“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.

“Our attraction to people who share our attributes is aided by the belief that those shared attributes are driven by something deep within us: one’s essence,” said lead author Charles Chu, PhD, an assistant professor at the Boston University Questrom School of Business. “To put it concretely, we like someone who agrees with us on a political issue, shares our music preferences, or simply laughs at the same thing as us not purely because of those similarities, but because those similarities suggest something more — this person is, in essence, like me, and as such, they share my views of the world at large.”

This thought process is driven by a type of psychological essentialism that is applied specifically to people’s ideas about the self and individual identity, according to Chu, adding that people “essentialize” many things — from biological categories such as animal species to social groups such as race and gender — and do so in virtually all human cultures.

“To essentialize something is to define it by a set of deeply rooted and unchanging properties, or an essence,” said Chu. “For example, the category of ‘wolf’ is defined by a wolf essence, residing in all wolves, from which stems attributes such as their pointy noses, sharp teeth and fluffy tails as well as their pack nature and aggressiveness. It is unchanging in that a wolf raised by sheep is still a wolf and will eventually develop wolf-like attributes.”

Recently, researchers have begun to focus on the category of the self and have found that just as we essentialize other categories, we essentialize the self, according to Chu.

“To essentialize me is to define who I am by a set of entrenched and unchanging properties, and we all, especially in Western societies, do this to some extent. A self-essentialist then would believe that what others can see about us and the way we behave are caused by such an unchanging essence,” he said.

To better understand how self-essentialism drives attraction between individuals, researchers conducted a series of four experiments. The research was published in the Journal of Personality and Social Psychology.

In one experiment, 954 participants were asked their position on one of five randomly assigned social issues (abortion, capital punishment, gun ownership, animal testing, or physician-assisted suicide). Half the participants then read about another individual who agreed with their position, while the other half read about an individual who disagreed with their position. All participants then completed a questionnaire on how much they believed they shared a general view of the world with the fictitious individual, their level of interpersonal attraction to that person and their overall beliefs in self-essentialism.

Researchers found that participants who scored high on self-essentialism were more likely to express an attraction to the fictitious individual who agreed with their position and to report a shared general perception of reality with that individual.

A similar experiment involving 464 participants found the same results for a shared attribute as simple as the participants’ propensity to overestimate or underestimate a number of colored dots on a series of computer slides. In other words, the belief in an essential self led people to assume that just a single dimension of similarity was indicative of seeing the entire world in the same way, which led to more attraction.

In another experiment, 423 participants were shown eight pairs of paintings and asked which in each pair they preferred. Based on their responses, participants were identified as either a fan of the Swiss-German artist Paul Klee or the Russian painter Wassily Kandinsky. Half of each fan group was then told that artistic preference was part of their essence; the other half was told it had no connection. All were then exposed to two hypothetical individuals, one of whom had the same artistic preference and one who differed. Participants who were told that artistic preference was connected to their essence were significantly more likely to express an attraction to a hypothetical person with the same artistic preferences than those who were told artistic preference had nothing to do with their essence.

A final experiment categorized 449 participants as fans of one of the two artists and then presented them with information about whether using one’s own essence was useful or not in perceiving other people. This time, one-third of the participants were told that essentialist thinking could lead to inaccurate impressions of others, one-third were told that essentialist thinking could lead to accurate impressions of others and the final third were given no information.

As expected, researchers found participants who were told that essentialist thinking could lead to accurate impressions of others were more likely to report attraction to and shared reality with hypothetical individuals with similar art preferences.

Chu said he was most surprised to find that something as minimal as a shared preference for an artist would lead people to perceive that another individual would see the world the same way as they do. Self-essentialist thinking, though, could be a mixed blessing, he warned.

“I think any time when we’re making quick judgments or first impressions with very little information, we are likely to be affected by self-essentialist reasoning,” said Chu. “People are so much more complex than we often give them credit for, and we should be wary of the unwarranted assumptions we make based on this type of thinking.”

Scientists at Scripps Institution of Oceanography at UC San Diego and the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) report that extremely intense days of rain or snow will be more frequent by the end of this century than previously thought — as often as once every 30 or 40 years in the Pacific Northwest and southeastern United States.

The conclusions come from analyzing a 30-terabyte data set that models temperature and precipitation at scales roughly the size of urban ZIP codes: six kilometers (3.9 miles). Researchers developed the data set, called Localized Constructed Analogs Version 2 (LOCA2), to provide climate information that is useful for local planners. In contrast, most of the existing advanced climate models look at regions that range from 50 to 250 kilometers (30 to 400 miles).

“With this data set, we’re able to look at the impacts of actual weather pattern changes across the United States at an extremely granular level,” said Dan Feldman, staff scientist at Berkeley Lab and the project’s principal investigator. “We see that there is a lot more extreme weather that is likely to happen in the future — and by looking at actual weather patterns, we show that changes in extreme precipitation will actually be more extreme than previously estimated. Land use managers and planners should expect more extremes, but location matters.”

The LOCA2 data set updates a similar analysis conducted in 2016 in advance of the Fourth National Climate Assessment (NCA), which was released in 2018 by the U.S. Global Change Research Program. The NCA is intended to assist the U.S. government with planning for, mitigating, and adapting to changes in climate that will affect the country. The Fifth NCA is expected to be issued later this year.

LOCA2 projections cover the lower 48 states of the United States, southern Canada, and northern Mexico. The data set draws on more than 70 years of weather data and incorporates 27 updated climate models from the Coupled Model Intercomparison Project (CMIP6), the latest iteration of an international effort to simulate climate that includes the “coupling” of natural systems such as the ocean and atmosphere to understand how they will act in concert as climate changes.

“We’ve spent a lot of effort improving the representation of extreme wet days, which is important for understanding both the likelihood of flooding and the availability of water for agricultural, commercial, and residential use,” said David Pierce, a scientist at Scripps Oceanography and the developer of LOCA and LOCA2.

The LOCA2 climate projections are available through the end of the century down to the daily level, and for three different greenhouse gas emissions scenarios known as SSPs, or Shared Socioeconomic Pathways. The three scenarios are a medium level of emissions that is slightly less than current levels (SSP 245), medium-high (SSP 370), and high, where emissions greatly increase (SSP 585). The data set is freely available for planners and decision makers to use.

The projection reinforces what climate scientists have long predicted: Future weather events will become more extreme in a warming world. LOCA2 finds that the heaviest days of rain and snowfall across much of North America will likely release 20 to 30 percent more moisture than they do now. Much of the increased precipitation will occur in winter, potentially exacerbating flooding in regions such as the upper Midwest and the west coast.

“The big picture is clear: it’s getting warmer and wetter,” Feldman said. “This research translates that bigger picture into more practical data for infrastructure and operations planning. With this more detailed look at local impacts, we can help local officials make better-informed decisions, such as how long to make an airport runway, how much resilience to include for constructing buildings or bridges, or where to put crops or culverts.”

The improved set of LOCA2 data was created by better identifying and preserving extreme weather events in the past, training models to more accurately reflect extremes in simulations of the future.

“We undertook a Herculean effort of personnel and computer time not just to produce a bunch of numbers, but to produce local projections that are relevant and useful,” Feldman said. “We do so by recognizing how heat waves and storms have occurred and will occur at the local level, and projecting those forward.”

Seasonal and regional predictions

While the data varies at the local level, researchers found substantial trends across the area covered by LOCA2 at the end of the century.

Across most seasons, a major part of North America will see roughly the same or fewer number of days with precipitation, roughly the same or fewer number of days with light and medium amounts of precipitation, and a large increase in the number of days with the most extreme precipitation (the top 1 percent and 0.1 percent of storms).

“People will be more affected by the really rare and most extreme events, because those are showing the biggest increase,” said Pierce, who is the lead author of the paper on extreme precipitation published in the Journal of Hydrometeorology. “The wettest day you would expect to see in five years, or 50 years, or 500 years — those extreme events are going to be substantially wetter, and that’s a really big issue, because it has implications for flooding and run-off.”

Southern Canada and most of the United States will see increases in extreme precipitation days that occur primarily in winter. The wettest days of precipitation will increase by 20-30 percent, depending on the emissions scenario and how extreme the storm is.

Arizona, New Mexico, and northern Mexico can expect increases in extreme precipitation days that occur primarily in autumn. The wettest days of precipitation increase by 10-30 percent, depending on which emissions scenarios come to be and how extreme the storms are. While the region becomes drier overall, the number of days with extreme precipitation events still goes up, meaning the precipitation that does come will often do so in larger storms.

“It’s quite interesting that you see the same kind of pattern of fewer low- and medium- precipitation days and more extreme precipitation days across pretty much the entire country,” Pierce said. Knowing the changing character of precipitation and the frequency of extreme events is useful in two ways, Pierce added. “One is for building new infrastructure in the future, and one is for understanding impacts upon existing facilities already there.”

Funding for this research was provided by the Department of Defense and Department of Energy through the Strategic Environmental Research and Development Program (SERDP). The NASA High-End Computing Capability (HECC) Program provided resources supporting this work through the NASA Earth Exchange (NEX), Earth Science Division, and the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.