►
From YouTube: DevoWorm (2021, Meeting 36): Hacktoberfest, Oct4 Day, Zebrafish Animations, Marine Invertebrates.
Description
Hacktoberfest: how to participate, 10 years of brain emulation with OpenWorm. Papers and animations on Zebrafish embyros, Octopus aquaculture, and diverse of prehistoric marine invertebrates. Attendees: Bradly Alicea, Jesse Parent, and Susan Crawford-Young.
B
A
A
A
A
B
A
B
Yeah,
that's
good,
I
don't
know
who
else
is
joining
us
today,
but
why
don't
we
just
start
and
then
people
can
join
in
as
they
come
through
so
yeah?
Let's
see,
I
have
a
number
of
things
to
talk
about
today.
This
is
you'll
you'll
see.
This
is
october.
4Th
and
you'll
see
what
the
significance
of
that
is
in
a
bit
okay
and
then
we
have
some
other
things
to
review
here:
some
papers
and
some
other
things.
B
A
B
So
this
is
a
couple
things
that
we
had
this
last
week
or
two.
So
the
first
thing
was
our
annual
meeting
was
on
this
last
tuesday
or
monday.
I
think
it
was
tuesday
yeah,
and
so
we
talked
about
all
updates
from
all
the
different
groups
in
open
worm,
and
we
talked
about
diva
worm
and
I
last
week
I
showed
the
paper
from,
or
I
showed
our
presentation
what
I
was
going
to
present
to
the
group.
B
It
was
a
review
of
our
papers
and
presentations
and
events
and
activities
from
this
past
year,
and
so
everyone
was
pretty
impressed
and
we
had.
You
know
this
is
a
meeting
where
they
have
all
the
board
of
directors
and
all
the
other
people
who
were
kind
of
behind
the
scenes
to
make
open
worm
work,
and
we
also
had
the
other
group
leaders.
B
We
call
the
senior
contributors
who
are
the
people
who
are
who
have
done
a
significant
set
of
contributions.
We
have
like
16
senior
contributors
and
some
of
those
people
a
lot
of
those
people
run
projects,
and
so
we
had
some
updates
on
the
projects.
It's
actually
some
pretty
interesting
advances
in
open
norm
over
the
past
year.
B
The
cybernetic
project,
for
example,
released
a
new
version
where
they're
able
to
so
they
do
this
thing
where
they
have
a
like
a
finite
element
model
of
the
worm.
They
have
a
physical
model
of
the
worm
where
they
take
the
worm
and
they
break
it
into
pieces,
and
then
they
use
those
pieces
to
they
put
them
into
a
multi-physics
model
where
they
have
like
the
physics
of
the
surface
that
the
worm
is
moving
against,
and
so
they
can
calculate
all
the
physics
as
this
process
of
movement
is
going
on.
So.
B
So
you
can
imagine
that
you
know
you're
simulating
the
worm
crawling
through
it's
along
its
surface
you're,
getting
these
hydrodynamic
forces
because
they're
crawling
through,
like
a
in
in
a
plate,
they're
crawling
through
you
know
a
medium
they're.
Also
you
know
in
the
under
the
underground
they're
crawling
through.
You
know
dirt,
but
they're
also
crawling
through.
B
It
decreases
the
amount
of
runtime
that's
needed
for
the
simulation,
so
the
cybernetic
simulation
is
one
of
the
most
computationally
challenging
simulations
in
the
in
the
suite
of
simulations
that
we
have,
and
so
they've
been
able
to
cut
the
size
of
that
down
enough,
so
that
you
know
it's
a
better
run,
has
a
faster
run
time
and
a
better,
you
know,
allows
people
to
run
it
on
their
machines.
We.
B
In
the
docker
package
that
open
worm
has
that's
one
of
the
components
of
it
and
you
know
we're
running
into
problems
with
it
when
it
generates
images
generating
too
much
information
for
some
people's
computers,
so
that
was
a
bottleneck
that
was
kind
of
solved
this
year.
We
have
some
interesting
work
going
on
with
a
lot
of
work
with
like
data
processing
and
secondary
data
and
and
things
like
that
and
data
science.
B
We
know
we
have
four.
You
know
we
have
people
working
on
different
formatting
projects.
We
have
people
working
on,
you
know
taking
data
from
different
sources
and
using
it
in
an
analysis
putting
it
into
a
simulation
format.
So
one
of
the
projects-
it
was
a
google
summer
code
project
in
another
one
of
the
projects
where
they
took
some
data
from
a
lab.
B
You
know
where
they
just
generated
the
data
hot
off
the
presses
and
they
put
it
into
the
simulation,
so
they
were
able
to
plug
all
that
in
so
there's
a
lot
of
advent,
a
lot
of
a
lot
of
good
stuff
going
on
and
one
of
the
programs,
and
I
don't
think
that
they've
really
fully
announced
this.
Yet,
but
one
of
the
things
they
want
to
do
is
they
want
to
take.
B
You
know,
converted
into
something
more
friendly
to
data.
You
know
like
a
universal
data
format
or
some
sort
of
simulation,
so
it
would
be
like
a
training
program
to
teach
people
who
generally
are
you
know
biologists
on
in
their
everyday
lives?
Maybe
they
don't
have
a
lot
of
experience
with
data
science
or
or
you
know,.
B
A
And
that
that
might
be
interesting
for
even
someone
like
myself,
who
hasn't
worked,
that
much
with
with
data
yeah
and
also
I
I
am
really
interested
in
the
programming
that
they
used
and
because
I
might
like
to
use
something
like
that.
Myself
and
open
foam
comes
to
mind
like
the
open
package,
open
foam.
A
B
B
Yeah,
I
I
think,
yeah,
I
think
a
lot
of
it
is
like
their
own,
their
own
code
and
so
yeah.
I
don't
think
they've
they've
built
on
top
of
a
lot
of
platforms.
For
that
which
is
you
know
both
good
and
bad.
You
get
the
benefit
of
the
platform,
but
then
you're
well
sometimes
you're
locked
into
that
format.
B
A
B
B
So
you
know
you,
you
basically
get
some
worms,
they
have
stocks
that
you
can
order
from
not
only
the
wild
type
but
the
mutants.
And
then
you
put
you
know
you
get
some.
You
get
some
petri
dishes,
you
put
some
media
down
on
the
bottom
gel
and
then
you
put
some
food,
which
is
usually
this
bacterial
mix,
and
you
put
just
you
know
you
get
a
what
they
call
a
worm
pick
you
pick
them
out,
you
put
them
in
the
new
petri
dish
and
you
incubate
them
usually
at
room
temperature.
B
That's
all
that's
required
and
then
they
grow
and
you
can
do
a
lot
of
things
with
them.
You
can
harvest
the
eggs.
You
can
look
at
the
adults
under
a
microscope.
You
can
do
different.
Assays
of
in
larval
development,
where
you
can
arrest
their
development
or
you
can
you
know,
do
do
all
sorts
of
different
experiments.
B
B
So
that's
why
people
use
it
for,
like
aging
research,
for
different
types
of
metabolic
disorders.
Things
like
that
so
and-
and
you
know,
the
methods
are
it's
kind
of
hard
because
the
methods
you
know
a
lot
of
that
you
just
have
to
like
do
by
hand.
It's
like
a
lot
of
the
wet
lab
training
that
I
had,
for
example,
was
in
the
classroom.
B
A
B
Not
yeah,
I'm
not
sure
that
people
have
used
that
method
on
it.
It
would
definitely,
I
think
it
would
be
workable
because
you
know
there
isn't
a
lot
of
it's
a
fairly
transparent
organism.
In
that
sense,
like
you
can
see,
you
know,
through
the
skin
or
through
the
epidermis
as
you're
working
with
it.
So
like
a
lot
of
the
microscopy
light
microscopy,
you
can
see
the
cells
you
know
but
yeah.
I
think
that
would
be
great.
A
B
A
B
B
It's
a
proposed
technique
which
involves
transferring
the
information
contained
within
a
brain
onto
a
computing
substrate.
The
brain
can
then
be
simulated,
creating
machine
intelligence.
This
is
discussed
in
the
context
of
scanning
the
brain
of
a
person
known
as
mind
uploading,
and
then
they
have
a
definition
here
for
mind
uploading,
which
is
pretty
obvious.
Given
what
I
told
you
so
this
is
this
whole
area.
You
know
the
idea
of
c
elegans
is
that
you
have
this
relatively
small
connectome
302
cells
and
it's
tractable
enough,
so
that
you
could
understand
the
entire
connectome.
B
It's
not,
it
wouldn't
be
impossible.
So
the
idea
was,
you
know.
If
you
have
302
cells
with
a
tractable
number
of
connections,
could
we
just
simply
simulate
that
entire
nervous
system
and
download
it
onto
a
computer,
which
you
know
this
is
one
thing
that
openworm
has
done
is-
is
to
take
the
connectome
data
and
plug
it
in
with
electrophysio
simulations
of
electrophysiology
of
ion
channels
and
look
at
how
those
circuits
work.
B
You
know
the
different
cells
in
a
circuit,
how
they
work,
how
they
produce
behavior,
and
so
that's
been
something
that
we've
had
now.
Their
argument
was
that
you
know
they
want
to
actually
download
the
c
elegans
brain
and
simulate
it,
which
I
don't
exactly
know
what
that
means,
especially
in
the
context
of
c
elegans,
where
we
can
simulate,
you
know
basic
movements,
we
can
simulate
it
in
a
you
know,
a
biophysical
context.
We
can
simulate
some
of
the
basic
movements.
We
know
some
of
us
what
some
of
the
circuits
do,
what
they
generate.
B
So
I
mean
you
know,
there's
that
part,
but
then
there's
also
this
idea
of
like
the
entire
brain.
Being
this
autonomous
thing-
and
I
don't
know-
maybe
this
is
a
a
bad
question
for
c
elegans,
because
we
don't
really
know
what
I
mean
beyond
that.
We
don't
really
know
what
is
involved
in
a
cl
against
brain.
It's
just
that.
You
know
it's
generating
movement
and
you
know
it
can
behave,
it
can
behave
adaptively
and
you
know
that's
that's.
B
Maybe
all
we
can
ask
for
because
we
couldn't
get
the
experience
of
worm
say
you
know,
worms
c
elegans
as,
for
example,
nociceptors
can
those
nociceptors
detect
pain?
And,
if
that's
true,
then
do
you
see
elegans
actually
feel
pain,
or
you
know
these
sorts
of
questions.
I
don't
know
if
we
can
answer
those.
It
might
be
something
interesting
to
look
into,
but
I
don't
know
what
that
would
look
like
you
know.
We
don't
have
a
project
on
an
open
world.
So
so
then
this
just
kind
of
goes
through
this
10-year
quest.
B
B
So
that's
that's
from
10
years
ago,
so
open
worm
at
that
time
focused
on
the
anatomical
data
from
dead
worms,
but
very
little
data
exists
on
loving
animal
cells,
which
is
not
true
now,
because
we
have
real-time
measurement
techniques
that
you
know
when
they
say
dead,
dead
worms,
they
mean,
like
cryo,
section
images
where
you
know
they
can
get
really
high
resolution
images
of
the
worm.
But
it's
fixed.
It's
it's
a
static
view
with
other.
You
know
there
might
be.
You
know
there
are
other
imaging
techniques
that
we
can
use
with.
B
You
know,
as
the
worm
is
behaving
as
the
worm
is
still
alive
and
try
to
get
to
some
of
these
anatomical
components
and
model
them.
So
you
know
this
is
something
that
we
don't
really
have
a
lot
of
an
open
worm.
I
mean
you
know
we're
adding
in
things
all
the
time,
but
maybe
not
at
the
rate.
We
would
like.
B
And
so
they
kind
of
get
into
the
confidence
of
complete
functional
simulation
of
the
c
elegans
nervous
system.
So
this
is
someone
in
the
chat
group
here
they
said
that
they
had
confident
76
confidence
that
it
would
be
achieved
by
2014
and
99.8
confidence.
It
would
be
achieved
by
2020
and
so
that
wasn't
obviously
wasn't
fulfilled
here
as
they're
making
the
point.
So
you
know
I
don't
know,
first
of
all
what
they
mean
by
whole
brain
simulation
in
the
context
of
c
elegans.
B
I
guess,
if
you
can
simulate
a
connectome,
you
know,
that's
that's,
probably
pretty
good.
If
you
can
stimulate
movement
or
some
other
behaviors,
that's
even
better.
I
don't
really
know
beyond
that.
What
you
can
say
about
it
I
mean
you
can
say
if
you're,
a
human
and
your
brain
was
uploaded
into
a
machine.
You
could
evaluate
that
and
you
know,
because
you
know
what
you
know,
and
you
know
what
you
feel
and
you
can
verify
that,
but
I
don't
know
what
it
means
to
be
like
to
upload
the
brain
of
an
organism.
B
What's
what's
the
like
the
minimal
representation
there?
I
don't
really
know,
but
in
any
case
now
there
are
a
lot
of
things
we
don't
take
into
account
when
we
build
the
model.
Of
course,
we
don't
take
into
account
a
lot
of
the
molecular
detail.
For
example,
so
cells
have
a
lot
of
molecular
mechanisms
going
on
in
them
that
modulate
some
of
the
ion
channel
activity
that
you
know
in
response
to
stimuli.
So
we're
not
simulating
any
of
that.
We're
just
simulating
the
ion
channel
activity,
which
is
you
know,
is
fine.
B
On
the
other
hand,
we
don't
know
that
for
sure
we
don't
know
that,
like
adaptive,
changes
might
require
things
that
are
sort
of
built
in
that
we
don't
really
understand
yet,
and
so
so
that
that's
the
problem
with
you
know
what
a
lot
of
the
you
know
we're
trying
to
simulate
organisms
and
the
question
there
are
two
questions
here.
I
think
one
is
that
you
have.
B
You
know
this
sort
of
idea
of
representation
like
what
level
representation
do
we
want
do
we
want
to
have
like
something
that's
very,
absolutely
complete
in
every
way
in
every
detail,
even
if
we
don't
understand
how
it
works
or
do
we
want
to
like
simulate
the
parts
that
we
know
work
and
then
figure
out.
Maybe
how
things
are
regulated,
how
adaptive
change
happens
and
then
maybe
we
can
fill
in
the
gaps
from
say
you
don't
need
a
molecular
level.
B
You
just
need
to
understand
what
the
molecular
level
does,
which
we
don't
really
know,
but
you
know
that
would
be
enough
and
then
the
other
question
is
like
how
do
we
know
the
entirety
of
like
c
elegans
behavior?
What
is
it
doing?
You
know
we
know
it's
moving.
We
can
observe
that
we
don't
know
if
it's
thinking
about
anything.
B
We
know
that
it
feels
that
their
nociceptors,
but
we
don't
know
what
their
pain
sensation
translates
into
in
the
world
other
than
like.
You
know,
changes
in
movement
and
in
direction
of
movement,
and
so
the
same
holds
cheer
for
chemo
sensation
as
well.
They
experience
a
chemosensory
gradient,
they
move
towards
it
or
against
it
or
whatever
it's
just
a
signal,
and
so
we,
but
we
don't
know
beyond
that,
what
the
brain
is
actually
doing.
B
Sometimes
you
know
signals
are
integrated
in
in
interneurons
in
poly
model
interneurons,
but
we
don't
know
you
know
what
that
if
there's
anything
more
than
just
a
simple
integration
function,
so
I
you
know,
I
don't
know
the
whole
brain
thing
it's
interesting,
but
I'm
not
sure
that
that's
really
the
the
true
scientific
value
of
what
you
can
do
with
you
know
creating
digital
organisms
or
creating
digital
c
elegans
or
whatever
you
want
to
call
it.
I
don't
I
don't
know
if
that's
really
the
value
of
it,
but
in
any
case
they
they
had
this.
B
B
B
This
goes
back
a
ways,
but
about
nine
years,
nine
years
ago
now
this
week
I
think
the
nobel
prize
in
physiology
or
medicine
for
2012
was
announced,
and
we
had
two
people
win.
This
award
in
2012,
john
gordon
and
shunya
yamanaka,
and
the
way
they
do
nobel
prizes
is
interesting.
They
usually
take
like
two
or
three
people
and
they
try
to
find
a
body
of
work.
That's
really
significant
and
then
they
share
the
prize.
B
You
know
generated
by
expressed
genes,
usually
in
the
form
of
rna
and
you
what
they
call
transfecta
cell,
which
means
you
take
the
rna
and
you
put
it
into
the
cell
using
some
sort
of
retrovirus,
and
so
you
can
get.
If
you
can
get
these
transcription
factors
into
the
cell,
you
can
get
the
you
can
trigger
the
expression
more
of
these
genes
and
you
can
change
things
in
the
cell.
So
you
can
change
the
expression
of
the
cell's
genetic
program.
B
What's
interesting
about
his
work
is
that
he
identif
they
screened
a
bunch
of
genes
for
this
and
they
came
down
to
four
or
five
genes
and
this
in
the
prize
here
it's
it's
a
four
gene
cocktail,
so
it's
oct4
sucks
to
see
mick,
and
I
can't
remember
the
other
one
now
you
know
I
had
this
like
stamped
on
my
forehead
at
one
time,
but
it's
been
a
while.
So
so
this
is
these.
Are
these
four
factors
and
they?
Basically
you
know
this
is
the
circuit
that
controls
pluripotency.
B
So
if
you
upregulate
those
four
genes,
you
get
this
change
in
in
the
state
of
the
cell,
so
these
two
were
awarded
the
prize
together.
So
the
the
prize
was
for
the
discovery
that
mature
cells
can
be
reprogrammed
to
become
pluripotent.
That
was
the
name
of
the
surprise.
It
just
seems,
like
you
know,
if
you
go
back
and
you
look
at
the
nobel
prizes
like
the
names
are
like
the
the
award
is
like
it's,
this
really
complicated
thing.
You
know
it's
not
like.
I
discovered
penicillin.
B
Well,
maybe
penicillin,
but
actually
that's
not
what
they
give
the
prize
for
it's
like
things
that
relate
to
the
discovery
of
penicillin.
It's
it's
quite
something
hi
jesse.
How
are
you
in
any
case?
This
is?
This
is
the
2012
nobel
prize.
This
is
the
announcement
here,
and
so
they
kind
of
go
through
yeah.
So
they
talk
about
the
frogs
john
gordon.
B
He
was
able
to
do
this.
Self-Somatic
nuclear
transfer
in
frogs
and-
and
so
people
were
skeptical
about
this.
This
was
in
1962.
and
it
wasn't
until
1996,
where
they
did
this
in
sheep,
and
that
was
dolly
the
sheep,
and
that
was
where
it
was
really
kind
of
taken
mainstream.
I
guess
rodon's
research
taught
us
that
the
nucleus
of
a
mature
specialized
cell
can
be
returned
to
an
immature
pluripotent
state,
but
his
experiment
involved.
B
This
was
in
the
thoughts
about,
I
think,
the
his
the
paper
on
the
four
factors
was
2007.,
and
so
they
did
this
paper,
and
it's
been
you
know
ever
since
people
have
just
been
working
on,
reprogramming
they've
actually
been
able
to
repro
that
the
four
four
factor
cocktail
works
to
create
stem
cells
or
things
that
look
like
stem
cells.
I
should
say,
because
they're
not
really
true
stem
cells
there's
a
lot
of
variability
there.
B
So
you
know
you
put
these
factors
into
the
cell
into
a
population
of
skin
cells,
and
those
skin
cells
will
then
start
to
sprout
axons
and
they'll
start
to
find
their
neighbors,
and
maybe,
if
you're,
lucky
they'll
form,
maybe
a
connection
or
two
they're.
Not
like
you
know,
you
don't
get
a
full-blown
nervous
system
out
of
this
method
necessarily,
but
you
do
get
neurons
that
are
somewhat
functional,
so
it's
really
interesting
work.
B
So
it's
also
known
as
pow
five
f1,
that's
not
a
date,
so
we
can't
have
fun
with
that
is
a
protein
that
human
in
that
in
humans
is
encoded
by
the
pal5
f1
gene
op4
is
a
homeodomain
transcription
factor
the
pow
family.
It
is
critically
involved
in
the
self
renewal
of
undifferentiated
embryonic
stem
cells.
B
So
this
is
actually.
This
is
an
embryonic
stem
cell
colony.
As
you
can
see
here,
you
can
see
the
sort
of
how
they
form
this
colony
of
stem
cells
and
then
they're,
surrounded
here
by
skin
cells
or
fibroblasts,
and
this
is
what
they
look
like
when
they're
reprogrammed
you
get
a
a
cell
that
transforms,
and
then
it
starts
as
a
stem
cell.
It
starts
to
divide
and
form
this
colony,
and
then
you
can
pick
these
colonies
out
and
passage
them
to
new
plates.
B
So
the
in
stem
cells,
unlike
say,
like
fibroblasts,
are
known
for
their
ability
to
self
renew,
so
fibroblasts
will
maybe
can
maybe
make
about
20
divisions
before
they
start
to
die
off,
and
they
have
this
program
cell
death
that
they
undergo
it's
it's.
What
they
call
the
hate
flick
limit,
which
is
where
you
get
a
certain
number
of
divisions
before
the
cell
line
terminates
just
kind
of
becomes
blobby
and
the
cells
become
non-functional
with
stem
cells.
B
Even
in
embryonic
stem
cells,
you
don't
see
that
they
can
divide
infinite
for
an
infinite
length
of
time
and
never
the
line
never
goes
extinct.
It
just
keeps
going.
That
being
said,
these
stem
cell
colonies
are
pretty
fragile,
so
you
have
to
really
work
to
keep
them
going,
but
that's
not
due
to
the
hayflip
limit,
that's
due
to
like
the
the
limitation,
the
metabolic
limitations
of
the
environment.
B
So
this
is,
you
know
this
is
how
we
can
really
make
some.
You
know
we
can
make
some
biological
changes.
Maybe
they
say
you
can
really.
You
know
see
these
things,
so
you
have
opt
for
sucks
too.
They
work
together.
Those
two
genes
usually
work
together.
You
can
the
you
know.
There
are
different
ways:
you
can
do
this
reprogramming.
B
There
is
a
paper
early
on
that
was
like
at
the
same
time
as
the
yamanaka
paper,
where
they
did
five
jeans,
the
two
octorin
socks,
two
are
really
the
ones
that
drive
this
process,
although
they're
other
genes.
That
kind
of
support
it,
and
so
this
is,
you
know
this
speaks
to
the
nature
of
a
genetic
network
that
you
have,
the
all
these
genes.
Working
together,
co-regulating
each
other,
okay,
nano
is
the
other
one.
B
C
B
Okay-
well,
that's
fine
thanks
for
attending,
so
the
next
thing
I
want
to
talk
about
was,
let's
see
if
my
screen
share
is
working
better.
Okay.
The
next
thing
I
want
to
talk
about
are
abstract
submissions,
so
this
is
something
that
we've
been
working
on
for
this
neuromatch
conference,
and
so
I
think
I
mentioned
the
neuromatch
conference
last
week
and
these
were
making
a
list
of
things
that
we
might
submit.
B
B
B
B
There
are
other
topics
I
I
don't
know
how
deeply
they
want
to
go
into
developmental
biology,
it's
a
neural
conference,
so
there
has
to
be
sort
of
a
narrow
angle
to
it,
but
you
know
maybe
there's
some
there's.
Some
topics
in
that
have
like
a
developmental
biology,
neural
overlap
that
people
might
be
interested
in
this
one
here,
neural
morphogenetic
patterns
the
theory
of
deep
learning.
This
is
a
poster
that
we
did
and
so
that
we
can.
Maybe
we
can
turn
that
into
a
submission
as
well.
B
A
B
Yeah
yeah,
it's
always
tough
to
do
stuff.
Wouldn't
do
that,
so
it's
fine!
I
just
wanted
to
bring
that
up.
The
next
thing
I
want
to
talk
about
is
hacktoberfest
and
our
involvement
in
it.
So
this
is
again
if
you
can't
see
the
screen
share,
I'll,
just
kind
of
mention
it
and
it'll
be
online
later,
so
we
I
set
up
the
repositories
for
oktoberfest,
so
it
started
the
first
so
get
a
slow
start
usually,
but
I
wanted
to
promote
this
a
little
bit.
B
I
promoted
a
little
bit
on
twitter,
so
let
me
see
if
I
can
find
the
tweets
here.
This
is
so.
This
is
our
banner
here
oktoberfest,
it's
like
two
embryo
cells.
It's
like
a
green
and
orange
theme.
So
it's
you
know
it's
the
hacktoberfest
logo
and
then
there
are
two
tweets.
One
is
where
it
just
mentions
the
different
repositories
and
then
the
second
one
which
mentions
a
different
set
of
repositories.
B
So
the
repositories
are
there's
in
diva
worm,
there's
digital
basil
area
and
I
put
a
little
badge
on
each
one,
so
in
the
readme
on
each
repo.
So
you
can
see
if
you
follow,
along
with
the
digital
vessel
area,
one
you'll
see
that
there's
a
badge
in
the
readme
and
that's
at
the
top.
And
so,
if
you
look
for
these,
these
badges
you'll
see
which
ones
are
open
to
contribution,
so
the
digital
basilarian.
B
We
actually
have
some
people
working
so
eligible
on
azimut
singh
who
worked
on
it
back
in
2019
or
reviving
some
of
their
work
with
a
couple
people.
I
think
thirune
and
I
think
that's
it
for
now,
but
they're
trying
some
new
algorithms
they're
trying
to
they
have
a
new
data
set
that
they're
working
with
so
it'll
be
interesting
to
see
what
they
come
up
with.
But
if
people
want
to
contribute
in
some
way
to
this,
they
can
leave.
B
They
can
leave
a
message
on
one
of
the
issues
or
you
know
some
other
way
to
get
in
touch
with
the
repository,
and
so
you
know
there's
a
lot
of
stuff
in
here
from
like
the
last
from
maybe
about
two
years
ago
about
a
year
ago,
but
we're
working
on
getting
some
new
things
in
line.
There's
also
the
open
papers,
the
bessel,
whereas
psychophysics,
which
is
something
that's
in
the
process
of
being
submitted
out,
and
so
that's
another
thing
that
we
can
kind
of
go
over.
B
B
So
we
have
a
badge
on
that
readme
as
well,
and
we
have
a
project
board
that
has
issues
and
a
lot
of
those
issues
are
things
we
mentioned
in
the
meetings.
So
people
are,
you
know
they're
also.
I
don't
know
how
many
issues
around
this
board-
probably
about
at
this
point
about
70
or
80
active
ones,
so
we
have
in
different
states
have
done
so.
A
lot
of
them
are
finished.
B
Some
are
off
the
radar,
but
these
are
things
that
people
can
address
or
ask
questions
about,
and
then
jesse
asked
about
the
virtual
developmental
worlds,
which
is
something
that
issue
113,
and
I
think
I
answered
them
on
that.
There's
a
discussion
in
one
of
our
lab
meetings
about
two
weeks
ago,
where
I
kind
of
go
over
some
like
it's
some
simulation
of
cells
that
I
found
in
someone's
talk.
They
they
had
this.
B
They
had
these
slides
where
they
had
these
simulations,
and
it
was
kind
of
odd
because
it
was
like
I've
never
seen
this
before
in
a
talk
where
they
actually
have.
The
simulations
is
like
background-
and
I
looked
it
up
and
there's
this
project
where
they
do
this,
where
they're
doing
people
do
this
for
a
number
of
reasons,
because
it's
easy
to
visualize,
it's
easy
to
understand
what
you
can
visualize.
B
So
you
know
you
have
these
cellular
and
subcellular
processes
that
they
simulate
this
company.
That's
doing
the
simulation,
so
I
you
know
it
got
me
thinking
about.
Like
you
know,
how
can
we
make
sort
of
like
development,
or
you
know
things
like
that
into
a
more
of
a
virtual
experience
that
people
can
understand
some
of
these
processes
a
bit
better?
B
But
this
is
something
that
I've
been
kicking
around
for
a
while,
and
I
I've
tried
to
get
people
to
you
know
get
involved
in
like
3d
simulations
of
of
an
embryo
where
you
have
like
you
know
you
build
like
something
in
blender,
which
is
a
3d
model.
You
build
a
cell
and
then
you
build
maybe
things
inside
the
cell
and
you
can
navigate
the
entire
embryo.
B
You
know
that's
the
sort
of
thing
that
would
be
kind
of
interesting,
both
from
just
sort
of
an
educational
standpoint,
but
also
you
know
you
can
do
other
things
with
it
like
kind
of
get
get
a
sense
of
like
the
spatial
scale,
or
you
know
the
spatiality
of
some
of
these
things
and
you
look
at
microscopy
images.
It's
kind
of
like
a
two-dimensional
experience
in
a
lot
of
ways,
and
sometimes
you
know
people
are
generating
these
three-dimensional
cell
tracking
data
sets
where
they're
able
to
simulate
the
you
know.
B
B
There.
Also,
very
early
on
the
project,
I
was
trying
to
get
some
people
to
help.
You
know
develop
this
platform.
It
was
like
a
phys,
it's
a
physics
platform
and
humpy
cell
3d
is
the
name,
and
the
idea
is
that
you
have
the
you
can
build
like
you
know
entire
tissues
organs
and
you
can
build
them
using.
You
know
different.
B
It's
a
series
of
cells
like
if
you
want
to
build
a
virtual
liver,
you
can
build
a
three-dimensional
liver
with
all
these
cells
and
then
each
cell
has
processes
inside
of
it
and
you
calibrate
it
using
real
data.
So
you
collect
data
from
like
wherever
you
know,
physiological
data
developmental
data,
you
plug
it
into
the
cells
and
then
they
the
simulation,
runs
as
if
those
cells
are
autonomous
agents
within
like
this
tissue
that
you're
simulating.
B
So
you
can
simulate
things
like
disease.
You
can
simulate
things
like
you
know.
Regulatory
states,
like
you,
know,
homeostatic
states,
or
you
know
whatever
you
want
to
do,
and
so
this
would
be
it's
a
very
hard
program
to
use
as
it
turns
up.
So
that's
a
problem,
but
I
think
the
output
would
be
great
and
you
know
this
is
the
kind
of
thing
it's
just
like
it's
never
been
put
together
in
any
meaningful
way.
So
I
don't.
C
B
How
to
go
forward
on
that,
but
that
you
know
this
is
a
thing
like
I
I
want
to
like
for
hacktoberfest
and
throughout
the
year.
I
want
to
make
this
easy
to
contribute.
I
want
people
to
be
able
to
contribute
code,
but
also
like
you
know,
how
would
we
do
something?
You
know
what's
the
road?
What's
the
number
the
set
of
stages
we
need
to
get
there
or
you
know
even
just
some
project,
like
you
know
getting
data
and
turning
it
into
you
know,
turning
it
into
a
different
format.
B
B
You
know
being
developed,
we're
at
0.3.0
and
we're
probably
going
to
release
a
new
version
soon.
So
there's
the
diva
learn.
This
is
the
diva
learned
software
all
right,
and
then
we
have
the
divoler
organization,
which
has
another
repository
which
has
a
badge
on
it,
which
is
this.
The
data
science,
demos
and
I
know
a
lot
of
people
who
are
regulars
of
this
group
have
contributed
to
this.
B
These
are
like
jupiter,
notebooks,
collab,
notebooks,
different
tutorials
for
different
topics
and
data
science.
So
that's
as
usual,
always
open.
That's
something.
People
can
contribute
to
there's
a
badge
there
and
we
have
a
nice
collection
now,
but
it's
like
you
know,
I'd
like
to
expand
that
as
much
as
possible.
B
So
that's
if
you
want
to
contribute
to
oktoberfest
there
you
go
those
are,
and
throughout
the
month,
we'll
be
going
through
some
of
these
things.
That
kind
of
you
know
maybe
say
more
about
like
what
you
know,
and
I
want
this
to
be
like
something
a
starting
point,
not
necessarily
just
like
you
know
we're
going
to
do
this
this
month
and
then
that's
it
november.
1St
forget
about
it.
I
want
people
to
you
know,
be
able
to
use
this
as
as
a
tool
for
you
know
developing
projects.
B
Okay,
so
I
think
we're
at
the
point
where
we
get
into
papers-
and
I
don't
want
to
do
too
much
today,
because
we
don't
have
our
screen
visible,
so
it
makes
the
zebrafish
embryo
thing
kind
of
hard
to
hard
for
people
to
see
but
like
I'll
just
go
through
this
and
if
you'll
see
actually,
why
don't?
I
just
give
a
link
to
this
window
or
this
folder
and
you
can
maybe
follow
along
with
some
of
the
files
opening
them
up
and
so
forth.
B
So
let
me
do
this:
that's
the
drive
and
so
starting
off
here,
okay.
So
this
is
a
light
sheet
image
of
an
embryonic
zebra
fish
heart,
and
this
is
accredited
to
this
person
on
twitter.
Here,
sanchez
she's,
a
scientist
I
can't
remember
where
she's
located,
but
this
is
this
nice
light
sheet
image.
So
white
sheet
microscopy
is
really
nice.
It's
really
high
resolution.
B
We've
looked
into
doing
this
for
different
organisms,
it's
it's
a
very
nice
technique
and
a
lot
of
the
stuff
that
a
lot
of
the
data
we've
used
for
c
elegans
in
a
lot
of
the
microscopy
data
for
gsoc
was
light
sheet
microscopy,
and
so
this
is
better
than
right
field
microscopy
for
a
number
of
reasons.
But
you
get
these
really
high
resolution
images
that
give
you
some
nice.
You
know
you
some
nice
contrast
and
capture
a
lot
of
detail.
B
But
if
I
open
up
the
gif
or
the
mp4,
I
think
this
will
play
okay.
You
get
this.
You
can
see
that
it's
like
the
blob
of
cells
that
starts
out.
It
starts
on
sort
of
the
top
of
the
zebra
fish,
and
then
the
cells
migrate
downward
towards
the
posterior
pole.
There's
like
this
vegetal
pole
at
the
bottom,
and
then
there
are
these
cells
that
migrate
across
the
surface
and
then
they're
going
to
form
sort
of
the
different
parts
of
the
zebrafish.
B
So
this
is
an
interesting
diversion
from
like
what
we
see
in
c
elegans
or
even
in
drosophila.
I
think.
Last
week
we
saw
drosophila
embryo
where
they
have
this
mode
of
of
development,
where
there's
a
cellularization,
so
you
have
this
big.
You
have
this
bean
shaped
embryo
and
then
the
constants
of
that
are
cellularized
and
in
the
zebrafish
it's
a
little
bit
different.
You
have
this
concentration
of
cells
at
one
pole
and
then
they
come
out
all
the
way
around
the
embryo,
and
then
they
start
to
form
the
zebra
fish
around.
B
A
Sorry,
zebra
fish
fish
are
tallest
embryo
and
fish,
so
there
they're
different
from
amphibian
and
different
from
mammal
and
amphibian,
have
a
variety
of
different
methods
of
producing
a
notochord
etc.
Like
they're,
it's
interesting
there's
a
huge
variety
of
of
mechanisms
in
play
in
different
animals.
B
You
know
that
at
one
time
people
thought
that,
like
they
were,
you
know
that
there
was
this
well
there
is
this
thing
called
the
phylotypic
stage,
but
that
you
know
that
a
lot
of
that
there's
this
idea
that
development
recapitulates
phylogeny
and
it
kind
of
looks
that
way
when
you
look
superficially
at
the
embryos,
but
that's
probably
not
true
to
a
large
extent,
because
there
are
a
lot
of
mechanisms
that
are
different
across
different
develop.
You
know
different
organisms
in
their
development,
so
yeah.
A
Yeah
dick
gordon
gave
me
a
paper
about
frogs
and
that
just
shows
a
variety.
I
also
have
a
1950s
paper
on
amphibians
and
and
frog's
brains
get
developed
differently
depending
on
the
species
is
sort
of
interesting.
It's
like
they're,
either
sort
of
like
salamanders
or
or
some
of
them
just
have.
They
have
a
layer
of
cells
on
top
of
the
neural
plate
as
it
develops
and
some
have
a
partial
one.
B
Embryos,
why
don't
we
talk
about
that
since
we're
getting
into
the
diversity
of
embryos?
And
this
is
the
folder
for
that
put
it
in
the
chat.
We
start
with
this
tweet,
it's
on
a
new
octopus
pre-print.
We
present
octopus
churchier
a
promising
study
organism
for
neuro
and
beyond
big
brains,
see
through
embryos,
cool
reproductive
patterns,
and
you
can
rear
them
in
the
lab.
B
So
this
is
the
picture
of
the
octopus
model
that
they're
proposing
this
lesser
specific
striped
octopus.
You
have
an
emerging
laboratory
model
for
the
study
of
octopuses,
so
octopuses,
of
course,
have
a
pretty
complex
behavior
repertoire,
pretty
big
brains
for
a
cephalopod
and
also
have
an
interesting
development.
B
So
then,
the
next
paper
in
the
order
in
the
folder
is,
I
think
this
is
the
paper
it's
related
to
it.
The
western
pacific,
striped
octopus,
octopus
churchier
an
emerging
laboratory
model
for
the
study
of
octopi
or
octopuses.
I
guess
it's
octopuses,
but
so
the
abstract
green
cephalopods
have
the
potential
to
become
useful
experimental
models
in
various
fields
of
science,
including
neuroscience
physiology
behavior,
their
complex
nervous
systems,
intricate
color
and
texture
changing
body
patterns.
So
they
have
these.
B
You
know
a
lot
of
marine
organisms
have
this:
they
have
these
chromophores,
where
they
can
change
their
color
or
their
hue,
depending
on
whether
they're
in
the
presence
of
predators
or
they're,
trying
to
remain
hidden
from
prey
that
they
might
capture.
Or
you
know
there
are
a
number
of
reasons
why
they
would
use
them.
But
it's
not
something
that
we
do
so
it's
an
interesting
system
and
octopi
also
have
problem
solving
abilities.
B
So
often
in
aquariums
settings
octopi
will
there
are
known
to
escape
their
tank
and
like
walk
around
the
aquarium
and
do
like
all
sorts
of
mischief
which
is
like
kind
of
mind-blowing.
If
you
think
about
it,
because
it's
like
well,
we
didn't
really.
We
haven't
really
octopus
proof
the
aquarium.
I
think
they
do
that
now,
though,
not
that
they
they're
aware
of
this
behavior
and
so
they're
very
smart,
and
they
have
a
lot
of
like
interesting
properties
as.
B
Model
organism
so
they
have
high
growth
rates
and
short
life
cycles
in
some
species.
So,
in
this
case,
like
we
said
with
c
elegans,
that
makes
them
suitable
for
a
laboratory
model
organism
or
laboratory
culture
setting.
So
you
know
you're
going
to
be
raising
them
in
a
laboratory.
You
need
to
have
like
a
tank.
You
need
to
have
different.
You
know,
facilities
for
this
sort
of
thing,
so
so
what
it
makes
octopus
really
interesting
here
in
terms
of
a
model
organism.
B
So
it's
a
small
octopus.
It's
predict
has
a
predictable
reproduction,
a
short
time
to
maturity,
small
adult
size
and
the
ability
to
lay
multiple
egg
clutches.
So
this
is.
These
are
all
positive
things
here.
We
describe
novel
methods
for
culture
of
the
species,
with
an
emphasis
on
enclosure,
designs,
feeding
regimes
and
breeding
management.
B
So
this
demonstrates
the
feasibility
of
multi-generational
culture
of
these
organisms,
the
bread
in
the
laboratory.
They
grow
from
a
three
point:
3.5
millimeter
mantle
length
at
hatching
to
an
adult
mantle
length
of
20
to
30
millimeters
in
250
to
300
days,
so
they
could
have
a
14
15
survivorship
to
over
400
days,
which
is
old
age
for
them
in
the
first
and
second
generations.
B
So
you
can
observe
you
know
aging,
you
can
observe
develop.
You
know
embryonic
development,
early
development
and
the
entire
life
cycle
in
about
400
days.
So
that's
you
know
pretty
decent
for
a
model
organism
c
elegans,
of
course,
is
much
shorter,
but
we
don't
have
the
like
a
lot
of
the
features
of
an
octopus
like
the
brain
size
and
some
of
the
other
physiology.
B
So
this
you
know
they
lay
multiple
clutches
of
eggs.
So
critically,
in
a
lot
of
you
know,
model
organisms.
You
want
to
be
able
to
reproduce
them.
You
want
to
be
able
to
produce
a
breeding
colony,
and
I
know
in
axolotl-
that's
pretty
tough,
but
here
they're
saying
that
it's
a
much
easier
system
to
work
with
in
that
respect
so-
and
I
guess
they're
also
transparent,
they're,
translucent
enough
that
they
can
work
with
them.
So
that's
always
a
plus
as
well.
A
Sorry
I
want
to
put
a
plug
in
for
axolotl.
You
just
have
to
know
how
to
work
with
them,
so
I've
discovered
that
cold
water
really
helps.
So,
if
you're,
if
they
go
from
room
temperature
to
sort
of
just
above
freezing
that
they
get
excited
so
yeah
you
put
a
male
and
a
female
in
in
some
sort
of
icy
water,
and
usually
you
get
results
and
you
have
to
make
sure
they're
they're
young
enough
to
be
foolish
like
teenage
ones,
work
better.
A
You
know
what
I
mean
yeah
anyway,
so
yeah.
They
need
to
be
one
two
three
years
old
and
anyway
place
in
ice
water
and
that
usually
works
actually,
okay.
So
it's,
but
it's
tricky
to
know
like
there
needs,
there's
a
formula
for
doing
this
and
it's
not
necessarily
apparent,
but
anyways
yeah
they're,
not
transparent,
very
opaque
anyways.
These
octopus,
the
octopus,
sound
very
interesting.
B
B
Lipid
diet
so
self
safe
containment.
There
are
life
history
attributes
most
leftover
species
are
similar
paris,
they
die
after
one
reproductive
event,
so
this
is
where
the
females
commonly
will
lay
only
one
clutch
of
eggs
in
their
lives.
Octopus
hatchlings
can
undergo
one
or
two
mechanisms
of
development,
indirect
and
direct
development,
so
small
egg
species
undergo
indirect
development
where
they
lay
large
numbers
of
small
eggs
that
develop
into
peril
rv
before
undergoing
metamorphosis
into
achieving
the
benthic
stage,
conditions
which
are
extremely
difficult
to
replicate
in
the
lab.
B
So
you
have
these
two
different
modes
of
development
in
different
species
of
octopus,
and
so
the
direct
development
seems
to
be
more
suitable
for
the
lab,
although
that
has
disadvantages
because
the
survival
rate
isn't
that
high,
so
it's
yeah,
they
mean
they
consider.
You
know
a
number
of
species
and
they're
all
like.
I
said,
they're
trade-offs
in
terms
of
you
know
what
you
want
to
look
at
versus.
You
know
what,
if
you
can
actually
keep
a
population
of
them
alive,
long
enough
to
do
the
studies
that
you
want
to
do
so.
B
B
So
this
is
kind
of
like
the
eye
area
here
in
the
front,
and
then
they
show
the
arms
here
and
the
male
and
the
female,
so
their
differences
in
the
arms
and
and
and
that
sort
of
anatomy
in
the
male
and
female
they
possess.
Sucker
females
possess
suckers
along
the
full
length
of
all
arms,
whereas
the
male
do
not
male
male
adult
possesses
a
specialized
organ
at
the
tip
of
the
third
right
arm,
which
is
a
smooth,
suckerless
hook-like
appearance
and
is
used
to
pass
spermaphores
to
the
female
during
mating.
B
So
their
differences
in
you
know
male
between
male
and
female
physiology
water
chemistry
system
design.
So
they
have
a
lot
of
stuff
on
like
how
to
set
up
a
model
organism
platform,
and
I
mean
just
from
the
standpoint
of
looking
at
see
seeing
how
this
is
how
this
works.
How
do
you
set
up
a
model
organism
set
it
you
know
set
up.
B
This
is
I
think
this
is
informative,
for
you
know
if
you're
interested
in
you
know
why
people
might
use
a
cert
one
model
organism
over
another
that
might
be
important
to
kind
of
look
over
and
see
what
they
do
in
octopus.
So
then
the
other
paper
is
this
paper
on
cambrian
comb
jellies.
So
this
is
quite
a
different
I'm
on
octopus.
C
B
So
the
the
summary
is
is
the
following:
I'll
read
it
to
you.
Tina
fours
are
a
group
of
predatory
macroinvertebrates.
His
controversial
phylogenetic
position
has
prompted
several
competing
hypotheses
regarding
the
evolution
of
animal
organ
systems,
although
tinopours
date
back
to
at
least
the
cambrian,
they
have
a
poor
fossil
record
due
to
their
gelatinous
bodies.
So
that
means
that
they
don't
really
preserve
well,
because
they
don't
have
hard
parts
that
preserve
in
the
fossil
record.
B
But
you
still
get
like
things
like
imprints
and
other
things
that
they
can
detect,
like
you
know,
even
the
burgess
shale,
which
is
a
ancient
assemblage
that
you
know,
they've
have
things
that
are
imprinted
into
this.
Basically,
this
rock
they
got
imprinted
at
some
point,
and
these
these
organisms
are
all
like,
you
know,
had
soft
tissues,
but
they
are
all
preserved
pretty
well,
so
we
can
actually
say
something.
So
there
are
ways
that
soft
tissue
organisms
can
get
preserved
in
the
fossil
record,
but
it's
pretty
rare.
B
Actually
here
we
describe
to
tina
four
species
from
the
cambrian
of
utah,
which
illuminate
the
early
evolution
of
nervous
and
sensory
features
in
the
phylum
and
phylum
is
another
word
for
clade.
As
I
mentioned,
here's
another
elegance,
the
lasso
stephalo's
elegance
has
16
comb
rows,
an
oral
skirt
and
an
apical
organ
with
polar
fields.
B
This
other
keener,
hebdos
campbell
uniformis,
I
think,
has
24
comrose
an
oral
skirt,
apical
organ
enclosed
by
a
capsule
and
a
neurological
and
neurological
tissues
preserved
as
carbonaceous
films.
These
are
concentrated
around
the
apical
organ
insulated,
furrows,
which
connect
to
a
nerve
ring
by
longitudinal
axons,
and
then
this
other
species.
B
So
it's
it's.
You
know
basically
they're
able
to
show
the
diversity
of
life
somehow
these
early
nervous
systems
sort
of
evolved
and
how
they
differ
from
what's
alive
today,
so
so
most
of
these
competing
phylogenies.
Then
they
talk
about
in
tenoforms.
There's
a
lot
of
ambiguity
as
to
how
things
are
related
necessitate
a
complex
pattern
of
convergent,
evolution
of
animal
tissues
and
organ
systems.
B
For
example,
the
placement
of
tenophobes
is
the
sister
group
of
all
with
her
metazoans,
which
are
animals
raises
the
possibility
that
the
muscle
and
nervous
systems
of
tenophobes
and
other
complex
animals
have
evolved
independently.
So
this
is
interesting.
This
is
what
they're
saying
is
that
the
nervous
systems
and
muscle
found
in
this
order
of
metazoans,
where
this
group
of
medicines
is
different
than
other
animals
which
include
us
or
or
you
know,
other
mammals
or
fishes
or
any
other.
B
You
know
even
like
insects,
and
so
that's
interesting,
living
tinoforce
possess
a
suite
of
unique
morphological
features
that
makes
comparisons
with
extant
members
of
other
animal
phyla
difficult.
These
are
called
a
biradial
body
symmetry
which
is
like
two
halves
it.
Actually
it's
a
radial,
it's
two
radii,
you
know
they're
or
their
bodies
are
organized
along
radial
organization,
but
it's
like
two
halves
of
that.
B
Eight
locomotive,
a
sensory
apical
organ
and
a
pair
of
tentacles
with
adhesive
coal
blasts.
So
this
is
the
basic
body
plan
and
then
they
have
a
number
of
different
other.
You
know
things
in
common,
so
they
share
features
with
pilotarians,
including
a
functional
through
gut
with
two
anal
pores
and
a
mesoderm-like
muscle
structure,
so
they
have
different.
They
have
they're
related
to
different
types
of
marine
invertebrate.
They
have
different
traits
that
you
find
in
marine,
invertebrates,
and
so
the
the
point
here
too,
is
that
these
stenophores
are
sparsely
represented
in
the
fossil
record.
B
Although
alternative
interpretations
of
the
devonian
have
been
proposed,
cambrian
tina
forest
possessed
character,
combinations
distinguishing
them
from
any
living
species.
So
we
saw
in
that
in
that
clade
at
the
beginning,
where
you
had
those
dashes
across
the
tree.
Those
slashes
were
different
character,
traits
that
either
appear
or
disappear
in
in
the
phylogeny,
and
so
what
they're
saying
here
is
that
there's
a
unique
mix
of
these
kind
of
traits
in
these
organisms
that
they've
they've
seen,
and
so
this
is
interesting
because
you
know
far
back
in
life.
B
So
I
think
I'll
stop
here
for
today.
I
think
that's
enough.
I
was
kind
of
hoping
for
some
nice
pictures,
but
they
didn't
have
anything
that
I
saw
in
there.
But
in
any
case
I
hope
that
maybe
next
week
we
can
hear,
I
don't
know
if
what
susan's
schedule
is
on
that
talk
that
she
mentioned,
but
in
any
case
that
we'll
have
we'll
probably
continue
without
a
hacktoberfest
next
week
and
we'll
talk
more
about
some
other
things.