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3, 2, 1, 0.
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Welcome aboard the System
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76 transmission Log.
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Our broadcast is about to begin.
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This is the latest on System 76
computers, manufacturing, and pop OS.
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Now for your in-orbit crew.
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Hello, hello, everyone.
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Welcome back to the System
76 transmission Log.
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I'm Alex, our resident, Normy.
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And I'm Emma, wondering why we're doing
the intro when it was supposed
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to be the banter section, but whatever.
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I mean, intro always comes first, right?
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No.
I feel like we always do banter first.
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Well, in the end, the intro comes first.
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All right.
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Just as a as a movie magic
secret, we're actually doing this last.
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We're not.
We're doing this first.
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Oh my God.
Just trying to confuse me.
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I did want to point out
I'm wearing pink today.
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I came prepared for our hosting here.
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What's the rest of that shirt?
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The rest of the shirt is…
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It's Slither's Snakeman.
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It's a snake disguised as a human.
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Is that a cartoon?
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No, it's just a shirt I found
on the internet from a cartoonist.
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But I was curious about if
we could get into some Emma lore here.
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When was the first moment you were like,
Oh, pink, that's my jam now?
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Well, when I really embraced it,
I was probably 15 or 16.
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I mean, I loved it when
I was a kid, of course.
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I was really into Barbie, and I just
think pink was the best world to live in.
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But yeah, when I was 18 with my first
apartment, everything was pink.
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There was a pink pillow floor area.
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There was a bright red couch
and a pink table, and everything
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on the walls was pink.
The dishes were pink.
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Ever since then, I was just
collecting pink stuff.
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And slowly, pink clothes
have been making a comeback.
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It was really hard to find a pink shirt
or a pink dress for a really long time,
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but now I can pretty much
find that stuff anywhere.
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Do you have a favorite shade of pink?
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I like a berry type of pink.
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It's not quite hot pink.
It's not dark.
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It's in the middle.
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So the inside of a strawberry.
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No.
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That's pretty light pink, I think.
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The color of my shoes is a very good pink.
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Okay, that's like a hot pink, I would say.
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Well, I like it a little more
subtle, but can't choose them all.
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True.
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All right.
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I have another topic to
discuss really quick.
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What's that?
The balloon game.
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Oh, yeah.
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I wasn't there for it,
but I hear it's pretty popping.
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I just realized that you
weren't there for it.
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How are you going to talk about it?
Well, I mean, I know.
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First of all, what is the balloon game?
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Oh, I'm glad you asked.
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So everyone at the company
in the factory at the beginning
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of the day gets a balloon with helium.
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And at the end of the day, whoever is left
with a balloon gets a prize.
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But throughout the day, people
can hide their balloon, pop people's
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balloons or steal people's balloons.
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We had some really competitive people,
had some tricky people hiding them.
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I thought Young Chin did a great job
hiding his behind the curtains
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in the window because you could totally
see it outside, but you couldn't see it
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on the inside, and nobody found it.
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He was one of the people left standing.
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Then Josh, I went and had a conversation
with him while he was in the shipping
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container, asking him if he had seen any
stray balloons It turns out his balloon
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was in the top corner of the shipping
container while I was talking to him.
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And I ended the contest and I was like,
Congratulations, everybody.
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Then he comes in at the last second.
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He's like, I still have mine.
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Then I asked him to expose
his spot, and it was right there.
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And I couldn't believe it.
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So I am not very good at the game, but I
thought those two spots were very clever.
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Then we had Grace,
who got all the balloons.
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She was taking people's balloons
and popping them left and right.
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She came in my office at the end
of the day and pulled out her balloon
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from under her sleeve.
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She was very proud.
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She got the most.
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Youngchin and Josh were probably
the most clever about it and sneaky,
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but all around, it was very fun.
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How did she fit an unpopped
balloon up her sleeve?
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Very carefully.
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She was pretty baggy clothes,
but her balloon had been losing air
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throughout the day, too.
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So it was a little smaller
than the other balloons.
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But yeah, I think she
called it George or something.
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She named it.
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Well, we're glad George survived.
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What would you say was the best technique
that was used for popping these balloons?
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I didn't witness any popping.
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I only heard it.
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But every time you heard a pop, you heard
a giggle, too, which was really funny.
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No matter where you were in the factory,
you could hear somebody
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laughing about it.
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So the balloons weren't
just filled with helium.
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They were filled with laughing gas.
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They were filled with laughing gas, yes.
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Or possessed by goats.
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Ghosts, not goats.
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I'm pretty sure I- Possessed by goats.
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I said it again.
What?
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We're leaving that in.
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Yeah, that was really fun.
I think we got to do it again, though.
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But I have some different ideas.
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The person with the most at the end
should probably win an extra prize.
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They have to throw a dart
at a wall of balloons that
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each contain a prize in them.
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I like that.
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Yes.
I like that a lot.
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Okay, well, now we know which games
to implement in the future, but maybe
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we should get into the news.
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Yeah, let's get into the
System 76 lore from the past month.
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Okay.
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Well, it's the opposite of last month
because we had a ton of news.
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Went from lots of releases to gearing up
for some releases down the line.
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Yeah.
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So this month, we have the tiniest bit
of news, and it's about the smallest bit
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of hardware, the little mirror cat.
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It's the little baby with big biceps.
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It's our compact mini little desktop
in the NUC form factor fits right
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on your desk or mounted on
the back of your monitor.
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And it is rocking
some Intel Core Ultra CPUs with up to 4.
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8 gigahertz processors.
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And and a whopping up to 96 gigabytes
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of RAM and up to 16 terabytes storage.
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So much in such a little
package, still rocking Intel.
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But also we added
I can't read your lips there.
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I thought you would just know.
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I thought you were going
to be like, open firmware.
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Oh, yeah.
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Look, I'm one of the first stages
in the product release.
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Once the product is ready to go,
I absorb all the information about it.
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I output it into something for
the website, and then my mind is cleared
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of it because I'm on to the next thing.
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What you're saying is you forgot that
Meer cat has open from rent.
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It's not I forgot so much as I've
stopped thinking about the Meer cat
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for the past three weeks.
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Okay.
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It's been a while for being able
to spout off the highlights, which
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is what we're here for on this podcast.
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So yes, Meer cat now has openFirmware.
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It's our core boot-based
System76 openFirmware.
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Part of it is speeding up boot
times, minimizes the Firmware,
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so it's more secure, less
vulnerability, all that good stuff.
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Yep.
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Do you think if you tied a balloon
around a meer cat, it would float?
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That's a great question.
We should try that.
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Well, I'm pretty sure
one balloon won't do the trick,
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but we should see how many balloons
it will take to float a mirror cat?
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My guess is three.
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Helium is pretty strong in a balloon.
Okay.
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They floated a house with it.
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Yeah, but he used like
a thousand balloons.
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He had a lot of balloons.
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It was a big house, though.
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You do like a townhouse.
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It's half the balloons.
Yeah.
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Meer cat, since it's just a little mirror
cat, probably three or four, yeah.
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With no mirror potential.
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Well, I'm going to test it and we'll
come back to you next month
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on the podcast and tell you how it went.
Okay.
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Yeah, we got to test it for science.
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Yeah, I need to know.
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All right.
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Well, that's our hardware news.
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We have some community news, right?
We do.
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We are going to be at a couple
of conferences in March
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and also April and May.
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To start off in March, we'll be at
SCALE in Pasadena, California,
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which is March sixth through 10th.
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We'll be hanging out at our booth
along with our friends at Ampere,
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who will be next to us.
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We'll have two Thelio ASTRO demos
at the Ampere booth showing us
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all the cores, all the power,
all the Blender renders you can think of.
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And a driving simulator as well, I think.
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Yeah, Tier 4's driving simulation
that they had at CES will be doing
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the same simulation at our booth
at scale at the Ampere booth.
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Then also on the other side of the booth,
we'll have a cosmic section where you can
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design your own universe and then
see your cosmic theme in action.
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Then also we'll be giving away
two launch keyboards and a lot of swag.
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You can get your hands on some hardware
and talk to some of your fellow nerds.
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Yeah, Garret, who's on our QA team, he
worked on a really cool universe designer
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using the cosmic theming system, so
we're really excited to share that off.
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Then also in March, we're going
to be at the PNY my booth at GTC
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in San Jose, March 17 through the 21st.
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Pny will be showcasing the Thaleomega
and just showing off what
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a high-end workstation optimized for
multi-GPU compute and heavy graphics
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and peripherals would be performing.
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Yeah, subject to change potentially.
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But what we've been told is
there will be a demo showing off
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some new stuff, and it'll be using
a production workflow, a combination
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of Blender, Unreal Engine, and OBS.
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So all tools available on Linux,
but just showing off the power
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of our hardware is combined.
Cool.
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That one's going to be a cool demo.
Yeah.
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And then let's see, April,
we have Linux Fest Northwest,
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which is my favorite one of the year.
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That one's in Bellingham,
Washington, the end of April.
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And we'll be doing a talk on how far
Cosmic has come and also have a booth.
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And then we'll be hosting
the Saturday barbecue.
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I could use some barbecue right about now.
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It's actually not really barbecue.
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It's just like whatever food
is a good idea that day.
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But maybe be just hot dogs like last year.
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If you bring some barbecue
sauce, it's barbecue.
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00:11:34,685 --> 00:11:35,980
Okay, that's fair.
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00:11:36,005 --> 00:11:41,760
Join us with your barbecue sauce,
B-Y-O-B-S.
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00:11:42,520 --> 00:11:45,640
Bring your own BS.
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00:11:45,680 --> 00:11:49,660
To the Linux Fest Northwest Barbecue.
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00:11:49,685 --> 00:11:51,460
Hope to see you guys there.
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All right, what's happening
in the cosmic universe?
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Cosmic universe.
Let's see.
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00:11:59,965 --> 00:12:05,760
There is potential for delaying
a week to get some more features
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00:12:05,785 --> 00:12:07,940
into the Alpha 6 release.
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So I could say some stuff
that's potentially wrong.
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An easier way to go about this
is just head to the blog
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00:12:14,725 --> 00:12:18,895
for actual up to date news blog.
System76.
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Com.
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00:12:19,185 --> 00:12:24,780
But what I know and I've heard inklings of
is we now have desktop icons.
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You right click the desktop,
you go to desktop view settings,
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and that'll bring up some toggles
for showing desktop icons, showing
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00:12:32,665 --> 00:12:37,900
the trash icons, adjusting the size
and spacing, things of that nature.
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There's also some more polish updates
to the way workspaces functions.
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Multimonitor setups, you can now
drag and drop windows into the other
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workspace overview on another display.
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00:12:49,665 --> 00:12:53,980
You can also drag and drop
minimized windows.
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When you drag a window out of a stack
to another workspace, it'll now only move
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00:12:58,585 --> 00:13:03,700
that window like you'd expect I think
previously it would move the whole stack.
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00:13:03,725 --> 00:13:07,660
There's more updates along
those lines just to make workspaces
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feel polished and intuitive.
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There's file menu features
built out for the cosmic media player
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00:13:14,485 --> 00:13:18,560
and potential for the initial setup
239
00:13:18,585 --> 00:13:21,860
applet, OS update settings, and
240
00:13:21,885 --> 00:13:25,460
a desktop zoom feature for accessibility.
241
00:13:25,485 --> 00:13:28,460
Those things may or may
not have been released.
242
00:13:28,485 --> 00:13:33,580
Check the blog for accurate information,
but if not released in Alpha 6,
243
00:13:33,605 --> 00:13:35,700
probably by Alpha 7.
244
00:13:35,725 --> 00:13:38,860
So all that said,
exciting things for Cosmic.
245
00:13:38,885 --> 00:13:40,980
I very much enjoy using it.
246
00:13:41,005 --> 00:13:45,180
I think I'm still on
2204 LTS, I've learned.
247
00:13:45,205 --> 00:13:49,420
But even being there, I mean,
it's been working great for my workflow.
248
00:13:49,445 --> 00:13:53,340
I don't really get into the
nitty-gritty and more surface level,
249
00:13:53,365 --> 00:13:54,660
but it's been going well.
250
00:13:54,685 --> 00:13:59,000
I have seen that the compositor on 2204
251
00:13:59,025 --> 00:14:01,920
LTS will no longer be getting updates, so
252
00:14:01,945 --> 00:14:04,180
make sure you're upgraded to 2404.
253
00:14:04,205 --> 00:14:07,660
I think the ISOs are 2404,
so you should be all right.
254
00:14:07,685 --> 00:14:10,375
Thank you for that piece of advice.
Yeah.
255
00:14:10,400 --> 00:14:11,180
All right.
256
00:14:11,205 --> 00:14:15,180
Well, we have a very
interesting interview today.
257
00:14:15,205 --> 00:14:16,720
Yeah, we have R.
J.
258
00:14:16,745 --> 00:14:21,620
Noling here to talk about
his work in genomic research.
259
00:14:21,645 --> 00:14:27,580
It's been really bugging me all week
to get this interview out there.
260
00:14:27,605 --> 00:14:30,100
Here we go.
261
00:14:30,125 --> 00:14:34,815
My name is R.
J.
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00:14:34,840 --> 00:14:35,120
Noling.
263
00:14:35,145 --> 00:14:37,460
I'm an Associate Professor
of Computer Science
264
00:14:37,485 --> 00:14:39,340
at the Milwaukee School of Engineering.
265
00:14:39,365 --> 00:14:42,820
I'm also the program director
for our master's in machine learning.
266
00:14:42,845 --> 00:14:46,540
My research is focused
on what I call genomic data science.
267
00:14:46,565 --> 00:14:50,140
I work with biologists
to process the raw sequencing data
268
00:14:50,165 --> 00:14:54,380
into a usable form and then
analyze it using data science techniques.
269
00:14:54,405 --> 00:14:58,080
In many cases, the data
we're collecting during an experiment,
270
00:14:58,105 --> 00:15:01,560
there's a particular research question
in mind, and often I'm helping them
271
00:15:01,585 --> 00:15:05,500
determine what evidence the data might
provide to help answer that question.
272
00:15:05,525 --> 00:15:08,700
Earlier, you mentioned you
looked at my website, you saw insects.
273
00:15:08,725 --> 00:15:11,060
Yeah, I primarily work
with insect genomes.
274
00:15:11,085 --> 00:15:14,600
A lot of my collaborators
work on mosquitoes such as Anopheles
275
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gambiae, that vector malaria,
as well as Edo gypti, which is
276
00:15:18,265 --> 00:15:19,900
a mosquito vector is Dengue fever.
277
00:15:19,925 --> 00:15:24,620
So these are medically important
organisms to public health.
278
00:15:24,645 --> 00:15:28,655
So it's mostly for public health
that you're doing all of this,
279
00:15:28,680 --> 00:15:29,780
or are there other reasons?
280
00:15:29,805 --> 00:15:33,420
So I'm doing it mostly
from a pure biology aspect.
281
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I want to better understand
the biological systems.
282
00:15:36,205 --> 00:15:40,200
We hope that by studying these insects,
we'll also learn things that
283
00:15:40,225 --> 00:15:43,820
are transferable to other organisms.
284
00:15:43,845 --> 00:15:46,680
But it so happens that
the biologist I work with
285
00:15:46,705 --> 00:15:48,620
may have more specific questions.
286
00:15:48,645 --> 00:15:52,880
For example, one of my collaborators
is trying to better understand why
287
00:15:52,905 --> 00:15:57,200
some of the Anopheles mosquitoes,
their immune system can fight off
288
00:15:57,225 --> 00:15:59,535
the malaria parasite and some can't.
289
00:15:59,560 --> 00:16:01,960
Even Even if they're from the same
family, literally the same mom
290
00:16:01,985 --> 00:16:04,540
and dad, there's differences.
291
00:16:04,565 --> 00:16:07,420
She's trying to understand
a genetic basis of that.
292
00:16:07,445 --> 00:16:11,000
On my end, most of the work
I'm doing isn't necessarily
293
00:16:11,025 --> 00:16:12,360
specific to the mosquito.
294
00:16:12,385 --> 00:16:16,360
The data I have is specific
to the mosquito, but the techniques
295
00:16:16,385 --> 00:16:18,100
I'm using are general.
296
00:16:18,125 --> 00:16:25,175
In the case of malaria, that could lead to
breeding mosquitoes to eventually...
297
00:16:25,200 --> 00:16:25,920
Yeah.
298
00:16:25,945 --> 00:16:30,655
If there's a gene associated to eliminate
the vulnerability to malaria.
299
00:16:30,680 --> 00:16:31,720
Yeah, you're actually right.
300
00:16:31,745 --> 00:16:37,000
There's been some experiments, what
they call gene drive, to try to basically
301
00:16:37,025 --> 00:16:40,500
produce sterile males and such.
302
00:16:40,525 --> 00:16:44,700
Other groups have been developing
techniques to like, okay, can we release
303
00:16:44,725 --> 00:16:49,680
some genetically modified mosquitoes
into a population, and hopefully, they
304
00:16:49,705 --> 00:16:52,760
will basically take over that population.
305
00:16:52,785 --> 00:16:57,440
And now, most the population will no
longer do a certain thing or something.
306
00:16:57,465 --> 00:17:02,440
So in this particular case, yeah, if
we We can find what makes it so that some
307
00:17:02,465 --> 00:17:05,980
of the mosquitoes, their immune system,
can't destroy the malaria parasite.
308
00:17:06,005 --> 00:17:11,280
We can then use gene drive techniques
to basically ensure that the mosquitoes
309
00:17:11,305 --> 00:17:14,810
can fight off the malaria parasite
so that they don't transmit it to humans.
310
00:17:14,840 --> 00:17:17,010
That's very interesting.
Yeah.
311
00:17:17,040 --> 00:17:18,810
How did you get into this?
312
00:17:18,840 --> 00:17:23,320
Actually, I was exposed to bioinformatics
through a summer internship
313
00:17:23,345 --> 00:17:24,980
arranged by a family member.
314
00:17:25,005 --> 00:17:28,760
When I was in high school, I interned
at the University of Connecticut Health
315
00:17:28,785 --> 00:17:33,090
Center, making a simple website
with some data from a paper,
316
00:17:33,120 --> 00:17:34,700
and I really fell in love with it.
317
00:17:34,725 --> 00:17:38,000
I loved that I could apply
my computing skills to understanding
318
00:17:38,025 --> 00:17:39,810
something in the real world.
319
00:17:39,840 --> 00:17:41,600
I like the nature of the work itself.
320
00:17:41,625 --> 00:17:44,010
Like data processing is interesting to me.
321
00:17:44,040 --> 00:17:48,050
I made it I focused in college,
and then I went and got a PhD.
322
00:17:48,080 --> 00:17:51,500
During college, while many
of my colleagues were doing
323
00:17:51,525 --> 00:17:55,380
internships with companies,
I was doing research with professors.
324
00:17:55,405 --> 00:17:58,160
I did spend a couple of years
in industry after my PhD,
325
00:17:58,185 --> 00:18:00,140
so I was at Red Hat for two years.
326
00:18:00,165 --> 00:18:03,570
And then an online advertising company
called Adroll for two years.
327
00:18:03,600 --> 00:18:05,920
I started my faculty position
at the Milwaukee School
328
00:18:05,945 --> 00:18:09,180
of Engineering in fall 2018.
329
00:18:09,205 --> 00:18:12,560
When along that journey
did you discover Linux, was it
330
00:18:12,585 --> 00:18:14,855
at Red Hat or was it before then?
331
00:18:14,880 --> 00:18:17,320
It involved Red Hat, but
it was not while I was at Red Hat.
332
00:18:17,345 --> 00:18:18,740
It's actually middle school.
333
00:18:18,765 --> 00:18:21,495
Someone gave me a box
copy of Red Hat Linux 6.
334
00:18:21,520 --> 00:18:22,010
2.
335
00:18:22,040 --> 00:18:25,660
I want to clarify, I did not
omit the word enterprise.
336
00:18:25,685 --> 00:18:28,530
This was before it became
Red Hat enterprise Linux.
337
00:18:28,560 --> 00:18:30,050
This was old Red Hat.
338
00:18:30,080 --> 00:18:33,680
And so if I remember correctly,
the version I had ran
339
00:18:33,705 --> 00:18:35,980
Nome desktop version one.
Oh, wow.
340
00:18:36,005 --> 00:18:40,095
And so then I started using Slackware
in high school, and I later
341
00:18:40,120 --> 00:18:40,960
switched to Debian.
342
00:18:40,985 --> 00:18:45,720
And so now all my research machines,
I use Debian for all my workstations
343
00:18:45,745 --> 00:18:47,050
and servers, basically.
344
00:18:47,080 --> 00:18:51,050
And then my laptop and desktop,
I use Apple products.
345
00:18:51,080 --> 00:18:55,420
With regards to your workflow, what
software tools are you using, mainly?
346
00:18:55,445 --> 00:18:59,380
So when we get the raw data,
we clean it with something
347
00:18:59,405 --> 00:19:05,700
like trim PEEK, and then we align it
to genomes using something like BWA.
348
00:19:05,725 --> 00:19:12,240
We'll use a software package
called GATK to try to identify where
349
00:19:12,265 --> 00:19:15,090
individual samples differ from a genome.
350
00:19:15,120 --> 00:19:19,940
We might also use, depending on if it's
a different workflow, a tool called MAX
351
00:19:19,965 --> 00:19:21,895
for what's called peak calling.
352
00:19:21,920 --> 00:19:25,040
There's certain experiments that basically
highlight certain parts of the genome.
353
00:19:25,065 --> 00:19:28,740
It's like this part of the genome
has activity of interest.
354
00:19:28,765 --> 00:19:34,520
Then once we get all have produced
that output, then we can switch to
355
00:19:34,545 --> 00:19:38,740
more traditional data science tools
like using pandas in Jupiter notebooks
356
00:19:38,765 --> 00:19:43,140
and scikit learn and things like that.
357
00:19:43,165 --> 00:19:44,180
I see.
358
00:19:44,205 --> 00:19:46,900
For your desktop, I know
you have a Thelioastra.
359
00:19:46,925 --> 00:19:52,290
How has working with Thelioastra
affected your workflow and your project?
360
00:19:52,320 --> 00:19:54,980
It's massively increased my throughput.
361
00:19:55,005 --> 00:19:57,960
The data sets I work
with have a lot of samples.
362
00:19:57,985 --> 00:20:03,090
The biologist will seek the DNA
from a thousand mosquitoes.
363
00:20:03,120 --> 00:20:08,440
Well, it's hard to get much peralysm
for an individual sample, but the samples
364
00:20:08,465 --> 00:20:10,380
can be processed independently.
365
00:20:10,405 --> 00:20:15,800
The ability to have 120 cores instead
of 24 cores means I can process
366
00:20:15,825 --> 00:20:17,660
a lot more samples at once.
367
00:20:17,685 --> 00:20:20,330
It also tends to be very memory-intensive.
368
00:20:20,360 --> 00:20:26,640
So the fact that I can have 512 gig
of ECC RAM is also an important component
369
00:20:26,665 --> 00:20:28,250
of scaling up that pipeline.
370
00:20:28,280 --> 00:20:31,530
And these pipelines
often take weeks to run.
371
00:20:31,560 --> 00:20:36,140
So it can cut it down from a month
to a week or a few days.
372
00:20:36,165 --> 00:20:37,895
Wow.
It's a big improvement.
373
00:20:37,920 --> 00:20:38,220
Yeah.
374
00:20:38,245 --> 00:20:43,330
What led to your decision to go with
System 76 and the Thaleo ASTRA?
375
00:20:43,360 --> 00:20:48,480
Honestly, it was priced So I've
been wanting an Ampere ultra system
376
00:20:48,505 --> 00:20:53,600
for a while because I had a workstation
with an AMD Threadripper processor
377
00:20:53,625 --> 00:20:57,140
and a 24 cores and 120 gigs
around that I got five or six years ago.
378
00:20:57,165 --> 00:20:59,180
And that's been my main research machine.
379
00:20:59,205 --> 00:21:00,770
It was time for an upgrade.
380
00:21:00,800 --> 00:21:04,520
But whenever I would look at other systems
with AMD Threadripper processors,
381
00:21:04,545 --> 00:21:10,140
I just couldn't get more cores
or much more memory within my budget.
382
00:21:10,165 --> 00:21:14,800
The Ampere ultra platform offers a really
nice price point for getting a lot of
383
00:21:14,825 --> 00:21:17,530
cores and a lot of RAM at the same time.
384
00:21:17,560 --> 00:21:19,500
And I need both to do my work.
385
00:21:19,525 --> 00:21:25,440
And System76 was the only company
I could find that really was supporting
386
00:21:25,465 --> 00:21:27,860
a workstation option for Impair.
387
00:21:27,885 --> 00:21:32,695
And I can say also I'm glad
I went with System 76 because support
388
00:21:32,720 --> 00:21:35,460
has been super helpful.
I haven't run into any major problems.
389
00:21:35,485 --> 00:21:37,140
It's just my ignorance.
390
00:21:37,165 --> 00:21:40,700
I didn't realize that because
it's running a server motherboard,
391
00:21:40,725 --> 00:21:45,080
that it takes a really long time
to post before the boot screen shows.
392
00:21:45,105 --> 00:21:49,000
I thought something was wrong with it
when I got it, when it's like, why isn't
393
00:21:49,025 --> 00:21:51,500
this showing anything after five minutes?
394
00:21:51,525 --> 00:21:53,740
And it turns out I just
needed to wait longer.
395
00:21:53,765 --> 00:21:58,040
But the support was super
helpful, and I really appreciate
396
00:21:58,065 --> 00:21:59,660
my interactions with the company.
397
00:21:59,685 --> 00:22:03,700
I I think that has potentially turned me
into a repeat buyer in the future.
398
00:22:03,725 --> 00:22:04,780
That's awesome.
399
00:22:04,805 --> 00:22:08,090
Yeah, support has been
a long time pride point for us, for sure.
400
00:22:08,120 --> 00:22:12,535
Do you have advice for people who
want to get into genomic data science?
401
00:22:12,560 --> 00:22:13,330
Yes.
402
00:22:13,360 --> 00:22:17,740
So unfortunately, most of the people
I know doing this are at universities.
403
00:22:17,765 --> 00:22:21,800
There are some companies in industry like
Illumina, which provides some
404
00:22:21,825 --> 00:22:25,800
of the sequencing technology,
also hires a number of bioinformaticians
405
00:22:25,825 --> 00:22:29,960
and data scientists and software
engineers, but still primarily most of the
406
00:22:29,985 --> 00:22:32,000
people in this field are in universities.
407
00:22:32,025 --> 00:22:33,460
So you pretty much need a PhD.
408
00:22:33,485 --> 00:22:37,400
That said, I have worked with people
in PhDs in a wide range of fields,
409
00:22:37,425 --> 00:22:42,220
from computer science to biology
to statistics, physics, chemistry,
410
00:22:42,245 --> 00:22:46,660
applied math, whatever, that
we all work on these projects together.
411
00:22:46,685 --> 00:22:49,760
And so I think one thing
I had to figure out is that
412
00:22:49,785 --> 00:22:52,135
you have to pick a home field.
413
00:22:52,160 --> 00:22:54,880
So in my particular case, I'm very
comfortable teaching computer science
414
00:22:54,905 --> 00:22:56,290
and data science courses.
415
00:22:56,320 --> 00:23:00,320
So getting a PhD in computer science was
the right fit for me, even if my research
416
00:23:00,345 --> 00:23:02,330
itself is very interdisciplinary.
417
00:23:02,360 --> 00:23:06,200
And so the trick is to find
a faculty member who's working
418
00:23:06,225 --> 00:23:10,680
in bioinformatics who can advise students
in a PhD program that you would
419
00:23:10,705 --> 00:23:12,740
be successful in, basically.
420
00:23:12,765 --> 00:23:16,160
And for college students,
you need to start talking to professors
421
00:23:16,185 --> 00:23:21,180
and seeing if you can get opportunities
to do research with them in an undergrad.
422
00:23:21,205 --> 00:23:25,215
That'll tell you if you like the work,
and that's how you build up your resume
423
00:23:25,240 --> 00:23:27,680
to increase your chances
of getting accepted into a PhD program.
424
00:23:27,705 --> 00:23:30,050
That's awesome advice.
Thank you.
425
00:23:30,080 --> 00:23:30,810
Yeah.
426
00:23:30,840 --> 00:23:34,980
Looking forward to the future
with machine learning and genomic data
427
00:23:35,005 --> 00:23:39,440
science, how can you combine those
in the future to make innovations?
428
00:23:39,480 --> 00:23:42,570
Yeah, some of the work that
I find most interesting recently.
429
00:23:42,600 --> 00:23:45,420
One thing to know is our genome
doesn't just have genes.
430
00:23:45,445 --> 00:23:49,460
It has other non-coding elements
that play an important role.
431
00:23:49,485 --> 00:23:51,860
So one example of these
are called enhanceers.
432
00:23:51,885 --> 00:23:56,090
Enhancers help determine
how many copies of the gene are produced.
433
00:23:56,120 --> 00:23:59,880
And we're actually just
learning that a lot of diseases
434
00:23:59,905 --> 00:24:04,330
are caused not by changes in the gene
themselves, but in the enhanceers.
435
00:24:04,360 --> 00:24:07,420
So your body just may not
produce as many copies of the gene.
436
00:24:07,445 --> 00:24:10,240
So one thing, though,
is that the enhanceers are hard
437
00:24:10,265 --> 00:24:11,575
to find computationally.
438
00:24:11,600 --> 00:24:14,600
Generally, people use experimental
techniques, but there's been some
439
00:24:14,625 --> 00:24:18,360
really interesting work lately using
deep learning to identify enhanceers
440
00:24:18,385 --> 00:24:20,090
in an accurate mannerism.
441
00:24:20,120 --> 00:24:23,860
So because it's finally pretty
accurate, we could actually go through
442
00:24:23,885 --> 00:24:27,320
all the publicly available genomes
of the NCBI database provided by the NIH
443
00:24:27,345 --> 00:24:30,940
and find the enhanceers for all of them.
444
00:24:30,965 --> 00:24:34,380
And then that would allow us
to do cross-species comparisons.
445
00:24:34,405 --> 00:24:38,880
Another thing is that enhanceers
are generally not located near
446
00:24:38,905 --> 00:24:40,500
the genes they interact with.
447
00:24:40,525 --> 00:24:41,940
They're pretty far apart.
448
00:24:41,965 --> 00:24:46,020
So there's been some work
to try to computationally determine
449
00:24:46,045 --> 00:24:50,620
which enhanceers act on which genes,
which will help us better understand
450
00:24:50,645 --> 00:24:54,570
what genes they impact and thus
what potential disease is.
451
00:24:54,600 --> 00:24:58,960
The other thing is trying
to understand how genetic variation
452
00:24:58,985 --> 00:25:00,810
within the enhanceers has an impact.
453
00:25:00,840 --> 00:25:03,700
Sometimes the DNA changes
and it does nothing.
454
00:25:03,725 --> 00:25:07,440
Sometimes it increases the number
of copies of a DNA, sometimes it
455
00:25:07,465 --> 00:25:09,810
decreases it, but it's hard to predict.
456
00:25:09,840 --> 00:25:14,200
So all of these things,
if we can build accurate machine learning
457
00:25:14,225 --> 00:25:18,440
models to do this,
we can vastly speed up research and reduce
458
00:25:18,465 --> 00:25:22,600
costs and study organisms that we can't
study right now because it may be
459
00:25:22,625 --> 00:25:26,780
infeasible to do the actual experimental
lab work for whatever reason.
460
00:25:26,805 --> 00:25:30,700
Is that what you're most excited
about for the future your field?
461
00:25:30,725 --> 00:25:34,860
I get really excited when I can understand
the mechanisms of the biology.
462
00:25:34,885 --> 00:25:39,570
In some ways, I'm a biologist
who uses a computer as my instrument.
463
00:25:39,600 --> 00:25:44,320
I'm excited for advancements
that will allow us to better
464
00:25:44,345 --> 00:25:46,180
understand how the biology works.
465
00:25:46,205 --> 00:25:47,975
That's my main motivation.
466
00:25:48,000 --> 00:25:50,200
How can people get in touch
with you if they want to
467
00:25:50,225 --> 00:25:52,760
learn more or connect with you?
468
00:25:52,785 --> 00:25:57,480
They can check out my website, rnowling.
Github.
469
00:25:57,505 --> 00:26:03,330
Io, or they can send me
an email email at nowlind@msoe.
470
00:26:03,360 --> 00:26:06,090
Edu.
Awesome.
471
00:26:06,120 --> 00:26:09,460
Is there anything else you'd
like to add or share with the audience?
472
00:26:09,485 --> 00:26:13,740
Just that I've really enjoyed my
experience with the Thelio Aster so far.
473
00:26:13,765 --> 00:26:17,920
It's been a great machine
and worth every dollar, and it really
474
00:26:17,945 --> 00:26:21,215
does offer a lot for the price.
Highly recommend it.
475
00:26:21,240 --> 00:26:23,090
We're so happy to hear that.
Thank you so much.
476
00:26:23,120 --> 00:26:26,720
We really appreciate you
coming on to share your experience
477
00:26:26,745 --> 00:26:28,740
and your story and your work.
478
00:26:28,765 --> 00:26:33,080
I'm happy to be here.
479
00:26:33,105 --> 00:26:34,120
All right.
480
00:26:34,145 --> 00:26:35,980
Well, that's our...
481
00:26:36,005 --> 00:26:39,620
Well, no, it's not our show
because we have a game.
482
00:26:39,645 --> 00:26:41,780
We have a game.
Yes.
483
00:26:41,805 --> 00:26:43,700
What's our game?
Okay.
484
00:26:43,725 --> 00:26:47,980
In the spirit of the balloon game,
I got here first.
485
00:26:48,005 --> 00:26:50,380
As you know, I always get here first.
486
00:26:50,405 --> 00:26:54,180
I hid a balloon in this studio,
and you have to find it.
487
00:26:54,205 --> 00:26:57,855
But you only have 30 seconds.
488
00:26:57,880 --> 00:26:58,810
Thirty seconds?
489
00:26:58,840 --> 00:27:02,095
Am I able to from where I'm sitting
or do I have to get up?
490
00:27:02,120 --> 00:27:02,920
There are no hints.
491
00:27:02,945 --> 00:27:05,570
I don't know why you're
asking questions about hints.
492
00:27:05,600 --> 00:27:07,880
I'm asking because I have headphones on.
493
00:27:07,905 --> 00:27:11,175
Do I have to take them off
and get up and walk around?
494
00:27:11,200 --> 00:27:12,180
Yes.
Okay.
495
00:27:12,205 --> 00:27:15,280
It's going to take me 10 seconds
to get out of this chair.
496
00:27:15,305 --> 00:27:17,215
Hold on.
I got to start the Timer.
497
00:27:17,240 --> 00:27:18,480
Can you start it once
I'm out of the chair?
498
00:27:18,505 --> 00:27:19,560
Yes.
499
00:27:20,440 --> 00:27:21,700
Okay, ready?
500
00:27:21,725 --> 00:27:24,800
Stop looking.
Okay, go.
501
00:27:28,920 --> 00:27:30,540
Not under the piano.
502
00:27:30,565 --> 00:27:34,400
Looking under the piano.
Not there.
503
00:27:39,680 --> 00:27:40,800
Not behind here.
504
00:27:41,040 --> 00:27:47,020
Is it in your jacket?
505
00:27:47,045 --> 00:27:48,520
No.
506
00:27:48,840 --> 00:27:53,040
Five, four, three, two, one.
I found it.
507
00:27:53,065 --> 00:27:54,080
No, you didn't.
508
00:27:54,105 --> 00:27:56,160
No, I didn't.
509
00:27:57,040 --> 00:27:59,810
All right.
510
00:27:59,840 --> 00:28:02,360
Finally, I stumped you on a game.
511
00:28:02,385 --> 00:28:05,540
I feel like every single podcast you win.
512
00:28:05,565 --> 00:28:09,215
So I really needed a win here.
What's your prize?
513
00:28:09,240 --> 00:28:10,180
Pride.
514
00:28:10,205 --> 00:28:13,840
I'll show you where it's at.
515
00:28:13,865 --> 00:28:17,200
I have to get out of the chair.
516
00:28:18,600 --> 00:28:21,560
You only have 20 seconds to find it.
517
00:28:23,560 --> 00:28:25,440
I even looked there.
I know.
518
00:28:25,465 --> 00:28:27,920
That's why I was so lucky.
519
00:28:27,945 --> 00:28:30,460
All right.
520
00:28:30,485 --> 00:28:35,860
It was between two large foam boards.
521
00:28:35,885 --> 00:28:37,160
Like sound.
522
00:28:37,185 --> 00:28:39,660
Sound suppressant boards.
Sound blockers.
523
00:28:39,685 --> 00:28:41,290
But table-sized.
524
00:28:41,320 --> 00:28:43,440
All right.
Well, I'm not saying that.
525
00:28:43,465 --> 00:28:46,080
I'd say good job, but you
didn't do good that time.
526
00:28:46,105 --> 00:28:47,500
No, but I want a balloon.
527
00:28:47,525 --> 00:28:48,535
All right.
528
00:28:48,560 --> 00:28:50,280
Well, thanks for joining us today, guys.
529
00:28:50,305 --> 00:28:51,660
We'll see you next month.
530
00:28:51,685 --> 00:28:54,800
On the System 76 transmission log.
531
00:28:54,920 --> 00:28:56,720
Over and out.
532
00:28:57,360 --> 00:29:00,600
Three, two, one.
533
00:29:00,625 --> 00:29:12,080
This has been the System
534
00:29:12,105 --> 00:29:14,380
76 transmission log.
535
00:29:14,405 --> 00:29:18,620
For more inspiration, check out the
website and follow us on social media.
536
00:29:18,645 --> 00:29:21,840
On your descent back to Earth,
please keep your hands and feet
537
00:29:21,865 --> 00:29:24,700
inside the transport beam at all times.
538
00:29:24,725 --> 00:29:26,960
Captain, sign off in transmission.
00:00:02,440 --> 00:00:05,160
3, 2, 1, 0.
2
00:00:05,185 --> 00:00:16,720
Welcome aboard the System
3
00:00:16,745 --> 00:00:19,100
76 transmission Log.
4
00:00:19,125 --> 00:00:21,540
Our broadcast is about to begin.
5
00:00:21,565 --> 00:00:27,180
This is the latest on System 76
computers, manufacturing, and pop OS.
6
00:00:27,205 --> 00:00:31,560
Now for your in-orbit crew.
7
00:00:32,280 --> 00:00:34,440
Hello, hello, everyone.
8
00:00:34,465 --> 00:00:37,300
Welcome back to the System
76 transmission Log.
9
00:00:37,325 --> 00:00:39,740
I'm Alex, our resident, Normy.
10
00:00:39,765 --> 00:00:43,040
And I'm Emma, wondering why we're doing
the intro when it was supposed
11
00:00:43,065 --> 00:00:45,120
to be the banter section, but whatever.
12
00:00:45,145 --> 00:00:48,620
I mean, intro always comes first, right?
13
00:00:48,645 --> 00:00:51,920
No.
I feel like we always do banter first.
14
00:00:51,945 --> 00:00:54,300
Well, in the end, the intro comes first.
15
00:00:54,325 --> 00:00:55,540
All right.
16
00:00:55,565 --> 00:01:00,520
Just as a as a movie magic
secret, we're actually doing this last.
17
00:01:01,320 --> 00:01:03,600
We're not.
We're doing this first.
18
00:01:03,625 --> 00:01:06,770
Oh my God.
Just trying to confuse me.
19
00:01:06,800 --> 00:01:10,520
I did want to point out
I'm wearing pink today.
20
00:01:10,545 --> 00:01:13,440
I came prepared for our hosting here.
21
00:01:13,465 --> 00:01:15,130
What's the rest of that shirt?
22
00:01:15,160 --> 00:01:17,130
The rest of the shirt is…
23
00:01:17,160 --> 00:01:18,400
It's Slither's Snakeman.
24
00:01:18,425 --> 00:01:21,740
It's a snake disguised as a human.
25
00:01:21,765 --> 00:01:23,210
Is that a cartoon?
26
00:01:23,240 --> 00:01:27,490
No, it's just a shirt I found
on the internet from a cartoonist.
27
00:01:27,520 --> 00:01:32,490
But I was curious about if
we could get into some Emma lore here.
28
00:01:32,520 --> 00:01:38,700
When was the first moment you were like,
Oh, pink, that's my jam now?
29
00:01:38,725 --> 00:01:42,820
Well, when I really embraced it,
I was probably 15 or 16.
30
00:01:42,845 --> 00:01:45,020
I mean, I loved it when
I was a kid, of course.
31
00:01:45,045 --> 00:01:49,680
I was really into Barbie, and I just
think pink was the best world to live in.
32
00:01:49,705 --> 00:01:53,850
But yeah, when I was 18 with my first
apartment, everything was pink.
33
00:01:53,880 --> 00:01:56,160
There was a pink pillow floor area.
34
00:01:56,185 --> 00:02:00,920
There was a bright red couch
and a pink table, and everything
35
00:02:00,945 --> 00:02:03,660
on the walls was pink.
The dishes were pink.
36
00:02:03,685 --> 00:02:07,540
Ever since then, I was just
collecting pink stuff.
37
00:02:07,565 --> 00:02:12,540
And slowly, pink clothes
have been making a comeback.
38
00:02:12,565 --> 00:02:16,760
It was really hard to find a pink shirt
or a pink dress for a really long time,
39
00:02:16,785 --> 00:02:19,580
but now I can pretty much
find that stuff anywhere.
40
00:02:19,605 --> 00:02:21,380
Do you have a favorite shade of pink?
41
00:02:21,405 --> 00:02:23,880
I like a berry type of pink.
42
00:02:23,905 --> 00:02:26,890
It's not quite hot pink.
It's not dark.
43
00:02:26,920 --> 00:02:28,420
It's in the middle.
44
00:02:28,445 --> 00:02:30,660
So the inside of a strawberry.
45
00:02:30,685 --> 00:02:31,920
No.
46
00:02:33,080 --> 00:02:35,800
That's pretty light pink, I think.
47
00:02:35,825 --> 00:02:38,400
The color of my shoes is a very good pink.
48
00:02:38,425 --> 00:02:41,170
Okay, that's like a hot pink, I would say.
49
00:02:41,200 --> 00:02:44,300
Well, I like it a little more
subtle, but can't choose them all.
50
00:02:44,325 --> 00:02:45,335
True.
51
00:02:45,360 --> 00:02:45,740
All right.
52
00:02:45,765 --> 00:02:48,200
I have another topic to
discuss really quick.
53
00:02:48,225 --> 00:02:49,815
What's that?
The balloon game.
54
00:02:49,840 --> 00:02:50,340
Oh, yeah.
55
00:02:50,365 --> 00:02:52,580
I wasn't there for it,
but I hear it's pretty popping.
56
00:02:52,605 --> 00:02:54,280
I just realized that you
weren't there for it.
57
00:02:54,305 --> 00:02:56,380
How are you going to talk about it?
Well, I mean, I know.
58
00:02:56,405 --> 00:02:58,260
First of all, what is the balloon game?
59
00:02:58,285 --> 00:03:00,300
Oh, I'm glad you asked.
60
00:03:00,325 --> 00:03:03,480
So everyone at the company
in the factory at the beginning
61
00:03:03,505 --> 00:03:05,400
of the day gets a balloon with helium.
62
00:03:05,425 --> 00:03:11,060
And at the end of the day, whoever is left
with a balloon gets a prize.
63
00:03:11,085 --> 00:03:15,480
But throughout the day, people
can hide their balloon, pop people's
64
00:03:15,505 --> 00:03:18,660
balloons or steal people's balloons.
65
00:03:18,685 --> 00:03:24,420
We had some really competitive people,
had some tricky people hiding them.
66
00:03:24,445 --> 00:03:28,535
I thought Young Chin did a great job
hiding his behind the curtains
67
00:03:28,560 --> 00:03:31,800
in the window because you could totally
see it outside, but you couldn't see it
68
00:03:31,825 --> 00:03:33,600
on the inside, and nobody found it.
69
00:03:33,625 --> 00:03:35,600
He was one of the people left standing.
70
00:03:35,625 --> 00:03:41,200
Then Josh, I went and had a conversation
with him while he was in the shipping
71
00:03:41,225 --> 00:03:46,680
container, asking him if he had seen any
stray balloons It turns out his balloon
72
00:03:46,705 --> 00:03:52,260
was in the top corner of the shipping
container while I was talking to him.
73
00:03:52,285 --> 00:03:57,520
And I ended the contest and I was like,
Congratulations, everybody.
74
00:03:57,545 --> 00:03:59,360
Then he comes in at the last second.
75
00:03:59,385 --> 00:04:01,340
He's like, I still have mine.
76
00:04:01,365 --> 00:04:05,580
Then I asked him to expose
his spot, and it was right there.
77
00:04:05,605 --> 00:04:07,140
And I couldn't believe it.
78
00:04:07,165 --> 00:04:12,060
So I am not very good at the game, but I
thought those two spots were very clever.
79
00:04:12,085 --> 00:04:16,740
Then we had Grace,
who got all the balloons.
80
00:04:16,765 --> 00:04:19,780
She was taking people's balloons
and popping them left and right.
81
00:04:19,805 --> 00:04:23,000
She came in my office at the end
of the day and pulled out her balloon
82
00:04:23,025 --> 00:04:24,860
from under her sleeve.
83
00:04:24,885 --> 00:04:27,180
She was very proud.
84
00:04:27,205 --> 00:04:28,980
She got the most.
85
00:04:29,005 --> 00:04:33,320
Youngchin and Josh were probably
the most clever about it and sneaky,
86
00:04:33,345 --> 00:04:35,420
but all around, it was very fun.
87
00:04:35,445 --> 00:04:39,260
How did she fit an unpopped
balloon up her sleeve?
88
00:04:39,285 --> 00:04:40,740
Very carefully.
89
00:04:40,765 --> 00:04:44,015
She was pretty baggy clothes,
but her balloon had been losing air
90
00:04:44,040 --> 00:04:44,880
throughout the day, too.
91
00:04:44,905 --> 00:04:47,580
So it was a little smaller
than the other balloons.
92
00:04:47,605 --> 00:04:51,260
But yeah, I think she
called it George or something.
93
00:04:51,285 --> 00:04:52,940
She named it.
94
00:04:52,965 --> 00:04:55,700
Well, we're glad George survived.
95
00:04:55,725 --> 00:05:01,660
What would you say was the best technique
that was used for popping these balloons?
96
00:05:01,685 --> 00:05:03,240
I didn't witness any popping.
97
00:05:03,265 --> 00:05:04,580
I only heard it.
98
00:05:04,605 --> 00:05:09,820
But every time you heard a pop, you heard
a giggle, too, which was really funny.
99
00:05:09,845 --> 00:05:13,415
No matter where you were in the factory,
you could hear somebody
100
00:05:13,440 --> 00:05:14,320
laughing about it.
101
00:05:14,345 --> 00:05:15,840
So the balloons weren't
just filled with helium.
102
00:05:15,865 --> 00:05:17,380
They were filled with laughing gas.
103
00:05:17,405 --> 00:05:19,300
They were filled with laughing gas, yes.
104
00:05:19,325 --> 00:05:21,180
Or possessed by goats.
105
00:05:21,205 --> 00:05:22,980
Ghosts, not goats.
106
00:05:23,005 --> 00:05:24,900
I'm pretty sure I- Possessed by goats.
107
00:05:24,925 --> 00:05:27,540
I said it again.
What?
108
00:05:27,565 --> 00:05:29,760
We're leaving that in.
109
00:05:29,785 --> 00:05:35,100
Yeah, that was really fun.
I think we got to do it again, though.
110
00:05:35,125 --> 00:05:37,220
But I have some different ideas.
111
00:05:37,245 --> 00:05:41,900
The person with the most at the end
should probably win an extra prize.
112
00:05:41,925 --> 00:05:45,240
They have to throw a dart
at a wall of balloons that
113
00:05:45,265 --> 00:05:47,260
each contain a prize in them.
114
00:05:47,285 --> 00:05:48,640
I like that.
115
00:05:49,040 --> 00:05:50,800
Yes.
I like that a lot.
116
00:05:50,825 --> 00:05:54,520
Okay, well, now we know which games
to implement in the future, but maybe
117
00:05:54,545 --> 00:05:55,980
we should get into the news.
118
00:05:56,005 --> 00:06:00,420
Yeah, let's get into the
System 76 lore from the past month.
119
00:06:00,445 --> 00:06:02,600
Okay.
120
00:06:02,625 --> 00:06:08,300
Well, it's the opposite of last month
because we had a ton of news.
121
00:06:08,325 --> 00:06:15,255
Went from lots of releases to gearing up
for some releases down the line.
122
00:06:15,280 --> 00:06:16,220
Yeah.
123
00:06:16,245 --> 00:06:22,120
So this month, we have the tiniest bit
of news, and it's about the smallest bit
124
00:06:22,145 --> 00:06:24,340
of hardware, the little mirror cat.
125
00:06:24,365 --> 00:06:28,060
It's the little baby with big biceps.
126
00:06:28,085 --> 00:06:33,920
It's our compact mini little desktop
in the NUC form factor fits right
127
00:06:33,945 --> 00:06:36,700
on your desk or mounted on
the back of your monitor.
128
00:06:36,725 --> 00:06:42,240
And it is rocking
some Intel Core Ultra CPUs with up to 4.
129
00:06:42,265 --> 00:06:43,880
8 gigahertz processors.
130
00:06:43,905 --> 00:06:47,880
And and a whopping up to 96 gigabytes
131
00:06:47,905 --> 00:06:51,700
of RAM and up to 16 terabytes storage.
132
00:06:51,725 --> 00:06:56,860
So much in such a little
package, still rocking Intel.
133
00:06:56,885 --> 00:07:02,620
But also we added
I can't read your lips there.
134
00:07:02,645 --> 00:07:05,420
I thought you would just know.
135
00:07:05,445 --> 00:07:07,775
I thought you were going
to be like, open firmware.
136
00:07:07,800 --> 00:07:08,620
Oh, yeah.
137
00:07:08,645 --> 00:07:12,460
Look, I'm one of the first stages
in the product release.
138
00:07:12,485 --> 00:07:16,760
Once the product is ready to go,
I absorb all the information about it.
139
00:07:16,785 --> 00:07:21,960
I output it into something for
the website, and then my mind is cleared
140
00:07:21,985 --> 00:07:24,340
of it because I'm on to the next thing.
141
00:07:24,365 --> 00:07:29,080
What you're saying is you forgot that
Meer cat has open from rent.
142
00:07:29,105 --> 00:07:33,560
It's not I forgot so much as I've
stopped thinking about the Meer cat
143
00:07:33,585 --> 00:07:35,820
for the past three weeks.
144
00:07:35,845 --> 00:07:37,380
Okay.
145
00:07:37,405 --> 00:07:43,300
It's been a while for being able
to spout off the highlights, which
146
00:07:43,325 --> 00:07:45,700
is what we're here for on this podcast.
147
00:07:45,725 --> 00:07:49,300
So yes, Meer cat now has openFirmware.
148
00:07:49,325 --> 00:07:53,420
It's our core boot-based
System76 openFirmware.
149
00:07:53,445 --> 00:07:58,800
Part of it is speeding up boot
times, minimizes the Firmware,
150
00:07:58,825 --> 00:08:02,375
so it's more secure, less
vulnerability, all that good stuff.
151
00:08:02,400 --> 00:08:03,100
Yep.
152
00:08:03,125 --> 00:08:07,460
Do you think if you tied a balloon
around a meer cat, it would float?
153
00:08:07,485 --> 00:08:10,655
That's a great question.
We should try that.
154
00:08:10,680 --> 00:08:13,000
Well, I'm pretty sure
one balloon won't do the trick,
155
00:08:13,025 --> 00:08:16,580
but we should see how many balloons
it will take to float a mirror cat?
156
00:08:16,605 --> 00:08:18,380
My guess is three.
157
00:08:18,405 --> 00:08:21,380
Helium is pretty strong in a balloon.
Okay.
158
00:08:21,405 --> 00:08:23,640
They floated a house with it.
159
00:08:23,680 --> 00:08:26,055
Yeah, but he used like
a thousand balloons.
160
00:08:26,080 --> 00:08:27,140
He had a lot of balloons.
161
00:08:27,165 --> 00:08:28,560
It was a big house, though.
162
00:08:28,585 --> 00:08:30,580
You do like a townhouse.
163
00:08:30,605 --> 00:08:33,140
It's half the balloons.
Yeah.
164
00:08:33,165 --> 00:08:37,720
Meer cat, since it's just a little mirror
cat, probably three or four, yeah.
165
00:08:37,745 --> 00:08:39,695
With no mirror potential.
166
00:08:39,720 --> 00:08:41,920
Well, I'm going to test it and we'll
come back to you next month
167
00:08:41,945 --> 00:08:45,020
on the podcast and tell you how it went.
Okay.
168
00:08:45,045 --> 00:08:46,980
Yeah, we got to test it for science.
169
00:08:47,005 --> 00:08:49,000
Yeah, I need to know.
170
00:08:49,200 --> 00:08:50,720
All right.
171
00:08:50,745 --> 00:08:52,660
Well, that's our hardware news.
172
00:08:52,685 --> 00:08:57,740
We have some community news, right?
We do.
173
00:08:57,765 --> 00:09:01,460
We are going to be at a couple
of conferences in March
174
00:09:01,485 --> 00:09:03,740
and also April and May.
175
00:09:03,765 --> 00:09:08,960
To start off in March, we'll be at
SCALE in Pasadena, California,
176
00:09:08,985 --> 00:09:11,300
which is March sixth through 10th.
177
00:09:11,325 --> 00:09:15,160
We'll be hanging out at our booth
along with our friends at Ampere,
178
00:09:15,185 --> 00:09:16,660
who will be next to us.
179
00:09:16,685 --> 00:09:21,580
We'll have two Thelio ASTRO demos
at the Ampere booth showing us
180
00:09:21,605 --> 00:09:26,200
all the cores, all the power,
all the Blender renders you can think of.
181
00:09:26,225 --> 00:09:28,960
And a driving simulator as well, I think.
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Yeah, Tier 4's driving simulation
that they had at CES will be doing
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00:09:34,185 --> 00:09:39,280
the same simulation at our booth
at scale at the Ampere booth.
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Then also on the other side of the booth,
we'll have a cosmic section where you can
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design your own universe and then
see your cosmic theme in action.
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00:09:50,085 --> 00:09:55,660
Then also we'll be giving away
two launch keyboards and a lot of swag.
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You can get your hands on some hardware
and talk to some of your fellow nerds.
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Yeah, Garret, who's on our QA team, he
worked on a really cool universe designer
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00:10:06,385 --> 00:10:10,460
using the cosmic theming system, so
we're really excited to share that off.
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Then also in March, we're going
to be at the PNY my booth at GTC
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in San Jose, March 17 through the 21st.
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Pny will be showcasing the Thaleomega
and just showing off what
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00:10:25,145 --> 00:10:31,300
a high-end workstation optimized for
multi-GPU compute and heavy graphics
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and peripherals would be performing.
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Yeah, subject to change potentially.
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00:10:37,065 --> 00:10:40,980
But what we've been told is
there will be a demo showing off
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some new stuff, and it'll be using
a production workflow, a combination
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00:10:46,905 --> 00:10:51,060
of Blender, Unreal Engine, and OBS.
199
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So all tools available on Linux,
but just showing off the power
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of our hardware is combined.
Cool.
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That one's going to be a cool demo.
Yeah.
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And then let's see, April,
we have Linux Fest Northwest,
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which is my favorite one of the year.
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That one's in Bellingham,
Washington, the end of April.
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And we'll be doing a talk on how far
Cosmic has come and also have a booth.
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And then we'll be hosting
the Saturday barbecue.
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I could use some barbecue right about now.
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00:11:22,920 --> 00:11:24,160
It's actually not really barbecue.
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00:11:24,185 --> 00:11:27,860
It's just like whatever food
is a good idea that day.
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But maybe be just hot dogs like last year.
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If you bring some barbecue
sauce, it's barbecue.
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Okay, that's fair.
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00:11:36,005 --> 00:11:41,760
Join us with your barbecue sauce,
B-Y-O-B-S.
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00:11:42,520 --> 00:11:45,640
Bring your own BS.
215
00:11:45,680 --> 00:11:49,660
To the Linux Fest Northwest Barbecue.
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Hope to see you guys there.
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All right, what's happening
in the cosmic universe?
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00:11:57,925 --> 00:11:59,940
Cosmic universe.
Let's see.
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00:11:59,965 --> 00:12:05,760
There is potential for delaying
a week to get some more features
220
00:12:05,785 --> 00:12:07,940
into the Alpha 6 release.
221
00:12:07,965 --> 00:12:11,140
So I could say some stuff
that's potentially wrong.
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00:12:11,165 --> 00:12:14,700
An easier way to go about this
is just head to the blog
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00:12:14,725 --> 00:12:18,895
for actual up to date news blog.
System76.
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00:12:18,920 --> 00:12:19,160
Com.
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00:12:19,185 --> 00:12:24,780
But what I know and I've heard inklings of
is we now have desktop icons.
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You right click the desktop,
you go to desktop view settings,
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00:12:28,205 --> 00:12:32,640
and that'll bring up some toggles
for showing desktop icons, showing
228
00:12:32,665 --> 00:12:37,900
the trash icons, adjusting the size
and spacing, things of that nature.
229
00:12:37,925 --> 00:12:43,420
There's also some more polish updates
to the way workspaces functions.
230
00:12:43,445 --> 00:12:47,860
Multimonitor setups, you can now
drag and drop windows into the other
231
00:12:47,885 --> 00:12:49,640
workspace overview on another display.
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00:12:49,665 --> 00:12:53,980
You can also drag and drop
minimized windows.
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When you drag a window out of a stack
to another workspace, it'll now only move
234
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that window like you'd expect I think
previously it would move the whole stack.
235
00:13:03,725 --> 00:13:07,660
There's more updates along
those lines just to make workspaces
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feel polished and intuitive.
237
00:13:09,725 --> 00:13:14,460
There's file menu features
built out for the cosmic media player
238
00:13:14,485 --> 00:13:18,560
and potential for the initial setup
239
00:13:18,585 --> 00:13:21,860
applet, OS update settings, and
240
00:13:21,885 --> 00:13:25,460
a desktop zoom feature for accessibility.
241
00:13:25,485 --> 00:13:28,460
Those things may or may
not have been released.
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00:13:28,485 --> 00:13:33,580
Check the blog for accurate information,
but if not released in Alpha 6,
243
00:13:33,605 --> 00:13:35,700
probably by Alpha 7.
244
00:13:35,725 --> 00:13:38,860
So all that said,
exciting things for Cosmic.
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00:13:38,885 --> 00:13:40,980
I very much enjoy using it.
246
00:13:41,005 --> 00:13:45,180
I think I'm still on
2204 LTS, I've learned.
247
00:13:45,205 --> 00:13:49,420
But even being there, I mean,
it's been working great for my workflow.
248
00:13:49,445 --> 00:13:53,340
I don't really get into the
nitty-gritty and more surface level,
249
00:13:53,365 --> 00:13:54,660
but it's been going well.
250
00:13:54,685 --> 00:13:59,000
I have seen that the compositor on 2204
251
00:13:59,025 --> 00:14:01,920
LTS will no longer be getting updates, so
252
00:14:01,945 --> 00:14:04,180
make sure you're upgraded to 2404.
253
00:14:04,205 --> 00:14:07,660
I think the ISOs are 2404,
so you should be all right.
254
00:14:07,685 --> 00:14:10,375
Thank you for that piece of advice.
Yeah.
255
00:14:10,400 --> 00:14:11,180
All right.
256
00:14:11,205 --> 00:14:15,180
Well, we have a very
interesting interview today.
257
00:14:15,205 --> 00:14:16,720
Yeah, we have R.
J.
258
00:14:16,745 --> 00:14:21,620
Noling here to talk about
his work in genomic research.
259
00:14:21,645 --> 00:14:27,580
It's been really bugging me all week
to get this interview out there.
260
00:14:27,605 --> 00:14:30,100
Here we go.
261
00:14:30,125 --> 00:14:34,815
My name is R.
J.
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00:14:34,840 --> 00:14:35,120
Noling.
263
00:14:35,145 --> 00:14:37,460
I'm an Associate Professor
of Computer Science
264
00:14:37,485 --> 00:14:39,340
at the Milwaukee School of Engineering.
265
00:14:39,365 --> 00:14:42,820
I'm also the program director
for our master's in machine learning.
266
00:14:42,845 --> 00:14:46,540
My research is focused
on what I call genomic data science.
267
00:14:46,565 --> 00:14:50,140
I work with biologists
to process the raw sequencing data
268
00:14:50,165 --> 00:14:54,380
into a usable form and then
analyze it using data science techniques.
269
00:14:54,405 --> 00:14:58,080
In many cases, the data
we're collecting during an experiment,
270
00:14:58,105 --> 00:15:01,560
there's a particular research question
in mind, and often I'm helping them
271
00:15:01,585 --> 00:15:05,500
determine what evidence the data might
provide to help answer that question.
272
00:15:05,525 --> 00:15:08,700
Earlier, you mentioned you
looked at my website, you saw insects.
273
00:15:08,725 --> 00:15:11,060
Yeah, I primarily work
with insect genomes.
274
00:15:11,085 --> 00:15:14,600
A lot of my collaborators
work on mosquitoes such as Anopheles
275
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gambiae, that vector malaria,
as well as Edo gypti, which is
276
00:15:18,265 --> 00:15:19,900
a mosquito vector is Dengue fever.
277
00:15:19,925 --> 00:15:24,620
So these are medically important
organisms to public health.
278
00:15:24,645 --> 00:15:28,655
So it's mostly for public health
that you're doing all of this,
279
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or are there other reasons?
280
00:15:29,805 --> 00:15:33,420
So I'm doing it mostly
from a pure biology aspect.
281
00:15:33,445 --> 00:15:36,180
I want to better understand
the biological systems.
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00:15:36,205 --> 00:15:40,200
We hope that by studying these insects,
we'll also learn things that
283
00:15:40,225 --> 00:15:43,820
are transferable to other organisms.
284
00:15:43,845 --> 00:15:46,680
But it so happens that
the biologist I work with
285
00:15:46,705 --> 00:15:48,620
may have more specific questions.
286
00:15:48,645 --> 00:15:52,880
For example, one of my collaborators
is trying to better understand why
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00:15:52,905 --> 00:15:57,200
some of the Anopheles mosquitoes,
their immune system can fight off
288
00:15:57,225 --> 00:15:59,535
the malaria parasite and some can't.
289
00:15:59,560 --> 00:16:01,960
Even Even if they're from the same
family, literally the same mom
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00:16:01,985 --> 00:16:04,540
and dad, there's differences.
291
00:16:04,565 --> 00:16:07,420
She's trying to understand
a genetic basis of that.
292
00:16:07,445 --> 00:16:11,000
On my end, most of the work
I'm doing isn't necessarily
293
00:16:11,025 --> 00:16:12,360
specific to the mosquito.
294
00:16:12,385 --> 00:16:16,360
The data I have is specific
to the mosquito, but the techniques
295
00:16:16,385 --> 00:16:18,100
I'm using are general.
296
00:16:18,125 --> 00:16:25,175
In the case of malaria, that could lead to
breeding mosquitoes to eventually...
297
00:16:25,200 --> 00:16:25,920
Yeah.
298
00:16:25,945 --> 00:16:30,655
If there's a gene associated to eliminate
the vulnerability to malaria.
299
00:16:30,680 --> 00:16:31,720
Yeah, you're actually right.
300
00:16:31,745 --> 00:16:37,000
There's been some experiments, what
they call gene drive, to try to basically
301
00:16:37,025 --> 00:16:40,500
produce sterile males and such.
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00:16:40,525 --> 00:16:44,700
Other groups have been developing
techniques to like, okay, can we release
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00:16:44,725 --> 00:16:49,680
some genetically modified mosquitoes
into a population, and hopefully, they
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00:16:49,705 --> 00:16:52,760
will basically take over that population.
305
00:16:52,785 --> 00:16:57,440
And now, most the population will no
longer do a certain thing or something.
306
00:16:57,465 --> 00:17:02,440
So in this particular case, yeah, if
we We can find what makes it so that some
307
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of the mosquitoes, their immune system,
can't destroy the malaria parasite.
308
00:17:06,005 --> 00:17:11,280
We can then use gene drive techniques
to basically ensure that the mosquitoes
309
00:17:11,305 --> 00:17:14,810
can fight off the malaria parasite
so that they don't transmit it to humans.
310
00:17:14,840 --> 00:17:17,010
That's very interesting.
Yeah.
311
00:17:17,040 --> 00:17:18,810
How did you get into this?
312
00:17:18,840 --> 00:17:23,320
Actually, I was exposed to bioinformatics
through a summer internship
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00:17:23,345 --> 00:17:24,980
arranged by a family member.
314
00:17:25,005 --> 00:17:28,760
When I was in high school, I interned
at the University of Connecticut Health
315
00:17:28,785 --> 00:17:33,090
Center, making a simple website
with some data from a paper,
316
00:17:33,120 --> 00:17:34,700
and I really fell in love with it.
317
00:17:34,725 --> 00:17:38,000
I loved that I could apply
my computing skills to understanding
318
00:17:38,025 --> 00:17:39,810
something in the real world.
319
00:17:39,840 --> 00:17:41,600
I like the nature of the work itself.
320
00:17:41,625 --> 00:17:44,010
Like data processing is interesting to me.
321
00:17:44,040 --> 00:17:48,050
I made it I focused in college,
and then I went and got a PhD.
322
00:17:48,080 --> 00:17:51,500
During college, while many
of my colleagues were doing
323
00:17:51,525 --> 00:17:55,380
internships with companies,
I was doing research with professors.
324
00:17:55,405 --> 00:17:58,160
I did spend a couple of years
in industry after my PhD,
325
00:17:58,185 --> 00:18:00,140
so I was at Red Hat for two years.
326
00:18:00,165 --> 00:18:03,570
And then an online advertising company
called Adroll for two years.
327
00:18:03,600 --> 00:18:05,920
I started my faculty position
at the Milwaukee School
328
00:18:05,945 --> 00:18:09,180
of Engineering in fall 2018.
329
00:18:09,205 --> 00:18:12,560
When along that journey
did you discover Linux, was it
330
00:18:12,585 --> 00:18:14,855
at Red Hat or was it before then?
331
00:18:14,880 --> 00:18:17,320
It involved Red Hat, but
it was not while I was at Red Hat.
332
00:18:17,345 --> 00:18:18,740
It's actually middle school.
333
00:18:18,765 --> 00:18:21,495
Someone gave me a box
copy of Red Hat Linux 6.
334
00:18:21,520 --> 00:18:22,010
2.
335
00:18:22,040 --> 00:18:25,660
I want to clarify, I did not
omit the word enterprise.
336
00:18:25,685 --> 00:18:28,530
This was before it became
Red Hat enterprise Linux.
337
00:18:28,560 --> 00:18:30,050
This was old Red Hat.
338
00:18:30,080 --> 00:18:33,680
And so if I remember correctly,
the version I had ran
339
00:18:33,705 --> 00:18:35,980
Nome desktop version one.
Oh, wow.
340
00:18:36,005 --> 00:18:40,095
And so then I started using Slackware
in high school, and I later
341
00:18:40,120 --> 00:18:40,960
switched to Debian.
342
00:18:40,985 --> 00:18:45,720
And so now all my research machines,
I use Debian for all my workstations
343
00:18:45,745 --> 00:18:47,050
and servers, basically.
344
00:18:47,080 --> 00:18:51,050
And then my laptop and desktop,
I use Apple products.
345
00:18:51,080 --> 00:18:55,420
With regards to your workflow, what
software tools are you using, mainly?
346
00:18:55,445 --> 00:18:59,380
So when we get the raw data,
we clean it with something
347
00:18:59,405 --> 00:19:05,700
like trim PEEK, and then we align it
to genomes using something like BWA.
348
00:19:05,725 --> 00:19:12,240
We'll use a software package
called GATK to try to identify where
349
00:19:12,265 --> 00:19:15,090
individual samples differ from a genome.
350
00:19:15,120 --> 00:19:19,940
We might also use, depending on if it's
a different workflow, a tool called MAX
351
00:19:19,965 --> 00:19:21,895
for what's called peak calling.
352
00:19:21,920 --> 00:19:25,040
There's certain experiments that basically
highlight certain parts of the genome.
353
00:19:25,065 --> 00:19:28,740
It's like this part of the genome
has activity of interest.
354
00:19:28,765 --> 00:19:34,520
Then once we get all have produced
that output, then we can switch to
355
00:19:34,545 --> 00:19:38,740
more traditional data science tools
like using pandas in Jupiter notebooks
356
00:19:38,765 --> 00:19:43,140
and scikit learn and things like that.
357
00:19:43,165 --> 00:19:44,180
I see.
358
00:19:44,205 --> 00:19:46,900
For your desktop, I know
you have a Thelioastra.
359
00:19:46,925 --> 00:19:52,290
How has working with Thelioastra
affected your workflow and your project?
360
00:19:52,320 --> 00:19:54,980
It's massively increased my throughput.
361
00:19:55,005 --> 00:19:57,960
The data sets I work
with have a lot of samples.
362
00:19:57,985 --> 00:20:03,090
The biologist will seek the DNA
from a thousand mosquitoes.
363
00:20:03,120 --> 00:20:08,440
Well, it's hard to get much peralysm
for an individual sample, but the samples
364
00:20:08,465 --> 00:20:10,380
can be processed independently.
365
00:20:10,405 --> 00:20:15,800
The ability to have 120 cores instead
of 24 cores means I can process
366
00:20:15,825 --> 00:20:17,660
a lot more samples at once.
367
00:20:17,685 --> 00:20:20,330
It also tends to be very memory-intensive.
368
00:20:20,360 --> 00:20:26,640
So the fact that I can have 512 gig
of ECC RAM is also an important component
369
00:20:26,665 --> 00:20:28,250
of scaling up that pipeline.
370
00:20:28,280 --> 00:20:31,530
And these pipelines
often take weeks to run.
371
00:20:31,560 --> 00:20:36,140
So it can cut it down from a month
to a week or a few days.
372
00:20:36,165 --> 00:20:37,895
Wow.
It's a big improvement.
373
00:20:37,920 --> 00:20:38,220
Yeah.
374
00:20:38,245 --> 00:20:43,330
What led to your decision to go with
System 76 and the Thaleo ASTRA?
375
00:20:43,360 --> 00:20:48,480
Honestly, it was priced So I've
been wanting an Ampere ultra system
376
00:20:48,505 --> 00:20:53,600
for a while because I had a workstation
with an AMD Threadripper processor
377
00:20:53,625 --> 00:20:57,140
and a 24 cores and 120 gigs
around that I got five or six years ago.
378
00:20:57,165 --> 00:20:59,180
And that's been my main research machine.
379
00:20:59,205 --> 00:21:00,770
It was time for an upgrade.
380
00:21:00,800 --> 00:21:04,520
But whenever I would look at other systems
with AMD Threadripper processors,
381
00:21:04,545 --> 00:21:10,140
I just couldn't get more cores
or much more memory within my budget.
382
00:21:10,165 --> 00:21:14,800
The Ampere ultra platform offers a really
nice price point for getting a lot of
383
00:21:14,825 --> 00:21:17,530
cores and a lot of RAM at the same time.
384
00:21:17,560 --> 00:21:19,500
And I need both to do my work.
385
00:21:19,525 --> 00:21:25,440
And System76 was the only company
I could find that really was supporting
386
00:21:25,465 --> 00:21:27,860
a workstation option for Impair.
387
00:21:27,885 --> 00:21:32,695
And I can say also I'm glad
I went with System 76 because support
388
00:21:32,720 --> 00:21:35,460
has been super helpful.
I haven't run into any major problems.
389
00:21:35,485 --> 00:21:37,140
It's just my ignorance.
390
00:21:37,165 --> 00:21:40,700
I didn't realize that because
it's running a server motherboard,
391
00:21:40,725 --> 00:21:45,080
that it takes a really long time
to post before the boot screen shows.
392
00:21:45,105 --> 00:21:49,000
I thought something was wrong with it
when I got it, when it's like, why isn't
393
00:21:49,025 --> 00:21:51,500
this showing anything after five minutes?
394
00:21:51,525 --> 00:21:53,740
And it turns out I just
needed to wait longer.
395
00:21:53,765 --> 00:21:58,040
But the support was super
helpful, and I really appreciate
396
00:21:58,065 --> 00:21:59,660
my interactions with the company.
397
00:21:59,685 --> 00:22:03,700
I I think that has potentially turned me
into a repeat buyer in the future.
398
00:22:03,725 --> 00:22:04,780
That's awesome.
399
00:22:04,805 --> 00:22:08,090
Yeah, support has been
a long time pride point for us, for sure.
400
00:22:08,120 --> 00:22:12,535
Do you have advice for people who
want to get into genomic data science?
401
00:22:12,560 --> 00:22:13,330
Yes.
402
00:22:13,360 --> 00:22:17,740
So unfortunately, most of the people
I know doing this are at universities.
403
00:22:17,765 --> 00:22:21,800
There are some companies in industry like
Illumina, which provides some
404
00:22:21,825 --> 00:22:25,800
of the sequencing technology,
also hires a number of bioinformaticians
405
00:22:25,825 --> 00:22:29,960
and data scientists and software
engineers, but still primarily most of the
406
00:22:29,985 --> 00:22:32,000
people in this field are in universities.
407
00:22:32,025 --> 00:22:33,460
So you pretty much need a PhD.
408
00:22:33,485 --> 00:22:37,400
That said, I have worked with people
in PhDs in a wide range of fields,
409
00:22:37,425 --> 00:22:42,220
from computer science to biology
to statistics, physics, chemistry,
410
00:22:42,245 --> 00:22:46,660
applied math, whatever, that
we all work on these projects together.
411
00:22:46,685 --> 00:22:49,760
And so I think one thing
I had to figure out is that
412
00:22:49,785 --> 00:22:52,135
you have to pick a home field.
413
00:22:52,160 --> 00:22:54,880
So in my particular case, I'm very
comfortable teaching computer science
414
00:22:54,905 --> 00:22:56,290
and data science courses.
415
00:22:56,320 --> 00:23:00,320
So getting a PhD in computer science was
the right fit for me, even if my research
416
00:23:00,345 --> 00:23:02,330
itself is very interdisciplinary.
417
00:23:02,360 --> 00:23:06,200
And so the trick is to find
a faculty member who's working
418
00:23:06,225 --> 00:23:10,680
in bioinformatics who can advise students
in a PhD program that you would
419
00:23:10,705 --> 00:23:12,740
be successful in, basically.
420
00:23:12,765 --> 00:23:16,160
And for college students,
you need to start talking to professors
421
00:23:16,185 --> 00:23:21,180
and seeing if you can get opportunities
to do research with them in an undergrad.
422
00:23:21,205 --> 00:23:25,215
That'll tell you if you like the work,
and that's how you build up your resume
423
00:23:25,240 --> 00:23:27,680
to increase your chances
of getting accepted into a PhD program.
424
00:23:27,705 --> 00:23:30,050
That's awesome advice.
Thank you.
425
00:23:30,080 --> 00:23:30,810
Yeah.
426
00:23:30,840 --> 00:23:34,980
Looking forward to the future
with machine learning and genomic data
427
00:23:35,005 --> 00:23:39,440
science, how can you combine those
in the future to make innovations?
428
00:23:39,480 --> 00:23:42,570
Yeah, some of the work that
I find most interesting recently.
429
00:23:42,600 --> 00:23:45,420
One thing to know is our genome
doesn't just have genes.
430
00:23:45,445 --> 00:23:49,460
It has other non-coding elements
that play an important role.
431
00:23:49,485 --> 00:23:51,860
So one example of these
are called enhanceers.
432
00:23:51,885 --> 00:23:56,090
Enhancers help determine
how many copies of the gene are produced.
433
00:23:56,120 --> 00:23:59,880
And we're actually just
learning that a lot of diseases
434
00:23:59,905 --> 00:24:04,330
are caused not by changes in the gene
themselves, but in the enhanceers.
435
00:24:04,360 --> 00:24:07,420
So your body just may not
produce as many copies of the gene.
436
00:24:07,445 --> 00:24:10,240
So one thing, though,
is that the enhanceers are hard
437
00:24:10,265 --> 00:24:11,575
to find computationally.
438
00:24:11,600 --> 00:24:14,600
Generally, people use experimental
techniques, but there's been some
439
00:24:14,625 --> 00:24:18,360
really interesting work lately using
deep learning to identify enhanceers
440
00:24:18,385 --> 00:24:20,090
in an accurate mannerism.
441
00:24:20,120 --> 00:24:23,860
So because it's finally pretty
accurate, we could actually go through
442
00:24:23,885 --> 00:24:27,320
all the publicly available genomes
of the NCBI database provided by the NIH
443
00:24:27,345 --> 00:24:30,940
and find the enhanceers for all of them.
444
00:24:30,965 --> 00:24:34,380
And then that would allow us
to do cross-species comparisons.
445
00:24:34,405 --> 00:24:38,880
Another thing is that enhanceers
are generally not located near
446
00:24:38,905 --> 00:24:40,500
the genes they interact with.
447
00:24:40,525 --> 00:24:41,940
They're pretty far apart.
448
00:24:41,965 --> 00:24:46,020
So there's been some work
to try to computationally determine
449
00:24:46,045 --> 00:24:50,620
which enhanceers act on which genes,
which will help us better understand
450
00:24:50,645 --> 00:24:54,570
what genes they impact and thus
what potential disease is.
451
00:24:54,600 --> 00:24:58,960
The other thing is trying
to understand how genetic variation
452
00:24:58,985 --> 00:25:00,810
within the enhanceers has an impact.
453
00:25:00,840 --> 00:25:03,700
Sometimes the DNA changes
and it does nothing.
454
00:25:03,725 --> 00:25:07,440
Sometimes it increases the number
of copies of a DNA, sometimes it
455
00:25:07,465 --> 00:25:09,810
decreases it, but it's hard to predict.
456
00:25:09,840 --> 00:25:14,200
So all of these things,
if we can build accurate machine learning
457
00:25:14,225 --> 00:25:18,440
models to do this,
we can vastly speed up research and reduce
458
00:25:18,465 --> 00:25:22,600
costs and study organisms that we can't
study right now because it may be
459
00:25:22,625 --> 00:25:26,780
infeasible to do the actual experimental
lab work for whatever reason.
460
00:25:26,805 --> 00:25:30,700
Is that what you're most excited
about for the future your field?
461
00:25:30,725 --> 00:25:34,860
I get really excited when I can understand
the mechanisms of the biology.
462
00:25:34,885 --> 00:25:39,570
In some ways, I'm a biologist
who uses a computer as my instrument.
463
00:25:39,600 --> 00:25:44,320
I'm excited for advancements
that will allow us to better
464
00:25:44,345 --> 00:25:46,180
understand how the biology works.
465
00:25:46,205 --> 00:25:47,975
That's my main motivation.
466
00:25:48,000 --> 00:25:50,200
How can people get in touch
with you if they want to
467
00:25:50,225 --> 00:25:52,760
learn more or connect with you?
468
00:25:52,785 --> 00:25:57,480
They can check out my website, rnowling.
Github.
469
00:25:57,505 --> 00:26:03,330
Io, or they can send me
an email email at nowlind@msoe.
470
00:26:03,360 --> 00:26:06,090
Edu.
Awesome.
471
00:26:06,120 --> 00:26:09,460
Is there anything else you'd
like to add or share with the audience?
472
00:26:09,485 --> 00:26:13,740
Just that I've really enjoyed my
experience with the Thelio Aster so far.
473
00:26:13,765 --> 00:26:17,920
It's been a great machine
and worth every dollar, and it really
474
00:26:17,945 --> 00:26:21,215
does offer a lot for the price.
Highly recommend it.
475
00:26:21,240 --> 00:26:23,090
We're so happy to hear that.
Thank you so much.
476
00:26:23,120 --> 00:26:26,720
We really appreciate you
coming on to share your experience
477
00:26:26,745 --> 00:26:28,740
and your story and your work.
478
00:26:28,765 --> 00:26:33,080
I'm happy to be here.
479
00:26:33,105 --> 00:26:34,120
All right.
480
00:26:34,145 --> 00:26:35,980
Well, that's our...
481
00:26:36,005 --> 00:26:39,620
Well, no, it's not our show
because we have a game.
482
00:26:39,645 --> 00:26:41,780
We have a game.
Yes.
483
00:26:41,805 --> 00:26:43,700
What's our game?
Okay.
484
00:26:43,725 --> 00:26:47,980
In the spirit of the balloon game,
I got here first.
485
00:26:48,005 --> 00:26:50,380
As you know, I always get here first.
486
00:26:50,405 --> 00:26:54,180
I hid a balloon in this studio,
and you have to find it.
487
00:26:54,205 --> 00:26:57,855
But you only have 30 seconds.
488
00:26:57,880 --> 00:26:58,810
Thirty seconds?
489
00:26:58,840 --> 00:27:02,095
Am I able to from where I'm sitting
or do I have to get up?
490
00:27:02,120 --> 00:27:02,920
There are no hints.
491
00:27:02,945 --> 00:27:05,570
I don't know why you're
asking questions about hints.
492
00:27:05,600 --> 00:27:07,880
I'm asking because I have headphones on.
493
00:27:07,905 --> 00:27:11,175
Do I have to take them off
and get up and walk around?
494
00:27:11,200 --> 00:27:12,180
Yes.
Okay.
495
00:27:12,205 --> 00:27:15,280
It's going to take me 10 seconds
to get out of this chair.
496
00:27:15,305 --> 00:27:17,215
Hold on.
I got to start the Timer.
497
00:27:17,240 --> 00:27:18,480
Can you start it once
I'm out of the chair?
498
00:27:18,505 --> 00:27:19,560
Yes.
499
00:27:20,440 --> 00:27:21,700
Okay, ready?
500
00:27:21,725 --> 00:27:24,800
Stop looking.
Okay, go.
501
00:27:28,920 --> 00:27:30,540
Not under the piano.
502
00:27:30,565 --> 00:27:34,400
Looking under the piano.
Not there.
503
00:27:39,680 --> 00:27:40,800
Not behind here.
504
00:27:41,040 --> 00:27:47,020
Is it in your jacket?
505
00:27:47,045 --> 00:27:48,520
No.
506
00:27:48,840 --> 00:27:53,040
Five, four, three, two, one.
I found it.
507
00:27:53,065 --> 00:27:54,080
No, you didn't.
508
00:27:54,105 --> 00:27:56,160
No, I didn't.
509
00:27:57,040 --> 00:27:59,810
All right.
510
00:27:59,840 --> 00:28:02,360
Finally, I stumped you on a game.
511
00:28:02,385 --> 00:28:05,540
I feel like every single podcast you win.
512
00:28:05,565 --> 00:28:09,215
So I really needed a win here.
What's your prize?
513
00:28:09,240 --> 00:28:10,180
Pride.
514
00:28:10,205 --> 00:28:13,840
I'll show you where it's at.
515
00:28:13,865 --> 00:28:17,200
I have to get out of the chair.
516
00:28:18,600 --> 00:28:21,560
You only have 20 seconds to find it.
517
00:28:23,560 --> 00:28:25,440
I even looked there.
I know.
518
00:28:25,465 --> 00:28:27,920
That's why I was so lucky.
519
00:28:27,945 --> 00:28:30,460
All right.
520
00:28:30,485 --> 00:28:35,860
It was between two large foam boards.
521
00:28:35,885 --> 00:28:37,160
Like sound.
522
00:28:37,185 --> 00:28:39,660
Sound suppressant boards.
Sound blockers.
523
00:28:39,685 --> 00:28:41,290
But table-sized.
524
00:28:41,320 --> 00:28:43,440
All right.
Well, I'm not saying that.
525
00:28:43,465 --> 00:28:46,080
I'd say good job, but you
didn't do good that time.
526
00:28:46,105 --> 00:28:47,500
No, but I want a balloon.
527
00:28:47,525 --> 00:28:48,535
All right.
528
00:28:48,560 --> 00:28:50,280
Well, thanks for joining us today, guys.
529
00:28:50,305 --> 00:28:51,660
We'll see you next month.
530
00:28:51,685 --> 00:28:54,800
On the System 76 transmission log.
531
00:28:54,920 --> 00:28:56,720
Over and out.
532
00:28:57,360 --> 00:29:00,600
Three, two, one.
533
00:29:00,625 --> 00:29:12,080
This has been the System
534
00:29:12,105 --> 00:29:14,380
76 transmission log.
535
00:29:14,405 --> 00:29:18,620
For more inspiration, check out the
website and follow us on social media.
536
00:29:18,645 --> 00:29:21,840
On your descent back to Earth,
please keep your hands and feet
537
00:29:21,865 --> 00:29:24,700
inside the transport beam at all times.
538
00:29:24,725 --> 00:29:26,960
Captain, sign off in transmission.
