Each year, about eight million tons of plastic float out to sea.

Pacific Garbage Patch May Be 16 Times Bigger Than Once Thought

<div class="related"></div>
<p>
 In the vast stretch of ocean between California and Hawaii, there’s a patch of plastic twice the size of Texas—and it’s between four and 16 times the size scientists previously thought.
</p>
<p>
 Researchers drew this surprising conclusion by analyzing 7,000 aerial photographs of the so-called Great Pacific Garbage Patch. This method, they say, is more precise than earlier estimates made based on observations from boats. But there’s likely a secondary reason that the patch is so much larger than last estimated: it’s growing.
</p>
<p>
 Laurent Lebreton, the lead researcher of the study, saw the growing patch for himself. Here are Merrit Kennedy and Christopher Joyce, reporting for NPR:
</p>
<blockquote>
<p>
 Lebreton, an oceanographer with The Ocean Cleanup Foundation, describes the experience of flying over the trash.
</p>
<p>
 “You start to see one debris, two debris, three debris and so on,” he told NPR. “Oh, there’s a crate, oh, there’s a buoy, oh, there’s a bottle. And it’s crazy because there’s nothing else around. There’s no land mass, there’s no humans, there’s nothing.”
</p>
</blockquote>
<div class="ad"></div>
<p>
 About eight million tons—that’s roughly 200,000 tractor trailers’ worth—of plastic garbage drifts out to sea each year. Half of what makes it to the garbage patch is fishing nets; much of the rest is debris that comes through rivers or washes off coasts. Based on the Japanese text discernible on about 30% of the debris, researchers suspect that a great deal of it was washed out to sea by the tsunami that struck Japan in 2011.
</p>
<p>
 But the patch, created by a Pacific Ocean-wide whirlpool caused by the Earth’s rotation, only accounts for about 76,000 tons of plastic. While that’s a lot, it’s less than a tenth of what gets washed to sea in a single year. No one knows exactly what happens to the rest. Researchers believe that much of the less buoyant plastic sinks to the seafloor, while some other portion of it gets consumed by sea creatures or washed back up to shore.
</p>
<p>
 As for the plastic that does make it to the patch, out of sight does not mean out of mind. As the debris breaks down into microplastics, or often-invisible particles less than five millimeters across, it’s easily ingested by marine life and begins to make its way up the food chain. Scientists are
 <a href="http://theconversation.com/how-microplastics-make-their-way-up-the-ocean-food-chain-into-fish-69148">
  still working on understanding
 </a>
 how ingesting microplastics can harm different living things.
</p>
<p>
 Before attempting a cleanup, researchers say that the first priority needs to be prevention—keeping fishing materials from getting lost at sea and stopping debris from leaving our coasts in the first place.
</p>
<div class="newsletter"></div>
<p>
 Photo credit:
 <a href="https://commons.wikimedia.org/wiki/File:Litter_on_Singapore%27s_East_Coast_Park.jpg">
  vaidehi shah / Wikimedia Commons (CC BY 2.0)
 </a>
</p>

<p>In the vast stretch of ocean between California and Hawaii, there’s a patch of plastic twice the size of Texas—and it’s between four and 16 times the size scientists previously thought.</p>

Archaeologists found these 115,000-year-old shell beads—the oldest personal ornaments known in the world.

Neanderthals Could Be the World's Oldest Artists

<div class="related"></div>
<p>
</p>
<p>
 For a long time, it seemed like art has separated humans from our less cultured relatives. But the recent discovery of cave art predating human settlement has challenged that story.
</p>
<p>
 In southeastern Spain, archaeologists discovered more than a dozen paintings dating back to more than 65,000 years ago—before the first humans arrived in Europe. The artists, most likely Neanderthals, mixed pigments, made seashell beads, and painted symbols of their lives on the walls of caves. These discoveries suggest that our reputedly boorish cousins were just as sophisticated as humans.
</p>
<div class="ad"></div>
<p>
 In the same caves, archaeologists also found 115,000-year-old shell beads—the oldest personal ornaments known in the world. The wide range of dates—all of which predate
 <i>
  Homo sapiens
 </i>
 —make it unlikely that an isolated group of precocious Neanderthals were responsible. More likely, it suggests the species as a whole was innately creative.
</p>
<p>
 Here’s Michael Greshko, reporting for National Geographic:
</p>
<blockquote>
<p>
 The [discoverers] argue that, despite their oafish reputations in pop culture, Neanderthals were the cognitive equals of Homo sapiens. If their results hold, the finds imply that the smarts underpinning symbolic art may date back to the common ancestor of
 <i>
  Homo sapiens
 </i>
 and Neanderthals, some 500,000 years ago.
</p>
<p>
 “Neanderthals appear to have had a cultural competence that was shared by modern humans,” says John Hawks, a paleoanthropologist at the University of Wisconsin-Madison who wasn’t involved with the study. “They were not dumb brutes, they were recognizably human.”
</p>
</blockquote>
<p>
 The find clarifies a murky narrative that has taken shape over recent decades, which depicts Neanderthals as more sophisticated than their stereotype. During this time, archaeologists at various sites in Europe found paintings and crafts from around 40,000 years ago, when Neanderthals lived alongside early humans. Yet it was hard to prove that any of this art had been made by Neanderthals, and few archaeologists humored the idea.
</p>
<p>
 While that question now appears to be settled, the debate over what it implies about human evolution is just beginning.
</p>
<div class="newsletter"></div>
<p>
 Photo credit: J. Zilhão
</p>

<p>The recent discovery of cave art predating human settlement is challenging the age-old narrative that symbolism makes us human.</p>

The Supercomputer That Could Map the Human Brain

<div class="related"></div>
<p>
 Bobby Kasthuri has a problem.
</p>
<p>
 In an effort to understand, on the finest level, what makes us human, he’s set out to create a
 <a href="https://video.wired.com/watch/neuroscientist-explains-one-concept-in-5-levels-of-difficulty">
  complete map of the human brain
 </a>
 : to chart where every neuron connects to every other neuron. The problem is, the brain has more connections than the Milky Way has stars. Just one millionth of the organ contains more information than all the written works in the Library of Congress. A map of the brain would represent the single largest dataset ever collected about anything in the history of the world.
</p>
<figure class="richtext-image full-width">
<img alt="" class="richtext-image full-width" src="https://www.pbs.org/wgbh/nova/media/images/mira-cables-cropped.width-990_56RHYBi.png"/>
<figcaption>A supercomputer, called Mira, at Argonne National Laboratory
        
    </figcaption>
</figure>
<p>
 Making that map seems like
 <a href="http://www.pbs.org/wgbh/nova/next/body/significant-science-of-2013-brain-mapping-gets-big-boost/">
  a task that could consume not just one lifetime
 </a>
 , but dozens. Yet in just three years, it might just be possible.
</p>
<p>
 Kasthuri, a neuroscientist at Argonne National Laboratory, is one of many scientists whose research will use a new supercomputer the lab is building, which is scheduled to be deployed by 2021. The computer, called
 <a href="http://www.sciencemag.org/news/2018/02/racing-match-chinas-growing-computer-power-us-outlines-design-exascale-computer">
  Aurora 21
 </a>
 , will run one quintillion operations in parallel—a billion billion calculations—putting it on par with the processing power of the human brain. For the U.S., which has lagged behind China in an intensifying supercomputing race since 2013, this milestone—exascale computing power—is both a national status symbol and a scientific game-changer.
</p>
<p>
 The demands for a simulation of the brain are immense, and just building a computer like Aurora 21 is a massive undertaking. The finished computer
 <a href="https://www.hpcwire.com/2018/04/11/us-plans-1-8-billion-spend-on-doe-exascale-supercomputing/">
  is expected to cost
 </a>
 hundreds of millions of dollars. It will occupy around a quarter-acre, have thousands of miles of wiring, and, if supercomputer trends continue, draw as much electricity as a medium-sized city.
</p>
<p>
 Aurora 21 is designed for
 <a href="https://www.livescience.com/6392-9-super-cool-supercomputers.html">
  more than just simulating our brains
 </a>
 . It will be able to perform computationally demanding simulations for tasks as diverse as predicting the weather, tracing the evolution of the cosmos, and understanding how new medicines will interact with the human body.
</p>
<div class="pullquote">
<p>
 “The metric of success is what kind of science you enable.”
</p>
</div>
<p>
 Every computing milestone brings new possibilities for research, says Rick Stevens, Argonne’s associate laboratory director for computing, environment, and life sciences. But this one holds particular promise for neuroscience. In providing the capacity to simulate the brain, the supercomputer could illuminate the largely mysterious processes that underpin human learning, behavior, and even psychiatric disorders.
</p>
<p>
 The promise of breakthroughs like these drive this arms race, says Michela Taufer, professor of high-performance computing at the University of Tennessee, Knoxville. The victor won’t necessarily have the biggest computer or the most medals, she says. “The metric of success is what kind of science you enable.”
</p>
<h2>
 Mapping the Brain
</h2>
<p>
 Early in his career, Kasthuri was struck by the fact that a fruit fly hatches from its egg fully competent, already knowing how to fly, while a human baby is born so utterly helpless that it’ll die of starvation without someone to feed it. He knew that this helplessness, the years we spend developing into functional adults, had to be key to what makes us human.
</p>
<p>
 “We trade off being competent for being able to learn almost anything,” Kasthuri says. We’re born with a blank slate of some 100 billion neurons that get arranged and rearranged over time to create the hardware we run on.
</p>
<p>
 But no one really understands how, exactly, that hardware is wired. So it’s at the level of these neurons that Kasthuri has set out to explore the brain.
</p>
<p>
 With the help of Aurora 21, he’ll be able to piece together millions of two-dimensional images, reconstructing the brain in three dimensions—essentially creating a map, known as a connectome. In many ways, it’s like a city map. “That map of the streets in the city is going to tell you something about what that dynamic city is like,” Kasthuri says, like which parts sustain the most traffic and which parts are directly connected to others.
</p>
<p>
 An exascale computer would be the first machine capable of crunching through such a massive amount of data at an efficient pace—in theory, letting scientists like Kasthuri map multiple brains. “There’s no way we want to do just one brain,” Kasthuri says. The most interesting findings, he expects, will come from comparisons. How does the connectome vary between two adults with different skills? Between an adult and a baby?
</p>
<p>
 Kasthuri even hopes to compare a human brain with that of an octopus. Our last common ancestor was probably some
 <a href="http://www.latimes.com/books/la-ca-jc-other-minds-octopus-20161128-story.html">
  worm-like creature that lived 600 million years ago
 </a>
 , meaning that the octopus, which can learn and solve problems much like humans, evolved independently. But Kasthuri wonders what structural principles our brains share and what those principles reveal about how we think and learn. “Is there only one plan for a brain that can problem-solve?” Kasthuri wonders. “Or is there more than one way to skin that cat?”
</p>
<figure class="richtext-image full-width">
<img alt="" class="richtext-image full-width" src="https://www.pbs.org/wgbh/nova/media/images/summit-innards.width-990_OMQY4WT.jpg"/>
<figcaption>The Aurora 21 supercomputer will perform computationally demanding simulations for tasks as diverse as predicting the weather, tracing the evolution of the cosmos, and understanding how new medicines will interact with the human body.
        
    </figcaption>
</figure>
<p>
 That’s not something an unaided human could discern. It would be like looking at New York City’s roads, subway lines, air traffic, and shipping lanes and trying to understand where everyone is going. Fortunately, it’s just the job for a high-performance computer. Stevens, who has been working on plans for Aurora 21 since 2007, jokes that it helps that supercomputers don’t get bored poring over millions of images. “We need this kind of idiot-savant brain to understand the real brain,” he says.
</p>
<h2>
 Beyond the Structural Map
</h2>
<p>
 The structural map is just one part of the story, though. With just a street map, Kasthuri says, you never really know where traffic might build up or why. Likewise, the structure of the brain is just a starting point.
</p>
<p>
 Kasthuri hopes to combine a structural map with collaborators’ maps of the brain’s electrical activity, or “traffic,” to see how the two together influence a person’s learning and behavior.
</p>
<div class="pullquote">
<p>
 Many disorders, such as autism and schizophrenia, are likely rooted in anomalies in the structure of neurons.
</p>
</div>
<p>
 If successful, the technology could make waves in the medical field. Susie Huang, a radiologist at Massachusetts General Hospital and researcher with the
 <a href="http://www.humanconnectomeproject.org/">
  Human Connectome Project
 </a>
 , says that many disorders, such as autism and schizophrenia, are likely rooted in anomalies in the structure of neurons.
</p>
<p>
 “A lot of how we diagnose disease is looking under a microscope and saying, ‘OK, these cells are altered, so therefore you have this kind of disease,’ ” she says. But MRIs and other current brain-imaging methods are too coarse and can’t easily suss out such anomalies. They can’t tie together cause and effect.
</p>
<p>
 A fine-grained map of the brain could change that, she says, and help doctors diagnose psychiatric disorders or possibly even predict them.
</p>
<div class="ad"></div>
<h2>
 A Changing Field
</h2>
<p>
 For other neuroscientists, like Columbia University’s Rafael Yuste, the most exciting part is not the map itself but how a national lab for neuroscience could transform the field. “Neuroscience has operated always a little bit like a ma-and-pa store,” he says, with small labs working within the limits of their budgets and the tools they can develop. But more recently, it’s begun to outgrow that model.
</p>
<p>
 Kasthuri says that neuroscience has quietly evolved into a big-data field—“and we didn’t realize it as a community till five or ten years ago.”
</p>
<p>
 Other fields have had to cope with similar growing pains. It’s a phase that the field of physics outgrew decades ago as researchers around the world started getting their data from large observatories and particle accelerators. Now, Yuste and Kasthuri believe, neuroscience needs to scale up, too.
</p>
<p>
 Aurora 21 will help catalyze that transformation. It’s happened in other fields like physics, where massive, expensive tools push scientists to work together more by sharing time on the machines to gain access to their potential for discovery. In the process, those collaborations advance the field in a way that a lone machine or hundreds of independent scientists never could. Yuste hopes that this is the beginning of more collaborative and ambitious neuroscience.
</p>
<p>
 Yuste led the team that first proposed a detailed map of the brain’s activity in the summer of 2011 at a meeting discussing the future of the field. He argued that the holy grail of neuroscience was to break the neural code—that is, to read the activity of every neuron in the brain and map that activity to a behavior. It was a goal separate from Kasthuri’s connectome but similar in scope. “Many people were very critical,” Yuste recalls. “They said that you couldn’t do it—it was impossible.”
</p>
<p>
 Then, he says, George Church, one of the pioneers of the
 <a href="http://theconversation.com/explainer-what-is-the-human-genome-project-7559">
  Human Genome Project
 </a>
 , stood up from the seat next to him. Church said he’d heard these criticisms before. People had said similar things about the Human Genome Project, and they’d been wrong. Yuste says that’s when the conversation shifted.
</p>
<p>
 Church is more modest about his role. “I’m not one to twist arms,” Church says. “There are a lot of things that I’ve run up the flagpole that, if nobody salutes, I just slink away with my tail between my legs.” But he says the moment was right for this one. The technology, the excitement, the ambition—they were all there.
</p>
<p>
 Despite the
 <a href="https://qz.com/1128704/china-has-way-more-of-the-worlds-most-powerful-computers-than-the-us/">
  stiff competition
 </a>
 between the U.S. and China, ultimately the supercomputer age is not about any one country or any one project, Taufer says, but about the diversity of science being made possible. “I look at this machine and I see a key to a solution.”
</p>
<div class="newsletter"></div>
<p>
 Yuste and Church supported what ended up being the multi-billion-dollar BRAIN Initiative, an Obama-era grand challenge which funds research that attempts to understand how the brain works. Both Yuste’s and Kasthuri’s work on mapping the brain are just two of the “impossible” projects that the initiative has set in motion.
</p>
<p>
 “It’s not exactly analogous, but I often think of the moonshot,” Kasthuri says. He thinks about how the average age of a NASA scientist was only 28 when the first crew landed on the moon in 1969 and about how the challenge fascinated a generation of scientists.
</p>
<p>
 Kasthuri can’t be sure how his project will play out. In some respect, it’ll probably fail, he says with a laugh. “It seems enormous and monumental. A lot of those things don’t work,” he adds. “That’s just the nature of trying to do something incredibly hard.” But he’s inspired by having a challenge that captures his imagination.
</p>
<p>
 For Taufer, the high-performance computing scientist, supercomputers like Aurora 21 swing open the door to possibilities that don’t exist in real life—the ability to test medicine, infrastructure, even weaponry free of the cost, safety, and ethical concerns that constrain real-life experiments.
</p>
<p>
 But as grandiose as the possibilities are, Taufer emphasizes that the applications will work their way into our everyday lives, from predicting the weather to assessing the safety of our aging bridges and fighting common diseases like Alzheimer’s.
</p>
<p>
 Photo credits:
 <a href="https://images.nigms.nih.gov/PublicAssets/5871/Connectome%20MRI%20movie%20screen%20shot3.jpg">
  NIH
 </a>
 ,
 <a href="https://www.alcf.anl.gov/files/field/image/mira-cables.png">
  Argonne National Laboratory
 </a>
 ,
 <a href="https://www.flickr.com/photos/olcf/26037021428">
  Jason Richards/Oak Ridge National Laboratory/Flickr (CC BY 2.0)
 </a>
</p>

<p>A planned “exascale” supercomputer may be powerful enough to map the human brain.</p>

How Dust Built the Universe

<div class="related"></div>
<p>
 If you’ve ever driven into the sunset with a dirty windshield or taken a drive after a snowstorm, your windshield caked with salt, you can probably relate to one of astronomers’ ongoing frustrations: seeing through dust.
</p>
<p>
 Cosmic dust, which collects in galaxies in loose fogs or thick clouds, has often plagued astronomers. The tiny grains, each 10,000 times smaller than the eye of a needle, absorb light, scatter it, or change its wavelength so it’s invisible to the eye. In doing so, dust steals some of the few clues we have to understand the nature of the universe.
</p>
<p>
 But astronomers are discovering that dust plays important roles in both creating our universe and helping us understand it. It plants the seeds for stars, planets, and life as we know it. In the past two decades, astronomers studying dust have pulled back the curtain on important pieces of the universe that were hiding in plain sight. The more we learn about dust, the more we realize that it is part of the puzzle—not the rascal hiding the puzzle pieces.
</p>
<h2>
 Fertilizing the Universe
</h2>
<p>
 In the clouds of swirling gas that produce stars and planets, dust serves as a wingman for hydrogen. As a cloud condenses under its own gravity, star formation begins when hydrogen atoms meet and form molecules. But the compressing gas raises temperatures to the point where hydrogen begins whizzing around too fast to form bonds. It’s easier for the atoms to latch onto a piece of relatively big, slow dust. There, on the dust’s surface, two atoms can form a bond, making forming the first building blocks of a star. But dust is more than a matchmaker. As nearby stars blaze hot and bright in the ultraviolet, clouds of dust can act as a shield, sheltering stars-to-be from the barrage of radiation, which can break their chemical bonds and thwart their path to stardom.
</p>
<figure class="richtext-image full-width">
<img alt="" class="richtext-image full-width" src="https://www.pbs.org/wgbh/nova/media/images/milky-way2_1024x576.width-990_lZHCc32.jpg"/>
<figcaption>The stars and dust clouds of the Milk Way
        
    </figcaption>
</figure>
<p>
 When the obstacles are finally overcome, a new star blossoms out of a cloud. Some of the remaining dust and gas begins to spin around the star and flatten into a disk. Specks of dust collide, and as their gravity increases, they pull more dust and gas onto their surface, accreting material. Over time, they become pebbles, then boulders and, sometimes, a few million years later, planets.
</p>
<p>
 Xuening Bai, a research associate at the Harvard Center for Astrophysics, studies the processes that create planets and the stuff of life. Without dust, he says, the world would be a different place.
</p>
<p>
</p>
<div class="ad"></div>
<h2>
 Seeing the Universe in a New Light
</h2>
<p>
 Indeed, most of what we see in space—not to mention all that we are, all that we eat, all that we breathe—owes its existence, in some way, to a grain of dust that formed the seed of a star or planet. But despite its fundamental importance, astronomers have only begun to understand what dust really is and how it affects the way we see the universe.
</p>
<div class="pullquote">
<p>
 Most of what we see in space owes its existence to a grain of dust.
</p>
</div>
<p>
 Dust itself is a mishmash of mostly carbon-based ashes cast off from dying stars. “It’s a catch-all term for what we would refer to on Earth as soot,” says Caitlin Casey, an astronomer at the University of Texas at Austin. Until recently, this “soot” was poorly understood. For centuries, the practice of astronomy was limited to what people could observe at visible wavelengths—in other words, what people could actually see. Dust absorbs light that can be seen by the naked eye and re-emits it at longer, infrared wavelengths, which are invisible to us. As a result, for most of history, dust was seen only as dark blobs, riddling galaxies with holes.
</p>
<p>
 Then, in the 1960s, the first infrared telescopes pioneered the study of dust emissions. But these telescopes were not able to detect all radiation from dust. Very distant galaxies, such as the ones Casey studies some 10 billion light-years away, are receding so quickly that the light re-emitted by their dust gets stretched, shifting its wavelength into the submillimeter range and making the galaxies practically invisible, even in infrared telescopes.
</p>
<p>
 It wasn’t until 1998 that a group of astronomers in Mauna Kea, Hawaii, pointed a submillimeter telescope at a blank field of sky and made a discovery that rocked the field. A few years earlier, the Hubble Space Telescope had revealed that this blank sky was swarming with distant galaxies, but now, an entirely new population of galaxies lit up in submillimeter wavelengths. It was like turning on a light in a room where astronomers had fumbled in the dark for centuries. Galaxies glowed with dust, and the earliest, most distant galaxies, showed the most dust of all.
</p>
<h2>
 Dust Bunnies in the Edges of the Universe
</h2>
<p>
 Submillimeter wavelengths were the last piece of the electromagnetic spectrum to be observed by astronomers, so in some ways, the 1998 discovery seemed to complete a picture of the universe. Large swaths of the sky were now imaged at every wavelength. Dust, the quiet catalyst behind star formation, had been unmasked.
</p>
<p>
 But in another way, astronomers had merely stumbled upon more pieces to a puzzle they thought they had completed. Because if dust comes from stars, the universe should get dustier the more stars have lived and died. What business did the earliest galaxies have being so dusty? The universe has been around for nearly 14 billion years, but most of these dusty galaxies formed when the universe was a tender 2 or 3 billion years old. By then, only a few generations of stars had ever existed. So where did all that dust come from?
</p>
<div class="pullquote">
<p>
 It was like turning on a light in a room where astronomers had fumbled in the dark for centuries.
</p>
</div>
<p>
 Desika Narayanan, an astronomer at the University of Florida, probes for answers by developing numerical simulations to model the early universe. He says that one clue lies in the earliest galaxies, which were probably ungainly, messy galaxies a far cry from the elegant spiral that is our Milky Way. Galaxies like ours pop out a star about once or twice a year. But these old, dusty galaxies were firecrackers, bursting with up to 1,000 to 2,000 new stars a year. As the first stars died, dust billowed from them and filled the galaxy—perhaps enough to account for the levels of dust seen today.
</p>
<div class="newsletter"></div>
<p>
 But telescope data can only confirm so much. In the short lifetime of submillimeter astronomy, Narayanan says, telescope sensitivity has drastically improved, outpacing even camera phone technology, which has raced from blurry images taken by flip-phones to the latest, sharpest shots on iPhones in roughly the same period.
</p>
<p>
 Still, even the greatest telescopes strain against the vastness of the universe. They have to be extremely large to detect and resolve light from the most distant galaxies. At 15 meters in diameter, the world’s largest submillimeter dish belongs to the James Clerk Maxwell Telescope at the summit of Mauna Kea, Hawaii. It was the first telescope to detect these galaxies in 1998. In Chile, the Atacama Large Millimeter Array/Submillimeter Array, or ALMA, is made up of 66 dishes that can be arranged to span nearly 10 miles in an attempt to resolve the universe’s faintest, most distant galaxies.
</p>
<p>
 Unbox the universe with Greg Kestin in the
 <a href="https://www.youtube.com/watch?v=NGv77BceBXg">
  latest episode
 </a>
 of "What the Physics?!"
</p>
<p>
 It’s no coincidence that both of these observatories were built in extreme environments, both well above 10,000 feet and in dry places where the air is thin. Water vapor in the air soaks up most of the infrared radiation passing through it that’s so critical to observing dust. Meanwhile, the Earth itself radiates enthusiastically in the infrared, creating a noisy background for any signal that does get through. “Doing infrared astronomy from the ground is like trying to observe a star in the daylight out of a telescope made of light bulbs,” George Rieke, an infrared astronomer, once said.
</p>
<p>
 For now, this difficulty has left some mysteries intact. Although astronomers are better able to observe galaxies and create simulations, some galaxies remain too old and too dusty to fit into existing models. The size, peculiar structure, and dustiness of early galaxies is not fully explained.
</p>
<p>
 The next surge in science, expected to help explain some of the mysteries surrounding dusty, star-forming galaxies, will come from the James Webb Space Telescope, a massive instrument with a six-and-a-half-meter dish—a piece of highly polished metal as wide as a giraffe is tall—set to for launch in 2018. Free from the interfering atmosphere, this telescope will peer into the dusty edges of space in finer detail than any other telescope.
</p>
<p>
 Narayanan says that the astronomy community is excited for these new measurements, and expect it will reveal new avenues for exploration. “Immediately, you start to open up as many questions as you think you’re going to answer,” he says.
</p>
<h2>
 Twenty Years of Dusty Galaxies
</h2>
<p>
 On July 31, astronomers will meet in Durham, U.K., to celebrate the 20th anniversary of the discovery of dusty star-forming galaxies and share what they have learned over the last two decades. But the elusiveness of hard data has left many questions about ancient dusty galaxies still open for debate. “I suspect we’re still going to walk away from this meeting saying, ‘Theorists still haven’t figured out where they come from,’” Narayanan says.
</p>
<p>
 But the mystery is part of what fascinates him. Twenty years ago, “We had no idea these things existed,” he says. “Then they just lit up in the infrared and have posed a huge challenge ever since then.”
</p>
<p>
 Despite all the research on dust, it is only a small fraction of the universe. Even in moderately dusty galaxies like our own, dust accounts for less than 1% of the mass. Yet its ability to transform the light passing through it completely changes the way we see the universe.
</p>
<p>
 For astronomers like Casey and Narayanan, this leaves plenty of mysteries to probe. “It’s really cool to me that something that is so negligible in terms of the mass budget of the universe can have such a tremendous impact on how we perceive it,” Casey says. “There is so much to discover and rediscover.”
</p>
<p>
 Image credit:
 <a href="https://apod.nasa.gov/apod/image/0801/16500feetmilkywaykc2_brunier.jpg">
  NASA
 </a>
</p>

<p>Astronomers are uncovering the role of dust in creating our universe—and in helping us understand it.</p>

Artist's rendering of how the first stars in the universe may have looked.

Signals From the First Stars Could Show Hints of Dark Matter

<div class="related"></div>
<p>
 Astronomers studying the dawn of the universe may have made a new detection of dark matter interacting with ordinary matter.
</p>
<p>
 In the four decades since its discovery, dark matter has eluded every attempt to directly detect it. It doesn’t interact with light or regular matter, so the only way astronomers have been able to infer its presence is in how its gravity tugs on regular matter.
</p>
<p>
 But when the universe first went from dark to light, 180 million years after the Big Bang, the particles in the cold gas throughout the universe seem to have bumped up against their dark-matter cousins, according to
 <a href="https://www.nature.com/articles/nature25791">
  new research
 </a>
 published in
 <i>
  Nature
 </i>
 on Wednesday. It’s the first sign that dark matter interacts with regular matter through anything other than gravity.
</p>
<p>
 “Finding this minuscule signal has opened a new window on the early universe,” lead author Judd Bowman of Arizona State University told The Guardian.
</p>
<p>
 The discoverers, led by Bowman, originally set out to find what’s known as “cosmic dawn”: the moment in time when the first stars flicked on. Any light these stars emitted is too faint for ordinary telescopes to pick up, but in theory, the moment should be detectable in a radio signal released when light from these stars excited the cold gas around it.
</p>
<p>
 For a long time, there was little hope. The signal seemed impossible to disentangle from other radio signals reaching Earth. But the milestone discovery published on Wednesday, radio astronomers using a small, table-shaped antenna in remote western Australia detected this blip in history.
</p>
<div class="ad"></div>
<p>
 As if that weren’t momentous enough, it came with a surprise: The signal was more than twice as strong as they predicted.
</p>
<p>
 Their predictions were based on the temperature of the gas pervading the early universe. They knew this was the coldest point in the universe’s history: Heat from the Big Bang had faded, and the first stars had yet to heat up the gas around them. But in order for the signal to be as strong as it was, the gas must have been even colder than they predicted.
</p>
<p>
 Something has to have cooled down the gas—and the most likely contender is cold dark matter. If this interpretation is correct, it reveals more than ever about what these mysterious particles may be like.
</p>
<p>
 Scientists currently know little about the nature of dark matter. “We have no other positive statement about it, really, other than it exists and it’s 25% of the universe,” said Matt Buckley, a professor of astrophysics at Rutgers University who studies the particle nature of dark matter.
</p>
<p>
 Buckley emphasizes the need for caution in interpreting these new results, which are based on a single observation. But if the findings are confirmed, he said, the result would be tremendously exciting. “It wouldn’t tell us what it is, but it would tell us where to start looking.”
</p>
<div class="newsletter"></div>
<p>
 Image credit: N.R.Fuller/National Science Foundation/Nature
</p>

<p>When the universe first went from dark to light, 180 million years after the Big Bang, the particles in the cold gas throughout the universe seem to have bumped up against their dark-matter cousins.</p>

The 5,000-year-old tattoos on two Ancient Egyptians suggest that the practice of tattooing has a longer history than we thought.

Ancient Egyptians May Have Worn the World’s First Tattoos

<div class="related"></div>
<p>
 The practice of tattooing appears to be 1,000 years older than anyone knew.
</p>
<p>
 Since his discovery about 100 years ago, an Egyptian mummy housed in the British Museum appeared to have nothing more special than a dark smudge on his right arm. But recent infrared imaging revealed the outlines of two animals in the skin: a bull and a Barbary sheep. The discovery makes these the oldest figurative tattoos ever found.
</p>
<p>
 Soon after, anthropologists found symbols inked into the shoulder of a second mummy, a female from the same time and region. She wore a more abstract tattoo, a series of four tilde-shaped lines.
</p>
<p>
 The mummies had been unearthed from shallow graves, naturally preserved by the desert climate. In both cases, the tattoos were likely made of soot and probably served as a status symbol. Anthropologists speculate that the man’s tattoos represented strength and virility, like the bull in ancient Egyptian legends, while the woman’s tattoos signified bravery and knowledge of magic.
</p>
<p>
 For the people studying these tattooed mummies, the symbols in the ink hold meaning as well.
</p>
<div class="ad"></div>
<p>
 Here’s Pallab Ghosh,
 <a href="http://www.bbc.com/news/science-environment-43230202">
  writing
 </a>
 for BBC News:
</p>
<blockquote>
<p>
 Daniel Antoine, one of the lead authors of the research paper and the British Museum’s Curator of Physical Anthropology, said that the discovery had “transformed” our understanding of how people lived in this era.
</p>
<p>
 “Only now are we gaining new insights into the lives of these remarkably preserved individuals. Incredibly, at over 5,000 years of age, they push back the evidence for tattooing in Africa by a millennium,” he told BBC News.
</p>
</blockquote>
<p>
 Until now, the oldest known tattoo belonged to a mummy found in the Alps, nicknamed Ötzi, who lived earlier than 3100 B.C. and whose skin was marked with a series of lines. The discovery of these more ornately tattooed ancient Egyptians suggests that tattooing was likely widespread across the ancient world, much like it is today.
</p>
<div class="newsletter"></div>
<p>
 Photo credit:
 <a href="https://phys.org/news/2018-03-egyptian-mummies-oldest-figurative-tattoos.html">
  British Museum / phys.org
 </a>
</p>

<p>At over 5,000 years of age, these soot-based tattoos push back the evidence for tattooing in Africa by a millennium.</p>

This Machine Learning System Thinks About Music Like You Do

<section class="entry-content instapaper_body clearfix" itemprop="articleBody">
<div class="add-container clearfix" id="add-block-placer">
</div>
<p>
  If you’ve ever let Spotify DJ your party and then found yourself asking, a half an hour in, “Spotify, what are you
  <em>
   thinking
  </em>
  ?”—well, it actually may be thinking a lot like you. Scientists reported in
  <a href="https://www.eurekalert.org/pub_releases/2018-04/miot-msp041718.php">
   a new study
  </a>
  from the Massachusetts Institute of Technology that they’ve created a machine-learning system that processes sound just like humans, whether it’s discerning the meaning of a word or classifying music by genre.
 </p>
<p>
  It’s the first artificial system to mimic the way the brain interprets sounds—and it rivals humans in its accuracy. The research, published today in the journal
  <em>
   Neuron
  </em>
  , offers a tantalizing new way to study the brain.
 </p>
<figure class="richtext-image full-width">
<img alt="" class="richtext-image full-width" src="https://www.pbs.org/wgbh/nova/media/images/music-neural-network_1024x576.width-990_qolUDyi.png"/>
<figcaption>Despite the ubiquity of machine-learning systems—in the software that gives you music recommendations, for example—even the engineers who design these systems often don’t know how they “think,” or how human-like their inner workings are.
        
    </figcaption>
</figure>
<p>
  The researchers’ model was based on what’s called a deep neural network, a system whose structure is loosely inspired by neurons, or brain cells. It processes information in layers, with the deepest layers doing the most complex work. Scientists can train systems like these to “learn” human tasks, such as, in this case, interpreting sounds.
 </p>
<p>
  The researchers gave their model two tasks. In one, they played a two-second snippet of speech and tested its ability to identify the middle word. In another, they played two seconds of music and tested how well the model could name the genre. They put background noise under each clip to complicate the job.
 </p>
<p>
  It took thousands of examples to train it, but by the end, the model performed as well as humans. It knew dozens of genres of music—it could tell dubstep from ska or gothic rock. It even made errors in the same places that tripped up humans, struggling the most with the clips played over city sounds.
 </p>
<p>
  But the researchers still weren’t sure if the model was processing these signals the same way a brain does—or if it had found its own way to solve the same problem. To settle that, they needed to look at some brains.
 </p>
<p>
  Lead author Alex Kell, of MIT, examined data from an fMRI scanner to see which regions of the brain were hardest at work as subjects listened to an array of natural sounds. Then he played those same sounds to their model. He found that when the model was processing relatively basic information—such as the frequency of a sound or pattern—that corresponded with one region of the brain. More complex processing—such as discerning meaning—lined up with a different region.
 </p>
<p>
  This suggests that the brain processes information the same way as their model, in a hierarchy that goes from simplest to most complex.
 </p>
<p>
  This ability to connect the inner workings of a deep neural network to the brain is exciting, said Andrew Pfalz, a Ph.D candidate in Experimental Music and Digital Media at Louisiana State University whose research applies neural networks to sound. Despite the ubiquity of machine-learning systems—in the software that gives you music recommendations, for example, or obeys spoken orders to help find you someplace to eat—even the engineers who design these systems often don’t know how they “think,” or how human-like their inner workings are.
 </p>
<p>
  “It’s sort of a black box,” Pfalz said. “The sort of funny part about neural networks is we train them and we see that they produce the right answers, but we don’t necessarily know what’s going on inside.”
 </p>
<p>
  But through many queries, the MIT researchers were able to shed light on which layers of the system were engaged when—and how that aligned with activity in brains processing the same sounds.
 </p>
<p>
  Pfalz appreciates the irony of a machine-learning system originally inspired by the brain (hence the name “neural network”) now helping scientists understand it.
 </p>
<p>
  Yet computer scientist Ching-Hua Chuan, whose research focuses on the use of machine-learning systems to generate music at the University of North Florida, emphasizes the immensity of this claim. “[Neural networks] were never intended to model how our brain works,” she said, adding that the difficulty of peering into the “black box” suggests to her that it would take more research to prove that the model truly mimics the brain.
 </p>
<p>
  The MIT team believes it comes close. If they’re right, it could help scientists understand and simulate how the brain processes sound and other sensory signals, said MIT’s Josh McDermott, senior author on the study. And since running tests on a simulation tends to be quicker, safer, and cheaper than experimenting on a real brain, this could boost some neuroscience research into the fast track.
 </p>
<p>
  Computing power and neural network technology haven’t always been up to the task of modeling parts of the human brain, but these last five years mark the beginning of a new era, Kell said. “A lot of these tasks that have historically been insurmountably difficult for machine systems have actually become solvable.”
 </p>
</section>
<p>
 Image credit: Public Domain
</p>

Samia Bouzid

Articles