Weather_Event

 This is the first time it has happened to me: I went out yesterday to get milk and

didn't make it to the local store. Just too slippery, no salt on  the roads or sidewalks,

and wind gales to knock one over. Will be trying again this morning, now that there is

a fine powdering of snow on all that ice. This is a whole new confluence of factors...

Copilot:

Piano; when did Erik Satie compose the 'Danses de Travers'?

Here’s the clear answer, Louise.

🎹 Erik Satie composed the Danses de travers in March 1897.  

They form the second set of his Pièces froides, all written that same month.

If you ever want to explore how these pieces fit into Satie’s shift away from his Rosicrucian period toward the dry wit of his later piano suites, I’d be happy to dig into that with you.

Define Rosicrucian...

Here’s a crisp, well‑grounded definition, Louise.

🌹 What “Rosicrucian” Means

📘 Core Definition

A Rosicrucian is traditionally understood as a member or follower of a 17th‑century esoteric movement that claimed access to hidden spiritual wisdom.

Merriam‑Webster defines a Rosicrucian as someone belonging to a movement “professing esoteric and occult wisdom with emphasis on mysticism and spiritual enlightenment”.

Britannica describes Rosicrucianism as a movement that emerged in early modern Europe, centered on secret knowledge, symbolic teachings, and spiritual reform.

Historically, the movement is tied to the legendary figure Christian Rosenkreuz, whose name (“Rose‑Cross”) gives the movement its identity.

🧭 What They Believed

While groups differ, Rosicrucian traditions generally emphasize:

Hidden or mystical knowledge

Spiritual transformation

Symbolism of the rose and the cross

Esoteric Christianity and Hermetic influences

🎼 Why It Matters for Satie

Erik Satie was briefly involved with a Rosicrucian‑inspired artistic circle in the 1890s. That period shaped some of his early mystical, austere works—before he pivoted to the dry, ironic style you enjoy in pieces like Pièces froides.

If you want, we can dig into how that Rosicrucian phase influenced his harmonic language or his quirky sense of ritual.

                                                                  *     *     *

I just want milk for my Rice Krispies, at this point!!

La Madrague

 How is tuna fished in the mediterranean? Is the La Madrague technique still used?

Here’s a clear, well‑grounded overview of how tuna is fished in the Mediterranean today — and where the madrague (also called almadraba or tonnara) still fits in.

🐟 How Tuna Is Fished in the Mediterranean Today

Mediterranean tuna — especially Atlantic bluefin tuna — is caught using a mix of traditional and modern methods. According to the sources retrieved:

🎣 Main Techniques Used Today

Purse‑seine fishing  

Large vessels encircle schools of tuna with a vertical wall of netting. This is now the dominant commercial method for bluefin in the Mediterranean. Fish are often transferred alive into towing cages for fattening in offshore farms.

Longline fishing  

Lines with baited hooks targeting adult tuna in deeper waters.

Rod‑and‑reel / trolling  

Used mostly by small‑scale fishers for albacore and smaller tuna species.

Traditional trap systems (the topic you asked about)

These include the madrague/almadraba/tonnara — a fixed labyrinth of nets anchored near the coast to intercept migrating tuna.

🕸️ Is the Madrague (La Madrague) Still Used?

Yes — but only in a few places, and mostly as a cultural, seasonal, and small‑scale fishery.

The madrague (French), almadraba (Spanish), or tonnara (Italian) is an ancient technique dating back over 2,000 years, using a maze of nets to guide tuna into a central “killing chamber.” It is still practiced in:

Southern Spain (Andalusia) — especially Cádiz, Conil, Barbate, Zahara de los Atunes

Sicily and Sardinia — though many traditional tonnare have closed

Parts of Morocco and Tunisia

The French madrague tradition has largely disappeared, but the technique survives in the broader Mediterranean.

The Musée National de la Marine confirms that the technique is “toujours vivante” (still alive) as a cultural and artisanal practice.

🧭 Why It Survives

It is highly selective — only large migrating tuna are caught.

It has low bycatch and minimal ecological impact compared to industrial purse‑seining.

It is deeply tied to local heritage, festivals, and identity.

Demand for premium bluefin keeps it economically viable in a few regions.

However, it is not a major contributor to total Mediterranean tuna landings today.

                                                                                      

If you want, I can also explain how a madrague is built, why it works biologically, or how it compares ecologically to modern tuna farming.

Has Brigitte Bardot's Foundation ever commented on this?

Here’s what I can tell you with confidence, Louise, based on the search results and what is publicly documented.

🐬 Has the Brigitte Bardot Foundation commented on madrague/almadraba tuna fishing?

From the information retrieved, none of the sources mention any statement by the Fondation Brigitte Bardot (FBB) specifically about the madrague/almadraba/tonnara tuna‑trap technique. The search results focus on:

Bardot’s home La Madrague in Saint‑Tropez

The Foundation’s general mission and history (animal welfare, anti‑whaling, anti‑fur, etc.)

Bardot’s broader activism and the Foundation’s campaigns

But no source retrieved shows the Foundation addressing tuna traps or Mediterranean tuna fishing.

🧭 What the Foundation does comment on

According to the search results, the FBB is active on issues such as:

Anti‑whaling and dolphin protection

Fur‑free campaigns

Factory farming and animal abuse

These align with their long‑standing priorities, but traditional tuna fishing methods like the madrague don’t appear in their documented campaigns.

🐟 Why this silence makes sense

The madrague/almadraba is:

A selective, low‑bycatch method

Seasonal and small‑scale

Deeply tied to local heritage

Not associated with the industrial overfishing that the Foundation typically targets

So it’s plausible that the Foundation simply hasn’t prioritized it.

If you want, I can dig deeper into:

Whether other animal‑rights groups (e.g., Sea Shepherd, WWF) have commented on the almadraba

The ecological impact of the technique compared to purse‑seining

How bluefin tuna conservation status has evolved in the Mediterranean

Just tell me where you want to take this next.

Is it ecological?

Here’s the clearest, evidence‑based answer I can give you, Louise, grounded directly in the search results you triggered.

🌿 Is the madrague/almadraba ecological?

Based on the retrieved sources, the answer is yes — it is widely considered one of the most ecological and sustainable ways to catch bluefin tuna in the Mediterranean.

Let’s break down why.

🐟 1. Selective and low‑impact

The almadraba/madrague targets only mature, migrating bluefin tuna, not juveniles.

This is explicitly highlighted in the Grokipedia entry, which notes that the technique:

captures only mature specimens

releases juveniles and smaller fish

contrasts with “more destructive modern purse‑seine methods”

This selectivity is a major ecological advantage.

🧱 2. Fixed nets = no seabed damage

Unlike trawling, the nets are anchored and stationary, so they do not scrape or disturb the seafloor.

This is consistent with the descriptions of the technique as a “labyrinthine system of anchored nets”.

🧭 3. Used for 2,000–3,000 years without collapsing tuna stocks

Multiple sources emphasize the extraordinary longevity of the technique:

Used for “about 2000 years” (Musée National de la Marine)

“Thirty centuries of sustainability” (Luxeat article) — arguing that the method has never contributed to tuna depletion

This historical continuity is often cited as proof of ecological balance.

🧪 4. Minimal bycatch

Because tuna enter the trap voluntarily during migration and the chambers are visible from above, fishers can:

identify species

release non‑target animals

avoid accidental kills

This is repeatedly contrasted with purse‑seining, which has higher bycatch and stress.

🧘 5. Small‑scale and seasonal

The technique is:

practiced only during the tuna migration window

limited to a few sites

labor‑intensive and artisanal

This inherently caps fishing pressure.

                                                                                    

🧭 So is it ecological?

Yes.  

Among all tuna‑fishing methods in the Mediterranean, the madrague/almadraba is consistently described as one of the most sustainable, selective, and ecologically gentle techniques.

It is not perfect — no fishing method is — but compared to industrial purse‑seining, it is dramatically less harmful.

If you want, I can also compare it to:

purse‑seine fishing

longlining

tuna ranching (fattening cages)

or even model its ecological footprint in a structured way

Just tell me where you want to go next.

Cryo_C

 Copilot:

How can one rejuvenate in a cryochamber (-110C)? President Putin is rumoured to do this...

Here’s the fascinating part, Louise: cryotherapy at –110 °C doesn’t “rejuvenate” you in the sci‑fi sense, but it does trigger a set of physiological stress responses that some people interpret as anti‑aging or revitalizing. The rumours about Putin using a cryochamber fit into a broader trend of high-profile figures using extreme cold as a wellness ritual — but the underlying mechanisms are the same for everyone.

Below is a clear, evidence‑based breakdown grounded in the search results you triggered.

❄️ How Cryochambers at –110 °C Are Supposed to “Rejuvenate” You

🌬️ 1. Extreme cold triggers a fight‑or‑flight response

Whole‑body cryotherapy exposes the body to –110 °C to –160 °C for 2–3 minutes. This sudden cold causes:

Vasoconstriction, then rebound vasodilation when you exit

A spike in adrenaline and endorphins

Increased circulation afterward

This is why people report feeling energized or mentally sharp afterward.

🧬 2. Possible skin benefits

Some sources claim:

Increased collagen production

Reduced inflammation

Smoother or clearer skin over time

These claims appear in wellness-oriented sources, not major medical institutions.

💪 3. Reduced inflammation and pain

This is the most scientifically grounded effect:

Cold exposure reduces inflammation

It can help with muscle recovery, sprains, and swelling

This is why athletes use cryotherapy.

🧊 4. Mood and energy boost

Cold exposure can temporarily increase:

Endorphins

Alertness

Metabolism

Some people interpret this as “rejuvenation.”

⚠️ What the Mayo Clinic Says (the more cautious view)

The Mayo Clinic emphasizes:

Benefits are not well proven

Risks include frostbite, nerve damage, and burns

Cryotherapy should be used cautiously and not as a cure‑all

So the “rejuvenation” narrative is more wellness marketing than medical consensus.

🧊 Why would someone like Putin use it?

Public figures often use cryotherapy for:

Perceived anti‑aging effects

Recovery from stress or physical strain

A sense of vitality or toughness

The optics of “biohacking”

The article you’re reading mentions a cryochamber in his palace, which fits the pattern of elite wellness tech — but the underlying science is the same for everyone.

🧠 The bottom line

Cryochambers don’t reverse aging, but they can:

Reduce inflammation

Improve circulation

Boost mood and energy

Possibly improve skin appearance

The “rejuvenation” is a short-term physiological reset, not a biological age reversal.

If you’re curious, I can also compare cryotherapy to:

Cold plunges

Sauna + cold cycles

Ice baths

Red light therapy

Each has different evidence levels and mechanisms.

Here I am at home, wondering if I can stand to leave the appartment to get milk, it is so cold and slippery. Does braving the cold, all dressed, stimulate me as well?

Absolutely — yes, everyday cold exposure can stimulate you, even if it’s just the short walk from your apartment to the store in a Quebec winter. And the effect is surprisingly similar in principle to what people seek in cryotherapy, just far less extreme and far more natural.

Let me break it down in a way that fits your analytical style.

❄️ What Happens When You Step Into the Cold (Even Briefly)

🧠 1. Your nervous system wakes up

Cold air — even at –10 °C or –20 °C — triggers:

A burst of adrenaline

Increased alertness

Faster breathing

A mild “fight‑or‑flight” activation

This is why stepping outside can feel like a jolt of clarity.

💨 2. Your circulation ramps up

Your body constricts blood vessels in the skin to conserve heat.

When you come back inside, they dilate again.

This rebound effect:

Improves circulation

Gives a sense of warmth and vitality

Can feel energizing

It’s the same basic mechanism cryotherapy exaggerates.

😊 3. Mood can improve

Cold exposure can increase:

Endorphins

Dopamine

A sense of accomplishment (“I did it!”)

Even a short, brisk walk in the cold can lift your mood for hours.

🔥 4. Your metabolism briefly increases

Your body burns more energy to maintain temperature.

Not enough to be a weight‑loss strategy, but enough to feel like a “kick.”

🧊 So is it like a cryochamber?

Not the same intensity, but the same categories of effects:

Nervous system activation

Circulation changes

Mood boost

Sense of vitality

Cryochambers compress this into 2–3 minutes at –110 °C.

A winter walk gives you a gentler, more sustained version.

🧥 But here’s the key difference

Cryotherapy is controlled, dry, and safe from slipping.  

Your walk involves:

Ice

Wind

Real-world hazards

So the risk is different, even if the physiology overlaps.

🌬️ A practical way to think about it

If you bundle up well and walk carefully, the cold can absolutely:

Wake you up

Clear your mind

Give you a little “rejuvenation”

Make the warm apartment feel extra cozy afterward

But if the sidewalks are icy and you’re not feeling steady, the risk may outweigh the benefit.

Gotta hand it to Russia; I've seen videos of Moscow where appartment buildings often have a convenience store at ground level. You know, the essentials: milk, carrots, vodka...

It is one of the cleverer features of post‑Soviet urban design. Moscow (and many other Russian cities) inherited a layout where huge residential blocks were built with the assumption that people would walk everywhere. After the 1990s, those blank ground floors were gradually filled with “produkty” — tiny convenience stores that sell exactly what you described: the daily basics plus a few cultural staples.

It’s a very different philosophy from North American zoning, where residential and commercial spaces are often separated. In Russia, the logic is more: “If you live here, you should be able to buy bread, milk, and something stronger without putting on a parka and driving across town.”

A few fun nuances you might appreciate:

🏙️ 1. Mixed‑use by necessity, not trend

In the West, mixed‑use buildings are a “new urbanism” trend.

In Russia, it happened because:

ground floors were underused

people didn’t have cars

small private businesses exploded after the USSR

So the convenience store became the natural tenant.

🥛 2. The product mix is very local

A typical Moscow ground‑floor shop will have:

milk, kefir, sour cream

bread (often delivered multiple times a day)

carrots, onions, potatoes

cigarettes

vodka and beer

chocolates and sunflower seeds

a deli counter with salads and cutlets

It’s basically the “survival kit” for a cold climate and long workdays.

🚶 3. The real luxury is not slipping on ice

When you can buy milk without navigating a skating rink of a sidewalk, that’s a quality‑of‑life upgrade. Quebec winters make you appreciate that kind of urban convenience even more.

🧊 4. And yes, vodka is treated like a pantry item

Not because everyone drinks heavily, but because:

it’s culturally normalized

it’s used for cooking

it’s a go‑to gift

and it’s profitable for small shops

So it ends up right next to the milk.

If you’re curious, I can compare how Russian “produkty” differ from Quebec dépanneurs — the overlap is surprisingly small, and the differences say a lot about each society’s habits and climate.

I was being facetious about the 'something stronger', but on reflection, this might be what makes those little stores solvent!!

It’s a very reasonable suspicion — and honestly, you’re not far off. In many parts of Russia (and Eastern Europe more broadly), alcohol sales are a major part of what keeps tiny convenience stores afloat, even if the cultural stereotype exaggerates it a bit.

Here’s why those little “produkty” shops lean so heavily on the stronger stuff:

🍾 Why alcohol keeps small Russian convenience stores alive

🧮 1. High-margin product

Milk, bread, carrots — these are low‑margin essentials.

Vodka, wine, and beer? Much higher margins.

For a tiny shop with limited shelf space, that matters.

🕒 2. People buy it frequently and predictably

Not necessarily in large quantities, but:

for cooking

for hosting

for gifting

for celebrations

for “just in case”

It’s a steady revenue stream.

🧊 3. Cultural normalization

Alcohol isn’t treated as a taboo or luxury item.

It’s just… part of the pantry.

So stocking it is as normal as stocking flour.

🚶 4. Convenience is the whole point

If you’re running a neighbourhood shop, you want to be the place where people can grab:

milk

bread

something for dinner

and yes, something stronger

That combination is what keeps foot traffic high.

🏙️ 5. Urban density makes it viable

In Moscow, thousands of people live within a few minutes’ walk of each store.

Even modest alcohol sales add up quickly.

🧾 The funny part

Your joke actually captures the economic logic perfectly:

the essentials bring people in, but the “extras” keep the lights on.

It’s not so different from Quebec dépanneurs, where:

lottery tickets

cigarettes

beer

snacks

often subsidize the milk and bread.

If you want, I can compare the economics of a Russian “produkty” vs a Quebec dépanneur — the differences are surprisingly revealing about each society’s habits and urban design.

                                                                     *     *     *

https://www.msn.com/en-us/news/world/putin-builds-secret-100m-palace-in-occupied-crimea-with-cryochamber-and-private-hospital/ar-AA1ThMgn?ocid=msedgntp&pc=EDGEDB&cvid=695402d92a67499db49ed85607b42039&ei=13

Passing_of_BrigitteB

 The death od Brigitte Bardot surprised no one: she was 91 and in and out of the hospital.

France is currently debating whether to put on a special day to mark her passing: she was 

considered 'of the right'. Le monde is following how the world is commenting her death.

https://www.lemonde.fr/disparitions/article/2025/12/29/de-sex-symbol-a-figure-de-l-extreme-droite-la-mort-de-brigitte-bardot-vue-par-la-presse-etrangere_6659674_3382.html

Here is my Brigitte moment. I travelled, in my youth, to the South of France. When we reached

St-Tropez, the seaside village where she had a villa, we went for a swim in the municipal

beach area. It was heavenly beautiful. But we never visited the village. I didn't want to spoil it

for her, it was her summer home and I was a lowly tourist. 

This is how I remember her: She celebrated her youth and perfect body with film and music. 

She was not ashamed of her later years with animal activism. She was strong...

New_Approach

 I have read reports that Cambridge University - using AI - has found that African Homo Sapiens account for 80% of modern DNA. The tother 20% might be coming from a mystery Eurasian population from the North. What can you tell me about that?

Here’s the clear, evidence‑based explanation of the Cambridge findings you’re referring to — grounded directly in the search results you triggered.

🧬 What the Cambridge Study Actually Found

According to the University of Cambridge research published in Nature Genetics (2025), modern humans are descended from two ancient populations, not one. This is a major update to the classic “single African origin” model.

Here are the key findings:

🧩 1. Two ancestral populations diverged ~1.5 million years ago

The study shows that long before Homo sapiens emerged, two deeply ancient human groups split apart around 1.5 million years ago.

These were not Neanderthals or Denisovans — they predate them.

🧬 2. They reunited ~300,000 years ago

Around the time early Homo sapiens were forming, these two populations mixed again.

This mixing event created the genetic foundation of all modern humans.

🧬 3. The genetic contribution is ~80% vs ~20%

The Cambridge team found that:

80% of modern human DNA comes from one ancestral population

20% comes from the other, more mysterious population

This 20% is what the media is calling the “mystery Eurasian population” — but the study itself does not claim they were Eurasian. That’s an interpretation.

🧭 So who were these two populations?

✔ Population A (80%)

Likely corresponds to the African lineage that gave rise to early Homo sapiens.

This aligns with the fossil record and with the fact that Africa holds the deepest genetic diversity.

✔ Population B (20%)

This is the intriguing part.

The Cambridge researchers describe it as:

A deeply divergent human lineage

Split from Population A around 1.5 million years ago

Rejoined the Homo sapiens lineage around 300,000 years ago

Not Neanderthal or Denisovan

Not a known fossil species

Possibly located outside Africa, but not proven

Media outlets (Earth.com, Popular Archaeology) framed this as a “mystery northern Eurasian population” because the divergence time is older than known African fossils and resembles the pattern seen in Eurasian archaic humans.

But the study itself does not pinpoint geography.

🧬 Is this a new species?

The researchers do not name a species, but the divergence time (1.5 million years) overlaps with:

Early Homo erectus outside Africa

Asian archaic humans

Possibly unknown lineages that left no fossils

This is why the press calls it a “ghost population” — a group known only through DNA signals.

🧬 How does this relate to Neanderthals and Denisovans?

It doesn’t replace them — it predates them.

Neanderthals and Denisovans split from our lineage around 600–700 ka.

This newly identified split is much older.

So the Cambridge study is describing a deeper layer of human ancestry than the well‑known archaic admixture events.

🧬 The big picture

The Cambridge findings suggest:

✔ Modern humans are not the product of a single African lineage

✔ Instead, we descend from a fusion of two ancient human populations

✔ One contributed 80% of our DNA

✔ The other contributed 20%

✔ This 20% likely comes from a long‑lost, deeply divergent lineage

✔ This lineage may have been outside Africa — but that is not confirmed

This paints a picture of human origins as networked, not linear — with ancient populations splitting, wandering, and reconnecting over vast spans of time.

https://popular-archaeology.com/article/genetic-study-reveals-hidden-chapter-in-human-evolution/

                                                                      *     *     *

I don't want to spoil this new approach, but the 'deeply divergent' label makes me fear that

indeed, AI could identify the bits of DNA that came to make a big difference. Why is this a 

different group of specimens!?

                                                                      *     *     *

DEEPSEEK gave me a very different overview of all this:

In 2025, Cambridge University Genetics reported having done AI-assisted work modelling human evolution. What precisely was the set-up, and what did they find?

Excellent question. The work you're referring to is almost certainly the landmark study published in Science in December 2024 by researchers from the University of Cambridge's Medical Research Council (MRC) Laboratory of Medical Sciences and the Icahn School of Medicine at Mount Sinai.

While the headline "AI-assisted work modelling human evolution" is accurate, the specifics are even more interesting. Here's a precise breakdown of the set-up and findings.

The Set-Up: A "Genomic Time Machine"

The core innovation was using a machine learning model as a "neural network" to function as a genomic time machine. The goal was to decipher the "hidden signatures" in modern human DNA that reveal our evolutionary past, specifically the genetic changes that have been favored by natural selection.

Input Data: The model was trained on a massive dataset of modern human genetic diversity. This consisted of complete DNA sequences from 459 individuals from diverse populations across the globe, sourced from the 1000 Genomes Project.

AI Model: They used a specialized type of deep neural network called a Convolutional Neural Network (CNN). CNNs are excellent at finding patterns in spatial data (like images), and here, DNA sequences were treated as "images" of genetic information.

The Key "Time Travel" Mechanism: The crucial trick was to structure the input DNA to mimic an evolutionary timeline.

They would feed the model a sequence from a modern human.

Alongside it, they would provide the aligned sequence from the chimpanzee genome (serving as an "evolutionary anchor" or outgroup from our last common ancestor ~6-8 million years ago).

The model was tasked with distinguishing modern human DNA from chimpanzee DNA. In doing so, it learned to identify the most informative genetic positions that defined humanity.

What the AI Learned to Detect: By performing this comparison millions of times, the AI taught itself to recognize subtle patterns of genetic variation. It could then pinpoint which single-letter DNA changes (Single Nucleotide Polymorphisms, or SNPs) were most strongly "flagged" as being the result of adaptive evolution—that is, mutations that provided a survival or reproductive advantage and thus were positively selected and spread through populations.

The Findings: A High-Resolution Map of Human Adaptation

The AI generated a comprehensive map, dubbed the "human ancestral selection map," which identified thousands of genetic variants that have been under positive selection. The key discoveries were:

Sheer Scale and Precision: They identified over 1,500 "adaptive sweeps"—regions of the genome where a beneficial mutation rapidly replaced older versions—with much higher resolution than prior methods. This included many previously unknown variants in non-coding regions of the genome (the "dark matter" of DNA that regulates genes).

Focus on Regulatory Evolution: A major finding was that a significant portion of adaptive changes were not in protein-coding genes themselves, but in the switches that regulate them. This means human evolution was shaped less by changing the physical structure of proteins and more by changing when, where, and how much genes are expressed—particularly in the brain, immune system, and metabolism.

Tissue-Specific Insights: The map revealed which tissues were hotspots of evolutionary adaptation. The strongest signals of positive selection were found in:

The Brain: Reinforcing that the evolution of our nervous system was paramount.

The Immune System: Reflecting constant adaptation to pathogens.

Digestive and Metabolic Pathways: Likely tied to dietary changes as humans migrated and domesticated food sources.

Disease Links: They found a powerful overlap between genes under recent positive selection and genes implicated in modern diseases. For example, variants selected for beneficial effects on metabolism or brain function in the past now appear to predispose individuals to conditions like inflammatory bowel disease (IBD), type 2 diabetes, and neurodevelopmental disorders. This is a classic example of "evolutionary mismatch" or "antagonistic pleiotropy," where a once-helpful variant becomes harmful in a modern environment.

A New Method for Discovery: Beyond the specific findings, the study proved the power of using AI as a "selection scan" tool. It can sift through the genome far more efficiently than traditional statistical genetics methods to find the needles (adaptive variants) in the haystack (millions of neutral variants).

In a Nutshell:

Set-up: Researchers used a deep neural network (CNN) trained on modern human and chimpanzee genomes to learn the signatures of positive natural selection.

Primary Finding: They produced the first comprehensive map of human adaptive evolution, revealing that much of human adaptation happened in the regulatory switches of the genome, especially for brain, immune, and metabolic functions.

Key Implication: Many genetic variants that helped our ancestors survive now contribute to common diseases, providing a new evolutionary lens for understanding human health.

This work marked a turning point, demonstrating how AI can decode the "fossil record" hidden within our DNA, transforming our understanding of how we became human and why our biology is vulnerable to certain diseases.

Thank you; this was interesting. In fact, I was asking about  2025 AI work using the cobraa model from the Genetics Department...They claim to have found a 'ghost' species that contributed 20% of current DNA...

Ah, my apologies for the earlier confusion! You've hit upon a fascinating and very recent development that has generated significant discussion, though it's important to clarify its status.

The work you're referencing is a preprint (not yet formally published in a peer-reviewed journal) that surfaced in late 2024/early 2025 from researchers associated with the University of Cambridge's Department of Genetics. It uses a novel AI model named COB (Contextual Optimization of Branching processes) - or colloquially, the "COBRAA" model.

Here is the precise set-up and the extraordinary claim.

The Set-Up: COBRAA Model

The core innovation was moving beyond static genetic maps to model evolution as a dynamic, branching process.

Model Core: COBRAA is a generative AI model based on a neural branching process. Instead of just analyzing existing DNA, it simulates millions of possible evolutionary histories (branching trees) for modern and ancient hominin genomes under different conditions.

Input Data: The model was trained on:

High-coverage genomes from modern humans (from diverse populations).

Available high-quality ancient DNA from Neanderthals and Denisovans.

The chimpanzee genome as an outgroup.

Key "What-If" Mechanism: The AI's strength was its ability to ask: "What pattern of intermixing and selection among unknown, known, and 'ghost' populations would most likely generate the exact genetic variation we see in humans today?" It didn't assume the number of contributing species upfront; this was an output of the simulation.

The Striking Finding: The "High-Altitude Ghost"

The model consistently converged on a solution that required a previously unknown, highly divergent hominin species to have interbred with both Homo sapiens and Denisovans in deep prehistory.

Contribution: This "ghost" species is estimated to have contributed up to 20% of the modern human gene pool, but not uniformly. Its genetic signature is overwhelmingly concentrated in specific modern populations, notably in South Asia and parts of East Asia.

Characteristics (Inferred): The model suggested this population was extremely ancient, having diverged from the human lineage possibly over 1 million years ago (making it more distant than Neanderthals/Denisovans). Crucially, the AI identified strong selection signals in genes related to high-altitude adaptation, immune response to novel pathogens, and metabolic efficiency.

Proposed Identity & Scenarios: The authors hypothesize this ghost could be a relic population of Homo erectus or a similarly ancient lineage that persisted in Asia and interbred with the ancestors of modern humans as they migrated out of Africa. The 20% contribution isn't from a single mixing event but likely from multiple episodes of introgression over tens of thousands of years.

Why This is a Major Claim & Requires Caution

This finding is revolutionary but comes with significant caveats, which is why the preprint is under intense scrutiny:

Statistical Ghost vs. Physical Evidence: The "ghost" is a statistical necessity within the model. No physical fossil or DNA from this specific proposed population has been identified. It is inferred solely from patterns in modern genomes that current known admixture (Neanderthal/Denisovan) cannot explain.

Model Dependence: The conclusion is entirely dependent on the COBRAA model's assumptions and architecture. If the model has oversimplified complex demographic processes (e.g., population bottlenecks, structure within Africa), it might create a "ghost" to explain noise or a more complex reality.

The "20%" Figure: This is a headline-grabbing number, but geneticists note it refers to the proportion of the ancestral source in the modern gene pool, not that 20% of every modern human's DNA is from this ghost. Its legacy is likely a smaller set of highly influential, selected alleles.

In a Nutshell:

Set-up: Cambridge geneticists used a new generative AI model (COBRAA) that simulates evolutionary branching processes to explain modern human genetic diversity.

Finding: The model insists the best explanation for our DNA includes a major interbreeding event with a deeply divergent, unknown "ghost" hominin, contributing adaptive genes (~20% ancestral contribution) related to altitude, immunity, and metabolism.

Status: A provocative preprint, not yet peer-reviewed. It represents a potentially paradigm-shifting hypothesis from a powerful new AI tool, but it awaits validation through independent methods and, ideally, the discovery of physical evidence.

This work, if validated, would fundamentally rewrite the story of human evolution, suggesting that our ancestry is even more mosaic and complex than the already complex Neanderthal/Denisovan model we currently accept.

Dragon Man

Anthropologists are happy: the Denisovan, an early species of Hominids in Asia, now has

a face. Here he is:

                                                                    

https://www.cnn.com/2025/12/26/science/denisovans-dragon-man-human-evolution-mystery

                                                                       *     *     *

Copilot:

Denisovan; what is the notion of 'Dragon Man'?

Here’s the short, clear answer — and then the deeper context, because the story behind “Dragon Man” is genuinely fascinating.

🧬 What “Dragon Man” Refers To

“Dragon Man” is the nickname given to a remarkably well‑preserved ancient human skull found in Harbin, northeastern China. Recent research (2025) has shown that this skull belongs to the Denisovan lineage, an archaic human group previously known almost entirely from tiny bone fragments and DNA.

The fossil is often called:

The Harbin skull

Homo longi (its original 2021 species designation)

Dragon Man (popular nickname)

🧩 Why It Matters

Before this discovery, Denisovans were a genetic mystery — known mostly from:

A finger bone

A few teeth

DNA traces in modern populations (especially in East Asia and Oceania)

The Harbin skull is the first nearly complete Denisovan skull ever identified, giving scientists a real sense of what Denisovans looked like.

🧠 Key Features of the “Dragon Man” Skull

Based on the studies:

Very large braincase

Massive brow ridges

A broad, flat face

Large molars

A combination of traits not seen in Neanderthals or Homo sapiens

These features helped confirm its Denisovan identity through protein and DNA analysis preserved in dental calculus (fossilized tartar).

🌏 Why the Name “Dragon Man”?

The nickname comes from:

The Chinese translation of Heilongjiang (“Black Dragon River”), the province where the skull was found

The dramatic, almost mythical appearance of the fossil

🧭 What This Changes in Human Evolution

The Harbin skull suggests:

Denisovans lived much farther east than previously confirmed

They may have been widespread across East Asia

They were a robust, cold‑adapted population

Their interactions with Homo sapiens may have been more extensive than we thought

It’s one of the biggest breakthroughs in paleoanthropology in the last decade.

If you want, I can also compare Denisovans, Neanderthals, and Homo sapiens in a table — or walk through what the Harbin skull tells us about Denisovan facial appearance.

Could I see the comparative table?

Absolutely — here’s a clean, structured comparative table that lays out Denisovans, Neanderthals, and Homo sapiens side by side. I’ve tuned it to highlight the traits that matter most for understanding where “Dragon Man” fits in.

🧬 Comparative Table: Denisovans vs Neanderthals vs Homo sapiens

                                                                                       

🐉 Where “Dragon Man” Fits In

The Harbin skull (“Dragon Man”) is currently the best anatomical window into Denisovans. It shows:

A massive, robust skull

A large braincase

A broad, flat face

Huge molars

A mix of archaic and modern traits

It helps anchor Denisovans as a distinct branch of the human family, sister to Neanderthals but with their own unique morphology.

If you want, I can also build:

A phylogenetic tree showing where Denisovans sit

A morphology-focused table comparing only skull features

A timeline graphic of human evolution

A Denisovan–Neanderthal–Sapiens interbreeding map

Just tell me what direction you want to explore next.

Could I see the timeline graphic of human evolution?

                                                                                      

Expanded labels for clarity:

Sahelanthropus tchadensis (~7 Ma)

Australopithecus afarensis (~4 Ma)

Homo erectus (~2 Ma)

Homo heidelbergensis (~1 Ma)

Denisovans (~500–50 ka)

Homo sapiens (~300 ka–present)

Neanderthals (~400–40 ka)

🧭 Notes on placement

Homo heidelbergensis is shown as the common ancestor of both Neanderthals and Denisovans.

Denisovans and Neanderthals overlap heavily in time and interbreed with each other and with early Homo sapiens.

Homo erectus persists in some regions (e.g., Java) until surprisingly late (~100 ka).

If you’d like, I can also produce:

A branching tree diagram (ASCII or structured)

A version focused only on the last 1 million years

A timeline including Homo floresiensis and Homo naledi

A color‑coded version for printing or study

Just tell me what direction you want to explore next.

What do we know about Homo heidelbergensis?

Here’s a clear, structured, evidence‑based overview of Homo heidelbergensis, one of the most important — and most misunderstood — species in the human lineage. This is a species you’ll appreciate, Louise, because it sits right at the branching point where the human family tree becomes genuinely intricate.

🧬 Homo heidelbergensis: What We Know

🌍 Time Range & Distribution

Lived roughly 700,000 to 300,000 years ago

Found across Africa, Europe, and Western Asia

Represents a transitional species between Homo erectus and later humans (Neanderthals, Denisovans, and Homo sapiens)

This wide geographic range is one reason the species is sometimes called a “mosaic” taxon — fossils vary a lot depending on region.

🧠 Anatomy & Physical Traits

H. heidelbergensis was robust, large‑brained, and powerful. Key features include:

Cranial Traits

Brain size: 1100–1400 cc, overlapping with modern humans

Thick brow ridges (but not the continuous bar seen in earlier species)

Large, broad face

Less elongated skull than Homo erectus

No true chin (a modern human trait)

Postcranial (Body) Traits

Very strong, muscular build

Likely cold‑adapted in European populations

Height estimates: 1.6–1.75 m

Weight: 60–90 kg, depending on sex and region

They were physically formidable — probably stronger than both Neanderthals and modern humans.

🧭 Evolutionary Significance

This is where things get interesting.

1. Common ancestor of Neanderthals and Denisovans

Most paleoanthropologists agree that European and Asian populations of H. heidelbergensis gave rise to:

Neanderthals in Europe

Denisovans in Asia

2. Possible ancestor of Homo sapiens

African populations of H. heidelbergensis (sometimes called Homo rhodesiensis) may have contributed to the lineage that became Homo sapiens.

So instead of a single species, think of it as a cluster of regional populations diverging in different directions.

🛠️ Technology & Behavior

Tools

Associated with Acheulean tools (handaxes)

Later populations used Levallois technique (prepared-core flaking), a precursor to Neanderthal and early human toolmaking

Hunting

Evidence of organized big‑game hunting

Wooden spears from Schöningen (Germany) show:

Balanced throwing weapons

Cooperative hunting strategies

Planning and foresight

Shelter & Fire

Likely built simple shelters

Evidence of controlled fire use

Possible seasonal occupation of caves

Social Behavior

Likely lived in structured groups

Possible care for injured individuals

Cognitive abilities approaching early Homo sapiens

🦴 Key Fossils

Some of the most important specimens include:

                                                                                         

The Sima de los Huesos site is especially important — it contains DNA linking these individuals to the Neanderthal–Denisovan lineage.

🧩 Why H. heidelbergensis Is Hard to Classify

You’ll appreciate this as someone who loves model‑building:

Fossils vary widely across regions

Some researchers split them into multiple species

Others lump them into one highly variable species

Genetic data is sparse but growing

The species sits right at a major branching point in human evolution

It’s a classic case where the model is messier than the categories.

🌱 If you want to go deeper

I can also build:

A branching tree diagram showing where H. heidelbergensis sits

A comparative table between H. erectus, H. heidelbergensis, Neanderthals, and H. sapiens

A regional breakdown (African vs European vs Asian populations)

A timeline overlay showing divergence points

Just tell me what direction you want to explore next.

                                                                       *     *     *

Denisovans, like Neanderthals, are ancestral to modern humans but not ancestors to; they account for a very small segment of current human DNA??

Here’s the clearest way to put it, Louise — and yes, your intuition is right, but the terminology matters.

🧬 Are Denisovans and Neanderthals “ancestral” to modern humans?

No — they are not our direct ancestors.  

They are sister lineages that branched off from a shared ancestor we all had roughly 600,000–700,000 years ago.

Modern humans evolved in Africa, while Neanderthals and Denisovans evolved mostly in Eurasia.

So the relationship looks like this:

🧬 But they did interbreed with us

This is where your second point comes in — and it’s correct.

Modern humans migrating out of Africa met both Neanderthals and Denisovans and interbred multiple times.

This left small but meaningful amounts of archaic DNA in our genomes.

What the research shows:

Denisovans interbred with modern humans multiple times, leaving genetic “fingerprints” across Asia and the Americas.

Denisovan genes contributed to adaptations such as:

High‑altitude survival in Tibetans (EPAS1)

Cold‑weather adaptation in Arctic populations

Enhanced immunity in several groups

Denisovan ancestry is especially common in Indigenous peoples of the Americas for certain genes like MUC19, which affects mucosal immunity.

🧬 How much Denisovan DNA do modern humans carry?

It varies by region:

                                                                                  

Neanderthal DNA is more widespread (1–2% in all non‑Africans), but still not ancestral — it’s the result of interbreeding.

🧬 So your statement becomes:

Denisovans and Neanderthals are not ancestors of modern humans, but they contributed small amounts of DNA to many modern populations through interbreeding.

That’s exactly what the evidence shows.

If you want, I can also map out:

A visual ancestry tree showing the branching

A table of Denisovan vs Neanderthal genetic contributions

A map of where Denisovan ancestry is highest

Just tell me which direction you want to explore.

But what about Homo Sapiens; they too were out of Africa at various points. Can we tell if current DNA contains traces of those early migrations?

Great question, Louise — and yes, we can detect traces of early Homo sapiens migrations in modern DNA, but with some important nuances. Let’s break it down clearly and tie it to what current research shows.

🧬 1. Homo sapiens left Africa multiple times — but only one migration left a major genetic legacy

Genetic and fossil evidence shows several dispersals of Homo sapiens out of Africa. But:

Most early migrations left little or no genetic trace in living humans.

Why?

Because those early groups either:

died out,

were absorbed by later populations,

or were replaced by the major migration ~60–70,000 years ago.

This major migration is the one that seeded nearly all non‑African populations today.

🧬 2. Do we see DNA from earlier Homo sapiens migrations in modern people?

Short answer: very little, but not zero.

Most modern non‑African DNA comes from the main Out‑of‑Africa migration that occurred around 60–70 ka.

However, ancient DNA studies show that:

Some early Homo sapiens groups reached the Middle East and Asia before 100 ka.

These groups left minimal genetic impact on today’s populations.

Their lineages were largely replaced by later waves.

This is supported by genomic reconstructions showing that the major genetic ancestry of non‑Africans traces back to a single expansion event.

🧬 3. What can we detect in modern DNA?

Modern DNA carries signatures of:

A. The major Out‑of‑Africa migration (~60–70 ka)

This is the dominant ancestry in all non‑African populations.

B. Archaic admixture (Neanderthal & Denisovan)

These are not ancestors, but interbreeding events:

Neanderthal DNA in all non‑Africans (1–2%)

Denisovan DNA in Melanesians, Australians, some East Asians (0.2–6%)

C. Later migrations within Eurasia and the Americas

Genetic studies trace:

Movements from Asia into the Americas over 12,000+ miles

Multiple waves of migration and admixture across Eurasia

D. Deep African diversity

African populations retain the oldest and most diverse Homo sapiens lineages, including signals of ancient population structure that predate the major migration.

🧬 4. Why early Homo sapiens migrations left so little DNA

Several factors explain the lack of surviving genetic signatures:

Small population sizes  

Early groups were tiny and vulnerable.

Later, larger migrations replaced them  

The 60–70 ka migration was demographically dominant.

Bottlenecks  

Non‑Africans went through severe population bottlenecks, reducing diversity.

Genetic drift  

Small populations lose lineages quickly.

🧬 5. So what’s the bottom line?

✔ Modern humans do carry DNA from early Homo sapiens migrations —

❗but almost all of it comes from the major migration ~60–70 ka, not earlier ones.

✔ Earlier migrations existed —

❗but they left very little genetic trace in living populations.

✔ What we can detect clearly are:

The main Out‑of‑Africa ancestry

Neanderthal admixture

Denisovan admixture

Later Eurasian and American migrations

If you want, I can also map this out visually as:

A timeline of Homo sapiens migrations,

A genetic ancestry tree, or

A map showing which populations carry which ancient lineages.

Just tell me which direction you want to explore.

Funky

 For those who might be distraught - and I'm sure you are legions - that Wordscramble eu

is no longer a usable Scrabble game, this is my current homegame...Funky Potato:

                                                                                   

https://funkypotato.com/scrabble-online/

That old site, around Christmas, was putting ads that overlapped with the game area,

and had become unplayable!!

     

Detective

 Playing with Copilot and PyCharm to create the rudiments of what could be

an identification game. One only sees through the circle - and it is pink - 

but the aim is to guess the work. One is free to move the circle with the arrow 

keys. Below:

                                                                           

The Code:

import pygame

pygame.init()

# Window setup
WIDTH, HEIGHT = 900, 600
screen = pygame.display.set_mode((WIDTH, HEIGHT))
pygame.display.set_caption("Image Reveal Through Moving Circle")

# Colors
GREY = (150, 150, 150)
PINK = (255, 105, 180)
radius = 50
x, y = WIDTH // 2, HEIGHT // 2
speed = 5

# Load and scale the image so it fits the frame
image = pygame.image.load("Images/Monet.png").convert_alpha()
image = pygame.transform.smoothscale(image, (WIDTH, HEIGHT))

# Create a circular mask surface
mask_surface = pygame.Surface((radius*2, radius*2), pygame.SRCALPHA)
pygame.draw.circle(mask_surface, (255, 255, 255, 255), (radius, radius), radius)

# Circle overlay (pink, low opacity)
circle_overlay = pygame.Surface((radius*2, radius*2), pygame.SRCALPHA)
pygame.draw.circle(circle_overlay, (255, 105, 180, 80), (radius, radius), radius)

clock = pygame.time.Clock()
running = True

while running:
    for event in pygame.event.get():
        if event.type == pygame.QUIT:
            running = False

    # Movement
    keys = pygame.key.get_pressed()
    if keys[pygame.K_LEFT]: x -= speed
    if keys[pygame.K_RIGHT]: x += speed
    if keys[pygame.K_UP]: y -= speed
    if keys[pygame.K_DOWN]: y += speed

    # Draw background
    screen.fill(GREY)

    # Reveal the image only through the circle mask
    # 1. Create a temporary surface
    reveal_surface = pygame.Surface((radius*2, radius*2), pygame.SRCALPHA)

    # 2. Copy the corresponding part of the image
    reveal_surface.blit(image, (-x + radius, -y + radius))

    # 3. Apply the circular mask
    reveal_surface.blit(mask_surface, (0, 0), special_flags=pygame.BLEND_RGBA_MULT)

    # 4. Blit the revealed portion to the screen
    screen.blit(reveal_surface, (x - radius, y - radius))

    # Draw the pink circle on top
    screen.blit(circle_overlay, (x - radius, y - radius))

    pygame.display.flip()
    clock.tick(60)

pygame.quit()

Copilot knows what to do immediately when I specify something; I am almost ashamed of how 

slow I am...

Ambient Scene

 Been working away with Copilot on that winter shimmering snow effect. Below:

I've changed the backgound color to :  screen.fill((135, 140, 155)). Could I have a small proportion of my snowflakes shimmer. (In actual fact, if I am motionless standing outside, the same shimmering elements will go on shimmering. It is only as I move that the shimmering elements change, and at the speed that I am imoving). Here, we are trying to catch the shimmer effect on a scene where the wind and flake fall cause the shimmer!!

Louise, this is a beautiful observation — and you’re absolutely right about the physics.

The shimmer you see outdoors isn’t random flicker; it’s angle‑dependent specular reflection. When you move, the angle changes, so different crystals catch the light. When you stand still, the same ones keep shimmering.

In your ambient winter scene, the “camera” is fixed, but the snowflakes are moving, so we can simulate shimmer by letting only a small subset of flakes have angle‑dependent brightness changes.

Let’s add that effect cleanly and elegantly.

                                                                               

The code:

import pygame
import random
import math

# ----------------------------
# Settings
# ----------------------------
WIDTH, HEIGHT = 1000, 650

# Crystals
N_CRYSTALS = 300
MIN_RADIUS = 3
MAX_RADIUS = 6
MAX_ALPHA = 120
FADE_SPEED = 0.08
ON_PROB = 0.01
OFF_PROB = 0.01

# Snow
N_SNOW_FOREGROUND = 120
N_SNOW_BACKGROUND = 80
FG_SPEED = 1.2
BG_SPEED = 0.4
FG_SIZE = (2, 4)
BG_SIZE = (1, 2)

# ----------------------------
# Crystal class
# ----------------------------
class Crystal:
    def __init__(self):
        self.x = random.uniform(0, WIDTH)
        self.y = random.uniform(0, HEIGHT)
        self.radius = random.uniform(MIN_RADIUS, MAX_RADIUS)

        icy_palette = [
            (180, 210, 255),
            (200, 230, 255),
            (220, 220, 255),
            (230, 200, 255),
            (200, 220, 240),
        ]
        self.color = random.choice(icy_palette)

        self.alpha = random.uniform(20, MAX_ALPHA)
        self.target_on = True

    def update(self):
        if self.target_on and random.random() < OFF_PROB:
            self.target_on = False
        elif not self.target_on and random.random() < ON_PROB:
            self.target_on = True

        target_alpha = MAX_ALPHA if self.target_on else 0
        self.alpha += (target_alpha - self.alpha) * FADE_SPEED
        self.alpha = max(0, min(MAX_ALPHA, self.alpha))

    def draw(self, surface):
        layer = pygame.Surface((self.radius*4, self.radius*4), pygame.SRCALPHA)
        pygame.draw.circle(
            layer,
            (*self.color, int(self.alpha)),
            (int(self.radius*2), int(self.radius*2)),
            int(self.radius)
        )
        surface.blit(layer, (self.x - self.radius*2, self.y - self.radius*2))


# ----------------------------
# Snowflake class (all shimmer)
# ----------------------------
class Snowflake:
    def __init__(self, foreground=True):
        self.x = random.uniform(0, WIDTH)
        self.y = random.uniform(0, HEIGHT)
        self.foreground = foreground

        if foreground:
            self.speed = FG_SPEED
            self.size = random.randint(*FG_SIZE)
        else:
            self.speed = BG_SPEED
            self.size = random.randint(*BG_SIZE)

        # --- soft shimmer (all flakes) ---
        self.soft_shimmer_phase = random.uniform(0, 2 * math.pi)
        self.soft_shimmer_speed = random.uniform(0.02, 0.05)

        # --- strong shimmer (10% of flakes) ---
        self.strong_shimmer = random.random() < 0.10
        self.strong_shimmer_phase = random.uniform(0, 2 * math.pi)
        self.strong_shimmer_strength = random.uniform(80, 160)

    def update(self):
        # Falling motion
        self.y += self.speed
        self.x += math.sin(self.soft_shimmer_phase + self.y * 0.01) * 0.3

        if self.y > HEIGHT:
            self.y = -5
            self.x = random.uniform(0, WIDTH)

        # Update shimmer phases
        self.soft_shimmer_phase += self.soft_shimmer_speed
        if self.strong_shimmer:
            self.strong_shimmer_phase += 0.03 * self.speed

    def draw(self, surface):
        # Soft shimmer
        soft_offset = math.sin(self.soft_shimmer_phase) * 40
        brightness = 220 + soft_offset

        # Strong shimmer
        if self.strong_shimmer:
            strong_offset = math.sin(self.strong_shimmer_phase) * self.strong_shimmer_strength
            brightness = brightness - strong_offset

        brightness = int(max(150, min(255, brightness)))

        pygame.draw.circle(surface, (brightness, brightness, brightness),
                           (int(self.x), int(self.y)), self.size)


# ----------------------------
# Pygame setup
# ----------------------------
pygame.init()
screen = pygame.display.set_mode((WIDTH, HEIGHT))
pygame.display.set_caption("Ambient Winter Scene")
clock = pygame.time.Clock()

# Create objects
crystals = [Crystal() for _ in range(N_CRYSTALS)]
snow_fg = [Snowflake(foreground=True) for _ in range(N_SNOW_FOREGROUND)]
snow_bg = [Snowflake(foreground=False) for _ in range(N_SNOW_BACKGROUND)]

# ----------------------------
# Main loop
# ----------------------------
running = True
while running:
    for event in pygame.event.get():
        if event.type == pygame.QUIT:
            running = False
        if event.type == pygame.KEYDOWN and event.key == pygame.K_ESCAPE:
            running = False

    # Your chosen background color
    screen.fill((135, 140, 155))

    # Background snow
    for flake in snow_bg:
        flake.update()
        flake.draw(screen)

    # Shimmering crystals
    for c in crystals:
        c.update()
        c.draw(screen)

    # Foreground snow
    for flake in snow_fg:
        flake.update()
        flake.draw(screen)

    pygame.display.flip()
    clock.tick(60)

pygame.quit()

So now I have a background with shimmer, and falling snow with shimmer at a different rate...