reduce the chaos.

“A long list of silly examples of complications caused by poor taxonomic governance” pushed conservation biologist Stephen Garnett of Charles Darwin University in Darwin to cowrite the piece. Standardizing species concepts across broad groups, mammals and reptiles, for instance, would reduce the chaos, says coauthor Leslie Christidis, a taxonomist at Southern Cross University in Coffs Harbour. The notion of standard-setting in determining species has stirred a bit of agreement and a lot of dissent. “We united the taxonomic community — unfortunately against us,” he says.

The furor illustrates the diversity of ways that people are sorting out what a species is among life’s various organisms. Historian and philosopher of biology John S. Wilkins of the University of Melbourne in Australia was almost kidding when he wrote that there are “n+1 definitions of ‘species’ in a room of n biologists.”
The commons
Thinking about the seemingly intractable ambiguities of the species concepts got a lot easier for me after my visit with de Queiroz. His office was the opposite of the Hollywood biologist’s jumble of dessicated specimens, dangling skeletons and tottering towers of books. The long room was mostly filled with rows of librarian-tidy metal bookcases hiding a desk cave at the far end. When I asked him what a species is, he didn’t laugh. He explained that there’s more agreement than the swarm of species concepts might suggest.

The concepts have in common their references to organisms in a population lineage, or line of descent. As evolutionary time passes, a lineage moves away and its various connected populations grow separate from others of the same ancestry. The concepts share the basic idea that a species is a “separately evolving metapopulation lineage,” he says.

To identify those lineages in practice, however, requires finding evidence of interbreeding or patterns of shared traits. Adding such criteria to the concepts is what creates the crazy diversity. Defining the term species is “not the problem,” he says. “The problem is in identifying a species.”

He calls up a map on his computer from a recent paper a former lab member published on fringe-toed lizards. Colored blobs float over dark lines of a map of the western United States. Three blobs are clearly designated species based on multiple lines of evidence. Three lizard patches, however, are perplexing. Various ways of testing these lizard populations lead to contradictory results.

No matter how badly we want the process of applying a species definition to be clear-cut for all creatures in all cases, “it just isn’t,” de Queiroz says. And that’s exactly what evolutionary biology predicts. Evolution is an ongoing process, with lineages splitting or rejoining at their own pace. Exploring a living, ever-evolving world of life means finding and accepting fuzziness.

A plasma cocoon lets growing stars keep their X-rays to themselves. Laboratory experiments that mimic maturing stars show that streams of plasma splash off a star’s surface, forming a varnish that keeps certain kinds of radiation inside.

That coating could explain a puzzling mismatch between X-ray and ultraviolet observations of growing stars, report physicist Julien Fuchs of École Polytechnique in Paris and colleagues November 1 in Science Advances.

Physicists think stars that are less than 10 million years old grow up by drawing matter onto their surfaces from an orbiting disk of dust and gas. Magnetic fields shape the incoming matter into columns of hot, charged plasma. The same disk will eventually form planets (SN Online: 11/6/14), so knowing how quickly stars gobble up the disk can help tell what kinds of planets can grow.
When disk matter hits a stellar surface, the matter heats to about 1,700° Celsius and should emit a lot of light in ultraviolet and X-ray wavelengths. Measuring that light can help scientists infer how fast the star is growing. But previous observations found that such stars emit between four and 100 times fewer X-rays than they should.

One theory why is that something about how a star eats absorbs the X-rays. So Fuchs and his colleagues re-created the feeding process in a lab. First, the team zapped a piece of PVC representing the edge of the disk with a laser to create plasma, similar to the columns that feed stars. In space, a star’s gravity draws the plasma onto its surface at speeds of about 500 kilometers per second. The star’s strong magnetic field guides the charged plasma into organized columns millions of kilometers long.
There’s not enough room or gravity in the lab to reproduce that exactly, but the plasma physics is the same on smaller scales, Fuchs says. His team applied magnetic fields up to 100,000 times stronger than Earth’s to the plasma to shape it into columns and accelerate it to the same speed it would have in space. The researchers placed a target made of Teflon representing the star’s surface just 11.7 millimeters away from the PVC, a distance equivalent to about 10 million kilometers in space.

When the plasma hits the Teflon surface, the plasma begins to ooze sideways. But the magnetic field that holds the plasma in a column stops the plasma’s spreading. Plasma and magnetic field push against each other until the buildup of pressure between them forces the plasma to curve away from the surface and back up the column, coating incoming plasma with outgoing plasma.

“This cocoon is building up,” Fuchs says. It absorbs enough X-rays to explain the surprisingly wimpy X-ray emission of growing stars, the experiment found. The team also compared the experiment setup with computer simulations of feeding stars to show that the lab configuration was a good representation of real stars.

The comparison with computer simulations makes the experiment more reliable, says experimental physicist Gianluca Gregori of the University of Oxford. “There is this reality check,” he says. “In the astrophysical community, there’s a tendency to think that there are observations, and there are simulations. But what this paper tells is that there are other ways you can understand what happens in the universe.”

The most distant quasar yet spotted sends its light from the universe’s toddler years. The quasar, called J1342+0928, existed when the universe was only 690 million years old, right when the first stars and galaxies were forming.

Quasars are bright disks of gas and dust swirling around supermassive black holes. The black hole that powers J1342+0928 has a mass equivalent to 800 million suns, and it’s gobbling gas and dust so fast that its disk glows as bright as 40 trillion suns, Eduardo Bañados of the Carnegie Institution for Science in Pasadena, Calif., and his colleagues report December 6 in Nature.
“The newly discovered quasar gives us a unique photo of the universe when it was 5 percent [of] its present age,” Bañados says. “If the universe was a 50-year-old person, we would be seeing a photo of that person when she/he was 2 1/2 years old.”

This quasar is only slightly smaller than the previous distance record-holder, which weighs as much as 2 billion suns and whose light is 12.9 billion years old, emitted when the universe was just 770 million years old (SN: 7/30/11, p. 12). Scientists still aren’t sure how supermassive black holes like these grew so big so early.

“They either have to grow faster than we thought, or they started as a bigger baby,” says study coauthor Xiaohui Fan of the Steward Observatory in Tucson.

The temperature of the gas surrounding the newfound quasar places it squarely in the epoch of reionization (SN: 4/1/17, p. 13), when the first stars stripped electrons from atoms of gas that filled interstellar space. That switched the universe’s gas from mostly cold and neutral to hot and ionized. When this particular black hole formed, the universe was about half hot and half cold, Fan says.
“We’re very close to the epoch when the first-generation galaxies are appearing,” Fan says.

NEW ORLEANS — The New Horizons team may get more than it bargained for with its next target. Currently known as 2014 MU69, the object might, in fact, be two rocks orbiting each other — and those rocks may themselves host a small moon.

MU69 orbits the sun in the Kuiper Belt, a region more than 6.5 billion kilometers from Earth. That distance makes it difficult to get pictures of the object directly. But last summer, scientists positioned telescopes around the globe to catch sight of MU69’s shadow as it passed in front of a distant background star (SN Online: 7/20/17), a cosmic coincidence known as an occultation.
Analyzing that flickering starlight raised the idea that MU69 might have two lobes, like a peanut, or might even be a pair of distinct objects. Whatever its shape, MU69 is not spherical and may not be alone, team members reported in a news conference on December 12 at the fall meeting of the American Geophysical Union.

Another stellar flicker sighting raised the prospect of a moon. On July 10, NASA’s airborne Stratospheric Observatory for Infrared Astronomy observed MU69 pass in front of a different star (SN: 3/19/16, p. 4). SOFIA saw what looked like a new, shorter dip in the star’s light. Comparing that data with orbit calculations from the European Space Agency’s Gaia spacecraft suggested that the blip could be another object around MU69.

A double object with a smaller moon could explain why MU69 sometimes shifts its position from where scientists expect it to be during occultations, said New Horizons team member Marc Buie of the Southwest Research Institute in Boulder, Colo.

The true shape will soon be revealed. The New Horizons spacecraft set its sights on the small space rock after flying past Pluto in 2015, and will fly past MU69 on January 1, 2019.

Our solar system is no longer the sole record-holder for most known planets circling a star.

An artificial intelligence algorithm sifted through data from the planet-hunting Kepler space telescope and discovered a previously overlooked planet orbiting Kepler 90 — making it the first star besides the sun known to host eight planets. This finding, announced in a NASA teleconference December 14, shows that the kinds of clever computer codes used to translate text and recognize voices can also help discover strange new worlds.
The discovery, also reported in a paper accepted to the Astronomical Journal, can also help astronomers better understand the planetary population of our galaxy. “Finding systems like this that have lots of planets is a really neat way to test theories of planet formation and evolution,” says Jeff Coughlin, an astronomer at the SETI Institute in Mountain View, Calif., and NASA’s Ames Research Center in Moffett Field, Calif.

Kepler 90 is a sunlike star about 2,500 light-years from Earth in the constellation Draco. The latest addition to Kepler 90’s planetary family is a rocky planet about 30 percent larger than Earth called Kepler 90i. It, too, is the third planet from its sun — but with an estimated surface temperature higher than 400° Celsius, it’s probably not habitable.

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The seven previously known planets in this system range from small, rocky worlds like Kepler 90i to gas giants, which are all packed closer to their star than Earth is to the sun. “It’s very possible that Kepler 90 has even more planets,” study coauthor Andrew Vanderburg, an astronomer at the University of Texas at Austin, said in the teleconference. “There’s a lot of unexplored real estate in the Kepler 90 system.”
Astronomers have identified over 2,300 new planets in Kepler data by searching for tiny dips in a star’s brightness when a planet passes in front of it. Kepler has collected too much data for anyone to go through it all by hand, so humans or computer programs typically only verify the most promising signals of the bunch. That means that worlds that produce weaker light dips — like Kepler 90i — can get passed over. Vanderburg and Christopher Shallue, a software engineer at Google in Mountain View, Calif., designed a computer code called a neural network, which mimics the way the human brain processes information, to seek out such overlooked exoplanets.
Researchers previously automated Kepler data analysis by hard-coding programs with rules about how to detect bona fide exoplanet signals, Coughlin explains. Here, Vanderburg and Shallue provided their code with more than 10,000 Kepler signals that had been labeled by human scientists as either exoplanet or non-exoplanet signals. By studying these examples, the neural network learned on its own what the light signal of an exoplanet looked like, and could then pick out the signatures of exoplanets in previously unseen signals.

The fully trained neural network examined 670 star systems known to host multiple planets to see whether previous searches had missed anything. It spotted Kepler 90i, as well as a sixth, Earth-sized planet around the star Kepler 80. This feat marks the first time a neural network program has successfully identified new exoplanets in Kepler data, Jessie Dotson, an astrophysicist at NASA’s Ames Research Center said at the teleconference.

Vanderburg and Shallue now plan to apply their neural network to Kepler’s full cache of data on more than 150,000 stars, to see what other unrecognized exoplanets it might turn up.

Coughlin is also excited about the prospect of using artificial intelligence to assess data from future exoplanet search missions, like NASA’s TESS satellite set to launch next year. “The hits are going to keep on coming,” regarding potential exoplanet signals, he says. Having self-taught computer programs help humans slog through the data could significantly speed up the rate of scientific discovery.

Globs of an inflammation protein beckon an Alzheimer’s protein and cause it to accumulate in the brain, a study in mice finds. The results, described in the Dec. 21/28 Nature, add new details to the relationship between brain inflammation and Alzheimer’s disease.

Researchers suspect that this inflammatory cycle is an early step in the disease, which raises the prospect of being able to prevent the buildup of amyloid-beta, the sticky protein found in brains of people with Alzheimer’s disease.
“It is a provocative paper,” says immunologist Marco Colonna of Washington University School of Medicine in St. Louis. Finding an inflammatory protein that can prompt A-beta to clump around it is “a big deal,” he says.

Researchers led by Michael Heneka of the University of Bonn in Germany started by studying specks made of a protein called ASC that’s produced as part of the inflammatory response. (A-beta itself is known to kick-start this inflammatory process.) Despite being called specks, these are large globs of protein that are created by and then ejected from brain immune cells called microglia when inflammation sets in. A-beta then accumulates around these ejected ASC specks in the space between cells, Haneke and colleagues now propose.
A-beta can directly latch on to ASC specks, experiments in lab dishes revealed. The two proteins were also caught in close contact in brain tissue taken from people with Alzheimer’s disease. Researchers didn’t see any ASC specks mingling with A-beta in the brains of people without the disease.
Mice engineered to produce lots of A-beta had telltale signs of its accumulation in their brains at 8 and 12 months of age, roughly comparable to middle age in people. But in mice that also lacked the ability to produce ASC specks, this A-beta brain load was much lighter, and these mice performed better on a memory test. Similar reductions in A-beta loads came when researchers used an antibody to prevent A-beta from sticking to ASC specks, results that suggest the specks are needed for A-beta to clump up.

The details show “a quite new and specific mechanism” that’s worth exploring for potential treatments, says Richard Ransohoff, a neuroinflammation biologist at Third Rock Ventures, a venture capital firm in Boston.

To be effective as a treatment, an antibody like the one in the study that kept A-beta from sticking to ASC would need to be able to enter the brain and persist at high levels — a big challenge, Ransohoff says. Still, the results are promising, he says. “I like the data. I like the line of experimentation.”

Many questions remain. The results are mainly from mice, and it’s not clear whether ASC specks and A-beta have similar interactions in human brains. Nor is it obvious how to stop the A-beta from accumulating around the specks without affecting the immune system more generally.

What’s more, the role of the microglia immune cells that release ASC specks is complex, Colonna says. In some cases, microglia serve as brain protectors by surrounding and sequestering sticky A-beta plaques in the brain (SN: 11/30/13, p. 22). But the current results suggest that by releasing ASC specks, the same cells can also make A-beta accumulation worse. The dueling roles of the cells — protective in some cases and potentially harmful in others — make it challenging to figure out how to tweak their behavior therapeutically, Colonna says.