[ Music ]
>> We will do-- I'll do a
little bit of an introduction
of traditional and
geometric morphometrics.
That's the basis of
3D-ID, the software
that you have downloaded on
our laptops and that we'll get
to use and I'll tell you also
the design of the project
that we did and it was
many years in the making
and I'm glad that
we finished it.
[Laughter] Essentially,
what the software does,
it will determine ancestry and
biological sex of an unknown set
of remains or a skull--
from a skull.
So, we will transition
from the lecture part
and we'll actually go and get
to go play with some real crania
and the other part of the lab,
we'll go over the landmarks
that we've pared down from
a standard set that we--
using and also give you some
tips on how to use a software
because sometimes it's not that
intuitive and some tricks on how
to upload your data from the
text files, that you don't have
to cut and paste them all
in there which will be nice
and other things that are--
may not listed on the website
that will help you sort
through some of the issues
when you actually use it to
analyze your remains, okay?
Alright, so, fundamentals of
traditional and craniometrics--
craniometrics and
geometric morphometrics
and if you don't mind,
I may have to use my cheat sheet
a couple of times with this one.
Alright, so historically
speaking,
traditional craniometrics
has been based on sets
of caliper measurements.
So what you end up using--
you have a set of
caliper measurements
and what you are
measuring is the distance
between the endpoints
of your calipers, right.
So it's 2 dimensional.
And what you have
here as you can see--
and also use those
sets of measurements
and you use multivariate
statistical methods
like discriminate
function analysis.
Sometimes, you use angles
to allocate crania
into certain groups.
But because it is 2D and because
it's only measuring a distance
between the endpoints
of 2 landmarks,
you get an incomplete set
of biological information
for whatever object it
is that you're measuring.
So you don't have the
full biological archive
of that object or form
that you're using.
Geometric morphometrics
on the other hand,
it's using coordinate landmark
data, it's the one that we used,
are various types of
geometric morphometrics
and we'll just briefly cover it.
We use the same-- what's
nice about it, we use--
if you use landmark data
or landmark coordinates,
you use the same anatomical
landmarks that you would use
in traditional craniometrics
so it shouldn't be too much
of a leap to get there,
but it fully archives
the biological forms,
'cause you get the X,
Y and Z coordinates
for each anatomical landmarks.
So for example, when you do
traditional craniometrics,
there's no way to reconstruct
that object in real space,
whereas you can reconstruct
that object or the skull
in real space by using
geometric morphometrics.
And it's also a lot of
more visually appealing
so you can see some of the
changes, right, visually,
whereas when you just have
a list of coefficients
and statistics, it's a
little bit more difficult
to actually see where some of
these landmarks are changing.
For example, in traditional
craniometrics, you can say,
well, if you take maximum
cranial breadth, right,
the maximum breadth of the
cranium, you can decide, well,
this skull is broader
than this skull,
but in geometric morphometrics,
you can say perhaps
like the left asterion
is more anteriorly placed
in this population
or X population
where you can't actually
tease that out
in traditional craniometrics.
Alright, so a few
definitions to get us started.
This should be on-- I
guess on your jump drive
and also you should have
the PowerPoints on there
and some other things.
So shape basically is
the geometric properties
of an object which are
invariant to location,
scale and orientation.
So, say you take your coordinate
data, even if you enlarge it
or reduce it, you still have
that same shape information.
That shape information is not
lost, 'cause it's invariant.
It's supposed to be invariant to
location, scale and orientation
of that said object, okay?
Shape variable is the variables
that we use a lot of times
to look at these population
differences between groups,
between species and
other things.
It's a geometric measure of
the object that's invariant
to location, scale
and orientation
so it's a similar
definition than the other one.
It's just the variable
that we're actually using
in our analyses, okay?
[ Pause ]
>> Okay, so the size measure--
and the one thing also that--
let me go back a little bit.
In traditional craniometrics,
there is no real
adjustment for size.
You can do ratios.
People have done ratios.
You can do Darroch and
Mosimann where you--
also, it's kinda ratios
where you try to account
for size difference but there's
no true measure to extract size
from what it is that
you're studying.
Whereas in geometric
morphometrics, you can choose
to look at only the shape of
those groups or the species
that you're looking at.
If you're looking at
fish or some other things
that you're looking at, you can
extract size from the equation,
only look at the
shape differences.
And that becomes really
important when you're looking
for example at populations
for different time periods.
Populations have
changed overtime.
They get generally larger due
to positive secular changes.
So, if you are wanting to look
at maybe the same population
and their time, shape changes.
You can extract size and only
look at those shape changes
and not worry about size
being a confounding effect
on your analyses.
And also, it's important due to
things like when we're looking
at sexual dimorphism, right.
So you'll have differences
in sexual dimorphism
between populations, different
groups and those are things
that you can look at, also just
look at shape whereas not worry
about the size component
affecting your analysis
or your results.
So the size measure is any
positive real valued measure
of an object that scales
as a positive power
of the geometric
scale for the form.
In geometric morphometrics,
generally we use the
term centroid size, okay?
And form just means if we're
looking at both size and shape,
but it's only size and shape
and one thing to note is
that we do not look at
the color of the object,
those do not go into it.
We do not look at the
surface composition.
The only thing that we
are essentially interested
in is size and shape
of the object.
And I say object because
it could be anything.
It could be a toy car, you know,
we can use this 'cause
we actually borrowed this
from computer sciences and
other biological sciences
and we call it the new
morphometry but it's not
so new anymore 'cause I
started doing this in 1998,
so how many decades
ago is that, right?
So, it's not that new but
it's still relatively new
to forensic scientists because
it's very cumbersome to apply.
Caliper measurements
are really easy.
You just measure
them, you plug them
in to whatever statistical
software or equation
that you have and it runs.
Whereas geometric morphometrics,
there is some period
where you have to get your
data ready to be analyzed
and that actually could leave
you to throw your computer
out the window if you want.
Yes?
>> This is a quick question.
You're using more than just
the standard landmarks, right?
>> We use-- it depends on what
your standard landmarks are.
For example, we were trained--
a lot of us from Tennessee were
trained using the Howells set
so that's a minimum
of 77 landmarks
that we collect that's using
the Howells standard set.
But if you're basing
your standard landmarks
on the forensic set, I think
that's 24 not including
the mandible.
So it depends what
standards that you're using,
but they are standard anatomical
landmarks that are standard in--
for clinicians, for
anthropologist,
so they are standard
anatomical landmarks.
Okay, does that answer
your question?
>> Yes.
>> Okay so, geometric
morphometrics is the collection
of all of the methods in
acquisition, processing
and analysis of shape
variables that retain all
of the geometric information
and that's one thing
about geometric morphometrics.
It retains the geometric
information of that object.
Whereas traditional
craniometrics does not retain
that geometric information
of that object, right?
>> Alright, again morphometrics
is the study of shape,
shape variation and the
covariation of shape
within extrinsic factors,
but that can also include
different modalities
of analyses.
Most of us use Generalized
Procrustes Analysis
which I'll talk about
in a second.
But there are other things
that you can look at.
For example, you can look at
outline data, you can look
at landmark data,
semi-landmark data and I'll go
through those things
in a minute.
The shape again is the
geometric properties
of a specimen invariant to
location, orientation and scale
and I know I keep harping
on this, but this is one
of the main key points and
then form is both your shape
and your size together
and again,
maximum geometric information is
what geometric morphometrics is
doing and this is why
it's a different process
that we're using in 3D-ID.
Some of the good sources
are in Slice's paper
where he does a really
good overview
of different methodologies
or modalities
in geometric morphometrics.
Alright, so what kind
of data do we have?
We have linear distances, right,
traditional craniometrics.
You can also extract
linear distances
from geometric morphometrics
using EDMA
or Euclidean Distance
Matrix Analysis.
But unfortunately, they
are kinda like 2 camps.
Right now, you have the camp
that does Generalized
Procrustes Analysis
and the camp that does EDMA.
Quite frankly, I don't see
why not everyone can play
in the same sandbox
because one method is better
for doing certain things and
another method is better doing
for other things, but I guess
that's a story for another time.
Then we have outline
data which is part
of geometric morphometrics
and this is an example
of outline data at the bottom
here where you look at a leaf.
They have used this
also to look at things
like mosquito wings
and other thing.
So what basically outline data--
EFA stands for Elliptical
Fourier Analysis.
So outline data kinda
look similar to landmarks,
but the difference is
when you're dealing
with landmark data, you
are comparing specific
or individual landmarks
across specimens
and then across populations.
So for example, if
you're looking at bregma,
you will be comparing
bregma to bregma to bregma
across specimens and
across populations.
Whereas in outline data, you
are comparing the entirety
of that shape across
populations.
You don't take the
individual points.
And the way you analyze this
is, it's in 2 dimensions.
You can't do it in 3 dimensions
so it is in 2 dimensions.
What you do is you get
a suite of harmonics.
So for example, the
more harmonics you get
with one harmonic, you
see it doesn't represent
that shape very well, right.
So the more harmonics that
you add, you have 5 harmonics,
it's getting a little
bit better, right,
at adapting to the shape.
And then with 10 harmonics, it
pretty much has that shape down.
So the harmonics act almost
like principle components,
so then you would
take those harmonics
and you would analyze
those harmonics,
your normal way using
multivariate statistics, okay.
So I've tried doing EFA.
It's very complicated and my
brain just doesn't quite work
that way so I have
done it but it--
I find it a little
bit cumbersome
but some people really like it.
And they have done studies--
actually, Angie Christensen
was able to look
at the frontal sinus
patterns and she used EFA
or shape outline data.
Alright, Bookstein--
and when we look--
when we talk about
geometric morphometrics,
we talk about book
colors and Bookstein came
out with the Orange Book
in 1991 and in that book,
he defined 3 types of primary
landmarks that we utilize.
The first one is
type 1 landmarks.
Those are the most
biologically-meaning landmarks,
okay?
So these are the most
biologically-meaningful
landmarks.
And they're usually at the
intersections of 3 sutures.
So I have here as an
example of [inaudible]
and that's a type 1 type of
landmark and we'll be able
to identify all of
these points in the lab.
So this is a true
type 1 landmark.
Then we have type 2
landmarks and these are--
have some pretty good biological
meaning to it and usefulness
to it, but they're
not as good as type 1,
but these are still good to use.
These are curvature maxima.
For example, if you were
to measure the height
of the mastoid process,
you know, the inferior part
of the mastoid would be
a type 2 landmark 'cause
when you're looking at
the anterior portion
or superior point of a specific
landmark, then you're dealing
with a type 2 landmark, okay?
Type 3 landmarks are the least
biologically-meaningful types
of landmarks and these are great
for traditional caliper
measurements, keep using them
but I prefer not to utilize them
when I am using geometric
morphometrics
and we have excluded all of
type 3 landmarks from 3D-ID,
so that's the first thing that
we did was to exclude all those.
So for example, type 3 landmarks
is maximum cranial breadth
eurion to eurion, right?
So when you're taking
your spreading calipers
and you're measuring
maximum cranial breadth,
so let's say you get
143 millimeters, okay?
That 143 millimeters can be
in a generalized area, right?
So there's a larger area
that's 143 millimeters.
But when you're trying to
identify a single area or point
with your stylist where that
143 is it's almost impossible,
so there's a lot of intra and
interobserver error associated
with his type of landmark, okay,
'cause there could be a lot
of areas on that breadth of the
skull that will be 143, alright,
so there's no way to
point out accurately
where that one position
is, alright?
[ Pause ]
>> Alright, semi-landmarks,
apparently, you know,
unfortunately for me now
that I've got landmark
coordinate data down,
it's taking me 10 years, right?
Now, Bookstein comes
out and says, "No,
you need to use semi-landmarks."
Well, you know what, I'm not
listening to him, not doing it.
This is complicated so you
have a lot of corner points
so you have 2 different types.
You can have semi-landmarks
where you have points
that are equidistant to each
other, so you're kinda looking
at an area of outlines where
they don't have a lot of--
so these are really useful
for areas for example
like the eye orbit where
you don't have a lot
of anatomical landmarks
and you wanna get visually
for example the shape
of the entire orbit.
So using semi-landmarks that are
equidistant, then you would tale
at say 100 points,
right, around the orbit.
But then you would align those
points between the individuals
like every 10 points so that
they would be homologous
across 2 specimens
across your populations.
The other one that I think
Bookstein is a major proponent
of is the sliding
semi-landmarks.
And what the sliding
semi-landmarks are--
and this one I like a little
bit better, so for example here,
you wouldn't take the
whole area because we--
honestly, we don't have software
that can actually analyze
so many points, everything
crashes,
so we don't have a software
that can actually really
analyze all those points
and when you think
about the samples
that anthropologists have, our
samples are not large enough
to actually be able to
analyze all those points.
So if we do semi-landmarks--
so this is an example,
you have 2 points that are fixed
in real anatomical landmark,
so Pro Osteon and
[inaudible], right.
So these 2 points would be fixed
and then you would have the
semi-landmarks and you can have,
what 30 points in between,
but when you look at
and compare different specimens
and different individuals
or populations, you would align
the fixed anatomical points
and then that's how you
would align your individuals
and you would be
able to use those
to actually look at your data.
So this-- yes.
>> In the same landmarks, do you
get them by using the slide--
caliper to apply them?
>> No, you use the
computer, so for example,
when you're collecting
your data, you're just--
you make sure you get your
fixed points on this one
and the same goes
for the other one.
You just point, point, point,
point, point and then you do it
after the fact where you
get your fixed points
or your equidistant points
or whatever it is
that you're using.
So that-- you know, you have
a set number that you just go
and mark as you're
going and it happens
after you've collected
your data, okay?
So you've probably have to dump
a bunch of points afterwards.
[ Pause ]
>> Alright, so then we have
another type of landmarks
and these are constructed
points and these are also
like type 3 landmarks, not
as biologically meaningful
and this is from a paper from
Williams and Richtsmeier, 2003.
Some of the descriptions
for example,
number 4, that's right here.
And this one is mental foramen
and it's the anteromedial edge
of the mental foramen.
Okay, so a constructed point
is-- had some anatomical meaning
but it's constructed
meaning that it's an area
that doesn't have a
true landmark, okay?
And then we have
fuzzy landmarks.
I like the name of fuzzy 'cause
I feel fuzzy a lot of days.
[Laughter].
So, fuzzy landmarks sounds good.
This was actually-- came
out in 1998 and I remember
when Valeri ET AT actually
presented this at one
of the conferences and it
was like the next big thing.
But what you used as a
fuzzy landmark is kind
of an anatomical area
that you can see,
but it doesn't have a specific
point like the frontal boss.
You can see it like your
parietal boss as you can see it,
so that would be kind
of a fuzzy landmark.
I don't think too many people
use fuzzy landmarks nowadays,
but this is another type of
landmark that you can look at
and I think people were trying
to accommodate the areas
of vault in the skull where
they don't have true landmarks
or true anatomical landmarks
that you can analyze.
Any questions so far?
No? Good? Alright, so
reliability of landmarks.
Best, most confidence
type 1, right?
Intermediate, type 2.
So generally, what we kept
for 3D-ID were the type1
and type 2 landmarks.
Type 3 landmarks are
the least confident
and the worst are fuzzy and
constructed landmarks, okay?
And we're not saying don't
use them ever, I mean,
every time that you
set up your study,
you incorporate your own
biases, so all of the studies
when you're selecting things for
geometric morphometrics is based
on the samples that
you're using, the questions
that you're asking, the
availability of the data,
all those things
actually go into play
when you're selecting the types
of landmarks and the types
of study that you're using.
So if you can incorporate
for example your own error
for type 3 landmarks for your
own study, there's nothing wrong
with doing that, but if you
are actually using data I think
from multiple individuals,
I think that's
when it gets a little
bit sketchy 'cause you--
there's no way to know how much
error is being introduced based
on those types of landmarks.
Alright, data acquisition
devices,
that was the old school
MicroScribe digitizer.
It used to be the
low red machine.
Now, it's a black machine.
They have different types.
You can even have one
now with a scanner on it
to scan which I haven't use.
They have the tablet
form, the Polhemus,
all of these can be
used to collect your X,
Y and Z coordinates and if a lot
of people also have used point
extraction of scanned images
that you can get your points off
of your scanned images as well.
Unfortunately, when you
do the scanned images,
you don't get 3D, you're
actually doing in 2D
and I think it's
better to keep all
of the biological information so
I would suggest just going ahead
and digitizing your skulls, but
if you don't have that available
to you, you can use-- and a lot
of times these are automated,
they have software out
there that you can automate
and extract your landmarks.
These are listed so if you're
interested in these digitizers,
you can go and look at them.
Okay, so you've collected
your data, now what do you do?
You have pages and pages and
pages of 3D data and remember,
your 3D data, every landmark
that you have, you have an X,
Y and Z coordinate, right,
for how ever many landmarks
that you have, so that's
when it gets cumbersome.
So unlike traditional
craniometrics,
you can't just take
those numbers and plug it
in to whatever software program
to run a discriminate function.
It just won't work.
So, we used Generalized
Procrustes Analyses
where you have to
scale each individual
to the same coordinate system.
So what does that mean?
You have to scale, rotate
and translate each individual
to the same coordinate system.
So for example, you
take whatever specimen,
say your first specimen, it'll
just align everything together
so that everybody's on the
same coordinate system.
We'll see it in a second here.
So first, we superimpose
our landmark configurations.
You can derive Euclidean
distances.
This is another method
that's the EDMA method.
You have to maintain
a constant orientation
or you can partially
superimpose landmarks.
This is for the error rate that
we were studying right now.
So we did a study
that looked at--
with all of these new software
and all of these new studies
that were coming out doing
geometric morphometrics,
nobody actually tested the
landmarks that we were selecting
for use in these
projects using a digitizer
or using different
methodologies.
So this is what we need.
But in order to do an error
test or a precision test,
you have to-- if you're using
geometric morphometrics,
you have to have the skull
in the same orientation.
You can't move it.
So that's one key
thing to remember.
Let's say if you're
digitizing your skull
and if slips are moved, you
have to start over again.
You can't just continue
digitizing
because you're introducing
all kinds of error, okay?
So what we ended up doing is
using Euclidean distances,
the other camp, right, we
used interlandmark distances
because our study was
based on crania that were
at the University of Florida,
my colleague there digitized--
Shanna Williams,
digitized her crania.
She shipped them to me.
I digitized the same crania.
So obviously, that would
introduce quite a bit
of error, right?
So we used interlandmark
distances to look at the error.
So Generalized Procrustes
is the preferred method.
So what does that is that
you select a specimen
to approximate the
mean of that specimen.
And then you fit all
of your other specimens
to that one mean.
So that's when you scale,
translate and rotate.
So the one specimen X as
your approximation mean.
It doesn't matter
which one it is, okay?
So it could be random.
And then you recompute the
mean as the simple average
of all the fitted coordinates.
So it's a good thing that with
3D-ID, this is all done for you.
You don't have to worry
about these things.
So that's why we simplify that
it was Geometric Morphometrics
for Dummies 'cause we knew
that if we ask people to go
through this and I know
Kate has as well as I have,
you have been reduced to
tears at times when trying
to work this-- Joan too, right?
[Laughter] So you fit
your entire new sample
to the new estimate and
then you do the last 2 steps
and this is all done
on the computer
until you have convergence in
that so everybody's aligned
and I have here this
shows it really nicely.
So, the picture over there is
you're raw landmarks, right?
So you have 2 specimens.
Okay, same landmarks,
same anatomical landmarks.
Then what do you do?
You have to center your
landmarks, okay, right in here.
So all your landmarks
are centered, right?
And they're not in the same
coordinate system that's why
they look like they're different
sizes, but they're really not.
They were just collected
in different coordinate
systems, okay?
So then you center and
you scale your landmarks.
Now, they're all kinda
the same size, right?
So they're all scaled.
And then finally, you
center, scale and rotate
so that this is what your end
result is and that's what you do
with every specimen, okay?
Easy peasy, right?
>> Long as I don't
have to do it?
>> Right. [Laughter]
Right, that's--
yes, as long as you
don't have to do it.
So Euclidean distance,
what does it mean?
It's a straight line
distance between objects
and you just take as many
interlandmark distances
from whatever anatomical
points that you're studying
as what Euclidean distance uses.
So it's invariant to location.
It's a coordinate-free system so
you don't have to scale these,
right, because if you do, all
the interlandmark distances
for all of your specimens,
you don't have to worry
about that scale thing.
So that's why we used EDMA
when we did our precision study
because the skulls were shipped
from one state to
another, alright?
So see, it does have its uses.
These are some area-- if
you go to the life bio
at SUNY Stony Brook, you have
all of the software for free.
It's all free ware.
You have morphologic-- a
past system, one that we used
for interlandmark distances.
Morphometrica is for
Mac, Morpheus et al.
and now there's MorphoJ which
is another good one to look
at population differences.
But unfortunately with
those, you still have to mess
with your data, you still have
to run it through a GPA system
and learn how to
deal with it, okay?
But just in case, if you
feel you have a free weekend
or something, right?
>> So here comes 3D-ID
and we developed this pretty
much Geometric Morphometrics
for Dummies.
All you need to do
is collect your data
in Excel whatever it is that you
wanna do and you plug your X,
Y and Z coordinates
into the software.
You don't need to know what's
going on in the background,
okay, that's thanks
to Dennis Slice.
So why did we do this?
We wanted to develop population
specific classification criteria
using the newer,
more robust methods,
the traditional methods
still work really well but--
and they actually are very
comparable to the older methods,
but the nice thing
about 3D-ID is
that you can find
subtle differences
that you may not
be able to detect
in more traditional means,
so we're still working on it.
We're still adding and
actually Dr. Slice wanted me
to let you know that
there's gonna be a new update
with new samples soon
for you to download,
but he said to let you
know that the new version
of java I guess introduced a
slight glitch in the system
so he's cleaning that up.
It's-- to compile-- we wanted
to compile an extensible
population database derived
from 3D data and Kate
has been gracious enough
to donate the Guatemalan samples
and various other researchers
have been able to donate too.
So right now we have close to
I wanna say 1300 individuals
in that database and what
we've done is also group them
by Mesoamerican,
South American--
some of our samples make sure
before you run it though to look
at the sample sizes for
each one because some
of them are still kinda empty.
So, if you're having problems
when you run the software,
it may be because some
of the reference populations are
still lacking some individuals,
but what we're doing is working
to keep collecting the data
so we keep updating
and filling those gaps
and as we see-- as we need them.
Okay, so when we did
our reliability study,
we looked at-- we wanted to
look at type 3 landmarks,
type 1 and type 2 landmarks
so this is the first test
that we did to look at--
well, you know, we're saying
everybody should do 3D
but you know, what is the
error and the precision
of using this technology.
So this is the 3 skulls
from the Pound lab.
We had 2 observers.
We had 3 digitizing
sessions per skull
so each individual
digitize each skull 3 times.
The skulls were not fixed, hence
we did interlandmark distances
and how you determine your
interlandmark distances is you
take your N. So N
minus 1, right,
we had what, 17 landmarks,
right?
Divide it by 2, or
I guess it was--
yeah, divided by 2 and then you
take your landmark number minus
1 divided by 2 and then you get
100 so we ended up with a total
of 171 total interlandmark
distances and that was the suite
of distances that we
used to analyze the data.
So we looked at all of these
interlandmark distances
and 32 percent showed digitizing
error in excess of 5 percent
or 54 interlandmark distances
out of the 171, okay?
So 37 of these included
eurion, oops, okay?
Twenty eight percent
included alare, alright.
And the radiometer point and
opisthocranion were problematic.
And the radiometer point are
not part of the forensic set
but some of those you who take
the house measurements will see
that those are very problematic.
And opisthocranion, right?
It's the maximum length
where you run your--
it's kinda like eurion where
you have to run your calipers
to get your maximum length.
So all of these as you can
see was the type 3 landmarks
that gave you error in
excess than 5 percent
which is unacceptable.
And between observer variation,
all of the variation here
that you see that was
significant also had radiometer
or a type 3 landmark so
this is between observer.
So not only was it intraobserver
but interobserver
variation was significant
when using type 3 landmarks.
So, what did we say to that
is you have to be cautious
when using type 3 landmarks
so we decided to take all
of those out for 3D-ID.
Alright, so we originally
started out with 75 landmarks.
First of all, can you get busy
people to take 75 landmarks,
probably not and
some of the landmarks
with very obtuse
descriptions at best, right?
Accuracy and repeatability
of type 3 errors was poor
so that was the first
thing we decided.
Okay, so let's start by taking
out the type 3 landmarks.
We still continue-- when I still
go on data collection trips,
I still continue the
entire suite just
because that's what
I was trained to do
and I feel guilty
if I don't do it.
So that can be for future use
or for my own personal use later
or to share data
or what have you.
So, our 3D-ID reference
data now uses 34 landmarks
for classification, okay?
The definitions come from
Howells and they come
from the Moore-Jansen,
Ousley and Jantz, 1994,
so there's a standard set.
So it's a few more than the
standards but not too many
and these are our standards
that's in here and one thing,
we have included now I collect
the right orbit traditionally,
we didn't collect the right
orbit only the left orbit
when we did data collection.
So when you run analysis
in 3D-ID,
make sure that you omit
your right orbit superior
and inferior border because most
of our reference sample
unfortunately were collected
in the early days and some
people still have not moved
to colleting both
right and left.
So, when you want
a larger sample,
we have a larger set
using just the left orbit.
So we have it in there
'cause we're trying
to move towards collecting
both orbits, okay?
So as you can see, they
all are standard sets.
This is the posterior aspect,
the inferior, and we will go
through all of these in the lab.
We have-- this is at the time
of when we released 3D-ID,
we had 1089 individuals.
I've added about almost
100 Angolans, Portuguese,
Cambodians as well
are included--
that's gonna be released soon.
And we would like-- so these are
the people that actually helped
with providing either
collections
or assisted with providing data.
So this is the data
pane, it's pretty easy.
It's easier to collect
your data in the format.
It's in alphabetical order so
you start out with asterion
and all you do is literally cut
and paste into coordinate data.
But if you want to import, you
can also import the data sets.
So if you're collecting
in Excel,
you make sure you collect
your data, actually not
in the new version of Excel
but in the compatibility mode
of Excel 'cause we
found that out yesterday
that MicroScribe will not
work with the new Excel.
It will only work in the
compatibility version.
So, if you collect
your data that way--
and the best way to do it is
just type in your landmarks
in your Excel sheet on the left
and you can just
start collecting
and it'll just collect
all the way down in a row.
Then what you will do is
save just your landmarks,
just your X, Y and Z landmarks
without the landmark
definitions or--
excuse me, the landmark
names on the left.
Save it as a text tab delimited
file and then when you go
in here, you will
go into program
and you will say read file,
and it will pull up your file.
So-- oh, one other thing to
make sure you leave a space
for the right upper
and right lower orbit
and another space between-- it
was nasion and left orbit also.
So it has to be a space in there
or the data will come
in in the wrong area.
So then you save it as
your text and all you have
to do is read file and then
your data will be in there.
And that's because we bothered
Dennis over and over again.
It's just crazy when you have
several cases you wanna put
in to cut and paste
every landmark
and putting it in there.
So he listened thank goodness.
Now, we can import our data
from a text file, okay?
So we'll work on
that in a second.
So then you can go
in here, just--
I would leave the shape
dimensions to use,
it's just a leave
what's in there.
>> You can determine
group and sex.
One thing I will say that
when you add sex to the mix,
it lowers your allocation
because once you start looking
at different populations, sizes
of factors, so that happens
to traditional methods.
When you start doing
discriminate functions based
on ancestry and sex,
your correct classifications
will be reduced.
So I would go in here
and then look at--
we only have a few inserted
Caribbean area right now.
I believe it's Panamanians,
a couple of Bahamians
that are in there.
So, know your demographics
of your population
and then you can click
some of these areas.
Africans might be skimpy still.
So click what you want and then
you just hit process, okay.
And it should give you the group
that your skull is more
closely related to.
And we have used it and I know
Aaron Kimberly has used it
in Florida and it has worked
really well with Mesoamericans
and with the young
boy-- it doesn't do--
it can't do sex for juveniles,
but it has been classifying
are juveniles
into the correct ancestral group
or geographic area
especially with Hispanics.
Here's your data option and
then what you would hit is the
process button at the bottom.
This is what your
data will look like.
You'll have 3 columns of X, Y,
and Z. It can do
missing variables
so if you have a
fragmented skull,
you just plug in what you have.
But if you're importing
it as a text file,
make sure you have those
missing rows in there
so that your data goes in
to the correct row, okay?
And this is typically
what you would get.
You'd get a posterior
probability,
you'd get typicality and then
this will give you the sample
sizes of the groups that you
picked, okay, for allocation.
Alright, some of
the applications,
I've been testing
it on the cases
from the medical
examiner's office that we get
and we positively
identified one case
as European-American
versus European.
We have a European
group 'cause as we know,
European-Americans do
not look like Europeans.
And it had a posterior
probability of 0.65
in a typicality of 3 so
that worked pretty well
and we also tested it--
we're still using Fordisc so
we can compare our results
and actually Fordisc is how we
align ourselves and make sure
that we're working how
we're supposed to work.
As you can see, the
results are very similar
and they had it in white male.
And then we also were able to
allocate, we have an adult group
that will be concluding there.
We tested this using
subadults of Portuguese
and it correctly allocated
the Portuguese juveniles
into that European or
the Portuguese group,
so it is working on which
is nice to say working
with the juveniles as well.
We used canonical
variate analysis.
Dennis is working on
alternative fitting methods.
He also has where he
tests everything using
cross validation.
So we're trying to get age
adjusted but that's gonna be
in the future as well and we're
gonna try to incorporate some
of Joan's postcranial
data in this as well.
Future applications,
what I'm very excited
about are the juveniles because
like I said, we were able
to look at the juveniles
from Portugal
and they allocated correctly--
the Mesoamerican 10-year-old,
allocated correctly.
And I wouldn't use them
in the really young,
but I think once you get to
that 10-year period, right,
because number 1 we
have reached, what,
85 percent of our size by 10.
So I think that's okay and
also ancestry Unna determined
that you could actually
distinguish that in utero
so a lot of those things, so--
I think you use it for the
older kids or teenagers
that will be safe to do it
so the applications have been
from going just using for
adults that we may be able
to use it now for children.
This is a study that we did
that we looked at Cuban,
historic African slaves.
We had children for that
Africans, Americans,
Portuguese adults, and
Portuguese subadults
and as you can see that
our Portuguese subadults,
50 percent classified into
the subadult Portuguese
and 50 percent classified
into the adult Portuguese
so we got the correct
allocation for the group
so we were pretty
excited about that.
So again, we can correctly
characterize ancestry
and subadult crania.
So these are some of the things
that we would like to add
in the future, but again it all
depended if we can have samples
of the same population
of subadults and adults
so maybe we can move to maybe CT
scans or something in the future
to get some data on that.
Thank you.
Any questions on
geometric morphometrics.
No?
>> I have a question.
>> Yes?
>> When you were doing
the error testing
with the type 3 landmarks, will
you still have some marking
where your eurion was or--
>> Yes.
>> Okay.
>> Yes, I was 'cause--
yeah, you can't--
yes, still problematic, yeah.
But still pencil marking in.
Yeah and there were-- and
opisthocranion also pencil mark.
>> Its tough.
>> Yes. You can't eyeball
that one fortunately.
>> Alright.
So Anne, when you're
saying they're not fixed--
>> Yeah.
>> How do you setup.
I mean I'm sure I get
[inaudible] to see it but--
>> Oh, they were not fixed
meaning that they did not move
between observers
and between sessions,
that's what I meant by fixed.
>> Okay.
>> Yeah, so I do have them in
a stand but if you move them
from place and then put them
back in or between skulls
and stuff, that means
it's not fixed, okay?
Yeah, sorry, yeah.
Okay. Thank you.
>> So this is all goning
magically make sense
when go next door[inaudible].
>> Yes.
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