Category: 7. Science

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.

Transposable elements (TEs) are repetitive DNA sequences in the genome that originated from ancient viruses. Over millions of years, they spread throughout the genome via copy-and-paste mechanisms. Today, TEs make up nearly half of the human genome. While they were once thought to serve no useful function, recent research has found that some of them act like “genetic switches,” controlling the activity of nearby genes in specific cell types.

However, because TEs are highly repetitive and often nearly identical in sequence, they can be difficult to study. In particular, younger TE families like MER11 have been poorly categorized in existing genomic databases, limiting our ability to understand their role.

To overcome this, the researchers developed a new method for classifying TEs. Instead of using standard annotation tools, they grouped MER11 sequences based on their evolutionary relationships and how well they were conserved in the primate genomes. This new approach allowed them to divide MER11A/B/C into four distinct subfamilies, namely, MER11_G1 through G4, ranging from oldest to youngest.

This new classification revealed previously hidden patterns of gene regulatory potential. The researchers compared the new MER11 subfamilies to various epigenetic markers, which are chemical tags on DNA and associated proteins that influence gene activity. This showed that this new classification aligned more closely with actual regulatory function compared with previous methods.

To directly test whether MER11 sequences can control gene expression, the team used a technique called lentiMPRA (lentiviral massively parallel reporter assay). This method allows thousands of DNA sequences to be tested at once by inserting them into cells and measuring how much each one boosts gene activity. The researchers applied this method to nearly 7000 MER11 sequences from humans and other primates, and measured their effects in human stem cells and early-stage neural cells.

The results showed that MER11_G4 (the youngest subfamily) exhibited a strong ability to activate gene expression. It also had a distinct set of regulatory “motifs,” which are short stretches of DNA that serve as docking sites for transcription factors, the proteins that control when genes are turned on. These motifs can dramatically influence how genes respond to developmental signals or environmental cues.

Further analysis revealed that the MER11_G4 sequences in humans, chimpanzees, and macaques had each accumulated slightly different changes over time. In humans and chimpanzees, some sequences gained mutations that could increase their regulatory potential during in human stem cells.Young MER11_G4 binds to a distinct set of transcription factors, indicating that this group gained different regulatory functions through sequence changes and contributes to speciation,leading researcher Dr. Xun Chen explains.

The study offers a model for understanding how “junk” DNA can evolve into regulatory elements with important biological roles. By tracing the evolution of these sequences and directly testing their function, the researchers have demonstrated how ancient viral DNA has been co-opted into shaping gene activity in primates.

“Our genome was sequenced long ago, but the function of many of its parts remain unknown,” co-responding auther Dr. Inoue notes. Transposable elements are thought to play important roles in genome evolution, and their significance is expected to become clearer as research continues to advance.