🧬 AI-Designed Universal Coronavirus Vaccine Passes First Human Trial

Researchers at the University of Cambridge and their spinout DIOSynVax have just completed the first-ever human trial of a vaccine whose active ingredient was designed entirely by artificial intelligence. The results, published in the Journal of Infection, show the vaccine is safe, well-tolerated, and generates immune responses against not just one coronavirus — but an entire family of them.

Unlike traditional vaccines that chase after individual variants, this vaccine uses an AI-designed "super-antigen." Machine learning algorithms analysed genetic data from every known Sarbeco coronavirus — the family that includes SARS-CoV-2, SARS, and multiple bat coronaviruses with pandemic potential — and stitched together their shared molecular features into a single immune-training molecule. The result: one shot that teaches the body to recognise the whole family, including viruses that haven't jumped to humans yet.

The Phase 1 trial enrolled 39 healthy volunteers in Southampton and Cambridge. The vaccine was administered via a needle-free micro fluid jet — no syringes involved. Participants developed immune responses against SARS-CoV-2, the original SARS virus, and against bat coronaviruses flagged as future spillover risks. No significant side effects were reported.

— First AI-designed vaccine antigen ever tested in humans
— Single "super-antigen" targets the entire Sarbeco coronavirus family
— Needle-free delivery via micro fluid jet — no injections required
— Immune responses detected against SARS-CoV-2, SARS, and bat viruses with pandemic potential
— Same platform adaptable for Ebola, influenza, and other rapidly mutating viruses

"We've converted vaccine development from being reactive to being future-proof." — Professor Jonathan Heeney, Cambridge

Why it matters: By the time a vaccine is developed against a circulating strain, the virus has already evolved. This AI-driven approach builds immunity against entire virus families before the next outbreak begins. If it works across coronaviruses, Ebola, and influenza, it could fundamentally change how humanity prepares for pandemics.

📄 Journal of Infection: https://www.journalofinfection.com/article/S0163-4453(26)00084-8/fulltext
📖 ScienceDaily: https://www.sciencedaily.com/releases/2026/06/260605023357.htm

#UniversalVaccine #AIBiology #PandemicPreparedness #Coronavirus #FutureOfMedicine

🤖 A 100-billion-parameter AI model was just trained across random GPUs scattered around the globe — not in a billion-dollar datacenter. And it worked.

Macrocosmos, building on the Bittensor network, has demonstrated Orion-100B: a 100B-parameter language model trained across geographically distributed Nvidia A100 GPUs.

Their system, called IOTA, splits the model itself across many machines using 16 pipeline-parallel stages — unlike earlier decentralized approaches that often required each participant to host the full model.

The result: more than 30% model FLOP utilization and roughly 65% of the efficiency of a comparable datacenter setup.

The technical challenge was serious. Macrocosmos had to reduce massive inter-GPU traffic, handle unstable nodes, work with heterogeneous hardware, and keep the training process alive across a decentralized network. Their ResBM activation compression technique reportedly reduced traffic from around 150MB to 2.2MB per stage. The team says it ran more than 700 experiments before scaling from a 1.5B test model to 100B in about a month.

Nikolas Bush’s Take:

This story matters far beyond the technical achievement.

First, if this approach scales, it could change the economics of AI training. A 100B-parameter model trained on geographically distributed A100 GPUs at roughly 65% of comparable datacenter efficiency is not yet a replacement for hyperscaler infrastructure — but it is a serious signal. It suggests that large-scale AI training may not always require a single billion-dollar GPU cluster.

Second, the Bittensor layer is important. This is not just a distributed computing experiment; it is an incentive system. GPU owners can be rewarded for contributing compute, which creates the foundation for a market around idle hardware. In simple terms, this could become something like “Airbnb for AI training”: monetizing unused GPU capacity the way Airbnb monetized unused rooms.

Third, the uncomfortable part: decentralized AI training has often been dismissed by the mainstream AI community as impractical. Orion-100B does not prove that decentralized training will beat datacenters tomorrow. But it does prove that the idea deserves to be taken much more seriously.

The next phase — permissionless participation from consumer hardware — will be the real test. If that works, the AI infrastructure map could become much more distributed than many people expected.

Original report: https://macrocosmosai.substack.com/p/orion-100b-distributed-pretraining
Summary: https://www.tao.media/macrocosmos-unveils-orion-100b-a-100b-parameter-distributed-ai-training-run/

#AI #DecentralizedAI #Bittensor #LLM #DeepLearning @science

✨ Pterosaurs Shimmered in Iridescent Greens and Magentas — 120-Million-Year-Old Fossil Rewrites the Look of Earth's First Flying Vertebrates

For decades, paleoartists have imagined pterosaurs in vivid, colorful hues. Now, a stunning new fossil analysis suggests that at least one species really did shimmer with shifting iridescent colors, much like modern starlings and pigeons.

The discovery comes from a specimen of Sinopterus dongi, unearthed in northeastern China. Scanning electron microscopy revealed layered arrays of melanosomes within the creature's pycnofibers — structures that closely resemble those producing iridescence in modern bird feathers. Computer simulations predict deep greens and magentas that shifted with viewing angle.

The diversity and organization of melanosomes matches patterns seen only in warm-blooded birds and mammals, suggesting elevated metabolisms and sophisticated thermoregulation — traits long debated among paleontologists. The finding also hints that iridescent displays may have played a role in courtship rituals.

"This is one of the most intriguing and surprising fossil discoveries of the past few years." — Dr. Steve Brusatte, University of Edinburgh

📄 Original paper (bioRxiv) · Science News summary

#paleontology #pterosaurs #fossil #evolution #iridescence
#science

One of the biggest mysteries in astrophysics may be getting closer to an answer.

A new study published in Physical Review Letters suggests that the famous “Amaterasu particle” — one of the most energetic cosmic rays ever detected — may not have been a proton at all. Instead, it could have been an atomic nucleus heavier than iron.

Discovered in 2021 by the Telescope Array in Utah, the Amaterasu particle carried an astonishing 240 exa-electron volts (EeV) of energy. That’s roughly the same kinetic energy as a fast-moving tennis ball — compressed into a single atomic nucleus.

What puzzled scientists most was its apparent origin. The particle seemed to arrive from a vast cosmic void, a region of space with no obvious object capable of accelerating particles to such extreme energies.

Using detailed simulations, researchers found that ultraheavy nuclei may survive intergalactic journeys far better than protons. While lighter particles lose energy through interactions with background radiation, nuclei heavier than iron can retain much more of their original energy over cosmic distances.

If correct, the finding could help explain how particles like Amaterasu reach Earth from seemingly impossible locations.

Possible sources include:
• Collapsing massive stars
• Neutron star mergers
• Gamma-ray bursts

Future instruments such as AugerPrime and the proposed Global Cosmic Ray Observatory may reveal whether these ultraheavy nuclei are truly responsible for some of the most extreme particles ever observed.

Every ultrahigh-energy cosmic ray is a messenger from one of the universe’s most violent events. Understanding what these particles are made of may help us uncover where they come from — and how nature accelerates matter to energies far beyond anything humans can create.

What do you think is the most likely source of particles this extreme?

📄 Original paper · ScienceDaily summary

#CosmicRays #Astrophysics #ParticlePhysics #SpaceScience #NeutronStars

🗂 The @science archive now lives on the web

Every post from this channel — searchable, filtered, and mapped on an interactive timeline. One page, no apps, no logins. Thanks to AI and just 1 prompt.. crazy!


🔹 356 posts and counting — the full archive since 2024, auto-synced with the channel several times a day

🔹 Five frontiers: AI, Space, Biotech, Physics and FutureTech — filter by category or search any keyword across titles and summaries

🔹 An interactive timeline of scientific breakthroughs from 2012 to 2025 — from AlexNet to room-temperature superconductor claims, hover any dot for the story

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🔹 Built lightweight: a single page that loads in under a second, works on any phone

The archive grows automatically as new posts appear on the channel.

🔗 http://144.172.108.222/science/

🧪 Could Tiny Mineral Particles Have Helped Spark Life on Earth?

One of science's biggest unanswered questions is how life emerged from nonliving matter. A new hypothesis suggests that the answer may lie in something surprisingly small: mineral nanoparticles.

Prof. Yongdong Jin of Shenzhen University has proposed the "Nanozyme Hypothesis" — the idea that naturally occurring mineral nanoparticles may have acted as primitive catalysts on the early Earth, helping transform simple chemicals into increasingly complex organic molecules.

Billions of years ago, our planet was a vast chemical laboratory. Around volcanoes, hydrothermal vents, and hot springs, intense heat and pressure produced nanoparticles made of metals, metal oxides, and sulfides. According to the hypothesis, these particles behaved like enzyme-like catalysts, accelerating reactions that otherwise would have occurred far too slowly.

Jin describes this process as a form of "inorganic photosynthesis" — chemistry powered by minerals long before biological cells existed.

What makes the idea particularly interesting is that it may help bridge several competing origin-of-life models. Rather than choosing between an RNA world, metabolism-first, or lipid-first scenario, nanozymes could have provided the chemical platform that enabled all of them to emerge.

The proposed functions of nanozymes include:

• Catalyzing key chemical reactions
• Concentrating molecules on their surfaces
• Protecting fragile compounds from UV radiation
• Using light to promote specific reactions
• Converting environmental energy into chemically useful forms

Remarkably, mineral nanoparticles are still abundant on Earth today, and many are known to exhibit enzyme-like behavior. The paper also highlights gold nanoparticles as particularly efficient catalysts under certain prebiotic conditions.

If future experiments support this hypothesis, it could reshape the search for life beyond Earth. Worlds with volcanic activity, liquid water, and the right mineral chemistry might possess the same ingredients that once helped kick-start biology here.

Was life an extraordinarily rare accident — or a natural consequence of chemistry under the right conditions?

📄 Original paper (Research, Dec 2025) · ScienceDaily summary

#OriginOfLife #Nanozymes #Abiogenesis #Astrobiology #EarthScience #science

“Most people do not really want freedom, because freedom involves responsibility, and most people are frightened of responsibility.”

— Sigmund Freud

🧬 Ancient “Language Switches” Hidden in Human DNA — And Neanderthals Had Them Too

A new study from the University of Iowa suggests that a tiny set of ancient genetic regulators may have played an outsized role in shaping human language ability.

These sequences are called HAQERs — Human Ancestor Quickly Evolved Regions. They make up less than 0.1% of the genome, yet appear to have around 200 times more influence on language-related traits than other genomic regions.

The findings, published in Science Advances, push the biological roots of language much deeper into our evolutionary past.

Researchers analyzed genetic and language-development data from 350 Iowa children followed over decades, then used an evolutionary-stratified polygenic score to trace how different layers of our genome contributed to language ability across roughly 65 million years of evolutionary history.

HAQERs are not genes themselves. Think of them more like molecular “volume knobs”: regulatory switches that dial the activity of genes up or down, especially during brain development. Even FOXP2 — the famous gene long associated with speech and language — appears to interact with this regulatory network rather than acting as a single “language gene.”

The most intriguing part: these same regulatory regions were already present before modern humans and Neanderthals split. Some language-associated variants may even have been more common in Neanderthals than in us.

That does not prove Neanderthals spoke like modern humans. But combined with archaeological evidence of tool-making, symbolic behavior, and complex social life, it strengthens the case that sophisticated communication may have emerged long before Homo sapiens stood alone.

There is also an evolutionary tradeoff. HAQERs are linked to fetal brain development — but larger brains and bigger infant skulls would have made childbirth more dangerous before modern medicine. In other words, evolution may have hit a ceiling: better language “hardware” came with a serious survival cost.

Key takeaways:

• HAQERs occupy less than 0.1% of the genome
• They may have ~200× more influence on language-related traits than other genomic regions
• These regulatory switches predate the human-Neanderthal split
• FOXP2 appears to be part of a broader regulatory system, not a standalone “language gene”
• The evolution of language may have been constrained by the risks of childbirth

The next step is to separate inherited genetic effects from the language environment parents create for their children — a question that could eventually help us better understand language disorders.

If Neanderthals had part of the same biological toolkit for language, how close were they to truly speaking?

📄 https://doi.org/10.1126/sciadv.aed5260


#genetics #language #neanderthals #evolution #neurosciencex

🌌 Dark Energy Survives Its Latest Crisis

For a moment, cosmology had a real scare.

A 2025 study suggested that the universe’s accelerating expansion might be partly an illusion — not because dark energy disappeared, but because Type Ia supernovae, the “standard candles” used to measure cosmic distances, may change their brightness depending on the age of the stars that produce them.

If that were true, one of modern cosmology’s biggest discoveries would need a serious rethink.

Now, an international team including Nobel laureates Adam Riess and Brian Schmidt has pushed back hard. In a new paper in Monthly Notices of the Royal Astronomical Society, led by Dr. Phil Wiseman of the University of Southampton, the researchers argue that the evidence for cosmic acceleration remains robust.

The problem, they say, was not dark energy — it was the correction.

The 2025 analysis made two major mistakes. First, it treated the age of a host galaxy as if it were the age of the specific star system that later exploded as a supernova, exaggerating the age difference between nearby and distant supernovae by a factor of three to five. Second, it left out a standard correction for the mass of the host galaxy — something modern supernova cosmology already uses because galaxy environments affect observed brightness.

Once those effects are included, the dramatic claim largely disappears.

• The claimed ~5-billion-year age gap between nearby and distant supernovae was overstated
• After standard corrections, there is no significant brightness difference between young and old supernova environments
• Data from the Dark Energy Survey show no meaningful evolution of the host-mass effect
• Including the proposed bias shifts the dark-energy equation-of-state parameter by less than 0.01

That does not mean we understand dark energy. We still don’t.

It makes up roughly 68% of the universe’s mass-energy budget, yet we have no clear physical explanation for what it actually is. A cosmological constant? Vacuum energy? Something that changes over time? A sign that gravity itself is incomplete on cosmic scales?

The new result does not solve the mystery. It simply says the original signal — the accelerating expansion of the universe — is still standing.

And that matters. Because the next generation of sky surveys, including the Vera C. Rubin Observatory’s 10-year Legacy Survey of Space and Time, is designed to measure exactly this kind of cosmic acceleration with far greater precision.

So the crisis may be averted.

But the real question remains: what kind of invisible “something” can dominate the universe — and still refuse to show itself directly?

https://doi.org/10.1093/mnras/stag797



#DarkEnergy #Cosmology #Astrophysics #Supernova #Physics #science

🚨 The U.S. Government Just Forced Anthropic to Switch Off Fable 5 and Mythos 5

This may be the first real “game over” moment for the old AI deployment model.

On June 11, 2026, Anthropic received a U.S. government export-control directive citing national security authorities. The order required the company to suspend access to Claude Fable 5 and Claude Mythos 5 for any foreign national — not only outside the United States, but also inside the country.

That includes foreign-national employees of Anthropic itself.

To comply, Anthropic says it had to disable Fable 5 and Mythos 5 for all customers globally. Other Claude models remain available. For now.

The reason appears to be a claimed jailbreak method for Fable 5.

Anthropic reviewed the demonstration and argues that the method only identifies a small number of previously known, simple vulnerabilities — the kind of tasks already possible with other public frontier models. According to the company, it did not receive a single example of a jailbreak producing a genuinely harmful result.

And this is where the conflict becomes much bigger than Anthropic.

The real issue is the standard of proof.

If asking a model to read a codebase and identify bugs is enough to trigger a national-security shutdown, then almost every next-generation frontier model becomes politically vulnerable by default. Future models will not get weaker. They will get stronger. So the regulatory question is no longer theoretical.

Who gets access?
Who counts as trusted?
And which jurisdiction gets to decide?

This is a tectonic shift in AI regulation.

Until now, governments mostly relied on voluntary commitments, safety frameworks, evaluations and post-release pressure. Now we have something much more direct: a forced shutdown of a commercial frontier model after deployment.

If this precedent holds, any advanced AI release can be stopped by a government letter.

And the location of frontier AI development may become less about talent, compute or product — and more about citizenship, export law and political risk.

There is also a very awkward human side to this.

If access to leading AI systems starts being restricted by nationality or “U.S. person” status, the blast radius could reach some of the most important people in AI:

• Andrej Karpathy — recently joined Anthropic; publicly described as Slovak-Canadian
• Demis Hassabis — British co-founder and CEO of Google DeepMind
• Geoffrey Hinton — British-Canadian pioneer of deep learning
• Yoshua Bengio — Canadian AI researcher and safety advocate
• Ilya Sutskever — publicly described as Israeli-Canadian; co-founder of Safe Superintelligence
• Mustafa Suleyman — British CEO of Microsoft AI
• Aidan Gomez — British-Canadian co-founder and CEO of Cohere

The point is not that all of them are immediately blocked from anything. The point is that a citizenship-based access regime for frontier AI would create absurd edge cases almost instantly.

The U.S. could end up restricting the very people who built the field.

So no, this probably does not mean AI progress is over.

But it may mean the era of “just ship the model globally” is over.

Order a truckload of popcorn.

China is definitely watching.

#Anthropic #Fable5 #Mythos5 #AIRegulation #ExportControl #FrontierAI #AISafety #science

🦜 Parrots Don't Just Mimic — They Use Names Like Humans Do, a Massive Study Confirms

For decades, we've known parrots can mimic human speech with uncanny precision. But a new study suggests something far more remarkable: they may actually understand and use names the way humans do — assigning specific vocal labels to specific individuals, and using them flexibly in social situations.

Researchers from the University of Northern Colorado, the University of Pittsburgh, and the University of Vienna analyzed recordings and survey data from nearly 900 captive parrots through the ManyParrots project, a global research network studying parrot cognition.

Out of 413 audio clips submitted by parrot owners, 88 showed clear evidence of birds using names as labels for specific people or animals — not just mimicking sounds, but deploying them in context-appropriate ways.

The team found that parrots don't just categorize broadly ("that's a person"). They can zero in on one specific individual. Some birds even used names to refer to someone who wasn't physically present — a cognitive leap that requires holding an abstract representation of another being in mind.

At the same time, parrots showed their own quirky twists: some would say their own name simply to attract attention, a behavior humans rarely exhibit.

• Nearly half of 889 surveyed parrot owners reported their birds using names
• 88 of 413 audio clips showed parrots labeling specific people or animals
• Parrots can refer to individuals who aren't present — a sign of abstract thinking
• Some birds use their own name as an attention-getting call, unlike humans
• The ability spans multiple parrot species, not just famous talkers like African Greys

While dolphins use signature whistles and some primates have distinct alarm calls, no previous study had shown such a diverse group of animals producing and flexibly using proper names under human linguistic conventions. It challenges our assumptions about what makes human language unique — and suggests the cognitive building blocks of naming may be more widespread than we ever imagined.

If a parrot can hold an abstract name for someone who isn't even in the room, what else is going on in that feathered brain?

📄 Original paper (PLOS ONE) · SciTechDaily summary

#AnimalCognition #Parrots #Language #Biology #PLOSONE #science

⚡ Google TurboQuant Cracks the AI Memory Wall — And It's Not About Bigger Models

At ICLR 2026, Google Research introduced TurboQuant, a new two-stage compression method that can reduce transformer KV cache memory usage by 40–60% without retraining and with minimal impact on model quality.

The KV cache — which stores information about every token processed during a conversation or document — has become one of the biggest bottlenecks in modern LLM inference. As context windows expanded from thousands to millions of tokens, KV caches often began consuming more GPU memory than the model weights themselves.

TurboQuant tackles this problem directly. The first stage, called PolarQuant, rotates cached vectors into a representation that is more friendly to quantization. The second stage uses a quantized Johnson–Lindenstrauss projection to compress the remaining error signal into just one bit per dimension. Together, these techniques reduce KV cache storage requirements to roughly 3–4 bits per element.

The implications are significant. Lower memory consumption means more concurrent users per GPU, larger context windows, and lower inference costs without changing the underlying model. In a world where AI infrastructure spending is growing at an unprecedented pace, improvements in efficiency can be just as valuable as improvements in model capability.

Nikolas Bush Take

1. The industry is entering an efficiency era.

For the last several years, the default answer to better AI has been bigger models, larger datasets, and more compute. TurboQuant is part of a growing trend suggesting that algorithmic efficiency may deliver some of the largest gains going forward. A 50% reduction in memory requirements achieved through mathematics rather than billion-dollar infrastructure investments changes the economics of AI deployment.

2. Infrastructure is becoming the real battleground.

Model quality is increasingly converging among frontier AI labs. The next competitive advantage may come from serving those models faster, cheaper, and at larger scale. Techniques such as TurboQuant directly target one of the most expensive components of large-scale inference: memory. In that sense, this is not merely a research paper — it's an infrastructure play.

3. The most important signal is reproducibility.

Breakthroughs matter only if the broader ecosystem can adopt them. If TurboQuant proves effective across different model architectures and hardware environments, it could evolve into a standard optimization layer for inference stacks, much like FlashAttention became a standard component of modern training and inference pipelines.

Caveats

The reported 40–60% memory reduction comes from benchmarked experiments and may vary depending on model architecture, context length, and hardware configuration. Some social media claims of extreme compression ratios refer to edge-case theoretical scenarios rather than typical production deployments. And importantly, TurboQuant addresses inference efficiency — not the still-unsolved challenge of reducing training costs.

What Comes Next?

If efficiency-focused innovations continue delivering meaningful gains, 2026 may be remembered as the year the AI industry began shifting its attention from model size to resource efficiency. The next major breakthroughs may come not from adding more parameters, but from using existing compute far more intelligently.

📎 Google Research blog · Lanceum analysis · Weekly AI roundup

#TurboQuant #ICLR2026 #AIInfrastructure #LLMInference #EfficiencyOverScale #science

🐱 Oxford Physicists Just Made Schrödinger’s Cat Even Weirder

Schrödinger’s cat was never really about a cat. It was a way to show how strange quantum mechanics becomes when one object is treated as being in two states at once.

Now physicists at the University of Oxford have created a new family of “cat-like” quantum states — but with an extra twist: the two parts of the superposition are not ordinary, classical-looking wave packets. They are already deeply quantum objects.

In standard lab versions of Schrödinger-cat states, researchers usually combine coherent states — the closest thing quantum physics has to classical motion. The Oxford team went further. Using a single trapped strontium-88 ion, they built superpositions from squeezed, trisqueezed and quadsqueezed motional states: exotic states where quantum uncertainty is reshaped in unusual ways.

The setup is elegant. The ion’s internal electronic state acts like a qubit, while its motion behaves like a quantum harmonic oscillator — a system that can occupy many energy levels. By entangling these two parts and then performing a mid-circuit measurement, the team could “sculpt” the ion’s motion into highly programmable quantum superpositions.

Why is this interesting?

• The states are built from nonclassical components, not just classical-like wave packets
• Their size, orientation and separation can be tuned experimentally
• Wigner-function measurements showed interference and negativity — signatures of genuinely quantum behavior
• Some states displayed striking geometric patterns, including sixfold symmetry in a trisqueezed example
• At the same average energy, these states can be more “quantum-resourceful” than standard cat states or Fock states

This matters because future quantum computers may not rely only on simple qubits. Quantum oscillators can store information across many energy levels, opening a richer route toward bosonic quantum error correction — where information is encoded in oscillator states rather than many separate physical qubits.

It is still early-stage physics, not a ready-made quantum computer. But it gives researchers a new way to build, control and study quantum states that sit far beyond everyday intuition.

And it brings us back to the original question Schrödinger wanted to provoke:

Where does the quantum world end — and the classical world begin?

Source: https://doi.org/10.1103/k1xk-yt42

#QuantumPhysics #SchrodingersCat #QuantumComputing #Physics #Oxfordx #science