Welcome back to The Nature Podcast.
This week, delivering drugs through the skin without needles.
And how noise becomes language.
I'm Nick Parchichal.
And I'm Sharmini Vandell.
First up on the show this week, we've got a story about a promising new way to get insulin into the body, and it's using a cream that's absorbed through the skin.
It's early in development, but this method could ultimately offer an alternative to the vital and often frequent insulin injections that many folk need to manage their diabetes.
Here's reporter Benjamin Thompson with the story.
Diabetes can, if left unchecked, cause serious health issues, even death.
For many people, controlling the elevated blood glucose levels associated with the condition requires regular self-injections of the hormone insulin, which prompts tissues in the body to remove glucose from the blood and store it.
But these injections can be inconvenient to say the least, and pain and phobia of needles can make folk reluctant to inject themselves.
To get around this, researchers have been looking for other ways to get insulin into the people who need it.
One of these ways is by getting it in through the skin.
Delivering therapies by this method, sometimes called transdermal drug delivery, has worked for several different drugs using things like patches stuck to the skin.
But this route only really works for small molecule drugs.
Larger drugs, such as insulin, still need to be delivered via injection.
And that's because evolution has come up with a very good barrier to prevent things on the outside getting in.
Our body is well protected by skin to isolate us from outside.
This is Yuqing Shen from Zhejiang University in China.
The skin is very complicated.
The top layer is is the stratum corneum.
This is a dead cell buried in the lipids.
Therefore, this layer is very hydrophobic.
Under the stratum corneum is the viable dermis composed of alive cells.
This is a very hydrophilic environment.
And this double whammy of the hydrophobic stratum corneum layer on the outermost part of the skin covering the hydrophilic viable epidermis underneath makes things tough for delivering large drugs transdermally.
A hydrophobic water-repelling molecule can make it past the oily outermost layer but will be foiled by the layer underneath.
A hydrophilic water-loving molecule won't even make it that far.
This has made it challenging to get insulin into the body via this route, with other attempts involving techniques that puncture or remove some of the layers first.
Yuqing and his colleagues wanted to take a different approach to get insulin past this formidable barrier, one that doesn't require any damage to the skin.
But before they could even think about insulin, they needed something that could deliver it, ultimately developing a polymer that can sneak through the skin.
It's called poly-2-N-oxide-N-N-dimethylaminoethylmethacrylate.
IA is simply OP.
OP is a hydrophilic polymer which normally wouldn't be able to get through the skin's oily outer layer.
But OP is designed to change its properties depending on where it is taking advantage of another key characteristic of the skin.
On the surface, our skin is actually weakly acidic.
The pH is about 4 to 5.
In the middle of the stratum corneum, the pH becomes pH around 7.
And this gradient from acidic at the outer surface down to neutral is key for OP's action.
When in an acidic environment, the polymer becomes positively charged.
This enables it to bind to negatively charged lipid molecules.
Even though it is very hydrophilic, this polymer would bind on the skin surface.
Once concentrated On the surface, OP begins to diffuse into the skin itself, where the pH tapers, ultimately becoming neutral.
At this neutral pH, OP loses its positive charge and lets go of the lipids.
Now this water-loving polymer is in a watery environment and able to make its way into circulation around the body.
With their delivery system in hand, the team needed to attach a cargo.
In lab tests, they showed that insulin attached to OP, which they call OPI, was still able to function.
To see how it worked at controlling blood sugar levels, they performed various animal tests.
Specifically, they looked at animal analogues of type 1 diabetes which, in humans, is characterised by the inability of the body to make insulin.
In mice, the team found that OPI applied to the skin quickly got to work.
Within 30 minutes, the glucose level would be lowered from 250 milligrams per decimal liter to below 200, considered to be normal.
Further, after one hour, the glucose level reached around 100.
This is a perfect level of the blood glucose.
Yuqing says that this performance is almost as quick as injected insulin and that the transdermal OPI accumulated in skeletal muscles, fat tissues and the liver.
Key sites for the uptake of glucose from the blood.
But of course, a mouse is not a human.
So the team moved to test OPI on mini pigs, animals with skin that's closer in structure to ours.
We just mirror cream and the systemic skin of the mini pig.
Within two hours, the glucose level reached the normal, around 100 mg per decimal liter.
OPI worked slower in the pigs than the mice, likely because the pigskin has a much thicker layer of fat.
Yuqing says that OPI caused no irritation or damage to the structure of the skin in the pigs and a single application lasted a long time.
Right now the very preliminary formulation.
It can maintain glucose level to normal range at least 12 hours.
Yuching hopes they can improve on the formulation to get it past the 12-hour mark and says it would be great to get it to work for a full 24-hour period.
Injected insulin can work for between around 5 and 24 hours, depending on the formulation and need.
However, despite the promise that using their newly developed polymer OP to deliver insulin has shown in this work, there are still a lot of questions surrounding things like safety that will need to be answered before it can be used in humans.
We evaluated this polymer.
There's no sign of any kind of toxicity, but we need to do more on this.
We have to know the long-term safety.
Second is how to make the dose more accurate.
That's very important because insulin is very potent.
Taking too much insulin can be very dangerous, and this is where injections currently have a clear advantage in terms of controlled dosage.
Answering these questions is the next step for Yuqing and the team.
He hopes that ultimately, if successful, their system will help lift some of the injection burden off of those who need regular insulin to control their condition.
But beyond that he also hopes their method could be adjusted to carry other cargo, perhaps GLP-1, weight loss drugs or other large therapeutic molecules.
For this work.
We try to demonstrate that the carrier, the polymer OP, can indeed quickly and efficiently penetrate through the skin into the body.
We use this insulin to show the efficiency.
This may be open a new way for the delivery of biomicrobial molecules.
That was Yuqing Shen from Zhejiang University in China.
To read his paper, head over to the show notes for a link.
Coming up, neuroscientists have been figuring out how our brains interpret language.
Right now, though, it's time for the research highlights.
We've done, folks.
What would humanity's biggest challenge be in the years following a huge volcanic eruption?
Extreme drought, according to a team of researchers.
Large volcanic eruptions can drastically lower global temperatures if the sulphur they spew out forms enough aerosol particles to block considerable amounts of sunlight.
Now scientists have looked at what other effects eruptions of various sizes could have.
Using a climate model, the team simulated eruptions that injected up to 160 million tonnes of sulphur into the stratosphere.
They saw that for eruptions that injected up to 40 million tonnes of sulphur the planet cooled, but with larger eruptions that cooling effect didn't become more pronounced.
Global rainfall, however, continued to decline as the size of eruptions increased.
Something the authors say could pose the greatest global scale threat after super eruptions.
Read that research in full in Geophysical Research Letters.
A few genetic tweaks can turn a bacterium into a miniature pigment factory.
Xanthomatin is a pigment that creates vibrant colours and patterns on butterfly wings and allows octopuses to change colours to camouflage themselves.
Its colour-shifting properties are attracting increased interest for applications in cosmetics, skincare products, sensors and displays.
Researchers modified a soil bacterium to produce the pigment by engineering a gap in its metabolic cycle that it could fill only by making xanthamatin.
The bacterium had to make the pigment to stay alive and grow, a reinforcing mechanism that boosted production of the pigment.
The authors say that the approach might be used for bioproduction of other natural products from bacteria as well.
You can find that paper in Nature Biotechnology.
I'm getting there, but I still have a ways to go.
It's such a different language, I feel as though I'm having to retrain my brain.
Things that were once noises have now become familiar words and phrases that I can respond in kind to.
And that is precisely what our next story is about.
New research into how familiar and unfamiliar languages are represented in the brain.
For more, let's hand over to a familiar voice to regular listeners, Noah Baker.
My name is Shankari.
There are over 7,000 spoken languages.
Language for me is... such a special ability in humans that we don't see in other animals.
It's something very intrinsic to our species as a behavior.
But at the same time, the way that it's expressed is so diverse to the point where you know, for the most of these languages they're uninterpretable, unintelligible to another.
But when does sound become language?
When do noises gain meaning?
These are questions which have fascinated neuroscientists for decades.
But the answers are complex.
Now new research published in Nature is shedding light on how language is represented in the brain, in search of the neural basis of the mother tongue.
When I hear a language I'm not familiar with, it's just noise to me.
This is Shankari Shankar.
She's a science communication master's student at University College London.
It makes no sort of connections.
I don't feel anything towards it.
I don't feel the need to acknowledge it or participate towards it.
But if I hear Tamil anywhere in the room remotely, it's an instant connection.
I can recognise it almost instantly and it just draws to me.
When I started investigating this story, I was in the midst of teaching at UCL.
And my students, like Shankari, came from all over the world.
And so I thought I'd start by asking them a bit about their experiences of language.
In particular, what it feels like to hear a language they understand versus one that they don't.
I don't think much about the language if I don't understand it.
It kind of just sounds like music to me.
When it comes to languages that are nearby mine, so in the Germanic or the Slavic groups I'm frequently reaching for things that are similar and trying to make sense of a kind of puzzle.
But when it's a completely foreign language, like a tonal language, I'm really lost in the musicality of it.
I'll see someone who might, who I might, might be.
Tongue might not be, but then it's not until I hear them speak the tongue that I'm like.
I literally like I turn into a hound.
I can feel like my ears turn.
I'm like oh, yep.
The languages I do understand.
I don't have to think through it to understand, I guess, when another language comes.
Yeah, it's just really lovely to listen to to me.
Yeah, these were common threads that came across.
Often my students felt like they could identify when a sound was language, even enjoy listening to it, but unless they spoke the language, it carried little meaning.
Many described unfamiliar languages as being akin to music.
And yet when they hear their own language, immediately something changes and consciously they tune into more.
So what happens to change this perception?
How is the brain turning music, into meaning?
These are the kinds of questions that neuroscientists like Eddie Chang have been asking for some time.
My name is Eddie Chang.
I'm a neurosurgeon at the University of California, San Francisco.
Eddie and his team are dedicated to understanding the myriad factors involved in language representation in the brain.
The one that we're particularly focused on right now is trying to understand really what aspects of languages are shared across different languages.
For example, between what you recognize and what you can interpret in your own native language, versus when you're listening to a foreign language.
When trying to compare elements of language, a sensible place to start is with the building blocks, phonemes.
These are the individual sounds that can be used to make up words.
Those are the basic phonological constructions of speech.
And then how those are assembled is syllables.
And when you compare that to other kinds of sounds, like music for example, it's a different set of structure, or environmental sounds and so on.
And so speech by itself is a sort of unique and specialized class structure, the sounds that our brains are very, very much evolved to
But these individual sounds that make up syllables in the context of speech still mean little alone until they're grouped together in a specific way to form words.
I mean, we all have this experience when you listen to a foreign language, that you can hear the sounds clearly, but you can't parse where the words are.
You can't parse what the words begin and end.
For example, take these two sounds.
Put them together on a loop, and suddenly, words can appear.
Here's another one, this time with the sounds ter and bo.
And I think that that's a very interesting challenge, because in most languages, when you have continuous speech, we don't have little spaces between all of the words.
Many times, you know, these words run together.
In fact, many times there's spaces within words, just as much as there are spaces and pauses between the words.
And so our brain is effectively taking this continuous signal and then chunking it into word size bits.
And it seems like this is something that's very specific to your native language.
It's essentially what you learn to do.
It's a very complex piece of processing, made even more complicated when you get into the details.
For example, the two excerpts I played you earlier.
These are both stimuli which Eddie and his team have used in other studies.
But to an English speaker's ear, there are multiple different words that can be gleaned.
Say can become ace, or even says.
Turbo. can become boater.
Eddie and his team wanted to get to the bottom of how our brains are able to pass sounds into syllables and syllables into speech.
But to do this, they needed to wait for a very specific opportunity.
This kind of research occurs in a very rare scenario basically, where people are actually having brain surgery to help cure cancer.
Their seizures,
And as part of the clinical care for patients with seizures, we put electrodes on the brain surface that can record from areas as small as a millimeter, but they also have millisecond resolution.
So it gives us very, very fine spatial resolution and temporal resolution.
And that's the kind of tool that we wanted to use in order to address this question, because we think the difference is are fairly subtle right between trying to discriminate what is happening in your native language versus another one.
Eddie was particularly interested in one very small area of the brain.
Within this area, there is a code that allows us to essentially hear things like the different aspects of consonants and vowels, or the pitch in your voice, all the things that give rise to intonation.
And so...
A big question over time has been, is that all that this is doing?
Is it just detecting the elements of speech or is it doing something even more?
For example, you know, processing things at the level of words.
By asking their subjects to listen to speech in their native language, as well as languages they were unfamiliar with.
Eddie's team could start to investigate how the brain responded.
And what they found came as a bit of a shock.
What we expected and predicted early on in the study was that there was going to be a very obvious difference in the area of the brain that was going to be activated for one or the other language.
In particular, we thought, well, maybe for your native language, we would see a much larger area.
And we saw neither.
And so what was surprising to us actually was that, regardless if you were listening to your native or foreign language, actually The brain activity looked remarkably similar.
When you looked at the magnitude of the responses, which is how big the responses were, when you looked at what part of the brain and the extent of its action, it looked remarkably similar between the two at the level of processing individual consonants and vowels.
It appeared that the individual building blocks of language were represented in a remarkably similar way in the brain, regardless of linguistic experience.
There's something about language which is shared amongst all of us.
And yet the experience of hearing a language you know versus a language you don't is wildly different.
So Eddie wanted to understand what else was going on.
They looked even deeper.
When you see these areas of the brain respond to speech, we can measure, you know, the electrical activity.
And basically, we would have this signal that goes up and down, kind of fluctuating.
And when it's to a foreign language, it's fairly continuous, like just modulating up and down.
In contrast, when you're actually listening to continuous speech of your native language, what we saw was that the neural activity would actually dip, would drop between every successive word.
And basically what we see happening is that it's no longer a continuous signal, like it is when it enters our ear, but the brain is actually parsing the signal into those word units.
While each sound may be represented somewhat universally, the way that those sounds are parsed into words is really distinctive and unique to the person's experience.
And importantly, it's the same part of the brain that's doing both of these jobs.
There's one area that is involved in not only processing the consonants and vowels, it's also processing at a word level.
It's also recognizing something that we once thought was happening, perhaps in a different brain area that was adjacent.
But it turns out that it's all integrated in one area.
These things may be a lot more integrated than we ever imagined.
And it could be the reason why, when we hear our native language, we more or less automatically hear words.
It's not like we can experience the individual phonetic elements.
We really just perceive them as words and it's quite automatic.
And it's one of the really amazing things about the way that we hear.
But what about people that speak more than one language?
Well, Eddie and his team also looked at that and found yet more surprises.
We also had the opportunity to look at people who were bilingual and also different levels of how good they were.
And what's really fascinating about this is that it's this one area, the superior temporal gyrus that is processing both languages, both your first and second language, which is really remarkable to think about because it has to essentially have a code that has become specialized.
There's going to be a lot of future work that we need to do to understand.
How can you have two codes that exist in the same part of neural machinery?
Neuroscientists have still just scratched the surface of how hearing people understand and reproduce spoken language in the brain.
But what appears clear is that the nuance of spoken language can be attributed to a deeply interlinked and integrated combination of innate ability and and an even more complicated web of experience and learning.
You know, we're all born with the ability to hear the vast majority of the speech sounds of any language.
And then through our experience that becomes more and more refined and specialized.
So it's certainly true that what is innate is that we have the machinery.
The other thing that's innate is that this machinery has extraordinary plasticity for learning.
And on top of that, it also has a capacity to not only learn but recognize not only the elements of speech and their uniquenesses for different languages, but also the longer elements of speech, these things that give rise to words as sound sequences.
And this happens probably even without meaning, right?
It's probably likely that we're recognizing words before we even know what many of them even mean.
What we've done now is look at the consequences of all that learning, the effects of all of that learning directly from the brain.
A, B, W, G, D, J, E, J, Z, I, J, K, L, L, M, N, N, O, P, R, S, T, T, U, F, H, C, J, J, S.
This podcast segment was produced by Noah Baker and featured the voice and work of Eddie Chang from the University of California, San Francisco, in the US.
Auditory stimulus excerpts were from another paper published by Eddie's team in Neuron.
You also heard from Shankari Shankar, Eugenia Sarpong, Vinusha Shimakunthan, Alex Vidic, Carlos Boca, Iyengu Shengu Zheng Misaki, Azuma Jamko and Sulanga Chatterjee from University College London here in the UK.
To read more, look out for a link in the show notes.
Finally on the show, it's time for the briefing chat, where we're going to discuss a couple of stories we've read in the Nature Briefing, Nature's daily email roundup of science news.
So, Nick, what have you picked this time?
Well, I'm sure that many of our listeners are interested in science and therefore interested in climate change, and it probably will not have escaped their notice that COP30 is going on at the moment in Brazil.
This is the UN's annual conference where scientists, all the world leaders, get together and decide what we need to do about climate change.
So I've been reading a story in Nature related to that about when emissions will peak.
So a lot of people at previous COPs have been putting in a lot of work to try and stop emissions rising.
So presumably, if they are going to peak, that means they will start reducing, which is kind of what we want.
But yeah, it Is that going to be any time soon?
That is the big question.
It's unclear exactly when emissions will peak and, if they do, whether it will be soon enough, and also what happens after they peak.
Because peaking is one thing, but that still means we have a lot of emissions.
What we need to do is for them to go down and eventually we need negative emissions.
We need to pull some carbon dioxide out of the atmosphere to try and cool things down a bit because, as our current trajectory is to go past the 15 degrees, which was the big target that was agreed in Paris that people may remember
So yeah, the question of whether and when is quite hard to work out.
There's a lot of uncertainties in there.
The world is a big system.
There's a lot of different countries, a lot of different things going on.
And a lot of it will depend on China, because China is now responsible for a third of the world's emissions.
Now European countries.
Industrialised countries in the US have been responsible for the most emissions historically, but now China is currently emitting the most emissions.
But it could be that China's emissions are about to peak, or maybe they already have.
Scientists are not quite sure.
And is answering this question of when emissions might peak.
Is that depending on what's going on right now and all the things we're trying?
Or does this involve a little bit of trying to predict the future and hope that we might do better, have different technologies and be able to reduce emissions that way?
Yeah, it's a bit of both, to be honest.
So there are things that are happening now that will obviously influence whether or not emissions will peak.
So I mentioned China.
One of the things that is adding to the uncertainty there is exactly how their emissions will change going forward.
Now, some researchers believe that China's emissions have already peaked.
Researchers at the Centre for Research and Energy on Clean Air, they believe have documented a downward trend, and the carbon monitor, which is a tracker for carbon emissions, suggests that China's emissions peaked in 2024 and will fall this year.
Some of that may be down to a decline in demand for cement and steel because China's real estate market has collapsed.
So people are not building as many houses, and that's resulting in less demand for these things, which are very carbon intensive.
Cement and steel are particularly important carbon hungry, but other researchers say that they may go up again.
Things could change.
But for its part, china has really put a lot of effort into renewable energies and it is the world leader in sort of solar panels and these sort of more renewable energies.
That could really make a difference here.
And what about the other big emitters?
Well, there's obviously a big question mark about the US and it's unclear exactly what will happen.
Industrialised nations, including the US, have been declining in their emissions for some time.
And that is a positive trend.
The question mark is over the developing nations and the places like India and China have had rapid economic development, which has been great for lifting people out of poverty. but also not great for trying to rein in emissions.
The difficulty is that obviously we want people to be lifted out of poverty and it's good that places are developed, but it would be better if obviously there's not the resultant increase carbon emissions as well.
And that has often been the debate at COPS and things like that and how do we avoid that and who pays for it if we do?
There's been a lot of effort to try and get richer nations, who have benefited from producing a lot of these emissions, to then pay to help these developing nations increase their ability to produce energy and lift people out of poverty, without also, you know, burning lots of coal and things like that, and coal is the real big culprit of emissions, like in China it's where most of their emissions are coming from and India as well.
They use quite a bit of coal because it's a very cheap energy source.
So, yeah, it'll be things like that that will be the things that will need to be.
There are also other things other than coal, like deforestation and stuff that we need to rein in.
And is this story?
All people saying oh, there's lots of factors, or is anyone actually putting any possible numbers on this?
Who is making some predictions here and what are they?
Yeah, there's a few different predictions.
And as I say, it depends on all of these factors.
So there is uncertainties.
But there is a projection by Climate Analytics, a non-profit consultancy, who.
They think it will be around 2030.
They don't see it's happening before, but 2030 will be when there is a peak.
And another commentator has suggested that it might happen in 2035, because that's when they believe that China's emissions will peak.
And when China's emissions peak, they predict that world's emissions will peak.
So, yeah, some uncertainties there. possibly in the next few years, which is good.
We want emissions to peak.
The question will be what happens after, because after they peak, they need to start coming down.
If we want to try and keep warming within 1.5 degrees, there's a lot more that needs to be done.
And even if we want to keep things under two degrees, there's still a lot of work to be done.
And as always with these sort of stories, more done, bit quicker.
And there's a nice quote, which I think sort of wraps this up nicely.
This is from climate scientist Catherine Hayhoe, and she compares the management of greenhouse gas levels to maintaining a swimming pool.
And they say we have to turn off the hose, but we also have to make the drain bigger and we have to learn how to swim.
So a lot of different factors there.
An expansive metaphor there.
Well, yeah, this is a story obviously not going away.
Thanks for giving us that update.
And I'm going to jump in now with a story about dogs.
Because I went to the nature briefing and I was like, this one's about dogs.
I'm going to talk about that.
Great.
This was two papers published in Science on the same day by different groups and an article in Nature News that covered it.
And what is your favourite, Nick?
Here's a question for you.
Like weird... dog breed weird dog breed that is my favorite i'm trying to think like my friends have a cockapoo which is quite a fun one mix obviously between a poodle and a cocker spaniel which is actually an enormous dog as it turns out they thought it'd be quite small they were very really oh i'm quite surprised poodles poodles are surprisingly big they are yeah i was thinking of these weird dogs with these sort of big long noses i think they're called balls i'm not sure if i'm pronouncing that right A Russian wolfhound, apparently.
And they look kind of like a cartoon of a dog, just as if you sort of exaggerated all the lengths of the limbs and the nose.
They just sort of look a little bit sad.
Just like, oh, hello, I'm a dog.
And for a lot of the weird dog breeds or certainly weird compared to, you know, dogs' ancestors as wolves, especially if you're thinking of, like those really flat-nosed ones like pugs and things where they just don't have a snout.
A lot of these dogs came from the Victorian period.
Victorians, well, that's certainly what we think of, really keen on their dog breeding, like breeding the weirdest, sometimes unhealthiest features they could think of.
But these papers are sort of going way back in dog's history and trying to sort of figure out a bit more of where dogs came from, how they changed not quite how they were domesticated.
This doesn't sort of answer that yet, but that's like one of the big questions that people are really interested in is sort of when and where did humans first domesticate wolves into dogs?
And there's a lot of archaeological evidence from a long time of you know apparently, domestic dogs, canids with humans.
They've got a sort of long and really interlinked history with us, and one of these papers was looking at skull shapes and the other one was looking at how sort of genetic groups moved around the world.
Okay, so they're going really back in time.
Then what are they trying to understand with like, the skulls?
Well, so skulls is something that we have more in many cases than genetics.
What i mean is skulls of old dogs are something that we have not you and i?
Okay good, we we also have skulls um, but no, like in archaeology.
We have examples of wolf and dog skulls going back a while.
So we potentially have a bit more data than we do genetic data, and that's sort of one avenue of understanding how dogs were domesticated, how they changed, and what they've done is they've looked at skull shape and they've tried to sort of classify skulls into sort of more wolf-like and more dog-like and basically created a sort of definition of of doggy skull right,
These ones we're going to put in the dog group.
These ones we're going to put in the wolf group.
And they found these dog-like skulls going back around 11000 years ago, which is sort of matching the genetics of when we think domestic dogs were existing, living with humans.
But what they also found is, a little while after that there was quite a clear.
They referred to it as an explosion in the variety of dog skull shapes and sizes.
And actually what they sort of found which was quite surprising to them is that the diversity of dog shapes happened quite early.
There was a lot of it going on you know, this was obviously well before the Victorian period and that there was this sort of branching out different types of shapes which might suggest that even then maybe people were breeding dogs on purpose, maybe people were adapting them to different needs, whether it's hunting or safety or companionship.
So this sort of pushes back sort of what we know about the richness of different varieties of domestic dogs.
Yeah, because I've always imagined, like you say, that like dogs were pretty sort of similar-ish, and then the Victorians came along and then like nah, none of this, we want loads of different kinds.
But actually it sounds like from the very earliest times that dogs were domesticated, humans were doing what humans do and breeding them for various purposes.
Yeah, although it's really hard, this is so hard.
What i got from reading the paper on this one was that it's so hard to really figure out what was going on, like just from skulls, for example.
Because you know, i said there's like here's the dog category, here's the wolf category.
There are plenty of examples of modern dogs whose skulls fit into the wolf category.
Right you, you cannot just draw a line and distinguish what is dog, what is wolf.
It's all mixed up together and this is part of why it's been so hard to try and disentangle where dogs domestic dogs originally came from.
Yes, but I also remember you saying that there was a second paper.
So does the second paper shed any light on this?
No, it doesn't.
I'm sorry.
It's an anticlimax now, but it is a very interesting paper.
This one is about humans and dogs and sort of where they go in the world.
So this one is genetics, so necessarily far fewer.
They sequenced the genomes of 17 dogs from the past 10 000 years, compared them with previously published genomes from around this time and they saw a pattern where different human ancestries were associated with particular dog populations.
So you know, the humans who live in a certain place, a certain sort of strain of dogs also live in that certain place.
But what's really nice is that they can see that when human populations moved they took the dogs with them.
So you've got the people from northeastern siberia, for example, sort of spreading out and you can see like ah, the dogs from the areas that they've now settled in also have that same ancestry of arctic dogs, for example.
I mean, that's kind of sweet in a way.
I mean, I guess it makes sense that the dogs would go with the humans, but I am maybe imagining this as like oh, they're their dear pets and they wanted to take them with them.
I don't know if that's quite the case, but that's certainly what I'm imagining.
I suppose we don't know for sure, but I think there's a lot of archaeological questions on that, on sort of what people's attitudes towards their pet and or working creatures would have been.
And then the other thing that they sort of saw in some examples in this genetic study was sometimes you don't have that matching sort of genetic location-based ancestry.
So they think that in situations where there's this disconnect, it might be a sign of dogs being traded between human populations and sent to different places.
So again, sort of suggests you know, you've got these different types of dogs which might have different features and attributes, and those would have been then maybe found to be really useful and started to spread to different places, even without their owners, their humans.
And this does still speak to that question of like, where did the dogs come from?
Where did dogs get domesticated?
And it's still sort of providing more evidence about the the history of the dogs, sort of going back, at least the sort of first 10 000 years of it.
And maybe we'll have to look even further back to find the sort of very first domesticated dog.
But that's obviously much harder to do and possibly lost in the mists of time.
Well, I'm always interested to hear more about our fairy companions and their history and long and intertwined history of humans, it seems.
So thanks for that, Sharmini.
And listeners for more on these stories and a link of where you can sign up to the Nature Briefing to get more like them straight to your inbox.
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I'm Sharmini Bundel.
And I'm Nick Perchichow.
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