Using light to describe the ancient world

Archive for the ‘Raman’ Category

#’Random’ R code for simulating Raman spectra

 

R figures 2

Simulated Raman spectra representing two different datasets. Simulated spectra were generated using R.

 

Multivariate statistical methods are commonly used by researchers who study fossils with spectroscopy. Moreover, these methods are often performed with the free software language ‘R’. However, it can sometimes be a real challenge to assemble R code for a new stats method you are not overly familiar with. How do you know if your R code is working properly? Actually, the answer is pretty simple – just analyse a dataset that has a known outcome. For example, if the method is supposed to distinguish spectra into groups, then work with a dataset that contains different groups of spectra. Simple, right?

Well, it’s often the case that a good dataset is not easily available for simply testing code. My solution? Simulated data. The following lines of R code simulate two groups of Raman spectra that vary slightly within each group, and vary substantially between each group. This code will let you test a whole range of multivariate methods.

Code for generating two ‘random’ sets of Raman spectra (feel free to use, adapt and modify – attribution would be nice but is not necessary).

##Code Starts Here##

wavenumber=200:3500

nspectra=20

set.seed(2)
baseline_1=runif(length(wavenumber),min=0,max=0.1)
group_1_peaks=sample(wavenumber[10:(length(wavenumber)-10)],10)
dataset_1=matrix(0,ncol=nspectra,nrow=length(wavenumber))

for(i in 1:ncol(dataset_1)){
for(j in 1:length(group_1_peaks)){
width=round(runif(1,min=1,max=10),digits=0)
peak_position=dnorm((group_1_peaks[j]-width):(group_1_peaks[j]+width),mean=group_1_peaks[j],sd=width)
peak_intensity=peak_position*round(runif(1,min=1,max=100),digits=0)
baseline_1[(group_1_peaks[j]-width):(group_1_peaks[j]+width)]=peak_intensity
}
dataset_1[,i]=baseline_1
}

set.seed(20)
baseline_2=runif(length(wavenumber),min=0,max=0.1)
group_2_peaks=sample(wavenumber[10:(length(wavenumber)-10)],10)
peak_intensity_means_2=runif(length(group_2_peaks),min=10,max=100)
dataset_2=matrix(0,ncol=nspectra,nrow=length(wavenumber))

for(i in 1:ncol(dataset_2)){
for(j in 1:length(group_2_peaks)){
width=round(runif(1,min=1,max=10),digits=0)
peak_position=dnorm((group_2_peaks[j]-width):(group_2_peaks[j]+width),mean=group_2_peaks[j],sd=width)
peak_intensity=peak_position*(peak_intensity_means_2[j]*runif(1,min=0.5,max=2))
baseline_2[(group_2_peaks[j]-width):(group_2_peaks[j]+width)]=peak_intensity
}
dataset_2[,i]=baseline_2
}

##Code Ends Here##

All spectra within each dataset have been overlain.

Stacked spectra, with all simulated spectra within each dataset overlying one another.

The following principal component analyses is a good example of how these simulated spectra might be used. The following code calls on two libraries (signal, baseline), so be aware that you will need to install the signal and baseline packages to run it. This code first runs through a set of preprocessing steps before the principal component analysis. The citation for this code is: Thomas D B, Chinsamy A, 2011. Chemometric analysis of EDXRF measurements from fossil bone. X-ray Spectrometry 40: 441-445

##Code Starts Here##

 

library (signal)

library(baseline)

combined.data=cbind(dataset_1,dataset_2)

combined.baseline=matrix(0,ncol=length(combined.data[,1]),nrow=length(combined.data[1,]))
combined.bc=c()
combined.data=t(combined.data)
for (n in 1:(length(combined.data[,1]))) {
combined.bc=baseline(combined.data[n,, drop=FALSE],lambda=1,hwi=20, it=30, int=800, method=’fillPeaks’)
combined.baseline[n,]=combined.bc@corrected[1:length(combined.bc@corrected)]
}
combined.data=combined.baseline
combined.data=t(combined.data)

combined.min=matrix(rep(apply(combined.data,2,min),length(combined.data[,1])),ncol=length(combined.data[1,]),byrow=T)
combined.max=matrix(rep(apply(combined.data,2,max),length(combined.data[,1])),ncol=length(combined.data[1,]),byrow=T)
combined.minmax=(combined.data-combined.min)/(combined.max-combined.min)

combined.mc=matrix(rep(colMeans(t(combined.minmax)),length(combined.minmax[1,])),ncol=length(combined.minmax[1,]))
combined.minmax.mc=combined.minmax-combined.mc

combined.minmax.mc=t(combined.minmax.mc)
combined.pc=prcomp(combined.minmax.mc)
combined.loadings=combined.pc$rotation
combined.scores=combined.pc$x

eigenval=combined.pc$sdev^2
explained=round(eigenval/sum(eigenval) * 100, digits = 1)

PCx=1
PCy=2

combined.scores.PCx=combined.scores[,PCx]
combined.scores.PCy=combined.scores[,PCy]
combined.loadings.PCx=combined.loadings[,PCx]
combined.loadings.PCy=combined.loadings[,PCy]

layout(matrix(c(1,1,2,1,1,3),nrow=3,ncol=2))

plot(combined.scores.PCx,combined.scores.PCy,font=5, ann=FALSE,col=c(rep(“red”,(nspectra)),rep(“blue”,(nspectra))))

title(xlab=paste(“Principal component “, PCx, ” (“,explained[PCx],”%)”,sep=””))
title(ylab=paste(“Principal component “, PCy, ” (“,explained[PCy],”%)”,sep=””))

plot(wavenumber,combined.loadings.PCx,type=”l”,xlab=”wavenumber (1/cm)”,ylab=paste(“Principal component”, PCx))
plot(wavenumber,combined.loadings.PCy,type=”l”,xlab=”wavenumber (1/cm)”,ylab=paste(“Principal component”, PCy))

##Code Ends Here##

 

The results of a PCA applied to a simulated dataset of Raman spectra.

The results of a PCA applied to a simulated dataset of Raman spectra.

 

Cool beans. We can clearly recover two groupings of PC scores along PC1. Here the two groups (red, blue) correspond to the two simulated Raman spectral datasets.

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#Fluorescence, Raman enemy?

Raman spectroscopy gives us some amazing insight into ancient life. To pick just example, Raman researchers have analysed ancient microbes on Earth to teach us how to recognise life on other planets (Marshall et al 2006). Rather than highlight the advantages of Raman spectroscopy though, I thought I might focus on one of the biggest drawbacks of this technique: Autofluorescence, which is more commonly just called fluorescence.

“The natural enemy of Raman spectroscopy is fluorescence” – Olaf Hollricher, Raman Instrumentation for Confocal Raman Microscopy.

[Check out this post for a basic refresher on Raman spectroscopy]. Fluorescence is what happens when a substance absorbs and then immediately emits light. In essence, the substance glows when a light is shone on it. This is kind-of but-not-quite like a glow-in-the-dark toy: you shine light on the toy, and when you remove the light you can still see the toy. The difference here is that the toy has stored the light and is slowly releasing it, instead of receiving and releasing the light at nearly the same time. Glow-in-the-dark is an example of phosphorescence. We are talking about fluorescence.

Have you ever been to a museum with rocks and minerals that are being lit with a ‘black light’? Did the minerals look all bright red and green and purple like in the picture from Wikipedia below? The bright colours of these minerals are examples of fluorescence.

Fluorescent minerals. Click to link back to the  Wikipedia source.

Fluorescent minerals. Click to link back to the Wikipedia source.

For a plain-language video explanation of fluorescence (…that uses lasers), check out this Youtube clip:

So the important point here is that a higher energy (shorter wavelength) light source can make substances emit lower energy (longer wavelength) light. A Raman spectrometer that uses a green wavelength (shorter wavelength) might cause a bone to emit near infrared light (longer wavelength). How does this make fluorescence the “…natural enemy…” of Raman spectroscopy? Quite simply, it’s because the business end of a Raman spectrometer is a CCD or CMOS detector, like the one in your camera. The Raman instrument is designed around producing and gathering light, and because of the way Raman scattering  works, the detectors need to be very sensitive. Also, detectors don’t discriminate between Raman scattered light and the light produced by fluorescence. Light emitted during fluorescence is much more intense than Raman scattered light: emitted light drowns out the scattered light.

So, why is fluorescence the “…natural enemy…” of Raman spectroscopy? A sample that fluoresces during a Raman analysis is unlikely to give you a meaningful Raman spectrum. So the trick is to stop a sample from fluorescing…

 

Marshall CP, Carter EA, Leuko S, Javaux EJ. 2006. Vibrational spectroscopy of extant and fossil microbes: Relevance for the astrobiological exploration of Mars. Vibrational Spectroscopy 41: 182-189

#Red-feathered dinosaurs?

Figure 2: Raman spectra from a fossil feather preserved in amber and from a carbonised compression fossil.

Raman spectra from a fossil feather preserved in amber and from a carbonised compression fossil.

In an earlier post I talked about the ‘insects in amber’ study that Professor Howell Edwards (University of Bradford) and his colleagues reported in 2007. In this study, Edwards et al. used a non-destructive technique (Raman spectroscopy) to analyse the bodies of insects preserved for many millions of years in amber. You can read all about it here and here.

What an amazing idea – amber is not just as a time capsule for ancient life, but also a vault for ancient biomolecules. Edwards and colleagues used this idea to learn more about insect preservation, but what else might be preserved in amber? What about feather pigments?

Birds today are highly coloured – they have colourful skin, scales, eggs, eyes, beaks… and colourful feathers. There are at least six distinct pigments that birds used to add colour to feathers. Carotenoid pigments are the most common colour molecules in the red, orange and yellow feathers of living birds. Were there carotenoids in the feathers in the ancient ancestors of birds? Did dinosaurs have red feathers?

I want an answer to this question!

My first step in getting that answer was to assemble an all-star team. Dr David Grimaldi and Mr Paul Nascimbene are amber experts at the American Museum of Natural History in New York, Dr Carla Dove (Smithsonian Institution) is a plumage expert who describes feathers at a microscopic-level, and Dr Helen James (Smithsonian Institution) is an ornithologist and paleontologist with an amazing understanding of bird evolution. Brilliant.

Next, we had a special Raman microscope shipped to us in the Birds Division at the Smithsonian Institution. This Raman microscope would let us analyse feathers in amber because it had a 1064 nm laser and confocal optics. 1064 nm is is a low energy laser, which means it doesn’t cause amber to  fluoresce (i.e. ‘glow’) – fluorescence is bad for Raman analyses. The confocal optics mean we can analyse a feather through the amber without worrying too much about the surrounding amber. Lynn Chandler at BaySpec did an amazing job of arranging the Raman microscope for us.

OK, I have the right team and the right tools, but do I have the right feathers? Carotenoids are not common in the feathers of living birds – one third of birds have feathers with carotenoid pigments, and most birds only have carotenoids on the outermost feathers. So think about it: if you randomly pluck one feather from any possible bird alive right now, the chances of that feather having carotenoids are pretty low. Imagine also that these feathers were plucked from a bird or dinosaur millions of years ago, they have to survive through deep time, be found by a paleontologist, and then be given to me for analysis. These may be be low odds, but we will never find red feathered dinosaurs if we don’t look.

Feathers in amber are amazingly precious, and we had the opportunity to work with seven ancient feathers from across the world. Alas, we didn’t find evidence for carotenoid pigments in these seven specimens. BUT, we did show that this type of work could be done without damaging the fossil feathers. It’s now just a matter of searching through every feather in amber that is found, to see if we can find our red-feathered dinosaur.

Thomas DB, Nascimbene PC, Dove CJ, Grimaldi DA, James HF. 2014. Seeking carotenoid pigments in amber-preserved fossil feathers. Scientific Reports 4, Article number: 5226 doi:10.1038/srep05226

#Smithsonian intern coauthors scientific article

Charlotte Doney during her undergraduate internship at the Museum Conservation Institute. Charlotte is shown here sitting in front of an FT-Raman spectrometer in Dr. Odile Madden's Modern Materials lab. Photo source: http://www.si.edu/mci/english/professional_development/MCI-REU2012Projects.html

Charlotte Doney during her undergraduate internship at the Museum Conservation Institute. Charlotte is sitting in front of an FT-Raman spectrometer in Dr. Odile Madden’s Modern Materials lab. Photo source.

My colleagues at the Smithsonian Institution and I have recently published an article that explores the preservation of old collagen. I think this is a great methods paper that could lead on to some really interesting applications, and I will get to the details of the article in a little bit. First though, I want to highlight one of the most fun aspects of this paper – a good chunk of the work was done by Charlotte Doney, an undergraduate intern from George Washington University.

In 2012, Dr. Christine France successfully attracted funding for undergraduate students to take up research projects in the Museum Conservation Institute. This Institute is housed within the Smithsonian Institution’s Museum Support Center, in Suitland, Maryland. Charlotte Doney was interested in working with Dr. France and Dr. Odile Madden on a project these senior researchers had discussed some years prior – can Raman spectroscopy tell us if collagen in an ancient bone is well preserved? Charlotte was interested in both the challenge and answer.

Raman spectroscopy provides chemical information about a sample, and in the case of an old bone, is useful for studying both the collagen and the bone mineral. Furthermore, the isotopic compositions of carbon and nitrogen in collagen can tell us about the lifestyle of the person or animal the bone is from. Raman spectroscopy doesn’t report on the isotopic composition of collagen – this is the job of a mass spectrometer. Instead, Raman spectroscopy gives us an idea about how much collagen is present in the bone. As collagen degrades, the isotopic composition becomes less meaningful about the original lifestyle of the person or animal. As collagen degrades, there is less and less of it left in the bone, and we can detect this with Raman spectroscopy. Charlotte collected Raman spectra from bones that had known isotopic compositions.

I was working in Dr. Madden’s lab at the time and had the privilege of training Charlotte to collect Raman spectra, and then later I analysed the data and we each cowrote the now published manuscript.

During my time at the University of Otago and the University of Cape Town I hadn’t worked alongside undergraduate interns, so this was one of the new experiences I encountered at the Smithsonian. I had undertaken (and later, worked with) summer studentships at the University of Otago, and looking back there are many similarities. A dedicated research project, a short and fixed time frame, an opportunity to work with professional researchers. I can’t value these experiences highly enough, and if the student is particularly motivated, like Charlotte, then the work can be recognised as a formal publication. How good is that!

France, C. A. M., Thomas, D. B., Doney, C. R. and Madden, O. 2014. FT-Raman spectroscopy as a method for screening collagen diagenesis in bone. Journal of Archaeological Science 42: 346–355

#An ancient penguin with yellow feathers

Heard Island emerged from the Indian Ocean millions of years ago and is now savaged by snow and battered by wind. The island is among the most remote places on Earth and our record of its existence only began in the mid 19th century. Victorian-era access to Heard Island was by schooner or barque, from a harbour on the Kerguelen Islands 450 km away. In 1874 a collection of Heard Island wildlife was brought to the Kerguelen Islands and given to the crew of the visiting USS Swatara. The animals from this remote wilderness were passed along to Dr. Jerome Kidder, surgeon and naturalist for Swatara. Kidder prepared and studied the specimens, and 17,000 km away in Washington, DC, he stored the bounty at the Smithsonian Institution.

The specimens from Heard Island included this macaroni penguin:

Crest feathers from a macaroni penguin Eudyptes chrysolophus (USNM 533533). Photo by Daniel Thomas.

Crest feathers from a macaroni penguin Eudyptes chrysolophus (USNM 533533). Photo credit D Thomas.

The penguin that Dr. Kidder brought back from Heard Island has recently helped us with an interesting feather colour puzzle (Thomas et al. 2013). Yellow is a very common feather colour and is achieved in a variety of ways. For example, a canary will eat seeds that contain yellow pigments, and those pigments will be deposited in feathers. A parrot will use biochemical pathways to make yellow pigments while the feather is growing. The yellow pigment in penguin feathers is… a mystery. By studying yellow penguin feathers we could not only learn about the pigment chemistry, but we could also discover when the pigment first evolved and which fossil penguins had yellow feathers.

Raman spectroscopy has given us some new insight into the chemistry of the yellow penguin pigment. As mentioned elsewhere in this blog, Raman spectroscopy describes interactions between atoms in molecules or minerals. A Raman spectrum is a set of bands (or peaks…), and each band describes the energy of a particular atomic interaction. For example, a Raman spectrum of bone contains a band at 960 cm-1, and that band relates to an interaction between phosphorus and oxygen atoms. The Raman spectrum from the penguin pigment was brand new; it is not like anything that we have found in the published literature, and it is completely different from other feather pigments. The spectrum contains important bands at 1578, 1491, 1285 and 683 cm-1, which are hallmarks for a nitrogen-bearing, heterocyclic aromatic ring.

Nitrogen heterocycles in a pterin (left) and a porphyrin (right).

Nitrogen-bearing aromatic heterocyclic rings, highlighted in orange and shown in a pterin (left) and a porphyrin (right). Our best guess at this stage is that the penguin pigment contains something similar. Images adapted from Wikimedia Commons.

We still need more information before we can completely solve the structure, but at this stage we know for certain that the pigment is unlike anything so far observed in nature, and it is not likely to be a pigment that the penguins are eating. This means that penguins have evolved a biochemical pathway for making the pigment themselves. Armed with this new knowledge, we can look at the evolutionary history for the yellow penguin pigment. We do this by lining up the relevant evidence. First, we know that ten species of living penguin make the yellow pigment, including king penguins, yellow-eyed penguins and macaroni penguins. Second, we have a good idea that all living penguins are the descendants of an ancestor that lived more than 13 years ago (Ksepka and Thomas, 2012). Third, only penguins have this yellow pigment, and it is absent from albatrosses, shearwaters or petrels. This means:

1)     Living penguins inherited the yellow pigment from the ancestor that lived 13 years ago.

2)     The yellow pigment evolved after penguins had separated from albatrosses, petrels and shearwaters, at least 62 million years ago (Slack et al. 2006).

So the yellow penguin pigment is ancient, evolving sometime between 62 and 13 million years ago. We can use this information to add colour to an ancient penguin from South America.

Madrynornis mirandus (the ‘wonderful bird of Madryn’) is a 10 million year old penguin from Argentina (Acosta Hospitaleche et al. 2007). Madrynornis is more closely related to crested-penguins, like Kidder’s macaroni penguin, than any other group of penguins that we know of. Yellow-eyed penguins are also close relatives of both Madrynornis and the crested penguins, and all three groups shared a common ancestor that probably lived between 10 and 13 million years ago. We know that two of the groups descended from this common ancestor now have yellow feathers (crested and yellow-eyed penguins), so this means that the common ancestor probably had yellow feathers. And, because Madrynornis is a descendant of a penguin with yellow feathers, it very likely had them as well.

So, using Raman spectroscopy of modern feathers, we can add yellow pigments to Madrynornis mirandus, a 10 million year old penguin.

Image credits: Yellow-eyed penguin photo from Christian Mehlfuhrer, Macaroni penguin photo from Liam Quinn, Madrynornis images from Acosta Hospitaleche et al. 2007.

Image credits: Yellow-eyed penguin photo from Christian Mehlfuhrer, Macaroni penguin photo from Liam Quinn, Madrynornis images from Acosta Hospitaleche et al. 2007.

 

Acosta Hospitaleche C, Tambussi C,  Donato M, Cozzuol M. 2007. A new Miocene penguin from Patagonia and its phylogenetic relationships. Acta Palaeontologica Polonica 52, 299-314.

Ksepka DT, Thomas DB. 2012 Multiple Cenozoic invasions of Africa by penguins (Aves, Sphenisciformes). Proceedings of the Royal Society B 279, 1027–1032.

Slack K, Jones C, Ando T, Harrison G, Fordyce RE, Arnason U, Penny D. 2006. Early penguin fossils, plus mitochondrial genomes, calibrate avian evolution. Molecular Biology and Evolution 23, 1144–1155.

Thomas, DB, McGoverin CM., McGraw KJ. James HF, Madden O. 2013. Vibrational spectroscopic analyses of unique yellow feather pigments (spheniscins) in penguins. Journal of the Royal Society Interface (doi: 10.1098/​rsif.2012.1065)

Image links: macaroni penguin, Madrynornis mirandus, porphyrin, pterinyellow-eyed penguin

 

P.S. We called the Raman spectrum ‘spheniscin’, and eventually we adopted this as the name for the pigment. Unfortunately, this was a very embarrassing mistake. A quick google search will show that the name ‘spheniscin’ has already been taken, and I will make sure this mistake is formally corrected in a follow-up publication.

What will be our final trace?

I met a traveller from an antique land
Who said: Two vast and trunkless legs of stone
Stand in the desert. Near them, on the sand,
Half sunk, a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them and the heart that fed:
And on the pedestal these words appear:
“My name is Ozymandias, king of kings:
Look on my works, ye Mighty, and despair!”
Nothing beside remains. Round the decay
Of that colossal wreck, boundless and bare
The lone and level sands stretch far away.

Percy Bysshe Shelley

 The legacy of Ozymandias provides an interesting metaphor for the fossil record. It is easy to recognise a living entity while it is still alive. It can be a little difficult to say that something was alive if we only ever saw it dead, and it becomes even harder to claim that something was alive if there is very little of it left. Time will eventually reclaim everything. So, what are the minimum traces that life leaves behind?

Biochemicals.

One group of biochemicals of particular interest are carotenoids. As the name suggests, carotenoids can be found in carrots – the carrot colour is ‘β-carotene’. Carotenoids are found elsewhere as well, and critically, they are only made by plants, algae, bacteria, fungi and one animal, an aphid. Carotenoids are not made by geological processes, but are instead evidence for life. Like all traces of life, carotenoids are eventually broken down and recycled by Earth processes. A plant rich with carotenoids will die and settle into the Earth, and even without the aid of bacteria, the atoms in the carotenoid will disassociate and break the biochemical apart. So, eventually these minimal traces of life will eventually fade away. These biochemicals break down into smaller and less complex molecules. So, carotenes in the fossil record, or carotanes – the breakdown products – are unambiguous signals that life once existed, even if those remnant molecules are all that remain. Like Ozymandias’ “…vast and trunkless legs of stone…”

Pigments like β-carotene are evidence for life, but these biochemicals can be altered during diagenesis.

So, when we investigate the time worn sediments from an aeons-old Earth, or Mars, we can look for carotenoid degradation products as vestiges of life. This was the focus of a 2010 study by Craig Marshall and Alison Olcott Marshall. Diagenetic alteration can result in “…hydrogenation of the polyene chain…”, the long carbon backbone of the carotenoid that gives the molecule it’s colour. In essence, the unaltered carotenoid has many carbon atoms bound to each other with ‘double’ bonds, but in the altered carotenoid, these carbon atoms are only bound with ‘single’ bonds. Marshall and Olcott Marshall (2010) describe the Raman spectra β-carotane and lycopane and show us what to look for in the fossil record. These altered carotenoids could be the only traces of a once teeming ecosystem, and we might otherwise never know about them if not for spectroscopy.

Marshall P, Olcott Marshall A. 2010. The potential of Raman spectroscopy for the analysis of diagenetically transformed carotenoids. Philosophical Transactions of the Royal Society A 368: 3137–3144.

Images compiled from Wikimedia Commons, here and here

Red blooded Ötzi

Raman spectroscopy has revealed that red blood cells preserved in the Iceman still contain oxygen-transporting porphyrin molecules.

Ötzi the Iceman was an important member of his ancient Tyrolean tribe. He planned to return to his village, but he took an arrow to the… shoulder… and instead was mummified in a glacier until discovery in 1991. His body has been so well preserved over the last 5300 years that the collagen in his skin is still intact. Ötzi is still contributing to science, and a recent study from Marek Janko and colleagues analysed the blood in Ötzi’s veins (Janko et al. 2012).

Small tissue samples were taken from wounds on Ötzi’s right hand and left shoulder. The Ötzi samples and fresh human tissues (from a volunteer) were prepared so that the red blood cells could be studied. Three analytical techniques were applied to the tissues, including Raman spectroscopy. Raman spectra from the ancient tissues were very similar to the spectrum of modern human blood. The peaks in each Raman spectrum were characteristic of a porphyrin – heme – which proved two things. First, there is still blood frozen in Ötzi’s arteries and veins. Second, the molecule heme, which has the important role of transporting oxygen around a living body, can be preserved for 5300 years. The heme in Ötzi’s tissues has degraded slightly, but it is still the red blood pigment we are all familiar with.

Janko M, Stark RW, Zink A. 2012. Preservation of 5300 year old red blood cells in the Iceman. Journal of the Royal Society Interface.doi:10.1098/rsif.2012.0174

Images compiled from Wikimedia Commons ( here, here and here)

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