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Common leukemia mutation derails protein positioning during cell division, study reveals

Experts from the University of Nottingham have discovered how a common genetic mutation disrupts the positioning of a key protein during cell division, revealing a new mechanism behind acute myeloid leukemia and a potential avenue for future treatment.

Acute myeloid leukemia (AML) is an aggressive blood cancer that disrupts the production of healthy blood cells. Around one-third of patients carry a mutation in a gene called NPM1, which causes its protein to accumulate in the wrong part of the cell and plays a key role in driving the disease.

Until now, research has largely focused on how the mutated protein moves between the cell’s nucleus and the surrounding cytoplasm. But a new study, led by Dr. Daniel Booth and Dr. Claire Seedhouse at the University of Nottingham’s School of Medicine, reveals that what happens during cell division itself is also critical.

Linus Torvalds says AI is a gateway drug for coding

Linus Torvalds is using AI to develop Linux kernel code now https://www.zdnet.com/innovation/linus-torvalds-uses-ai-to-w…nel-patch/

Linux creator Linus Torvalds says AI is like a “gateway drug”

Linus Torvalds AI stance has softened as he says tools boost productivity but still create false bug reports. Read what his comments reveal.

I Tested AI Cloning… and It’s Way Worse Than You Think!

I used AI to reverse engineer real binaries, understand how they work, and then clone their functionality into brand-new applications. In this video I show the full process — what the AI can actually figure out, where it falls short, and how far you can push the technique of turning an existing binary into something new.

This is the practical side of AI-assisted reverse engineering, not theory.

What you’ll see:

How AI helps tear apart and understand binaries.
Extracting logic, behavior, and structure.
Cloning that functionality into a clean new application.
Real examples of what works and what still needs a human engineer.
The limits, risks, and surprising capabilities of current tools.

If you’ve ever wondered how far AI can go in reverse-engineering and recreating software from binaries, this is the deep dive.

For a free download of Task Manager TMOG, visit: https://tmog.org

Continue reading “I Tested AI Cloning… and It’s Way Worse Than You Think!” | >

Jamming the flow to enable memory in quantum materials

Understanding and controlling phase transitions are fundamental to physics and central to many technological revolutions. There is currently strong interest in materials with coupled structural and electronic phase transitions, where controlling the transition plasticity naturally yields built-in memory—a key step in emulating neurons and synapses for “neuromorphic” or brain-like computing technologies.

A team of UC San Diego researchers led by physics professor Oleg Shpyrko developed a new approach to coherent X-ray diffraction—a microscopy technique that is highly sensitive to specific nanoscale domain configurations —that allowed them to observe dynamics within a neuristor (a man-made device that mimics the brain’s neurons) made of vanadium dioxide while electricity was applied.

By taking measurements while operating the neuristor, they discovered a strategy to make the material learn and forget a specific state, opening an avenue toward developing energy-efficient brain-inspired hardware.

Rocks from hydrothermal vents can spontaneously combust, posing potential risks for deep-sea mining operations

Projects involving the mining of seafloor massive sulfide (SMS) deposits around hydrothermal vents already face criticism over their destructive nature, and now these projects may face a new concern: spontaneous combustion. A new study, published in Scientific Reports, describes how certain samples of rock brought up from SMS deposits have unexpectedly caught fire. The study indicates that conditions at the surface, combined with processing activities, may turn some of these rocks into a hazard.

Hydrothermal vents on the ocean floor release heated water rich in minerals that result in metal-rich deposits over time. These deposits can become valuable sources of copper, zinc, gold and silver. However, the deposits also contain sulfur-bearing minerals that can release heat when they react with oxygen in the air. If that heat builds up, the material can spontaneously combust.

Spontaneous combustion has been occasionally documented in sulfide mines on land, referred to as volcanogenic massive sulfide deposits (VMS). Some safety practices have been developed for land-based mines, but seafloor rocks are more porous and likely require different safety practices.

New evidence points to two feeding black holes just 175 parsecs apart lurking in the early universe

Astronomers have found two actively feeding supermassive black holes separated by just 175 parsecs (570 light-years) in a galaxy seen when the universe was only about 2 billion years old. The system, MG B2016+112, lies at a redshift of 3.273 and is the most compact dual AGN known at this redshift. The study was posted to the arXiv preprint server on Sept. 25.

Models predict that galaxy mergers drive gas onto central supermassive black holes, so merging galaxies should sometimes host dual active galactic nuclei (AGN). Finding close pairs is hard because of observational limitations in distinguishing nearby bright spots.

Strong gravitational lensing is useful for observing these distant objects. It occurs when a massive galaxy between the object and observer warps spacetime so strongly that the background object’s light is bent and magnified, producing multiple images, arcs or rings. By magnifying distant sources, it lets astronomers reconstruct the object and study it in depth.

AI-assisted math advance highlights gap between finding answers and judging their significance

AI models are good at finding answers to mathematical puzzles but are not as good at research judgment, according to theoretical computer scientists.

The researchers, co-led by Princeton’s Pravesh Kothari, documented their use of AI in a case study shared on arXiv, outlining the ways in which AI tools excelled and fell short in reaching an important solution to an open mathematical problem.

AI is excellent at finding answers, they argue, because the models are trained on published papers, textbooks and solved problems. But, as of August 2026, these models were not as good at the process of recognizing the value of a theoretical result over numerical evidence or determining the significance, generality or downstream use of a result, according to the authors. This is because training data rarely includes information on the long process of mathematical discovery, which includes failed approaches, unlikely results and changes in strategy.

Rare particle decays hint at deviations from Standard Model predictions

Studying rare processes is part and parcel of particle physics, providing stringent tests of the Standard Model. At the recent International Conference on High Energy Physics (ICHEP), the CMS and LHCb collaborations presented their latest research into one such process: the transformation of a beauty quark into a strange quark, a muon and the oppositely charged antimuon (b→sμ⁺μ⁻).

Flavor transitions—when one type of quark turns into another—can only take place via the weak interaction, one of the four fundamental forces. Quantum physics describes how other particles, not directly involved in a particular transition, can influence how that transition takes place. If a process happens differently than is predicted by the influence of the particles we know about, which make up the Standard Model, this difference could point to the existence of as-yet-undiscovered particles and interactions.

Some of these transitions are extremely rare, which makes them very sensitive to any potential contributions from particles that may exist beyond the Standard Model. The b→sμ⁺μ⁻ transition is one such process, only occurring approximately once in every 10 million decays of particles containing beauty quarks, known as beauty particles. Over the past decade, signs have begun to appear that this transition may not occur as expected.

Quantum network test checks entanglement without having to trust detectors

Quantum networks are systems that connect different users or devices using principles of quantum mechanics. In the future, these networks could help improve the security of communications.

For entanglement-based quantum networks to work, however, researchers need to be able to verify that particles are entangled and assess the extent of their entanglement. Entanglement is a quantum mechanical effect in which the shared states of distant particles cannot be described as separate or independent.

Researchers at Shandong University recently introduced a new approach for verifying entanglement in a quantum network that does not require trusting the equipment used to analyze and detect photons (i.e., light particles), as most previously developed strategies do.