►
From YouTube: DevoWorm (2021, Meeting 44): Microscopy Image Analysis, Diatom Patterning, Cambrian Development
Description
Presentation on Image Processing of Microscopy (Axolotl) and Cell Tracking (Zebrafish) data. Patterning in Pennate Diatoms. Optimization of Lineage trees and spatial lineage differentiation in eutelic organisms. Cambrian embryos and the emergence of metazoan body plans. Attendees: Susan Crawford-Young, Richard Gordon, Karan Lohaan, and Bradly Alicea.
B
C
A
F
A
A
C
A
A
A
B
A
I
I
I
D
I
Say
angles
and
things
like
the
depth
of
this
thing:
that
is
there
and
the
at
least
you
know
some
scales
with
respect
to
the
x
y
and
z
axis
so
because
those
kind
of
details
are
not
mentioned
so
far.
So
what
I've
done
right
now
is
I'm
trying
to
you
know
I've
plotted
contours
of
first
time.
You
know
done
some
pre-processing
here,
like
I've
scaled,
these
images
to
a.
I
I
I
I
I
B
And
they
just
have
the
numbers,
they
don't
really
have
names
for
the
cells,
but
then,
if
you
plot
them
out
by
an
xyz
value,
you
get
a
a
plot
like
a
three-dimensional
plot.
It
kind
of
looks
like
the
embryo
would
look,
and
you
know
it's
just
kind
of
this
point
cloud.
That's
what
it
should
look
like.
That's
what
that's
what
you
should
be
able
to
get
out
of
those
data.
J
I
H
H
I
C
I
I
B
H
I
D
I
Is
still
something
that
I
need
to
look
into
a
lot
that
I
just
because
there
was
this
again
an
idea
you
know
like
we
were
doing.
You
know
powering
implantable
medical
devices,
so
the
stress
model
of
something
like
a
tissue
would
kind
of
give
more
perspective
into
how
you
know
we
can
have
some
more
powering
methods
for
implantable
medical
releases,
because
a
lot
of
them
have
these
piezo
electric.
So
if.
I
C
C
I
I
I
This
was
again
the
these
are
different
techniques
used,
for
you
know,
modeling
that
same
stress,
related
tensor
models
or
anything
for
anything
related
to
finding
stress
and
strain.
What
do
you
say?
It's
like
various
ways
of
you
know,
gathering
data
like
2d
data.
Is
there
3d
data?
Is
there
in
different.
C
C
C
D
I
I
Yeah,
like
these
are
the
only
two
papers
that
had
some
leads.
You
know
that
I
need
to
go
through
for
creating
that
3d
tensor
model
like
some
ideas,
because
the
thing
is
python.
Libraries
have
very
little
support
for
model
issues
like
this.
You
know
so
I'd
have
to
find
some
third-party
software.
You
know,
along
with
this,
to
maybe
get
some
more
data,
but
then
again
you
know
this
will
take
some
more
time.
I
C
I
With
respect
to
each
other
and
I'll
be
able
to
model
that
you
know,
but
with
respect
to
the
act,
it's
a
lot
of
thing
yeah,
I
think
you're
using
a
different
way
of
modeling.
This,
I
think,
would
be
more
practical
and
still
have
to
go
through
more
things
but
yeah.
These
are
the
three
things
that
are
currently
there
that
need
to
be
moved
here.
B
B
C
D
B
C
I
J
B
We've
had
people
do
this
a
lot
in
the
group.
You
know
trying
to
work
with
embryo
images
and
and
trying
to
figure
out
the
best
algorithm.
So
you
know
really
depends
on
the
kind
of
microscopy
image
that
you
have.
You
know
it
depends
on.
If
you
have
good
good
edges
or
you
know
sometimes
you'll
need
to
do
some
pre-processing
or
augmentation
of
the
data
set
to
get
really
good
results.
B
I
I
B
I
Stages,
yeah
yeah,
yeah
yeah,
so
like
these
ids
are
there
and
you
know
combined
there,
because
these
resources
were
not
available
had
to
access
their
json
files.
So
they
have
this
weird
schema.
You
know
of
getting
their
data
using
their
api,
so
it's
still
I'm
still
trying
to
work
work
around
it
because
they're,
like
63
objects.
I
D
I
B
B
Cells,
but
you
won't
know
the
id
of
you
can't
go
from
like
you
can't
track
anyone's
cell
through
time.
Necessarily
it's
just
going
to
be
like
one
through
63
and
then
one
through
you
know
74
and
then
it's
going
to
grow
and
then
you
know
you'll
see
definitely
that
you're
getting
more
cells.
But
it's
kind
of
hard
to
get.
D
B
B
G
D
B
G
B
B
First
thing
to
note:
is
that
there's
a
conference
coming
up
dynamics
days
2022,
and
this
is
something
that's
been
held
every
year.
It's
a
this
is
a
non-linear
and
dynamic
chaos,
a
non-linear
dynamics
conference
and
it's
usually
held.
You
know
different
universities
around
north
america.
They
have
a
european
version
which
is
separate
from
this.
B
I
think
I
did
the
european
version
this
summer,
so
they
have
one,
but
this
is
the
north
american
version,
and
this
year
it's
being
hosted
at
georgia,
tech,
but
it's
being
held
virtually
because
of
you
know
they
still
hold
them
virtually
last
year.
They
didn't
have
one
at
all
and
then
the
year
before,
I
can't
remember
where
it
was.
They
move
around
to
different
universities,
but.
B
You
know
it's
a
pretty
broad
range
of
things
and
this
I've
put
in
an
abstract
for
this
conference,
so
you
know
you
might
want
to
attend
and
they
have
a
lot
of
good
speakers.
They
have
people
from
randy
bear
who
does
a
lot
of
agent
modeling
he's
doing
a
lot
of
like
minimal
cognition
and
things
like
that?
Brad
ehrman,
trout
is
a
brain
scientist.
B
You
know
they
have
some
other
people
here.
Elizabeth
bunch
is
a
a
topological
data
analysis
person,
so
it's
gonna
be
a
nice
set
of
talks
and
this
is
held
the
january
7th
or
january
8th.
So
it's
like
right
at
the
beginning
of
the
year,
it's
kind
of
like
in
the
still
in
the
holiday
break
period
for
people.
B
You
know
this
term
hasn't
really
started
yet,
and
I've
been
to
the
one
in
in
evanston
illinois,
which
is
chicago
in
january,
and
it's
not
typically
a
time
you
want
to
visit
chicago,
but
it
was
a
pretty
good
time.
So,
there's
a
link
to
the
here's,
a
link
to
the
site
for
the
conference
if
you're
interested
in
attending
this
is
virtual
again
so
check
it
out
if
you're
interested
I've
submitted
this
abstract.
This
is,
I
think
it
did
this
one
for
dynamics
days
europe.
B
B
So
looking
at
things
like
the
shape
of
eggs
or
pattern
formation,
or
these
different
types
of
things
where
agents
are
trying
to
solve
a
maze
and
this
idea
of
developmental
agents-
and
this
is
a
developmental
agent
developing
and
then
there's
an
antigenetic
agent
and
there's
coordination
or
trade-offs,
and
it
kind
of
goes
through
a
lot
of
things
here
that
we're
interested
in
which
you
can
do
with
developmental
game
theory.
You
can,
you
know,
have
a
number
of
different
types
of
games
like
zero
player
games,
which
is
like
this
kind
of
game
of
life.
B
F
B
So
that's
and
then
kind
of
get
into
developmental,
stable
states,
which
is
a
idea
that
builds
off
of
evolutionary
stable
states
and
then
a
route
to
understanding,
combinatorial
complexity
of
development.
So
I'm
gonna.
I
again.
This
is
on
the
youtube
channel
this
video
from
dynamics
days,
europe
and
you
can
check
it
out
if
you're
interested.
B
Oh
yeah
yeah,
no
problem,
I
will
the
second
one
is
that
I
know
we've
talked
about
this
for
a
long
time,
but
this
I
finally
got
a
chance
to
get
into
this
a
bit
more
and
this
is
this
diva
learn
preprint
that
we
wanted
to
put
together.
So
this
is
my
knock,
of
course,
in
my
yook
and
usual
myself,
and
this
is
the
work
that
we've
done
on
divo
learn
over
the
past.
B
So
we've
done
a
lot
of
work
on
the
software
done,
a
lot
of
work
on
sort
of
the
educational
aspect,
the
web
presence
and
then
promoting
it
at
different
talks,
and
then
now
we
have
this
preprint.
So
it's
a
short
abstract,
a
summary
of
the
platform
we
have.
This
is
in
markdown,
so
it's
this
is
interactive.
You
know
you
have
this.
Gif
of
the
sort
of
this
is
something
I
think
my
knock
made
of
this
sort
of
his
this.
Oh,
this
is
him
using
the
interface.
B
So
this
is
a
heroku
app
that
he
built
on
top
of
the
software
and
so
he's
able
to
show
kind
of
how
it
works.
So
you
go
into
the
interface.
You
click
with
your
selection.
What
you
want
to
do,
you
can
predict
lineages,
you
can
do
this
sort
of
thing
and
you
can
then
it
shows
you
the
interface
here.
You
can
do
some
interactive
analysis
in
it.
B
I
need
to
I've
been
kind
of
putting
in
different
parts
like
accessibility,
then
there's
a
statement
of
need
and
I
have
the
technical
details.
I
need
to
add
a
couple
more
things
in
here
and
the
formatting
needs
to
be
worked
out
a
little
bit
better,
but
coming
along
on
this,
so
I
just
wanted
to
give
an
update
on
that.
It's
a
lot
better
than
it
was,
and
so
I
don't
know
where
we're
gonna
submit
this
we'll
have
it
as
ever.
B
At
the
very
least,
I
want
to
make
sure
that
we
have
it
in
place
for
the
gsoc
period.
That's
coming
up
at
the
you
know
in
mid-january
or
late
january.
I
think
we
need
to
submit
projects
for
gsox,
so
this
is
going
to
be
like
you
know
something
that
people
can
see
and
people
oh
yeah.
This
is
something
that
they
did
over
two
summers
worth
of
work.
So
yeah,
like
I
said
I
might
add
some
things
on
the
lineage
prediction
which
I
didn't
do
but
I'll
just
write
that
down.
B
All
right-
and
maybe
some
other
things,
but
I
wanted
to
get
that
you
know
I
want
to
get
this
going
and
I
don't
exactly
know
how
we're
going
to
put
this
together,
because
we
have
a
lot
of
gifts
in
it.
I
don't
want
to
publish
it
to
a
oh
I'll,
probably
have
like
a
static
version,
but
I
like
these
gifs
in
here,
so
we'll
see
what
we
do
here.
B
So
the
next
thing
I
want
to
talk
about
actually
I'll
talk
about
gsoc
in
a
minute,
but
I
wanted
to
talk
about
this,
so
this
week
is
the
neurops
conference
and
for
those
of
you
interested
in
machine
learning,
deep
learning.
This
is
a
sort
of
one
of
the
hallmark
conferences
of
the
field,
and
this
is
again,
you
know
the
it
costs
something
to
attend.
H
B
B
D
B
B
Able
to
get
access
to
some
of
the
videos
on
on
youtube,
or
you
know,
on
the
website.
I
think
they
have
links
to
some
of
the
websites
here.
So
some
of
these
workshops
are,
you
know,
very
specialized.
I
don't
think
they
have
anything
for
biology
again,
but
they
have
things
like
you
know
if
you're
interested
in
federated
learning
or
metacognition
in
ai
or
databases
in
ai,
which
is
you
know,
closer
to
kind
of
what
we're
doing
here.
B
Machine
learning
and
structural
biology
openworm
had
a
workshop
here
in
2017
on
was
called
w,
nip
or
worm
neural
information
processing,
and
so
they
had
a
bunch
of
papers
on
like
you
know
how
you
know
like
a
lot
of
papers
on
simulating
biology
and-
and
you
know,
computational
neuroscience-
that
sort
of
thing.
So
you
know
there's
some
interesting
things
buried
in
here.
If
you
want
to
look
through
and
see
if
you
can
find
something,
that's
interesting
to
you,
but
there's
a
lot
of
stuff
here,
deep
reinforcement
learning.
B
So
that's
that
then,
I'm
going
to
talk
about
google
summer
of
code,
which
is
coming
up
like
I
said
the
application
period
starts
in
the
spring,
but
we
have
to
have
the
proposals
out
by,
I
think
the
middle
or
end
of
january.
So
this
is
this
year.
I
think
I
mentioned
it
a
couple
weeks
ago.
It's
a
much
more
flexible
program
this
year,
they're
going
to
do
you
know
short
projects
or
long
projects.
We
can
do
different
things
depending
on
what
people
want
to
sponsor.
B
We
can
do
you
know
a
very
short
project
or
a
longer
project,
and
then
we
submit
it
to
the
incf,
which
is
our
sponsoring
organization.
Then
they
give
us
some
slots
and
the
reason
I
bring
that
up
is
because
every
year
we
have
a
couple
of
projects
and
then
we
have
one
project
that
gets
selected.
Usually
so
we
want
to
make
sure
that
we
have
a
range
of
projects,
and
so
we
can
see
who
applies
to
what
and
then
you
know
we
don't
really
know
which
project's
going
to
be
accepted
ahead
of
time.
B
B
B
B
So
these
are
these
little
single
cell
or
colonial
organisms
that
move
around
and
they
have
these
very
simple
morphologies
compared
to
c
elegans
and
so
we're
working
on
techniques
for
analyzing
that
we
also
have
the
axolotl,
which
is
the
you
know,
working
with
an
image
or
an
embryo.
That's
moving
around
it's
flipping,
so
we
can
have
that
sort
of
information
about
its
surface
and
about
its
movement
along
this.
You
know
rotational
movement,
so
we
have
a
lot
of
different
things.
B
We
can
focus
on
so
I'm
going
to
work
on
thinking
about
some
of
the
maybe
the
potential
projects
and
then
we'll
I'll
pull
it
together.
Maybe
our
first
meeting
of
next
year,
maybe
I'll,
go
over
some
of
these
projects,
because
I'm
planning
on
maybe
having
one
more
meeting
this
year
for
diva
worm
and
then
we'll
in
the
new
year,
we'll
start
over
and
we'll
get
it.
C
B
Yeah,
well,
that's
fine
yeah!
I
mean
I
just
you
know
we.
I
don't
know
how
we
want
to
do
like
an
axolotl
project
last
year.
I
know
we
suggested
that
people,
you
know
just
kind
of
model
it
as
a
3d
sphere
and
then
put
the
data
on
top
of
that
and
that
you
know
we
didn't.
I
mean
people,
we've
had
people
attempt
it,
but
you
know
we
didn't
get
any.
We
didn't
get
a
finished
product
and
of
course
it
didn't
get
funded.
B
B
Coming
up
with
something
that
will
analyze
something
like
an
axolotl
embryo
which
is
much
different
than
a
c
elegans
embryo,
and
so-
and
I
think
you
know
from
the
pre-trained
model-
work
that
the
pre-trained
model
is
going
to
be
very
specific
to
a
certain
species
or
a
certain
type
of
data.
You
know
it's
going
to
be
limited
and
will
allow
you
to
like
really
kind
of
just
identify
stuff
pretty
accurately,
but
it's
going
to
be
very
limited
in
it.
Where
it
can,
you
know
what
it
can
analyze.
C
If
you
can
make
programs
that
did
each
species,
that
would
be
fine,
I
mean
each
species
has
different
ways
of
developing
one
salamander
from
another
salamander.
They
do
it
differently,
so
I
mean
in
a
way:
that's
that's!
Okay!
It's
just!
I
guess
more
work
to
come
up
with
the
product,
but
they
are
different
species.
They
develop
differently.
B
D
B
Leave
that
open,
so
there
may
be
some
emerging
techniques
that
we
might
be
able
to
leverage
that
would
allow
you
to
do
something
with
you
know:
different
species.
I've
been
interested
in
this
because
I'm
like
well,
you
know
it's
great,
that
we
have
something
that
we
can
work
with
one
species,
but
you
know
development's
pretty
diverse
in
in,
in
the
sense
that
you
have
all
these
different
types
of
embryogenesis
and
embryos.
B
At
least
you
know,
in
terms
of
from
the
standpoint
of
the
algorithm,
like
it's
not
doing
exactly
the
same
thing
for
every
embryo,
it's
it's
got
to
find
cells
and
the
cells
are
dividing,
and
then
you
know
it
may
be
picking
up
on
something
in
c
elegans,
that's
very
specific
to
c
elegans,
like
the
cell
lineage.
You
know
the
organization
of.
G
B
But
then
you
know
in
another
species:
that's
not
going
to
work
at
all
because
it's
not
organized
in
the
same
way.
So
that's
going
to
be
you
know
a
problem
and
I
don't
even
know
if
there's
a
general
solution
to
that.
But
it's
you
know
something
that
we
have
to
kind
of
find.
Maybe
the
right
data
set
to
train
the
models
on
or
to
to
figure
out
like
what
exactly
the
algorithms
are
picking
up
on.
C
F
B
A
A
A
A
Okay,
the
the
part
which
is
motor
doesn't
go
across
the
whole
cell.
It's
broken
and
the
patterns
radiate
from
from
this
shape,
and
I
have
about
well,
I
guess
around
90
electron
micrographs
of
cells,
each
one
different
okay,
so
it
might
be
fun
to
see
if
one
could
isolate
the
shape
of
the
wraith
and
produce
the
pattern
from
it.
E
E
B
Yeah,
that
sounds
like
something
that
we
could
well
like.
A
cellular,
automata
of
some
type
might
be
useful
and
maybe
one
of
the
neural
cellular
automata
that
we've
been
talking
about
recently
in
meetings.
So
I
don't
know.
I
know
that
shruti
was
interested
in
that
at,
for
you
know,
I
don't
know
if
she's
still
interested
in
doing
that,
but
okay
yeah,
we
worked.
A
A
C
A
C
A
G
A
B
So,
let's
see
I'm
going
to
move
to
papers,
can
you
see
my
screen?
Yeah?
Okay,
thank
you.
So
I
have
a
couple
things
I
want
to
go
over
before
the
end
of
the
meeting
and
if
you
need
to
leave
at
the
top
of
the
hour,
that's
fine.
The
first
one
is
this
paper
on
volume.
Segregation
programming-
and
this
is
an
early
morphogenesis
new
paper
that
came
out
in
physical
review
e.
So
e
is,
I
believe,
it's
like
materials,
soft
materials
and
sort
of
biological
stuff.
B
In
physics,
you
know
it's
a
physical
review,
they
have
like
a
through
e
and
they
have
x,
which
is
a
open
source
journal,
but
this
or
open
access
journal.
This
is
e,
so
this
is
volume.
Segregation,
programming
and
nematodes,
early,
embryogenesis,
and
so
nematode
species
are
well
known
for
their
invariant
selenium
pattern
during
development,
the
mining
knowledge
about
the
fate
specification
induced
by
asymmetric
division
and
the
anti-correlation
between
cell
cycle
length
and
cell
volume.
B
B
They
take
up
a
certain
volume
of
the
embryo
by
virtue
of
them
dividing.
So
you
have
your
eight
cells
and
then
you
have
the
descendants
of
those
eight
cells
and
they
form
a
lineage
and
but
then
they
take
up
space
in
the
embryo,
and
so
I
think
what
they're
doing
in
this
paper
is
they're,
actually
kind
of
simulating
some
of
this
and
kind
of
figuring
out
what
the
optimal
pattern
is
for
this,
and
so
when
you
go
back,
let's
see
what
they
have
in
here.
So
they
have
something
called
a
logistic
cleavage
model.
B
So
they
say
that
each
lineage
pattern
is
specified
by
a
set
of
cleavage
timings
on
the
genealogical
tree.
So
what
they're
doing
is
they're
taking
a
cell,
and
we
did
actually.
We
did
a
little
bit
of
this
in
a
couple
of
our
papers.
I
think
the
one
that
we
published
in
2016,
where
we
had
the
c
elegans
data
set
and
the
obsidian
data
set,
and
we
were
looking
at
the
cell
division
timing.
B
This
is
something
where
they're
taking
a
cell
like
a
what
they
call
a
lineage
tree
of
of
different
cells,
so
the
cells
divide
and
differentiate
along
this
lineage
tree
and
they're
playing
around
with
the
timing,
so
they
have
the
sequence
of
cell
cleavage
events
during
early
embryo
genesis.
These
10
means
are
illustrated
by
solid
circles.
B
So
each
of
these
circles
is
a
is
a
cleavage
or
a
division,
and
this
is
over
time
and
they're
not
showing
the
tree,
but
you
can
imagine
that
at
this
at
this
circle
there,
this
single
line
branches
into
two
lines
at
this
circle,
the
two
lines
you
know
you
might
have
an
asymmetric
division
where
one
one
of
the
lines
appear
divides
into
two
and
then
this
one
continues
until
this
circle.
Where
then,
that
divides
into
two?
And
then
here
you
have
a
four
cell
embryo
that
undergoes
three
divisions.
B
B
Sublineage,
this
is
the
p
sublineage,
which
is
the
the
set
of
germ
cells.
This
is
ems,
c
d
and
so
forth,
and
so
what
they're
trying
to
do
is
they're
trying
to
first
of
all
figure
out
from
a
set
of
founder
cells.
You
know
what
the
optimal
division
pattern
is
that
fills
up
this
embryo
volume.
So
this
is
the
embryo
volume.
If
you
fill
it
up
with
different
sublineages.
B
You
know:
there's
some
optimality
there
that
they're
trying
to
capture
so
they
use
this
technique
where
they
just
look
at
these
cell
cleavage
events
on
a
one
dimensional
time
representation
of
time,
and
then
they
can
measure
using
this
parameter
so
a
which
is
a
window
of
size,
s
which
is
this
window
of
events,
and
then
you
have
this
a
which
is
the
interval
between
events.
So
you
basically
for
any
s
you
want
to
have
you
know
a
can
be
long
or
it
can
be
short,
you
can
have
multiple
a's.
B
You
can
have
one
a
and
so
forth,
so
this
is
basically
what
they
found
so
based
on
our
experimental
cell
trajectory
analysis.
We
set
s
to
1.5
minutes
and
a
to
three
minutes
and
the
following.
We
shall
use
cleavage,
clustering,
classify
and
compare
lineage
patterns,
so
they
do
this
for
a
number
of
different
lineages
and
they
come
up
with
these
results
here,
where
there's
fake
diversity
and
proliferation
speed
so
they're
looking
at
like
you
know,
you
know
what
kind
of
fates
do
you
get
or
you
know
what?
B
B
B
So
the
continuous
addition
of
new
cells,
following
sequential
cleavages,
could
jeopardize
a
canonical
cell
movement
during
mechanical,
equal
equilibration
inside
the
egg
leading
to
defect
patterns.
So
some
of
these
things,
where
you
have
you
know
some
of
the
things
where
cells
are
dividing
and
finding
their
place
in
space,
so
you
just
keep
adding
cells.
B
You
know
this
can
jeopardize
sort
of
the
mechanical
equilibration
and
lead
to
defects
in
the
embryo.
So
c,
elegans
solves
this
problem
by
synchronizing
cell
divisions
in
a
temporally
clustered
set
of
events
so
c
elegans
has
this
clustered
set
of
events
that
solves
this
problem
that
they're
supposing
to
enforce
this
property
among
lineage
patterns
generated
for
a
model?
We
introduced
two
time
constants.
D
B
So
that's
interesting.
I
didn't
know
that
this.
I
guess
this
paper
just
came
out,
so
there
is
actually
a
mechanical
reason
why
embryos
deviate
from
the
normal
distribution
there,
and
so
then
they
just
live
a
little
bit
more
simulation
work.
Here.
The
fast
proliferating
solutions
of
a
relatively
small
number
of
distinct
lineages
within
each
lineage
they
mean
sublineage
cell
divisions
are
synchronized.
B
We
will
name
this
phenomenal
lineage
interference,
so
this
means
that
your
your
lineage
reaches
a
maximum
number
so
as
they
sort
of
these
two
sublineages
unfold
or
multiple
supplenages
unfold
in
the
same
embryo,
they
interfere
with
each
other,
and
so
they
can't
proliferate
uncontrollably.
They
have
sort
of
an
upper
bound
on
how
they
can
proliferate
beyond
the
cell
number,
the
solution
space
for
lineage
optimization
shrinks
rapidly.
D
B
Of
any
cells
in
a
single
sublineages
lineages
are
limited
and
then
beyond
this
cell
number
you
get.
You
know
you
can't
optimize
for
these
different
properties
anymore.
So
this
is
an
interesting
paper.
I
didn't
know
it
just
ties
into
a
lot
of
the
work
that
we've
done
in
different
papers
and
I
think
it's
really
good
work
again.
I
haven't
really
delved
into
this
paper
a
lot,
but
this
really
applies
to
utelic
organisms,
mainly
so
like
c
elegans
utilic
is
where
this
organism
has
the
same
number
of
cells
in
the
adult.
B
So
in
c
elegans
you
have,
you
know
959
and
then
a
few
more
in
the
male
in
the
hermaphrodite
you
have
959
and
in
the
male
you
have
1024,
I
think,
or
something
like
that,
but
there's
always
from
you
know
individual
to
individual
within
that
species.
They
always
have
the
same
number
of
cells
unless
there's
some
unless
it's
a
mutant
of
some
type.
So
this
is
what
they
mean
by
utelic,
and
so
that
means
that
they
develop
to
a
certain
number
of
cells
and
then
it
stops
like
the
process
stops.
B
So
if
you're,
looking
at
like
a
mammalian
embryo,
this
wouldn't
necessarily
apply
because
the
divisions
would
continue.
You
know
they
would.
There
would
be
continual
divisions.
You'd
have
a
lot
of
cell
death.
You'd
have
a
lot
of
cells
that
can,
you
know,
take
on
different
fates
and
then
you
have
regeneration,
which
you
don't
really
see
in
c
elegans
too
much.
So
this
is
only
really
something
you
would
observe
and
a
utelic
c
elegans
like
organism.
B
B
So
I
know
I've
talked
a
lot
about
like
the
early
embryos
like
er
early,
meaning
like
geologically
back
in
the
cambrian
or
pre-cambrian
era,
and
the
dashanto
assemblage
of
fossils
in
china
has
yielded
us
one
of
the
earliest
embryos,
and
this
is
an
embryo
that
is
something
from
just
after
the
great
oxygenation
event.
When
you're
starting
to
get
a
lot
of
proliferation
of
different,
you
know
orders
and
kingdoms
of
life,
and
so
you
get
these
early
embryos.
B
D
B
You
can
definitely
see
they
have
properties
of
differentiation
and
they
have
these
properties
of
you
know.
Cell
cell,
you
know,
like
colonies,
not
really
sell
colonies,
but
they're
groups
of
cells
that
are
developing
into
something
else
as
an
adult,
and
so
you
can
start
to
see
this
lifespan
unfold
start
to
unfold.
B
So
this
is
the
first
paper
I
found
from
this
week.
So
they
have
this.
This
is
a
paper
on
current
understanding
of
the
cambrian
explosion,
questions
and
answers.
So
this
is
just
a
very
general
review
in
this
paper.
They're
talking
about
sort
of
the
cambrian
explosion
and
how
that
period
in
the
cambrian
period
is
sort
of
where
we
start
to
see
a
lot
of
animal
diversity
and
plant
diversity.
B
Actually,
they
focus
on
animal
body
plans
here
and
why
this
is
relevant
to
embryos
is
because,
if
you
can
imagine
the
early
embryos
being
this
very
generic
embryo,
that
we
can
maybe
recognize
some
features
of
differentiation
and
life.
History
or
stages
of
life
span
in
the
cambrian
explosion
then
means
that
these
embryos
had
to
sort
of
incorporate
these
different
body
plans
that
were
emerging
at
the
time
and
so
during
the
cambrian.
B
B
So
you
know
you
might
have
a
say
like
radial
symmetry
emerge
or
bilateral
symmetry,
which
just
means
that
there
are
two
sides
of
the
organism
that
are
similar,
that
they're
sort
of
like
not
copies
of
each
other
but
they're,
symmetrical,
meaning
that
there's
a
corresponding
set
of
cells
on
the
left
side
and
the
right
side
or
you
could
have
radial
symmetry
where
you
have
maybe
six
volt
symmetry.
So
you
have
six
parts
of
the
organism
that
are
similar.
B
B
Why
do
we
care
about
homeobox
gene
duplications,
as
I
think
in
a
previous
meeting
I
talked
about
how
homeobox
genes
are
these
hox
genes
that
are
very
important
in
development,
and
so
you
know
hops.
Genes
are
well
known
for
their
sort
of
being
clustered
on
a
genome
in
the
same
sort
of
location
and
they
kind
of
contribute
to
the
different
segments
of
an
embryo.
B
So
you
might
have
like
an
anterior
and
a
hox
gene
or
a
middle
segment,
hox
gene
or
a
hox
gene
at
the
posterior
end
of
the
animal,
and
so
they
all
map
to
that
to
give
it
like
sort
of
an
orientation
in
space
in
in
the
adult
and
so
homeobox.
Gene
duplication
just
simply
means
that
these
homeobox
genes
diversified
and
duplicated.
B
So
now
you
have
more
segments,
you
have
more
opportunities
for
new
types
of
segments
and
if
you
think
about
what
was
happening
during
the
cambrian
explosion,
you
had
this
period
where
you
had
a
lot
of
different
body
plans
emerge.
So
if
you
have
gene
duplications
in
this
homeobox
region,
then
you
have
a
lot
of
different
possibilities
for
new
phenotypes
and
if
you
get
rid
of
some
of
those
genes
in
you
know
different
lineages,
then
you
can
have
radically
different
body
plans.
So
this
is
where
this
this
paper
kind
of
goes.
B
B
B
This
is
on
fossils
molecules
and
embryos,
new
perspectives
on
the
cambrian
explosion,
so
they
kind
of
bring
everything
together
here.
So
this
is
what
we
talked
about
in
the
last
paper,
the
ox
genes
in
the
paper
before
we
talked
about
the
general
tenor
of
the
cambrian
explosion,
how
it
led
to
these
new
body
types
and
in
this
paper
they're
talking
about
sort
of
this
they're,
bringing
it
all
together.
B
B
There
was
nothing
new
after
that
that
emerged
in
terms
of
new
phyllis,
so
this
was
when
a
lot
of
that
was
established
and
then
molecular
divergences
among
lineages,
leading
to
phyla
record
speciation
events
that
have
been
earlier
than
the
origins
and
new
body
plans,
so
the
body
plans
actually
maybe
arose
later.
These
new
branches
of
life
arose
earlier,
but
then
you
know
this
so
there's
a
molecular
sort
of
precursor
to
these
body
plans
and
then,
of
course,
development
is
a
way
of
mediating
this
into
a
phenotype
and
then
into
the
adult.
B
So
they
kind
of
talk
about
these
brain
through
the
timing
of
the
branches
and
how
you
know
the
molecular
mechanisms,
sort
of
predate
the
emergence
of
these
phenotypes
and
then
well.
The
timing
of
evolution
of
the
developmental
systems
of
living
metazoans,
which
are
animals
of
these
body
plans,
is
still
uncertain.
The
distribution
of
hawks
and
other
developmental
control
genes
among
metazones
indicates
an
extensive
patterning
system
was
in
place
prior
to
the
cambrian,
so
these
patterning
systems
existed
prior
to
the
cambrian,
but
they
became
diversified
along
with
the
lineages
in
the
cambrian.
B
So
that's
all
I
have
to
say
about
that.
I
know
that
we've,
I
think
we've
talked
about
some
of
these
things
before
in
terms
of
you
know
these
sort
of
the
origins
of
the
embryo
and
you
wrote
a
paper
on
any
origins
of
the
embryo,
but
it
was
actually
quite
theoretical
in
nature,
so
this
is
like
putting
some
real
data
to
the
to
this
concept
and
yeah.
So
I
think
that's
that's
all
I
have
for
today
in
terms
of
papers,
did
we
have
any
comments
on
that.
B
Yeah,
so
that's
I
mean
that's
one
area,
it's
actually
I
mean
you
know.
The
hot
genes
are
really
kind
of
putting
together
these
little
modules.
If
you
think
about
it
in
terms
of
like
you
know,
single
hox,
gene
encodes,
a
single
module,
and
this
is
clearer
than
something
like
a
an
insect
or,
like
you
know,
other
some
like
crustacean
or
something
like
that,
where
you
actually
see
the
segments
pretty
clearly
in
the
embryo
and
then
again
in
the
adult
phenotype,
our
segments
are
less
visible.
We
have
segments,
and
you
know
in
yeah.
B
But
yeah,
I
think
it's
it.
I
think
we
did
a
paper
and
I
can't
remember
it
was
several
weeks
ago
where
they
talked
about
this
sort
of.
You
see
this
sort
of
patterning
and
the
sort
of
the
physics
of
the
embryo
as
a
very
early
embryo
or
a
ver.
I
guess
maybe
it's
a
very
basal
embryo
evolutionarily.
So
it's
like
something.
That's
not
you
know
it's
it's
like
a
very.
It
represents
a
very
early
organ.
B
It's
something
that
would
have
existed
a
long
time
ago,
such
as
you
can
find
them
extent
today,
but
they
had
this.
They
did
some
investigations
into
like
the
biophysics
of
it
as
well.
So
I
think
it's
kind
of
interesting
that
those
things
are
coming
together,
I'm
going
to
have
to
bring
together
some
of
these
readings
and
do
a
more
formal
list,
because
I
think
it's
an
interesting.
C
B
Yeah,
so
I
I
yeah,
I
mean
I'm
thinking
more
seriously
about
writing
a
paper
on
this,
but
you
know
it's
like
one
of
those
things
where
I
don't
know.
We
have
the
expertise,
although
we
don't
have
to
be
like
paleontologists
to
do
this,
we
can
just
simply
say
this
is
what's
been
found.
This
is
what
we're
interested
in
we're
kind
of
interested
in
like
biophysics
and
we'd
like
to
know,
maybe
how
you
get
these
early
embryos
and
maybe
what
the
biophysics
were,
what
they
were
doing.
You
know.
B
Maybe
it
was
like
you
had
some
embryo
that
was
like
free
living
and
then
you
know
you
added
on
like
life
like
life
stages
afterwards,
but
you
know
that,
like
I
think
in
the
paper
that
we
wrote
back
in
2018,
we
kind
of
made
some
assumptions
about
like
what
was
needed
for
an
embryo
to
kind
of
form.
You
know
they
were
like
energetic
constraints
and
there
were
like
strategic
constraints
by
the
cells,
and
so
those
are
all
kind
of
in
there,
but
it
wasn't
really
grounded
in
any
paleontology.
B
I
think
you
could
imagine
a
paper
where
you
know
you
had
like
a
couple
of
hypotheses
or
you
said
well,
you
know.
Maybe
we
have
this
early
embryo
and
maybe
we
suggest
that
you
know
physical
forces
were
important
to
sort
of
keeping
these
collection
of
cells
together
in
a
developmental
stage.
You
know-
or
maybe
it
was
something
else.
You
know
you
have
these
segments
that
emerge
and
then
you
know
how
do
they
diversify?
How
do
they
not
only
in
development,
but
how
do
you
get
new
stages
of
life?
B
And
the
answer
maybe
is
just
you
know
you
gotta
get
from
like
you,
gotta
reproduce,
you
don't
wanna,
you
know
you
can
clone
yourself
and
you
could
be
a
toady
potent
cell
have
toty
potent
cells
like
the
flatworm,
but
then
you
know
those
are
the.
Are
those
the
only
strategies?
Now
I
mean
there
are
other
strategies
you
can
lay
eggs
and
that's
where
you
need
an
embryo,
because
you
have
a
single
cell.
B
C
B
And
then
we
can
tie
it
into
the
the
oxygenation
events,
because
we've
noticed
that,
like
there's,
this
relate
as
soon
as
you
get
like
a
certain
amount
of
oxygen
in
the
atmosphere.
You
know
like
early
in
life,
you
had
very
little
oxygen.
Then
there
were
these
two
oxygenation
events
and
between
those
is
this
boring
billion
period,
where
you
don't
see
any
diversification
of
life
and
then
you
start
to
see
it
take
off
after
you
get
a
certain
amount
of
oxygen.