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From YouTube: DevoWorm (2021, Meeting 10): GSoC Demos, Cellular Automata, Modeling of Emergence in Embryogenesis.
Description
Fuzzy c-means clustering for detecting Diatom filaments, spatial modeling of the C. elegans embryo, Cellular Automata and the Game of Life for Pattern Formation, GSoC (and DevoWorm) Onboarding guide, and the modeling of emergence in embryogenesis. Attendees: R Tharun Gowda, Aayush Kumar, Yash Vadi, Krishna Katyal, Bradly Alicea, Mayukh Deb, Mainak Deb, Akshay Nair, Susan Crawford-Young, Richard Gordon, Markus Heimerl, Param Mirani, Kush Kothari, Abhishek Tiwari, and Shruti Raj Vansh Singh
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A
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B
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B
B
B
A
Okay,
well,
I
guess
I
don't
know
like
I
said
there
was
a
time
change
in
north
america
so
and
I
sent
out
reminders,
but
I
don't
know
what
people
got
and
what
they
didn't.
So
welcome
to
the
meeting
I
can.
I
guess
I
can
start
now.
We
we
had
some
things
planned
from
some
of
the
people
who
are
working
on
g-suck,
but
I
don't
know
if
they're
gonna,
when
they're
gonna
be
here
so
we'll
we'll
start
and
we'll
go
over
regular
meeting
business
and
then
we'll
get.
A
C
A
A
I
bet
you
so
yeah.
I
was
just
saying
that
we
were
gonna.
I
wanted
to
start
by
having
some
the
new
people
introduce
themselves.
We
have
param
and
kush,
and
I
don't
know
if
we
have
anyone
else.
So
if
you
want
to
use
the
chat,
if
you
can't
speak,
that's
fine,
but
I
just
I
want
to
get
a
sense
prom.
Why
don't
you
go
first?
A
A
D
A
Yeah,
that's
great
yeah.
That
would
be
good
yeah.
If
you
talk
about
gsoc,
but
you
know
it's
not
it's
pretty
competitive.
So
I
mean
you
know
there
are
always
opportunities
to
contribute
through
the
project
and
then
maybe
you
know
make
that
into
something
a
little
bit
more
substantial.
So
it's
always.
B
G
B
A
Yeah,
that's
great
yeah
thanks
for
you
know,
just
checking
it
out
and
seeing
what's
going
on
so
this
you
know.
Each
meeting
is
not
necessarily
representative
of
everything
that's
going
on.
We
just
cover
different
topics
as
they
come
up
and
we
can
certainly
talk
about
things
if
you're
interested
in
them
from
the
meeting
or
if
you
find
something
you
know
that
you
might
be
interested
in.
We
can
talk
about
it
and
you
know
put
it
on
the
schedule
and
and
talk
about
it
in
more
depth.
A
So
it's
definitely
there's
a
lot
going
on
in
the
group.
We
have
a
number
of
long-term
members
and
then
we
also
have
people
who
come
in.
You
know
every
once
in
a
while
and
check
in,
so
I
would
get
in
touch
well
I'll.
Show
you
in
a
minute
we're
building
an
onboarding
guide.
So
we'll
we'll
talk
about
that
in
a
little
bit,
but
I
wanted
to
talk.
I
wanted
to
ask
people.
I
know
there
have
been
a
number
of
people
in
the
slack
this
week
who
have
been.
A
C
B
C
A
H
B
I
H
A
H
H
B
H
H
H
We
compute
all
the
distance
from
the
center
and
the
center
of
our
update.
Accordingly,
every
situation.
First,
we
have
initialized
the
member
value.
Then
we
have.
Then
we
find
the
cluster
center
by
this
formula
by
calculating
the
mixtures
and
then
we
update
memory.
Then
we
again
calculate
the
cluster
center.
Then
we
update
update
membership
function.
We.
H
H
H
H
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I
H
A
D
D
Okay,
so
I'll
start,
so
my
presentation
would
be
about
exploring
the
spatial
distribution
of
cells
in
the
c
elegans
embryo
using
clearly
interactive
plots,
so
I'll
go
back
to
the
next
slide.
Okay,
so
the
data
set
that
we
are
dealing
with
is
basically
the
epic
data
set,
which
is
really
really
popular,
so
the
data
is
like
it.
It
looks
somewhat
like
this,
so
it
has
many
many
columns
like
it
has
this
column
called
cell,
which
basically
says.
A
A
A
D
D
D
D
To
visualize
the
progress
of
time
through
these
static
plots
and
like
if
we,
if
we
have
to
visualize
time,
then
we'll
have
to
save
each
plot
separately
and
then
create
a
gift
later
on,
which
is
very,
very
tedious,
and
this
actually
no
way
to
quickly
find
the
coordinates
of
each
point.
Suppose,
like
you,
want
to
find
the
coordinates
of
this
corner
point,
so
it's
almost
impossible
to
prove
that
without
any
specific
code,
so
we'll
go
ahead
to
the
next
slide:
yeah.
Okay.
So
this
is
where
these
interactive
plots
coming.
A
D
D
And
we
can
zoom
in
or
zoom
out,
and
so
this
gives
us
a
much
better
insights.
It
just
gives
us
actually
a
much
better
insight
of
what
we
are
dealing
with
like
in
the
previous
slide.
We
had
the
cell
image
dpa,
so
the
path
can
be
so.
The
path
is
traced
here,
using
this
blue
line,
so
but
there's
no
way
to
change
the
perspective
or
to
zoom
in
or
out
or
to
find
the
coordinates.
D
D
D
So,
as
you
can
see,
there's
the
scrubber,
so
you
can
scrub
to
the
so
you
can
scrub
through
the
time
points
and
you
can
stop
at
any
point
of
time
and
change
the
perspective
zoom
in
and
find
the
coordinates
of
each
cell,
lineage
and
yeah.
Another
thing
that
I
would
like
to
say
is
that
the
time
takes
here
they
are
not
in
the
correct
order.
That's
actually
an
error
in
my
code,
so
I'll
be
fixing
that
soon.
D
F
D
D
D
D
So
I
will
go
back
to
the
next
slide:
okay,
so,
okay,
so
this
is
something
that
I
actually
plan
to
do.
I
don't
think
I'll
be
able
to
do
it
until
at
least
a
month
from
now,
but
this
is
something
that
I
got
planned,
so
my
plans
to
basically
take
these
segmentation
maps
from
the
developer
model,
outputs
and
stitch
them
together
in
form
of
a
3d,
closed
surface
segmentation
map,
because
these
frames
they
basically
represent.
D
B
D
Way
easier
for
the
scientist
or
the
researcher
to
find
to
find
insights
from
the
given
segmentation
map
rather
than
having
these
2d
slices
of
rather
than
having
these
just
2d
images.
So
this
is
what
I
plan.
So
I
guess
that's
it
from
my
presentation
and
I
would
really
like
some
feedback
from
you
guys
right
now.
So
thank
you.
A
A
D
E
E
D
A
Well,
I
had
some
comments,
so
I
wanted
to
go
over
like
this
is
good
work,
very
good
work.
I
wanted
to
go
over
some
of
the
aspects
of
the
data
set.
Could
you
go
up
to
the
the
first?
Let's
see
third
slide,
three,
maybe
maybe
slide
four
when
you
say
that
there's
a
lineage
and
you
say
dpa,
what
are
you
measuring?
Are
you
measuring
just
the
position
of
dpa
or
like
dpa
and
all
the
cells
that
come
before
dpa?
A
D
D
A
A
A
A
Is
we
have
to
normalize
the
data,
somehow
the
position,
data
because
they're
kind
of
raw
positions
and
if
you
take
like
a
bunch
of
them
from
different
embryos,
you
know
they're,
not
all
in
the
they're
in
in
a
similar
frame,
but
you
can't
like
make
you
can't
say,
like
each
point
is
equivalent.
So,
like
you
know
the
the
track
cells
for
a
number
of
embryos,
then
there
has
to
be
a
way
to
normalize
across
the
different
embryos
that
were
sampled
to
get
the
to
get
numbers
that
are
comparable.
A
So
we've
used
different
strategies
for
that,
like
pre-processing
the
data,
so
you
know
you
could
do
like
an
average
position.
You
could
start
like
from
a
center
position
for
each
embryo,
and
just
say
you
know,
there's
a
center
point:
zero,
zero
and
then
there's
like
a
you
know,
maybe
like
a
z-score,
a
plus
or
minus
away
from
that
center
point.
And
then
you
know
that
gives
you
like
a
a
framework
to
work
with,
so
that
you
can
make
comparisons.
A
So
this
is
this
plot
here
would
be
like
the
cell
as
it's
dividing
it's
kind
of
moving
because
they
do
sample
it
at
different
times.
But
I
I
mean
you
know
this
is
this
is
good
work,
but
I
don't
know
what
the
you
know
if
we
play
around
with
the
data
and
normalize
the
data
a
little
bit
more
and
you
know
maybe
look
at
like
how
cells
go
from
like
as
they
divide
where
they
move
to.
That
might
be
interesting
as
well.
B
D
A
And
that's
and
that's
something
that
you
know
when
we
use
different
data
sets.
I
we
don't
really
have
a
mechanism
for
that.
With
our
I
mean
some
of
the
data
that
we
have
that's
been
processed
in
devozu,
for
example,
has
that
property,
where
we've
normalized
it
but
like
the
raw
data
sets,
and
we
don't
really
have
community
standards
for
us?
A
What
I'm
saying-
and
I
don't
know-
maybe
that's
something
that
we
can
develop
as
people
start
to
use
the
diva
or
divalearn
platform
more
and
start
getting
into
some
of
other
data
sets.
You
know
thinking
about
strategies
of
how
to
normalize
the
data,
because
it's
it
you
know,
depends
on
the
data
set
that
you
have.
If
you
have
something
that's
been
acquired.
A
Very
rarely
are
data
acquired
very
uniformly
a
lot
of
times
they'll
acquire
data
they'll
get
like
samples,
they'll
measure,
a
sample
and
they'll.
You
know
not
get
an
ex
the
exact
interval
that
the
other
sample
was
sampled
at.
So
you
know
there's
a
lot
of
that
sort
of
sort
of
improvisation
that
goes
on
in
data
analysis,
and
it's
it's
sometimes
it's.
You
know
people
think
you
know
it's
science,
it
has
to
be
all
you
know
systematic,
which
is
what
we're
trying
to
do.
A
D
A
D
A
A
D
D
A
Okay,
yeah
yeah
good
yeah.
It
was
a
paper
on
4d
embryo
modeling,
so
they
took
like
you
know
the
three
dimensions
of
space
and
then
the
one
dimension
of
time,
and
then
they
created
like
a
virtual
model
of
the
cells
and
that
you
know
that
was
what
they
were
doing
but,
like
you
know,
we
could
do
this
in
a
number
of
different
ways.
That
would
be
you
know,
yeah.
A
So
when
a
cell
is
a
single
cell
and
it
divides
into
two
cells
and
then
they
either
stay
next
to
one
another
or
what
more
likely
happens
is
they
move
they
shift
around
in
the
embryo,
and
so
we
can
measure
that
use
that
as
like
a
fifth
parameter,
which
is
like
angle,
you
know
so
we
can
go
on
like
you
know,
in
a
number
of
different
parameters
that
we
can
use
to
characterize
the
embryo,
not
just
space.
But
that's
that's
a
good!
That's
good
start!
Thank
you.
D
D
A
Yeah
yeah.
That
would
be
good
if
we
had
like
a
way
to
integrate
it
into
the
web
like
a
well.
Ideally,
it
would
be
like
a
resource
where
someone
could
punch
a
button
and
get
a
visualization,
but
I
don't
think
I
don't
know
how
hard
that
would
probably
be
very
hard
to
do,
especially
if
you
have
like
you
know,
you'd
have
to
have
your
data
set,
pretty
defined,
which
is
you
know,
part
of
the
the
part
that
we
don't
really
talk
about.
A
We
talked
about
the
machine
learning
part,
but
then
you
know
you
know
you
have
to
host
your
data
like
we
saw
in
the
last
demo.
You
know
you
have
your
co-lab
notebook
and
then
you
have
underneath
your
co-lab
notebook.
You
have
a
data
set
somewhere
that
you're
drawing
from,
and
so
that's
that's
another
part
that
we
have
to
kind
of
figure
out
or
you
know
that
isn't
like
just
trivial.
But
so
yes
thank
you
for
that.
Yeah.
Absolutely.
B
C
We
make
a
complex
system,
the
simplest
one,
so
this
is
something
what
cellular
order
looks
like.
These
are
different,
which
are
made
from
different
rules
that
we'll
see
in
a
moment
how
they
are
made.
So
first
of
all,
is
are
opportunisational
models
that
are
represented
by
this
that
you
can
see
here.
This
is
the
one
thing
I'll
give
you
an
example
for.
B
C
B
C
C
Was
the
first
one
would
be
depending
upon
the
neighbor
at
the
time,
so
what
this
actually
means
is
suppose
we
want
to
find
out
what
will
be
the
value
of
here
while
here
we
would
have
to
consider
the
element
decision
reposition,
one,
the
fourth
and
one
after
it,
so
for
computing.
That
value
like
zero.
C
B
C
C
The
ones
that
are
representing
the
black
color
so
once
we
do
this
this
step
many
many
number
of
times.
This
is
a
pattern,
a
very
beautiful
pattern
which
we
will
get
and
those
are
something
we
expected
out
of
this
numbers,
that
is
as
a
pc
strangle,
and
this
triangle
was
particularly
found
out
using
rule
number
90.
C
C
Said
rules
only
a
handful
produces
a
compelling
outpost
like
this
one
which
you're
seeing
is
not
a
very
you
know,
something
which
you
would
find
different
or
attractive,
and
this
is
yet
another
example
of
one
of
the
rules.
So
what
is
the
use?
This
is
something
which
actually,
like
stephen
one
person
or
the
person.
C
C
C
D
Yeah
the
presentation
was
really
nice.
That's
the
first
thing
that
I
would
like
to
say,
and
the
second
thing
is
that
you
are
speaking
about
the
natural
world,
so
another
thing
that
I
actually
found
interesting
way
back.
I
guess
a
couple
of
months
ago,
when
I
was
reading
about
security,
that
you
could.
B
D
D
B
B
A
A
Yes,
yes,
and
for
those
of
you
who
are
new
to
the
group,
you
know
we
do
a
lot
of
we
like
to
explore
pattern
formation
in
because
we're
interested
in
development.
We
we're
interested
in
pattern
formation,
and
especially
in
cells
and
in
life.
So
you
saw
before
you
have
the
you
know:
you
have
these
3d
models
of
the
embryo.
Where
you
have
you
know
different
patterns
forming.
You
have
cells.
A
Different
places-
and
you
know
sometimes
you
have
on
like
things
like
seashells,
you
have
surface
patterns
of
different
chemicals
that
get
deposited
and
you
know
we
would
like
to
understand
that
process.
So
I
think
this
is
definitely
a
part
of
this.
You
know
we
we
did
a
couple
years
ago.
We
did
something.
We
had
a
platform
that
we
used.
We
have
a
paper
on
something
called
morphozoic
and
I
don't
know
if
shirty
read
that
or
saw
that
paper.
C
A
I'll
send
you
the
link
of
the
paper
and
then
there's
actually
a
github
repo
as
well,
where
it's
housed
and
then
you
know,
that's
one
version
of
it,
but
I
mean
you
might
be
find
it
interesting
to
play
with
so
yeah
we'll
be
continuing
this
discussion
in
the
coming
weeks.
Yeah,
let's
check
the
chat.
Okay
susan
says
place
the
show
on
my
new
microscope
yeah.
You
could
do
that.
A
A
A
C
A
A
E
A
B
A
It's
just
it's
harder
to
implement,
but
it
it's.
You
know,
people
do
it
yeah
and
then
finally,
mayak
has
some
news
here:
diva
learns,
0.3.0
has
112
weekly
downloads
on
february
2021.,
so
this
is
the
this.
Is
the
project
3.1.
This
is
the
software
that
mayork
published
last
year
and
we've
been
working
on
version
updates
on
this,
and
so
we
had
112
weekly
downloads,
so
it
seems
like
people
are
using
it
or
at
least
interested
in
it.
So
congratulations
on
that.
A
Yes,
this
contains
the
stats
for
diva,
learn.
Okay,
so
this
link
here
snyk
dot,
io
advisor
okay.
So
you
can
go
to
that
link
if
you're
interested
in
seeing
those
I'll
look
at
those
later.
So,
let's
move
on
here,
we
will
go
over
past
the
top
of
the
hour.
If
you
have
to
leave
that's
fine,
but
I'm
going
to
go
on
maybe
about
20
minutes
after
the
hour
on
some
various
things
that
I
wanted
to
cover.
So
we
have
some
deadlines.
A
Just
to
remind
you,
we
have
deadlines
coming
up
for
various
submissions,
so,
if
you're
interested
in
contributing
to
a
submission,
let
me
know-
and
I
can
point
you
to
the
right
place-
we
have
this
list
of
submissions
things
that
have
been
submitted,
things
that
are
in
process
of
being
submitted
and
things
that
are
farther
out.
A
couple
of
things
that
are
new
this
week
is
that
I
submitted
a
let's
see
if
it's
on
here
yeah.
A
I
submitted
us
abstract
to
the
imcf
neuroinformatics
assembly,
and
this
is
an
overview
of
diva
learn.
So
this
is
similar
to
the
devil
in
flash
talk
given
at
the
osf
virtual
conference
for
online
education
and
we'll
be
going
through
those
slides
in
the
coming
weeks,
and
the
reason
this
is
important
is
because
incf
also
sponsors
our
google
summer
code,
and
so
it
would
be
nice
to
get
that
out
in
front
of
all
of
incf
to
show
them
the
progress
we're
making
on
that.
A
A
This
is
something
that's
actually
a
full
paper,
a
conference
paper,
so
we
don't
know
yet
if
it's
going
to
be
accepted,
but
it
it
should
be
actually
out
on
the
archive
pretty
soon
so
be
on
I'll,
send
that
out
in
the
channel
and
then
nur
ips
is
coming
up.
So
there's
a
paper
submission
deadline
for
their
ips.
A
You
have
to
submit
the
abstract
by
may
19th
and
the
full
paper
by
may
26
and
the
paper
details
are
on
the
website.
It's
like
an
eight
page,
double
column
format
paper.
So
people
have
if
people
want
to
submit
a
paper
that
they
are
working
on.
That's
fine,
it's
very
competitive,
of
course,
but
you
know
you
can
always
try,
and
so
that's
that's
for
their
ips
there's
also.
A
A
So
we're
probably
going
to
work
on
this,
maybe
flushing
this
out
into
a
paper
or
a
longer
document
right
now,
it's
like
two
pages
with
references,
and
the
idea
here
is
to
talk
about
like
the
morphogenetic
patterns
like
we
saw
with
cellular
automata
with
some
of
the
stuff
with
the
microscopy
analysis,
but
to
think
about
this
in
terms
of
like
the
theory
of
deep
learning
like.
Why
is
it
that
deep
learning
networks
can
maybe
generate
some
of
these
things
or
or
inform
us
about
some
of
these
things?
A
So
those
are
all
opportunities
to
contribute
to
the
group.
So,
if
you're
interested,
let
me
know
if
you
have
things
that
you
think
would
be
interesting
to
submit
to,
let
me
know,
and
that
would
be
good
also.
I
wanted
to
pull
up
the
slack.
I
wanted
to
make
sure
everyone
knew
where
we
were
with
the
slack.
A
A
There
are
other
projects
like
c302
and
a
couple.
Other
projects
you'll
see
the
channel
list,
but
I
would
recommend,
if
you're
interested
in
working
in
this
group,
joining
three
channels,
maybe
two
channels,
depending
on
your
interests,
so
one
would
be
the
diva
worm
group,
and
this
is
the
channel
and
see
it's.
You
know
we
talk
about
the
meetings
we
have
people
joining
in,
and
people
post,
like
things
like
this
is
josh's
work
that
he
presented
on
today.
A
He
just
kind
of
put
a
pointer
in
this
channel
for
us
to
pay
attention
to
it
and
give
further
instruction,
which
is
what
we're
this
channel
is
for.
This
is
this
channel
diva
worm
is,
is
sort
of
like
for
everything,
but
if
you're
really
interested
in,
like
the
diva
learn
platform
and
the
devorum
soft
learn
software
and
machine
learning
more
more
specifically,
then
you
have
joined
the
diva
learn
channel
and
that's
this
channel
where
we
have
different
things.
A
A
A
So
if
you're
specifically
interested
in
gsoc
2021,
then
this
is
a
channel
you
can
join,
and
this
is
kind
of
like
a
more
general
sort
of
conversation
about
the
different
projects.
So
you
know
we
don't
want
to
like
fill
up.
Divo
learn
with
things
about
gsoc.
Specifically,
you
know
we
want
to
keep
this
for
kind
of
like
the
machine
learning
computational
stuff,
evil
worm.
A
We
want
to
like
make
it
very
general,
but
also
have
a
lot
of
biology
in
here
and
then
gsoc
21
would
2021
would
be
very
specific
to
gsoc
projects
and
q
a
related
to
that.
So
that's
our
slack,
and
I
wanted
to
also
talk
about
this
onboarding
manual.
So
this
is
something
that
I'm
trying
to
find
it
here.
A
A
So
we
have
each
project.
We
have
some
references
listed
here,
so
you
have,
you
know
their
papers
for
3.3.
There
are
some
papers
you
need
to
read
for
3.2
there's.
Some
data
sets
that
you
can
link
to,
and
there
are
all
sorts
of
things
that
you
would
need
to
get
started
on
these
projects,
we'll
be
updating
this
regularly,
but
I
wanted
to
put
it
in
github
so
that
we
could
all
access
this.
A
These
are
the
slides
that
krishna
came
up
with,
and
I
wanted
this
these
slides
in
here,
because
I
wanted
to
point
people
to
things
like
understanding
what
the
relationship
between
incf,
divaworm
group
and
openworm
foundation
are
so
incf
sponsors.
The
projects
openworm
is
sort
of
our
home
organization
and
then
divaworm
is
our
research
group,
and
so
these
things
will
maybe
be
a
little
bit
helpful
in
the
application
process.
A
One
thing
I
do
we
do
need
to
add
to
this
is
a
section
on
how
to
create
a
timeline
for
your
project,
so
I'm
planning
on
putting
that
together
soon
too,
and
I
hope
to
have
this
ready
by
beginning
of
the
application
period.
So
I
know
that
you
know
and
and
I'll
organize
it
so
that
people
can
like
index
it
so
that
people
can
go
to
a
certain
point
in
the
in
the
application
process.
A
If
they,
you
know,
if
they're
interested
in
a
specific
part
of
the
onboarding
guide,
so
that's
that's
over.
So
if
you're,
I
think
I
put
it
in
the
channel,
but
if
you're
interested
in
diva
or
if
you're
interested
in
gsoc
you're
interested
in
contributing.
You
should
look
this
over,
but
even
if
you're,
just
interested
in
contributing
to
diva
worm
in
general.
You
might
want
to
take
a
look
at
this
and.
A
A
A
So
one
of
the
things
we
do
in
the
meetings
is
go
over
some
papers
and
so
the
last
maybe
20
minutes
I'll
go
over
some
papers
and
for
some
of
the
new
people,
this
don't
let
this
be
too
intimidating.
We
talk
about
developmental
papers
and
it
I
think
it
helps.
People
understand
the
development
side
quite
a
bit
more.
A
So
today
I'm
going
to
talk
about
a
couple
of
new
papers,
and
I
think
dick
has
sent
me
some
papers
this
week,
and
so
thank
you
for
that,
and
so
let's
see
first
paper
is
this:
this
is
lab
on
a
chip.
This
is
a
magazine
on
the
biological
magazine,
they're
interested
in
working
with,
like
microfluidics
and
other
types
of
things
where
you
can
have
a
what
they
call
lab
on
a
chip.
A
A
So
this
is
so
c
elegans.
They
lay
eggs
and
they
lay
sometimes
several
hundred
eggs
throughout
their
lifetime
and
the
eggs
kind
of
are
derived
from
the
germ
line,
which
forms
actually
very
early
in
development,
and
so
they
form
these.
They
they
form
these
eggs,
which
are
basically
germline
cells,
and
then
they
start
laying
them
as
they
become
an
adult.
A
So
they
do
this
in
a
micro
channel,
it's
discovered
and
correlated
with
neuro
and
muscular
activities.
So
basically,
they're
developed,
they're,
delivering
an
electric
field
they're,
putting
the
c
elegans
in
this
micro
channel
and
they're
affecting
the
egg
laying
capacity
and
egg
laying.
Of
course
involves
neuromuscular
behavior.
A
A
So
the
quantitative
effects
of
worm,
aging
and
ef
strength,
direction
and
exposure.
Duration,
unangling,
are
studied
using
egg
count
body
length,
head
movement
and
transient
neuronal
activity,
readouts,
so
they're,
stu
they're,
looking
at
like
the
number
of
eggs
laid
they're
looking
at
the
properties
of
the
worm
and
then
they're
looking
at
the
neural
activity
is
of
the
worm
in
in
the
muscular
activity
in
the
electrical
activity
there,
electric
egg
laying
rate
increases
significantly
when
worms
face
the
an
anode
which
is
one
pole
of
the
dipole,
and
the
response
is
ef
dependent
and
then
longer.
A
Ef
exposure
results
in
shorter
egg,
laying
responses
during
shorter
egg
laying
response
duration.
So
basically
they
can
control
the
duration
of
egg.
Laying
with
this
electric
field,
worm
aging
significantly
deteriorates
the
electric
egg
laying
behavior
with
an
88
decrease
in
the
egg
count
from
day
one
to
day
four
post
young
adult
stage,
so
they
lay
their
eggs
in
rounds,
and
sometimes
you
know
they
can
be
pretty
old
when
they
keep
laying
eggs,
but
there's
a
certain
window
within
their
adult
life
that
they
can
lay
eggs,
worm
aging
significantly
deteriorates
the
electric
giggling
behavior
yeah.
A
We
got
the
fluorescent
imaging
of
intracellular,
calcium
dynamics
and
the
main
parts
of
the
egg
laying
neuro
neural
circuit
demonstrates
the
involvement
and
sensitivity
of
serotonergic
hermaphrodite
specific
neurons.
So
these
are
all
neurons
that
are
involved
in
angling,
that
they're
identifying
then
there's
a
mutation
that
also
results
in
the
reduced
rate
of
egg,
laying
allowing
the
use
of
this
technique
for
cellular
screening
and
mapping
the
neural
basis
of
electrosensation
in
c
elegans.
A
So
they
actually
say
that
c
elegans
can
sense,
electric
fields
and
they're
showing
they're
demonstrating
that
with
this
technique,
this
novel
essay
can
be
parallelized
and
performed
in
a
high-throughput
manner.
So
they
can
do
a
very
a
lot
of
different.
They
can
do
a
lot
of
c
elegans
at
once
and
identify
some
of
these
behaviors.
A
A
A
So
what
is
that?
So?
The
abstract
of
this
paper
is
segmental
organization
of
the
vertebrate
body
plan
is
established
by
something
called
the
segmentation
clock,
and
so
this
is
a
molecular
oscillator
that
controls
the
timing
and
periodicity
of
somite
formation.
So
you
have
these
cells
that
are
formed
and
there's
a
certain
periodicity
to
it.
Given
the
dynamic
nature
of
the
segmentation
clock
in
vivo
studies
and
invertebrate
embryos,
post
technical
challenges,
so
vertebrate
embryos
are
like
fishes,
humans,
mice,
etc.
A
So
these
are
different,
experimental
models
that
we
use
to
understand
some
of
these
processes,
so
we
use
c
elegans
as
one
model
organism,
but
we
also
use
things
like
explants
and
pluripotent
stem
cells,
which
are
cell
models,
cellular
base
models
of
these
processes.
It's
much
easier
to
study
these
complex
biological
processes
in
a
in
a
model
system,
and
so
we
use
these
types
of
model
systems.
A
They
enable
more
quantitative
analysis
of
the
oscillatory
properties
and
expand
the
experimental
repertoire
of
the
segmentation
clock.
So
we
can
study
more
things
using
these
models,
crucially
by
eliminating
the
need
for
model
organisms,
so
they
don't
use
model
organisms
or
using
these
cell
models
in
vitro
models.
Allow
us
to
study
the
segmentation
clock
in
new
species,
including
our
own.
A
The
human
oscillator
was
recently
recapitulated,
or
you
know,
replicated
using
induced
pluripotent
stem
cells
providing
a
window
into
human
development,
so
we
can
actually
take
like
our
our
own
body
cells
like
skin
cells
and
turn
them
into
stem
cells
using
a
a
certain
molecular
process-
and
I
can
talk
about
this
in
an
upcoming
meeting,
but
suffice
it
to
say
that
this
is
a
common
model
of
sort
of
these.
These
types
of
processes,
certainly
a
combination
of
in
vivo
and
in
vitro
work-
holds
the
most
promising
potential
to
unravel
the
mechanisms
behind
vertebrate
segmentation.
A
So
let
me
see
if
they
have
any
images
in
here.
It
shows
a
little
bit
more
about
what
what
they're
doing
so.
If
people
don't
know
in
development,
we
have
this
thing
called
segmentation.
So
an
embryo
is
this
mass
of
cells.
It
starts
out
as
mass
of
cells
and
then
you
get
differentiation
into
different
cell
types,
but
you
actually
also
get
organization
based
on
location.
So
this
is
why
we're
measuring
position
of
cells
we're
interested
in
the
sort
of
the
organization
of
the
embryo,
but
in
other
organisms
other
than
c
elegans.
A
So
this
is
an
example
from
like
gastroloids
we've
talked
about
these
before
these
are
these
groups
of
cells?
That
kind
of
you
know
that
people
grow
in
a
in
a
culture
like
in
a
bioreactor.
To
sort
of
you
know
develop
these.
You
know
cell
multi-cell
bodies
that
that
exhibit
these
developmental
properties,
so
they
you
know
differentiate
and
partially
differentiate
and
show
all
these
interesting
properties
3d
differentiation,
which
is
where
they
use
a
three-dimensional
culture
and
then
2d
differentiation
where
they
use
a
standard
cell
culture.
A
A
So
this
is
a
paper
on
where
the
part
mirrors
the
whole
interactions
beyond
simple
location,
and
this
is
a
paper
about
emergence
in
development
and
how
we
might
understand
the
difference
between
different
locations
in
an
embryo
or
in
a
system
more
generally,
and
then
how
those
interactions
sort
of
allow
us
to
see
emergent
phenomena.
So
we
looked
at
the
cellular
automata.
We
know
the
cell
in
its
location,
isn't
the
whole
story.
We
have
these
patterns
that
emerge
across
the
whole.
A
The
whole
grid
that
we
have
in
our
cellular
automata
in
this
case
you'll,
have
emergent
properties
and
an
embryo
that
emerge
from
the
interactions
between
individual
cells,
and
so
they
talk
about
this.
They
talk
about.
You
know
how
to
because
this
idea
of
emergence
is
very
hard
to
get
your
head
around
and
to
use
in
like
quantitative
measures.
So
they're
actually
looking
here
more
at
the
sort
of
the
philosophy
of
this
and
some
of
the
other
types
of
things
that
are
considered
so
they.
A
This
goes
back
to
the
reductionism
versus
emergentism
debate.
So
reductionism
is
where
we
want
to
look
at
the
things
that
are
the
most
the
easiest
to
sort
of,
isolate
and
understand
in
their
isolation
like
a
specific
gene
or
a
specific
cell
and
emergentist
means,
we
want
to
understand
what
the
cells
are
doing
collectively,
where
the
genes
are
doing
collectively,
and
so
they
lay
out
a
lot
of
this.
A
This
is
a
recent
paper
in
development
they
talk
about
in
this
paper.
They
talk
about
these
common
principles
of
mammalian
embryo
self-organization,
so
c,
elegans
are
mammals,
they're
much
simpler
than
mammals,
because
mammals
actually
have
a
lot
of.
They
don't
have
this
sort
of
pro
these
programmed
cell
lineages.
A
So
just
by
decades
of
research,
we
haven't
really
defined
any
principles
for
this.
The
in
this
paper,
though
they
talk
about
the
role
of
physical
forces,
molecular
and
cellular
mechanisms
and
how
those
drive
self-organization
and
the
formation
of
these
lineages,
and
so
again
the
lineages
are
just
basically
going
from
a
single
cell
to
multiple
cells
and
looking
at
cell
divisions
in
that
process
and
tracking
it
and
saying,
is
there
something
about
the
summoning?
That's
important,
and
so
this
is
how
we
kind
of
get
in
in
mammals.
A
A
A
lot
of
this
literature
and
it's
probably
pretty
hard
to
read
for
someone
who's
not
familiar
with
development,
but
I'd
just
like
to
point
out
that
there
are
three
things
here
that
we
talk
about
in
the
meeting.
Quite
often
physical
forces
molecular
mechanisms
and
cellular
mechanisms.
So
that's
a
that's
something
you
can
follow
up
on.
A
If
you
would,
if
you
want
to
finally
there's
this
four
simple
rules
that
are
sufficient
to
generate
the
mammalian
blastocyst,
and
this
is
related
to
the
other
paper,
and
that
if
you
read
the
other
paper,
you
should
also
read
this
paper
and
I'll
go
through
the
abstract
really
quickly.
Here.
Early
mammalian
development
is
both
highly
regulative
and
self-organizing,
meaning
that
there's
regulation
between
cells,
so
cell
cell
interactions,
are
determining
what
happens
in
the
embryo
and
self-organizing,
meaning
that
the
interactions
between
cells
are
really
determining
what
goes
on.
A
It
involves
the
interplay
of
cell
position,
predetermined
gene
regulatory
networks
and
environmental
interactions
to
generate
the
physical
arrangement
of
blastocysts
with
this
timing
mechanism.
So
you
know
things
have
to
come
together
to
form
this
blastocyst,
so
the
physical
arrangement
is
determined
by
all
these
factors.
Here,
however,
this
process
occurs
in
the
absence
of
maternal
information
in
the
presence
of
transcriptional
stochasticity,
which
means
like
when
genes
are
expressed
they're
expressed
in
a
way.
That's
not
deterministic,
they're
kind
of
expressed,
probabilistically
and
stochasticity
here
does
not
mean
the
same
thing.
A
It
means
in
machine
learning
just
so,
you
know
how
does
the
pre-implantation
embryo
ensure
robust
reproducible
development
in
this
context?
And
so
the
question
is
what
are
the
principles
here
in
this
paper?
Then
they
talk
about
it,
utilizes
a
versatile
toolbox
that
includes
complex
intracellular
networks,
coupled
the
cell
cell
communication,
segregation
by
differential
adhesion,
which
means
the
cells
are
adhering
to
things
differently
and
apoptosis,
which
is
cell
death
program
cell
death,
and
so
this
these
are
all
different
factors
that
they
point
to
here.
A
So
whether
you
have
like
you
know
mutants,
where
they
have
a
gene
knocked
out
and
they
have
different
phenotypes
from
what
they
call
the
wild
type
or
you
know
what
what
that
means,
and
so
they
look
at
a
bunch
of
things
here.
They
build
a
ma,
a
quantitative
model.
They
build
this
sort
of
so
they're,
actually
building
they're,
putting
all
these
factors
into
a
developmental
set
of
developmental
rules
and
then
they're
modeling,
using
an
agent-based
model
of
physically
interacting
cells
and
so
the
from
this
quantitative
model.
A
We
conclude
that
a
minimal
set
of
rules
enables
the
embryo
to
experiment
with
stochastic
gene
expression
and
could
provide
the
robustness
necessary
for
evolutionary
diversification
of
a
gene
regulatory
network,
and
so
that's
a
lot
of
jargon
in
there.
But
this
is
basically
the
idea.
So
you
have
you:
have
a
single
cell,
you
start
to
see
cell
division.
You
see
this
first
rule
implemented
polarity,
so
cells
sort
of
form
sort
of
basic
polarity
between
each
other.
Then
you
have
this
signal
of
this
molecular
signaling,
which
guides
the
cells
in
different
directions.
A
You
have
differential
adhesions,
so
these
cells
are
adhering
to
one
end
of
the
embryo
and
then
apoptosis,
where
some
of
these
cells
die
off
purposefully
to
shape
the
embryo,
and
so
this
is.
This
is
how
they
implement
these
rules
and
they
observe
it
here
in
this,
and
I
think
this
mouse
embryo
here
so
they're
doing
this
they've
built
a
model.
A
Then
they
build
this
in
silicon
model
or
this
computational
model
where
they
actually
look
at
physical
interactions
between
the
cells,
so
they're,
looking
at
these
different
cells,
different
cell
positions
and
they're,
assigning
different
physical
forces
to
them
and
then
they're
looking
at
the
model
with
different
parameters,
so
a
potential
versus
distance
plot
here,
where
they're
showing
differences
between
what
they
call
the
trifecta
derm
and
the
inner
cell
mass.
So
this
is
the
inner
cell
mass.
This
is
the
trifecta
derm
and
so
they're.
B
A
So
I
think
that's
enough
for
the
papers
for
today
and
let
me
look
at
my
chat
here.
Let's
see
if
we
have
anything
so
dix
is,
let's
read
his
latest
paper
here
and
we
have
susan
molecular
signaling,
maybe
due
to
forces
on
cells.
That's
true.
If
you
you
know,
molecular
signaling
is
influenced
by
environmental
forces.
One
of
those
are
forces
on
the
cells
themselves.
A
Dick
says:
are
we
on
the
cusp
of
a
new
paradigm
for
biology?
So
this
is
his
paper
that
he
just
published
in
biosystems
and
then
he
says
embryo
arises
from
the
inner
cell
mass
so
that
inner
cell
mass,
that
we
saw
is
where
the
embryo
a
lot
of
the
differentiating
part
of
the
embryo,
arises
from,
and
then
inner
cell
mass
is
exposed
to
fracking,
which
is
a
paper
that
susan
presented
on
a
while
back
talking
about
like
hydraulic
pressures
between
cells
in
an
embryo.
A
I
A
A
What
is
environment?
That's
yeah!
That's
a
good
question.
It's.
It
could
be
like
just
anything
outside
of
the
cell
or
outside
of
the
biology,
but
that's
yeah.
That's
a
good
question
what
that
means.
I
mean
people
use
that
term
pretty
freely,
but
it's
not
you
could.
If
I
guess
you
could
define
it,
but
it's
basically
anything
outside
the
cell
or
outside
the
embryo
or
organism.
I
guess
what
is
information
content
of
the
environment?
That's
another
good
question,
that's
something
that
depends
on
how
you
operationalize
it.
A
I
guess
you
know
if
it's
like
something
that
we
can
detect
through
information
theory
or
just
something
we
say
is
like
information,
which
is
often
what
people
do
in
some
of
these
papers.
They
just
say:
well,
you
know,
there's
this,
you
know
we
introduce
a
perturbation,
that's
information.
We
don't
know
that.
For
sure.
Really
I
mean
that's
my
answer.
A
A
You
know
we
kind
of
keep
on
I'll.
Keep
on
top
of
that
and
and
myoca
will
keep
on
top
of
that.
But
if
you
have
any
specific
questions,
don't
hesitate
to
ask
us
the
guest
embryos
for
axolotl's
growing,
distilled
water,
constant
temperature.
What
does
environment
do
I
mean?
I
don't
know,
I
guess.
If
you
varied
the
temperature,
it
would
be
the
that
would
actually
be
information
content,
but
I'm
not
sure.
A
B
A
Yes,
thank
you,
nice
to
see
everyone
and
if
you
have
any
questions,
we'll
be
in
slack
or
email,
and
so
thank
you
for
joining
us
at
the
new
time
we
have.
We
had
a
time
change
in
north
america
once
again,
so
everyone
outside
in
north
america,
it's
going
to
be
a
time
change,
maybe
for
the
next
couple
weeks.
I
don't
know
how
your
time
changes
go,
but
we
had
our
daylight
savings
time.
So,
thanks
for
attending
have
a
good
week.