Science in telegram
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Science that matters: AI, space, biotech, physics, future tech — explained sharply
Показати більше📈 Аналітичний огляд Telegram-каналу Science in telegram
Канал Science in telegram (@science) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 120 310 підписників, посідаючи 104 місце в категорії Факти та 178 місце у регіоні США.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 120 310 підписників.
За останніми даними від 14 вересня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -532, а за останні 24 години на 17, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 4.66%. Протягом перших 24 годин після публікації контент зазвичай збирає 2.21% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 5 603 переглядів. Протягом першої доби публікація в середньому набирає 2 660 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 52.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як medicine, cell, researcher, scientist, u.s.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Science that matters: AI, space, biotech, physics, future tech — explained sharply”
Завдяки високій частоті оновлень (останні дані отримано 15 вересня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Факти.
120 310
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| 15 вересня | +12 | |||
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Дописи каналу
| 2 | Немає тексту... | 1 568 |
| 3 |  +2Немає тексту... | 1 563 |
| 4 | Немає тексту... | 2 497 |
| 5 |  Немає тексту... | 3 733 |
| 6 | Немає тексту... | 2 654 |
| 7 | 🌌 Astronomers Found 84 Cosmic Objects We Somehow Missed for Decades
They were already sitting in NASA’s data.
We simply weren’t looking at the right kind of light.
Astronomers mining observations from the Chandra X-ray Observatory have uncovered 84 mysterious objects across six nearby galaxies, including Andromeda and the Pinwheel Galaxy. They appear to belong to a previously unrecognized population researchers are calling hypersoft X-ray sources.
The strange part is their spectrum.
Most bright X-ray binaries radiate strongly above 0.3 keV. These objects do almost the opposite: they appear primarily below 0.3 keV, right near the boundary between X-rays and extreme ultraviolet light. That makes them exceptionally difficult to see, because this radiation is easily absorbed by gas between the stars — and because standard astronomical surveys were not optimized to search this faint corner of the spectrum.
Some of the objects may contain white dwarfs, neutron stars or black holes feeding on companion stars. Their true energy output could be enormous, with much of it emerging as invisible extreme-ultraviolet radiation.
That matters for two big reasons.
Such systems could provide a previously hidden source of radiation capable of ionizing gas throughout galaxies. And some may be accreting white dwarfs — systems astronomers suspect can eventually become Type Ia supernovae, the stellar explosions we use as cosmic distance markers.
Researchers do not yet know exactly what these objects are. “Hypersoft source” currently describes what astronomers observe, rather than one confirmed type of star system.
But the discovery carries a wonderful scientific lesson:
Sometimes the Universe does not need a new telescope to reveal something new.
Sometimes you just need to ask an old telescope a question nobody asked before.
#Astronomy #Space #Chandra #Xrays #BlackHoles #Supernovae #NASA #Science
https://www.nature.com/articles/s41550-026-02959-7 | 2 681 |
| 8 | Немає тексту... | 3 182 |
| 9 | 🌌 A Detector One Mile Underground May Have Seen Dark Matter
For decades, dark matter has been one of physics’ strangest certainties.
We can see its gravity shaping galaxies and the large-scale Universe — yet no one has ever directly detected the particle responsible for it.
Now the LUX-ZEPLIN experiment, buried nearly a mile underground in South Dakota, has recorded one unusually difficult-to-explain event.
LZ contains about 10 tonnes of ultrapure liquid xenon. Researchers watch for a dark-matter particle hitting a xenon nucleus and making it recoil. In 220 days of previously collected data, they found one event depositing about 248 keV of recoil energy — far more energetic than the simplest WIMP models normally predict.
The team spent months trying to explain it as radioactive contamination, neutrons or another known background process.
So far, none fits particularly well.
Under their background model, the result reaches 2.6 sigma, corresponding to roughly a 0.5% probability of obtaining such an event from known backgrounds. Particle physicists normally demand 5 sigma before claiming a discovery. And this entire result rests on exactly one event.
If it really was dark matter, the responsible particle would probably be unusually heavy — at least around 200 times the mass of a proton — and its interaction with ordinary matter would be more complicated than the simplest WIMP scenario.
The good news is that LZ is still collecting data.
If similar events begin appearing, the statistical significance should grow.
If they do not, today’s mysterious flash will probably become another extremely interesting piece of background noise.
For now, after decades of searching, dark matter may have knocked once.
Scientists are waiting to see whether it knocks again.
Status: preliminary candidate event; not a confirmed detection of dark matter.
#DarkMatter #Physics #Cosmology #ParticlePhysics #LUXZEPLIN #WIMP #Science
https://newscenter.lbl.gov/2026/09/01/lz-sees-surprising-result-in-search-for-dark-matter/ | 3 049 |
| 10 | Немає тексту... | 3 696 |
| 11 | ⚛️ A Quantum Operation That Took Thousands of Cycles May Now Take Just One
One of quantum computing’s biggest enemies is time.
Quantum states are extraordinarily fragile. The longer a computation takes, the more opportunities there are for noise to destroy the information before the answer arrives.
Researchers at Chalmers University of Technology have now developed a method that could make some advanced quantum operations more than 1,000 times faster.
The work focuses on bosonic quantum codes — a promising approach in which quantum information is stored in oscillating electromagnetic fields rather than simply in individual two-level qubits. Existing Floquet-control techniques can require thousands of repeated driving cycles to prepare and manipulate these protected states.
The new approach uses what the team calls quantum lattice gates to perform the same class of transformations within a single driving period. In their calculations, the researchers could prepare several important bosonic code states and execute logical quantum gates with high fidelity.
Why does 1,000× matter?
A quantum state has a limited lifetime before decoherence begins to erase it. If the useful operation becomes dramatically shorter, there is simply less time for errors to accumulate — potentially making fault-tolerant quantum computing easier to achieve.
But there is an important distinction: the researchers have developed and numerically demonstrated the method; they have not yet shown a thousand-fold acceleration on a physical quantum computer. The next challenge is translating the control scheme into hardware.
Quantum computing is usually presented as a race for more qubits.
This result suggests another route may be just as important:
make every quantum operation finish before the quantum state has time to fall apart.
#QuantumComputing #QuantumPhysics #Qubits #Physics #Technology #Science
https://journals.aps.org/prl/abstract/10.1103/tnb8-3m8m | 659 |
| 12 | Немає тексту... | 4 227 |
| 13 | Немає тексту... | 4 222 |
| 14 | 🧬 Scientists Just Watched Two DNA Molecules “Zip” Together
DNA has a basic physics problem.
Every DNA molecule carries a negative electrical charge. Put two of them next to each other, and they should repel.
Yet inside cells, DNA molecules somehow come close enough to recognize matching regions — an interaction relevant to genome organization, recombination and gene regulation.
Now researchers from the Universities of York and Sheffield have directly imaged how this may happen.
Using high-resolution atomic force microscopy, they observed two DNA double helices aligning with remarkable precision, with their grooves matching groove-to-groove. Atom-by-atom simulations suggest the trick comes from positively charged metal ions such as magnesium, calcium and nickel: the ions settle into DNA’s grooves and form tiny electrostatic bridges between the two helices.
The interaction is not completely random. Certain DNA sequences form stronger contacts than others, creating potential “pairing hotspots.” In some simulations, the ion bridges propagate along the molecules, producing something that looks remarkably like a molecular zipper.
The idea that DNA helices could align this way has existed for around two decades. What was missing was direct structural evidence.
Now we can actually see it.
There is an important caveat: these experiments used short DNA fragments under controlled laboratory conditions. Researchers have not shown that this exact mechanism alone explains how long chromosomes find matching sequences inside living cells.
Still, it reveals something surprisingly elegant:
DNA may recognize DNA not only through the information written in its bases —
but through the physical shape of the molecule itself.
#DNA #Genetics #MolecularBiology #Biophysics #Genome #Science
https://academic.oup.com/nar/article/54/16/gkag817/8769959 | 4 046 |
| 15 | Немає тексту... | 4 081 |
| 16 | Scientists Just Found the Missing Denisovans of Southern China
For years, genetics has told us something strange.
People living today in Southeast Asia and Oceania carry substantial amounts of Denisovan DNA — yet confirmed Denisovan fossils have been extraordinarily rare, and a huge geographic gap remained across southwestern China.
Now that gap has started to close.
Researchers examined more than 60,000 bone fragments from Bianfu Cave in China’s Yunnan–Guizhou Plateau. Most were too fragmented to identify by shape, so the team analyzed the ancient proteins preserved inside them. The result: three bone fragments and two teeth were molecularly identified as Denisovan, dating to roughly 167,000–134,000 years ago.
Among them is something particularly valuable: part of a radius — a forearm bone. Until now, scientists had almost no securely identified Denisovan postcranial remains, making it extremely difficult to reconstruct what these mysterious humans actually looked like below the skull.
The cave is now the richest confirmed Denisovan fossil site outside the original Denisova Cave in Siberia. Its location is also tantalizing: southwestern China lies on a natural corridor connecting East Asia, the Tibetan Plateau, South Asia and Southeast Asia — precisely the region through which Denisovan populations may have spread before interbreeding with ancestors of people alive today.
Denisovans were discovered not from a skull, but from DNA in a tiny finger bone.
Sixteen years later, we are still assembling an entire human population almost one fragment at a time.
And proteins are now finding fossils that bones alone could not reveal.
#Denisovans #HumanEvolution #Genetics #Archaeology #Anthropology #AncientDNA #Science | 4 009 |
| 17 | Немає тексту... | 4 761 |
| 18 | Немає тексту... | 4 395 |
| 19 | 🧬 Google DeepMind Just Precomputed 9 Billion Possible Human DNA Mutations
This may be one of DeepMind’s most ambitious biology releases since AlphaFold.
AlphaGenome Atlas contains AI predictions for the molecular effects of essentially every possible single-letter substitution in the human genome — around 9 billion variants.
The resulting dataset is about 1 petabyte, more than 30 times larger than the AlphaFold Database.
Why does this matter?
Only around 2% of our genome directly encodes proteins. Much of the remaining 98% regulates when, where and how strongly genes are switched on — and contains huge numbers of variants associated with human traits and disease.
AlphaGenome predicts how mutations may alter processes including gene expression, RNA splicing, chromatin accessibility and regulatory activity. DeepMind then combines these predictions with AlphaMissense into a single AlphaGenome Variant Impact — AVI — score, allowing researchers to rapidly rank variants across both coding and non-coding DNA.
In an analysis of whole-genome data from more than 54,000 UK Biobank participants, the approach uncovered 22% more associations involving rare non-coding variants that had previously been buried in statistical noise.
And there is another important shift happening alongside it.
DeepMind has released Science Skills — an open collection of agent tools connecting AI workflows to resources including AlphaGenome, AlphaFold DB, UniProt, ClinVar and dozens of other scientific databases.
This does not turn an AI agent into a doctor or make consumer DNA tests clinically diagnostic.
But it does move genomics toward something fundamentally new:
A human genome is becoming a dataset an AI agent can systematically interrogate, prioritize and explain.
We sequenced the human genome 25 years ago.
Now we are starting to make it searchable.
#AlphaGenome #DeepMind #Genetics #AI #Bioinformatics #Biotechnology #Science
Atlas:
https://alphagenome.google/atlas
Science Skills:
https://github.com/google-deepmind/science-skills | 4 805 |
| 20 | 🤖 Robots Are Now Building Robots
China’s XPeng has switched on a new production line for its humanoid robot IRON — and more than 80% of the line’s core manufacturing processes are automated.
The first production-line IRON completed assembly and then walked off the line by itself.
XPeng describes the facility as the world’s first automated production line for advanced general-purpose humanoid robots. The important nuance: this is not yet a completely human-free “self-replicating robot factory.” But it is a serious step from handcrafted prototypes toward industrial-scale humanoid production.
And IRON is not exactly a conventional industrial robot.
Its body has human-like proportions, flexible skin, highly articulated hands and movements realistic enough that, during XPeng’s 2025 AI Day, some viewers suspected there might actually be a person inside. CEO He Xiaopeng responded in the most convincing possible way: he cut open the robot’s leg on stage to reveal the machinery underneath.
XPeng plans to begin mass production before the end of 2026, with commercial deliveries expected in China and overseas in 2027.
For decades, factories used robots to manufacture cars.
Now a car company has built a factory where robots manufacture humanoid robots.
The recursion has officially begun.
#Robotics #AI #XPeng #HumanoidRobots #China #PhysicalAI #Technology
https://www.xpeng.com/news/01a080371029a057bc8e8a02a2c6012b | 4 589 |
