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From YouTube: DevoWorm (2021, Meeting 41): Diatom Modeling, Cellular Automata, Movement and Morphogenesis
Description
Diatom oscillations and modeling movement and upsampling microscopy images. Cellular Automata and Developmental Complexity, and papers on movement as a driver of morphogenesis. Attendees: Susan Crawford-Young, Richard Gordon, and Bradly Alicea.
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So
this
is
a
sort
of
a
phase
diagram
of
of
a
I
guess:
it's
of
a
single
cell,
that's
moving
back
and
forth
or
the
you
know,
measure
of
the
oscillation.
So
this
is
where
you
know
you
have
this
pattern
here,
which
is
the
cell.
That's
going
back
and
forth
the
red
yeah,
the
the
black
line
is
the
sine
wave
it's
a
sine
wave
and
then
the
red
line
is
the
actual
movement
fitted
to
a
sine
wave.
So
if
you
go
up
to
this
figure
see
yeah
yeah.
D
So
position
is
the
posit
like
the
just
it's
maybe
kind
of
like
displacement,
but
it's
stated
differently
where
you
move,
maybe
about
120
microns
in
you
know,
total
60
in
either
direction
and
then
the
velocity
changes
as
you're
moving.
So
you
know
you
get
to
the
end
here
and
then
it
speeds
up
and
slows
down,
and
all
this
here.
D
A
A
A
A
F
A
A
H
D
Okay,
it's
off
to
you,
okay!
Thank
you!
Yeah
yeah,
that's
an
interesting
looking
graph!
I
mean
I've
been
thinking
about
it
actually,
in
the
context
of
this
next
paper
that
I've
been
doing
on
the
information,
processing
and
psychophysics,
and
you
know
kind
of
thinking
about
how
that's
regulated
yeah.
It
could.
C
A
The
the
question
is
whether
or
not
vascillary
movement
is
smooth,
so
I
think
the
simplest
way
to
do
that
is
to
take
the
accelerations.
You
have
your
paper
and
the
subunku
paper
and
compare
it
to
those
and
see
if
it
falls
in
the
range
or
where
it's
up
the
range
okay,
yeah
and
that
could
just
that
might
be
worth
a
short
note.
Someplace
to
settle
the
question
right
and
we
may
not
need
high
speed
movies
at
all.
A
A
A
A
A
I
want
to
make
a
simple
claim
and
I'm
not
sure
it's
true-
I'm
not
sure
how
to
claim
it.
If
you
put
a
simple
grid
over
this
that
and
did
and
and
took
the
center
of
the
grid
instead
of
the
exact
point
which
they're
calculating,
in
other
words,
each
each
point
where
chain,
where
the
red
dot
changes,
directions
we
go
in,
the
middle
of
a
grid.
Point
will
be
moved
to
the
middle
of
the
vehicle
yeah,
okay
and
in
a
sense
that
represents
a
spatial
resolution
of
the
observation:
yeah,
okay,
okay.
A
A
And
I
I
think
it
would
be
a
matter
of
getting
a
hold
of
a
program
that
does
something
like
this
and
just
putting
the
grid
on
there.
And
the
idea,
then,
is
the
great.
As
I
said,
each
point
on
this
line
would
get
represent,
they're,
always
starting
over
reset
rate
reset.
Okay,
each
point
on
the
line
would
be
in
the
middle
of
the
grid.
Point:
okay,
all
right!
So,
every
time
it
changes
direction.
A
A
D
A
A
A
C
A
A
Okay,
now,
the
reason
I
bring
this
up
is
that
when
we
did,
when
I
did
that
work
with
subaku,
we
we
did
890
frames
per
second
okay.
So
it
was
a
time
resolution
thing
and
we
did
very
high
spatial
resolution
and
when
we
compared
it
to
edgar's,
edgar's
work
was
done
at
10
frames
per
second,
so
we
went
from
10
to
890.,
okay
and
we
wrote
paper.
We
were
surprised
because
we
got
bigger
accelerations
than
edgar
got
okay
yeah.
B
A
C
A
Emotion:
okay,
okay,
it
doesn't
have
to
be
sarcastic
in
itself.
Well,
it
could
be
okay,
yeah,
a
deterministic
force
would
give
you
you
know
in
this
case,
for
example,
if
the
red
dot
was
pushed
to
the
right,
for
example,
which
it's
not
in
this
case
right.
That
would
be
roughly
motion
with
drift
and
you
would
see
it
bouncing
about,
but
eventually
ending
up
on
the
right
side
right
now.
A
A
C
A
G
A
D
D
E
They
they're
using
a
least
squares,
weighted
least
squares
method,
and
then
they
have
another
method,
they're
calling
the
vector
method
and
they
say
it's
better
because
it's
it
gives
smoother
results
and
it's
it
was
more
accurate
and
it
had
it's
more
resistant
to
noise.
So
I
don't
know
if
you
want
to
know
more
about
the
vector
method,
I
have
to
figure
out
what
it
is.
A
Okay,
okay
yeah.
This
stopped
after
a
modest
number
of
where
there
isn't
too
much
overlap.
So
one
could
possibly
isolate
the
line
visually,
get
rid
of
all
those
little
circles
and
then
find
the
corners
on
the
line
right
and
then
move
them
over
to
square
grid
of
a
given
width,
the
given
width
of
the
grid
and
then
populate
the
accelerations
right,
okay!
Well,
that's
that's
sneaky
way
to
do
it.
A
D
D
It
was
like
the
morphology
just
doing
static,
morphology
and
and
trying
to
you
know,
extract
the
cells
and
their
positions
now
they're,
looking
at
motion
so
they're
looking
at
you
know,
movies
across
many
frames
and
they're.
Looking
at
that
series
of
motion
and
they're
having
problems
with
some
of
the
cells
where
they're
overlapping
as
they
move
so
yeah,
you
have
that
problem,
but
it's
it's
it's
something
I
think
they're,
overcoming
they're,
just
trying
out
different
techniques
of
segmentation
and
trying
to
define
you
know
right.
A
D
E
D
D
He
presented
a
paper
on
this
where
they,
you
know,
did
really
high
speed
sampling
of
some
video,
like
they
oversampled
the
video
and
they
used
some
techniques
to
avoid
aliasing
so
that
they
could
get
a
you
know,
a
nice
representation
of
this
higher
frame
rate
that
didn't
exist
in
the
actual
movie.
They
just
tried
to
up
sample
everything
and
so
yeah.
Now
those
techniques,
I
think,
are
a
bit
much
for
us,
because
I
think
you
need
really.
D
A
A
A
Okay,
okay,
so
look
see
if
you
can
just
do
that.
I
think
this.
The
quickest
thing
to
do
is
the
phase
diagram
with
acceleration
instead
of
velocity,
okay,
yeah
and
then
let's
compare
the
values
you
get
to
the
values
that
are
in
the
two
papers,
all
right.
Okay,
all
right.
It
sounds
good
and
then
yeah
whether
we
go
for
the
the
speculation
on
the
acceleration
getting
bigger
the
finer,
the
grid
for
brownian
motion.
A
You
know
the
closest
I've
been
able
to
find
in
the
literature
is
that
true
brownian
motion
is
what
they
call
differentiable
nowhere:
okay,
oh
yes,
yes,
okay,
but
that
doesn't
tell
you
what
that
that's
a
that's
a
qualitative
statement.
I
haven't
been
able
to
turn
that
into
a
quantitative
statement.
Okay,
it's
differential
nowhere.
What
is
the
differentiation
for
a
given
grid
size
right.
A
A
G
A
F
A
To
eliminate
questions
like
that,
okay,
that's
that's!
Why
I'd
like
you
to
look
at
the
tobacco
paper,
because
we
were
really
I
mean
look.
We
were
able
to
get
down
to.
I
think
five,
something
like
five
resolution
in
terms
of
spatial
location
of
the
diatom,
and
we
did
that
by
assuming
the
diatom
is
rigid
and
finding
the
best
algorithm
for
finding
its
centroid,
okay,
yeah.
So
it's
way
beyond
the
resolution
of
the
light
microscope
yeah,
even
though
we're
stuck
with
light
micro
microscope
images.
A
A
D
E
D
So
these
are
some
images
here
of
some
of
the
cellular
automata
and,
as
you
can
see,
they're
not
grid-based,
they're,
actually
particle-based
and
they're
able
to
form
these
patterns.
A
lot
of
some
of
these
look
very
much
like
things
you'd
find
in
biology.
This
looks
like
a
packing
of
rod.
Shaped
bacteria.
D
D
So
these
type
of
models
produce
emerging,
complex
emerging
patterns,
often
featuring
robust
local
structures
similar
to
solitons
and
solitons.
Are
these
things
from
physics
that
people
often
use
to
demonstrate
complex
systems?
The
unique
properties
of
these
structures
offer
a
vast
increase
in
the
diversity
resulting
phenomena
in
comparison,
a
single
neighborhood
cellular
automata.
D
So
we
were
talking
about
random
brownian
motion
earlier
in
the
meeting
where
we're
talking
about
how
to
maybe
constrain
brownian
motion
or
to
show
you
know
things
that
emerge
out
of
the
patterns
of
brownian
motion.
As
those
particles
are
moving
around
in
this
quasi-random
way.
You
know
what
kinds
of
patterns
do
they
show,
so
so
these
types
of
structures,
after
a
vast
increase
in
the
diversity
of
resulting
phenomena
in
comparison
to
single
neighborhood,
cellular
automata.
D
So
I
know
that
we
do
a
lot
of
stuff
with
morphozoic
in
this
group,
and
morphozoic
is
similar
to
this
in
some
ways
because
it
has
nested
neighborhoods.
This
is
something
that
has
multiple
neighborhoods,
and
so
this
is
a
little
bit
different
model,
so
this
is
implemented
in
the
shader
toy
example
here.
So
this
is
an.
D
D
And
then
so,
for
the
sake
of
demonstrations
of
simplicity,
this
is
referred
to
as
a
two
state
discrete
model
but
of
course,
they're
continuous
and
autonomous
rotations.
So
this
post
gives
a
number
of
details
about
this
model.
We've
talked
about
the
neighborhoods
and
update
functions,
so
this
is
a
traditional
cellular.
Automata,
like
conway's
game
of
life,
defines
a
single
group
of
local
neighbors
for
each
pixel
and
sums
the
value
at
these
located
at
those
locations.
D
These
updates
are
accessed
in
the
order
they
are
executed,
with
light
of
later
updates,
potentially
overwriting
these
those
that
took
place
before
them.
So
in
other
words,
you
have
neighborhoods
that
are
sort
of
not
concurrent
time
they're
executed,
maybe
over
time
differentially.
So
you
have
one
neighborhood
at
one
point
and
have
another
neighbor
at
another
point.
In
time,
and
that
gives
you
differences
in
the
way
these
particles
interact
over
time.
D
Mnca
models
often
form
structures
that
exhibit
individualized
unit-like
local
identities
compared
to
solitons.
These
structures
are
significantly
different
to
those
found
in
other
cellular
automata,
exhibiting
incredible
resilience
as
they
interact
with
their
environment
in
a
robust
and
non-destructive
manner.
So
this
is
talks
about
salwaton
formation.
They
form
solitons,
they
form
emergent
structures,
they
have
a
reaction
to
attractive
and
repulsive
forces
like
most
cellular
automata.
D
They
form
collective
movements
in
flopping
and
they're,
able
to
model
compressive
and
elastic
interactions,
the
formation
of
crystalline
lattices
and
metamorphosis
of
structural
layouts
causing
different
behaviors.
So
you
see
all
these
different
attributes
not
only
of
life-like
things,
but
also
soft
materials,
and
we
talked
about
soft
materials
earlier.
D
You
know
come
up
with
a
model
of
something
like
a
an
embryo
or
some
developing
tissue
okay,
so
this
kind
of
just
walks
through
some
of
this.
This
is
the
conway's
game
of
life
simulation
here
and
you
can
see
what
conway's
game
of
life.
Does
you
get
these
moving
things
in
in
the
array
which
look
like
you
know,
some
of
them
are
what
they
call
gliders.
Some
of
them
are
pulsing,
and
so
you
can
see
that
there
and
then
this
is
an
example
of
a
neighborhood
for
conway's
game
of
life.
D
So
this
is
the
focal
cell
here,
so
it's
like
a
cell
in
the
middle
and
then
the
cells
surrounding
it
all
determine
its
state.
So
you
can
have
a
rule.
For
example,
that
says
if
all
the
neighbors
are
state
one,
the
focal
cell
is
of
state
zero,
and
so
you
can
see
that
in
this
case
we
could
have
another
rule.
That
says
if
a
majority
of
the
cells
are
state
one,
then
the
focal
cell
is
also
state
one.
D
So
you
can
have
rules
like
that
in
different
ways,
and
so
this
is
in
conway's
game
of
life.
There
are
a
couple
of
rules
here:
many
cell
with
one
or
fewer
live
neighbors
dies
any
cell
with
three
live,
neighbors
becomes
alive,
so
in
other
words,
if
your
neighbors
are
alive,
you
become
alive.
If
your
neighbors
die,
then
you
die
and
this
approximates.
You
know
something
like
you
know,
an
epidemiological
model
where
you
see
the
spread
of
disease,
any
cell
with
four
or
more
live
neighbors
dies.
D
All
other
cells
survive,
retaining
the
state
they
had
in
the
previous
time
step.
So
again,
this
this
resembles
something
like,
maybe
with
the
conversion
of
a
stem
cell
embedded
in
a
tissue
and
whatever
its
neighbors.
Are
it
takes
on
that
state
that
that
cell
type-
and
you
know
if
the
neighbors
change
their
identity,
then
the
vocal
cell
also
change
its
identity.
So
there
are
a
lot
of
parallels
with
life
and
living
systems,
so
this
is
a
larger
than
life
neighborhood,
and
this
is
of
course,
a
little
bit
bigger.
D
This
is
different
than
the
kind
of
neighborhood
that
you
see
here
in
the
conway's
game
of
life.
This
is
a
larger
than
life
neighborhood,
so
the
family
of
patterns
and
this
uses
neighborhoods
of
greater
size,
so
they're
bigger
than
this
nine
by
number,
this
three
by
three
grid
and
the
most
well-known
example,
is
probably
bugs
and
uses
a
radius
five
square
neighborhood.
So
what
that
means
is
that
you
have.
You
can
see
that
it
settles
down
here
and
you
start
getting
these
cells
that
are
moving
around.
D
So,
let's,
let's
go
over
that
again,
you
get
these
bubbles
looks
like
things
are
bubbling
around
here
and
that
activity
settles
down.
Then
you
start
getting
these
things
that
move
around
they're
kind
of
gliding
across
just
moving
across,
and
then
you
get
bubbling
again
like
these
things
that
are
sort
of,
I
don't
know
what's
which
state
they
want
to
be
in,
and
then
you
get
these
coherent
structures
that
move
around
the
area.
So
this
is
all
generated
by
this
bugs
neighborhood,
and
this
is
a
radius
five.
D
D
So
it's
pretty
big
neighborhood,
and
so
all
of
these
cells
determine
the
state
of
the
focal
style
in
some
way,
then
you
have
multiple
neighborhood
cellular
automata,
so
this
is
a
little
bit
different,
even
even
from
the
example
of
larger
than
life.
Where
now
it's
not
necessarily
the
large
size
of
the
neighborhood,
but
you
have
these
multiple
neighborhoods
in
time.
D
So
I
think
I
like
this.
This
is
nice.
This
is
a
basic
nmca
example.
This
is
a
simplistic
discrete
mnca
he's
manually
constructed
using
two
neighborhoods
and
six
update
functions.
So
this
is
the
number
of
update
functions,
so
you
have
the
focal
cell
here
with
some
area
around
it,
and
then
the
neighbors
are
way
out
here
where
the
neighbors
can
be
contiguous
to
the
focal
cell,
and
then
this
is
finally
using
the
same
two
neighborhoods.
D
A
different
set
of
update
functions
results
in
a
largely
different
kind
of
pattern,
so
this
is
a
different
kind
of
pattern.
Already.
This
looks
like
a
bunch
of
worms
expanding
out.
You
know
I'm
just
trying
to
use
a
some
sort
of
broad
metaphor
to
describe
this,
but
it
really
does
look
like.
Actually,
it
looks
like
a
chemical
reaction
and
so
it's
filling
in
the
space
the
empty
space
in
the
image,
so
you
can
see
that
this
area
is
being
filled
up
with
these
with
this
collective
movement
of
agents
or
the
state
of
these
different.
D
D
The
next
thing
I
want
to
talk
about
was,
I
think,
jesse
was
mentioned,
johanna,
johannes
jagger,
who
is
a
european
developmental
biologist,
he's
works
at
in
paris
in
vienna,
so
he's
interested
in
complexity,
theory
and
developments.
It
does
very
much
very
much
in
the
same
space
as
we
are,
and
so
I
wanted
to
point
this
out
jesse.
This
is
google
scholar
profile
here
for
johannes.
D
D
So
this
is
about
positional
information,
but
linking
this
to
pattern,
formation
and
feedback,
so
bioattractors
dynamical
systems,
theory
in
the
evolution
of
regulatory
processes
from
2014.
This
is
a
more
recent
paper
on
the
dynamic
nature
of
positional
information.
So
you
can
see
that
you
know
he's
covered
a
number
of
interesting
topics,
especially
of
interest
to
this
group.
D
D
The
next
thing
I
want
to
talk
about
is
this
evil
learned
paper,
and
this
is
a
long-suffering
paper.
I
know
I
promise
this
is
our
paper
that
describes
the
divo
learning
platform,
and
I
know
that
we
tried
to
do
this
once
before,
where
we
tried
to
follow
up
on
google
summer
of
code
2020,
and
we
were
going
to
submit
this
to
the
journal
of
open
source
software.
D
D
D
So
I'm
going
to
be
working
on
this
paper,
so
we
need
an
abstract
for
one
thing,
I'll
take
care
of
that.
But
if
you
have
any
ideas
of
what
you
want
to
contribute
to
this,
if
you
know
I
know
that
a
lot
of
people
have
contributed
to
the
data
science,
demos,
for
example-
and
they
will
have
to
have
a
section
on
that
as
well.
D
E
D
Sure
that
archive
would
well,
they
might
accept
it.
It
depends
on
the
length
of
the
paper.
So,
let's,
let's
keep
working
on
that.
I
know
that
we
have.
I
wanted
to
make
sure
I
had
the
current
state
of
the
paper,
and
this
is
actually
in
the
diva
learn
repository.
So
if
you
go
to
divo,
learn,
diva,
learn
and
that
will
take
you
into
this
paper.
It's
actually
paper.md.
D
So
it's
in
a
markdown
file
and
we're
going
to
work
in
markdown.
I
think
to
do
this
editing
and
we'll
see
you
know,
you
know
you
can
issue
a
pull
request
or
you
can
make
a
contribution
to
it
by
you
know,
adding
in
content
and
then
leaving
a
note
on
the
pull
request
or
leaving
a
note
on
the
commit
that
says
that
you
know
you
made
this
change,
so
I
you
know
encourage.
Maybe
if
you
want
to
contribute,
let
me
know
and
then
maybe
contribute
through
the
repository.
D
D
We
have
a
lot
of
these
issues
floating
around
and
some
of
them
are
very
old.
This
is
the
people
learn
preprint,
one
which
has
been
in
action
items
for
a
while,
and
I
I
apologize
that
I
haven't
been
more
proactive
on
that,
but,
as
you
can
see,
we
have
a
lot
of
things
moving
and
we
have
a
lot
of
things
like
the
neural
match.
4
tie-in,
which
I
still
haven't,
worked
out,
but
I
think
we're
going
to
do
something
for
narrow
match.
4.
neural
match.
D
4
is
right
at
the
beginning
of
december,
so
we'll
do
something
maybe
concurrently
with
neuromatch
4.,
so
keep
an
eye
out
for
that.
Some
of
these
things,
a
pattern
resumption
after
self-repair
and
seashells,
is
a
project
that
we
haven't
talked
about
in
many
many
months,
maybe
even
years.
So
if
you're
interested
in
that,
maybe
where
we
also
have
other
things
in
here
that
we
haven't
revisit,
you
know-
we've
talked
about
in
the
past,
but
maybe
we
need
to
revisit
more.
So
we
need
to
think
about
these
different
projects.
D
D
Currently
so
there's
some
opportunity
there
to
contribute,
but
also
in
the
action
items
as
well,
where
we
need
to
get
moving
on
and
we
need
to
get
some
momentum
behind
it.
So
I
think
that's
enough
of
that
and
if
you
feel
like
adding
an
issue,
please
try
to.
I
think
you
have
to
be
a
member
of
deborah.
D
There's
some
papers
here
that
I
found
interesting
this
week
and
this
these
are
on
embryonic
muscle
movements
as
strength
conditioning-
and
this
is
interesting.
We've
talked
about
embryonic
muscle
movements
in
c
elegans
and
what
happens
in
c
elegans
as
you
get
in
the
egg,
you
get
the
formation
of
muscle
and
as
soon
as
you
get
the
formation
of
muscle,
you
start
to
see
sort
of
this
spontaneous
twitching
that
occurs
and
it's
electrical
activity.
D
That's
sort
of
generated
by
the
muscle
fibers,
as
the
cells
become
muscle
fighters
and
the
center
of
the
muscle
start
to
become
active.
Now
the
thing
about
muscle
in
the
embryo
is
it's
not
necessarily
connected
to
the
connectome,
so
the
connectom
usually
drives
the
muscle
activity.
You
know
through
motor
neurons
and
other
you
know
in
the
synapses
that
are
involved
in
that.
So
there's
this
activation
of
muscles
that
are
purposeful.
I
guess
if
you
want
to
put
it
that
way,
that
they
move
the
organism
to
different
places.
D
So
you
know
when
the
c
elegans
is
making
different
movements.
The
muscle
is
being
driven
by
these
motor
neurons,
but
in
the
egg
in
the
embryo
they
don't
necessarily
do
that.
They
just
kind
of
twitch
spontaneously,
eventually
they'll,
become
wired
to
the
central
nervous
system,
the
connectome,
but
it's
not
automatic.
Now
in
some
organisms
and
we'll
see
this
in
some
of
these
papers,
those
those
connections
are
already
sort
of
made
in
the
embryo,
especially
in
the
later
embryo,
and
so
let's,
let's
dig
into
this.
D
So
the
first
paper
is
embryonic
motility
environmental
influences
and
evolutionary
innovation,
and
this
was
older.
It's
an
older
paper,
it's
from
evolution
and
development
in
january
2003.
This
is
jared
mueller,
it's
at
the
university
of
vienna,
and
so
this
this
is
a
paper
on
embryonic
motility.
So
this
is
about
movement
in
the
embryo,
so
this,
I
think,
focuses
on
what
organisms.
I
think
this
is
just
a
general
review
here,
so
embryo
is
going
to
passively
await
hatching
from
their
eggs
or
amniotic
containments,
but
we
get
active
movement.
D
D
So
this
is
actually
a
case
where
the
you
know,
victor
hamburger
and
some
other
people
have
explored
this
problem
and
they've
looked
at.
You
know
this
this
phenomena
in
the
embryo,
and
so
this
is
something
that
you
know
occurs
fairly
early
on
in
the
formation
of
the
muscle
and
then
sort
of
allows
for
this
sort
of
motor
activity.
D
But
it's
not
necessarily
linked
to
the
central
nervous
system
or
any
purposeful
movement.
It's
just
increasing
and
decreasing
motor
activity.
It
is
long
known
that
embryonic
motility
represents
an
important
epigenetic
component
of
development.
So
in
this
case,
when
they
say
epigenetic,
they
don't
mean
like
at
the
molecular
level.
They
mean
like
something
you
know
beyond
genetics,
so
this
is
a
component
of
development
that
you
know
the
muscle
has
to
be
active
to
start
to
keep
developing
so
yeah.
D
The
electrical
activity
is
actually
bootstrapping
the
development
of
the
tissue
and
making
it
stronger,
and
you
know
if
you,
if
you've
ever
revealed,
you
know
the
vl
is
where
they
take
cows
and
they
don't
let
them
move,
and
they.
You
know
that
results
in
a
softer
muscle
tissue
and
then
they
take
that
muscle
tissue
and
use
it
for
the
veal
and
it's
a
very
common
thing
actually
in
cows.
They
want
to
make
sure
like
when
they
make
meat
in
a
culture
dish.
D
They
have
to
ensure
that
the
the
tissue
in
the
culture
dish
is
stimulated
with
electricity
in
order
to
get
the
texture
of
meat.
So
this
is
a
very
important
thing
in
like
agriculture,
where
they
try
to
get
the
right
consistency
of
the
meat
or
they
modify
the
consistency
by
modifying
the
activity
of
the
muscle,
and
so
this
is
something
that
is
what
they
call
epigenetic,
meaning
that
it's
not
a
genetic.
D
It's
not
driven
by
the
genetics.
It's
driven
by
electrical
activity
and
sort
of
morphological
activity,
so
invertebrates,
active
movement
of
the
embryo
was
required
for
the
correct
development
of
cartilage
bone
and
joints
of
muscles
tendons
and
ligaments.
So
this
isn't
just
muscle:
this
is
everything
in
the
body
and
have
connectivity
is
in
the
central
nervous
system.
So
this
is
basically
a
lot
of
these
movements
are
driving
some
of
these
things
to
development
and
if
you
know
anything
about
how
the
central
nervous
system
forms,
you
know
that
you
have
neurons.
G
D
D
E
D
Of
that
process
and
sort
of
strengthening
the
first
synapses
that
form,
so
we
know
that
this
happens
because
we
see
the
activity,
but
also
when
you
have
disturbances
of
embryonic
movements.
This
can
lead
to
severe
malformation
and
functional
disorders.
This
is
in
the
form
of
pathological
motor
patterns
and
neurological
deficits
that
are
retained
into
adulthood.
D
D
You
know
is
this
something
that
is
we
know
we
see
these
spontaneous
muscle,
movements
and
c
elegans
is
something
that
even
going
back
to
c
elements
is
an
important
what
we
don't
really
know
I
mean
I
guess
we
kind
of
can
assume
that,
but
no
one's,
I
don't
think
anyone's
done.
Those
studies,
so
motor
activity
of
the
embryo
was
influenced
by
electrical
mechanical
thermal
and
chemical
factors,
so
there's
an
overview
from
1960,
that's
cited
here
and
it's
maybe
a
little
old.
D
A
decrease
of
temperature,
for
instance,
not
only
reduces
all
metabolic
activity
but,
as
a
consequence,
also
diminishes
motor
activity.
Okay,
also,
the
intensity
of
omni
at
light
conditions
were
recently
shown
to
affect
embryonic
motility.
So
this
is
another
thing
that
light
conditions
also
play
a
role.
Chicken.
E
D
D
D
So
this
is,
you
know,
cartilage
and
bone
formation
as
a
in
addition,
a
muscle
formation
or
effect
by
these
movements.
The
movements
actually
provide
and
why
it's
important
is:
it
provides
sort
of
a
set
of
forces
that
the
tissues
are
sort
of
moving
against
and
it
kind
of
conditions
them.
So,
in
the
case
of
the
the
in
the
case
of
like
meat,
what
I
was
saying
before
about
like
how,
when
they
make
artificial
meat
in
a
culture
dish,
they
have
to
keep
stimulating
it
with
electricity.
D
This
is
because
this
electric
electrical
activity
mimics
these
movements,
because
when
the
movements
occur,
they're
stimulated
by
endogenous
electrical
activity
and
then
that
electrical
activity
is
actually
you
know
the
the
movements
are
actually
moving
against
some
force
in
the
environment.
They
could
be
a
liquid
environment,
it
could
be
a
physical
environment
like
a
surface
hard
surface,
but
you
know
there's
movement
against
that
surface
and
it
provides
mechanical
feedback
and
then
ultimately
modulates
the
electrical
activity.
So
this
is
why
these
movements
are
important.
D
You
know
the
same
reason
why,
if
you
walk
a
lot
of
run
a
lot,
you
can
build
muscle
mass
as
an
adult,
it's
the
same
type
of
process,
so
you
can
see
the
frequency
of
embryonic
movements
in
the
check
over
days
of
development.
You
can
see
that
there's
this
period,
sort
of
towards
the
middle
end,
maybe
two
thirds
of
the
way
through
development,
where
it
maximizes
and
then
it
decreases
towards
the
end
of
development.
D
D
Okay.
So
now
and
then
this
is
an
example
of
the
effects
of
a
12-fold
increase
on
illumination
intensity
and
movement
frequencies.
So
this
is
an
example
of
where
the
open
bars
were
where
they
applied.
The
white
and
the
the
enhanced
light
source
and
the
shaded
bars
are
the
controls,
which
did
not
see
an
increase
in
light,
and,
as
you
can
see,
this
actually
follows
this
other
graph
here.
D
This
is
the
normal
sort
of
state
of
affairs
where
the
embryos
is
generating.
These
spontaneous
movements
about
two-thirds
of
the
way
through
development
and
again
you
see
the
same
pattern
here,
except
that
it
in
the
case
of
the
expo
enhanced
exposure
to
light
using
the
the
peak
of
these.
The
you
know,
the
peak
of
movement
occurs
earlier,
but
it's
also
much
more
frequent,
so
you
can
see
that
there's
sizeable
effect
of
light
stimulation.
D
Okay,
so
I
think
that's
it
for
this
paper
and
they
talk
well.
Actually
they
talk
about
the
origin
of
novelty
and
evolution,
so
there's
a
neglected
issue,
evolutionary
theory
and
that's
the
origination
of
morphological
novelty,
and
so
this
means
that
you
know
you
have
things
that
are
new
in
evolution
like
new
types
of
tissues
or
new
types
of
morphological
structures.
How
do
they
get
there?
D
D
So
the
other
paper
I
wanted
to
talk
about
here
is
this
is
an
well
in
2006.
So
it's
actually
not
a
newer
paper.
It's
newer
than
the
paper
that
we
reviewed
there.
This
is
early
effects
of
embryonic
movement,
a
shot
out
of
the
dark.
So
this
is
so
the
abstract
reads.
It
has
long
been
appreciated
that
the
study
of
the
embryonic
chick
and
ovo
provides
a
variety
of
advantages,
including
the
potential
to
control
the
embryo's
environment
and
its
movement
independently
of
maternal
influences.
D
We
have
exploited
the
system
by
developing
novel
models
and
schemes
to
examine
the
influence
of
defined
periods
of
movement
during
musculoskeletal
development,
so
they
use
a
number
of
drugs
to
sort
of
manipulate
these
movements
early
on
and
they
examine
the
role
of
movement
in
joint
osteochondrial
and
muscle
development.
So
osteocabrio
is
its
bone.
It's
heart,
you
know
lamellar
bone
and
also
cartilage.
D
The
endogenous
activity
was
enough
for
it
to
form,
by
contrast,
imposition
of
either
rigid
or
flaccid
paralysis
prior
to
cavity
formation,
completely
blocked
this
process,
and
with
time
previous
fusion
of
cartilaginous
elements
in
formation
of
continuous
single
cartilaginous
rods
across
locomotion
or
across
locations
where
joints
will
ordinarily
form.
So
this
is
interesting
when
they
enhance
these
movements.
D
D
This
means
that
you
can
completely
block
the
process
so
and
once
you
do
that,
you
can
actually
create
sort
of
a
deformed
anatomical
structure.
So
this
is
night.
This
is
an
interesting
result,
and
you
know
in
in
my
other
group
that
I
work
with
we're
very
interested
in
critical
periods
in
development,
so
mainly
we're
interested
in
critical
prairies
with
respect
to
the
nervous
system,
with
respect
to
the
brain
with,
with
you
know,
with
development
of
behaviors.
D
Another
nice
example
of
a
critical
period,
but
you
could
imagine
that
in
evolution-
or
you
know
in
in
some
type
of
scenario
where
you
have
changes
in
the
developmental
timing
of
these
things,
that
you
can
watch
in
real.
You
know
you
could
watch
this
process
where
these
things
get
decoupled
in
time
and
it
actually
affects
the
morphology
and
how
it
forms.
It's
a
very
interesting
evolution
of
development
question.
So,
if
you're
interested
in
evo
devo,
this
is
a
very
interesting
type
of
experiment.
D
So
this
is
so.
This
is
a
nice
set
of
experiments
and
it
kind
of
gets
into
this.
So
they
talk
about
critical
periods.
Here,
changes
in
cartilage
and
bone
growth
induced
by
three-day
periods
of
flaccid,
immobilization
mobilization
imposed
at
distinct
developmental
phases,
provide
support
for
dimmunition
and
cartilage
elaboration
in
an
early
phase
and
for
a
relatively
delayed
influence
of
movement
and
osteogenesis,
which
is
the
formation
of
bone.
Invoking
critical
periods
during
which
the
developing
skeleton
becomes
receptive
to
the
impact
of
movement.
D
So
this
means
that
this
critical
period
is
very
important
to
this
process,
and
the
timing
of
it
is
very
important
as
well.
This
skeleton
becomes
receptive
to
the
impact,
so
if
you
develop
the
movement,
if
you,
if
you
introduce
movements
later
or
earlier
in
development,
maybe
it
isn't
as
effective,
and
this
is
why
we
see
that
that
graph
in
the
last
paper,
where
there's
sort
of
a
peak
about
two
thirds
of
the
way
through
development
of
spontaneous
movements,
because
it's
actually
having
the
greatest
effect
there.
D
Finally,
our
preliminary
results
support
the
possibility
that
embryonic
hyperactivity
influences
the
potential
for
postnatal
muscle
growth.
So
this
is,
this
can
also
affect
postnatal
growth
as
well,
and
so
again,
it's
all
about
timing.
It's
all
about
sort
of
the
the
signals
that
are
generated.
You
know
you.
If
you
introduce
forces,
you
can
trigger
molecular
pathways,
which
might
be
persistent
over
time
if
you
deliver
it
at
the
wrong.
D
D
D
D
Okay,
so
I
don't
think
there
are
any
figures
in
this
paper
really
worth
kind
of
hitting
on,
but
there
so
yeah.
So
actually
I
don't
know
if
I
can
get
back
to
this
part
here.
I
just
saw
something
before
I
closed
it.
They
were
talking
about
critical
periods
and
again
I
wanted
to
point
that
out
that
there
is
okay,
so
some
evidence
points
to
the
existence
of
critical
periods
of
skeletal
development
and
our
studies
about
later
periods
of
development,
during
which
movement
begins
to
exert
a
dramatic
and
significant
contribution.