The early history of our home galaxy, the Milky Way, is one of joining smaller galaxies, which makes for fairly large building blocks. Now, Khyati Malhan and Hans-Walter Rix of the Max Planck Institute for Astronomy have succeeded in identifying what could be two of the earliest building blocks that can still be recognized as such today: proto-galactic fragments that merged with an early version of our Milky Way between 12 and 13 billion years ago, at the very beginning of the era of galaxy formation in the Universe. The components, which the astronomers have named Shakti and Shiva, were identified by combining data from ESA’s astrometry satellite Gaia with data from the SDSS survey. For astronomers, the result is the equivalent of finding traces of an initial settlement that grew into a large present-day city.

Tracing the origins of stars that came from other galaxies

When galaxies collide and merge, several processes happen in parallel. Each galaxy carries along its own reservoir of hydrogen gas. Upon collision, those hydrogen gas clouds are destabilized, and numerous new stars are formed inside. Of course, the incoming galaxies also already have their own stars, and in a merger, stars from the galaxies will mingle. In the long run, such “accreted stars” will also account for some of the stellar population of the newly-formed combined galaxy. Once the merger is completed, it might seem hopeless to identify which stars came from which predecessor galaxy. But in fact, at least some ways of tracing back stellar ancestry exist.

Help comes from basic physics. When galaxies collide and their stellar populations mingle, most of the stars retain very basic properties, which are directly linked to the speed and direction of the galaxy in which they originated. Stars from the same pre-merger galaxy share similar values for both their energy and what physicists call angular momentum — the momentum associated with orbital motion or rotation. For stars moving in a galaxy’s gravitational field, both energy and angular momentum are conserved: they remain the same over time. Look for large groups of stars with similar, unusual values for energy and angular momentum — and chances are, you might find a merger remnant.

Additional pointers can assist identification. Stars that formed more recently contain more heavier elements, what astronomers call “metals,” than stars that formed a long time ago. The lower the metal content (“metallicity”), the earlier the star presumably formed. When trying to identify stars that already existed 13 billion years ago, one should look for stars with very low metal content (“metal-poor”).

Virtual excavations in a large data set

Identifying the stars that joined our Milky Way as parts of another galaxy has only become possible comparatively recently. It requires large, high-quality data sets, and the analysis involves sifting the data in clever ways so as to identify the searched-for class of objects. This kind of data set has only been available for a few years. The ESA astrometry satellite Gaia provides an ideal data set for this kind of big-data galactic archeology. Launched in 2013, it has produced an increasingly accurate data set over the past decade, which by now includes positions, changes in position and distances for almost 1.5 billion stars within our galaxy.

Gaia data revolutionized studies of the dynamics of stars in our home galaxy, and has already led to the discovery of previously unknown substructures. This includes the so-called Gaia Enceladus/Sausage stream, a remnant of the most recent larger merger our home galaxy has undergone, between 8 and 11 billion years ago. It also includes two structures identified in 2022: the Pontus stream identified by Malhan and colleagues and the “poor old heart” of the Milky Way identified by Rix and colleagues. The latter is a population of stars that newly formed during the initial mergers that created the proto-Milky Way, and continue to reside in our galaxy’s central region.

Traces of Shakti and Shiva

For their present search, Malhan and Rix used Gaia data combined with detailed stellar spectra from the Sloan Digital Sky Survey (DR17). The latter provide detailed information about the stars’ chemical composition. Malhan says: “We observed that, for a certain range of metal-poor stars, stars were crowded around two specific combinations of energy and angular momentum.”

In contrast with the “poor old heart,” which was also visible in those plots, the two groups of like-minded stars had comparatively large angular momentum, consistent with groups of stars that had been part of separate galaxies which had merged with the Milky Way. Malhan has named these two structures Shakti and Shiva, the latter one of the principal deities of Hinduism and the former a female cosmic force often portrayed as Shiva’s consort.

Their energy and angular momentum values, plus their overall low metallicity on par with that of the “poor old heart,” makes Shakti and Shiva good candidates for some of the earliest ancestors of our Milky Way. Rix says: “Shakti and Shiva might be the first two additions to the ‘poor old heart’ of our Milky Way, initiating its growth towards a large galaxy.”

Several surveys that are either already ongoing or bound to start over the next couple of years promise relevant additional data, both spectra (SDSS-V, 4MOST) and precise distances (LSST/Rubin Observatory), should enable astronomers to make a firm decision on whether or not Shakti and Shiva are indeed a glimpse of our home galaxy’s earliest prehistory.

Finding viable alternatives to traditional petroleum-based plastics and microplastics has never been more important. New research from scientists at the University of California San Diego and materials-science company Algenesis shows that their plant-based polymers biodegrade — even at the microplastic level — in under seven months. The paper, whose authors are all UC San Diego professors, alumni or former research scientists, appears in Nature Scientific Reports.

“We’re just starting to understand the implications of microplastics. We’ve only scratched the surface of knowing the environmental and health impacts,” stated Professor of Chemistry and Biochemistry Michael Burkart, one of the paper’s authors and an Algenesis co-founder. “We’re trying to find replacements for materials that already exist, and make sure these replacements will biodegrade at the end of their useful life instead of collecting in the environment. That’s not easy.”

“When we first created these algae-based polymers about six years ago, our intention was always that it be completely biodegradable,” said another of the paper’s authors, Robert Pomeroy, who is also a professor of chemistry and biochemistry and an Algenesis co-founder. “We had plenty of data to suggest that our material was disappearing in the compost, but this is the first time we’ve measured it at the microparticle level.”

Putting it to the test

To test its biodegradability, the team ground their product into fine microparticles, and used three different measurement tools to confirm that, when placed in a compost, the material was being digested by microbes.

The first tool was a respirometer. When the microbes break down compost material, they release carbon dioxide (CO2), which the respirometer measures. These results were compared to the breakdown of cellulose, which is considered the industry standard of 100% biodegradability. The plant-based polymer matched the cellulose at almost one hundred percent.

Next the team used water flotation. Since plastics are not water soluble and they float, they can easily be scooped off the surface of water. At intervals of 90 and 200 days, almost 100% of the petroleum-based microplastics were recovered, meaning none of it had biodegraded. On the other hand, after 90 days, only 32% of the algae-based microplastics were recovered, showing that more than two thirds of it had biodegraded. After 200 days, only 3% was recovered indicating that 97% of it had disappeared.

The last measurement involved chemical analysis via gas chromatography/mass spectrometry (GCMS), which detected the presence of the monomers used to make the plastic, indicating that the polymer was being broken to its starting plant materials. Scanning-electron microscopy further showed how microorganisms colonize the biodegradable microplastics during composting.

“This material is the first plastic demonstrated to not create microplastics as we use it,” said Stephen Mayfield, a paper coauthor, School of Biological Sciences professor and co-founder of Algenesis. “This is more than just a sustainable solution for the end-of-product life cycle and our crowded landfills. This is actually plastic that is not going to make us sick.”

Creating an eco-friendly alternative to petroleum-based plastics is only one part of the long road to viability. The ongoing challenge is to be able to use the new material on pre-existing manufacturing equipment that was originally built for traditional plastic, and here Algenesis is making progress. They have partnered with several companies to make products that use the plant-based polymers developed at UC San Diego, including Trelleborg for use in coated fabrics and RhinoShield for use in the production of cell phone cases.

“When we started this work, we were told it was impossible,” stated Burkart. “Now we see a different reality. There’s a lot of work to be done, but we want to give people hope. It is possible.”

Full list of authors: Robert S. Pomeroy, Michael D. Burkart, Steven P. Mayfield (all UC San Diego), Marco N. Allemann, Marissa Tessman, Jaysen Reindel, Gordon B. Scofield, Payton Evans, Ryan Simkovsky (all Algenesis).

This research was supported by funding from the Department of Energy (DE-SC0019986 and DE-EE0009295).

Disclosure: Burkart, Mayfield and Pomeroy are co-founders of and hold equity positions in Algenesis Corporation.

“If you ask me, this is the main cause of systemic aging in the whole body,” says co-author and molecular biologist Jan Hoeijmakers of the Erasmus University Medical Center, Rotterdam; the University of Cologne; and Oncode Institute/Princess Maxima Institute, Utrecht.

The authors span four research groups from Spain, the Netherlands, Germany, and the United States, with each group arriving at the same conclusions using different methods.

Aging is associated with changes at the molecular, cellular, and organ level — from altered protein production to sub-optimal cell metabolism to compromised tissue architecture. These changes are thought to originate from DNA damage resulting from cumulative exposure to harmful agents such as UV radiation or reactive oxygen species generated by our own metabolism.

While a lot of research in aging has focused on specific genes that might accelerate or slow aging, investigations of exactly which genes are more susceptible to aging have revealed no clear pattern in terms of gene function. Instead, susceptibility seems to be linked to the genes’ lengths.

“For a long time, the aging field has been focused on genes associated with aging, but our explanation is that it is much more random — it’s a physical phenomenon related to the length of the genes and not to the specific genes involved or the function of those genes,” says co-author Ander Izeta of the Biogipuzkoa Health Research Institute and Donostia University Hospital, Spain.

It essentially comes down to chance; long genes simply have more potential sites that could be damaged. The researchers compare it to a road trip — the longer the trip, the more likely that something will go wrong. And because some cell types tend to express long genes more than others, these cells are more likely to accumulate DNA damage as they age. Cells that don’t (or very rarely) divide also seem to be more susceptible compared to rapidly replicating cells because long-lived cells have more time to accumulate DNA damage and must rely on DNA repair mechanisms to fix them, whereas rapidly dividing cells tend to be short-lived.

Because neural cells are known to express particularly long genes and are also slow or non-dividing, they are especially susceptible to the phenomenon, and the researchers highlight the link between aging and neurodegeneration. Many of the genes involved in preventing protein aggregation in Alzheimer’s disease are exceptionally long, and pediatric cancer patients, who are cured by DNA-damaging chemotherapy, later suffer from premature aging and neurodegeneration.

The authors speculate that damage to long genes could explain most of the features of aging because it is associated with known aging accelerants and because it can be mitigated with known anti-aging therapies, such as dietary restriction (which has been shown to limit DNA damage).

“Many different things that are known to affect aging seem to lead to this length-dependent regulation, for example, different types of irradiation, smoking, alcohol, diet, and oxidative stress,” says co-author Thomas Stoeger of Northwestern University.

However, although the association between the decline in long-gene expression and aging is strong, causative evidence remains to be demonstrated. “Of course, you never know which came first, the egg or the chicken, but we can see a strong relationship between this phenomenon and many of the well-known hallmarks of aging,” says Izeta.

In future studies, the researchers plan to further investigate the phenomenon’s mechanism and evolutionary implications and to explore its relationship with neurodegeneration.

The findings boost AI-driven medical diagnostics and bring health care professionals closer to being able to quickly diagnose patients with COVID-19 and other pulmonary diseases with algorithms that comb through ultrasound images to identify signs of disease.

The findings, newly published in Communications Medicine, culminate an effort that started early in the pandemic when clinicians needed tools to rapidly assess legions of patients in overwhelmed emergency rooms.

“We developed this automated detection tool to help doctors in emergency settings with high caseloads of patients who need to be diagnosed quickly and accurately, such as in the earlier stages of the pandemic,” said senior author Muyinatu Bell, the John C. Malone Associate Professor of Electrical and Computer Engineering, Biomedical Engineering, and Computer Science at Johns Hopkins University. “Potentially, we want to have wireless devices that patients can use at home to monitor progression of COVID-19, too.”

The tool also holds potential for developing wearables that track such illnesses as congestive heart failure, which can lead to fluid overload in patients’ lungs, not unlike COVID-19, said co-author Tiffany Fong, an assistant professor of emergency medicine at Johns Hopkins Medicine.

“What we are doing here with AI tools is the next big frontier for point of care,” Fong said. “An ideal use case would be wearable ultrasound patches that monitor fluid buildup and let patients know when they need a medication adjustment or when they need to see a doctor.”

The AI analyzes ultrasound lung images to spot features known as B-lines, which appear as bright, vertical abnormalities and indicate inflammation in patients with pulmonary complications. It combines computer-generated images with real ultrasounds of patients — including some who sought care at Johns Hopkins.

“We had to model the physics of ultrasound and acoustic wave propagation well enough in order to get believable simulated images,” Bell said. “Then we had to take it a step further to train our computer models to use these simulated data to reliably interpret real scans from patients with affected lungs.”

Early in the pandemic, scientists struggled to use artificial intelligence to assess COVID-19 indicators in lung ultrasound images because of a lack of patient data and because they were only beginning to understand how the disease manifests in the body, Bell said.

Her team developed software that can learn from a mix of real and simulated data and then discern abnormalities in ultrasound scans that indicate a person has contracted COVID-19. The tool is a deep neural network, a type of AI designed to behave like the interconnected neurons that enable the brain to recognize patterns, understand speech, and achieve other complex tasks.

“Early in the pandemic, we didn’t have enough ultrasound images of COVID-19 patients to develop and test our algorithms, and as a result our deep neural networks never reached peak performance,” said first author Lingyi Zhao, who developed the software while a postdoctoral fellow in Bell’s lab and is now working at Novateur Research Solutions. “Now, we are proving that with computer-generated datasets we still can achieve a high degree of accuracy in evaluating and detecting these COVID-19 features.”

The team’s code and data are publicly available here: https://gitlab.com/pulselab/covid19

Scientists have long known that COVID-19 increases the risk of heart attack, stroke, and Long COVID, and prior imaging research has shown that over 50% of people who get COVID-19 experience some inflammation or damage to the heart. What scientists did not know is whether the damage occurs because the virus infects the heart tissue itself, or because of systemic inflammation triggered by the body’s well-known immune response to the virus.

“This was a critical question and finding the answer opens up a whole new understanding of the link between this serious lung injury and the kind of inflammation that can lead to cardiovascular complications,” said Michelle Olive, Ph.D., associate director of the Basic and Early Translational Research Program at the National Heart, Lung, and Blood Institute (NHLBI), part of NIH. “The research also suggests that suppressing the inflammation through treatments might help minimize these complications.”

To reach their findings, the researchers focused on immune cells known as cardiac macrophages, which normally perform a critical role in keeping the tissue healthy but can turn inflammatory in response to injury such as heart attack or heart failure. The researchers analyzed heart tissue specimens from 21 patients who died from SARS-CoV-2-associated ARDS and compared them with specimens from 33 patients who died from non-COVID-19 causes. They also infected mice with SARS-CoV-2 to follow what happened to the macrophages after infection.

In both humans and mice, they found the SARS-CoV-2 infection increased the total number of cardiac macrophages and also caused them to shift from their normal routine and become inflammatory.

When macrophages are no longer doing their normal jobs, which includes sustaining the metabolism of the heart and clearing out harmful bacteria or other foreign agents, they weaken the heart and the rest of the body, said Matthias Nahrendorf, M.D., Ph.D., professor of Radiology at Harvard Medical School and senior author on the study.

The researchers then designed a study in mice to test whether the response they observed happened because SARS-CoV-2 was infecting the heart directly, or because the SARS-CoV-2 infection in the lungs was severe enough to render the heart macrophages more inflammatory. This study mimicked the lung inflammation signals, but without the presence of the actual virus. The result: even in the absence of a virus, the mice showed immune responses strong enough to produce the same heart macrophage shift the researchers observed both in the patients who died of COVID-19 and the mice infected with SARS-CoV-2 infection.

“What this study shows is that after a COVID infection, the immune system can inflict remote damage on other organs by triggering serious inflammation throughout the body — and this is in addition to damage the virus itself has directly inflicted on the lung tissue,” said Nahrendorf. “These findings can also be applied more generally, as our results suggest that any severe infection can send shockwaves through the whole body.”

The research team also found that blocking the immune response with a neutralizing antibody in the mice stopped the flow of inflammatory cardiac macrophages and preserved cardiac function. While they have yet to test this in humans, Nahrendorf said a treatment like this could be used as a preventive measure to help COVID-19 patients with pre-existing conditions, or people who are likely to have more severe outcomes from SARS-CoV-2 associated ARDS.

Study: Grune J, Bajpai G, Ocak PT, et al. Virus-induced ARDS causes cardiomyopathy through eliciting inflammatory responses in the heart. Circulation. 2024. doi:10.1161/CIRCULATIONAHA.123.066433.

Funding: This study was supported by NHLBI grants: HL139598, HL142494, HL155097, and HL149647.