Amazing Science

Astronomers using the NASA/ESA Hubble Space Telescope have confirmed the presence of ionized (electrically-charged) buckminsterfullerene in the interstellar medium — the gas and dust that fills interstellar space. The study was published in the Astrophysical Journal Letters.

Buckminsterfullerene, also known as ‘buckyballs,’ consists of 60 carbon atoms (C60) arranged in a hollow sphere. This molecule has been seen in space before. However, this is the first time its ionized version (C60+) has been confirmed to be present in the diffuse interstellar medium. C60 gets ionized when UV light from stars tears off an electron from the molecule.

“The diffuse interstellar medium was historically considered too harsh and tenuous an environment for appreciable abundances of large molecules to occur,” said Dr. Martin Cordiner, a researcher at NASA’s Goddard Space Flight Center and the Catholic University of America, Washington.

“Prior to the detection of C60, the largest known molecules in space were only 12 atoms in size. Our confirmation of C60+ shows just how complex astrochemistry can get, even in the lowest density, most strongly UV-irradiated environments in our Milky Way Galaxy.”

The Milky Way’s interstellar material is primarily located in a relatively flat disk, so lines of sight to stars in the Galactic plane traverse the greatest quantities of interstellar matter, and therefore show the strongest absorption features due to interstellar molecules.

Dr. Cordiner and colleagues used Hubble to analyze the light coming from seven blue supergiant stars in the plane of the Milky Way.

The Southern California erythracarid mite species, Paratarsotomus macropalpis, which is smaller than sesame seed, can run up to 322 body lengths per second, according to a study led by biologist Samuel Rubin of Pitzer College, Claremont. The previous record-holder, the Australian tiger beetle, can run at 171 body lengths per second. By comparison, a cheetah running at 100 km per hour attains only 16 body lengths per second.

“It’s so cool to discover something that’s faster than anything else, and just to imagine, as a human, going that fast compared to your body length is really amazing,” said Mr Rubin, who is the first author of a paper published in the FASEB Journal.

Paratarsotomus macropalpis is a large (up to 0.7 mm long) mite belonging to the family Anystidae. It has long legs and a body nearly twice as long as broad. This species is an endemic to California, the United States, and Upper Baja California, Mexico, and often found running along rocks or sidewalks. Earlier classified as belonging to the mite genus Tarsotomus, it was reclassified in 1999 to the genus Paratarsotomus.

Mr Rubin, his advisor Prof Jonathan Wright of Pomona College, and their colleagues used high frame-rate video cameras to record the mites’ sprints in the lab and in their natural environment.

They were very surprised to find that the Paratarsotomus macropalpis’ speed is equivalent to a human running roughly 2,100 km per hour. They were also surprised to find the mites running on concrete up to 60 degrees Celsius, a temperature significantly higher than the upper lethal temperature of most animals.

“They’re operating at temperatures that seem to preclude activities of any other animal group. We’ve seen them running where there were no other animals visibly active,” Prof Wright said. The mites also are adept at stopping and changing directions extremely quickly, attributes the researchers are investigating further for potential insights that may be relevant to bioengineering applications.

Over the past twenty years, a number of pharmaceutical and biotechnology companies have invested in cancer research and brought many new drug therapies to the market. These drugs have improved the prognosis for patients diagnosed with difficult-to-treat cancers, including non-small cell lung cancer (NSCLC) and multiple forms of leukemia.

A team of researchers who have built an artificially-intelligent writer say they are withholding the technology as it might be used for "malicious" purposes.

OpenAI, based in San Francisco, is a research institute backed by Silicon Valley luminaries including Elon Musk and Peter Thiel.

It shared some new research on using machine learning to create a system capable of producing natural language, but in doing so the team expressed concern the tool could be used to mass-produce convincing fake news.

Which, to put it another way, is of course also an admission that what its system puts out there is unreliable, made-up rubbish. Still, when it works well, the results are impressively realistic in tone - which is why I've shared a sample of it below.

Feeding the system
OpenAI said its system was able to produce coherent articles, on any subject, requiring only a brief prompt. The AI is "unsupervised", meaning it does not have to be retrained to talk about a different topic.

It generates text using data scraped from approximately 8m webpages. To "feed" the system, the team created a new, automated method of finding "quality" content on the internet.

Rather than scrape data from the web indiscriminately, which would have provided a lot of messy information, the system only looked at pages posted to link-sharing site Reddit. Their data only included links that had attracted a "karma" score of 3 or above, meaning at least three humans had deemed the content valuable, for whatever reason.

"This can be thought of as a heuristic indicator for whether other users found the link interesting, educational or just funny," the research paper said.

The AI generates the story word-by-word. The resulting text is often coherent, but rarely truthful - all quotes and attributions are fabricated. The sentences are based on information already published online, but the composition of that information is intended to be unique.

But how good is it? Judge by yourself.

Google has found another way to lure machine learning aficionados to its cloud, offering Deep Learning Containers to get ML projects up and running quicker.

The product consists of a set of performance-optimised Docker containers that come with a variety of tools necessary for deep learning tasks already installed. In the introductory blog entry, Google software engineer Mike Cheng illustrates the idea behind the project with a workflow spanning local prototyping and the integration of cloud tools to an eventual deployment to a cloud service. 

In this scenario, Deep Learning Containers are meant to ensure the availability of all necessary dependencies in the different environments. They are also supposed to improve the scalability of a project and facilitate the use of additional hardware, since for example Nvidia’s GPU tools are already part of the package.

Each of the available container images includes a Python 3 (including packages such as numpy, sklearn, scipy, pandas, and nltk) and a Jupyter environment, conda, the Nvidia tools CUDA, cuDNN, and NCCL2, and a few other supporting packages. They differ however in the framework installed, giving customers the choice between TensorFlow 1.x and 2.x, PyTorch, scikit-learn, and R.

Deep Learning Containers can be used locally and on Google’s Kubernetes Engine, the Compute Engine, or in the AI Platform Training. According to the documentation, Deep Learning Container instances are available free of charge. However, if they are combined with other Google services, such as GKE etc, which makes sense if scalability or more computational power is what you’re going for, the pricing of those offerings applies.

A human protein combined with stem cell therapy has been found to repair stroke damage to the brain, according to a new USC-led study on mice.

Permanent brain damage from a stroke may be reversible thanks to a developing therapeutic technique, a USC-led study has found.

The novel approach combines transplanted human stem cells with a special protein that the U.S. Food and Drug Administration already approved for clinical studies in new stroke patients.

“This USC-led animal study could pave the way for a potential breakthrough in how we treat people who have experienced a stroke,” said Jim Koenig, a program director at the National Institute of Neurological Disorders and Stroke, which funded the research. “If the therapy works in humans, it could markedly accelerate the recovery of these patients.”

Berislav Zlokovic, senior author of a recent Nature Medicine study, and his colleagues identified a protein that spurs neural stem cells to become functional neurons: 3K3A-APC, a variant of the human protein “activated protein C.” The created compound is being tested as a neuroprotectant. Researchers in a National Institutes of Health-funded Phase II clinical trial administer 3K3A-APC to patients who have very recently (within a few hours) suffered from an ischemic stroke, when a clot blocks blood from reaching the brain. About 87 percent of all strokes are ischemic, according to the Centers for Disease Control and Prevention.

However, Zlokovic, director of the Zilkha Neurogenetic Institute at the Keck School of Medicine of USC, said he and his colleagues are the first to use 3K3A-APC to produce neurons from human stem cells grafted into the stroke-damaged mouse brain. “We showed that 3K3A-APC helps the grafted stem cells convert into neurons and make structural and functional connections with the host’s nervous system,” said Zlokovic, a scientific founder of ZZ Biotech, a company devoted to developing therapeutics using variants of activated protein C. “No one in the stroke field has ever shown this, so I believe this is going to be the gold standard for future studies.”

Although other researchers have experimented with grafting stem cells into injured brain areas, they have met with limited success — partially because transplanted stem cells diminish with time. The therapeutic compound stops that from happening.

 very year more than 795,000 people in the United States have a stroke, according to the CDC. These debilitating seizures reduce mobility in more than half of stroke survivors age 65 and older.

Giant squid have the largest eye in the animal kingdom. At up to 10 inches in diameter, people often describe it as the size of a dinner plate -- or, in other words, as big as a human head. Here, National Museum of Natural History staffer Katie Velazco goes eye-to-eye with a preserved example from the Smithsonian's collection.

Why do they need such big eyes? The deep ocean is so dark that bigger eyes probably don't help the giant squids pinpoint and hunt small food. However, drawing in even a bit more light could help them see larger shadowy shifts in the depths -- like those produced by an enormous predator. A study suggested that giant squids could detect a moving sperm whale from 394 feet (120 meters) with those big eyes.

For the first time, astrophysicists have used artificial intelligence techniques to generate complex 3-D simulations of the universe. The results are so fast, accurate and robust that even the creators aren't sure how it all works.

"We can run these simulations in a few milliseconds, while other 'fast' simulations take a couple of minutes," says study co-author Shirley Ho, a group leader at the Flatiron Institute's Center for Computational Astrophysics in New York City and an adjunct professor at Carnegie Mellon University. "Not only that, but we're much more accurate."

The speed and accuracy of the project, called the Deep Density Displacement Model, or D3M for short, wasn't the biggest surprise to the researchers. The real shock was that D3M could accurately simulate how the universe would look if certain parameters were tweaked—such as how much of the cosmos is dark matter—even though the model had never received any training data where those parameters varied.

"It's like teaching image recognition software with lots of pictures of cats and dogs, but then it's able to recognize elephants," Ho explains. "Nobody knows how it does this, and it's a great mystery to be solved." Ho and her colleagues present D3M June 24 in the Proceedings of the National Academy of Sciences. The study was led by Siyu He, a Flatiron Institute research analyst.

Computer simulations like those made by D3M have become essential to theoretical astrophysics. Scientists want to know how the cosmos might evolve under various scenarios, such as if the dark energy pulling the universe apart varied over time. Such studies require running thousands of simulations, making a lightning-fast and highly accurate computer model one of the major objectives of modern astrophysics. D3M models how gravity shapes the universe. The researchers opted to focus on gravity alone because it is by far the most important force when it comes to the large-scale evolution of the cosmos.

The most accurate universe simulations calculate how gravity shifts each of billions of individual particles over the entire age of the universe. That level of accuracy takes time, requiring around 300 computation hours for one simulation. Faster methods can finish the same simulations in about two minutes, but the shortcuts required result in lower accuracy.

Ho, He and their colleagues honed the deep neural network that powers D3M by feeding it 8,000 different simulations from one of the highest-accuracy models available. Neural networks take training data and run calculations on the information; researchers then compare the resulting outcome with the expected outcome. With further training, neural networks adapt over time to yield faster and more accurate results.

Dr. Kyeongjae Cho and his collaborators expanded the vocabulary of the transistor, which may help the microelectronics industry find alternative technologies that will continue the quest for faster processing speed.

Computers and similar electronic devices have gotten faster and smaller over the decades as computer-chip makers have learned how to shrink individual transistors, the tiny electrical switches that convey digital information.

Scientists' pursuit of the smallest possible transistor has allowed more of them to be packed onto each chip. But that race to the bottom is almost over: Researchers are fast approaching the physical minimum for transistor size, with recent models down to about 10 nanometers -- or just 30 atoms -- wide.

"The processing power of electronic devices comes from the hundreds of millions, or billions, of transistors that are interconnected on a single computer chip," said Dr. Kyeongjae Cho, professor of materials science and engineering at The University of Texas at Dallas. "But we are rapidly approaching the lower limits of scale."

To extend the quest for faster processing speed, the microelectronics industry is looking for alternative technologies. Cho's research, published online April 30, 2019 in the journal Nature Communications, might offer a solution by expanding the vocabulary of the transistor.

Conventional transistors can convey just two values of information: As a switch, a transistor is either on or off, which translates into the 1s and 0s of binary language. One way to increase processing capacity without adding more transistors would be to increase how much information each transistor conveys by introducing intermediate states between the on and off states of binary devices. A so-called multi-value logic transistor based on this principle would allow more operations and a larger amount of information to be processed in a single device.

"The concept of multi-value logic transistors is not new, and there have been many attempts to make such devices," Cho said. "We have done it." Through theory, design and simulations, Cho's group at UT Dallas developed the fundamental physics of a multi-value logic transistor based on zinc oxide. Their collaborators in South Korea successfully fabricated and evaluated the performance of a prototype device.

Cho's device is capable of two electronically stable and reliable intermediate states between 0 and 1, boosting the number of logic values per transistor from two to three or four. Cho said the new research is significant not only because the technology is compatible with existing computer-chip configurations, but also because it could bridge a gap between today's computers and quantum computers, the potential next landmark in computing power.

While a conventional computer uses the precise values of 1s and 0s to make calculations, the fundamental logic units of a quantum computer are more fluid, with values that can exist as a combination of 1s and 0s at the same time or anywhere in between. Although they have yet to be realized commercially, large-scale quantum computers are theorized to be able to store more information and solve certain problems much faster than current computers.

"A device incorporating multi-level logic would be faster than a conventional computer because it would operate with more than just binary logic units. With quantum units, you have continuous values," Cho said. "The transistor is a very mature technology, and quantum computers are nowhere close to being commercialized," he continued. "There is a huge gap. So how do we move from one to the other? We need some kind of evolutionary pathway, a bridging technology between binary and infinite degrees of freedom. Our work is still based on existing device technology, so it is not as revolutionary as quantum computing, but it is evolving toward that direction."

The technology Cho and his colleagues developed uses a novel configuration of two forms of zinc oxide combined to form a composite nanolayer, which is then incorporated with layers of other materials in a superlattice.

A team of University of Copenhagen researchers has compiled the first and only evidence that narwhals and beluga whales can breed successfully. DNA and stable isotope analysis of an anomalous skull from the Natural History Museum of Denmark has allowed researchers to confirm the existence of a narwhal-beluga hybrid.

For the first time, scientists can record cells communicating in real time, opening the floodgates for new developments in cell therapy and other areas within cell biology.

A new technology discovered by UConn School of Dental Medicine researchers records cellular communication in real time -- providing a closer look into the dynamics of cell secretion and a greater understanding of how cells repair tissue.

In a recent study published in the Proceedings of the National Academy of Sciences, Kshitiz Gupta, an assistant professor (who goes by just his first name), and Yashir Suhail, a postdoctoral fellow, in the Dental School's Department of Biomedical Engineering, unlocked a breakthrough technology platform. Now for the first time, scientists can record cells communicating in real time, opening the floodgates for new developments in cell therapy and other areas within cell biology.

Cells -- like humans -- are in constant communication with each other. Whereas humans exchange words, cells deliver and receive messages through secreting proteins and changing their behavior accordingly. When we listen to humans speak to each other, we can understand how words are placed into sentences and how the conversation moves back and forth. When it comes to recording communication between cells, however, the key characteristics of the conversation have been largely unknown until now.

Communication between cells is necessary to maintain most functions in the body and can also help the body properly respond to an external cue -- such as an ailment or injury. Current technology only allows broad snapshots of these protein secretions. "This is akin to detecting what words were spoken in a sentence, but not really knowing their placement, the inflection, and tone of the message," says Kshitiz.

Prior to the current findings, he adds, understanding of the language of communication between cells has been very limited, and did not capture the complexity of messaging involved.

Using a combination of microfluidics and computer modeling, researchers created a platform to record cell messages in depth, uncovering the precise ways in which the words and messages are arranged in these intercellular conversations.

Cyanide and carbon monoxide are both deadly poisons to humans, but compounds containing iron, cyanide, and carbon monoxide discovered in carbon-rich meteorites may have helped power life on early Earth.

The extraterrestrial compounds found in meteorites resemble the active site of hydrogenases, which are enzymes that provide energy to bacteria and archaea by breaking down hydrogen gas (H2). Their results suggest that these compounds were also present on early Earth, before life began, during a period of time when Earth was constantly bombarded by meteorites and the atmosphere was likely more hydrogen-rich.

"When most people think of cyanide, they think of spy movies -- a guy swallowing a pill, foaming at the mouth and dying, but cyanide was probably an essential compound for building molecules necessary for life," explained Dr. Karen Smith, senior research scientist at Boise State University, Boise, Idaho. Cyanide, a carbon atom bound to a nitrogen atom, is thought to be crucial for the origin of life, as it is involved in the non-biological synthesis of organic compounds like amino acids and nucleobases, which are the building blocks of proteins and nucleic acids used by all known forms of life.

Smith is lead author of a paper on this research published June 25 in Nature Communications. Smith, along with Boise State assistant professor Mike Callahan, a co-author on the paper, developed new analytical methods to extract and measure ancient traces of cyanide in meteorites. They found that the meteorites containing cyanide belong to a group of carbon-rich meteorites called CM chondrites. Other types of meteorites tested, including a Martian meteorite, contained no cyanide.

"Data collected by NASA's OSIRIS-REx spacecraft of asteroid Bennu indicate that it is related to CM chondrites," said co-author Jason Dworkin of NASA's Goddard Space Flight Center in Greenbelt, Maryland. "OSIRIS-REx will deliver a sample from Bennu to study on Earth in 2023. We will search for these very compounds to try to connect Bennu to known meteorites and to understand the potential delivery of prebiotic compounds such as cyanide, which may have helped start life on the early Earth or other bodies in the solar system."

Cyanide has been found in meteorites before. However, in the new work, Smith and Callahan were surprised to discover that cyanide, along with carbon monoxide (CO), were binding with iron to form stable compounds in the meteorites. They identified two different iron cyano-carbonyl complexes in the meteorites using high-resolution liquid chromatography-mass spectrometry. "One of the most interesting observations from our study is that these iron cyano-carbonyl complexes resemble portions of the active sites of hydrogenases, which have a very distinct structure," Callahan said.

Hydrogenases are present in almost all modern bacteria and archaea and are widely believed to be ancient in origin. They are large proteins, but the active site -- the region where chemical reactions take place -- happens to be a much smaller metal-organic compound contained within the protein, according to Callahan. It is this compound that resembles the cyanide-bearing compounds the team discovered in meteorites.

An international team of physicists has theoretically predicted and experimentally generated light beams with a new property that they call the self-torque of light.

In ‘Application of high resolution DLP stereolithography for fabrication of tricalcium phosphate scaffolds for bone regeneration,’ researchers examine how to make complex, stable scaffolds based on β-tricalcium. Typically, there are obstacles to finding materials and techniques suitable for creating structures capable of sustaining cell life.

The authors are aware of the necessities in tissue engineering: the material cannot be toxic, obviously, as that would cause further health issues in a patient, biodegradability is key, with the material being absorbed along with suitable bone growth, and porosity and density must be suitable too, balanced out with proper strength.

DLP 3D printing has proven successful for creating scaffolds due to comprehensive irradiation over the whole cross-section, and shorter processing times in comparison to other processes. The researchers focused on DLP 3D printing for this study, in relation to the use of calcium phosphate structures that are not only complex and high resolution but also strong. The team assessed both rectilinear grid structure and hexagonal geometries (at 50 and 75 percent porosity) for mechanical properties, with complete chemical analyses performed before and after bioprinting.

Physicist Eugene Paul Wigner predicted more than 80 years ago that hydrogen, the most abundant element in the universe, could turn into an electricity-conducting solid metal at the right temperature and pressure. Scientists have spent decades since attempting to synthesize this material—and may have finally done so.

A team of researchers in France has posted a paper on the arXiv physics preprint server describing their observation of metallic hydrogen under pressures greater than those inside Earth’s core. Several times, other researchers have claimed to discover this phase of matter, claims that are generally met with varying levels of skepticism. But some experts think that this newest claim could be the real deal.

The CRISPR-Cas system (Clustered Regularly Interspaced Short Palindromic Repeats-CRISPR-associated protein system) is a bona fide adaptive (acquired) immunity system that is present in most archaea and many bacteria. CRISPR-Cas system is stored in the form of spacer sequences derived from foreign genomes and inserted into CRISPR arrays, providing sequence-specific protection against foreign DNA or, in some cases, RNA. 

CRISPR-Cas is a programmable form of immunity that can adapt to target any sequence and therefore is not under pressure to evolve an immense diversity of specificities as is the case for restriction-modification enzymes, the most abundant form of innate immunity in prokaryotes. Nevertheless, like any defense system, CRISPR-Cas is engaged in an incessant arms race with viruses, which results in rapid evolution of cas genes and notable diversity of the gene repertoires and architectures of the CRISPR-cas loci translating into diversification of the actual defense mechanisms. More specifically, the diversification of the CRISPR-Cas systems is likely to be partly driven by their competitive coevolution with virus-encoded dedicated anti-CRISPR proteins.

The evolution of CRISPR–cas loci, which encode adaptive immune systems in archaea and bacteria, involves rapid changes, in particular numerous rearrangements of the locus architecture and horizontal transfer of complete loci or individual modules. These dynamics complicate straightforward phylogenetic classification, but here we present an approach combining the analysis of signature protein families and features of the architecture of cas loci that unambiguously partitions most CRISPR–cas loci into distinct classes, types and subtypes. The new classification retains the overall structure of the previous version but is expanded to now encompass two classes, five types and 16 subtypes. The relative stability of the classification suggests that the most prevalent variants of CRISPR–Cas systems are already known. However, the existence of rare, currently unclassifiable variants implies that additional types and subtypes remain to be characterized.

We have seen intricate patterns that milk makes in coffee and much smoother ones that honey makes when stirred with a spoon. Which of these cases best describes the behavior of the hot gas in galaxy clusters? By answering this question, a new study using NASA's Chandra X-ray Observatory has deepened our understanding of galaxy clusters, the largest structures in the Universe held together by gravity.

Galaxy clusters are comprised of three main components: individual galaxies, multimillion-degree gas that fills the space between the galaxies, and dark matter, a mysterious form of matter that is spread throughout a cluster and accounts for about 80 percent of the mass of the cluster.

A team of astronomers used a set of long Chandra observations, totaling about two weeks of observing, of the Coma galaxy cluster to probe gas properties on spatial scales comparable with a typical distance that particles travel between collisions with each other. This measurement helped them to learn about the viscosity—the technical term for the resistance to the motion of gas lumps with respect to each other—of the hot gas in Coma.

"Our finding suggests that gas viscosity in Coma is much lower than expected," said Irina Zhuravleva of the University of Chicago, who led the study. "This means that turbulence can easily develop in the hot gas in galaxy clusters on small scales, analogous to swirling motions in a coffee mug."

The hot gas in Coma glows in X-ray light observed by Chandra. The gas is known to contain about six times more mass than all of the combined galaxies in the cluster. Despite its abundance, the density of the hot gas in Coma, which radio observations have shown is permeated by a weak magnetic field, is so low that the particles do not interact with each other very often. Such a low-density, hot gas cannot be studied in a laboratory on Earth, and so scientists must rely on cosmic laboratories such as the one provided by the intergalactic gas in Coma.

"We used Chandra to probe whether the density of the gas is smooth on the smallest scales we can detect," said Eugene Churazov, a co-author from the Max Planck Institute for Astrophysics in Garching and the Space Research Institute in Moscow. "We found that it is not, suggesting that turbulence is present even on these relatively small scales and the viscosity is low."

The polarization signature reveals magnetic fields in the universe's most powerful explosions to be much more patchy and tangled than first thought.

Good fortune and cutting-edge scientific equipment have allowed scientists to observe a Gamma Ray Burst jet with a radio telescope and detect the polarization of radio waves within it for the first time -- moving us closer to an understanding of what causes the universe's most powerful explosions.

Gamma Ray Bursts (GRBs) are the most energetic explosions in the universe, beaming out mighty jets which travel through space at over 99.9% the speed of light, as a star much more massive than our sun collapses at the end of its life to produce a black hole.

Studying the light from Gamma Ray Burst jets as we detect it traveling across space is our best hope of understanding how these powerful jets are formed, but scientists need to be quick to get their telescopes into position and get the best data. The detection of polarized radio waves from a burst's jet, made possible by a new generation of advanced radio telescopes, offers new clues to this mystery.

In a finding that may overthrow our understanding of where Earth's heavy elements such as gold and platinum come from, new research by a University of Guelph physicist suggests that most of them were spewed from a largely overlooked kind of star explosion far away in space and time from our planet.

Some 80 percent of the heavy elements in the universe likely formed in collapsars, a rare but heavy element-rich form of supernova explosion from the gravitational collapse of old, massive stars typically 30 times as weighty as our sun, said physics professor Daniel Siegel.

That finding overturns the widely held belief that these elements mostly come from collisions between neutron stars or between a neutron star and a black hole, said Siegel.

His paper co-authored with Columbia University colleagues appears today in the journal Nature. Using supercomputers, the trio simulated the dynamics of collapsars, or old stars whose gravity causes them to implode and form black holes.

Under their model, massive, rapidly spinning collapsars eject heavy elements whose amounts and distribution are "astonishingly similar to what we observe in our solar system," said Siegel. He joined U of G this month and is also appointed to the Perimeter Institute for Theoretical Physics, in Waterloo, Ont.

Most of the elements found in nature were created in nuclear reactions in stars and ultimately expelled in huge stellar explosions.

Heavy elements found on Earth and elsewhere in the universe from long-ago explosions range from gold and platinum, to uranium and plutonium used in nuclear reactors, to more exotic chemical elements such as neodymium found in consumer items such as electronics.

Until now, scientists thought that these elements were cooked up mostly in stellar smashups involving neutron stars or black holes, as in a collision of two neutron stars observed by Earth-bound detectors that made headlines in 2017.

Ironically, said Siegel, his team began working to understand the physics of that merger before their simulations pointed toward collapsars as a heavy element birth chamber. "Our research on neutron star mergers has led us to believe that the birth of black holes in a very different type of stellar explosion might produce even more gold than neutron star mergers."

What collapsars lack in frequency, they make up for in generation of heavy elements, said Siegel. Collapsars also produce intense flashes of gamma rays. "Eighty per cent of these heavy elements we see should come from collapsars. Collapsars are fairly rare in occurrences of supernovae, even more rare than neutron star mergers -- but the amount of material that they eject into space is much higher than that from neutron star mergers."

Astronomers have a new tool in their search for extraterrestrial life -- a sophisticated bot that helps identify stars hosting planets similar to Jupiter and Saturn. These giant planets' faraway twins may protect life in other solar systems, but they aren't bright enough to be viewed directly. Scientists find them based on properties they can observe in the stars they orbit. The challenge for planet hunters is that in our galaxy alone, there are roughly 200 billion stars.

"Searching for planets can be a long and tedious process given the sheer volume of stars we could search," said Stephen Kane, UCR associate professor of planetary astrophysics. "Eliminating stars unlikely to have planets and pre-selecting those that might will save a ton of time," he said.

The astronomy bot is a machine learning algorithm designed by Natalie Hinkel, a researcher formerly in Kane's laboratory now with the Southwest Research Institute. Kane examined data produced by the bot and discovered three stars with strong evidence of harboring giant, Jupiter-like planets about 100 light years away.