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From YouTube: DevoWorm (2021, Meeting 19): Complex Networks, Developmental Trajectories in Embryos and Brains
Description
Review of Onboarding Guide and Submissions table, discussion of network science and accepted submissions to Networks 2021 (TopoNets, NetNeuro), updates and Bacillaria research, more Paramecium non-neuronal cognition, Developmental trajectories in embryos (cricket synticium) and human brains (from embryogenesis to life-history). Attendees: R Tharun Gowda, Vrutik Rabadia, Jesse Parent, Richard Gordon, Susan Crawford-Young, Tom Portegys, Bradly Alicea, Shruti Raj Vansh Singh, Aswarth Narayama, Yash
Vadi, Akshay Nair, Ujjwal Singh, and Mainak Deb.
A
B
B
B
C
B
B
B
B
Okay!
Yes,
all
right!
So
first
thing
I
wanted
to
mention
was
because
our
the
gsoc
results
are
being
announced,
we're
heading
into
the
community
period
and
so,
like
I
said,
even
if
you
don't
get
selected,
I
would
welcome
you
to
participate
in
some
other
way
in
the
group,
and
you
know
this.
This
will
lead.
This
might
lead
to
other
things
down
the
road,
so
don't
feel
discouraged,
but
I
also
wanted
to
point
out
to
something
that
we
have
and
we'll
be
continuing
to
build.
B
This
is
a
guide
that
we're
using
for
to
get
people
into
diva
worm
and
sort
of
get
them
used
to
the
community.
So
I
think
a
lot
of
you
have
been
the
meetings
already
know
kind
of
what's
going
on,
but
this
is
something
maybe
for
more
casual
participants
or
people
who
might
want
to
join,
but
don't
know
what
to
expect.
B
B
You
know
what
can
you
do
is
sort
of
like
a
good
first
step
into
the
community,
and
I
think
some
of
the
diva
or
some
of
the
gsoc
projects
were
sort
of
that.
You
know
they
were
allowed
people
to
do
some
quick
participation,
some
quick
commits
and
get
embedded
into
the
community
so
holyash
and
hello
usworth.
B
Okay,
I
just
wanted
to
check
if
you
do
have
something
let
me
know,
but
I'm
gonna
keep
going
okay,
so
I
think
next,
what
we'll
do
is
we'll
talk
about
the
submissions
documents,
so
the
submissions
again,
you
know
this
is
our
document
for
submissions.
This
is
stuff
that
we've
submitted
or
going
to
submit
that's
been
accepted
or
rejected,
so
we're
just
keeping
a
running
tab
of
things.
We've
had
a
couple
of
these
talks
on
divo,
divalearn
and
those
have
been
completed.
B
So
we
have
we
presented
at
the
incf
neuroinformatics
assembly
in
the
osf
virtual
conference
and
we're
still
this
pre-print
is
still
kind
of
hanging
out
there.
I
haven't
had
time
to
get
back
to
it,
but
there
are
a
couple
of
opportunities
that
have
come
up
in
the
last
couple
weeks
and
I
don't
know
if
I
mentioned
well.
I
mentioned
the
net
neural
and
the
topo
nets
submissions
before
so
that's
25
and
five,
and
they
got
accepted
this
week.
So
the
abs,
the
abstracts,
got
accepted
to
the
satellite
session.
B
B
B
Actually,
the
the
the
net
neuro
is
actually
a
poster,
but
that's
okay.
It
was
a
pretty
competitive
venue,
so
this
is
the
net
neuro
2021
satellite.
So
this
is
going
on
over
several
days
and
it's
a
pretty
big
group
of
people.
I
think
it's
one
of
the
bigger
satellites
of
the
conference,
and
so
this
is
a
lot
of
people
doing
stuff
with
their
own.
B
You
know
like
neuronal
networks
instead
of
neural
networks,
so
these
are
brain
networks
where
people
are
looking
at
connectivity
in
the
brain
and
they're
kind
of
figuring
out
what's
connected
to
what
during
behavior
or
maybe
just
anatomically-
and
so
I
know
that's
a
little
bit,
not
really
what
we're
doing
in
this
group.
That's
why
we
got
a
poster
accepted,
but
basically
there's
going
to
be
a
lot
of
information
about
this
area,
so
they're
going
to
be
a
lot
of
big
names
in
that
area.
B
Speaking
we'll
be
talking
about
the
c
elegans
connectome,
but
those
will
be
talking
about
human
brains,
I'll
be
talking
about
other
animal
brains
and
I'm
sure
it'll
be
a
good
time.
So
this
is
the
abstract
that
was
submitted
to
the
net
neuro,
and
this
is
the
okay.
This
is
the
abstract,
so
this
is
neuromorphogenetic
patterns
in
the
theory
of
deep
learning.
B
So
this
is
a
rather
short
abstract
with
a
lot
of
references,
and
this
was
contributed
by
a
number
of
people
in
the
group
like
mino,
christian
and
jesse,
you've
all
kind
of
commented
on
it
and
shaped
it
a
bit-
and
this
is
so
this
is
going
to
be
turned
into
a
poster.
So
the
poster
presentations
work
like
this.
You
have
a
person
presenting
in
a
room
and
they
may
put
a
poster
up
in
a
large
square.
B
It's
like
a
it's
like
a
single
slide
or
a
single
powerpoint
slide,
and
then
you
present
it
to
people
in
that
room.
So
it's
like
a
breakout,
zoom
room,
if
you,
if
you
know
what
those
are-
and
you
present
your
poster
at
the
allotted
time
and
so
at
the
you
know,
for
something
like
this:
a
poster
provides
both
benefits
and
drawbacks.
B
B
Your
ideas
and
put
you
know,
create
visually
compelling
displays
of
what
your
idea
is
all
about,
and
so
this
is,
I
think
this
will
make
for
a
pretty
decent
poster.
I
think
we
can
work
it
out.
I
think
this
will
work
out.
So,
if
you're
interested
in
participating
in
this,
please
let
me
know
you
know
I
think,
actually
we're
open
to
contributors.
B
A
B
B
I'll
be
circulating
a
draft
as
I
I
usually
do
for
these
things
so
and
I'll
just
put
it
in
the
slack
or
I'll
mention
it,
the
email.
So
that's
that
and
then
there's
this
toponets.
So
this
is
a
different
satellite
session,
and
this
is
it's
a
so.
The
the
aim
of
this
satellite
is
to
look
at
across
like
biological,
social
and
technological
systems
and
look
at
the
richness
of
interaction
among
their
units
and
so
the
way
network
science
usually
works.
B
B
B
B
Taking
then
trying
to
figure
out
sort
of
like
the
global
signal
across
the
network.
So
people
usually
look
at
like
motifs,
which
are
like
maybe
a
couple
nodes
connected
or
they
look
at,
like
you
know,
maybe
very
broad
statistical
signatures
and
with
topo
nets
they're
trying
to
figure
out
sort
of
like
what
the
structure
means
so
like
the
shape
of
the
network
or,
like
you
know,
if
you
have
spaces
in
between
the
nodes,
maybe
there's
some
spatial
significance
of
how
the
network
is
structured
and
you're.
B
E
F
B
Simply
a
statement
about
like
the
type
of
connectivity
so,
like
you
know
small
worlds
and
scale
free
those
all
basically
have
something
to
say
about
how
the
nodes
are
connected
to
one
another
in
the
aggregate.
It's
a
statistical
signature,
you
get
it
out
of
a
single
parameter
with
other.
You
know
other
types
of
approaches,
they're
much
broader
and
you
know
people
say
well.
You
know
you
can
say
a
lot
about
a
network
by
using
like
a
network
parameter.
But
what
about
like?
B
You
know
if
you
have
a
network
that,
isn't
you
know
a
standard
network?
What,
if
you
have
something?
That's
has
high
spatial
structure
or
something
that
is
you
know.
Has
you
know
information
in
the
in
the
edges
rather
than
the
nodes
that
are
you
know
it's
more
interesting,
so
none
of
those
things
are
covered.
You
know
under
the
typical
network,
parameter
space,
so
you
would
go
to
some
other
type
of
method.
That
would
give
you
that
information.
B
B
B
B
B
So
it's
not,
you
know
you'll
be
able
to
participate
in
it
at
some
level.
So
that's
that's
what
I
had
on
that.
Let
me
go
back
to
the
submissions
document
to
kind
of
tie
up
here,
so
we
have
those
and
then
the
google
summer
code,
of
course,
is
we'll
hear
about
that
today,
the
vassal
very
non-renal
cognition
stuff.
B
Well,
I
mean
we've
had
some
people
do
some
work
on
some
of
the
projects
for
we
have
like.
I
think
seven
people
apply
to
the
project,
and
so
people
have
tried
various
techniques
and
need
to
catalog
those
and
see
what
they
are.
But
so
we
have
a
number
of
different
techniques
that
are
candidates
for
doing
this.
E
E
B
A
B
Yeah,
we
have
a
bunch
of
different
methods
that
people
have
used,
and
so
what
would
we
required
for
this?
If
you're
interested
in
doing
this?
If
you've
done
a
project,
you
submitted
a
project
for
gsoc
on
this
is
to
take
images
and
it
would
be
over.
You'd
have
like
a
time
series
of
data
and
you'd
have
to
find
sort
of
the
derivatives
of
position.
B
So
it's
like
a
higher
order
derivative
of
position,
so
each
of
your
images
is
a
certain
position
and
then
that
position
you'll
have
to
sort
of
find.
You
know
there's
some
derivative
where
you
average
over.
You
know
different
frames
and
and
find
like
the
you're
finding,
like
acceleration,
you're
finding
motion,
and
then
you
know,
eventually
you
get
to
jerkiness,
which
is
a
derivative
of
sort
of
momentum.
I
guess,
and
so
you're
you're
looking
at
higher
order
effects
in
in
the
in
the
motion
of
these
things.
B
So,
like
you
know,
we
know
understand
the
motion
as
a
sinusoid,
but
there
are
different
variations
in
that
motion,
and
so
that's
really
what
we're
trying
to
get
at
or
some
of
those
variations
within
the
sinusoid
or
you
know
the
sinusoid
might
slow
down
and
speed
up
and
you've
seen
the
movies.
I
think
a
lot
of
you
where
you're
getting
these
kind
of
changes
over
time
in
the
in
the
acceleration.
B
F
B
24,
which
is
molecular
level
simulation,
that's
not
really
the
same
thing
so
so
yeah
we
have.
I
guess
we
actually
had
the
neuro
ips.
The
deadline
for
abstracts
is
this
week
and
may
26th
is
the
full
paper.
So
I
believe
that
ujwal
said
that
he
had
some
he's
working
on
paper
with
someone
on
this
in
the.
G
G
B
B
G
B
H
C
B
B
B
Let's
see,
then
we
have
two
other
things
on
the
list:
the
boring
billion,
which
is
this
potential
book
contribution,
and
this
is
about
you
know,
early
life
and
associated
things,
and
then
this
amazon
kindle
book,
which
was
another
thing
that
we
were
talking
about
doing
with
the
diva
worm
ml
material,
so
diva
worm
ml,
was
something
we're
working
on
in
2019.
It
was
a
set
of
lectures
on
machine
learning
and
biology
and
developmental
biology.
B
B
B
B
Sometimes
I
get
into
the
biological
papers,
and
I
don't
know
I
I
don't
know
how
much
people
are,
how
much
background
people
have
in
certain
things.
So
it's
always
a
challenge
to
know
like
what
you
know
what
people,
what
their
threshold
for
tolerance
is
on
some
of
this
stuff,
but
we'll
see
where
it
goes.
A
lot
of
the
submissions
I'm
going
to
talk
about
today
are
pretty
or
papers.
I'm
going
to
talk
about
today
are
pretty
biologically
oriented,
so
the
first
one
I'm
going
to
talk
about
is
this.
B
This
is
so
we
were
talking
earlier
in
the
meeting
about
non-neuronal,
cognition
and
single
cell
cognition
and
that
sort
of
model
explanation
for
this
stuff
and
a
couple
weeks
ago
we
talked
about
the
paramecium
being
a
model
organism
for
this,
and
so
that
was
a
different
paper
than
this.
The
first
paper
we
talked
about
was
an
e-life
and
it
was
a
paper.
It
was
largely
on
the
contributions
of
someone
who
has
been
somewhat
forgotten
in
the
history
of
this
area
and
their
contributions
and
some
of
the
experiments
they
did.
B
B
B
B
B
B
So
how
does
it
do
this
behavior
and
so
you're,
basically
stimulating
the
single
cell
in
different
modes,
and
then
it
interprets
that
signal
is
you
know,
swimming
backwards
and
then
turning
then
swimming
forward?
So
it's
you
actually
see
this
behavior
in
c
elegans.
If
you
look
at
our
data
for
the,
we
have
a
lot
of
movement
data
and
a
lot
of
the
movement
data
is
sort
of
you
know.
B
It
describes
certain
movements,
so
you
know
you
can
see
all
sorts
of
stereotypical
movements
and
c
elegans
movement
behaviors
with
respect
to
like
poking
it
with
a
little
pick
or
you
know,
putting
food
in
a
certain
location
and
they'll
explore
for
food
and
they'll
make
these
different
like
what
they
call
omega,
turns
and
other
types
of
movements,
they'll,
move
backwards
and
then
forwards
again
and
so
they're
doing
this.
But
c
elegans
has
a
circuit,
a
neural
circuit
for
this,
and
the
neural
circuit
is
actually
well
studied.
B
And
if
you
look
at
some
of
the
papers
that
have
been
published
on
like
feeding
circuits
or
like
chemosensory
circuits
and
tom
actually
has
worked
on
this
in
terms
of
making
this
into
a
robot.
You
can
see
that
there's
a
certain
pattern
in
that
circuit,
but
that
requires
a
number
of
neurons
that
are
connected
in
paramecium.
You
see
these
things
but
they're
they.
It
doesn't
involve
a
neuro,
a
nervous
system.
B
So
this
avoiding
reaction,
which
is
a
simple
avoidance
reaction,
is
triggered
by
a
calcium-based
action
potential.
So
it's
actually
an
action
potential
like
you
see
in
neurons,
but
it's
not
in
a
neuron,
it's
actually
in
a
cell,
and
so
we
talked
about
michael
levin's
work
where
he's
looked
at
the
electrical
activity
of
cells
in
our
neurons,
so,
for
example,
somatic
cells
of
different
types,
and
they
have
maybe
not
action
potentials,
but
they
have
electrical
potential.
B
For
this
reason,
some
authors
have
called
paramecium
a
swimming
neuron.
This
review
summarizes
current
knowledge
about
the
physiological
basis
of
behavior
and
paramecium.
So
I
think
the
last
paper
we
talked
about
paramecium
talked
about
the
ability,
maybe
of
of
paramecium,
to
learn
in
an
associative
manner,
so
associative
learning.
So
this
is
something
we
associate
with
like
a
simple
neural
network,
maybe
like
two
neurons
or
three
neurons
in
a
circuit-
and
you
know
like
there's
this
co-stimulation
that
goes
on
and
it
can
associate
one
thing
with
another
in
the
environment.
B
So
in
the
we
talked
about
the
sea,
slug
applausia,
for
example,
they
exhibit
this
very
simple
associative
learning,
and
so
you
can
see
that
in
a
simple
nervous
system,
you
can
see
it
even
more
so
in
larger
nervous
systems,
but
you
even
see
it
in
a
single
cell
here.
So
this
paper
kind
of
it's
a
review,
it
kind
of
goes
through
what
pyramicia
looked
like.
So
this
is
the
paramecium.
These
are
the
cilia
here,
so
they
look
like
little
hairs
and
they
move,
but
they
move
in
a
way.
B
B
He
drew
this
out
back.
Then
they
used
to
draw
things
out
really
in
a
detailed
manner.
Now
they
just
kind
of
take
high
resolution
pictures.
This
is
kind
of
a
lost
art
in
biology
all
these
kind
of
drawings
that
people
used
to
do
so
yeah.
This
isn't.
This
is
a
100
to
300
micrometers
long.
So
this
is
something
that
you
know
it's
about.
Actually
it's
maybe
a
little
bit
bigger
than
your
average
diatom.
B
It's
a
somewhat
popular
model
organism
so-
and
this
is
the
avoiding
reaction
here,
so
you
can
see
that
there's
this
like
so
a
here,
avoiding
reaction
against
an
obstacle
is
illustrated
by
jennings.
So
this
is
the
obstacle
here,
a
this.
This
circle,
the
paramecium,
encounters
this
obstacle.
So
when
it
does
this,
it
moves
backwards.
So
it's
it's
receiving
pressure,
stimulation
to
the
front
of
the
organism
or
the
cell.
B
It
starts
moving
backwards
and
then
it
makes
a
turn
and
it
moves
this
in
forwards
in
this
direction,
ostensibly
to
avoid
this
barrier
or
this
this
obstacle.
That's
that's!
Basically,
the
movement
we're
talking
about
and
in
response
to
this
movement,
you
see
an
action
potential
here
in
response
to
a
two
millisecond
current
pulse,
which
is
you
know,
there's
there's
a
whole
field
of
action
potential
analysis,
so
I
don't
want
to
get
into
that,
but
basically
it's
this
rapid
change
in
potential
within
the
cell
membrane.
B
B
B
So
we
I
think
we've
talked
about
texas
before
and
this
is
one
example
of
this
and
then
two
paramecium
and
conjugation
sexual
reproduction,
so
they
actually
sexually
reproduce
and
they
have
movements
associated
with
that,
and
so
they
also
can
navigate.
So
they
do
this
as
backing
up
and
moving
in
a
different
direction.
B
This,
if
we're
talking
about
non-neuronal
cognition,
this
is
one
example
of
how
this
works
in
single
cells.
And
of
course
we
don't
know
if
this
is
something
that
is
goes
beyond
paramecium,
but
it's
definitely.
We
have
a
good
model
for
understanding
sort
of
the
basis
of
it.
What
are
the
important
things
to
look
at
within
the
cell?
So
you
also
see
chemotaxis
and
social
behavior.
B
You
see
a
bunch
of
paramecium
gathered
around
the
circle.
It's
a
drop
weekly
acid
solution,
so
they
drop
some
acid
into
the
into
this
area.
Here
they
either
yeah.
So
they
aggregate
around
a
drop
of
weakly
acidic
solution
here
and
then
in
this
case
see
they
actually
are
avoiding
a
drop
of
sodium
carbonate,
so
there's
a
drop
of
sodium
carbonate
in
the
culture
and
they
disperse
and
they
actually
avoid
this
spot.
So
they,
this
is
a
social
behavior.
B
So
all
these
are
social
behaviors,
but
we
don't
know
like
you
know
when
they
say
social
behavior.
They
don't
really
talk
to
the
paramecia.
It's
just
kind
of
it
looks
like
collective
behavior.
So
so
this
is
that
paper.
That's
let
me
make
I'll
put
a
link
to
the
paper
in
the
chat
here,
but
you
can
go
through
the
rest
of
the
paper
and
really
kind
of
gets
into
a
lot
of
detail
about
a
lot
of
the
different
behaviors
and
they
have
a
very
diverse
set
of
behaviors
that
they
exhibit.
B
B
If
you
only
call
them
cilia,
that
would
be
silly
yeah
it
would
be.
I
was
gonna
make
that
pun.
I
didn't
go
there,
but
jesse
did
so.
Thank
you
yeah.
So
that
was
this
response
reaction
to
that.
Okay.
So
then
that's
that's
that
paper.
The
next
one
is
what
is
this
one?
This
is
an
image
that
I
found.
Oh,
this
is
a
kind
of
an
interesting
talk
that
I
didn't
see
the
talk,
but
this
is
just
something
to
be
aware
of
here.
This
person
gave
a
talk
on
floral
cell
type.
B
Diversity
reminds
animal
biologists,
how
beautiful
plants
are
and
almost
makes
you
want
to
do.
Cereal
em
on
a
flower,
so
flowers
are
very,
can
be
very
diverse
and,
and
you
know
make
for
some
very
good
subjects
for
imaging
studies.
So
you
know
this
would
be
you
know
this.
Is
they
do
a
lot
of?
We
don't
talk
much
about
plants
in
the
group.
I
would
have
just
wanted
to
make
the
point
that
plants
are
still
there
and
they're
very
much.
You
can
observe
you
know
development
in
plants.
B
B
Centititial
is
a
word
I
don't
know.
If
people
know
what
that
means,
it's
basically
a
bunch
of
it's
a
it's
a
sort
of
a
cell
mass
with
a
bunch
of
nuclei
in
it
so
often
c
elegans.
We
know
that,
like
muscle
forms
from
a
centititium,
so
centital
it
you
know,
centititium
is
a
tissue
that
has
a
lot
of
nuclei
in
it
and
then
it
diversifies
into
into
muscle,
and
so
this
is
a
type
of
thing,
they're.
B
Looking
at
nuclear
movements
during
this
blastoderm
stage
or
blastoderm
formation
stage,
and
so
I
think
we've
talked
about
cassandra
x-raver
before
her
work.
This
is
when
her
she's
on
this
paper.
So
the
abstract
reads:
animal
embryos
passed
through
an
early
stage
called
the
blastoderm
in
which
cells
are
arranged
in
a
continuous
layer
at
the
periphery
of
the
embryo
and.
B
This
is
yeah.
This
is
the
blastoderm.
Despite
the
broad
evolutionary
conservation
of
this
embryonic
stage.
The
cellular
behaviors
that
led
to
blasto
and
formation
vary
across
animals
and
the
mechanisms
that
regulate
these
behaviors
are
poorly
understood
in
most
insects.
Pre-Blastoderm
development
begins
as
a
centitium
that
as
many
nuclei
divide
and
move
throughout
the
single
shared
cytoplasm
of
the
embryo.
B
B
So
these
nuclei
basically
move
out
into
a
into
a
layer,
and
so
the
nuclei,
of
course,
is
where
the
dna
is,
and
the
thing
that
we
look
at
usually
when
we
look
at
microscopy
of
like
cells,
will
often
put
like
a
fluorescent
marker
in
the
nucleus
and
that's
how
we
can
mark
a
cell.
It's
one
way
to
mark
a
cell,
and
so
people
use
this.
It's
not
really
the
center
of
the
cell.
But
it's
it's
a
useful
point
of
reference.
B
When
you're
looking
at
cells,
especially
when
they're
migrating,
then
the
sentinel
nuclei
must
move
from
there.
Okay,
recent
work
has
shown
that
in
the
fruit
fly,
drosophila
melanogaster,
some
of
these
early
nuclear
movements
are
caused
by
pulses
of
cytoplasmic
flows
that
are
comfortable
to
synchronous
divisions.
B
Here
we
show
that
the
cricket
grilas
by
maculatus
as
an
altogether
different
solution
to
the
problem,
so
in
drosophila
they
do
one
there.
You
can
observe
one
type
of
thing
in
this
cricket
species.
You
observe
something
very
different:
we
quantify
nuclear
dynamics
during
the
period
of
central
cleavages
and
movements
that
lead
to
blastoderm
formation,
so
these
sential
structures
they
kind
of
you
know
they.
B
They
partition
themselves
into
cells
and
that's
what
they
call
the
cleavages
and
movements
that
lead
to
blastoderm
formation
in
the
embryos
with
transgenically
labeled
nuclei,
which
are
these
fluorescent
labels
that
we
often
see
in
these
in
in
the
microscopy
images.
So
when
you
see
a
black
background
with
a
lot
of
light,
you
know
points
of
light.
Those
points
of
light
are
these
nuclei
that
are
labeled
and
they
use
something
called
a
gfp
or
an
rfp,
or
some
other
color
of
fluorescent
marker
to
do
that,
labeling.
B
So
that's
what,
when
you
run
across
any
of
that
terminology?
That's
what
that
means.
We
found
that
one
cytoplasmic
flows
were
unimportant
for
nuclear
movement,
so
these
flows
were
unimportant
for
the
nucleus.
Moving
around
didn't
really
coordinate
the
movement
of
the
nuclei
and
two
division
cycles.
Nuclear
speeds
in
the
directions
of
nuclear
movement
were
not
synchronized
across
the
embryo,
as
in
d-melanogaster,
but
instead
were
heterogeneous
in
space
and
time,
so
they
weren't
coordinated,
like
you
see
in
in
drosophila.
They
were
kind
of.
B
I
don't
know
if
they
were
random,
but
they
were
definitely
heterogeneous.
Moreover,
several
aspects
of
nuclear
divisions
and
movements
were
correlated
with
local
nuclear
density.
We
show
the
previously
proposed
models
for
the
movement
of
d.
Melanogaster
sentinel
nuclei
cannot
explain
the
behaviors
of
the
nuclei
in
the
cricket.
We
introduce
a
novel
geometric
model
based
on
asymmetric
local,
pulling
forces
on
nuclei
which
recapitulates
the
density,
dependent
nuclear
speeds
and
orientations
without
invoking
common
paradigms
of
localized,
polarity,
cues
or
cell
lineages
determinants
of
nuclear
activity.
B
So
our
model
predict
accurately
predicts
nuclear
behavior
and
embryos.
We
show
the
model
can
be
used
to
genera,
generate
falsifiable
predictions
about
the
dynamics
of
blastoderm
formation
in
other
species.
So
this
is
the
they
kind
of
go
through
this.
Here
they
talk
about
the
comparison
with
drosophila
there's
some
methods
I
want
to
get
to
the
images
they're
in
here
usually
they're
at
the
end.
Okay,
I
think
they're
here.
B
So
this
is
the
first
image
here,
and
this
will
make
this
a
lot
clearer
when
I
what
I
just
said
so
this
is
an
example
of
a
time
lapse
here.
So
this
is
the
centital
blastoderm.
So
you
can
see
this
whole
thing
is
basically
like
open
cytoplasm
and
you
have
these
nuclei
that
are
stained.
So
these
black
dots
are
the
nuclei,
and
you
have
this
time
lapse
where
you
can
see
like
you
know,
150
minutes,
since
the
start
of
the
time
lapse.
300
minutes
450
minutes
and
then
24
hours.
B
So
you
start
off
with
a
few
new
identifiable
nuclei
here
and
you
get
more
nuclei
scattered
about
even
more
and
then
you
get
this
sort
of
they're
all
over
the
centititium.
And
then
you
get
this
and
as
this
is
happening,
it's
partitioning
this
cytoplasm
and
then
you
get
the
cellularization
and
embryonic
rudiment
formation.
So
now
you
get
this
cellularization
happening
at
12
at
this
time
point
here
and
then
at
24
hours
this
and
what
they
call
the
embryonic
rudiment
is
here,
and
so
you
can
see
that
there's
this.
B
B
And
then
this
b
here
is
nuclei
were
tracked
to
produce
a
3d
plus
t
data
set
of
nuclear
lineages.
So
here
they
visualize
this
as
a
set
of
nuclear
lineages.
Instead
of
cell
lineages,
which
is
something
we're
very
familiar
with
in
c
elegans,
there's
a
nuclear
lineages
to
look
at
how
these
things
divide
and
actually
move
around
the
centititium,
which
will
end
up
being
a
bunch
of
cells,
and
so
all
nuclear
tracts
are
displayed
for
an
example.
Embryo
with
a
lineage
descended
from
a
single
nucleus
highlighted.
B
I
B
Duration
was
calculated
in
the
vicinity
of
each
nucleus
by
measuring
the
time
elapsed
until
the
number
of
nuclei
within
a
150,
micron
or
micrometer
radius,
increased
by
25
percent
top
ratios
through
example.
Time
points
with
local
cell
cycle
duration
displayed
as
colored
volumes,
each
of
which
contain
a
single
nucleus.
B
B
And
so
then,
this
is
actually
figure.
3
actually
shows
this,
these
trees
that
they're
generating
from
this.
So
this
is
the
lineage,
the
the
nuclear
lineages
that
they
have,
so
they
have
the
nuclear
position,
anterior
being
the
front
posterior
being
the
back
end
of
this
structure,
the
nuclear
positions,
of
course,
are
spread
out
across
this.
They
start
off.
B
So
this
is
all
good
stuff,
and
then
this
is
an
example
of
how
they've
taken
these
nuclei
and
they've
done.
These
voronoi
models
and
a
voronoi
model
is
where
you
have.
You
take
a
bunch
of
points
and
you
try
to
draw
boundaries
around.
You
know
you
try
to
find
the
sort
of
the
midpoint
between
these
points
and
draw
a
boundary,
so
these
are
supposed
to
be
like
optimal
tilings.
B
They
sort
of
describe
these
points
as
sort
of
centroids
in
a
geometric
model,
so
you
draw
boundaries
at
roughly
the
midpoint
between
the
two
most
adjacent
points,
and
you
end
up
with
this.
This
set
of
boundaries
which
sort
of
define
maybe
what
the
cells
will
look
like
as
they
emerge
from
the
centititium.
B
B
It's
very
specific
to
you,
know,
sort
of
what
you
might
expect
the
optimal
outcome
to
be
so
there
are
a
lot
of
nice
figures
in
this
paper,
so
I
think,
if
you're
interested
in
this
area,
this
is
definitely
a
paper
to
check.
It's
definitely
a
different
model
system
than
we've
seen,
I'm
not
really
sure
what
the
I
know
that
crickets
aren't
really.
As
far
as
I
know,
they're,
not
really
a
very
popular
model
system,
but
it's
definitely
a
nice.
B
B
B
And
so
this
actually
drives
changes
to
cell
shape
during
organogenesis.
So
the
highlights
of
this
paper
are
that
new
physics-based
simulation
methods
allow
study
of
dynamic
tissue
structures
in
3d.
Movement
of
an
organ
through
tissue
generates
viscoelastic
drag
forces
on
the
organ.
These
drag
forces
can
generate
precisely
the
cell
shape
changes
seen
in
experiment
and
piv
analysis.
B
F
I'm
I'm
not
exactly
ready
to
talk
about
it
because
I've
been
writing
a
6
000
word
acid,
but
yeah.
There's
some
interesting
movements
here,
there's
a
circular
movement
and
then
the
whole
circular
movement
tends
to
move
up
the
embryo.
I
think
up.
I
don't
know
what
you
would
call
that,
but
up
or
down
which
way
it
looks
like
up
from
the
arrows
anyway.
The
cells
ahead
of
it
become
calmer
and
the
cell
behind
a
flat
note.
So
that
provides
the
driving
force
for
the
circle
circular
swirling.
F
B
B
B
I
think
I
think
so,
I'm
not
really
sure,
but
that's
that's,
maybe
true,
yeah
and
then
us
a
swarth
says
hello,
everyone,
my
name
is
a
sworth
and
I
am
a
csc
undergrad
from
india.
I
looked
into
the
repo
of
divalearns.
I
tried
to
solve
one
of
the
issues
there
attended.
One
of
the
previous
meetings
he's
interested
in
the
cellular
automata
work
that
shruti
is
doing
fascinated
him.
B
Okay,
well
I
mean
you
know
yeah.
We
we
often
if
you
join
the
evil,
learn
channel,
which
is
not
the
diva
worm
channel,
but
there's
another
channeled
evil
learn
there's.
Sometimes
issues
are
posted
there
and
you
know
just
keep
checking
out
the
diva
learn
channel
or
the
diva
worm
channel
as
well
and
you'll.
B
You
know
you
can
maybe
maybe
ask
around
you
can
inquire
about
things
that
people
want
to
talk
about
certain
issues
that
they're
interested
in
and
then
trudy
who's
in
the
meeting
was
doing
she's
been
presenting
on
the
cellular
automata
a
neural
cellular,
automata,
so
she's
working
on
this
thing
with
respect
to
that
this
project.
So
you
might
want
to
talk
to
her
about
maybe
some
of
the
things
that
she's
been
presenting
on.
B
Yes,
piv
analysis
is
used
to
measure
flows
of
planar
velocity
fields,
yeah,
that
was
the
that
was
the
they
had
this
right
here,
yeah,
so
this
that
was,
that
was
what
they
were
doing
in
this
paper.
That
type
of
analysis
dick
says:
slime,
molds.
Yes,
some
are
100
meters
long
in
seaweeds,
and
this
is
with
respect
to
yeah,
and
then
christian
is
not
here,
because
his
father
is
in
the
hospital
unconscious
of
the
stroke.
B
Well,
sorry
to
hear
that
krishna,
I
hope
your
father
is
feeling
better
soon
and
I
hope
you're
doing
well
yeah
sorry
to
hear
that
so
yeah,
that's
that's
our
chat
so
yeah.
That
was
the
paper
and
then
I
think
I'll
do
one
more
paper
before
we
go,
and
this
is
a
kind
of
a
shift
from
what
we've
been
talking
about
here.
It's
back
to
sort
of
humans
and
mammals,
and
I'm
not
gonna,
give
this
paper
the
what
maybe
the
attention
it
deserves.
B
B
Building
this
human
cns
requires
the
precise
orchestration
and
coordination
of
myriad
molecular
and
cellular
processes
across
the
staggering
array
of
cell
types
over
a
long
period
of
time.
You
know
it's
like
human
development,
of
course
is
we
have
we're
in
the
womb
for
nine
months
and
then
there's
a
lengthy
developmental
period
outside
the
womb.
You
know
going
from
maybe
like
you.
B
F
B
Pruned
you
know
we're
born
with
a
much
different
brain
than
what
we
have.
You
know
what
we
end
up
with
that
a
year
after
birth
and
then
after
about
seven
or
eight
years.
There
are
a
lot
of
neurochemical
changes
in
the
brain
and
then
in
puberty.
There
are
a
lot
more
neurochemical
changes
in
the
brain
and
our
brains,
don't
truly
mature
until
we're
in
our
20s,
and
then
people
have
questioned
whether
this
has
something
to
do
with
plasticity.
B
B
B
Like
a
lot
of
cognition
happens
there
and
the
insights
of
these
findings
provide
into
human
neural
evolution,
function
and
dysfunction.
So
that's
a
lot
of
a
lot
that
they're
going
to
set
out
to
do
here.
So
this
is
really.
You
know.
This
is
again
a
review,
so
you
have
a
lot
of
citations
in
the
front
in
the
introduction
and
they
talk
about
the
prolonged
developmental
course
and
period
of
dependency.
B
So
you
know
we're
more
than
other
primates.
We
need
to
be
in
a
parental
setting
for
18
years
or
you
know,
10.
You
know,
depending
on
your
culture,
you
know
people
like
maybe
a
couple
hundred
years
ago,
didn't
have
the
length
of
childhoods
that
we
do
today.
But
this
is
you
know.
This
is
all
due
to
our
very
long
developmental
trajectory
for
our
brains
and
so
we're
dependent,
often
on
our
parents,
for
a
long
period
of
time
and
in
a
lot
of
organisms.
B
You
know
they're
just
kind
of
born
if
they're
not
born,
live
if
they're
born
from
eggs
say
they're
all
ready
to
go
when
they
hatch.
So
that's
a
huge
difference
in
terms
of
like
the
biology
of
the
brain
c
elegans,
for
example,
the
eggs,
hatch
and
they're.
Basically,
they
go
through
these
larval
stages,
but
they
don't
need
parental
supervision
in
any
way
in
humans.
You
need
a
large
amount
of
parental
supervision
so,
and
a
lot
of
that
has
to
do
with
the
development
of
the
brain,
and
so
in
our
central
nervous
system.
B
We
have
things
like
proliferative
zones
and
diverse
subtypes
of
neural
stem
and
progenitor
cells,
which
actually
help
our
brains.
Our
brains,
have
expanded
across
evolution.
So
it
helps
with
that,
but
it
also
helps
with
neuroplasticity
and
some
of
the
things
involved
in
that,
like
neural
repair
and
other
types
of
things
and
there's
a
whole
literature
on
neuroplasticity
that
I
won't
go
into
my.
B
B
So
that's
interesting
that
a
lot
of
disorders
in
the
brain
may
be
due
to
the
complexity
of
the
brain,
and
so
we
know
that
from
model
organisms
we
can
study
things
about
the
human
brain
people
get
a
lot
of
funding
to
study,
say,
for
example,
different.
You
know
different
diseases
and
other
things
in
other
organisms.
B
So,
for
example,
c
elegans
is
used
to
study
aging
and
there
are
other
organisms
that
are
used
to
study
addiction
like
mice-
and
you
know
there
are
other
organisms
that
are
maybe
very
phylogenetically
distant
from
humans
that
are
used
to
study
diseases.
So
a
lot
of
these
diseases
have
sort
of
roots
in
the
genome
that
you
can.
You
know
use
like
phylogenetically
distant
organisms
to
understand,
but
some
of
these
things
are
really
kind
of
unique
to
you,
the
complexity
of
the
human
brain.
So
and
then
this
is
a
nice
figure.
B
This
is
actually
really
big
figure.
This
is
a
timeline
of
key
human
neurodevelopmental
processes
and
functional
milestones.
So
this
is
like
sort
of
the
if
you're
interested
in
neural
development
in
humans.
This
is
the
figure
right
here.
They
have
the
sort
of
the
time
series
of
different
things
in
in
the
brain
as
it's
developing,
so
this
is
in
the
embryo
in
the
fetus.
This
is
where
you
get.
Actually,
this
is
where
you
get.
This
isn't
fetal
development.
This
is
sort
of
like
embryonic
development.
Then
this
is
fetal
development,
these
three
brains.
B
So
you
get
this
these
structures,
you
know
you
get
a
neural
tube
and
then
you
get
elongation
and
you
get
these
different
structures
in
the
embryo
and
then
in
fetal
development.
You
get
these
other
structures
that
start
kind
of
looking
like
a
human
brain
or
at
least
a
mammalian
brain
and
then
at
birth.
B
You
start
to
get
these
bigger
brains
and
you
start
to
get
what
they
call
convolutions
on
the
outside
of
the
brain
that
are
forming
in
you
know,
as
you're
heading
towards
sort
of
in
between
this
birth
and
newborn
stage,
so
it's
very
early
and
then
you're
starting
to
get
this
is
this
is
like
three
years
old,
30
years
old
and
90
years
old
and
over
time,
the
brain
sort
of
that
after
about
30
years,
the
brain
starts
to
decay.
You
know
at
least
age
enough
so
that
it's
loses
cells.
B
B
So
they
try
to
link
this
to
development
and
not
only
development,
but
what
they
call
life
history,
which
is
this
period
of
you
know.
How
does
the
organism
sort
of?
How
does
the
organism's
trajectory
play
out
over
the
course
of
life?
So
you
know
you
have
development,
but
you
also
have
like
you
know,
life
history.
So
certain
things
happen
in
your
youth.
Certain
things
happen
in
your
middle
age
that
affect
your
later
life.
There
are
other
processes
that
unfold
during
the
life
span.
B
That
are
you
know
that
are
actually
evolutionarily
selected
for,
and
things
like
that.
So
there
are
a
lot
of
things
going
on
with
life
history
as
well,
and
then
they
have
this
human
cns
in
numbers,
which
is
where
they
give
a
lot
of
numbers,
and
these
are
you
know
these
are
like
cell
types
and
number
of
cells,
so
these
are
kind
of
estimates
that
people
have
come
up
with.
So
this
is
why
they
started
to
have
all
these
citations
in
here.
You
know.
B
The
number
of
neurons
in
the
human
brain
varies
by
as
much
as
a
factor
of
two,
so
we
don't
know
how
many
cells
there
are
like
in
the
c.
Elegans
connect
them.
We
know
exactly
how
many
cells
there
are,
and
it's
always
the
same
number
of
cells
across
every
c
elegans.
Unless
it's
a
mutant
and
the
human
brain.
We
have
no
idea
how
many
cells
are.
B
Furthermore,
there
could
be
a
lot
of
variation,
individual
differences
or
you
know
by
age,
so
you
can
have,
but
it's
immaterial
because
there's
so
many
neurons
that
you
know
it's
not
a
it
may
or
may
not
have
any
sort
of
functional
impact.
So
this
is
this.
Is
you
know
this
goes
on
for
quite
a
while
and
if
you're
interested
in
this
topic,
you
should
definitely
check
this
out.
B
They
talk
about
something
called
the
transcriptional
landscape,
which
is
where
they
look
at
gene
expression,
and
these
are
the
figures
that
they
use
to
look
at
that,
so
they
usually
get
like
a
bunch
of
data
on
gene
expression
and
then
they
look
at
the
data
and
they
use
them
sort
of
dimensionality
reduction
to
to
make
these
maps
or
basically,
if
you
have
these
clusters
and
we've
talked
about
dimensionality
reduction.
But
if
you
have
these
clusters,
that
tells
you
something
about
the
data
yeah.
B
So
that's
that's
that
paper
and
a
lot
of
references
in
here.
So
if
you're
interested
in
that,
I
don't
know
if
I
posted
the
paper
list
in
the
chat,
but
let
me
post
it
again
so
that
everyone
has
this
and
again.
If
the
sharing
is
off.
Let
me
know
I
can
put
the
share
or
put
the
thing
so
susan
said
she
has
to
leave
now.
The
farmers
here
need
a
ride.
Thank
you
for
attending
susan
jesse
says:
can
you
link
to
the
paper
here?
Okay,
this
is
the
link
here.
B
I
just
posted
the
the
folder.
So
if
you're
interested
in
that,
we
have
dick
said
tried
piv
once
on
a
diatom,
but
piv
software
couldn't
handle
the
diatoms
of
motion
and
maybe
now,
though
they
actually
have
better
software
for
it.
I
don't
know
if
this
was
a
long
time
ago
or
what
but
and
then
number
of
cell
types
in
the
brain
speculated
to
be
number
in
body
and
gordon
the
hierarchical
differentiation,
our
genome
differentiation
waves
yeah.
So
there
are
a
lot
of
estimates
about
a
number
of
cell
types.
B
E
B
And
it's
because
of
that
functional
that
need
for
functional
diversity
and
you
need
to
be,
you
know,
turned
on
and
off
as
as
by
you
know,
given
in
a
given
context.
A
lot
of
circuits
are
reused
for
different
things.
So
and
then
you
know
it's
there's
this
idea
of
lumping
and
splitting
when
we
talk
about
categories,
and
that
is
like,
if
you
want
to
make,
if
you
want
to
classify
things
in
the
world,
do
you
want
to
put
them
in
a
small
number
of
categories?
B
B
You
know
this
infinite
number
of
categories,
and
it's
just
really
anything.
That's
different
is
going
to
put
you
into
a
new
category,
and
so
this
has
been
a
an
issue
for
hundreds
of
years,
actually
in,
like
in
systematics
biological
systematics
and
in
other
types
of
classification,
where
really
you
have
to
make
a
decision
ahead
of
time
as
to
what
your
categories
look
like
and
what's
important
to
you
in
terms
of
understanding
your
data.
C
B
Approaches
are
useful
in
the
sense
that,
like
you
know,
you
may
not
want
a
thousand
categories.
You
may
only
want
to
know
about
a
certain
couple
of
certain
key
elements
to
your
data.
On
the
other
hand,
if
you
have
like
a
thousand
things
you
want
to
know
about,
then
splitting
is
is
better
and
it
says:
does
anyone
here
have
access
to
piv?
B
I
don't
know,
I
don't
think
so,
not
sure,
I'm
not
sure
what
yeah
well
anyways.
If
you
can
answer
dick's
question,
if
you
have
access
to
it
or
you
know
like
the
state-of-the-art
software
for
this,
let
us
know
so,
okay,
so
thanks
for
attending,
I
don't
know
if
anyone
has
anything
else,
they
want
to
say
before
we
wrap
up
for
today.
Does
anyone
else
have
any
comments
or
questions.
D
B
B
Yeah
yeah,
I
hope
christian,
is
doing
well.
Minoxis
have
a
great
week
ahead.
So
thank
you
and
again,
if
you
yeah
thank
you,
take
care
and
if
you
don't
get
selected
for
g
soccer,
don't
be
disheartened,
it's
very
competitive,
especially
last
year,
and
I
take
it
this
year
as
well.
So
we
shall
see
thanks
for
attending.
I
know
that
dick
and
bye-bye
tom,
I
know
that
dick
and
shretty
I
think,
are
going
to
meet
now.