Daily Science to all

Meanwhile, something interesting is happening on Telegram: @gadget is officially up for auction. Yes — the actual @gadget username. Telegram usernames can be turned into blockchain-based collectibles and traded through Fragment. Whoever wins the auction gets control of the handle and can assign it to a Telegram account, channel, group or bot. And @gadget is exactly the kind of digital property that could be valuable: short, memorable, universally understandable and sitting right in the middle of the global tech industry. It’s a strange new category of internet real estate — not a domain name, not quite an NFT, but a piece of identity infrastructure inside a platform used by more than a billion people. Let’s see what the market thinks @gadget is worth. https://fragment.com/username/gadget

⚡️ GPT-6 Astra deciphered a letter to a Napoleonic marshal that nobody had been able to read for 217 years Researcher Carter Church used GPT-6 Astra to decipher an encrypted letter to Marshal Auguste de Marmont, one of Napoleon's generals. The letter is dated March 1809, and the model took about six hours to do the whole job. The first step was simply to extract readable text from a poor scan of the manuscript. Astra recognised 1,300 cipher characters, among which there turned out to be 155 distinct signs. The model then found a published partial key by French cryptology historian Daniel Tant: 33 letters covering roughly 435 characters. For the remaining signs it wrote a simulated-annealing solver, and then checked the result against historical correspondence and corrected the document's date. Inside was a military briefing from Eugène de Beauharnais's headquarters: troop positions and Austrian movements on the eve of Austria's invasion in April 1809. Marmont is told not to fear "a few detachments or a gathering of rabble." It also turned up the ending of a sentence that breaks off in Napoleon's memoirs, published in 1865. The solution was reviewed by Satoshi Tomokiyo, who runs the historical ciphers site Cryptiana, and the cipher is now listed there as solved. https://runtimewire.com/article/gpt-6-astra-marmont-cipher-carter-church https://x.com/Machinelearrn/status/2105593763582107849

🩻 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

🪐 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

🧬 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