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📈 Аналитический обзор Telegram-канала Daily Science to all
Канал Daily Science to all (@sciencetoall) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 10 995 подписчиков, занимая 10 784 место в категории Технологии и приложения и 18 066 место в регионе Китай.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 10 995 подписчиков.
Согласно последним данным от 05 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -39, а за последние 24 часа — -8, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 4.11%. В первые 24 часа после публикации контент обычно набирает 1.54% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 452 просмотров. В течение первых суток публикация набирает 169 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 0.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как scientist, researcher, discovery, matter, plasma.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“5 newZ per day”
Благодаря высокой частоте обновлений (последние данные получены 06 октября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
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Посты канала
| 2 | Нет текста... | 254 |
| 3 | Нет текста... | 281 |
| 4 | 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 | 320 |
| 5 | ⚡️ 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 | 281 |
| 6 | Нет текста... | 347 |
| 7 |  🩻 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 | 523 |
| 8 | Нет текста... | 499 |
| 9 | 🪐 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 | 446 |
| 10 | 🧬 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 | 378 |
| 11 |  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 | 483 |
| 12 | ⚛️ 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 | 504 |
| 13 | 🧠 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 | 276 |
| 14 | 🧠 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 | 352 |
| 15 |  The brain of a dead male fruit fly was connected to a ROBOT — giving the fly a new body and allowing it to move freely through our world again.
The fly’s neural activity is translated into commands for motors that control the robot’s legs.
So, in a sense, the fly is… alive again.
“Black Mirror” was a documentary.
@science | 694 |
| 16 | 🧬 Life Uses 4 DNA Letters. Scientists Just Made 8 Work.
Every known organism on Earth writes its genetic instructions using the same four DNA letters: A, T, C and G.
Scientists have now shown that one of biology’s most fundamental molecular machines can read an alphabet containing eight.
Researchers tested E. coli RNA polymerase — the enzyme that reads DNA and turns its information into RNA — with synthetic DNA containing four additional chemical letters known as P, Z, B and S. Remarkably, the enzyme successfully recognized and transcribed the artificial base pairs using much of the same molecular machinery it employs for natural DNA.
Using cryo-electron microscopy at resolutions down to about 2.4 ångströms, the team could watch how the synthetic letters fit inside the polymerase. The artificial pairs adopted almost the same geometry as ordinary Watson–Crick DNA pairs, allowing the enzyme’s catalytic machinery to close around them and continue transcription. Researchers also engineered a modified version of one synthetic letter to reduce copying errors.
The implications are potentially enormous. A larger genetic alphabet could eventually produce RNA molecules with chemical capabilities unavailable to natural biology and might help scientists design new diagnostics, drugs and engineered biological systems. Expanded genetic alphabets have already been used experimentally to create molecules that recognize cancer cells.
But there is an important boundary: scientists have not created an eight-letter living organism here. The experiment demonstrates transcription by bacterial RNA polymerase; reliably replicating a full eight-letter genome and translating that expanded information into proteins inside living cells remain much harder problems.
For four billion years, life on Earth has been writing with four letters.
Apparently, biology’s machinery can read a bigger alphabet than evolution ever gave it.
#Genetics #DNA #SyntheticBiology #Biotechnology #RNA #Science
Primary paper — Nature Communications | 770 |
| 17 |  +1🌍 The US Was the Only Country to Vote Against the UN's New World Map
On Friday, the UN voted to adopt the Equal Earth map projection. 164 member states voted in favour of the resolution to change the map, 6 abstained — and only the United States voted against.
⚡️ The breakthrough in a nutshell:
The Equal Earth projection aims to "provide a fair representation of the real sizes of the world's regions, in particular Africa."
🔬 Key findings:
• The world — including Google Maps — still mostly uses the Mercator projection, created in 1569.
• Mercator is great for navigation: north–south lines keep constant true bearings relative to the equator. But it distorts country sizes: regions farther from the equator look disproportionately bigger than those closer to it.
• On a Mercator map, Greenland looks about the size of Africa. In reality, Africa is roughly 14 times larger than Greenland. Equal Earth is designed to fix that.
💼 Why it matters:
Critics of the most popular projection have long noted it isn't abandoned partly because it "enlarges and centres" Europe and North America. The UN resolution states that "the Mercator projection, due to its distortion, perpetuates an unbalanced representation of the world."
The first image shows the Equal Earth projection; the second shows the Mercator projection.
#Maps #Cartography #Geography #UN #Science
@science | 626 |
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