The three Americans and one Canadian slated to fly on NASA's Artemis II circumlunar mission had a "pinch me" moment Monday when they got their first chance visit the Orion spacecraft that will carry them around the Moon and back to Earth.

 

The astronauts had an opportunity to peer through the hatch of the Orion crew capsule for the Artemis II mission, now largely complete and going through some final tests before it is connected to its power and propulsion module at NASA's Kennedy Space Center in Florida.

 

"We all said when we walked up to it the first time, that it gave us chills, and it really does," said Christina Koch, a mission specialist on the Artemis II mission. "So it's a new way that I feel bonded with this crew and also with the team."

 

This week's crew visit to Kennedy Space Center is far from their last as the astronauts ready for their mission—the first flight by humans to the vicinity of the Moon since the last Apollo mission in 1972. On future trips to the Florida spaceport, Koch and her crewmates will climb inside the Orion crew module multiple times for testing and checkouts. Some time next year, they'll put on their orange pressure suits and strap into their seats for a full-up end-to-end test of the spacecraft's software, cockpit displays, and life support system.

 

"It was great to look inside," said Reid Wiseman, the Artemis II commander. "The fit and finish is gorgeous. It's neat to see the actual hardware all coming together. The things that we've learned about so far in training, to see it for real in the spacecraft, it just gave you a good sense of how far along this thing is. The hardware is is nearly ready."

Far along, but more work to do

Right now, the 16.5-foot-diameter (5-meter) Orion crew module—nearly 4 feet wider than the Apollo command module from the first era of lunar exploration—is situated at one end of the long hall of the cavernous Neil Armstrong Operations & Checkout Building at Kennedy. A stack of powerful speakers surround the Orion capsule on three sides.

 

The upper part of the gumdrop-shaped spacecraft is covered in black silica tiles similar in appearance to the thermal protection tiles that flew on NASA's space shuttle. On the bottom, the spacecraft's main heat shield is attached, with a reflective silver coating applied over an ablative material that will take the brunt of the nearly 5,000-degree Fahrenheit (2,800-degree Celsius) heat generated at the end of the mission when the capsule plunges back through Earth's atmosphere at a speed equivalent to 32 times the speed of sound.

 

Later this week, the speakers will start blasting the Orion crew module with sounds that mimic the acoustic energy from a rocket launch. The direct field acoustic test is designed to ensure the spacecraft can weather the intense sound from the engines and boosters of NASA's Space Launch System rocket, which will send the Artemis II crew into space.

 

It's one of the last big tests before technicians raise the Orion crew module, built by Lockheed Martin, on top of the Orion service module, made by Airbus through an agreement between NASA and the European Space Agency. The finishing touches on the Orion crew module have taken longer than expected, and the connection of the two Orion modules is now expected in mid-September, two-to-three months later than NASA forecast earlier this year.

 

Preparing the crew module element of the Artemis II mission—where the astronauts will live during their roughly 10-day loop flight around the far side of the Moon— is now driving the launch date, according to Jim Free, who leads the NASA division responsible for developing hardware for the Artemis lunar program.

 

The preparations are running a "number of weeks" behind the schedule NASA needs to maintain the target Artemis II launch date in late November 2024, Free said. Not surprisingly, that means a delay into 2025 is likely.

 

"The crew module is the critical path right now," Free said in a press conference Tuesday at Kennedy. "We have to get the crew module assembled and tested, and then mated to the service module, and turn it over to the ground systems folks here for processing."

 

 

NASA convened a review in late July to assess the results from the unpiloted Artemis I lunar mission last year, the first flight of the enormous SLS rocket and the first voyage of an Orion spacecraft to the distance of the Moon. While the space agency has heralded the Artemis I test as an unqualified success, there are a few lessons learned from the Artemis I still under review. None rise to the level to be considered "major issues," Free said.

 

One of those involves an electrical system issue on the Orion service module, and another has to do with release and retention bolts on the Orion spacecraft. Free said the most significant unresolved issue coming out of the Artemis I mission was a finding from post-flight inspections of the Orion heat shield. Ground teams discovered "charred material" that ablated, or burned off, from the heat shield in a different way than was predicted by computer models.

 

More of the charred material than expected came off the heat shield during the Artemis I re-entry, and the way it came off was somewhat uneven. There were more variations than expected in the appearance of different parts the heat shield. Ultimately, though, the heat shield protected the capsule and Orion safely splashed down under parachutes in the Pacific Ocean.

 

Debbie Korth, NASA's deputy Orion program manager, told Ars that engineers continue the investigation into the heat shield performance, with the help of arc jet and wind tunnel tests. It's not likely, she said, that any hardware changes will be made to the heat shield already installed on the Orion spacecraft for Artemis II. NASA could make changes to heat shields for downstream Artemis missions.

 

"We’re in the process of doing some modeling updates and some ground testing to try and duplicate the kind of condition that we saw on Artemis I," Korth said. "The heat shield we have on Artemis II is of the same design, so a lot of what we’re doing now is looking at the trajectories, what kind of trajectory will we fly to try to minimize that char loss."

 

NASA officials went out of their way Tuesday to commit to the safety of the Artemis II crew. "Obviously, we’re going to make the right decision to keep them safe," Free said, when asked about the heat shield investigation. "If that decision is we have to do something drastic, then we’ll do that, but right now we’re on a path ... to get to the root cause, and then we’ll make the final determination from there.”

 

Wiseman, the Artemis II commander, said he is heavily engaged in discussions on the Orion heat shield.

 

“I know we will find the right solution, and for sure, this crew, we’re not going to launch until we know we’re ready and until our team knows that the vehicle is ready," Wiseman said. "We’ll keep the pressure on, but so far, I think all the right things are being done."

 

 

A short walk from the crew module's acoustic test cell, the Orion service module is largely complete for Artemis II. Once the modules are mated, engineers will put the entire Orion spacecraft through a series of integrated pressure tests, which will exercise the spacecraft's environmental control and life support system. The full set of life support hardware has not flown in space before.

 

"I think the biggest new thing will be how these new systems are responding to the series of tests we have ahead of us," Korth said.

 

The handover of the Orion spacecraft from the production team to the operations team at Kennedy is planned for next April. Then Orion will move to a nearby fueling facility at Kennedy to be loaded with hypergolic propellants, before rolling to the Vehicle Assembly Building for stacking on the SLS rocket.

 

The Boeing-built core stage of the SLS Moon rocket is on track to arrive at Kennedy from its factory in New Orleans in November. That would set up for the start of stacking of the rocket inside the assembly building in February.

This Artemis II delay may not amount to much

A schedule slip of several months for Artemis II, if that's all it is, is not a big deal in the delay-ridden history of NASA's deep space exploration programs. The next Artemis mission after Artemis II is currently slated to attempt the program's first lunar landing, again using the SLS Moon rocket and the Orion spacecraft, which will link up with a commercial landing vehicle derived from SpaceX's Starship mega-rocket.

 

The landing on the Moon's South Pole on Artemis III hinges on the readiness of the Starship lander and new spacesuits developed by Axiom Space. Those projects aren't likely to be ready to support NASA's target launch schedule for Artemis III at the end of 2025. It's probably also a fair question whether the Orion spacecraft and SLS rocket for Artemis III would be ready at that time.

 

If the Artemis III landing mission moves to 2026 or later, it doesn't make much difference whether Artemis II flies in late 2024 or 2025. There's just no big rush. In fact, Free acknowledged on Tuesday that NASA is considering alternate mission profiles for Artemis III in case of significant delays to Starship and the Axiom spacesuits.

 

SpaceX first needs to get the Starship rocket into orbit. Another Starship test launch could happen in the next couple of months. Then there will need to be many more test flights, including a Starship refueling demonstration in orbit, a capability without which Starship can't reach the Moon. Finally, SpaceX plans to fly a Starship test mission to land on the Moon without astronauts before committing to a crew landing.

 

Free said NASA officials recently met with SpaceX's team at the Starship development site in South Texas. SpaceX provided NASA with an updated schedule of milestones to get to the Artemis III landing, but Free declined to discuss specifics of the timeline.

 

"I think we’ll look at that and update around that in the near future, after we have some time to digest it," Free said. "But we’re holding all the contractors to that December of ’25 date (for Artemis III).

 

"We may end up flying a different mission," Free said. "If we’re having these big slips, we’ve looked at can we do other missions, if the possibility exists there. Right now, we’re still taking a look at their schedule. The spacesuits are having a CDR (Critical Design Review) in October, so that’s obviously another piece of hardware that’s on the critical path for that mission.”

 

As machine-learning algorithms grow more sophisticated, artificial intelligence seems poised to revolutionize the practice of science itself. In part, this will come from the software enabling scientists to work more effectively. But some advocates are hoping for a fundamental transformation in the process of science. The Nobel Turing Challenge, issued in 2021 by noted computer scientist Hiroaki Kitano, tasked the scientific community with producing a computer program capable of making a discovery worthy of a Nobel Prize by 2050.

 

Part of the work of scientists is to uncover laws of nature—basic principles that distill the fundamental workings of our Universe. Many of them, like Newton’s laws of motion or the law of conservation of mass in chemical reactions, are expressed in a rigorous mathematical form. Others, like the law of natural selection or Mendel’s law of genetic inheritance, are more conceptual.

 

The scientific community consists of theorists, data analysts, and experimentalists who collaborate to uncover these laws. The dream behind the Nobel Turing Challenge is to offload the tasks of all three onto artificial intelligence.

Outsourcing (some) science

Outsourcing the work of scientists to machines is not a new idea. As far back as the 1970s, Carnegie Mellon University professor Patrick Langley developed a program he called BACON, after Francis Bacon, who pioneered the use of empirical reasoning in science. BACON was capable of looking at data and putting it together in different ways until it found something that looked like a pattern, akin to discovering a new physical law. Given the right data, BACON discovered Kepler’s laws, which govern the orbits planets make around the Sun. However, limited computing power kept BACON from taking on more complex tasks.

 

In the 1990s, with more computing power at their fingertips, scientists developed an automated tool that could search through formulas until it found one that fit a given dataset. This technique, called symbolic regression, bred formulas as if they were a species, with genetic inheritance and mutations, where only the ones that fit the data best would survive. This technique, and variants thereof, spurred on a new era of AI scientists, many with similarly referential names like Eureqa and AI Feynman.

 

These sophisticated algorithms can effectively extract new formulas, which may describe scientific laws, from vast datasets. Present them with enough raw information, and they’ll determine and quantify any underlying relationships, effectively spitting out plausible hypotheses and equations for any situation. They play the role of the data analyst, but experts say this approach isn't about replacing all human scientists.

 

“The biggest roadblock is knowledge representation,” says Ross King, a machine-learning researcher at the University of Cambridge. “Because if you look at big breakthroughs, like Einstein’s theory of special relativity, it came from a philosophical question about magnetism. And it’s a reformulation of our knowledge. We’re nowhere near a computer being able to do that.”

Leveraging existing knowledge

To truly make groundbreaking discoveries, King argues, the way the machines represent knowledge has to be more sophisticated than simply pushing around algebraic expressions until they find one that fits. There needs to be a way to represent more of the abstract, almost philosophical formulations of knowledge and understanding—they have to handle laws in both their mathematical and non-mathematical forms.

 

As a step in that direction, researchers at IBM have created a new AI scientist with a novel feature: incorporating prior knowledge. Human scientists often start with well-established basic principles and deduce more intricate or specific relationships from there; they don’t solely rely on new data.

 

The IBM program, named AI Descartes, merges data-driven discovery with a knowledge of theory for the first time. “This is what real scientists do,” said Cristina Cornelio, a research scientist now at Samsung AI who led the effort. Like many previous machine scientists, AI Descartes looks at new data and compiles a list of potential underlying formulas. Unlike previous software, however, it doesn’t stop there: it then considers relevant prior knowledge, checking how well the suggested formulas fit into the bigger picture.

 

AI Descartes is basically a three-step system that helps the software make the most sense out of a set of data, given some theoretical information. Its first step is similar to previous machine scientists: looking at noisy data and searching for a formula that would fit without being overly complicated. For example, one of the classic equations it re-discovered was Kepler's law, which describes how planets orbit the Sun. Descartes’ handlers fed the system the masses of the Sun and each planet, their distance to the Sun, and the number of days each takes to complete one revolution. The system used a version of symbolic regression to construct possible formulas from component terms and searched for one that can predict the orbital period of any planet based on mass and distance. Usually, this procedure results in a few possible formulas with varying levels of complexity (with simpler ones being less accurate).

 

For the second step, AI Descartes turns to the known background theory to check if any of the candidate formulas make scientific sense and help break the tie. To do this, it makes use of a “logical reasoning module” that basically works as a theorem prover—verifying logical connections without the need for actual data. It starts with fundamental rules and concepts, expressed as a set of equations entered by human researchers. For the case of Kepler’s law, this included expressions for gravitational and centrifugal forces, as well as basic premises like mass should always be positive. Then, the reasoning module tries to expand its background knowledge one logical step at a time, using the fundamental rules to generate more and more formulas that are still valid.

 

If one of the first step’s candidate formula pops up in that list, that immediately makes it the favorite, since it would be provable from background theory.

Imperfect matches

Of course, it’s more likely the theorem prover won’t generate an exact match for a candidate formula—if the formula is easily derived from the background theory alone, one might question the necessity of the data in the first place. In the Kepler’s law example, none of the three formulas it identified in the first step could be derived from existing knowledge alone.

 

But the ways in which the candidate formulas fall short can be enlightening. This comprises the crucial third step: determining which candidate formula is closest to the possibilities suggested by the background theory. To do this, AI Descartes uses three separate ways of describing the distance between the candidate data-driven formulas and those derivable from background theory—something that can be done even without an explicit ‘correct’ formula. “That’s the magic of the theorem prover,” Cornelio says.

 

These definitions of distance vary, but they’re all about trying to derive the candidate formula from the background theory with a few different assumptions. These distances help tease out why the formula might be underivable from the background theory and thus suggest future courses of action. One checks that the data itself isn’t inconsistent with the theory; the second examines whether the formula overfit the noisy data; and the third checks whether the candidate formula has a sensible dependence on each of the variables (for example, the masses and distances of the planets in the Solar System).

 

By looking at all three error measures, AI Descartes picked the least offensive version of Kepler’s law. All three candidate formulas did reasonably well on the first and second tests, but the third revealed that none of them had a theory-approved dependence on mass, and only one had the appropriate dependence on the distance between the planets and the Sun. So, the AI concluded that the distance-dependent formula is a good approximation for the range of masses of bodies in the Solar System.

 

To do better, the team turned to a dataset that included pairs of stars orbiting each other. Then, the AI learned the proper dependence on mass and fully re-discovered Kepler’s law.

 

If the program fails to find a formula that at least partially fits both data and theory, it can recommend follow-up experiments to produce additional data that would help it distinguish between candidate formulas.

A long road ahead

Consulting with prior knowledge allows the program to make meaningful inferences from far fewer data points. Aside from Kepler’s laws, AI Descartes has re-derived several well-known laws in physics and chemistry from as few as 10 pieces of data, and it may soon help scientists crack unsolved problems. “In many problems, making the measurement is difficult,” Cornelio says, “both from the experiment point of view and also in terms of cost. So in many cases, you have really noisy data with very few points. That’s where AI Descartes would be most useful.”

 

It's not going to win the Nobel Turing Challenge on its own, says King, but “AI Descartes is a step towards that. It’s one of the pieces that’s required.” Machine science expert George Karniadakis at Brown University agrees: “I commend the effort because it’s in the right direction,” he says, “but we are not at the point where we have enough intelligence yet.”

 

One issue is that, while AI Descartes can analyze data and recommend experiments, the system cannot perform the experiments itself. And even more serious is the lack of systematized sets of background knowledge axioms for it to build on in its second step. It will be even harder to give an AI the ability to re-formulate that knowledge by starting from completely different premises or an alternate conceptual framework, rather than by adding formulas to the existing structure. Yet that ability is critical for navigating fields where there are multiple competing hypotheses, like finding a quantum-compatible version of gravity.

 

“If you think about the history of science,” Karniadakis says, “the big discoveries came from ‘aha’ moments. An aha moment is like you're going down the road and you discover you’ve had the wrong assumptions and you realize they’re the wrong assumptions. These machines cannot realize that.”

 

But AI Descartes is showing one possible way to start getting us there, and the researchers are already at work on the next steps. “Theory can be incomplete, and at times incorrect,” says Lior Horesh, a senior manager at MIT-IBM Research who led the project. "So, our next question is, 'Can we somehow bring both numerical data and theorems to a common ground, where they can exchange value and simultaneously guide us towards the discovery of new models?’ One way or another, I hope that AI-Descartes and future AI advancements can help us unveil some of the mysteries of the Universe.”

 

Planets that go rogue orbit no star. They wander the vacuum of space alone, having been kicked out of their star systems by gravitational interactions with other planets and stars. Nobody really knows how many rogue planets could be out there, but that may change in a few years.

 

Researchers from NASA’s Goddard Space Flight Center and Osaka University in Japan have used the phenomenon of gravitational microlensing to estimate the number of rogue planets that could be revealed in the heart of the Milky Way. They analyzed data from the Microlensing Observations in Astrophysics (MOA) survey that searched for gravitational microlensing events from 2006 to 2014 to figure out how many more of these events we could expect to find with NASA’s upcoming Nancy Grace Roman Space Telescope.

 

There are currently only 70 known rogue planets, but there could be hundreds more out there. The researchers now suggest that Roman could discover at least 400 Earth-mass rogues meandering through our galaxy.

Using gravity as a magnifying glass

Anything that has mass bends spacetime since gravity is the curvature of spacetime. When an object passes in front of a distant star, galaxy, or galaxy cluster without being completely aligned with it, the light from that star (or other light-emitting body) will pass through the space that is bent by the object’s mass. This curved space can magnify the object like a lens would, amplifying the brightness of the background star, making it more visible. This phenomenon is known as gravitational lensing.

 

Most rogue planets tend to be on the smaller side because less mass puts a planet at greater risk of being flung out of its exosolar system. The small size and fact that they’re not associated with a star make them very hard to spot. But gravitational microlensing can give scientists an assist.

 

Gravitational microlensing events occur the same way other gravitational lensing events do, except microlensing refers to the lensing done by smaller objects. Because of the low mass of many rogue planets, they create a weaker lensing effect that makes events more difficult to see. Still, a number of these microlensing events have been detected, so we know rogue planets are out there.

 

Rogue planets that range from Mars mass to Earth mass will be future targets for the Roman telescope. As they cross in front of a star and bend spacetime (therefore bending the star’s light), most may not linger longer than a day, but that’s typically just long enough for observations to be made.

 

“Gravitational microlensing enables us to study a variety of objects with masses ranging from that of exoplanets to black holes,” the researchers said in the first of two studies soon to be published in The Astronomical Journal. The second study can be found here.

Lonely telescope seeks unattached planets

After it launches in 2027, the Roman telescope will search for rogue planets at the heart of the Milky Way. The research team decided to figure out how many planets we might expect it to find. To do so, they used data from MOA and the Optical Gravitational Lensing Experiment (OGLE) to estimate how many of these planets there are. That approximation was then used to make a prediction of how many could be found in the Milky Way’s central galactic bulge based on the Roman telescope’s capabilities.

 

“We estimate that our galaxy is home to 20 times more rogue planets than stars—trillions of worlds wandering alone,” said a co-author of both papers, senior research scientist David Bennett of NASA’s Goddard Space Flight Center, in a press release. “This is the first measurement of the number of rogue planets in the galaxy that is sensitive to planets less massive than Earth.”

 

Bennett and his colleagues also estimated that there are at least six times more small rogue planets than planets with wide orbits in our galaxy’s center, which means their orbits are distant from their star. The masses of these predicted rogues are between Mars’ and Earth’s, based on the cutoffs used by the researchers.

 

If some planets of this mass have wide orbits and their mass is consistent with many rogue planets, it could mean that at least some rogue planets around the mass of Earth were also once in wide orbits around stars but were thrown into space by intense gravitational interactions with objects in their former exosolar systems. They could have also been ejected during the tumultuous formation of their star systems.

 

Roman’s instruments should be able to pick up on more microlensing events that reveal these planets than the earlier surveys. It already has high expectations to live up to. For now, at least we have an idea of what might be lurking out there.

For a fourth consecutive summer, COVID-19 is on the rise, though this year's warm-weather wave appears milder than those in the emergency period of the pandemic.

 

COVID-19 indicators of hospital admissions, emergency department visits, test positivity, and wastewater levels have all been increasing in the past month, with a peak not yet clearly in sight, according to data tracking by the Centers for Disease Control and Prevention. From June 10 to July 29, test positivity rose from 4.1 percent to 8.9 percent. For reference, the most recent winter wave had a peak test positivity of 10.6 percent on December 31, 2022.

 

On the brighter side, however, weekly COVID-19 hospital admissions and deaths continue to be at their lowest points since the start of the pandemic. For now, deaths do not appear to be rising, though there are lags in data reporting. Weekly new hospital admissions are ticking up only slightly—with admissions rising to about 8,000 in the week of July 22, up from around 6,300 the week of June 24.

 

Death counts for the most recent weeks with complete data show tallies of 500 to 400. And excess deaths—the number of deaths above expected baseline levels—are no longer being observed in CDC data. That is, the weekly number of deaths in the US from all causes is currently tracking with the pre-pandemic number of expected deaths.

 

The seemingly milder wave is likely due to a combination of factors, including immunity from vaccines and past infections and the fact that many people vulnerable to the virus died in previous waves. The cumulative US death toll of the COVID-19 pandemic stands at over 1.1 million.

 

Still, the virus is surging again this summer, raising questions of whether summer waves will be a fixed seasonal cycle for this virus. Many health experts see SARS-CoV-2 as predominately a cold-weather virus, much like other respiratory germs, such as the common cold and flu viruses that thrive and surge in the fall and winter. The Food and Drug Administration, for instance, has modeled its COVID-19 vaccine booster plans around those used for annual flu shots.

 

But SARS-CoV-2's seasonality is still unclear, and researchers don't know exactly what's driving the summer waves, which often start in the southern part of the country. A leading hypothesis is that the upticks coincide with summer vacations, travel, and get-togethers.

 

Another potential factor for waves is newly emerging variants. Currently, a new omicron subvariant—EG.5, which is related to XBB.1.9.2—is gaining dominance in the US over the previously reigning variants, XBB.1.5 and XBB.1.16. However, monitoring for SARS-CoV-2 variants has declined so steeply that the CDC only has enough data to estimate variant prevalence for three of the country's 10 health regions (the areas around California, New York, and the Southeast).

 

Based on an FDA advisory committee meeting in June, vaccine manufacturers will likely roll out updated COVID-19 booster shots this fall aimed at an XBB subvariant lineage, possibly XBB.1.5.

 

US government scientists have achieved net energy gain in a fusion reaction for the second time, a result that is set to fuel optimism that progress is being made toward the dream of limitless, zero-carbon power.

 

Physicists have since the 1950s sought to harness the fusion reaction that powers the Sun, but until December no group had been able to produce more energy from the reaction than it consumes—a condition also known as ignition.

 

Researchers at the federal Lawrence Livermore National Laboratory in California, who achieved ignition for the first time last year, repeated the breakthrough in an experiment on July 30 that produced a higher energy output than in December, according to three people with knowledge of the preliminary results.

 

The laboratory confirmed that energy gain had been achieved again at its laser facility, adding that analysis of the results was underway.

 

“Since demonstrating fusion ignition for the first time at the National Ignition Facility in December 2022, we have continued to perform experiments to study this exciting new scientific regime. In an experiment conducted on July 30, we repeated ignition at NIF,” it said. “As is our standard practice, we plan on reporting those results at upcoming scientific conferences and in peer-reviewed publications.”

 

Fusion is achieved by heating two hydrogen isotopes—usually deuterium and tritium—to such extreme temperatures that the atomic nuclei fuse, releasing helium and vast amounts of energy in the form of neutrons.

 

Although many scientists believe fusion power stations are still decades away, the technology’s potential is hard to ignore. Fusion reactions emit no carbon, produce no long-lived radioactive waste, and a small cup of hydrogen fuel could theoretically power a house for hundreds of years.

 

The most widely studied approach, known as magnetic confinement, uses huge magnets to hold the fuel in place while it is heated to temperatures hotter than the Sun.

 

The NIF uses a different process, called inertial confinement, in which it fires the world’s largest laser at a tiny capsule of the fuel triggering an implosion.

 

 

US Energy Secretary Jennifer Granholm in December described the achievement of ignition as “one of the most impressive science feats of the 21st century.” In that experiment, the reaction produced about 3.15 megajoules, which was about 150 percent of the 2.05MJ in the lasers.

 

Initial data from the July experiment indicated an energy output greater than 3.5MJ, two of the people with knowledge of the preliminary results said. That energy would be roughly sufficient to power a household iron for an hour.

 

Achieving net energy gain has been seen for decades as a crucial step in proving that commercial fusion power stations are possible. However, there are still several hurdles to overcome.

 

Energy gain in this context only compares the energy generated to the energy in the lasers, not to the total amount of energy pulled off the grid to power the system, which is much higher. Scientists estimate that commercial fusion will require reactions that generate between 30 and 100 times the energy in the lasers.

 

The NIF also makes a maximum of one shot a day, whereas an internal confinement power plant would probably need to complete several shots a second.

 

However, the improved result at NIF, coming “only eight months” after the initial breakthrough, was a further sign that the pace of progress was increasing, said one of the people with knowledge of the result.

 

SpaceX on Sunday performed a static fire of a new Super Heavy booster at its launch site in South Texas. The ignition of 33 engines proved to be a spectacle, and there were positives and negatives to be taken away from the short-duration test firing.

 

On the plus side, the rocket—dubbed Booster 9, as it is the ninth to be built as part of SpaceX's iterative design methodology—survived the test and appeared to be in good shape afterward. Also on the positive side of ledger, the company's radically rebuilt ground systems, with an enhanced water suppression system, appeared to function well in protecting the rocket and the launch pad.

 

However, the test did not run a full duration. It ended after 2.74 seconds, according to SpaceX's webcast, short of the planned five seconds. Moreover, four of the rocket's 33 main Raptor engines shut down prematurely. This indicates that SpaceX is still struggling with the reliability of its Raptor engines despite intense work to improve their performance. This rocket is powered by "Raptor 2" engines, and SpaceX is working on an upgraded "Raptor 3" version to address reliability.

A step forward

Even so, Sunday's testing marked a step forward for SpaceX, bringing the company closer to a second launch of its Starship vehicle. A full stack of the rocket includes the Super Heavy booster and Starship upper stage. It is not known whether SpaceX plans to perform additional tests of this booster, or gleaned enough data on Sunday to press ahead with a launch attempt this fall.

 

Accordingly, it is also not clear how far SpaceX is from a second Starship launch attempt. For the sake of comparison, a period of 70 days elapsed between the static fire test of Booster 7, which powered the first Starship launch, and its liftoff. This debut launch attempt, on April 20, failed after engine issues and other problems doomed the flight of the booster stage.

 

However, the fact that this latest test took place on a Sunday—SpaceX can only close the road leading to its launch site and Boca Chica Beach on a few weekend days per year—indicates there is some sense of urgency with this launch campaign.

 

SpaceX has made considerable progress since the April 20 launch attempt, which caused serious damage to the company's Orbital Launch Mount and associated ground hardware in South Texas. Most notably, engineers and technicians have installed large water deluge system, and performed what appeared to be a successful test of it on July 28.

 

This system includes a thick, perforated steel plate beneath the rocket through which jets of water are fired to offset the heat and acoustic energy of 33 Raptor engines firing simultaneously. On Sunday, the result of this new water deluge system was the production of an immense amount of steam, as intended.

 

During the April launch attempt, the lack of a sound suppression system led to significant damage, including the rupture of concrete chunks from the launch pad that rained down debris for miles around the Starbase location. That is one area of concern being looked at by the Federal Aviation Administration, as SpaceX seeks a new launch license; and it is also the subject of a lawsuit filed by environmental groups against the Federal Aviation Administration to stop the issuance of a new license.

Data needed for regulators

It is likely that SpaceX collected copious amounts of data about the performance of the revamped launch site and water deluge system on Sunday in order to provide information needed by the Federal Aviation Administration as part of the launch licensing process.

 

Another issue yet to be resolved is the rocket's flight termination system, which would be used to destroy the rocket in case it veers off course during flight. Just less than 90 seconds into its debut flight, the Super Heavy booster's flight termination system was initiated. However, there was about a 40-second delay between the initiation of the system and the rocket breaking apart.

 

 

This time lag posed no safety issues with the rocket safely offshore, but it is an unacceptable lag for a system that is supposed to terminate flight almost immediately. Several days after this launch attempt, SpaceX founder Elon Musk said the problem could be solved with a "longer detonation cord" to make sure the propellant tanks are fully unzipped rapidly. However, he acknowledged that working through this issue with the Federal Aviation Administration may take some time.

 

"The longest lead item is probably requalification of the flight termination system," Musk said. Neither he nor the Federal Aviation Administration has provided any updates since.

 

 

A key function of our immune system is to detect and eliminate foreign pathogens such as bacteria and viruses. Immune cells like T cells do this by distinguishing between different types of proteins within cells, which allows them to detect the presence of infection or disease.

 

A type of T cell called cytotoxic T cells can recognize the mutated proteins on cancer cells and should therefore be able to kill them. However, in most patients, cancer cells grow unchecked despite the presence of T cells.

 

The current explanation scientists have as to why T cells fail to eliminate cancer cells is because they become “exhausted.” The idea is that T cells initially function well when they first face off against cancer cells, but gradually lose their ability to kill the cancer cells after repeated encounters.

 

Cancer immunotherapies such as immune checkpoint inhibitors and CAR-T cell therapy have shown remarkable promise by inducing long-lasting remission in some patients with otherwise incurable cancers. However, these therapies often fail to induce long-term responses in most patients, and T cell exhaustion is a major culprit.

 

We are researchers who study ways to harness the immune system to treat cancer. Scientists like us have been working to determine the mechanisms controlling how well T cells function against tumors. In our newly published research, we found that T cells become exhausted within hours after encountering cancer cells.

 

T cells recognize tumor cells by the specific proteins called antigens they display on their surfaces.

Timing T cell exhaustion

By the time most patients are diagnosed with cancer, their immune system has been interacting with developing cancer cells for months to years. We wanted to go back earlier in time to figure out what happens when T cells first encounter tumor cells.

 

To do this, we used mice genetically engineered to develop liver cancers as they age, similarly to how liver cancers develop in people. We introduced trackable cytotoxic T cells that specifically recognize liver cancer cells to analyze the T cells’ function and monitor which of the genes are activated or turned off over time.

 

We also used these same trackable T cells to study their response in mice infected with the bacteria Listeria. In these mice, we found that the T cells were highly functional and eliminated infected cells. By comparing the differences between dysfunctional T cells from tumors and highly functional T cells from infected mice, we can home in on the genes that code for critical proteins that T cells use to regulate their function.

 

In our previous work, we found that T cells become dysfunctional with dramatically altered genetic structure within five days of encountering cancer cells in mice. We had originally decided to focus on the very earliest time points after T cells encounter cancer cells in mice with liver cancer or metastatic melanoma because we thought there would be fewer genetic changes. That would have allowed us to identify the earliest and most critical regulators of T cell dysfunction.

 

Instead, we found multiple surprising hallmarks of T cell dysfunction within six to 12 hours after they encountered cancer cells, including thousands of changes in genetic structure and gene expression.

 

 

We analyzed the different regulatory genes and pathways in T cells encountering cancer cells compared to those of T cells encountering infected cells. We found that genes associated with inflammation were highly activated in T cells interacting with infected cells but not in T cells interacting with cancer cells.

 

Next, we looked at how the initial early changes to the genetic structure of T cells evolved over time. We found that very early DNA changes were stabilized and reinforced with continued exposure to cancer cells, effectively “imprinting” dysfunctional gene expression patterns in the T cells. This meant that when the T cells were removed from the tumors after five days and transferred to tumor-free mice, they still remained dysfunctional.

Boosting T cell killing

Altogether, our research suggests that T cells in tumors are not necessarily working hard and getting exhausted. Rather, they are blocked right from the start. This is because the negative signals cancer cells send out to their surrounding environment induce T cell dysfunction, and a lack of positive signals like inflammation results in a failure to kick T cells into high gear.

 

Our team is now exploring strategies to stimulate inflammatory pathways in T cells encountering cancer cells to make them function as though they are encountering an infection. Our hope is that this will help T cells kill their cancer targets more effectively.

 

NASA lost contact with its Voyager 2 spacecraft—the second-most distant object ever built by humans and flung into space—nearly two weeks ago due to an errant command sent to the probe. This caused Voyager to point its antenna slightly away from Earth.

 

At the time, the space agency said it wasn't panicking. The mission's scientists believed they had several options to restore communications with the half-century-old probe. And so they did.

 

In an update posted Friday, NASA said all is now well once again with Voyager 2. NASA's Deep Space Network facility in Canberra, Australia, was able to send a "shout" command to Voyager instructing the spacecraft to reorient itself into a proper position to facilitate communication with Earth.

 

It took 18.5 hours for the signal to reach the spacecraft, which is now 19.9 billion kilometers away from Earth. Finally, after a total of 37 hours, a signal returned from the probe. Shortly after midnight on Friday morning, at 12:29 am ET, Voyager 2 started streaming back science and telemetry data.

Accordingly, the venerable probe is healthy, on course, and communicating with NASA once again.

 

Prior to the launch of Voyager 1 and 2 in 1977 on two different rockets, humans had been gazing at fuzzy blobs in the outer Solar System for hundreds of years. Pioneer 10 and 11 provided some better views of Jupiter and Saturn, but still, very little was known about the planets or their moons. Next to nothing was known of Uranus and Neptune. The Voyagers uncovered complex planetary systems and incredible moons, such as volcano-covered Io, icy Europa, and Titan, with its methane seas.

 

And in their old age, the two probes have kept on exploring. Voyager 1, at a distance of 24 billion km from Earth, and Voyager 2 have both left the Solar System, exploring the barren but scientifically interesting interstellar medium.

 

In late July, a couple of startling papers appeared on the arXiv, a repository of pre-peer-review manuscripts on topics in physics and astronomy. The papers claim to describe the synthesis of a material that is not only able to superconduct above room temperature, but also above the boiling point of water. And it does so at normal atmospheric pressures.

 

Instead of having to build upon years of work with exotic materials that only work under extreme conditions, the papers seem to describe a material that could be made via some relatively straightforward chemistry and would work if you set it on your desk. It was like finding a shortcut to a material that would revolutionize society.

 

The perfect time to write an article on those results would be when they've been confirmed by multiple labs. But these are not perfect times. Instead, rumors seem to be flying daily about possible confirmation, confusing and contradictory results, and informed discussions of why this material either should or shouldn't work.

 

In this article, we'll explain where things stand and why getting to a place of clarity will be challenging, even if these claims are right.

What’s the original claim?

The more detailed of the two manuscripts describes how to make the material and measurements of its property. The material itself is a variation of a well-known chemical called lead apatite. Apatites are a class of chemicals that form similar crystal structures; this particular version is primarily composed of lead and phosphate groups—all of its constituents are cheap and readily available.

 

The version developed here, which has been termed LK-99, was made by reacting a lead sulfate with a copper-phosphorus compound (the reaction requires high temperatures for over a day under a vacuum). This strips the phosphorus from the copper, oxidizes it, and allows it to displace the sulfur from its compound with the lead. Critically, though, some fraction of the lead itself ends up replaced by copper in the resulting compound.

 

This has a significant impact on the apatite crystal structure because copper is quite a bit smaller than lead. The researchers claim the overall volume of the sample drops by about half of a percentage as a result, and that change is accompanied by shifts in the orientation of various atoms and bonds. That means changes in where the electrons reside within the material.

 

That change appears to be critical to the LK-99's behavior. Superconductivity is associated with a number of very specific properties, and the researchers measure two of them: the expulsion of magnetic field lines (called the Meissner effect) and the existence of a critical temperature at which conductivity changes.

 

It's hard to explain just how strange these experiments are. Under normal circumstances, the superconducting material starts out behaving as a normal chemical and has to be cooled down to the critical point where exceptional behavior emerges. LK-99, by contrast, starts out superconducting and has to be heated beyond the boiling point of water to reach its critical temperature.

 

The only somewhat strange result here comes at temperatures just below the critical temperature. At room temperature and above, the resistance of LK-99 remains at zero as far as the testing equipment is able to measure. But it starts to rise ever so slightly once temperatures reach 60°C and displays a smooth upward slope until the sample hits 90°C, at which point it stays flat until the critical temperature is reached. The researchers did not attempt to explain this.

So we have a simple superconductor, then?

Anything but. The synthesis process does not allow any control over how many lead atoms are swapped out for copper. LK-99's formula is given as Pb10-xCux(PO4)6O. Note the presence of the x, which means just what high school algebra taught you to think it does: unknown. The base unit of the crystal structure has 10 lead atoms in it, and that number is decreased in exact proportion to the number of copper atoms that get added. But what that number is can potentially vary.

 

And it keeps getting more complicated from there. We also don't have control over which of the lead atoms gets swapped out. There are lead atoms located at very specific places in each base unit of the crystal, and not all of these locations are equivalent to each other. There are some indications that it's energetically more favorable to substitute copper in at specific locations, but this synthesis takes place at high temperatures, so energetic favorability may not be a major determinant of what happens; there's energy around to spare.

 

In addition, there's no guarantee that neighboring base units in the same crystal will be identical. So neighboring units in the same crystal could have different numbers of copper atoms located in different places.

 

One bit of relevant information comes from data obtained by shining X-rays through LK-99, which suggests that the majority of the material is in a single conformation. But there are some notable differences between the X-ray data and computer-generated predictions of what that data should look like. These are minor, suggesting that any variation in composition is small. But it's always possible for small differences to radically impact a material's behavior.

 

Consistent with all this messiness, LK-99 is what's called a polycrystalline material. That means it is formed from multiple smaller crystals, each with different orientations, smushed up against each other. It's possible that any superconductivity is the product of a subset of the crystals within the bulk material.

 

One last thing worth mentioning: Theoretical considerations suggest that electrons from specific orbitals of the atoms within the crystal will be doing the superconducting. And those orbitals have an equally specific orientation within the crystal. It's possible that LK-99 only superconducts along certain axes of the crystal. So you could potentially get superconductivity if you put wires on the top and bottom of a crystal, but regular metallic behavior if the wires are on the left and right sides.

Has anyone reproduced this?

Maybe? Ish? While the original draft manuscript contains detailed synthesis instructions, there's a lot of potential for lab-to-lab variation in little things like glassware composition, water pH, and so on that could make reproducing LK-99 difficult. The huge potential for variation in LK-99 described above obviously increases the challenge of making the right version of the chemical. It's also possible for materials to have some partial aspects of a superconductor but not the full suite of expected behaviors.

 

Given all this, it's plausible to expect that we'd see a mix of confirmation of some reported results and failures to replicate, regardless of whether LK-99 was a superconductor or not. And social media has been filled with exactly that.

 

On the more compelling side, we have a video reportedly from a research group that synthesized some LK-99 (it appears to be from the people who posted this report) and showed that it rejects magnetic fields strongly enough to levitate away from them—a hallmark of the Meissner effect. With a strong enough magnet, it's possible to get nearly anything to levitate (including, apparently, mice), but this is done with not especially strong magnets, and clearly at room temperature. And the small chunk of material isn't lifted evenly, consistent with only a small crystal within the sample actually superconducting.

 

On the less compelling side, a different group has apparently synthesized the material but only finds that it superconducts up to about 110 K—nowhere near room temperature. Whatever was made here also doesn't seem to have a critical temperature, instead seeing a gradual increase in resistance above that point, and the Meissner effect tests came up negative. That's pretty inconsistent with the original results and suggests that what they have isn't a typical superconductor at all.

 

Meanwhile, there has been some support on the theory side, as Lawrence Berkeley Lab's Sinéad Griffin ran some density functional theory calculations to probe what the material might be up to. These calculations confirmed that swapping copper into a specific position in the crystal should cause a conformational change in the crystal itself. More significantly, this change causes the appearance of a set of conduction bands that are largely "flat," meaning the energy involved in getting electrons into them hovers around zero.

 

And that's consistent with existing ideas on superconductivity. "If previous assumptions about band flatness driving superconductivity are correct," Griffin writes, "then this result would suggest a much more robust (higher temperature) superconducting phase exists in this system, even compared to well-established high-TC systems." Again, that's a calculation, not experimental evidence. But it at least provides a reason to think the reported results might be valid.

What about those other superconductors?

The news comes at a somewhat awkward time for the field. A similar claim was made about a high-pressure material a few years ago, but that paper ended up being retracted because of problems with some of its data. The same research group came back with a different material that was said to work at room temperature, but that work hasn't been replicated, and the head of the lab has now been accused of scientific misconduct.

 

There is almost no overlap between that work and LK-99. None of the people involved are the same, so there's no reason to suspect problematic research practices. And the chemistry and physics involved are completely different. The earlier work used high pressure to create chemicals with lots of hydrogen and unusual orbital structures. LK-99 uses no hydrogen at all and gets its orbital structures via a conformational change in a crystal lattice that takes place at ambient pressures.

 

Hydrogen was the focus of the earlier work because its low atomic weight influences the behavior of vibrations within the material in a way that promotes the formation of superconducting pairs of electrons. The mechanism behind LK-99 is less clear, but clearly not that.

 

(The LK-99's creators suggest that the conformational change in the crystal creates a sort of standing wave of electrons called a "charge density wave," and superconductivity involves electrons tunneling between wave sites. The modeling paper, by contrast, suggests that giving electrons the opportunity to both superconduct and participate in additional processes like charge density wave formation increases the probability that they'll superconduct. In any case, neither idea involves phonons.)

So when will we actually know anything?

Hopefully soon. The researchers behind the original report are trying to get information out there. In addition to the drafts placed in the arXiv, they have already published a paper on LK-99, albeit in their native Korean. And a group of South Korean scientists working in the field have also announced that they're going to obtain LK-99 samples and try to confirm its reported behavior.

 

There's also lots of activity outside of South Korea. Producing LK-99 is within reach of a lot of labs, and testing it is much easier since it doesn't require low temperatures or high pressures. That will mean a lot of short-term confusion, but it's likely to enable a consensus to emerge sooner.

 

Whether or not this chemical superconducts at ambient temperatures might not be the final question, though. Assuming it does, there will be many questions about how to develop it into a useful material, how much current it can carry, and how to use it most effectively in the huge range of applications it can be put to. But I'm sure we'll all be happy if we end up needing answers to those questions.

 

Tesla is facing a class-action lawsuit filed by customers who say they were misled by the company's exaggerated range claims. The lawsuit was filed yesterday, days after a report revealed that Tesla exaggerated its electric vehicles' range so much that many drivers thought their cars were broken.

 

"Tesla marketed its electric vehicles as having a grossly overvalued range in an effort to increase sales to consumers," said the lawsuit filed in US District Court for the Northern District of California.

 

The lawsuit seeks class-action status to represent all people in California who purchased a new Tesla Model 3, Model S, Model Y, or Model X vehicle. The three named plaintiffs are James Porter, Bryan Perez, and Dro Esraeili Estepanian. All three bought Tesla cars in 2022.

 

The complaint said that when plaintiffs ordered their cars online or spoke to Tesla representatives about the car features, they were never warned that the advertised range was "grossly overestimated." The plaintiffs would not have purchased a Tesla car at the prices they paid if the company "truthfully revealed that the advertised range was exaggerated and not based on normal driving conditions," the lawsuit said.

 

The complaint says Tesla committed "violations of state consumer fraud statutes, fraud, negligent misrepresentation, breach of express warranty, breach of implied warranty, violation of California's Song-Beverly Consumer Warranty Act, and unjust enrichment."

 

"As a result of Tesla's tactics and false advertising, Plaintiffs and Class Members suffered an injury in fact, incurred damages, and have been harmed by Tesla's conduct," the complaint said. They are seeking financial damages in the lawsuit, which was prepared by class-action law firm Milberg Coleman Bryson Phillips Grossman.

Cars “failed to accurately account for external factors”

The complaint cited testing that found three Tesla models fell short of their advertised ranges by an average of 26 percent. In addition to alleging false advertising, the lawsuit said that range estimates provided by Tesla vehicles during car trips fail to account for temperature and other factors that reduce range.

 

Testing by analytics firm Recurrent "determined that Tesla model vehicles still overwhelmingly calculated that they could still deliver nearly the advertised full range, regardless of external factors—with Tesla vehicles calculating that they could travel more than 90 percent of their advertised range," the complaint said. "Put simply, Tesla vehicles failed to accurately account for external factors impacting battery performance and vehicle range, leading to a gross overestimate of the vehicle's range."

 

The lawsuit cites a recent investigation by Reuters. "Based upon information from those familiar with Tesla's early vehicle software designs, the Reuters report explains that Tesla developed algorithms for estimating the range of its electric vehicles, which would display to drivers 'rosy' projections for the distance the vehicle could travel on a full battery," the lawsuit said. "However, once the battery reached 50 percent capacity, the algorithm would change and begin showing the driver more realistic projections. This would cause the estimated range of the vehicle to fluctuate drastically from that point."

 

"The decision to include these algorithms to present inflated range estimates came directly from Tesla's chief executive officer, Elon Musk," the lawsuit continued.

 

Reuters reported that Tesla became inundated with complaints from drivers who thought their cars were broken when the actual driving range was much lower than advertised. When these drivers scheduled service appointments to address their range problems, Tesla allegedly canceled the appointments because there was no way to improve the actual driving distance. The Reuters report said that in mid-2022, Tesla started routing range complaints to a "Diversion Team" that fielded up to 2,000 cases a week and "was expected to close about 750 cases a week."

 

Tesla didn't respond to a request for comment when we wrote about the exaggerated EV range claims last week.

Plaintiffs describe their Tesla problems

Tesla's diversion team "ensured that customers' Tesla vehicles could not be reviewed to determine whether their range complaints arose, in fact, from a malfunctioning battery," the lawsuit said. "This effectively ensured that Tesla would not be required to potentially offer more warranty repair or replacement coverages. Tesla's conduct, effectuated through the Diversion Team, violated and breached its warranties."

 

The class-action plaintiffs say their experiences are similar to those described in the Reuters investigation. Porter, a resident of Petaluma in Sonoma County, bought a 2022 Tesla Model Y Performance vehicle for $72,000 and took delivery of the car in June 2022.

 

Shortly after getting the car, Porter's charge was reduced from 100 percent to 40 percent on a two-hour trip despite the Tesla's advertised range of 303 miles, the lawsuit said. The car "lost approximately 182 miles of range—despite only driving 92 miles," the lawsuit said.

 

When he complained, a "Tesla representative explained that it was normal and expected that the Tesla vehicle's range would drop below the advertised range with normal driving conditions and use of other vehicle features. The Tesla representative claimed that there was nothing wrong with the range Plaintiff Porter was experiencing in his vehicle, and the representative canceled the service appointment to review the vehicle and confirm whether the vehicle or battery was, in fact, malfunctioning."

 

Perez, a resident of Lancaster in Los Angeles County, bought a 2021 Tesla Model 3 Long Range vehicle. He paid $60,000 for his car, which had an advertised range of 333 miles, the lawsuit said.

 

After getting the car in March 2022, "Perez fully charged his vehicle to 100 percent battery charge and took an approximately 90-mile trip to visit his parents. After returning home from the approximately 180-mile round trip, he noticed that his vehicle showed that it had roughly 10-15 percent charge remaining... his vehicle lost approximately 283 miles of range—despite only driving approximately 180 miles round trip," the lawsuit said.

“Nothing was wrong with the vehicle”

After Perez complained about the car's actual mileage, a Tesla rep told him the advertised range was calculated "without use of any other electronics in the vehicle (including, for example, use of air conditioning, use of the radio, etc.) and without accounting for weather, headwinds, temperature, traffic, amount of passengers, and other driving conditions," the lawsuit said.

 

Previously, when Perez ordered the car, Tesla never told him that the estimated range "required the driver not to use other vehicle features (including other electronics), did not account for normal, expected driving conditions, or that it was exaggerated," the lawsuit said.

 

Estepanian, a resident of Palmdale in Los Angeles County, bought a 2022 Tesla Model 3 Long Range vehicle for $62,000. He test-drove the car and spoke with a Tesla dealership representative, "discussing various features of the vehicle, including its range. However, at no point did the Tesla representative warn or explain to Plaintiff Estepanian that the advertised range did not account for normal driving conditions or that it was exaggerated," the lawsuit said.

 

Estepanian's round-trip commute is 140 to 150 miles, but his car "consistently loses approximately 189 miles of range during his daily commute," the lawsuit said. After he complained, "Tesla representatives performed a mobile diagnostic on his vehicle and explained to him that nothing was wrong with the vehicle," the lawsuit said.

 

The Tesla car's advertised range "was one of the most important factors Plaintiff Perez considered when choosing to purchase his Tesla vehicle," the lawsuit said. Although he is disappointed in the car's performance, the complaint said that "Plaintiff Estepanian would consider buying another Tesla vehicle in the future if Tesla were ordered to truthfully reveal the actual range of the vehicle, based on normal driving conditions."

 

Human beings and other animals send electrical signals via the central nervous system. The Venus flytrap, which lacks such a nervous system, also sends rapid electrical impulses, which are generated in response to touch or stress. It's how the plant traps its prey to feed. Now scientists have developed a bioelectronic device to better understand the Venus flytrap's complex signaling mechanism by mapping how those signals propagate, according to a recent paper published in the journal Science Advances.

 

“We can now say with certainty that the electrical signal originates in the sensory hairs of the Venus flytrap," said co-author Eleni Stavrinidou of Linköping University in Sweden. "With our technology, we can also see that the signal mainly spreads radially from the hair, without any clear direction."

 

As we've reported previously, the Venus flytrap attracts its prey with a pleasing fruity scent. When an insect lands on a leaf, it stimulates the highly sensitive trigger hairs that line the leaf. When the pressure becomes strong enough to bend those hairs, the plant will snap its leaves shut and trap the insect inside. Long cilia grab and hold the insect in place, much like fingers, as the plant begins to secrete digestive juices. The insect is digested slowly over five to 12 days, after which the trap reopens, releasing the dried-out husk of the insect into the wind.

 

In 2016, Rainer Hedrich, a biophysicist at Julius-Maximilians-Universität Würzburg in Bavaria, Germany, led the team who discovered that the Venus flytrap could actually "count" the number of times something touches its hair-lined leaves—an ability that helps the plant distinguish between the presence of prey and a small nut or stone, or even a dead insect. The plant detects the first "action potential" but doesn't snap shut right away, waiting until a second zap confirms the presence of actual prey, at which point the trap closes. But the Venus flytrap doesn't close all the way and produce digestive enzymes to consume the prey until the hairs are triggered three more times (for a total of five stimuli).

 

 

In 2020, Japanese scientists genetically altered a Venus flytrap to gain important clues about how the plant's short-term "memory" works. They introduced a gene for a calcium sensor protein called GCaMP6, which glows green whenever it binds to calcium. That green fluorescence allowed the team to visually track the changes in calcium concentrations in response to stimulating the plant's sensitive hairs with a needle. They concluded that the waxing and waning of calcium concentrations in the leaf cells really do seem to serve as a kind of short-term memory for the Venus flytrap, though precisely how calcium concentrations work with the plant's electrical network remains unclear.

 

However, a mutant Venus flytrap dubbed Dyscalculia (DYSC) does not close in response to two sensory stimuli, nor does it process its prey in response to additional stimuli. It has somehow "forgotten' how to count. Earlier this year, Hedrich and his team found that the mutation did not seem to affect either the action potential or the underlying calcium signal in the first two-count stage of the process. The action potentials fire, yet the trap doesn't snap shut, suggesting that the touch-activation of calcium signaling is being suppressed.

 

Despite these recent advances, Stavrinidou et al. note that a scientific understanding of the underlying mechanisms and the precise relationship of action potentials with plant function is still incomplete, in part because it is challenging to accomplish high-resolution mapping of the signal. So they turned to bioelectronics, specifically noninvasive electrophysiological recording devices often used to study mammalian systems. The team adapted this approach to the Venus flytrap, building a multi-electrode micro-array made up of a very thin film embedded with electrodes.

 

The film wrapped around the outside of the lobes of the Venus flytrap, and the researchers would then poke one of the plant's sensory hairs and measure the resulting signal in the lobe. They also filmed how the plant moved so they could see if the closure of the Venus flytrap correlated with the electrical signal. Prior measurement attempts had relied on a single measuring point; Stavrinidou et al. used around 30 electrodes for their signal measurements, which helped them pinpoint the origin of the signal and the direction in which it spread. They were also able to investigate the role of ions in the spread of the electrical signal, thanks to pharmacological treatments involving ion blockers.

 

 

They found that the electrical signal starts in the plant's sensory hairs and then spreads radially outward with no clear preferred direction. And sometimes the signals were spontaneous, originating in sensory hairs that had not been stimulated. They don't yet know why this happens or what function this might serve. As for the ion experiment, the team didn't observe any significant impact in terms of how fast the signals spread when ion blockers were in play.

 

“One of the most important aspects of this study is that we show that bioelectronic technologies, which are extensively used in biomedical research, can be applied to plant physiology research as well, therefore opening possibilities for new discoveries," said Stavrinidou. "There is currently a great need for developing plants that are more stress resistant for us to be able to grow food and have healthy forests also in the future. That’s why it’s important that we understand how plants respond to stress, and I think that this new technology may contribute in this area of research.”

 

DOI: Science Advances, 2023. 10.1126/sciadv.adh4443  (About DOIs).