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From YouTube: DevoWorm (2021, Meeting 42): Diatom Modeling II, Turing Morphogenesis, Development Genetical-physics
Description
Cursory data analysis of Bacillaria colony velocity vs. acceleration. Discussion of how stress and physical manipulation can factor into experimental results. Updates on Google Summer of Code 2022 and DevoLearn preprint. Embryogenesis animations. Papers on Turing Morphogenesis, Tissue Biomechanics, and the many facets of C. elegans Commectomics. Attendees: Susan Crawford-Young, Shruti Raj Vansh Singh, and Bradly Alicea.
B
A
B
B
Welcome
to
the
meeting
if
you're
joining
us
on
youtube,
we
have
a
number
of
things
today.
I
have
been
communicating
with
thirun
and
usual
and
asmit
on
their
work
on
the
basil
area,
decomposing
the
basil
area,
colonies
and
they're,
making
some
progress,
although
they're
still
having
some
problems
with
the
edges
and
some
of
the
you
know
some
of
the
cases
where
the
cells
seem
to
overlap,
I
mean
they
don't
overlap
physically,
but
the
algorithm
can't
seem
to
pick
out
the
true
edge
or
like
an
edge.
B
A
A
B
B
B
A
B
A
Resolve
them,
it
has
to
do
with
these
experiments
that
we're
supposed
to
be
doing
on
tissue,
and
if
you
push
a
tissue,
is
it
a
trauma
to
the
tissue
or
is
it
just
a
normal
movement
like
a
normal,
I
know
to
build
muscley,
you
have
to
give
it
micro
tears,
but
so
that's
sort
of
an
in-between
situation.
I
suppose,
but
I
have
a
nice
graph
and
try
to
explain
it,
explain
this,
but
anyway,
I
wanted
to
share
that
yeah.
B
A
A
B
Right,
yeah,
that's
something
actually
that
I
think
yeah.
I
don't
think
a
lot
of
the
studies
look
at
like
when
they
do
a
lot
of
stuff
with
forces
and
that
like
and
like
even
with
stem
cells,
in
that
you
know,
you
have
self-renewal
and
you
have
a
lot
of
you
know
they
can
repair
themselves
quit
or
they
can.
You
know.
Sometimes,
if
you
stimulate
them
in
certain
ways,
they
can
go
down,
say
a
cancer
pathway
or
they
can
die,
but
sometimes
they
can.
A
A
B
A
A
B
Right,
yeah
yeah,
it
happens
a
lot
in
like
in
in
experimentation
where
you
know
some
of
your
sample
will
die
off.
It's
kind
of
like
you
just
harvest.
What
you
can
you
put
do
all
these
treatments
to
it,
and
then
you,
like
a
certain
amount
of
the
cells,
will
die
off
and
then
you
just
have
to
harvest
what
you
can
and
try
to
re-establish
it.
A
Apparently,
you
can
pre-stress
cells.
So
if
you
pre-stress
the
cells
before
your
experiment,
so
you
can
get
nice
results,
then
you're
not
measuring
what
was
there
before
right
now
yeah
and
I
they'll
pound
the
ultrasound
into
something
with
a
whole
bunch
of
clicks
and
I'm
going.
Is
that
a
trauma
to
the
tissue
it
might
be?
This
might
be
a
trauma
if
you
sent
ultrasound.
B
B
B
A
B
Yeah
well
yeah.
I
don't
know
if
people
in
our
group
have
a
lot
of
I
mean
dick.
Might
you
know
he
has
so
yeah?
But
I
don't
know
you
know
it's
like
one
of
those
things
like
it's.
I
don't
I
don't
think
it's
been
well
studied.
So
I
don't
think
people
have
a
very
hard
and
fast
answer
on
it,
but
I
think
I've.
B
B
A
B
B
So
this
is
what
the
graph
looked
like.
This
was
great
figure
15
in
the
paper,
and
so
you
got
these
phase
diagrams
where
we
had
position
on
the
x-axis
and
velocity
on
the
y-axis.
So
this
is
position
or
it
could
be
displacement
if
you
re,
arrange
the
numbers,
if
you
you
know,
and
then
velocity,
which
is
microns
per
second.
So
this
is
where
you
have
movement
in
a
certain
position:
location,
location,
and
then
this
is
velocity.
B
As
you
can
see,
the
velocity
increases
as
you
get
towards
the
middle
and
decreases
as
you
get
towards
the
ends
so
that
that
roughly
corresponds
to
the
each
cell
kind
of
like
coming
to
a
stop
at
its
extent
and
then
coming
back
and
then
making
it
swing
through
and
then,
as
it
passes,
the
zero
markets
it's
going
at
sort
of
its
maximum
velocity,
and
then
it
does
this
over
again.
So
it's
a
nice
pattern
and
I
looked
through
the
data.
I
went
back
to
the
data
and
I
I
got
some
interesting
things.
B
B
So
this
is
the
graph
for
cell
one
versus
cell
two.
So
this
is
a
pairwise
comparison
where
you
have
the
two
cells
that
are,
you
know,
expanding
against
each
other,
so
they're
actually
making
contact
and
they're
moving
against
one
another,
and
you
can
see
that
this
is
sort
of
this
position
is
sort
of
a
displacement.
B
B
Now
that's
one
versus
two.
This
is
two
versus
three
and
you
can
see
there's
a
little
bit
of
different.
Now
one
is
at
the
edge
of
the
colony.
So
you
know
one
is
sort
of
at
the
leading
edge
of
the
colony.
Two
is
the
next
cell
down
and
three
is
the
next
cell
down
from
two.
So
this
is
two
versus
three,
so
these
are
two
cells
that
are
within
the
colony,
so
there's
no
leading
edge
here.
This
is
within
the
colony
itself,
and
so
you
can
see
there's
a
little
bit
different
phase
diagram
pattern.
B
You
can
see
that
there's,
maybe
if
you
call
them
harmonics,
you
know
almost
like
a
slowing
down
or,
like
you
know,
there's
some
constriction
of
movement
here.
So
you
have
like
this
again,
you
go
out
to
negative
60,
you
go
to
positive
60,
but
then
you
have
these
slowing
down
periods
at
negative,
30,
negative,
40,
negative
50.,
and
you
also
have
this
kind
of
noise
here
which
you
know.
This
is
a
noisy
pattern.
It's
not
smooth
movement!
Necessarily,
it's
a
little.
B
B
You
can
see
it's
gaining
velocity
here,
losing
velocity
gaining
velocity,
losing
velocity,
and
so,
as
it
starts
to
lose
velocity.
There
starts
to
be
some
fluctuation
in
the
signal,
and
this
is,
of
course
one
versus
two,
so
this
is
where
the
leading
edge
is
moving
against
the
the
an
interior
cell
and
there's
a
slowing
down,
which
is
there's
some
instability
there.
Now,
in
this
case,
in
phase
in
the
comparison
of
two
versus
three,
you
see
this.
B
You
know
same
pattern
here
where
it's
slowing
down,
but
also
you
get
this
pattern
where
you
get
these
sort
of
slowing
down
events
at
different
positions,
positional
displacements
or
whatever
you
want
to
call
it
now
phase
diagram.
Three,
then,
is
quite
different.
This
is
one
versus
three,
and
so
this
is
where
you
have
the
leading
edge
cell
and
then
the
an
interior
cell.
But
this
isn't
a
pairwise
comparison.
B
It's
a
sort
of
a
you
know:
a
leading
edge
versus
an
interior
cell
comparison
they're,
not
necessarily
coupled
in
terms
of
their
movement
like
one
and
two
are
or
two
and
three
even
they're.
You
know
they're
they're
sort
of
coupled
indirectly,
and
so
you
can
say
this
is
maybe
a
second
order
effect,
and
so
you
can
see
it's
a
second
order
effect.
It's
quite
different.
B
You
have
this
phase
diagram,
it's
got
a
lot
of
noise
in
it
and
it
looks
like
there's
a
slowing
down
period
where
things
wind
down
into
this
sort
of
towards
the
end
of
this
sequence.
It
winds
down
sort
of
dampens
in
this
region.
So
I
don't
know
what
this
means
this
is
you
know
you
see
these
different
slowing
down
periods
and
many
scales
here
instead
of
at
the
end.
You
know
the
extreme
ends
of
the
of
the
displacement,
so
you
see
all
sorts
of
things.
B
B
B
That's
sort
of
next
to
it,
so
they're
actually
coming
into
contact
direct
contact,
and
there
are
all
sorts
of
you
know:
they're
they're,
different,
molecular
sort
of
like
connections
here
with
different
types
of
polymers
that
both
you
know
enable
movement
in
the
water
column
and
an
evil
movement
against
each
other.
So
there's
this
direct
connection.
One
versus
three,
on
the
other
hand,
are
not
directly
connected
they're
intermediately
connected
through
two,
so
two
is
moving
against.
One
two
is
moving
against
three
and
then
you're
measuring
this
one
versus
three.
B
B
B
B
You
know
there's
minimal
velocity
there,
so
there's
a
little
bit
of
a
plateau
there,
but
you
can
see
that
there's
this
rough
oscillatory
pattern
and
we
have
some
smooth
versions
of
this
in
figure
15,
which
are
just
kind
of
like
you
know
when
we
fit
it
to
a
sine
wave.
So
it
looks
more
like
a
sine
wave
in
that,
but
this
is,
if
you
don't
fit
it
to
a
sine
wave
and
just
plot
out
the
values.
B
B
This
is
where
you
have
this
acceleration
going
on
so
yeah.
This
one
is
velocity
versus
acceleration.
The
velocity,
of
course,
is
rough.
It's
not
position
it's
its
velocity.
This
is
the
acceleration,
the
blue
line,
and
you
can
see
that
this
is
not.
This
has
like
little
peaks
in
it
in
different
places,
so
this
has
a
peak
a
couple
peaks
within
some
of
these
some
of
these
oscillations.
So
this
cycle
here
you
have
some.
B
You
know
some
large
displacements
in
terms
of
acceleration
in
some
of
these
places
and
that
others,
it's
you
know
it's
kind
of
it
looks
it
doesn't
look
like
there's
any
sort
of
pattern
that
jumps
out,
but
this
gives
you
an
idea.
This
is
unsmooth
acceleration
data.
So
this
is
again
you
know.
Velocity
is
when
it's
moving
per
second,
you
know
microns
per
second,
the
acceleration
is
microns
per
second
per
second
and
you're
gonna
get
some.
You
know.
I
guess
this
is
in
some
ways
like
oscillatory.
B
Compared
to
this.
It's
much
you
know
a
much
shorter
period
and
it's
much
more
unstable
with
respect
to
its
own
sort
of
fitting
a
sine
wave.
So
you
can
see
it's
actually.
You
know.
This
is
something
that
you
could.
I
actually
did
do
a
little
bit
of
signal
processing
on
it.
I
didn't
get
any
well.
I
think
I
have
those
graphs
in
here
but
anyways.
That's
so
that's!
Okay!
So
then
there's
velocities!
B
This
is
a
histogram
here
of
velocities
for
cell
one
versus
cell
two,
so
you
can
see
that
in
the
in
terms
of
velocities,
you
have
you
know
this,
these
low
velocity
moments
and
then
you
have
some
high
velocity
moments
down
here
and
you
have
like.
We
talked
about
in
last
week's
meeting.
You
know
it
isn't
normally
distributed.
There's
a
long
tail
to
this,
and
you
can
see
that
there's
a
you
know,
it's
it's
a
it's
a
fairly
thick
tail
here
coming
out
into
this
region,
so
these
are
higher
velocity
movements.
B
Okay,
so
this
is
velocity
versus
acceleration
plotted
out
on
a
as
a
scatter
plot.
So
this
is
a
little
bit
different
way
of
expressing
it.
As
I
showed
you
before,
I
had
the
two
traces
kind
of
overlaid.
This
is
where
you
have
velocity
versus
acceleration
here.
So
the
velocity
is
here.
Acceleration
is
on
the
y-axis,
and
so
you
can
see
that
they're
high
velocity
high
acceleration
points
that
are
rather
rare
and
there
are
a
lot
of
low
velocity
low
acceleration
points.
B
Okay,
let's
see
we
also
have.
I
think
these
are
histograms
of
accelerations,
so
this
is
kind
of
what
I
showed
you
before
this
is.
This
is
for
acceleration
that
velocity.
So
these
are
the
histograms
for
accelerations.
You
see
that
there's
this
it's
you
know,
there's
a
very
thin
tail
here.
So
a
lot
of
someone
versus
cell
2,
the
accelerations,
are,
you
know,
distributed
at
low
accelerations.
B
There
are
a
couple
high
accelerations
in
here,
but
it
seems
like
it's
pretty
stable
if
you
compare
it
with
cell
one
versus
cell
three
there's,
maybe
a
little
bit
longer
tail,
because
you
have
some
events
out
here
like
you
have
four
and
five
point
two,
so
you
have
these
rare
events
out
here.
Rare
events
where
the
acceleration
increases
greatly
between
the
two
cells
and
then
cells,
two
in
cells.
Three,
these
are
the
cells
that
are
interior
to
the
colony.
You
have
the
same
thing
you.
This
is
not
the
same
graph.
B
This
is
a
different
graph,
but
you
get
a
very
similar
behavior.
So
I
don't
know
if
that's
what
cell
the
influence
of
cell
three
or
if
it's
just
the
influence
of
you,
know
two
versus
three
and
then
there's
it's
sort
of
similar
with
one
versus
three.
But
you
get
this
long
tail.
But
these
rare
events
out
here
where
there's
a
lot
of
you,
know
very
few
number
of
high
acceleration
events
and
then
a
lot
of
low
acceleration
events.
B
C
B
B
B
B
A
B
Well,
I
think
yeah,
it's
definitely.
There
are
different
ways
you
can
plot
them
out.
I
use
the
scatter
plots
because
I
wanted
to
see
like
you
know,
if
you
compare
the
two
versus
just
you
know
putting
them
on
top
of
one
another.
You
know
what
are
the
obviously
they
well,
they
may
not
share
like,
and
it
doesn't
show
you
like
a
correspondence
of
things
in
time,
but
it
does
give
you
an
idea
of,
like
you
know,
is
there?
B
Are
there
high,
accelerations
and
also
you
know
high
amounts
of
of
velocity,
so
it's
like
high
velocity
and
versus
high
acceleration.
You
know
you'll
see
like
some
sort
of
relationship
between
them,
so
I
I
you
know
I
I
that's
that's
the
relationship
that
came
out.
It's
not
very
clear,
there's,
no
linear
relationship
or
anything
like
that.
B
B
So
yeah
that
that's
that's
something
so
we'll
follow
up
on
that
in
another
time.
So
I
want
to
move
on
to
some
other
business
here.
So
I
I
know
that
we've
last
year
we
had
google
summer
of
code,
and
we
talked
about.
We
had
a
lot
of
applicants
and
there
has
been
a
lot
of
fluctuation
in
the
program
over
the
last
two
or
three
years
because
of
kovid.
B
You
know
getting
everyone
funded
and
things
like
that.
So
this
year,
they're
going
to,
I
think,
revamp
the
program.
It
looks
like
they
put
up
this
blog
post
this
a
few
days
ago,
and
so
I
wanted
to
go
over
this
for
people
and
this
keep
this
in
mind
as
we're
formulating
projects
for
this
coming
year.
So
this
is
for
2022.
C
B
But
this
year
they're
expanding
the
scope
of
it.
So
I
like
this,
what
they've
done
here?
So
what
they're
trying
to
do
here
is
they're
trying
to
bring
in
more
a
more
diverse
group
of
people
with
you
know,
different
sort
of
needs
and
and
goals,
and
so
their
first
thing
they've
done
is
expanded
eligibility,
so
they're
expanding
eligibility.
Now
the
only
constraint
is,
you
need
to
be
18
years
or
older,
so
you
don't
need
to
be
a
university
student.
A
recent
grad.
B
That
was
a
problem.
It's
not
a
problem
anymore!
So
that's
good,
but
the
other
thing
here,
the
other
two
things
is
that
they
first
of
all
they've
modified
the
size
of
the
project,
and
that
means
that
they
have
two
different
project
sizes.
Now
so
there's
the
shorter
project,
which
is
175
hours,
which
is
you
know?
Basically,
I
don't
know
how
many
weeks
that
is,
it's
like
10
to
12
weeks
and
then
350
hour,
projects
which
are
about
22
weeks.
B
So
this
is
a
something
I
think
they've
done
this
in
google
summer
of
docs,
which
is
a
competing
program
in
the
fall,
and
they
have
these
different
program
lengths.
So
the
idea
would
be
you
know
if
you
have
a
project
that
can
be
done
in
12
weeks.
You
know
a
small
scale
project
like
say
we
want
to
update
diva
learn
again,
but
we
want
to
do
this
in
a
shorter
period
of
time,
because
it's
not
like
doesn't
require.
B
A
lot
of
you
know,
lead
lead-up
development
and
all
this,
if
we
were,
you,
know
creating
something
new
like
if
we
were
creating
diva
learn
from
scratch.
We
might
want
the
22
week
project
if
we're
just
updating
it,
we
might
want
the
12-week
project
and
then
that
increases
our
chances
of
getting
a
spot
in
the
program.
B
If
you
wanna,
if
you
submit
an
idea
for
a
project,
you
wanna
be
a
mentor
say
or
you
wanna
comment
or
you
know
you
can
propose
a
project
of
these
different
sizes
and
you
know
it
allows
us
to
do
like
things
like
create
something
from
scratch
or
just
you
know,
update
things.
So
that's
good,
then
there's
increased
flexibility
of
timing
for
projects
so
related
to
this.
You
know
you
can
do
your
project,
you
know
over
a
22-week
window,
so
you
could
do
it
in
more.
B
B
My
advice
on
this
would
be
you
know
if
you're
interested
get
in
touch
now.
I
can't
you
know,
I
can't
guarantee
you
a
spot
or
I
can't
guarantee
you
a
project
or
anything
like
that,
but
if
you're
interested
in
doing
something
as
a
project
this
summer,
let
me
know
we
can
maybe
create
a
proposal
around
that
we
can
recycle
proposals
from
last
year.
We
can
you
know
if
susan
wants
to
pursue
the
the
the
stuff
we're
doing
the
facts
of
waddles.
You
know
we
can
revisit
that.
B
B
I
think
it's
like
the
the
project
can
be
of
the
different
lengths,
so
you
can
have
like
a
long
project
or
a
short
project,
and
then
you
can
have
like
you.
Can
you
have
flexibility
in
scheduling?
So
you
could
say
I
don't
want
to
start
this
until
like
august
and
go
is
do
a
short
project
and
do
it
to
like.
I
don't
know
october
november,
or
you
could
do
a
22-week
project.
C
B
And
then
you
know,
we'd
have
to
come
up
with
a
schedule
that
would
be
suitable,
so
you
know
like
it
would
be
like
you
know,
12-week
schedule
or
you'd
stretch
it
out
to
22,
and
then
it
would
have
to
be
like
you
know.
You
couldn't
just
like
take
a
12-week
project
and
stretch
it
out
to
22
weeks
unless.
B
A
B
With
I
think,
as
as
many
images
as
you
can
get
would
definitely
be
good,
and
I
think
another
way
to
you
know
get
people
interested
is,
you
know
have
like
we
didn't
do
this
last
year,
but
maybe
we
could
have
like
a
sort
of
showing
some
of
the
microscopes
like
just
maybe
like
a
video
or
like
some
pictures
of
the
microscopes
and
just
have
those
like
as
a
like.
If
people
are
interested
in
the
project,
we
can
create
like
an
onboarding
guide,
for
that.
B
Actually
we
had
the
onboarding
guide
from
last
year
and
we
can
just
add
to
that
and
say
this
is
this
project
and
here's
what
the
data
collection
looks
like
here's,
what
the
data
look
like
and
then
here's
what
we
want.
You
know
what
we
would
want
you
to
do
and
then
people
could
have
a
good
idea
of
what
what
to
do,
because
they
see
you
know
what
it
is
like.
This
is
a
axolotl.
B
C
A
C
A
Now
this
is
called
neural
tube
closure.
You
might
not
have
to
go
into
that,
but
yeah,
okay,
oh,
I
need
to
get
that
put
together.
I
have
a
new
one.
New
micro
microscope,
that's
easier
to
work
with,
and
I
hopefully
near
a
christmas
time
the
salamanders
will
probably
lay
eggs
like
this.
Is
I
have
a
friend
who
lives
nearby
and
she
has
a
whole
group
of
salamanders
and
and
they
they
tend
to
lay
eggs
at
christmas
time.
A
B
Yeah,
great
okay,
so
yeah
we'll
come
back
to
that
and
we'll
plan
out
you
know,
is
again.
If
you
have
any
ideas,
if
you
want
to
be
involved,
let
me
know
so.
The
next
thing
I'd
like
to
talk
about
is
I
I
know
I've
been
talking
about
this
for
a
long
time
and
I've
been
putting
it
off,
but
I
think
we're
finally,
here
to
some
extent
this
is
the
diva
learn
technical
paper
on
medieval,
learn
platform
that
I've
been
talking
about
finishing
up,
and
this
includes
my
knox
work
from
this
summer.
B
So
this
is
something
that
I'm
still
kind
of
working
on
getting
it
organized.
I
wanted
to
go
through
it
and
revise
it,
because
we
had
submitted
it
to
another
venue
and
they
didn't
like
it.
So
I
wanted
to
make
it
larger
and
then
incorporate
my
knox
work
from
this
last
summer,
and
so
this
is
where
this
is
headed,
and
so
I've
been
working
on
this
a
bit
getting
things
from
my
knock,
getting
things
from
you
know
putting
in
figures
and
tidying
up
some
of
the
references
and
things
like
that.
B
So
again,
our
authors
are
usually
my
hook.
My
knock
and
myself,
the
abs
have
a
short
abstract
and
it's
just
very
short,
just
just
kind
of
goes
over
the
platform.
So
this
is
a
summary
of
why
it's
important.
This
is
a
screenshot
from.
I
think
my
knox
work
from
this.
He
made
this
gif
that
we
have
in
here.
B
So
this
is
the
accessibility
section
which
discusses
sort
of
where
you
can
find
this,
and
you
know
this
is
a
gui.
So
this
is
all
right.
So
then
the
statement
of
need
is
here,
so
this
is.
Why
do
we
need
this
and
this
kind
of
goes
through?
The
again
I
like,
I
said
this
is
an
optimal.
I
need
to
work
on
it
more,
but
I
I
think
I'm
getting
getting
some
of
it
in
place
here
and
it's
it's
coming
along.
B
So
this
is
a
sort
of
a
schematic
of
how
the
software
works,
the
github
source
and
the
user
environment
technical
details.
So
you
know
from
my
nox
work:
we
have
hyper
parameter,
optimization,
meta,
feature,
detection.
This
is
both
myocard
mayuk
and
minox
work
and
then
the
divor
learn
platform,
which
is
the
broader
platform
which
has
educational
resources
and
other
things,
and
this
is
an
example.
B
This
is
the
diagram
of
that
and
then
future
directions
which
kind
of
talk
about
some
of
the
things
we
might
do
in
the
future
or
things
that
need
to
be
clarified,
and
I
yeah
so
then
references.
So
it's
still.
You
know
it
still
needs
a
little
bit
more
length.
I
think,
to
be
an
effective
preprint,
but
but
we're
getting
there.
B
B
So
now
I'm
going
to
move
into
papers,
I
don't
well.
We
have
some
a
couple
submission
items.
I
don't
know
if
we
have
anything
to
catch
up
on,
but
we
have
a
couple
things
here:
neuromatch
4.0
is
coming
in
this
early
december
december,
1
and
2..
My
other
group
submitted
a
bunch
of
stuff
a
number
of
made
a
number
of
submissions
to
this
conference,
which
all
got
accepted
as
short
talks,
so
we'll
be
doing
that.
B
B
This
is
a
number
a
lot
of
topics
in
neuroscience
and
in
they
always
have
some
really
good
talks,
some
good
keynotes
and
some
good
short
talks
and
then
neuro
ips,
which
is
coming
up
december
6th
through
the
14th.
So
if
you're
interested
in
machine
learning
deep
learning
that
sort
of
thing
near
ips
is
coming
up
so
again,
if
you're
a
student,
you
could
volunteer
or
you
can
attend
virtually.
B
You
can
attend
both
of
these
conferences
virtually
and
it
will
improve
your
understanding
of
these
areas,
so
the
mathematics
of
diva
worm.
That
is
something
that's
in
progress.
I
was
going
to
show
it
today,
but
I
really
haven't
made
a
lot
of
progress
on
that
to
really
cover
this
is
the
diva
learned
papers
this
has
been.
I
just
showed
that
and
then
we
have
a
number
of
different
other
things
that
are
outstanding.
I
don't
think
there's
been
any
progress
on
those
last
week.
B
B
B
B
B
All
right,
so
that's
good!
That's
those
are
some
nice
animations,
and
so
then
let
me
talk
about
some
of
these
other
things
in
here.
So
this
is
a
paper
and
graph
cellular
automata.
So
this
is
something
that
you
know:
we've
kind
of
talked
about
a
little
bit.
I
think
this
is
the
bright
paper,
but
I
don't
know.
B
Is
the
right
one
or
not?
I
don't
want
to
cover
this
one,
and
this
is.
I
thought
this
was
a
different
paper.
It
wasn't
there
I'm
going
to
talk
about
this
one.
Then
this
is
trying
morphogenesis.
This
is
a
special
issue
when
trying
morphogenesis-
and
this
just
came
out
recently.
This
is
actually
the
data,
and
this
is
december,
27
2021..
B
So
this
is
upcoming
issue
it's
out
now,
but
it's
going
to
be
out
in
what
they
call
print
in
a
few
months.
So
this
is
recent
progress
in
open
frontiers,
enterings
theory
morphogenesis.
So
we've
talked
about
this
a
lot.
This
is
a
picture
of
alan
turing.
This
is
morphogenesis
in
the
background.
We've
talked
about
what's
going
on
there
and
just
a
whole
bunch
of
papers
on
this
topic,
some
of
the
more
recent
developments.
B
So
you
know
they
talk
about
elucidating
pattern.
Forming
processes
is
an
important
problem
in
the
physical,
chemical
and
biological
sciences,
turing's
contribution,
after
being
initially
neglected,
eventually
catalyzed,
a
huge
amount
of
work
from
mathematicians,
physicists,
chemists
and
biologists,
and
this
work
was
aimed
towards
understanding
how
steady
spatial
patterns
can
emerge
from
homogeneous
chemical
mixtures
due
to
the
reaction
and
diffusion
of
different
chemical
species.
B
Their
bifurcation
results
indicate
a
number
of
relationships
between
classical
touring
and
what
they
call
hop
bifurcations,
so
hop
bifurcations.
Are
these
bistable
systems
where
you
get
bifurcations
between
one
state
and
another,
whereas
the
turing
system
doesn't
usually
have
these
bistabilities?
They
usually
have
this
monostability
and
then
with
more
exotic
structures
such
as
homoclinic
snaking,
and
then
you
get
into
this
higher
level.
Topological
set
of
relationships.
B
Unlike
the
complexity
insert
and
uncertainty
of
in
vivo
systems,
chemical
systems
can
exploit
extremely
well
characterized
reaction,
kinetics
and
diffusion
rates
to
design
and
engineer
a
range
of
pattern,
formation
scenarios
from
the
interaction
of
coupled
layers
to
growing
domains
using
photosensitive
reaction
kinetics.
So
you
get
these
different
types
of
you
can
get
these.
You
have
these
different
types.
You
have
this
in
vivo
system.
B
So
this
is
something
that
we
can
do
with
synthetic
biology.
We
can
engineer
turing
systems
that
will
allow
us
to
look
at
some
of
these
pattern
formation
regimes.
So
this
is
a
nice
sort
of
future
direction
step.
So
this
is
good.
They,
they
kind
of
discuss
a
lot
of
different
aspects
of
this.
So
if
you're
interested
in
this
reaction,
the
fusion
model-
you
really
they
they.
You
know
this.
This
special
issue
is
really
going
to
help
you
sort
of
think
about
the
possibilities
here.
B
B
These
resulting
turing
patterns
have
been
shown
to
correspond
to
various
spatial
phenomena
in
chemistry
and
biology,
and
so
this
is
a
steady
state
solution
of
a
system
of
spatially
homogeneous
reaction,
diffusion
equations,
so
the
classical
turing
mechanism
involves
a
single
sort
of
you
know,
differential
equation
that
allows
you
to
model
this
process
sort
of
homogeneously.
It
doesn't
matter
where
you
are
in
space.
The
same
process
is
working,
you
know
everywhere,
sort
of
it
with
the
same
type
of
dynamics
and
that's
typically
how
it
works.
B
But
of
course,
then
they
it.
You
know
the
system
can
lose
stability
due
to
spatial
perturbation,
with
the
system
evolving
away
from
the
steady
state
and
into
a
new
state
which
is
spatially
heterogeneous,
genius
or
patterned.
So
it
is
possible
to
consider
reaction,
diffusion
systems
which
are
explicitly
heterogeneous
in
space.
So
what
happens
here?
Is
you
have
this
single
equation?
B
It's
governing
this
system.
This
diffusion
reaction
system,
then,
when
there's
you
know
enough,
spatial,
heterogeneity,
there's
a
space
phase
transition
and
you
get
this
sort
of
this
pattern
formation,
but
you
don't
actually
have
equations
that
describe
that
process.
This
just
kind
of
emerges
out
of
the
out
of
which
you're
you
know
out
of
the
system,
so
turing
has
this
unpublished.
Work
called
outline
of
the
development
of
a
daisy,
and
so
this
was
something
that
he
worked
on
sort
of.
B
This
has
been
suggested
as
a
factor
in
the
rich
variety,
a
pattern
unseen
in
nature,
including
patterns,
emergent
and
developmental
biology,
ecology
and
spatial
epidemics.
So
there's
been
this
missing
piece
of
spatial
heterogeneity
in
these
systems,
and
this
is
what
this
article
is
introducing
or
reviewing
to
a
broader
audience.
B
So
spatial
heterogeneity
can
be
introduced
into
turing
systems
through
a
variety
of
mechanisms.
You
can
have
space-varying
reaction
kinetics,
which
is
a
popular
choice
amongst
people.
You
can
also
have
space-faring
diffusion
parameters,
which
is
also
a
popular
choice,
but
these
aren't,
like
you
know,
these
aren't
like
things
that
are.
These
are
just
possibilities
in
your
modeling.
B
B
So
the
literature
concerned
with
instabilities
either
considers
the
case
of
small
spatial
heterogeneity,
which
means
it's
really
restricted
to
certain
regions
or
strictly
numerical
simulations
in
the
case
of
large
spatial
heterogeneity.
So
they
haven't
really
solved
the
problem
of
large-scale
spatial
heterogeneity.
B
A
And
this
fits
in
with
active
matter
active
patterns
because
that
you've
got
movement.
Obviously,
when
you're
creating
patterns
in
a
living
system,
so
yeah
things
like
vortices,
which
have
always
intrigued
me
so
yeah.
I
really
need
to
get
that
organized
because
it's
the
end
of
my
presentation
that
I'm
doing
so,
I'm
I'm
working
on
that
this
week.
So
hopefully
I'll
get
get
to
that
point
because
I've
yeah
anyways
it
it
all
fits
together.
The
touring
patterns,
along
with
the
active
matter,
will
create
patterns
in
living
systems.
B
C
B
That's
that's
good,
and
you
know
I
yeah,
I
don't
know.
I
haven't
really
gone
through
this
special
issue
too
much,
but
I
know
that
there
are
probably
a
lot
of
articles
there
that
might
help
make
that
bridge,
and
I
don't
know
if
people
are
making
it
explicitly
in
terms
of
like
some
of
the
soft
active
materials
would
urge
her
like
if
they're
bringing
that
in
explicitly,
but
I
think
yeah
there
are
a
lot
of
links
there.
B
B
B
So
this
is
the
background
of
this
paper.
As
embryonic
development
involves
the
interplay
of
driving
forces
that
shape
the
tissue
mechanical
resistance
that
the
tissues
offer
in
response,
while
increasing
evidence
has
suggested
the
crucial
role
of
physical
mechanisms
in
embryo
development.
Tissue
biomechanics
is
not
well
understood
because
of
the
lack
of
techniques
that
we
can
use
to
quantify
the
stiffness
of
the
tissue,
so
they
use
two
all
optical
techniques,
optical
coherence,
tomography
and
really
microscopy
to
map
the
longitudinal
modulus
of
the
tissue,
and
this
is
in
mouse
embryos.
B
B
B
Before
you
really
get
a
you
know,
this
is
this
is
going
to
become
the
brain,
so
this
is
going
to
become
the
central
nervous
system,
but
you
need
to
have
this
neural
tube
and
the
neural
tube
closes,
and
then
you
get
this
structure
that
will
then
become
the
brain.
So
we
have
this.
So
we
can
see
this
difference
here,
and
this
is
a
nice
image
because
it
does
show
sort
of
the
difference
here
in
these
two
points.
B
So,
let's
see
the
modulus
of
neural
tube
tissue
is
a
gradient
along
dorsal.
Ventral
direction
and
fusion
region
is
much
softer
after
closure,
so
they
can
actually
measure
now
this
the
hardness
or
softness
relative
to
some
metric
of
this
neural
tube,
and
so
we
know
that,
like
they're
different
parts
of
the
neural
tube-
and
there
is
a
hardness
and
softness
of
those
tissues
depending
on
their
sort
of
their
morphogenesis,
you
know
as
they're
closing
or
as
they're
closed
there's
also
a
gradient
in
a
dorsal
ventral
direction.
B
So
there's
you
know
they
report
these
results
here
then
they
have
this
ectoderm
layer
which
can
be
distinguished
from
the
neural
tube.
So
you
have
this
ectoderm
and
is
much
softer,
so
this
technique
will
not
only
measure
the
hardness
or
softness
of
the
neural
tube
at
different
points
in
development,
but
can
also
get
the
ectoderm
layer
distinguished
from
the
neural
tube
itself.
So
you
get
this
these
differentiating
layers
and
you
can
actually
look
at
that.
B
So
these
are
all
things
that
involve
sort
of
thickening
lengthening,
elevating
and
bending
this
neural
tube
tissue,
so
the
tissue
itself
is
isn't
changing,
shape,
it's
changing
it's
consistency,
and
then
this
is
all
something
that
we
can
measure
in
this
using
these
techniques.
So
this
is,
you
know
this
opens
up.
I
think
a
lot
of
avenues
for
some
of
the
physical
sort
of
characterizing,
some
of
the
physical
aspects
of
these
processes.
A
Oh,
they
should
definitely
triax
a
lot
of.
I
can
think
of
an
axolotl
model
for
some
of
the
migrating
cells.
There's
the
white
salamanders,
like
the
they
called
leucistic
they're.
Apparently,
some
of
the
melanin
cells
are
blocked
from
migrating,
so
they
they're
white,
not
because
they
they're,
naturally
that
way,
but
somehow
they
get
blocked
in
that
phenotype.
A
D
B
Is
a
proof
of
concept?
Of
course
you
know
the
proof
of
concept
could
be
maybe
more
interesting,
but
I
think
they've
got
a
good,
so
this
is
like
showing
some
of
this.
You
know
part
of
like
different
parts
of
the
neural
fold
and
like
showing
the
softness
and
stiffness
peaks,
so
they're
the
stiffness
peaks.
The
fusion
region
can
be
defined
defined
here
as
sort
of
a
dip
in
the
stiffness.
B
A
A
B
Yeah,
so
this
is,
I
don't
know
there
are
a
lot
of
images
in
here
so
and
then
that's
the
end
of
the
paper
so
yeah.
This
is
a
good
paper
and
then
finally,
I'd
like
to
end
with
this
paper
on
wired
for
insight.
This
is
a
c
elegans
paper
on
c
elegans.
I
think
it's
on
the
let's
see
so
wired
for
insight.
Recent
advances
in
c
elegans
neural
circuit,
so
this
is
just
on
neural
circuits
and
again
we
have
some
really
nicely
worked
out:
neural
circuits
and
c
elegans
for
behavior.
B
So
this
is
kind
of
a
review
of
this
kind
of
goes
through
some
connectomix
advances.
So
this
is
good
if
you
want
to
know
the
state
of
the
art
in
the
field
of
neural
circuits
and
c
elegans.
So
this
is
the
abstract
says:
the
completion
of
a
c
elegans
connectomics
four
decades
ago,
so
c
elegans
conictomics
has
a
head
start
on
a
lot
of
other
organisms.
B
This
is
where
the
entire
connectome
has
been
worked
had
been
worked
out,
and
you
know
we're
now
kind
of
figuring
out
like
some
of
the
details
in
terms
of
development.
In
terms
of
you
know,
behavioral
connectivity
and
plasticity
and
things
like
that,
but
they
cannot
tell
the
connectome
for
c
elegans
has
been
worked
out
and
it's
been
a
long
time.
B
Given
some
you
know,
given
behavioral
data
given
neural
data
that
we
have
and
just
kind
of
working
that
out.
Recent
technological
advances
in
microscopy
and
computation
programs
have
aided
re-examination
of
this
connectome.
Expanding
your
knowledge
by
both
uncovering
previously
unreported
synaptic
connections
and
models
for
neural
networks
underlying
behaviors.
B
So
there
are
also
a
lot
of
these
synaptic
connections
which
are
have
different.
You
know,
strengths
have
different.
You
know
sometimes
they're
active
in
some
parts
of
life
history
like
in
development.
Sometimes
they
change
in
adulthood.
They
can
change
with
aging.
They
can
change
with
behavior,
so
they're
in
you
know.
You
have
like
the
dollar
stage
of
c
elegans
development,
where
you
know
there
are
significant
changes
to
the
nervous
system
in
terms
of
synapses,
so
these
are
all
very
important
things.
B
B
We
can
get
information
about
what
genes
are
expressed
in
single
cells,
one
cell
versus
another
cell,
so
this
is
very
useful
information,
and
this
really,
you
know
in
c
elegans.
It's
really
nice,
because
c
elegans
is
relatively
invariant.
With
respect
to
these
things.
So
you
know
in
other
organisms.
It
would
be
very
hard
to
put
together
all
this
into
a
single.
B
You
know,
model
of
the
brain
or
a
model
of
a
neural
circuit,
so
we're
in
advantage
of
c
elegans.
This
mini
review
aims
to
provide
an
overview
of
new
information
on
connectomics
and
progress
towards
the
molecular
atlas
of
c
elegans
nervous
system
so
and
and
discuss
emerging
findings
on
neural
circuits.
So
this
kind
of
goes
over.
Some
of
the
advances
in
connectomics
started
with
sydney
brenner,
and
it
goes
to
john
white,
who
did
a
lot
of
the
early
work
on
this
publishing
reconstruction
of
the
entire
nervous
system
back
in
1986..
B
So
these
are,
you
know
there
are
all
sorts
of
different
reconstruction
techniques
that
you
can
use.
These
are.
These
are
aiding
the
expansion
of
conectomic
studies
to
other
organisms,
these
a
lot
of
electron
microscopy
to
do
this
type
of
work,
and
we
also
have
the
cook
paper
that
is
fairly
recent.
This
reanalyzed,
the
original
electron
micrographs
of
pharyngeal
nervous
system,
neurons
and
added
anatomical
weights
for
synaptic
strength.
So
you
know
we're
trying
to
infer
different
synaptic
strengths
from
the
data.
B
This
reevaluation
of
the
pharyngeal
nervous
system,
connective
not
only
revealed
previously
unreported
synaptic
connections
and
additional
details
of
extensive
cross
connectivity,
but
also
provided
evidence
that
the
pharyngeal
neurons
may
both
have
sensory
and
motor
characteristics.
So
in
c
elegans
you
have
these
sensory
motor
neurons
that
are
both
sensory
and
motor,
and
so
this
is
a
table
of
the
different
experimental
technologies
for
extracting
these
connectomes.
B
So
you
know
you
have
electron
microscopy.
You
have
genomics
in
which
you
can
look
at
like
what
what
genes
are
doing
in
these
connectomes.
You
have
trap
seek,
which
is
a
technique
for
protein
expression.
Looking
at
protein
expression,
you
have
ribosome
tethered
gcamp,
which
is
another
technique,
and
then
you
have
chemo
and
optogenetics.
B
So
this
this
is,
you
know
where
you
actually
put
fluorescent
proteins
into
the
organism
and
you
use
a
certain
frequency
of
light
to
excite
those
fluorescent
components,
and
you
can
see
this
in
the
active
neurons.
This
is
a
very
powerful
technique.
We
also
have
sexual
dimorphism
c
elegans.
You
can
look
at
the
differences
between
the
male
and
the
hermaphrodite
connectome.
B
B
We
also
have
new
data
on
the
development
on
early
adult
and
l4
stage
c
elegans.
Looking
at
the
connectivity
as
well
as
the
number
of
cells
that
are
there,
we
have
finer
structures
and
expanded
microdomains.
So
we
have
these
super
resolution,
microscopy
techniques
that
allow
us
to
look
at
some
of
the
micro
structure
of
the
cells.
Looking
at
some
of
the
things
going
on
and
you
know
around
the
cells,
we
also
have
this
molecular
and
cellular
connectomics,
where
people
are
investigating
different
aspects
of
gene
expression
and
protein
expression,
and
things
like
that.
B
You
can
also
do
genetic
manipulations,
of
course,
which
is
what
we
mentioned
with
the
gcamp
technique
and
also
neuropal,
which
is
a
neuro
neuronal,
polychromatic
atlas
of
landmarks.
This
allows
us
to
identify
things,
identify
different
types
of
neurons,
which
can
be
distinguished
by
their
gene
expression.
This
has
been
an
open
topic
that
you
know.
We've
been
trying
to
figure
out
how
many
cells
there
are
in
the
human
brain
or
how
many
cells
are
even
the
c
elegans
brain.
We
assume
we
know
because
we
have
them
labeled
through.
B
You
know,
anatomical
observations,
but,
of
course,
if
we
look
at
the
gene
expression
profiles,
we
find
out
that
there's
some
a
lot
of
potentially
a
lot
of
different
types
and
subtypes
of
cells
that
we
really
can't
identify
using
traditional
microscopy
techniques.
So
these
are
things
that
we
want
to
know
as
well.
B
B
B
So
you're
looking
at
rna
transcripts
and
you
can
actually
identify
you
know-
maybe
you
know
different
transcripts-
have
different
levels
of
expression
in
different
cells
and
identify
differences
between
cells.
In
that
way,
it's
a
very
difficult
technique
to
sort
of
get
a
you
know
to
definitively
say
whether
you
have
one
cell
type
versus
another.
Usually
the
the
levels
of
rnas
are
fluctuating.
B
There
are
a
lot
of
I
mean
we've
kind
of
got
nailed
down
the
basic
neural
circuits,
but
we
have
yet
to
really
understand
the
roles
of
local
interneurons
and
some
of
these
other
things.
We
also
don't
know
exactly
the
extent
of
plant
neuroplasticity
that
exists
in
c
elegans.
We
assume
there's
very
little,
but
they
may
or
may
not
be
true
they're
ways.
B
You
can
manipulate
neural
circuits
using
ablation
techniques,
so
you
have
that
you
also
have
other
techniques
where
you
can
modulate
food
supply
or
you
can
modulate
things
like
dopamine
and
neuropeptides,
and
you
can
get
you
know
different
modes
of
locomotion
which
actually
allow
you
to
tease
out
some
of
the
circuit,
and
so
there's
here's,
the
the
connectome
here
in
the
adult
you
have
the
nerve
ring
the
head
ganglia,
the
ventral
nerve,
cord
motor
neurons
downstream.
Here
this
is
the
dorsal
nerve
cord
which
goes
up
the
top
of
the
worm
and
goes
from
head
to
tail.
B
And
then
you
have
these
tail
ganglia
cells,
which
is
you
know
this
emerges
fairly
early
as
well,
and
this
is
like
the
the
tail
end
of
the
nervous
system
as
well.
So
this
this
is
all
one
connectome
here
and
they
do.
You
know
this.
It
particip
these
cells
participate
in
different
behaviors,
chemo
sensation,
motor
behavior,
other
types
of
things-
and
this
is
these-
are
the
maps
that
we
usually
use
to
look
at
it.
So
we
have
these
different
cells.
B
These
are
the
names
of
the
cells,
but
in
terms
of
the
nomenclature,
and
then
you
propose
these
wiring:
either
an
inhibitory
relationship
or
an
excitatory
relationship,
so
one
cell
either
turns
another
cell
on
or
turns
another
cell
off,
and
then
this
is
how
this
works.
So,
given
the
stimulus,
we're
given
the
goal,
you
know
moving
left
or
moving
right.
These
neural
circuits
behave
in
different
ways
and
so.
B
So
that's
about
it
and
you
get
like
circuit
regulation
beyond
neurons,
even
with
the
neural
gut
axis,
so
they
even
consider
outside
of
the
traditional
connectome.
You
know
what
other
parts
of
the
body
you're
doing
and
then
c
elegans,
since
it
has
900
or
so
cells.
You
can
do
this
quite
easily
and
and
understand
some
of
these
relationships
on
a
cellular
level,
and
so
this
is
another
example
of
bacterial
aversion
and
how
the
motor
circuit
responds
to
this.
B
So
this
is
something
where,
if
they're
eating
bacteria
or
if
they
you
know,
if
they
I
don't
know
if
they
ingest
bad
bacteria
or
something
they
have
an
aversion
to
it,
and
they
actually
reverse
away
from
the
stimulus,
and
this
is
a
signal
that
they
get
from
their
gut.
They
get
intestinal
bloating.
And
then
you
know
this
is
like.
If
you
eat
a
bad
piece
of
cheese,
what
do
you
do?
You
don't
eat
the
cheese
anymore,
because
you
get
the
signal
from
your
gut
telling
you
not
to
do
it.
B
A
Well,
it
looks
interesting,
I
I'll
probably
try
to
read
it,
and
I
know
the
gut
brain
access
is
important
for
mammals
yeah
as
well,
so
we're
humans
actually
so
yeah.
It's
an
important,
well
a
study,
and
if
you
can
study
it
and
see
elegance,
you
might
get
some
insights
so
yeah.
Hopefully,
hopefully
they
can
figure
some
things
out,
because
anyways.