NASA’s Chandra Finds Unexpected Fireworks in Aftermath of Stellar Explosions - UNIVERSE

A composite image of the nearby galaxy Messier 83, and short timelapse videos of two curious supernova remnants hidden inside.

X-ray: NASA/CXC/SAO; Optical: NASA/ESA/AURA/STScI, Hubble Heritage Team, W. Blair (STScI/Johns Hopkins University) and R. O'Connell (University of Virginia); Image Processing: NASA/CXC/SAO/A. Jubett, L. Frattare and P. Edmonds

The aftermath of a supernova, a stellar explosion, is usually a slowly fading cloud of hot gas. So when astronomers pointed NASA's Chandra X-ray Observatory at the nearby galaxy Messier 83 (M83), they did not expect to find a population of supernova remnants, or the debris from these explosions, showing dramatic changes in their brightness. The new results were presented at the American Astronomical Society meeting in Pasadena, California, and published in The Astrophysical Journal.

The galaxy M83, located about 15 million light-years from Earth, is forming stars at a high rate. Researchers analyzed 14 years of Chandra data of the galaxy, spanning 2000 to 2014.

Using this extensive set of data, the researchers caught surprising variations in the X-ray brightness of sources previously identified as supernova remnants. The researchers expected supernova remnants older than a century or so to fade gradually in X-rays, but not change dramatically in brightness.

The team found that roughly half of the 22 X-ray sources associated with supernova remnants in their sample showed changes in X-ray brightness over the 14-year span of observations — a result that was completely unexpected.

"We knew that individual X-ray sources could vary dramatically," said Andrea Prestwich, of the Catholic University of America who led the study. “But finding that so many supernova remnants were behaving this way was a real surprise. Something unusual is going on in these objects. Pinpointing the cause remains a challenge, as M83's distance limits the detail we can observe.”

One of the 22 variable supernova remnants has a straightforward explanation: SN 1957D, the debris from a supernova first observed nearly 70 years ago, is ramming into material surrounding the explosion site, producing the observed X-ray flares. But this cannot explain the rest of the sample. There is no evidence to suggest that all 22 remnants were formed within the last century. Something else must be driving the variability.

The most likely explanation is that the team has uncovered a population of stellar survivors stars that lived through their partner's destruction in a supernova explosion. In this scenario, each variable X-ray source began as a pair of massive stars orbiting each other. The more massive star collapsed and exploded as a supernova, leaving behind a black hole or ultra-dense neutron star. Its companion survived.

Galaxy M83 in X-ray and Optical Light.

X-ray: NASA/CXC/SAO; Optical: NASA/ESA/AURA/STScI, Hubble Heritage Team, W. Blair (STScI/Johns Hopkins University) and R. O'Connell (University of Virginia); Image Processing: NASA/CXC/SAO/A. Jubett, L. Frattare and P. Edmonds

"It may be that this galaxy contains a collection of supernova remnants where one massive star survives the supernova and becomes locked into an orbit with a black hole or neutron star," said co-author Michael McCollough of the Center for Astrophysics | Harvard & Smithsonian (CfA). "The neutron star or black hole can then start pulling material from the massive star’s surface."

That infalling material is superheated by the intense gravitational pull, producing the X-rays Chandra detects. These types of systems, known as high-mass X-ray binaries (HMXBs), are among the most variable X-ray sources in the universe. Researchers say they may be the cause of the variations seen in M83’s supernova remnants.

Astronomers have known about HMXBs for decades, but the difference with this group in M83 is their connection to supernova remnants. Previously, only a handful of supernova remnants associated with HMXBs had been identified across observations of all galaxies. It is unprecedented to find more than 20 strong candidates in just one galaxy.

The authors found that the variable supernova remnants are in regions with higher concentrations of massive stars than in other parts of the galaxy, increasing the chances of a link between the remnants and HMXBs.

There is another possible explanation: Instead of pulling in material from a companion star, the black hole or neutron star may be recapturing some of the material blasted outward by the original explosion.

"This could be an example of cosmic recycling, where debris from the explosion falls back onto the very object the supernova created," said co-author Roy Kilgard of Wesleyan University. "And it's quite possible that both explanations are at play — different sources in our sample may have different origins."

These results are not unique to M83. A follow-up study of the nearby star-forming galaxy M51 by Zoe Hoiland of Vassar College and Kilgard has uncovered a similar population of variable X-ray sources associated with supernova remnants, suggesting that such systems may be a feature of galaxies undergoing vigorous star formation.

This is a composite image of the galaxy M51 combining data from NASA's Chandra X-ray Observatory (purple) with optical data (red, green and blue) taken with ground-based telescopes by a team of astrophotographers. A surprisingly high number of X-ray sources associated with supernova remnants in M51 show large changes in brightness, similar to the behavior seen in M83.

Chandra X-ray Data: NASA/CXC/SAO; Astrobin/Optical Groundbased: C.Björk, T.Bähnck, S.Donoso, J.Gentillon, A. and D.Grelin, S.Guberski, R. Hall, T.Heuberger, J.Jacks, P.Kent, Br.Meyers, W.Ostling, N.Puig, T.Schaeffer, F.Schöfbänker, M.Vasilev

The Chandra data for M83 began with single observations in 2000 and 2001, followed by 10 observations from 2010 to 2011 and another observation in 2014.

NASA's Marshall Space Flight Center in Huntsville, Alabama, manages the Chandra program. The Smithsonian Astrophysical Observatory's Chandra X-ray Center controls science operations from Cambridge, Massachusetts, and flight operations from Burlington, Massachusetts.

Visual Description

This release features a composite image of the nearby galaxy Messier 83, and short timelapse videos of two curious supernova remnants hidden inside.

In the composite image, Messier 83, or M83, is shown to have a spiral structure, viewed straight on. At the center is a brilliant white and yellow pool of light. From that light, spiral arms of hot pink cloud corkscrew out in wide, sweeping arches. The galaxy is covered in a faint grey haze, and flecked with red, green, blue, white, and yellow dots.

In an annotated version of the composite image, two tiny dots to our lower right of center are highlighted by white circles. These are two of the supernova remnants being considered by researchers. Each is examined further in a separate timelapse video.

Over a 14-year period from 2000 to 2014, astronomers pointed NASA’s X-ray observatory at the M83 galaxy. They discovered that about half of the X-ray sources believed to be supernova remnants, the aftermath of stellar explosions, were exhibiting dramatic changes in brightness. This result was entirely unexpected.

Those changes in brightness are highlighted in the timelapse videos. In each video, a series of static images flashes by, focused on one of the two X-ray sources once believed to be supernova remnants. In the videos, the X-ray sources appear as bright blue blobs with glowing cores. But in each image, taken months or years apart, the shapes change, as does the intensity of the blue color, and the brightness of the core. By presenting the substantively different images of the same objects one after another in quick succession, short timelapse videos are created.

The most likely explanation for the changes in brightness is that the team has uncovered a population of stellar survivors, stars that lived through an orbiting partner’s destruction in a supernova explosion. Material is being pulled from the surviving star onto the black hole or neutron star that formed in the supernova, a process known to cause rapid changes in X-ray brightness.

Read more from NASA’s Chandra X-ray Observatory

To learn more about NASA’s Chandra mission, visit: 

https://science.nasa.gov/chandra   

https://chandra.si.edu

Source: NASA's Chandra Finds Unexpected Fireworks in Aftermath of Stellar Explosions - NASA Science 

AI diagnoses brain tumors in minutes instead of weeks - medicalxpress

Credit: cottonbro studio from Pexels

Experts in Heidelberg, Germany, have developed an AI system that can classify brain tumors with unprecedented accuracy using standard microscopic tissue sections. Using digitized standard stains, the system identifies more than 100 molecular subtypes of central nervous system tumors, delivers results within minutes and could accelerate the diagnosis of brain tumors worldwide. The work appears in Nature Cancer.

Tumors of the brain and spinal cord are extremely diverse. In recent years, it has become clear that many of these tumors can be reliably diagnosed only if their molecular properties are examined in addition to their microscopic appearance. Of particular importance is so-called DNA methylation analysis, which is now considered the gold standard for the accurate classification of many brain tumors.

However, such tests are complex. They require specialized laboratories, expensive equipment and sufficient tumor material. In addition, it often takes about two weeks for the results to become available. In many regions of the world, the necessary technologies are not even available.

AI learns from over 11,000 tissue sections

A new AI system called "Hetairos" is expected to bring substantial improvements. It was developed by a team led by Moritz Gerstung (German Cancer Research Center, DKFZ) and Felix Sahm (Heidelberg Medical Faculty of Heidelberg University and Heidelberg University Hospital). The goal of the project was to predict which molecular subgroup a tumor belongs to based solely on routinely prepared and stained histological sections.

Hetairos was trained and validated using more than 11,000 digitized tissue sections from 9,606 patients. The diagnoses were primarily determined using DNA methylation diagnostics. The data came from 11 medical centers on four continents. In total, Hetairos distinguishes 102 different molecular tumor subtypes, covering nearly the entire spectrum of the current WHO classification of central nervous system tumors.

The AI not only evaluates its diagnosis but also indicates how confident it is in it. In approximately 50% to 70% of all cases, Hetairos made predictions with a high degree of certainty. In these cases, accuracy was around 87% to 88%.

Even when the AI was uncertain, it was usually able to significantly narrow the number of possible diagnoses. Instead of having to distinguish between more than 100 tumor subtypes, Hetairos often provides neuropathologists with only a few likely candidates. This can significantly simplify the selection of further diagnostic tests.

"The study shows that artificial intelligence is capable of deriving molecular information directly from routine tissue sections and thus fundamentally changing cancer diagnostics," said Darui Jin, one of the lead authors of the study.

Hetairos outperforms experienced specialists

Particularly noteworthy was the direct comparison with human experts. Five experienced neuropathologists from various international centers were given 210 cases and asked to make a diagnosis based solely on the tissue sections. Hetairos achieved an accuracy rate of 68%, while the specialists averaged 30%. When considering the three most likely diagnoses in each case, the AI scored 84%, while the specialists scored about 50%.

"The results show that modern AI systems are now capable of recognizing extremely subtle morphological patterns that are difficult even for experienced specialists to distinguish," said Sahm.

"Currently, the diagnosis of very rare tumor types still poses a major challenge for Hetairos; in this regard, experienced neuropathologists appear to be at least on par. However, we expect the system's performance to improve even further with larger and more diverse datasets," added Gerstung.

Diagnosis in 12 minutes instead of 12 days

In a prospective study, Hetairos was used in parallel with routine clinical practice. The system analyzed 210 tumor samples without the AI result influencing the actual diagnosis or treatment decision.

While complete molecular diagnostics took an average of about 12 days, Hetairos generated its findings in just 12 minutes on standard computer hardware after digitizing the stained tissue sections. Including preparation and digitization of the tissue sections, results could often be available within 24 hours to two days.

Assistance with difficult and unclear cases

Hetairos could be particularly valuable in situations where traditional molecular methods reach their limits, when there is insufficient tumor material for genetic testing, or when molecular tests do not yield clear results. In addition, the system highlights the areas in the tissue section that were particularly important to its decision. This allows doctors to understand the basis of the AI's diagnosis and identify which regions may be suitable for further investigation.

"We developed Hetairos primarily as a tool to support diagnostics," explained neuropathologist Sahm.

"It is not intended to replace molecular analyses, but rather to specifically complement and accelerate them. The technology could make an important contribution, particularly in countries or regions with limited resources, as it is based on standard tissue sections used worldwide."

The method could also offer economic advantages. While a DNA methylation analysis typically costs several hundred euros, Hetairos uses existing tissue sections for its analysis.

Gerstung said, "Hetairos demonstrates the enormous potential of AI-supported digital pathology to provide rapid and widely available diagnostic methods that were previously possible only with considerable technical effort." 

Provided by German Cancer Research Center  

by German Cancer Research Center

edited by Stephanie Baum, reviewed by Robert Egan 

Source: AI diagnoses brain tumors in minutes instead of weeks