Researchers from Integra Therapeutics, in partnership with the Pompeu Fabra University (UPF) and the Centre for Genomic Regulation (CRG), Spain, have used generative AI to design synthetic proteins that outperform naturally occurring proteins used for editing the human genome. Their use of generative AI focused on PiggyBac transposases, naturally occurring enzymes that have long been used for gene delivery and genetic engineering, and uncovered more than 13,000 previously unidentified PiggyBac sequences. The research, published in Nature Biotechnology, has the potential to improve current gene editing tools for the creation of CAR T and gene therapies.
“Our work expands the phylogenetic tree of PiggyBac transposons by two orders of magnitude, unveiling a previously unexplored diversity within this family of mobile genetic elements,” the researchers wrote.
For their work, the researchers first conducted extensive computational bioprospecting, screening more than 31,000 eukaryotic genomes to uncover the 13,000 new sequences. From this number, the team was able to validate 10 active transposases, two of which showed similar activity to PiggyBac transposases currently used in both research and clinical settings.
Flying manipulator robots have shown themselves to be useful in many applications, such as industrial maintenance or construction. Their utility in hard to reach or hazardous locations makes them particularly promising in applications that put humans at risk. While these machines have been continuously improving over the years, they are still lacking in certain areas.
One difficulty for drones in the past has been the ability to stack on top of one another and work cooperatively while in flight. This ability is useful for things like swapping tools, similar to the way a nurse might hand different tools to a doctor during a procedure—allowing the doctor (or manipulator drone) to work uninterrupted.
The difficulty comes from something called “downwash,” which is a strong movement of air generated between two drones that interferes with their precise movements and docking procedures. However, a team of researchers from Westlake University in China has designed a new system of micro-aerial vehicles (MAVs) capable of exchanging tools with impressive precision while flying. The design and experimental tests on the “FlyingToolbox” are documented in their new study, published in Nature.
Recently, the iGaN Laboratory led by Professor Haiding Sun at the School of Microelectronics, University of Science and Technology of China (USTC), together with the team of academician Sheng Liu from Wuhan University, has successfully developed the world’s first miniaturized ultraviolet (UV) spectrometer chip and realized on-chip spectral imaging.
Based on a novel gallium nitride (GaN) cascaded photodiode architecture and integrated with deep neural network (DNN) algorithms, the device achieves high-precision spectral detection and high-resolution multispectral imaging.
With a response speed on the nanosecond scale, it sets a new world record for the fastest reported miniaturized spectrometer. The work, titled “A miniaturized cascaded-diode-array spectral imager,” was published online in Nature Photonics on September 26, 2025.
Heat has always been something we thought we understood. From baking bread to running engines, the idea seemed simple: heat spreads out smoothly, like water soaking through a sponge. That simple picture, written down by Joseph Fourier 200 years ago, became the foundation of modern science and engineering.
But zoom into the nanoscale—inside the chips that power your smartphone, AI hardware, or next-generation solar panels—and the story changes. Here, heat doesn’t just “diffuse.” It can ripple like sound waves, remember its past, or flow in elegant streams like a fluid in a pipe. For decades, scientists had pieces of this puzzle but no unifying explanation.
Now, researchers at Auburn University and the U.S. Department of Energy’s National Renewable Energy Laboratory have delivered what they call a “unified statistical theory of heat conduction.”
Typically, the charge of electrons is used to store and process information in electronics-based devices. In spintronics, the focus is instead on the magnetic moment or on magnetic vortices, so-called skyrmions—the goal is smaller, faster, and more sustainable computers. To further increase storage density, skyrmions will not only be two-dimensional in the future, but will also conquer the third dimension.
Researchers from the Institute of Physics at Johannes Gutenberg University Mainz (JGU) have now succeeded in creating three-dimensional skyrmions, so-called hybrid skyrmion tubes, in synthetic antiferromagnets and have demonstrated for the first time that these skyrmion tubes move differently than two-dimensional skyrmions.
“Three-dimensional skyrmions are of interest for quantum computing and brain-inspired computing, among other things—here the higher storage density resulting from the third dimension is essential,” says Mona Bhukta from Professor Mathias Kläui’s research group. The results were published on September 26 in Nature Communications.
An international team of researchers from Forschungszentrum Jülich (Germany), Tohoku University (Japan), and École Polytechnique de Montréal (Canada) has made a significant discovery in semiconductor science by revealing the remarkable spin-related material properties of Germanium-Tin (GeSn) semiconductors.
Semiconductors control the flow of electricity that power everyday technology all around us (such as cars and computers). However, technology is progressing at such a breakneck speed that it is straining current semiconductor technologies.
“Semiconductors are approaching their physical and energy-efficiency limits in terms of speed, performance, and power consumption,” says Makoto Kohda from Tohoku University. “This is a huge issue because we need semiconductors that can keep up as we shift to more demanding needs such as 5G/6G networks and the increased use of artificial intelligence.”
WARNING: AI could end humanity, and we’re completely unprepared. Dr. Roman Yampolskiy reveals how AI will take 99% of jobs, why Sam Altman is ignoring safety, and how we’re heading toward global collapse…or even World War III.
Dr. Roman Yampolskiy is a leading voice in AI safety and a Professor of Computer Science and Engineering. He coined the term “AI safety” in 2010 and has published over 100 papers on the dangers of AI. He is also the author of books such as, ‘Considerations on the AI Endgame: Ethics, Risks and Computational Frameworks’
He explains: ⬛How AI could release a deadly virus. ⬛Why these 5 jobs might be the only ones left. ⬛How superintelligence will dominate humans. ⬛Why ‘superintelligence’ could trigger a global collapse by 2027 ⬛How AI could be worse than nuclear weapons. ⬛Why we’re almost certainly living in a simulation.
00:00 Intro. 02:28 How to Stop AI From Killing Everyone. 04:35 What’s the Probability Something Goes Wrong? 04:57 How Long Have You Been Working on AI Safety? 08:15 What Is AI? 09:54 Prediction for 2027 11:38 What Jobs Will Actually Exist? 14:27 Can AI Really Take All Jobs? 18:49 What Happens When All Jobs Are Taken? 20:32 Is There a Good Argument Against AI Replacing Humans? 22:04 Prediction for 2030 23:58 What Happens by 2045? 25:37 Will We Just Find New Careers and Ways to Live? 28:51 Is Anything More Important Than AI Safety Right Now? 30:07 Can’t We Just Unplug It? 31:32 Do We Just Go With It? 37:20 What Is Most Likely to Cause Human Extinction? 39:45 No One Knows What’s Going On Inside AI 41:30 Ads. 42:32 Thoughts on OpenAI and Sam Altman. 46:24 What Will the World Look Like in 2100? 46:56 What Can Be Done About the AI Doom Narrative? 53:55 Should People Be Protesting? 56:10 Are We Living in a Simulation? 1:01:45 How Certain Are You We’re in a Simulation? 1:07:45 Can We Live Forever? 1:12:20 Bitcoin. 1:14:03 What Should I Do Differently After This Conversation? 1:15:07 Are You Religious? 1:17:11 Do These Conversations Make People Feel Good? 1:20:10 What Do Your Strongest Critics Say? 1:21:36 Closing Statements. 1:22:08 If You Had One Button, What Would You Pick? 1:23:36 Are We Moving Toward Mass Unemployment? 1:24:37 Most Important Characteristics.
New artificial intelligence-generated images that appear to be one thing, but something else entirely when rotated, are helping scientists test the human mind.
The work by Johns Hopkins University perception researchers addresses a longstanding need for uniform stimuli to rigorously study how people mentally process visual information.
“These images are really important because we can use them to study all sorts of effects that scientists previously thought were nearly impossible to study in isolation—everything from size to animacy to emotion,” said first author Tal Boger, a Ph.D. student studying visual perception.
A new machine learning method has achieved what even AlphaFold cannot — the design of intrinsically disordered proteins (IDPs), the shape-shifting biomolecules that make up nearly 30% of all human proteins.
