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2025-07-05 22:00:00

The post This Week’s Awesome Tech Stories From Around the Web (Through July 5) appeared first on SingularityHub.

2025-07-04 22:00:00

A painless, non-invasive brain stimulation technique can significantly improve how young adults learn math, my colleagues and I found in a recent study. In a paper in PLOS Biology, we describe how this might be most helpful for those who are likely to struggle with mathematical learning because of how their brain areas involved in this skill communicate with each other.

Math is essential for many jobs, especially in science, technology, engineering, and finance. However, a 2016 OECD report suggested that a large proportion of adults in developed countries (24 percent to 29 percent) have math skills no better than a typical seven-year-old. This lack of numeracy can contribute to lower income, poor health, reduced political participation, and even diminished trust in others.

Education often widens rather than closes the gap between high and low achievers, a phenomenon known as the Matthew effect. Those who start with an advantage, such as being able to read more words when starting school, tend to pull further ahead. Stronger educational achievement has also been associated with socioeconomic status, higher motivation, and greater engagement with material learned during a class.

Biological factors, such as genes, brain connectivity, and chemical signaling, have been shown in some studies to play a stronger role in learning outcomes than environmental ones. This has been well-documented in different areas, including math, where differences in biology may explain educational achievements.

To explore this question, we recruited 72 young adults (18–30 years old) and taught them new math calculation techniques over five days. Some received a placebo treatment. Others received transcranial random noise stimulation (tRNS), which delivers gentle electrical currents to the brain. It is painless and often imperceptible, unless you focus hard to try and sense it.

It is possible tRNS may cause long-term side effects, but in previous studies, my team assessed participants for cognitive side effects and found no evidence for it.

Participants who received tRNS were randomly assigned to receive it in one of two different brain areas. Some received it over the dorsolateral prefrontal cortex, a region critical for memory, attention, or when we acquire a new cognitive skill. Others had tRNS over the posterior parietal cortex, which processes math information, mainly when the learning has been accomplished.

Before and after the training, we also scanned their brains and measured levels of key neurochemicals such as gamma-aminobutyric acid (gaba), which we showed previously, in a 2021 study, plays a role in brain plasticity and learning, including math.

Some participants started with weaker connections between the prefrontal and parietal brain regions, a biological profile that is associated with poorer learning. The study results showed these participants made significant gains in learning when they received tRNS over the prefrontal cortex.

Stimulation helped them catch up with peers who had stronger natural connectivity. This finding shows the critical role of the prefrontal cortex in learning and could help reduce educational inequalities that are grounded in neurobiology.

How does this work? One explanation lies in a principle called stochastic resonance. This is when a weak signal becomes clearer when a small amount of random noise is added.

In the brain, tRNS may enhance learning by gently boosting the activity of underperforming neurons, helping them get closer to the point at which they become active and send signals. This is a point known as the “firing threshold,” especially in people whose brain activity is suboptimal for a task like math learning.

It is important to note what this technique does not do. It does not make the best learners even better. That is what makes this approach promising for bridging gaps, not widening them. This form of brain stimulation helps level the playing field.

Our study focused on healthy, high-performing university students. But in similar studies on children with math learning disabilities (2017) and with attention-deficit/hyperactivity disorder (2023), my colleagues and I found tRNS seemed to improve their learning and performance in cognitive training.

I argue our findings could open a new direction in education. The biology of the learner matters, and with advances in knowledge and technology, we can develop tools that act on the brain directly, not just work around it. This could give more people the chance to get the best benefit from education.

In time, perhaps personalized, brain-based interventions like tRNS could support learners who are being left behind not because of poor teaching or personal circumstances, but because of natural differences in how their brains work.

Of course, very often education systems aren’t operating to their full potential because of inadequate resources, social disadvantage, or systemic barriers. And so any brain-based tools must go hand-in-hand with efforts to tackle these obstacles.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The post Could Electric Brain Stimulation Make You Better at Math? appeared first on SingularityHub.

2025-07-03 22:00:00

Vast swathes of the human genome remain a mystery to science. A new AI from Google DeepMind is helping researchers understand how these stretches of DNA impact the activity of other genes.

While the Human Genome Project produced a complete map of our DNA, we still know surprisingly little about what most of it does. Roughly 2 percent of the human genome encodes specific proteins, but the purpose of the other 98 percent is much less clear.

Historically, scientists called this part of the genome “junk DNA.” But there’s growing recognition these so-called “non-coding” regions play a critical role in regulating the expression of genes elsewhere in the genome.

Teasing out these interactions is a complicated business. But now a new Google DeepMind model called AlphaGenome can take long stretches of DNA and make predictions about how different genetic variants will affect gene expression, as well as a host of other important properties.

“We have, for the first time, created a single model that unifies many different challenges that come with understanding the genome,” Pushmeet Kohli, a vice president for research at DeepMind, told MIT Technology Review.

The so-called “sequence to function” model uses the same transformer architecture as the large language models behind popular AI chatbots. The model was trained on public databases of experimental results testing how different sequences impact gene regulation. Researchers can enter a DNA sequence of up to one million letters, and the model will then make predictions about a wide range of molecular properties impacting the sequence’s regulatory activity.

These include things like where genes start and end, which sections of the DNA are accessible or blocked by certain proteins, and how much RNA is being produced. RNA is the messenger molecule responsible for carrying the instructions contained in DNA to the cell’s protein factories, or ribosomes, as well as regulating gene expression.

AlphaGenome can also assess the impact of mutations in specific genes by comparing variants, and it can make predictions about RNA “splicing”—a process where RNA molecules are chopped up and packaged before being sent off to a ribosome. Errors in this process are responsible for rare genetic diseases, such as spinal muscular atrophy and some forms of cystic fibrosis.

Predicting the impact of different genetic variants could be particularly useful. In a blog post, the DeepMind researchers report they used the model to predict how mutations other scientists had discovered in leukemia patients probably activated a nearby gene known to play a role in cancer.

“This system pushes us closer to a good first guess about what any variant will be doing when we observe it in a human,” Caleb Lareau, a computational biologist at Memorial Sloan Kettering Cancer Center granted early access to AlphaGenome, told MIT Technology Review.

The model will be free for noncommercial purposes, and DeepMind has committed to releasing full details of how it was built in the future. But it still has limitations. The company says the model can’t make predictions about the genomes of individuals, and its predictions don’t fully explain how genetic variations lead to complex traits or diseases. Further, it can’t accurately predict how non-coding DNA impacts genes that are located more than 100,000 letters away in the genome.

Anshul Kundaje, a computational genomicist at Stanford University in Palo Alto, California, who had early access to AlphaGenome, told Nature that the new model is an exciting development and significantly better than previous models, but not a slam dunk. “This model has not yet ‘solved’ gene regulation to the same extent as AlphaFold has, for example, protein 3D-structure prediction,” he says.

Nonetheless, the model is an important breakthrough in the effort to demystify the genome’s “dark matter.” It could transform our understanding of disease and supercharge synthetic biologists’ efforts to re-engineer DNA for our own purposes.

The post New Google AI Will Work Out What 98% of Our DNA Actually Does for the Body appeared first on SingularityHub.

2025-07-01 22:00:00

It starts with flashes of light. Zig-zag lines float across your vision. You feel a slight tingling in your cheeks and limbs. Then comes a stabbing headache so intense you forget everything—what you were doing, where you are, and how to compose yourself.

Scientists still don’t fully understand why migraines happen. Unlike dull headaches during a cold or pulsing headaches after a night of overindulgence, migraines are debilitating and strike at seemingly random times. Stress, lack of sleep, and bright lights could spark an attack—but the triggers vary between people, making them hard to predict.

Despite decades of research, few medications are available. But a surprising newcomer may change the field. Called liraglutide, the drug is in the same family as the blockbuster weight-loss drugs Ozempic and Wegovy, which have taken the world by storm.

In a small trial of 31 people with chronic migraines who didn’t respond to other treatments, liraglutide slashed the number of days they experienced migraines by over half. The drug worked remarkably fast, with most participants feeling relief within the first week.

Although the volunteers were obese—which increases the chance of migraines—subsequent analysis showed the drug lowered migraines even with minimal weight loss.

“Liraglutide may operate via different mechanisms [than weight loss], and represent a promising new approach to migraine prevention,” wrote the team.

Headache on Headache

Migraine has been a headache to study for decades. Although it affects nearly 15 percent of people worldwide, its origins in the brain remain mostly mysterious. The condition isn’t just a severe headache—people also experience nausea, dizziness, and sensitivity to light, sound, and smell.

Scientists originally thought migraines occurred because of blood vessel problems in the brain and treated the headaches with standard pain medications. But these don’t work well. Recent studies paint a far more complex picture of the condition. Migraines seem to stem from dysfunctional neural networks in certain brain regions, where neurons release messengers called neuropeptides that spark inflammation and dilate blood vessels in the brain.

These chemicals potentially increase intracranial pressure—that is, the brain pressing against the skull—and could act as a trigger for migraines.

Scientists investigating neuropeptides have already designed a migraine treatment. Called anti-CGRP drugs, these medications can be injected into the bloodstream to treat or prevent chronic migraine attacks. One controlled clinical study in 667 patients found that those who received injections experienced fewer days of head-splitting pain.

Although these drugs are effective and have relatively mild side effects, they’re expensive. This motivated the team to look for another way to lower brain pressure.

Chemical Polymath

Enter GLP-1 agonists. Most famously represented by Ozempic, these drugs skyrocketed to fame for their ability to slash weight, manage diabetes, and lower the risk of heart disease.

That’s not all they can do. The drugs target proteins called GLP-1 receptors, which are dotted on the surfaces of multiple cell types, including neurons, suggesting that beyond managing weight, they could regulate the brain too. One study found that daily injections of a GLP-1 drug slowed cognitive decline in people with mild Alzheimer’s disease. Another trial suggested the drugs could tackle alcohol addiction. How they work is still under investigation, but these clinical trials suggest GLP-1 drugs can impact the brain through chemical signaling, or perhaps pressure.

Previous studies found the drugs tinker with the amount of fluid in the brain. The organ is bathed in a nutritious soup called cerebrospinal fluid, which cushions it and removes waste. But the fluid can build up and increase intracranial pressure—potentially leading to migraines.

GLP-1 drugs might lower that pressure. An early study found a drug normalized dangerously high brain pressure in rats, like deflating an overblown balloon. A small randomized clinical trial in people with high intracranial pressure found the drug nearly restored it to normal.

These promising results led Simone Braca at the University of Naples Federico II and colleagues to test liraglutide, a GLP-1 drug, as a treatment for chronic migraine. All 31 participants in the study were roughly middle-aged, obese, and had already tried at least two other drugs without any improvement in their symptoms.

“Obesity can worsen migraine by increasing headache frequency and reducing response to standard preventive treatments,” wrote the team.

Each participant received a daily injection of the drug for 12 weeks. They also kept a “headache diary” to track their migraines and log any potential side effects.

Almost everyone reported fewer days with migraines. On average, their headaches dropped from 20 days a month to roughly 11 days. Some people reported that the days they had headaches fell by roughly 75 percent. One participant remained completely migraine-free after the first injection and for the rest of the test period. Others weren’t so lucky: Four people didn’t respond to the treatment, suggesting it’s not a universal cure-all.

Those who benefited, however, said the drug improved their quality of life in just a week, despite minor side effects. Roughly 40 percent of participants experienced nausea or constipation, both of which are common side effects for those taking GLP-1 drugs. The symptoms eventually went away.

As expected, the participants dropped a few pounds, but additional statistical analysis found the weight loss didn’t contribute to migraine frequency. This suggests the effect of GLP-1 drugs on migraine “is independent of their weight loss effect,” wrote the authors.

At the Beginning

The team is just starting to untangle how GLP-1 drugs fight migraines. Because they lower intracranial pressure, the shots might reduce the amount of the neuropeptide CGRP pumped out in the brain. Existing anti-CGRP migraine drugs lower inflammation and reduce intracranial pressure, and liraglutide might have similar effects.

GLP-1 drugs can also play with salt and potassium levels in the brain, which controls how neurons activate. Tinkering with these levels could potentially alter a neuron’s ability to fire, changing the brain’s capacity to release CGRP and other neuropeptides.

Also, the study has limitations worth noting. Each participant knew they were receiving the drug, so placebo effects may have clouded their judgement. Although they experienced benefits for 12 weeks, a longer follow-up period could better gauge if the benefits last. And because the trial only recruited people with obesity, the results may not generalize to a broader population.

The team is already planning a large randomized controlled trial. “As an exploratory pilot study, these findings provide a foundation for larger-scale trials” that examine the role GLP-1 drugs may play in migraine management, wrote the authors.

The post This Ozempic-Like Drug Slashed Migraines by Half in a Small Trial appeared first on SingularityHub.

2025-06-30 22:00:00

Stroll through ancient churchyards in England, and you’ll likely see yew trees with bright green leaves and stunning ruby red fruits guarding the graves. These coniferous trees are known in European folklore as a symbol of death and doom.

They’re anything but. The Pacific yew naturally synthesizes paclitaxel—commonly known as Taxol, a chemotherapy drug widely used to fight multiple types of aggressive cancer. In the late 1990s, it was FDA-approved for breast, ovarian, and lung cancer and, since then, has been used off-label for roughly a dozen other malignancies. It’s a modern success story showing how we can translate plant biology into therapeutic drugs.

But because Taxol is produced in the tree’s bark, harvesting the life-saving chemical kills its host. Yew trees are slow-growing with very long lives, making them an unsustainable resource. If scientists can unravel the genetic recipe for Taxol, they can recreate the steps in other plants—or even in yeast or bacteria—to synthesize the molecule at scale without harming the trees.

A new study in Nature takes us closer to that goal. Taxol is made from a precursor chemical, called baccatin III, which is just a few chemical steps removed from the final product and is produced in yew needles. After analyzing thousands of yew tree cells, the team mapped a 17-gene pathway leading to the production of baccatin III.

They added these genes to tobacco plants—which don’t naturally produce baccatin III—and found the plants readily pumped out the chemical at similar levels to yew tree needles.

The results are “a breakthrough in our understanding of the genes responsible for the biological production of this drug,” wrote Jakob Franke at Leibniz University Hannover, who was not involved in the study. “The findings are a major leap forward in efforts to secure a reliable supply of paclitaxel.”

A Garden of Medicine

Humans have long used plants as therapeutic drugs.

More than 3,500 years ago, Egyptians found that willow bark can lower fevers and reduce pain. We’ve since boosted its efficacy, but the main component is now sold in every drugstore—Aspirin. Germany has approved a molecule from lavender flowers for anxiety disorders, and some compounds from licorice root may help protect the liver, according to early clinical trials.

The yew tree first caught scientists’ attention in the late 1960s, when they were screening a host of plant extracts for potential anticancer drugs. Most were duds or too toxic. Taxol stood out for its unique effects against tumors. The molecule blocks cancers from building a “skeleton-like” structure in new cells and kneecaps their ability to grow.

Taxol was a blockbuster success but the medical community was concerned natural yew trees couldn’t meet clinical demand. Scientists soon began trying to artificially synthesize the drug. The discovery of baccatin III, which can be turned into Taxol after some chemical tinkering, was a game-changer in their quest. This Taxol precursor occurs in much larger quantities in the needles of various yew species that can be harvested without killing the trees. But the process requires multiple chemical steps and is highly costly.

Making either baccatin III or Taxol from scratch using synthetic biology—that is, transferring the necessary genes into other plants or microorganisms—would be a more efficient alternative and could boost production at an industrial scale. For the idea to work, however, scientists would need to trace the entire pathway of genes involved in the chemicals’ production.

Two teams recently sorted through yew trees’ nearly 50,000 genes and discovered a minimal set of genes needed to make baccatin III. While this was a “breakthrough” achievement, wrote Franke, adding the genes to nicotine plants yielded very low amounts of the chemical.

Unlike bacterial genomes, where genes that work together are often located near one another, related genes in plants are often sprinkled throughout the genome. This confetti-like organization makes it easy to miss critical genes involved in the production of chemicals.

A Holy Grail

The new study employed a simple but “highly innovative strategy,” Frank wrote.

Yew plants produce more baccatin III as a defense mechanism when under attack. By stressing yew needles out, the team reasoned, they could identify which genes activated at the same time. Scientists already know several genes involved in baccatin III production, so these ingredients could be used to fish out genes currently missing from the recipe.

The team dunked freshly clipped yew needles into plates lined with wells containing water and fertilizer—picture mini succulent trays. To these, they added stressors such as salts, hormones, and bacteria to spur baccatin III production. The setup simultaneously screened hundreds of combinations of stressors.

The team then sequenced mRNA—a proxy for gene expression—from more than 17,000 single cells to track which genes were activated together and under what conditions.

The team found eight new genes involved in Taxol synthesis. One, dubbed FoTO1, was especially critical for boosting the yield of multiple essential precursors, including baccatin III. The gene has “never before been implicated in such biochemical pathways, and which would have been almost impossible to find by conventional approaches,” wrote Franke.

They spliced 17 genes essential to baccatin III production into tobacco plants, a species commonly used to study plant genetics. The upgraded tobacco produced the molecule at similar—or sometimes even higher—levels compared to yew tree needles.

From Plant to Microbes

Although the work is an important step, relying on tobacco plants has its own problems. The added genes can’t be passed down to offspring, meaning every generation has to be engineered. This makes the technology hard to scale up. Alternatively, scientists might use microbes instead, which are easy to grow at scale and already used to make pharmaceuticals.

“Theoretically, with a little more tinkering, we could really make a lot of this and no longer need the yew at all to get baccatin,” said study author Conor McClune in a press release.

The end goal, however, is to produce Taxol from beginning to end. Although the team mapped the entire pathway for baccatin III synthesis—and discovered one gene that converts it to Taxol—the recipe is still missing two critical enzymes.

Surprisingly, a separate group at the University of Copenhagen nailed down genes encoding those enzymes this April. Piecing the two studies together makes it theoretically possible to synthesize Taxol from scratch, which McClune and colleagues are ready to try.

“Taxol has been the holy grail of biosynthesis in the plant natural products world,” said study author Elizabeth Sattely.

The team’s approach could also benefit other scientists eager to explore a universe of potential new medicines in plants. Chinese, Indian, and indigenous cultures in the Americas have long relied on plants as a source of healing. Modern technologies are now beginning to unravel why.

The post Scientists Genetically Engineer Tobacco Plants to Pump Out a Popular Cancer Drug appeared first on SingularityHub.

2025-06-28 22:00:00

Imagine you’re a copper miner in southeastern Europe in the year 3900 BCE. Day after day, you haul copper ore through the mine’s sweltering tunnels.

You’ve resigned yourself to the grueling monotony of mining life. Then one afternoon, you witness a fellow worker doing something remarkable.

With an odd-looking contraption, he casually transports the equivalent of three times his body weight on a single trip. As he returns to the mine to fetch another load, it suddenly dawns on you that your chosen profession is about to get far less taxing and much more lucrative.

What you don’t realize: You’re witnessing something that will change the course of history—not just for your tiny mining community, but for all of humanity.

Despite the wheel’s immeasurable impact, no one is certain as to who invented it, or when and where it was first conceived. The hypothetical scenario described above is based on a 2015 theory that miners in the Carpathian Mountains—in present-day Hungary—first invented the wheel nearly 6,000 years ago as a means to transport copper ore.

The theory is supported by the discovery of more than 150 miniaturized wagons by archaeologists working in the region. These pint-sized, four-wheeled models were made from clay, and their outer surfaces were engraved with a wickerwork pattern reminiscent of the basketry used by mining communities at the time. Carbon dating later revealed that these wagons are the earliest known depictions of wheeled transport to date.

This theory also raises a question of particular interest to me, an aerospace engineer who studies the science of engineering design. How did an obscure, scientifically naive mining society discover the wheel, when highly advanced civilizations, such as the ancient Egyptians, did not?

A Controversial Idea

It has long been assumed that wheels evolved from simple wooden rollers. But until recently no one could explain how or why this transformation took place. What’s more, beginning in the 1960s, some researchers started to express strong doubts about the roller-to-wheel theory.

After all, for rollers to be useful, they require flat, firm terrain and a path free of inclines and sharp curves. Furthermore, once the cart passes them, used rollers need to be continually brought around to the front of the line to keep the cargo moving. For all these reasons, the ancient world used rollers sparingly. According to the skeptics, rollers were too rare and too impractical to have been the starting point for the evolution of the wheel.

But a mine—with its enclosed, human-made passageways—would have provided favorable conditions for rollers. This factor, among others, compelled my team to revisit the roller hypothesis.

A Turning Point

The transition from rollers to wheels requires two key innovations. The first is a modification of the cart that carries the cargo. The cart’s base must be outfitted with semicircular sockets, which hold the rollers in place. This way, as the operator pulls the cart, the rollers are pulled along with it.

This innovation may have been motivated by the confined nature of the mine environment, where having to periodically carry used rollers back around to the front of the cart would have been especially onerous.

The discovery of socketed rollers represented a turning point in the evolution of the wheel and paved the way for the second and most important innovation. This next step involved a change to the rollers themselves. To understand how and why this change occurred, we turned to physics and computer-aided engineering.

Simulating the Wheel’s Evolution

To begin our investigation, we created a computer program designed to simulate the evolution from a roller to a wheel. Our hypothesis was that this transformation was driven by a phenomenon called “mechanical advantage.” This same principle allows pliers to amplify a user’s grip strength by providing added leverage. Similarly, if we could modify the shape of the roller to generate mechanical advantage, this would amplify the user’s pushing force, making it easier to advance the cart.

Our algorithm worked by modeling hundreds of potential roller shapes and evaluating how each one performed, both in terms of mechanical advantage and structural strength. The latter was used to determine whether a given roller would break under the weight of the cargo. As predicted, the algorithm ultimately converged upon the familiar wheel-and-axle shape, which it determined to be optimal.

During the execution of the algorithm, each new design performed slightly better than its predecessor. We believe a similar evolutionary process played out with the miners 6,000 years ago.

It is unclear what initially prompted the miners to explore alternative roller shapes. One possibility is that friction at the roller-socket interface caused the surrounding wood to wear away, leading to a slight narrowing of the roller at the point of contact. Another theory is that the miners began thinning out the rollers so that their carts could pass over small obstructions on the ground.

Either way, thanks to mechanical advantage, this narrowing of the axle region made the carts easier to push. As time passed, better-performing designs were repeatedly favored over the others, and new rollers were crafted to mimic these top performers.

Consequently, the rollers became more and more narrow, until all that remained was a slender bar capped on both ends by large discs. This rudimentary structure marks the birth of what we now refer to as “the wheel.”

According to our theory, there was no precise moment at which the wheel was invented. Rather, just like the evolution of species, the wheel emerged gradually from an accumulation of small improvements.

This is just one of the many chapters in the wheel’s long and ongoing evolution. More than 5,000 years after the contributions of the Carpathian miners, a Parisian bicycle mechanic invented radial ball bearings, which once again revolutionized wheeled transportation.

Ironically, ball bearings are conceptually identical to rollers, the wheel’s evolutionary precursor. Ball bearings form a ring around the axle, creating a rolling interface between the axle and the wheel hub, thereby circumventing friction. With this innovation, the evolution of the wheel came full circle.

This example also shows how the wheel’s evolution, much like its iconic shape, traces a circuitous path—one with no clear beginning, no end, and countless quiet revolutions along the way.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The post How Was the Wheel Invented? Computer Simulations Reveal Its Unlikely Birth Nearly 6,000 Years Ago appeared first on SingularityHub.