Category: 7. Science

Today’s Image of the Day from the European Space Agency features a dazzling view of the globular cluster NGC 1786, which was captured by the Hubble Space Telescope. 

Unlike open clusters, which are younger and more loosely arranged, globular clusters are tightly concentrated and typically located in a galaxy’s halo.

The globular cluster NGC 1786 is located in the Large Magellanic Cloud (LMC) – a satellite galaxy of the Milky Way about 160,000 light-years from Earth. 

“NGC 1786 itself is in the constellation Dorado. It was discovered in the year 1835 by John Herschel,” noted ESA.

Observing globular clusters 

Globular clusters are densely packed, spherical groups of stars that orbit the cores of galaxies like satellites. 

Each cluster can contain tens of thousands to millions of stars, all bound tightly together by gravity. These stars are generally very old – often over 10 billion years – making globular clusters some of the oldest known structures in the universe.

The image of NGC 1786 is part of a broader observing program aimed at comparing old globular clusters in nearby dwarf galaxies with those found in the Milky Way. 

“Our galaxy contains over 150 of these old, spherical collections of tightly-bound stars, which have been studied in depth – especially with Hubble Space Telescope images like this one, which show them in previously-unattainable detail,” said ESA.

“Being very stable and long-lived, they act as galactic time capsules, preserving stars from the earliest stages of a galaxy’s formation.”

Stars in globular clusters

For many years, astronomers believed that the stars in globular clusters all formed at roughly the same time. However, studies of Milky Way clusters have revealed multiple stellar populations of different ages. 

To better understand how globular clusters form – and how they can be used to trace galactic evolution – researchers are now investigating whether clusters like NGC 1786 in external galaxies also show signs of multiple stellar generations. 

This research sheds light on the origins and development of the Large Magellanic Cloud. It also helps scientists study the formation history of the Milky Way itself.

Large Magellanic Cloud 

The Large Magellanic Cloud (LMC) is one of the most prominent and intriguing galaxies in our cosmic neighborhood. It is the largest satellite galaxy of the Milky Way and is visible to the naked eye from the Southern Hemisphere. 

A satellite galaxy is a smaller galaxy that orbits a larger one due to gravitational attraction. Just as the Moon orbits the Earth, satellite galaxies are bound to larger galaxies and can slowly orbit them over billions of years.

These satellites can range in size from relatively massive dwarf galaxies to tiny, faint collections of stars.

Though classified as a dwarf galaxy, the Large Magellanic Cloud spans roughly 14,000 light-years across and contains billions of stars, nebulae, and star clusters. 

One of its most famous features is the Tarantula Nebula – the most active star-forming region in the entire Local Group of galaxies. This stellar nursery is so intense that it’s forming some of the most massive stars ever observed.

Dynamic interaction with the Milky Way 

The Large Magellanic Cloud is also of great interest to astronomers because of its dynamic relationship with the Milky Way. It’s not just a passive companion – it’s interacting gravitationally with both the Milky Way and the Small Magellanic Cloud, another nearby dwarf galaxy. 

These interactions have created long streams of gas, such as the Magellanic Stream, that arc across the sky. In fact, recent studies suggest the Large Magellanic Cloud may be on its first infall into the Milky Way’s halo, rather than orbiting it for billions of years as once thought. 

This motion could have significant effects on the structure of our galaxy, possibly even perturbing the Milky Way’s dark matter halo.

Significance of the research

The detailed observations of NGC 1786 not only showcase the stunning capabilities of the Hubble Space Telescope but also deepen our understanding of how galaxies like the Milky Way and the LMC evolve over time. 

By examining ancient globular clusters in both the Milky Way and its neighboring galaxies, astronomers are uncovering vital clues about star formation and the processes that shaped our universe.

Image Credit: ESA 

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By systematically integrating systems metabolic engineering strategies—such as synthetic biology, genome editing, and pathway optimization—the group significantly boosted production titers, yields, and product diversity.

Corynebacterium glutamicum, first identified in 1956 for its exceptional glutamic acid production, has since become a cornerstone microorganism in industrial biotechnology, particularly for manufacturing monosodium glutamate and other amino acids. Thanks to its metabolic versatility, safety profile, and capacity to utilize a wide range of substrates, C. glutamicum has emerged as a promising platform for constructing sustainable microbial cell factories. Recent advances in genetic and metabolic engineering have expanded its product range to include organic acids and terpenoids. However, challenges remain in optimizing its performance on non-conventional feedstocks such as lignocellulosic sugars and one-carbon compounds. These limitations highlight the need for innovative strategies to enhance substrate utilization, stress tolerance, and multifunctional cell factory design.

A study (DOI:10.1016/j.bidere.2025.100008) published in BioDesign Research on 26 February 2025 by Chen-Guang Liu’s team, Shanghai Jiao Tong University, opens avenues for more sustainable and cost-effective bio-based manufacturing of chemicals and materials, paving the way for industrial biorefineries that can reduce reliance on petrochemicals.

To explore the potential of Corynebacterium glutamicum as a robust microbial chassis for industrial biotechnology, researchers implemented a variety of metabolic engineering strategies to enable the utilization of non-model feedstocks, including lignocellulosic sugars, glycerol, methanol, and formic acid. They introduced and optimized key heterologous pathways and transport systems to allow efficient assimilation and conversion of pentose sugars such as xylose and arabinose—two major components of lignocellulosic biomass. For xylose, combinations of the xylose isomerase (XI) and Weimberg (WMB) pathways, including the expression of genes such as xylA, xylB, and the xylXABCD operon, were engineered into C. glutamicum, enabling growth on xylose as a sole carbon source. Transporter enhancements, like introducing xylE or araE, further improved sugar uptake. Similarly, arabinose assimilation was enabled via the araBAD operon from E. coli, leading to strains capable of producing organic acids and isobutanol from arabinose. For glycerol utilization, researchers expressed enzymes from E. coli, Klebsiella pneumoniae, and Citrobacter freundii to construct an active glycerol pathway, boosting growth rates and glycerol conversion efficiency. To harness one-carbon compounds, methanol assimilation was achieved by introducing methanol dehydrogenase (mdh) and key RuMP pathway enzymes, while formate assimilation relied on the reductive glycine pathway. Further, they developed strains with optimized flux control and cofactor regeneration systems to metabolize these C1 substrates effectively. These engineering strategies were validated through isotope tracing and fermentation assays, demonstrating the successful transformation of C. glutamicum into a multi-substrate platform. Collectively, these modifications significantly broadened the substrate scope and biochemical output of C. glutamicum, offering a foundation for producing diverse bio-based chemicals from renewable and low-cost resources.

This review summarizes recent advances in engineering Corynebacterium glutamicum into a versatile microbial cell factory for biochemical production. Originally used for amino acid synthesis, C. glutamicum has been reprogrammed to utilize non-conventional carbon sources such as xylose, arabinose, glycerol, methanol, and formic acid. It has also been optimized to produce various high-value compounds, including organic acids (e.g., lactate, succinate), amino acids (e.g., glutamate, lysine), and terpenoids (e.g., lycopene, pinene). The paper highlights challenges such as carbon catabolite repression and byproduct formation, suggesting that future improvements will rely on systems biology, multi-omics integration, and adaptive laboratory evolution.

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References

DOI

10.1016/j.bidere.2025.100008

Original Source URL

https://doi.org/10.1016/j.bidere.2025.100008

Funding information

This work was supported by the National Natural Science Foundation of China [22208212]; Startup Fund for Young Faculty at SJTU (SFYF at SJTU).

About 

About BioDesign Research

BioDesign Research is dedicated to information exchange in the interdisciplinary field of biosystems design. Its unique mission is to pave the way towards the predictable de novo design and assessment of engineered or reengineered living organisms using rational or automated methods to address global challenges in health, agriculture, and the environment.

What makes the human brain distinctive? A new study published July 21 in Cell identifies two genes linked to human brain features and provides a road map to discover many more. The research could lead to insights into the functioning and evolution of the human brain, as well as the roots of language disorders and autism.

The newly characterized genes are found among the “dark matter” of the human genome: regions of DNA that contain a lot of duplicated or repeat sequences, making them difficult to study until recently. If assembling a DNA sequence is like putting together a book from torn-up pages, reconstructing it from repeat sequences would be like trying to match pages using only words like “and” and “the.” There are many opportunities for mismatches and overlap.

Although difficult to study, DNA repeats are also thought to be important for evolution as they can generate new versions of existing genes for selection to act on.

“Historically, this has been a very challenging problem. People don’t know where to start,” said senior author Megan Dennis, associate director of genomics at the UC Davis Genome Center and associate professor in the Department of Biochemistry and Molecular Medicine and MIND Institute at the University of California, Davis.

In 2022, Dennis was a coauthor on a paper describing the first sequence of a complete human genome, known as the ‘telomere to telomere’ reference genome. This referencencludes the difficult regions that had been left out of the first draft published in 2001 and is now being used to make new discoveries.

Identifying human brain genes

Dennis and colleagues used the telomere-to-telomere human genome to identify duplicated genes. Then, they sorted those for genes that are: expressed in the brain; found in all humans, based on sequences from the 1000 Genomes Project; and conserved, meaning that they did not show much variation among individuals.

They came out with about 250 candidate gene families. Of these, they picked some for further study in an animal model, the zebrafish. By both deleting genes and introducing human-duplicated genes into zebrafish, they showed that at least two of these genes might contribute to features of the human brain: one called GPR89B led to slightly bigger brain size, and another, FRMPD2B, led to altered synapse signaling.

It’s pretty cool to think that you can use fish to test a human brain trait.”

Megan Dennis, associate director of genomics, UC Davis Genome Center and associate professor, Department of Biochemistry and Molecular Medicine and MIND Institute, University of California, Davis

The dataset in the Cell paper is intended to be a resource for the scientific community, Dennis said. It should make it easier to screen duplicated regions for mutations, for example related to language deficits or autism, that have been missed in previous genome-wide screening.

“It opens up new areas,” Dennis said.

Additional coauthors on the work are: Daniela Soto, José Uribe-Salazar, Gulhan Kaya, Ricardo Valdarrago, Aarthi Sekar, Nicholas Haghani, Keiko Hino, Gabriana La, Natasha Ann Mariano, Cole Ingamells, Aidan Baraban, Zoeb Jamal, Sergi Simó and Gerald Quon, all at UC Davis; Tychele Turner, Washington University St. Louis; Eric Green, National Human Genome Research Institute, Bethesda, Md.; and Aida Andrés, University College, London.

The work was supported in part by grants from the National Institutes of Health, National Science Foundation and The Wellcome Trust.

Scientists reanalyzed the supernovae with a sophisticated statistical method (a “Bayesian Hierarchical Model”) that can better account for uncertainties, incorporating partial information and the probability of errors. It makes it possible to include factors the researchers might not know exactly, but with constraints on how well they do know them. For example, the new approach can take into account that the filters in a telescope might drift over time, changing the amount of light that gets through from a supernova. This kind of flexibility improves the accuracy of the analysis and was difficult to include in previous techniques.

The improved analysis approach will be used to incorporate additional supernovae. Over the next year, researchers plan to add three more datasets, one with low-redshift (nearer by) supernovae, and two with high-redshift supernovae that look further back in time.

“We wanted to set a baseline before we bring in several hundred new low-redshift supernovae, which is one of the areas where the calibration is most crucial and where we have some of the weakest datasets in the results so far,” said Greg Aldering, a co-author of the paper and physicist at Berkeley Lab who led the Nearby Supernova Factory project. “We think we really understand the calibration in a way no one has before, and we’re excited to add more supernovae and see what they can tell us about dark energy.”

The new analysis framework will also help incorporate the tens to hundreds of thousands of additional supernovae expected from the NSF/DOE’s Vera C. Rubin Observatory (which recently released its first images) and NASA’s Nancy Grace Roman Space Telescope over the coming decade.

To paint a more complete picture of how our universe works, researchers can then combine their findings with those from complementary studies of dark energy that use different approaches. The other current leading technique to investigate how dark energy varies over time is by measuring how galaxies cluster — a characteristic feature known as baryon acoustic oscillations, or BAO. This is the measurement that DESI performs.

“BAO can look further back in time to when dark energy played less of a role in the universe, and supernovae are particularly precise in the more recent universe,” Perlmutter said. “The two techniques are getting good enough that we can really start saying things about the dark energy models. We’ve been waiting to reach this point for a long time.” 

The joint result from supernovae and BAO used together is also a striking example of the successful focus that a national laboratory can bring to a scientific field. Berkeley Lab supported the Supernova Cosmology Project’s decade-long work leading to the discovery of the universe’s acceleration, as well as its subsequent supernova studies of the dark energy models that might explain it. The lab also initiated and leads the 70-institution DESI collaboration to address the same question with the BAO technique, and led a complementary series of cosmic microwave background (CMB) projects that provide crucial early universe measurements for these dark energy studies. 

Researchers in neighboring offices on the same hallway thus helped each other understand the strengths and weaknesses of the two time-varying dark energy approaches, supernovae and BAO, as they were brought together with the CMB to obtain joint results. The projects also have inspired each other’s research agendas, helping build these ambitious, world-leading projects that use some of the largest telescopes on the ground and in space.

This research was conducted with collaboration from Berkeley Lab, UC Berkeley, University of Hawai’i at Mānoa, France’s Laboratory of Nuclear and High-Energy Physics (LPNHE, CNRS/IN2P3), Space Telescope Science Institute, University of San Francisco, the Australian National University, Spain’s Institute of Fundamental Physics (IFF-CSIC), the Institute of Cosmos Sciences (UB-IEEC), and Florida State University. Computing support was provided by the University of Hawai’i’s high performance computing cluster, Koa.

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Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 16 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science.

DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.

It took about 50 exploding stars to upend cosmology. Researchers mapped and measured light from Type Ia supernovae, the dramatic explosion of a particular kind of white dwarf. In 1998, they announced their surprising results: Instead of slowing down or staying constant, our universe was expanding faster and faster. The discovery of “dark energy,” the unknown ingredient driving the accelerated expansion, was awarded a Nobel Prize.

Since the late ’90s, dozens of experiments using different telescopes and techniques have captured and published more than 2,000 Type Ia (pronounced “one A”) supernovae. But without correcting for those differences, using supernovae from separate experiments is often a case of comparing apples and oranges.

To unite the supernovae and more precisely measure dark energy’s role in our universe, scientists built the largest standardized dataset of Type Ia supernovae ever made. The compilation is called Union3 and was built by the international Supernova Cosmology Project (SCP), which is led by the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab).

Analysis of this supernova set hints that dark energy might be evolving over time. The findings, recently published in The Astrophysical Journal, are not strong enough to conclusively say that dark energy has started weakening. But they do point in the same direction as separate analyses by the Dark Energy Spectroscopic Instrument. The two complementary approaches seeing similar results have researchers intrigued. Moreover, a partially independent result from another supernova analysis (including supernovae from the DOE-led Dark Energy Survey) also appears to support the conclusion.

“I don’t think anyone is jumping up and down getting overly excited yet, but that’s because we scientists are suppressing any premature elation since we know that this could go away once we get even better data,” said Saul Perlmutter, who shared the 2011 Nobel Prize for discovering dark energy and is a scientist at Berkeley Lab, professor at UC Berkeley, and co-author of the paper. “On the other hand, people are certainly sitting up in their chairs now that two separate techniques are showing moderate disagreement with the simple Lambda CDM model. It’s exciting that we’re finally starting to reach levels of precision where things become interesting and you can begin to differentiate between the different theories of dark energy.”

In our reigning model, Lambda CDM, dark energy (“Lambda”) is assumed to have the same strength over time, and it counteracts the gravitational contraction due to cold dark matter (“CDM”). But other models that allow dark energy to change over time might be a better fit for what researchers see in the data. If that’s the case, there are big implications for the fate of the universe.

“Dark energy makes up almost 70% of the universe and is what drives the expansion, so if it is getting weaker, we would expect to see expansion slow over time,” said David Rubin, first author of the Union3 paper, associate professor at the University of Hawai’i at Mānoa, and a leading member of the Supernova Cosmology Project. “Does the universe expand forever, or eventually stall, or even start contracting again? It depends on this balance between dark energy and matter. We want to find out which wins, and we want to understand this underlying piece of our universe.”

Tracing the expansion history of the universe using supernovae is one way to figure it out. Because supernovae have predictable brightness, researchers can use them as “standard candles” to measure distance – the same way you could calculate the length of a dark hallway based on how bright the flames appeared from a set of matching candles. Scientists also study the redshift, a measure of how much the supernova’s light has shifted into redder wavelengths because of the expansion of space.

Union3 standardizes 2,087 supernovae from 24 datasets, and can be used to look back over roughly 7 billion years of cosmic history. It builds on Union2, released in 2010, which contained 557 supernovae. To combine supernovae from disparate datasets, researchers analyze the light curve: the way a supernova’s brightness characteristically peaks and dims over its life. That lets them find the intrinsic brightness and adjust the supernovae so they’re all on the same scale – like calibrating a candle from a different manufacturer.

Scientists reanalyzed the supernovae with a sophisticated statistical method (a “Bayesian Hierarchical Model”) that can better account for uncertainties, incorporating partial information and the probability of errors. It makes it possible to include factors the researchers might not know exactly, but with constraints on how well they do know them. For example, the new approach can take into account that the filters in a telescope might drift over time, changing the amount of light that gets through from a supernova. This kind of flexibility improves the accuracy of the analysis and was difficult to include in previous techniques.

The improved analysis approach will be used to incorporate additional supernovae. Over the next year, researchers plan to add three more datasets, one with low-redshift (nearer by) supernovae, and two with high-redshift supernovae that look further back in time.

“We wanted to set a baseline before we bring in several hundred new low-redshift supernovae, which is one of the areas where the calibration is most crucial and where we have some of the weakest datasets in the results so far,” said Greg Aldering, a co-author of the paper and physicist at Berkeley Lab who led the Nearby Supernova Factory project. “We think we really understand the calibration in a way no one has before, and we’re excited to add more supernovae and see what they can tell us about dark energy.”

The new analysis framework will also help incorporate the tens to hundreds of thousands of additional supernovae expected from the NSF/DOE’s Vera C. Rubin Observatory (which recently released its first images) and NASA’s Nancy Grace Roman Space Telescope over the coming decade.

To paint a more complete picture of how our universe works, researchers can then combine their findings with those from complementary studies of dark energy that use different approaches. The other current leading technique to investigate how dark energy varies over time is by measuring how galaxies cluster – a characteristic feature known as baryon acoustic oscillations, or BAO. This is the measurement that DESI performs.

“BAO can look further back in time to when dark energy played less of a role in the universe, and supernovae are particularly precise in the more recent universe,” Perlmutter said. “The two techniques are getting good enough that we can really start saying things about the dark energy models. We’ve been waiting to reach this point for a long time.”

The joint result from supernovae and BAO used together is also a striking example of the successful focus that a national laboratory can bring to a scientific field. Berkeley Lab supported the Supernova Cosmology Project’s decade-long work leading to the discovery of the universe’s acceleration, as well as its subsequent supernova studies of the dark energy models that might explain it. The lab also initiated and leads the 70-institution DESI collaboration to address the same question with the BAO technique, and led a complementary series of cosmic microwave background (CMB) projects that provide crucial early universe measurements for these dark energy studies.

Researchers in neighboring offices on the same hallway thus helped each other understand the strengths and weaknesses of the two time-varying dark energy approaches, supernovae and BAO, as they were brought together with the CMB to obtain joint results. The projects also have inspired each other’s research agendas, helping build these ambitious, world-leading projects that use some of the largest telescopes on the ground and in space.

This research was conducted with collaboration from Berkeley Lab, UC Berkeley, University of Hawai’i at Mānoa, France’s Laboratory of Nuclear and High-Energy Physics (LPNHE, CNRS/IN2P3), Space Telescope Science Institute, University of San Francisco, the Australian National University, Spain’s Institute of Fundamental Physics (IFF-CSIC), the Institute of Cosmos Sciences (UB-IEEC), and Florida State University. Computing support was provided by the University of Hawai’i’s high performance computing cluster, Koa.

What makes the human brain distinctive? A new study published July 21 in Cell identifies two genes linked to human brain features and provides a road map to discover many more. The research could lead to insights into the functioning and evolution of the human brain, as well as the roots of language disorders and autism.

The newly characterized genes are found among the “dark matter” of the human genome: regions of DNA that contain a lot of duplicated or repeat sequences, making them difficult to study until recently. If assembling a DNA sequence is like putting together a book from torn-up pages, reconstructing it from repeat sequences would be like trying to match pages using only words like “and” and “the.” There are many opportunities for mismatches and overlap.

Although difficult to study, DNA repeats are also thought to be important for evolution as they can generate new versions of existing genes for selection to act on.

“Historically, this has been a very challenging problem. People don’t know where to start,” said senior author Megan Dennis, associate director of genomics at the UC Davis Genome Center and associate professor in the Department of Biochemistry and Molecular Medicine and MIND Institute at the University of California, Davis.

In 2022, Dennis was a coauthor on a paper describing the first sequence of a complete human genome, known as the ‘telomere to telomere’ reference genome. This referencencludes the difficult regions that had been left out of the first draft published in 2001 and is now being used to make new discoveries.

Identifying human brain genes

Dennis and colleagues used the telomere-to-telomere human genome to identify duplicated genes. Then, they sorted those for genes that are: expressed in the brain; found in all humans, based on sequences from the 1000 Genomes Project; and conserved, meaning that they did not show much variation among individuals.

They came out with about 250 candidate gene families. Of these, they picked some for further study in an animal model, the zebrafish. By both deleting genes and introducing human-duplicated genes into zebrafish, they showed that at least two of these genes might contribute to features of the human brain: one called GPR89B led to slightly bigger brain size, and another, FRMPD2B, led to altered synapse signaling.

“It’s pretty cool to think that you can use fish to test a human brain trait,” Dennis said.

The dataset in the Cell paper is intended to be a resource for the scientific community, Dennis said. It should make it easier to screen duplicated regions for mutations, for example related to language deficits or autism, that have been missed in previous genome-wide screening.

“It opens up new areas,” Dennis said.

Additional coauthors on the work are: Daniela Soto, José Uribe-Salazar, Gulhan Kaya, Ricardo Valdarrago, Aarthi Sekar, Nicholas Haghani, Keiko Hino, Gabriana La, Natasha Ann Mariano, Cole Ingamells, Aidan Baraban, Zoeb Jamal, Sergi Simó and Gerald Quon, all at UC Davis; Tychele Turner, Washington University St. Louis; Eric Green, National Human Genome Research Institute, Bethesda, Md.; and Aida Andrés, University College, London.

The work was supported in part by grants from the National Institutes of Health, National Science Foundation and The Wellcome Trust.

After a long wait, astronomers have finally seen the stellar companion of the famous star Betelgeuse. This companion star orbits Betelgeuse in an incredibly tight orbit, which could explain one of Betelgeuse’s longstanding mysteries. The star is doomed, however, and the team behind this discovery predicts that Betelgeuse will cannibalize it in a few thousand years.

The fact that Betelgeuse is one of the brightest stars in the sky over Earth, visible with the naked eye, has made it one of the most well-known celestial bodies. And ever since the first astronomers began inspecting this fixture in the night sky, they have been baffled by the fact that its brightness varies over periods of six years.

This mystery is now solved.

The six-year dimming of this red supergiant star is not to be confused with an event that saw it drop sharply in brightness over 2019 and 2020. This event, known as the “Great Dimming,” sparked intense interest across the globe. The Great Dimming was so unexpected that it led some scientists to theorize that it could signal Betelgeuse was approaching the supernova explosion that will one day mark the end of its life.

Physicists from Ames National Laboratory and Iowa State University have demonstrated the emergence of a Higgs echo in niobium superconductors. Their discovery provides insight into quantum behaviors that could be used for next-generation quantum sensing and computing technologies.

Superconductors are materials that carry electricity without resistance.

Within these superconductors are collective vibrations known as Higgs modes.

A Higgs mode is a quantum phenomenon that occurs when its electron potential fluctuates in a similar way to a Higgs boson.

They appear when a material is undergoing a superconducting phase transition.

Observing these vibrations has been a long-time challenge for scientists because they exist for a very short time.

They also have complex interactions with quasiparticles, which are electron-like excitations that emerge from the breakdown of superconductivity.

However, using advanced terahertz (THz) spectroscopy techniques, the research team discovered a novel type of quantum echo, called the Higgs echo, in superconducting niobium materials used in quantum computing circuits.

“Unlike conventional echoes observed in atoms or semiconductors, the Higgs echo arises from a complex interaction between the Higgs modes and quasiparticles, leading to unusual signals with distinct characteristics,” said Dr. Jigang Wang, a researcher at Ames National Laboratory.

“The Higgs echo can remember and reveal hidden quantum pathways within the material.”

By using precisely timed pulses of THz radiation, the authors were able to observe these echoes.

Using these THz radiation pulses, they can also use the echoes to encode, store, and retrieve quantum information embedded within this superconducting material.

This research demonstrates the ability to control and observe quantum coherence in superconductors and paves the way for potential new methods of quantum information storage and processing.

“Understanding and controlling these unique quantum echoes brings us a step closer to practical quantum computing and advanced quantum sensing technologies,” Dr. Wang said.

A paper describing the discovery was published June 25 in the journal Science Advances.

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Chuankun Huang et al. 2025. Discovery of an unconventional quantum echo by interference of Higgs coherence. Science Advances 11 (26); doi: 10.1126/sciadv.ads8740

The European Space Agency (ESA) has taken a step toward diversifying its access to space.

ESA has chosen five rocket companies to pass through to the next round of its competition to encourage and support the development of new launch vehicles.