Scientists at the Weizmann Institute of Science have engineered Nicotiana benthamiana, a fast-growing relative of tobacco, to produce five different psychedelic compounds. 

The achievement, detailed in a study published in Science Advances, brings together biosynthetic pathways that nature distributes across three different kingdoms of life.

The five compounds (DMT, psilocybin, psilocin, bufotenin, and 5-MeO-DMT) are typically sourced from an Amazonian shrub, mushrooms, and the defensive skin glands of a desert toad. Extracting even one of these molecules from its natural source is a slow process. Producing all five in a single, easily cultivated plant had not been achieved before.

A Shared Chemical Ancestor

Despite coming from such different sources, all five of these molecules share a common origin: tryptophan, an amino acid found in every living thing. In humans, tryptophan is the building block for serotonin, which helps explain why these psychedelics interact with serotonin receptors in the brain. This shared chemistry gave the Weizmann team, led by Dr. Paula Berman and Prof. Asaph Aharoni, a logical starting point for their experiments.

The team began by focusing on DMT to address a previously unresolved question. Researchers knew the general biochemical pathway plants use to synthesize DMT, but they had not yet identified all the genes and enzymes responsible for each step. “At the heart of the study was the challenge of making DMT,” Aharoni says.

To identify the necessary genes, the team analyzed genetic material from the Amazonian chacruna shrub and an Australian acacia species. Introducing these genes into Nicotiana benthamiana allowed the plant to produce DMT within days.

Fixing a Bottleneck With One Amino Acid

The researchers applied the same gene transfer approach to the other four psychedelics, but the engineered plants initially produced only trace amounts of 5-MeO-DMT. Protein-design specialists Prof. Sarel Fleishman and Dr. Olga Khersonsky determined that the limitation was due to a poor molecular fit in one enzyme’s active site. Replacing a single amino acid resolved the issue.

“We mutated one amino acid in the sequence and got a 40-fold increase in the production of 5-MeO-DMT,” Berman says.

One Organism, Three Kingdoms

Once all five biosynthetic pathways were functioning separately, the researchers combined them within a single plant. The result was a tobacco relative capable of producing DMT from plants, psilocin and psilocybin from fungi, and bufotenin and 5-MeO-DMT from toads, all at the same time. “In effect, we created a kind of biological ‘cocktail’ — not by mixing substances externally, but by combining the underlying pathways inside one organism,” Aharoni says.

Combining all five pathways reduced the yield of each compound, as they competed for the same pool of tryptophan. The researchers also introduced bacterial enzymes to attach chlorine and bromine atoms to the psychedelic molecules, creating modified psychedelic molecules that do not naturally occur in the engineered plant. Some similar modified molecules have shown antidepressant-like effects in previous studies.

Why the Compounds Exist in the First Place

The bottleneck also highlighted a broader research question about the natural role of these compounds. These molecules likely function as defenses or as tools for interacting with microbes and insects in the organisms that produce them, rather than evolving to affect human consciousness.

“If we can move these pathways into a model plant that grows quickly and is easy to manipulate, we can start asking what these compounds actually do for the plant,” Berman explains.

A Sustainable Supply Line

There is a clear practical benefit to this approach. Plants used for ayahuasca are increasingly threatened by habitat loss and rising demand, and the Sonoran Desert toad is declining due to overcollection and habitat destruction. A fast-growing laboratory plant capable of producing these compounds within a week provides an alternative that does not rely on harvesting wild species.

The Weizmann researchers are now working on a plant that can produce the complete ayahuasca mixture, which includes both DMT and the compound required for it to be active when taken orally. They are also exploring whether these psychedelics could be produced in edible plants capable of delivering carefully regulated doses.

Whether this platform becomes a practical manufacturing system will depend on overcoming the metabolic bottleneck that currently limits production when multiple pathways operate simultaneously.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

The properties of water are unusual among liquids—it expands when it freezes, reaches its highest density just above freezing, and does not behave like the simple liquids chemists often use to understand matter.

Now, a new study in Communications Chemistry uses artificial intelligence to compare competing methods for measuring water’s hidden molecular order and determine which are truly effective.

Researchers at the University of Osaka developed a neural network to test 16 different ‘structural descriptors,’ which are mathematical tools for measuring how ordered or disordered water molecules are at the microscopic scale. Over the past 30 years, scientists have independently developed these descriptors, each with its own approach and scale, all aimed at explaining the same underlying puzzle.

Until now, researchers lacked a standard method for comparing them directly—a gap this study fills.

Water’s Split Personality

Distinguishing Hot From Cold

To establish a common standard, the Osaka team trained a neural network using molecular dynamics simulations of water at temperatures from 200 to 300 Kelvin. Using only the values from a single structural descriptor, it had to predict the temperature at which each molecular configuration was generated. If a descriptor captured meaningful structural information, the network could easily distinguish hot from cold.

“Past studies have shown that using machine learning to classify and understand structural data is effective,” says corresponding author Kang Kim. “We specifically wanted to incorporate a neural network model into this study to evaluate how accurate the descriptors were at capturing key structural information, in a way that is like human cognition.”

The network assessed each descriptor using a standard classification measure. To deepen their analysis, the team used an explainable AI technique called LIME. This method looks at how the model makes its decisions and checks whether its reasoning matches established physical principles, instead of relying only on statistical patterns.

Two Descriptors Pull Ahead

Out of the 16 descriptors tested, two stood out. The Local Structure Index (LSI) and another, referred to as zeta (ζ), both of which measure the distance between the first and second layers of neighboring molecules, achieved nearly perfect accuracy across the tested temperatures. Another group, based on how hydrogen bonds connect, also scored well but was not as accurate as the others. Older descriptors that rely on tetrahedral angles or interaction energies became less accurate at higher temperatures, where molecules move more, and the differences blur.

“The network used what it had learned to compare how 16 descriptors differentiated between LDL and HDL structures at different temperatures,” says senior author Nobuyuki Matubayasi. “In this way, we determined the most efficient descriptors.”

The LIME analysis backed up the network’s results with evidence consistent with scientists’ expectations. Molecules with high Local Structure Index and zeta values led the model to predict lower temperatures, which is consistent with the idea that more ordered, LDL-like structures appear as water cools. This suggests that the network captured more than just statistical correlations.

With a common framework for comparing descriptors in place, researchers could attempt to develop descriptors that directly identify HDL and LDL environments rather than infer them indirectly. Whether these new tools will finally solve one of water’s biggest mysteries, the point where its two proposed liquid forms fully separate, is something scientists are just starting to investigate.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

Engineers at Queen Mary University of London have created a tactile sensor, described in a study published in Science Advances, that allows robots to detect touch using a simple camera.  

The sensor converts mechanical forces into visible color patterns, reducing the need for dense arrays of embedded sensors. Giacomo Sasso, a postdoctoral researcher at Queen Mary’s School of Engineering and Materials Science, led the team that developed this new sensor. Rather than relying on embedded electronic sensors, they created a soft material that changes color when pressed or bent.

This effect, known as structural color, is the same reason butterfly wings look iridescent without pigment. Pressure causes microscopic structures within the material to shift, altering how light reflects and creating a real-time visual indicator of force.

Touch Without the Computation

Most vision-based tactile sensors can map contact and force in detail, but only at the cost of heavy computation that slows them down. Simpler systems are faster, but sacrifice detail. The new material developed by Sasso’s team sidesteps this problem by building the sensing function into the material itself. With just a standard USB camera, researchers can track color changes on the surface and immediately infer pressure, strain, and contact shape without complex data reconstruction.

“You won’t guess how much information is generated when your finger presses a light switch. A human hand contains more than 10,000 mechanoreceptors to do the job, yet touch sensing remains one of the major challenges in robotics,” Sasso said. “We were happy to capture the finger ridges, as no existing technology can reproduce such sensor density at [a] comparable scale and simplicity. The key idea behind this project was to think outside the box: instead of embedding dense and overengineered sensor arrays, sensing is moved into the material itself, where mechanical cues are directly transformed into colour fields and captured using a simple low-cost USB camera.”

Professor James Busfield, one of the paper’s co-authors, described the advantage more simply: “What is particularly powerful is that the information is already in the light signal. You are no longer reconstructing touch – you are observing it directly.”

From Precision Manufacturing to the Operating Room

Reducing the computational demands of touch sensing opens up new possibilities. A robotic gripper made with this material could handle tiny parts more gently, since changes in force are immediately detectable. Prosthetic limbs equipped with similar sensors could also provide users with more detailed tactile feedback.

The researchers also see potential applications in surgery, where detecting subtle pressure differences could help clinicians interpret tissue properties.

A Decade of Groundwork Behind One Material

Professor Federico Carpi from the University of Florence and Professor James Busfield from Queen Mary worked together on this project, merging years of separate work on stretchable sensors and polymer behavior into one material system. This allowed the team to use a mechanochromic material as the sensor itself, not just as a base for electronics.

Many approaches to robotic touch have relied on increasingly complex sensor arrays, wiring, and computing systems. Rather than adding more electronics, Sasso’s team’s approach allows the material itself to encode touch into visible color. It remains unclear whether this color-based method can be scaled for use in commercial robotic hands, but this work suggests that the future of robotic touch might rely as much on smart materials as on advanced electronics.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

A recent study documented a participant using an AI model to simulate a conversation with her late grandmother. According to her report, the experience offered a sense of closure that she had not experienced before.

This interaction was one of sixteen documented in a study published in the Proceedings of the 2026 ACM Designing Interactive Systems Conference. The research provides some of the first empirical data on how individuals respond to the opportunity to communicate with AI representations of deceased loved ones.

Researchers at the University of Colorado Boulder call these AI agents ‘generative ghosts.’ These systems use data such as writing, voice recordings, or photographs from the deceased to create simulated conversations. Some platforms, such as Project December and Séance AI, generate text-based versions of journal entries and messages. Others, such as HereAfterAI, include voice and images. A few companies have also developed virtual reality experiences that let users interact with holographic representations of loved ones.

Jack Manning, the PhD candidate in Information Science who led the study, expected the sessions to feel unsettling. “We originally thought it might feel very Black Mirror creepy to people and make them uncomfortable,” he said. “I ended up being completely wrong. People thought it was amazing.”

How To Build a Ghost

The study included 16 participants, aged 22 to 50, each of whom joined a Zoom interview about a deceased relative or friend. During the session, a facilitator led the conversation while another researcher entered details into a large language model, generating an AI representation of the deceased in real time.

Each participant interacted with two versions of the AI model for about twenty minutes each. One version spoke as the deceased, using the first person, while the other described the person in the third person. A facilitator stayed present to offer support if the conversation became emotionally difficult.

The results indicated a clear preference for the version that spoke as the deceased individual, rather than about them. Researchers described this as a preference for ‘reincarnation’ over ‘representation.’

What People Will and Won’t Forgive

Participants tended to overlook factual mistakes or made-up details from the AI. What mattered more was whether the AI captured the right tone and language. If the model used words or phrases that felt out of character, people were more likely to disconnect from the experience. The study found that getting the voice and manner right was more important than perfect factual accuracy.

How the AI communicated also shaped the experience. Participants preferred short, to-the-point sentences and even the use of emojis, rather than the longer, more formal responses that AI models often produce. This suggests that future versions of these systems should pay close attention to conversational style.

A Technology Both Wanted, and Feared

All sixteen participants indicated that they would be willing to use the technology again. However, most also expressed concerns about the potential risks of individuals in grief accessing these AI systems without supervision.

Lead researcher Jack Manning said his own experiences with loss have shaped his view of the risks and benefits of AI grief tools. He acknowledged that negative outcomes are possible but also noted that the technology could help some users find a sense of closure and peace.

Jed Brubaker, the study’s senior author and an associate professor of Information Science, said the lab took on the work precisely because the field had outpaced the research. “To our knowledge, we are conducting the first user experience studies of simulated AI ghosts,” he said.

The research team intends to involve mental health professionals in future studies. The next phase will evaluate the psychological risks and benefits of prolonged use of AI grief tools to ensure that empirical research guides product development.

Generative ghost technology is already available through commercial platforms, and people are actively using these systems. The study highlights that voice, tone, and pacing are often more important to users than factual accuracy. As the technology develops, researchers are working to address the challenges of providing appropriate safeguards and responsible design.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

A jellyfish can close a wound in real time, without stitches, scabs, or scars, a fascinating—and borderline miraculous—healing ability that has long perplexed scientists. 

Now, a new study in Molecular Biology of the Cell has finally revealed the secrets behind this process, identifying two cellular structures that act in a specific sequence.

Jocelyn Malamy, an associate professor at the University of Chicago, first observed this process a decade ago at the Marine Biological Laboratory. Using transparent, dime-sized jellyfish called Clytia hemisphaerica, she watched individual cells move toward each other to close a gap in the tissue.

Now, with colleagues Maxwell Sassaman and Manjula P. Mony, she has mapped the steps of this process and explored its broader significance.

A Flower That Never Stops Blooming

The free-swimming medusa most people picture when they hear “jellyfish” is only one phase of Clytia‘s life. It spends most of its life as a polyp colony attached to rocks, docks, or leaves, and it periodically produces medusae, much like a shrub produces flowers. While medusae live for only a few months, the polyp colonies can persist for much longer.

Clytia medusae are known for their ability to heal quickly. Small wounds close in minutes, and even larger injuries seal in under an hour, all without forming scar tissue. Malamy notes that this type of healing is similar to the scar-free repair seen in embryos.

Since Clytia are transparent, heal rapidly, and lack the immune-mediated inflammation that complicates wound healing in mammals, they serve as an excellent model organism. Scientists can watch epithelial cells close wounds in real time, without interference from scarring or immune reactions. The mechanisms uncovered in Clytia are likely relevant across many animals.

“A lot of the processes that we see in Clytia‘s wound healing are really similar to what you see in all other systems, including mammalian systems,” Malamy explains. “When you’re staring at these epithelial cells, you wouldn’t know this was a jellyfish.”

Two Structures, One Decision Tree

Epithelial cells form the surfaces of the body, including skin, the gut, and internal membranes, and must repair themselves after injury. Malamy previously described Clytia’s wound repair in 2017 and 2018. The new study addresses a longstanding question in epithelial wound healing: do wounds close by cells moving over the gap, or by an actin cable contracting to seal it? The findings show that both mechanisms operate in a specific sequence, with the basement membrane beneath the cells coordinating the transition between them.

The initial response involves lamellipodia, actin-rich extensions that move across the basement membrane, the protein layer beneath all epithelial tissues. These structures pull their parent cells forward to close the wound. Malamy’s team observed lamellipodia forming even in very small wounds within a single cell, a previously undocumented detail.

After the lamellipodia advance, an actomyosin cable forms and contracts after it covers the basement membrane. If damage or debris blocks the membrane, the cable closes the wound and removes debris that lamellipodia cannot cross.

For larger wounds, a third response occurs. When the gap is too wide for lamellipodia to bridge, regardless of how far the cells extend, the entire epithelial sheet lifts and migrates as a unit. Once lamellipodia from opposing edges meet, the same closing sequence resumes.

“This is a truly elegant mechanism where the system can rapidly adapt to heal all the kinds of wounds that might occur in nature,” Malamy says.

What’s Still Unrepaired

If epithelial repair mechanisms are conserved across animal evolution, as current evidence suggests, studies of jellyfish could reveal wound-healing processes that inflammation and scar formation obscure in mammals, making those processes difficult to observe directly.

Researchers have not yet fully understood the basement membrane, which coordinates these processes, and it remains the least understood aspect of wound repair. Scientists still do not know how organisms repair the basement membrane itself.

“It’s great that you can heal a wound by dragging the cells over it,” Malamy says, “but at some point, a damaged basement membrane has to get fixed.” That’s her next target.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

A single machine learning model just turned one of condensed matter physics’ most persistent problems into a navigable map.

In a study published in National Science Review, researchers led by Nanjing University used AI to sort millions of magnetoresistance curves into 13 clean categories, then charted exactly how tuning a material’s electronic properties pushes it from one behavior to another.

The work targets the anomalous Hall effect, a decades-old phenomenon that governs how electrons move through certain magnetic materials, and it could hand physicists a shortcut to some of the most sought-after states in modern electronics.

A Problem Too Complex to Map by Hand

The anomalous Hall effect occurs when electrons moving through a magnetic material exhibit behavior beyond the predictions of the conventional Hall effect. Standard Drude theory predicts that resistance should shift as electrons move through a magnetic field. Anomalous Hall systems break that rule, producing resistance curves with double peaks, plateaus, and non-saturating shapes that shift with the material’s band structure and the way its electrons scatter.

The challenge becomes even greater when multiple electron energy bands are active near the Fermi level. Each additional parameter increases the difficulty of interpreting the resulting resistance curves, and until now, constructing a comprehensive map linking electronic properties to resistance behaviors has not been possible.

The Nanjing team set out to create that map. They generated more than 2.27 million magnetoresistance curves from a two-band model spanning five electronic parameters, then set an unsupervised algorithm loose on the dataset with no instructions beyond finding the pattern.

Millions of Curves

Every curve in the dataset collapsed into just 13 distinct types, a verdict so blunt that even the researchers seemed surprised. A trained neural network took over from there, sorting new curves into those same 13 categories with 99% accuracy and turning what had looked like an unruly set of data into something closer to a field guide.

Next, the team created phase diagrams and topological networks to visualize how changing just one parameter could shift a material from one type of resistance curve to another. Instead of a fixed chart, the result is more like an interactive roadmap; adjust a setting, and you can see the transition happen in real time.

To check whether any of this held up outside a computer model, the researchers tested their framework against real measurements from gated Fe5GeTe2 nanoflakes, a magnetic material whose properties can be tuned electrically. The experimental data closely matched the AI-generated phase diagrams, providing the model with a real-world anchor rather than a purely theoretical one.

“This is just the beginning. We believe this framework can be extended to a wide range of intricate models and will help uncover many more complex problems in condensed matter physics,” said Hongtao Yuan, professor at Nanjing University and an author of the study.

A Map That Points to New Physics

The significance of this work extends beyond classification. The phase diagrams indicate which parameter combinations are likely to produce large magnetoresistance, including giant magnetoresistance, a phenomenon already used in hard drive technology. These maps also identify regions where the quantum anomalous Hall effect may occur, a state in which an electric current flows along the edges of a material without energy loss due to resistance.

That kind of lossless conduction has long been seen as a target for low-power electronics, but identifying the narrow parameter windows that produce it has historically relied on trial-and-error. A phase diagram that predicts those windows in advance changes the search from guesswork into targeting.

“Our model provides a framework for comprehensively addressing the complex magnetoresistance behavior in anomalous Hall systems, and serves as a platform for predicting parameter regions that may host intriguing quantum phenomena such as giant magnetoresistance,” said author of the study Ganyu Chen.

The team frames the two-band model as a starting point rather than a finished tool. The same machine-learning approach, they note, should extend to more complex systems beyond ferromagnets, including topological insulators and superconducting junctions, areas of physics that have their own tangled, high-dimensional parameter spaces still waiting for a map. Whether AI-built phase models can keep pace with that added complexity, or whether some of these systems will require entirely new modeling strategies, remains an open question the field is only beginning to test.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

While a patient is fully anesthetized and unresponsive, neurons in the hippocampus continue to process language, distinguish different types of words, and generate neural activity consistent with predicting upcoming words.

Researchers at Baylor College of Medicine directly recorded this neural activity, offering new insight into how unconsciousness works at the cellular level. Their findings, published in Nature, show that hippocampal neurons continue to process aspects of language even when the brain is unconscious.

“Our findings show that the brain is far more active and capable during unconsciousness than previously thought,” said Dr. Sameer Sheth, professor and Cullen Foundation Endowed Chair of neurosurgery and a McNair Scholar at Baylor. “Even when patients are fully anesthetized, their brains continue to analyze the world around them.”

a Rare Look Inside the Anesthetized Hippocampus

Sheth’s team recorded from hundreds of individual neurons in the hippocampus, a region of the brain essential for memory formation. Researchers collected data during epilepsy surgeries, specifically anterior temporal lobectomies, when patients were under general anesthesia and the hippocampus was temporarily accessible. This rare opportunity allowed researchers to directly record hippocampal activity, something scalp EEG cannot.

The researchers used Neuropixels probes, high-density recording devices that can monitor hundreds of neurons simultaneously. This study represents the first use of this technology in the human hippocampus during anesthesia. Seven patients participated, all under propofol anesthesia with consistent sedation levels maintained throughout the recordings.

The Unconscious Brain Still Learns

In the initial test, researchers played patients a repeating tone with occasional unexpected pitches. Hippocampal neurons consistently detected these outlier tones, and their sensitivity increased over approximately ten minutes. Researchers usually observe this kind of adaptive neural response during wakefulness.

To dig deeper, the researchers built a computer model of a recurrent neural network, trained to perform the same tone discrimination task. The model showed the same pattern as the human brain recordings, suggesting that this increased sensitivity to outlier tones can arise from local brain circuits alone, without the need for higher-level conscious processing.

Predicting Words Before They’re Spoken

The researchers also played short stories to anesthetized patients while recording from the same neurons. Hippocampal activity tracked language in real time, distinguishing between nouns, verbs, and adjectives. Neural activity reflected both the current word and prediction of the upcoming word in the sequence. “The brain appears to anticipate what comes next in a story, even without conscious awareness,” Sheth said.

“This kind of predictive coding is something we associate with being awake and attentive, yet it’s happening here in an unconscious state,” added Baylor neurosurgery professor Benjamin Hayden.

What This Means for the Science of Consciousness

The results show that the hippocampus can support neural computations associated with complex cognition even during unconsciousness. This suggests that at least some components of language processing and prediction may not require conscious awareness. Sheth’s team proposes that consciousness does not arise from a single brain region but rather emerges from interactions among multiple brain regions. The hippocampus may process information that later becomes part of awareness.

The researchers noted a parallel with artificial intelligence. Large language models generate text by predicting the next word in a sequence; the anesthetized hippocampus showed a similar pattern of prediction. This similarity may provide neuroscientists and AI researchers with a shared framework for studying language prediction based on biological circuitry.

“Can we use these signals to deploy and run a speech prosthetic for some of the parts of the brain that are damaged by stroke or injury? These are questions that we can now consider in relation to this part of the brain,” said study author and Baylor neurosurgery resident Kalman Katlowitz.

While these results are promising, the team acknowledged the study’s limitations. The research focused only on propofol-based general anesthesia, so the results may not apply to other unconscious states such as natural sleep or coma. Additionally, the researchers examined only the hippocampus, so they cannot yet determine how broadly this type of unconscious processing occurs in other brain regions.

“This work pushes us to rethink what it means to be conscious,” Sheth concluded. “The brain is doing much more behind the scenes than we fully understand.”

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

A new model suggests that asteroid impacts on Earth may have occasionally launched microscopic material into space that could have reached Venus’s atmosphere over billions of years. Some of this material may even have temporarily remained suspended in the planet’s cloud layers under certain conditions.

The idea is based on the theory of panspermia, which suggests that life, or the building blocks of life, can move between planets on debris created by asteroid impacts. Scientists have often discussed the possibility of microbes traveling between Earth and Mars, and even the possibility of Earthly organisms reaching locations as far away as Jupiter’s moons.

Now, interest in the possibility of microscopic life in Venus’s dense, sulfur-rich clouds has made Venus a focus for similar studies of interplanetary transfer.

A team from Arizona State University, the Johns Hopkins University Applied Physics Laboratory, and Sandia National Laboratories recently explored this idea in a study presented at the 2026 Lunar and Planetary Science Conference. They used the Venus Life Equation, a framework made in 2021 by planetary scientist Noam Izenberg and his team. This equation considers three main factors: origination, robustness, and continuity. These factors help estimate the likelihood that life exists on Venus today. If panspermia occurred, it would mean that any life on Venus, if it exists at all, would not necessarily have originated there.

A Rock Becomes a Delivery Vehicle

Before considering the possibility of life originating on Venus, the researchers first addressed a fundamental question: could any material from Earth realistically survive the journey to Venus?

When an asteroid hits Earth hard enough to send material into space, that material faces intense shock, high heat, radiation, and the vacuum of space. Studies of meteorites found on Earth show that organic compounds can survive both ejection and travel between planets. However, an important question remains: what happens when this debris enters Venus’s atmosphere at high speed?

To study this process, the researchers used the pancake model. This model helps researchers analyze how meteors break apart when they enter an atmosphere. In this model, a meteor entering Venus’s atmosphere experiences intense heating and breaks into fragments. These fragments spread out into a flattened cloud of debris called ‘cells.’ Some of these cells are small enough to stay suspended in Venus’s clouds, instead of falling to the surface, where survival would be much less likely.

The Math of Interplanetary Hitchhikers

Their calculations indicate that, depending on the assumptions used, between 2 and 4 billion cell-sized fragments may have reached the clouds of Venus. Even accounting for a 99% loss during transit, billions of cell-sized fragments could be delivered. The model further estimates that approximately 100 cells could arrive in Venus’s clouds each year, amounting to roughly 20 billion over the past billion years, which corresponds to the estimated age of Venus’s current surface.

Life on Venus?

Venus is considered a relatively low-risk target for spacecraft contamination under planetary protection guidelines. The results of this study imply that natural processes may have been transferring material from Earth to Venus for a billion years, independent of human activity.

So far, no mission has found life in Venus’s clouds. However, future astrobiology missions are expected to directly search for it. If life is discovered, these models show that scientists will then need to determine whether it originated on Venus or arrived from Earth.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

A team of physicists set out to recreate a 60-year-old quantum mystery using a large water tank. While their experiment produced the expected results, it also revealed an unexpected behavior in the water.

The experiment centered on the Aharonov–Bohm effect, a strange quantum phenomenon where electrons respond to a magnetic field even when they never actually pass through it. First predicted back in 1959, this effect has been notoriously difficult to confirm because the changes it causes in electron waves are incredibly subtle.

Instead of using electrons and a particle accelerator, researchers from the Okinawa Institute of Science and Technology (OIST), along with collaborators in Oslo and Chile, recreated the effect using water waves, making an otherwise invisible quantum effect visible in a tabletop experiment. The team reported their findings in Communications Physics.

A 1980 Idea Gets a Modern Upgrade

“With waves traveling the opposite direction, you see a mirror image pattern,” says Jonas Rønning, co- author and former postdoc in the OIST unit. “The question for us was, what happens if you send waves from both directions at the same time? We thought that the patterns might cancel each other out, or both pitchfork-like patterns would be visible, but our intuition was completely wrong.”

Two Waves Walk Into a Vortex

The researchers constructed a custom tank, created a vortex at its center, and sent waves from opposite sides to meet in the middle. They used a high-speed camera to observe how the waves interacted on the water’s surface.

Without a vortex, the peaks and troughs remain in place. When the vortex is there, the standing wave breaks down. The vortex shifts the phase of the waves as they travel around it, altering the standing-wave interference pattern and creating rotating nodal lines. These are lines of momentarily flat water that spiral outward and turn in the opposite direction of the vortex.

“This was something new and unexpected,” says Aditya Singh, a PhD student in the Nonlinear and Non-equilibrium Physics Unit and co-first author of the study. “That’s what makes this fluid analogue system so valuable. It reveals topological effects — wave behaviors that occur across the whole system — that can’t be seen in quantum experiments.”

At first, the team suspected their equipment was causing the rotating lines. However, the same pattern also appeared in their computer simulations.

“When we first saw these lines, we thought they were an experimental artifact,” Singh says. “But when we also saw them in our simulations, we dropped everything and quickly worked out the mathematics underlying how they arise.”

More Speed, More Lines

As the strength of the vortex increased, more rotating nodal lines appeared, always moving in the direction opposite to the vortex. The study showed that the number of lines is quantized, meaning it is fixed to whole-number values determined by the vortex’s circulation. In other words, the topology of the vortex determines how many rotating nodal lines appear. This effect was observed across the entire wave field, not just near the vortex core as earlier models suggested.

This finding differs from Berry’s original experiment, which observed changes only near the vortex. In this case, the researchers discovered a non-local effect, a pattern that covers the entire system and depends on the overall setup. Instead of being limited to the area near the vortex, the rotating nodal lines appeared throughout the whole standing-wave pattern.

Although the practical uses of these newly discovered nodal patterns are not yet clear, the study shows that simple classical systems can reveal behaviors that are challenging to observe in quantum experiments. By making these effects visible, the researchers plan to explore more complex wave systems that could offer new insights into superconductivity and other quantum phenomena.

“One direction is to make the system more complex by introducing multiple vortices and arranging them into a lattice,” senior author Mahesh Bandi says. “That setup would mirror conditions in some superconducting materials, with the water waves behaving like a supercurrent. We don’t yet know what we’ll see — and that’s exactly what makes it worth doing.”

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

A team of researchers examined 23 teeth from at least 20 individuals from South Africa‘s Rising Star Cave system and found no evidence of the male-specific Amelogenin-Y protein.

A recent study, published in Cell, marks the first ancient protein analysis of Homo naledi and raises the possibility that the Dinaledi Chamber may contain predominantly, or possibly exclusively, female individuals. If confirmed, Homo naledi would become the first known non-human species to practice sex-specific burial.

A Decade-Old Puzzle

Since its discovery in 2013, Homo naledi has puzzled paleoanthropologists. This extinct human relative lived between 335,000 and 241,000 years ago and combined ape-like anatomy with distinct human features. So far, Homo naledi remains have only been found in the Rising Star Cave system in South Africa.

For over a decade, scientists noted that the adult fossils from the Dinaledi Chamber were nearly identical, lacking the skeletal variation usually seen between males and females. The reason for this similarity remained unclear.

Proteins Where DNA Fails

To investigate further, a team from the University of York, the University of Copenhagen, and additional institutions analyzed the remains at the molecular level. They used a minimally destructive acid-etching method to extract protein fragments, or peptides, from the enamel of the 23 teeth.

“Unlike those found in other remains like bone fragments, proteins in tooth enamel are preserved because dental enamel—the hardest tissue in the human body—shields proteins from environmental contamination even for millions of years. This makes them ideal carriers of genetic information from deep time. Our study helps explain the long-standing mystery of why Homo naledi lacked significant variation; it’s probably because they could have all belonged to one sex,” said Palesa Madupe, who conducted the research during her postdoctoral fellowship at the University of Copenhagen.

A Missing Male Marker

The researchers specifically searched for Amelogenin-Y, a protein produced only by the male Y chromosome, but detected none of it in the sampled teeth. To make sure the results were not due to modern contamination, a specialized chemistry team at the University of York independently confirmed that the amino acids were genuinely ancient. The findings remained consistent.

“The lack of male markers within the group is truly fascinating. It is incredibly exciting to gain a window not only into the biology of our ancestors, but also into how they lived. These findings offer rare insights into a culture that has, until now, been difficult to access directly. Advances in ancient protein analysis are opening the door to a far richer and more nuanced understanding of ancient hominins,” said Dr. Marc Dickinson from the university’s Department of Chemistry.

Ritual or Genetic Fluke?

The findings allow for two possible explanations. One is cultural: if the chamber were reserved for females, it could indicate that Homo naledi practiced sex-specific, symbolic mortuary behavior, a trait previously thought to be unique to modern humans.

The second explanation is biological. The researchers note that a small, isolated Homo naledi population could have lost or mutated the Amelogenin-Y gene, leaving males without the protein marker typically used to identify male remains.

Either explanation could reshape what scientists know about Homo naledi. If researchers confirm the site as an all-female burial chamber, it would suggest that symbolic behavior emerged earlier in the human lineage than previously believed. On the other hand, a genetic change across the population would show how isolation can shape a species’ biology in ways that fossils alone cannot reveal. This study shows how ancient protein analysis from teeth can open new windows into human evolution.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

Before the prefrontal cortex evolved, another system in the brain may already have been directing attention, scientists have now revealed. 

In a recent study published in Nature Communications, researchers at Johns Hopkins University have identified a small group of brainstem neurons that act as an internal attention filter.

When these neurons were deactivated in mice, the animals became highly distractible, mirroring one of the hallmark features of ADHD. Restoring activity in these neurons allowed the mice to regain normal focus.

An Old Problem With No Modern Answer

Selective spatial attention is the brain’s ability to focus on relevant information while ignoring distractions. This process allows a person to follow a conversation in a noisy environment or recognize a familiar face in a crowd. Conditions such as ADHD and autism also often involve disruptions in selective attention. For many years, scientists believed the prefrontal cortex was primarily responsible for this function.

However, this view does not fully explain how attention works across different species. Primates have a well-developed prefrontal cortex, while other vertebrates have a less pronounced version, yet birds, fish, and turtles still effectively focus their attention.

“If we really go back in evolution, for hundreds of millions of years, birds have had this ability, fish have had this ability. And they do not typically have a highly developed prefrontal cortex, so how does the brain solve this problem?” said lead author Ninad Kothari, a postdoctoral fellow in Johns Hopkins’ Department of Psychological and Brain Sciences. “We were able to identify an evolutionarily old region in the brainstem which affords this ability.”

A Filter Hiding in Plain Sight

The researchers traced this behavior to a network of inhibitory neurons located deep within the brainstem, a region found in all vertebrates. Mysore’s laboratory had previously studied this area in birds, frogs, and turtles before examining it in mice.

The team created an attention task, similar to those used in human studies, to test the role of these neurons. Mice viewed visual cues on a screen and earned rewards for responding to a target in front of them while ignoring a distracting signal off to the side. This test measured how well the mice could pick out important information and ignore distractions. The mice successfully performed the task until researchers deactivated the brainstem neurons, at which point they became easily distracted.

Ruling Out the Obvious Explanations

Before concluding that these neurons controlled attention, the researchers tested for other possible explanations, such as vision or motor problems, that might account for the mice’s behavior. However, the researchers ruled out these alternative explanations.

“The only thing impaired was their ability to take the competing pieces of information, compare them, and pay attention to the location with the most important information,” said senior author Shreesh Mysore, a neuroscientist who studies neural circuits tied to behavior. “This part of the brain is like an attentional selection engine. It helps solve the question: ‘What is most important information I should pay attention to right now?'”

The attention deficit was not permanent. When the neurons were reactivated, the same mice that had previously been unable to ignore faint distractions were once again able to ignore even strong distractors.

“A hallmark of ADHD is that even faint distractors draw attention away — and that’s exactly what we see here when these neurons are silenced,” Mysore said. “But the very next day, when the neurons are turned back on, the same animal can ignore distractors again, even very strong ones.”

What This Could Mean for Human Brains

“All the evidence to date suggests that these neurons exist in humans too,” Mysore said. “But are they responsible for selective spatial attention in humans? An exciting hypothesis is that they play a crucial role.”

If researchers find that these neurons function differently in people with attention disorders, the circuit could become a target for future therapies.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

A butterfly that survives for nearly a year, while its closest relatives live only two weeks, offers a potential model for studying how aging and lifespan can evolve in nature.

A University of Bristol-led study published in Nature Communications reports that some Heliconius butterflies have evolved both longer lifespans and slower physical decline, distinguishing them from most other butterfly species studied to date.

Heliconius butterflies inhabit the rainforests of Central and South America, where most adult butterflies live only a few weeks. In the study, Heliconius hewitsoni reached a maximum lifespan of 348 days, while a related species, Dione juno, only survived about 14 days under observed conditions. This represents a 25-fold difference in maximum lifespan between two closely related species.

Old Age Without the Decline

Heliconius butterflies not only live longer than their relatives, but also show slower physical decline with age. The team, in collaboration with the Smithsonian Tropical Research Institute in Panama, measured grip strength in aging butterflies to assess physical decline. Older Heliconius hecale performed as well as the younger individuals tested, showing no detectable age-related decline in grip strength.

The team combined butterfly house data, mark–recapture field studies, and controlled lab experiments. In these cases, Heliconius butterflies consistently showed longer lifespans, lower mortality rates, and slower rates of aging than closely related species that do not feed on pollen.

The Pollen Connection

One hypothesis for their extended lifespan focuses on their unusual adult diet. While most butterflies feed only on nectar, Heliconius also consumes pollen, which was thought to provide the nutritional basis for its extended lifespan.

To test this idea, researchers compared pollen-feeding Heliconius hecale with its non-pollen-feeding relative, Dryas iulia. H. hecale maintained body mass and muscle performance longer than D. iulia. Even without pollen in its diet, H. hecale still outlived its relative by a wide margin. This suggests that diet alone does not explain Heliconius’s extended lifespan.

Dr. Jessica Foley, the study’s lead author from the University of Bristol’s School of Biological Sciences, explained that insects already show a huge range of lifespans. Some, like mayflies, live only a few days, while ant and termite queens can survive for decades. “This represents a roughly 5,000-fold difference within the class, compared with around a 100-fold difference in lifespan observed in mammals.”

Heliconius butterflies are notable because they have evolved both longer lifespans and slower rates of aging, allowing them to outlive close relatives from which they diverged only recently in evolutionary history.

A New Model for an Old Problem

Most aging research uses a limited set of laboratory organisms, such as mice, worms, and fruit flies, to study biological aging mechanisms. The heliconius butterfly provides a unique opportunity to study a long-lived species closely related to short-lived species, allowing for direct comparison in a natural context. Foley noted that comparing these two groups “can help reveal how lifespan is extended, making them a highly promising new model for research into the biology of aging and longevity.”

If pollen feeding and longevity evolved together but are not entirely dependent on each other, the next step is to identify the genetic and physiological changes responsible for both traits. Understanding these mechanisms in Heliconius butterflies could provide insight into how aging evolves across species.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

Moss has long been regarded as unusual among land plants for its resilience and apparent independence from fungal partnerships, which are common in other species. However, new research from UC Riverside indicates that this view may not apply in desert environments.

An Unexpected Partnership

More than 85% of land plants form relationships with fungi that exchange soil nutrients for sugars, and approximately 75% of these plants specifically associate with arbuscular mycorrhizal fungi, or AMF, which require a host to survive. Moss was considered the exception. All 10,000 known species were believed to exist without such fungal partnerships.

“That’s been the model,” said Jason Stajich, a UCR professor of microbiology and plant pathology and co-author of the study, published in New Phytologist. Mosses, he explained, simply didn’t need fungi. Recent findings now challenge that assumption.

Chasing Moss Through 100-Degree Heat

To investigate this, UCR doctoral researcher Kian Kelly collected samples from the Mojave and Sonoran deserts, focusing on biological soil crusts. These crusts contain complex communities of fungi, bacteria, algae, moss, and microscopic animals that stabilize desert soils. The team wanted to know whether desert mosses host different fungal communities than mosses in milder environments. “Sometimes I couldn’t find the same species of moss,” Kelly said of the search.

The team then processed moss samples and sequenced their DNA in the laboratory. They identified fungal genetic material, including AMF, which are typically unable to survive without a host plant. Furthermore, the fungal species present in desert moss differed from those found in moss from less extreme climates and from fungi in the surrounding soil. “We suspect that certain fungi are more helpful for surviving hotter, drier climates,” Kelly noted.

This mismatch made simpler explanations, such as contamination or fungi decomposing dead tissue, less likely. The evidence suggested a more specific association.

Unexpected Structures Inside Moss

Since DNA evidence alone could not confirm that fungi were living within moss tissue, Kelly stained samples with a dye that binds specifically to fungal cells and examined them under a microscope. He observed branching structures located inside the moss. “As soon as I saw that, I knew we had something really interesting,” Kelly said.

The structures resembled arbuscules, small, tree-shaped structures that fungi typically form within plant roots to facilitate nutrient exchange. Since moss lacks traditional roots, these structures were observed in the moss leaves. The researchers therefore refer to them as “arbuscule-like” rather than identifying them as true arbuscules. Whether nutrient exchange actually occurs between fungus and moss remains unconfirmed and will be the subject of future investigation.

Potential Implications

If this relationship is confirmed, its significance extends beyond desert ecology. Mosses belong to one of the oldest surviving lineages of land plants, which first colonized terrestrial environments approximately 470 million years ago. A fungal partnership present in moss tissue today could represent a remnant of the original alliance that enabled plants to adapt to life on land.

There are also more immediate implications. Biological soil crusts are fragile, and even small disturbances can take decades to heal. These ecosystems are increasingly threatened by rising temperatures and human activity. If fungi help desert moss survive heat and drought, understanding this relationship could guide future efforts to restore degraded soil crusts rather than merely documenting their decline. For now, the main conclusion is that moss, once thought to be independent of fungal associations, may have been getting help the entire time.

“The desert is full of things people overlook. Sometimes, the biggest surprises are the ones growing quietly beneath our feet,” Kelly concludes.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.