Humanity continues to complicate its own future.
Fruit flies have been loaded into drones and tiny vehicles.
@science

🧠 Scientists Replaced Most of a Mouse’s Cortex With Human Brain Tissue

This sounds like science fiction, but the experiment is real.

Stanford researchers genetically engineered mice so that most of their cerebral cortex and hippocampus never developed. The adult animals were left with only about 2% of the normal amount of corresponding cortical tissue.

Then, shortly after birth, scientists implanted tiny human cortical organoids — brain-like structures grown from human stem cells — into the empty space.

Three months later, more than 90% of the cortical tissue by volume was human-derived.

And it did not simply sit there.

Human neurons became integrated into the mouse nervous system, developed organized electrical activity and sent long-range projections — some extending as far as the spinal cord. The mice retained broadly normal movement, although researchers found specific differences in coordination and spontaneous behavior.

Then came an unexpected discovery.

Inside the transplanted human tissue, researchers found cells resembling von Economo neurons — extremely rare, large neurons associated with brain regions involved in social awareness and decision-making. They occur in humans, great apes, elephants, dolphins and whales, but scientists had never previously succeeded in generating them in laboratory brain cultures.

The team also demonstrated why the model could matter medically. When the animals experienced several hours of reduced oxygen, the human cortical tissue was severely damaged while comparable mouse tissue was largely spared — potentially giving researchers a living model for studying why the developing human brain is particularly vulnerable to oxygen deprivation.

An important distinction: these are not mice with human intelligence or a human brain. The transplanted tissue remained developmentally immature, and the experiment provides no evidence of human-like cognition or consciousness. It is a new animal model for studying human neural development and disease.

But the boundary scientists have crossed is remarkable.

We can now grow substantial amounts of developing human neural tissue not just in a dish —

but inside a living brain, connected to a living nervous system.

Where should the ethical boundary for experiments like this be?

#Neuroscience #Brain #Organoids #StemCells #Biotechnology #Stanford #Science

https://www.nature.com/articles/s41586-026-11032-2

⚛️ Scientists Found Quantum Entanglement Inside Higgs Boson Decays

Einstein famously disliked quantum entanglement enough to call it “spooky action at a distance.”

Now physicists have found strong evidence that the same bizarre quantum connection survives inside some of the most violent particle collisions humans can create.

Using the ATLAS Experiment detector at the Large Hadron Collider, researchers studied Higgs bosons decaying into pairs of Z bosons — massive particles that exist for only a tiny fraction of a second.

The question was simple:

Are the quantum states of those two particles independent — or entangled?

The Z bosons disappear far too quickly to measure directly. Instead, researchers reconstructed their spin states from the directions of the electrons and muons produced when they decayed.

The resulting correlations strongly favored quantum entanglement. A statistical analysis rejected a separable, non-entangled description at 4.7 sigma — strong evidence, although just below particle physics’ conventional 5-sigma discovery threshold.

There is another unusual detail.

A Z boson has three possible spin projections. So instead of the familiar two-state qubits used in quantum computing, the entangled Z bosons behave mathematically as qutrits — three-state quantum systems.

Entanglement itself is not new. Scientists have demonstrated it spectacularly with photons, atoms and other systems.

What is new is where it survived.

These Z bosons were created in proton collisions at energies of 13 and 13.6 TeV. They are enormously heavier and vastly shorter-lived than the particles used in traditional entanglement experiments. The result provides the first measurements of entanglement between pairs of Z bosons and strong evidence for entanglement between massive vector bosons at the electroweak scale.

Quantum mechanics, in other words, does not become less weird when you turn the energy up.

It just gets a much bigger laboratory.

#QuantumPhysics #HiggsBoson #CERN #LHC #QuantumEntanglement #Physics #Science

https://journals.aps.org/prl/abstract/10.1103/y1nh-1b82

In principle, once cameras everywhere capture enough high-resolution, overlapping views, ordinary photographs may start to feel obsolete.

Instead of saving a single flat image, those recordings could be used to reconstruct a navigable 3D representation of a scene — estimating the position, shape, depth, texture, and appearance of objects from multiple camera angles.

You could then return to a particular moment, move the virtual camera to almost any viewpoint, and generate a new image from that angle. Parts of the scene that were never directly visible to any camera wouldn’t be true recordings — AI would have to infer and reconstruct them from the surrounding visual information.

Author: joergkahlhoefer

#gaussian #splat #3D

🧬 An AI Just Made a Biological Discovery

Not summarized a paper.
Not predicted a protein structure.

Found something in nature that scientists apparently hadn’t noticed before.

Researchers gave Claude AI agents a relatively broad task: search enormous DNA databases for unusual reverse transcriptases — enzymes that copy RNA into DNA.

Then they mostly stepped aside.

About 950 AI agents spent 21 hours analyzing more than 200,000 enzymes, narrowing them to 3,500 candidate systems and eventually 20 especially interesting ones.

One agent noticed something strange.

Next to a reverse transcriptase gene in a bacteriophage — a virus that infects bacteria — it found a long, organized array of repeating DNA sequences.

The pattern looked oddly familiar.

It resembled the repeat architecture of CRISPR.

Claude investigated the sequence, measured the repeats, compared them with known systems and searched the scientific literature. It concluded that the combination appeared to represent a previously uncharacterized biological system.

Human scientists then took over in the laboratory.

Their initial experiments supported a key prediction: the mysterious DNA array is actually expressed into multiple short RNA molecules.

The researchers named the system ART — array-associated reverse transcriptases.

And this is where the story gets interesting.

CRISPR also contains arrays that generate short RNAs, which ultimately help make the system programmable. Several other recently discovered molecular systems with similar combinations of features can cut, copy or insert genetic material.

But an important warning: nobody yet knows what ART actually does.

This is an early preprint, not a peer-reviewed discovery, and there is currently no evidence that ART is a new gene-editing system. Experiments are still underway to determine its biological function.

The bigger story may therefore be Claude itself.

For decades, biological discovery depended partly on humans noticing something strange hidden inside enormous datasets.

Now we may have machines capable of doing the noticing.

What happens when thousands — or millions — of AI scientists start searching nature simultaneously?

#AI #Biology #CRISPR #Genetics #Biotechnology #Claude #Science

https://www.anthropic.com/news/claude-discovers-novel-enzyme-system

🪐 Astronomers Just Watched a Planet Being Built

We know surprisingly well how planets should form.

The problem is that the crucial parts of the process happen hundreds of light-years away, on scales so tiny that astronomers have mostly had to reconstruct them from simulations and indirect clues.

Now they have caught the process in action.

Using Atacama Large Millimeter/submillimeter Array, astronomers imaged gas moving around WISPIT 2b, a newborn planet about 430 light-years from Earth.

It is a monster in the making: roughly five times the mass of Jupiter, orbiting inside the disk of gas and dust from which its planetary system is still emerging.

And around the planet, the gas is doing something remarkable.

On one side of WISPIT 2b it is moving toward us; on the other, away from us.

Together, those motions reveal a swirl of gas around the growing planet — exactly the kind of interaction predicted by simulations of planet formation, but never directly observed around a known protoplanet before.

The system is unusually valuable because astronomers can now see essentially every major piece of the process at once:

the enormous protoplanetary disk, the planet itself, hydrogen emission showing that WISPIT 2b is still accreting material, the gap it has carved through the disk — and now the surrounding gas responding directly to the planet.

A second young planet, WISPIT 2c, is also reshaping the system by carving out a larger cavity. And at the center, astronomers recently discovered that there isn’t even one star.

There are two.

The scale of the observation is extraordinary.

At WISPIT 2’s distance, resolving a structure the size of Earth’s orbit around the Sun is roughly equivalent to reading a normal book from five kilometers away.

For decades, simulations have shown us beautiful animations of planets growing inside swirling disks.

Now nature has finally provided the footage.

We are beginning to watch solar systems assemble in real time.

#Space #Astronomy #Exoplanets #PlanetFormation #ALMA #WISPIT2 #Science

https://www.mpg.de/26990768/astronomers-produce-the-first-complete-picture-of-gas-planet-formation-in-action

🩻 AI that reads a CT scan in 3D—and explains its findings

NVIDIA, the NIH’s National Cancer Institute, and the University of Zurich have released NV-Reason-CT, an open model that analyzes full 3D CT volumes and generates reports with explanations.

⚙️ How it works
The model pairs the Qwen3.5-4B language model with Primus, a 3D visual encoder. Each scan becomes 13,824 visual tokens, passed to the language model without further compression. Three-dimensional positional encoding preserves spatial information, helping it distinguish, for example, a finding in the right kidney from one in the left.

📚 How it was trained
Training used 550,000 examples from 70,111 CT volumes. Supervised fine-tuning on radiologists’ analyses was followed by reinforcement learning, with rewards for correctly identifying abnormalities and following the required report structure.

📊 What the results show
On CT-RATE, NV-Reason-CT achieved an average precision of 0.614 across 18 abnormality categories, compared with 0.581 for VoxelFM and **0.398 for CT-CLIP**—without a separate classification head.

In a pilot study with radiologists, scan review and reporting time fell from 26.25 to 13.13 minutes: roughly half.

🧩 Part of a broader medical AI toolkit
NVIDIA’s open medical model family also includes:
• NV-Generate-CTMR — generates synthetic 3D CT and MRI volumes.
• NV-Segment-CTMR — segments organs and lesions.
• NV-Reason-CXR — analyzes chest X-rays.
• NV-Reason-CT — analyzes full 3D CT scans.

🔓 Weights and code are available under OpenMDW-1.1, alongside fine-tuning and reinforcement-learning examples and a web demo.

Promising early results for AI-assisted radiology—with the time savings demonstrated so far in a pilot study.

@science