To discover exoplanets, planets which orbit stars other than the Sun, by imaging astronomers have up until now used “blind surveys”: stars are selected for imaging consider factors such as age and distance but are otherwise unbiased. However, blind surveys find planets very infrequently. Knowing where to look would help increase detection rates.

An international research team led by Subaru Telescope, the University of Tokyo, the University of Texas-San Antonio, and the Astrobiology Center of Japan, searched for hints of unknown planets in the data from the European Space Agency’s Gaia mission and its predecessor, Hipparcos. The team identified a star, HIP 99770 located 133 light-years away in the constellation Cygnus, whose motion suggests that an unseen planet is gravitationally pulling on it. Direct imaging observations with the Subaru Telescope detected the planet, HIP 99770 b.

The newly discovered planet is 14-16 times more massive than Jupiter. Its orbit is just over 3 times further from its star than Jupiter is from the Sun. The planet is 10 times hotter than Jupiter, with signs of water and carbon monoxide in its atmosphere.

A decade from now, astronomers hope to image a potentially-habitable planet with a size and temperature like the Earth using observatories like the Thirty Meter Telescope (TMT). Compared with HIP 99770 b, this Earth twin will be smaller and closer to its star, traits that will make it harder to detect. But with precise motion measurements, researchers will know where to look in this game of planetary hide and seek.

“Our laboratory has developed a novel method that enables the production of photoreceptor progenitor cells resembling those in human embryos,” said Assistant Professor Tay Hwee Goon, first author of the study from Duke-NUS’ Centre for Vision Research. “Transplantation of these cells into experimental models has yielded partial restoration of the retinal function.”

The degeneration of photoreceptors in the eye is a significant cause of declining vision that can eventually lead to blindness and for which there is currently no effective treatment. Photoreceptor degeneration occurs in a variety of inherited retinal diseases, such as retinitis pigmentosa — a rare eye disease that breaks down cells in the retina over time and eventually causes vision loss — and age-related macular degeneration, a leading cause of vision impairment worldwide.

Asst Prof Tay and her team developed a procedure to grow human embryonic stem cells in the presence of purified laminin proteins that are involved in normal development of human retinas. In the presence of the laminins, stem cells could be directed to differentiate into photoreceptor progenitor cells responsible for converting light into signals that are sent to the brain.

When these cells were transplanted into damaged retinas, the preclinical models showed significant recovery of vision. A diagnostic test called electroretinogram also identified significant recovery in the retinas via electrical activity in the retina in response to a light stimulus. The transplanted cells established connections with surrounding retinal cells and nerves in the inner retina. They also survived and functioned for many weeks after transplantation.

Moving forward, the team hopes to refine their method to make it simpler and achieve more consistent results than earlier attempts to explore stem cell therapy for photoreceptor cell replacement.

“It is exciting to find these results, which suggest a promising route towards using stem cells to treat those forms of visual deterioration and blindness caused by the loss of photoreceptors,” said Dr Helder Andre, Head of Molecular and Cellular Research from Karolinska Institute’s Department of Clinical Neuroscience and a senior author of the study.

Associate Professor Enrico Petretto, Director of the Centre for Computational Biology at Duke-NUS and the study’s bioinformatics analysis lead, added: “Our method may also be useful for understanding the molecular and cellular pathways that drive the progression of macular degeneration, perhaps leading to the development of other therapeutic approaches.”

The next challenge for the researchers is to explore the efficacy of their method in models of photoreceptor degeneration that more closely match the human condition.

“If we get promising results in our future studies, we hope to move to clinical trials in patients,” said Professor Karl Tryggvason, from Duke-NUS’ Cardiovascular and Metabolic Disorders Programme, and the corresponding author of the study. “That would be an important step towards for being able to reverse damage of the retina and restore vision.”

The protocol underlying the procedure developed by Asst Prof Tay has since been licensed to Swedish biotech start-up Alder Therapeutics.

Diet can hugely impact your mental health and previous research has shown that some foods are particularly good at positively impacting your brain. Fermented foods are a source of tryptophan, an amino acid key to the production of serotonin, a messenger in the brain which influences several aspects of brain function, including mood. The foods may also contain other brain messengers (known as neurotransmitters) in their raw form. It’s no surprise then that research has shown that eating fermented foods may have various long- and short-term impacts on brain function, such as reducing stress. But which foods have the biggest impact on brain health?? 

Researchers at APC Microbiome, University College Cork, and Teagasc (Ireland’s Agriculture and Food Development Authority) in Moorepark, Cork, Ireland are currently working on a large study to finally answer this question. Ramya Balasubramanian and the team at APC compared sequencing data from over 200 foods from all over the world, looking for a variety of metabolites that are known to be beneficial to brain health.?? 

The study is still in it’s initial stages, but researchers are already surprised by preliminary results. Ramya explains, “I expected only a few fermented foods would show up, but out of 200 fermented foods, almost all of them showed the ability to exert some sort of potential to improve gut and brain health”. More research is needed to fully understand which groups of fermented foods have the greatest effects on the human brain, but results are showing an unexpected victor. 

“Fermented sugar-based products and fermented vegetable-based products are like winning the lottery when it comes to gut and brain health”, explains Ramya.  

“For all that we see on sugar-based products being demonised, fermented sugar takes the raw sugar substrate, and it converts it into a plethora of metabolites that can have a beneficial effect on the host. So even though it has the name ‘sugar’ in it, if you do a final metabolomic screen, the sugar gets used by the microbial community that’s present in the food, and they get converted into these beautiful metabolites that are ready to be cherry picked by us for further studies.”? 

These further studies are what’s next for Ramya. She plans to put her top ranked fermented foods through rigorous testing using an artificial colon and various animal models to see how these metabolites affect the brain.?? 

Ramya hopes that the public can utilise these preliminary results and consider including fermented foods in their diet as a natural way of supporting their mental health and general well-being. 

In a paper slated for publication April 14 in Science, Currie reports the first exoplanet jointly discovered through direct imaging and precision astrometry, a new indirect method that identifies a planet by measuring the position of the star it orbits. Data from the Subaru Telescope in Hawai`i and space telescopes from the European Space Agency (ESA) were integral to the team’s discovery.

An exoplanet — also called an extrasolar planet — is a planet outside a solar system that orbits another star. With direct imaging, astronomers can see an exoplanet’s light in a telescope and study its atmosphere. However, only about 20 have been directly imaged over the past 15 years.

By contrast, indirect planet detection methods determine a planet’s existence through its effect on the star it orbits. This approach can provide detailed measurements of the planet’s mass and orbit.

Combining direct and indirect methods to examine a planet’s position provides a more complete understanding of an exoplanet, Current says.

“Indirect planet detection methods are responsible for most exoplanet discoveries thus far. Using one of these methods, precision astrometry, told us where to look to try to image planets. And, as we found out, we can now see planets a lot easier,” said Currie.

The newly discovered exoplanet, called HIP 99770 b, is about 14 to 16 times the mass of Jupiter and orbits a star that is nearly twice as massive as the Sun. The planetary system also shares similarities with the outer regions of our solar system. HIP 99770 b receives about as much light as Jupiter, our solar system’s most massive planet, receives from the Sun. Its host star is surrounded by icy debris left over from planet formation, similar to our solar system’s Kuiper belt, the ring of icy objects observed around the Sun.

Currie and team used the Hipparcos-Gaia Catalogue of Accelerations to advance their discovery of HIP 99770 b. The catalogue consists of data from ESA’s Gaia mission and Hipparcos, Gaia’s predecessor, providing a 25-year record of accurate star positions and motions. It revealed that the star HIP 99770 is likely being accelerated by the gravitational pull of an unseen planet.

The team then used the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) instrument, which is permanently installed at the focus of the Subaru Telescope in Hawai`i, to image and confirm the existence of HIP 99770 b.

The discovery of HIP 99770 b is significant, because it opens a new avenue for scientists to discover and characterize exoplanets more comprehensively than ever before, Currie said, shedding light on the diversity and evolution of planetary systems. Using indirect methods to guide efforts to image planets may also someday lead scientists closer to the first images of other Earths.

“This is the first of many discoveries that we expect to have. We are in a new era of studying extrasolar planets,” Currie said.

In 2017, the EHT collaboration used a network of seven pre-existing telescopes around the world to gather data on M87, creating an “Earth-sized telescope.” However, since it is infeasible to cover the Earth’s entire surface with telescopes, gaps arise in the data — like missing pieces in a jigsaw puzzle.

“With our new machine learning technique, PRIMO, we were able to achieve the maximum resolution of the current array,” says lead author Lia Medeiros of the Institute for Advanced Study. “Since we cannot study black holes up-close, the detail of an image plays a critical role in our ability to understand its behavior. The width of the ring in the image is now smaller by about a factor of two, which will be a powerful constraint for our theoretical models and tests of gravity.”

PRIMO, which stands for principal-component interferometric modeling, was developed by EHT members Lia Medeiros (Institute for Advanced Study), Dimitrios Psaltis (Georgia Tech), Tod Lauer (NOIRLab), and Feryal Özel (Georgia Tech). Their publication, “The Image of the M87 Black Hole Reconstructed with PRIMO,” is now available in The Astrophysical Journal Letters.

“PRIMO is a new approach to the difficult task of constructing images from EHT observations,” said Lauer. “It provides a way to compensate for the missing information about the object being observed, which is required to generate the image that would have been seen using a single gigantic radio telescope the size of the Earth.”

PRIMO relies on dictionary learning, a branch of machine learning which enables computers to generate rules based on large sets of training material. For example, if a computer is fed a series of different banana images — with sufficient training — it may be able to determine if an unknown image is or is not a banana. Beyond this simple case, the versatility of machine learning has been demonstrated in numerous ways: from creating Renaissance-style works of art to completing the unfinished work of Beethoven. So how might machines help scientists to render a black hole image? The research team has answered this very question.

With PRIMO, computers analyzed over 30,000 high-fidelity simulated images of black holes accreting gas. The ensemble of simulations covered a wide range of models for how the black hole accretes matter, looking for common patterns in the structure of the images. The various patterns of structure were sorted by how commonly they occurred in the simulations, and were then blended to provide a highly accurate representation of the EHT observations, simultaneously providing a high fidelity estimate of the missing structure of the images. A paper pertaining to the algorithm itself was published in The Astrophysical Journal on February 3, 2023.

“We are using physics to fill in regions of missing data in a way that has never been done before by using machine learning,” added Medeiros. “This could have important implications for interferometry, which plays a role in fields from exo-planets to medicine.”

The team confirmed that the newly rendered image is consistent with the EHT data and with theoretical expectations, including the bright ring of emission expected to be produced by hot gas falling into the black hole. Generating an image required assuming an appropriate form of the missing information, and PRIMO did this by building on the 2019 discovery that the M87 black hole in broad detail looked as predicted.

“Approximately four years after the first horizon-scale image of a black hole was unveiled by EHT in 2019, we have marked another milestone, producing an image that utilizes the full resolution of the array for the first time,” stated Psaltis. “The new machine learning techniques that we have developed provide a golden opportunity for our collective work to understand black hole physics.”

The new image should lead to more accurate determinations of the mass of the M87 black hole and the physical parameters that determine its present appearance. The data also provides an opportunity for researchers to place greater constraints on alternatives to the event horizon (based on the darker central brightness depression) and perform more robust tests of gravity (based on the narrower ring size). PRIMO can also be applied to additional EHT observations, including those of Sgr A*, the central black hole in our own Milky Way galaxy.

M87 is a massive, relatively nearby, galaxy in the Virgo cluster of galaxies. Over a century ago, a mysterious jet of hot plasma was observed to emanate from its center. Beginning in the 1950s, the then new technique of radio astronomy showed the galaxy to have a compact bright radio source at its center. During the 1960s, M87 had been suspected to have a massive black hole at its center powering this activity. Measurements made from ground-based telescopes starting in the 1970s, and later the Hubble Space Telescope starting in the 1990s, provided strong support that M87 indeed harbored a black hole weighing several billion times the mass of the Sun based on observations of the high velocities of stars and gas orbiting its center. The 2017 EHT observations of M87 were obtained over several days from several different radio telescopes linked together at the same time to obtain the highest possible resolution. The now iconic “orange donut” picture of the M87 black hole, released in 2019, reflected the first attempt to produce an image from these observations.

“The 2019 image was just the beginning,” stated Medeiros. “If a picture is worth a thousand words, the data underlying that image have many more stories to tell. PRIMO will continue to be a critical tool in extracting such insights.”

Development of the PRIMO algorithm was enabled through the support of the National Science Foundation Astronomy and Astrophysics Postdoctoral Fellowship.