So, lightning talks are always interesting when a new team runs them. And I think we've done
this one pretty well, but of course the proof will be in the pudding. And firstly, before we
OK, I'm just seeing what's going on here. I should really be filling in. The caffeine hasn't
quite kicked in yet to allow me to do that, so I'll keep sort of babbling a bit. So firstly,
after this, we are going to be heading to Brutog, I think, which is near Central. So you're
very welcome to join us. How long we're taking order to get there? I'm not sure. There
may be some variants along the way, but if you want to carry on discussions or if you want
to meet us there, either way, hang about at the end and we will work it out. There isn't
anything. There are no more sessions either. We're going on after. So as mentioned
before, some of us will maybe go on from Brutog to Bite Night. Some of us won't. The Bite
Night is held at the Brussels Hackspace, which apparently is a pretty impressive place
to visit. So if you've never been to a Hackspace before, who's come here for the first time
for us, then, wow. So this is only my second time at Fosthen. The trick is to just go and
talk to everybody. There's a thing about not getting tired, you don't have to sit and
you've been amazing. We've been for pretty much the whole of the afternoon. And I think
that's kind of unusual actually. Yeah, so, okay, guys, how are we doing? How much time are
we giving each speaker? Three minutes. This is like slow lightning, but it's still lightning,
actually three minutes. So you will see messages appear at magical in front of your face.
Okay, so the trick is going to be, hold the mic. If people look like they can't hold
you at the back, you should say, we can't hear you. So it's all going to be very quickly.
I will try to do it quickly. It's not easy. So I'm small. I'm working in a university
and I will speak about geeks and workflow management and so on. So the question is scientific
production is about long-term trust. A long-term trust is about verification, be able to share
and so on. So open source is one mean, but it's not the only mean. Another mean is to have long-term
reproducibility. And the question is, how do we have a long-term reproducibility about workflow?
So what is a workflow is a second of computation, but in real life is more than just a couple of
steps. It's a concurrent, a multi-output, multi-output and so on and so on. So how do we have long-term
reproducibility workflow? So there is two questions and usually people are taking these two
questions completely orthogonal. The first question is, what is the computation environment
inside each box? So where do I come from? The binaries, how are they produced and so on? And the second
question is, how this language, how this graph is described? So which language and so on? So how
you know? I mean, maybe you guess that I use geeks for the binaries. So what is geeks? I mean,
it's in for your homework. Geeks is a package manager on steroid. So it's able to produce
reproducible computational environments. So check out this webpage where there is a lot of resources
and peerostokes in first them. So binaries is, okay, so the question is, is not about the binaries.
The question is, how can we, can we plug the description of the graph with geeks? So the idea is
the geeks workflow language is a really old idea by peerotero prints and rale. And the idea is to say,
okay, package management is about graph management. Workflow is also a graph management. So can we
build a workflow engine of the top on the top of the package management system? And it is, what is
a geeks workflow language? It's a workflow domain specific language built on the top of geeks
used as a as a library. So maybe you don't know. So yeah, I guess never heard about it. So yeah,
it's not adopted at all because first it's yet another language describes the graph and so on.
And it's based on this old kind of closing parenthesis, closing parenthesis language. And it's
a competitive market. Like, you know, you have snake mega likes you next floor and so on. And
maybe connecting both the description of of the workflow and the complexion of everyone was too
descriptive. So check out Revenant. Revenant is a yet another implementation,
powered by geeks. So we have a standard specification geeks workflow.
Join us with Revenant and time's up. So here we are. Time's up.
Please, please ping me on on email, uh, Master Don and so on. Let's share your long
story about long-term Roblesibia workflow. Thank you. Thank you.
Let's go. Okay. So I want to talk to you about a molecular visualization software called VTX.
So why would we need those? Well, there is biologists that needs to seek what's important in
directions. Sometimes for me, this micro molecular structure we don't see very well. And sometimes
there is like a computer chemist that want to see just small molecules interacting with, uh,
with proteins. So they have already, uh, some kind of tools like bi,
bi, mole, chimera, and the MD. So why another one? Well, when we take, uh, like a hundred
million martini beads from a world cell model, uh, it just doesn't work. So we had to do something
better. Except maybe for VMD, which was kind of opening the stuff, but it was very hard to
interact with the system as it was just only one, uh, point four, a frame per second. It's kind of,
difficulties like a slideshow, you know. But that's where VTX shiny. It's, um, it could read like,
this system and navigate it with 12 frame per second, which was quite nice. But what does it look
like? So on the left, you have, uh, the image, the picture, uh, taken with VTX and on the right,
it's VMD. So you, you, yeah, a picture, a big thousand words. And that's,
because I only have three minutes. Um, and the upcoming version will feature a new,
wrong-breaking way to see, um, protein surface. So currently, um,
chimeraics and VMD and all that stuff, they use meshes to display, uh,
molecular surface. So if you, uh, take a closer look, you see straight lines,
trying to form round shapes, which is quite a satisfying to look at. When we, uh,
the, the next version will feature, it will implicit like meshless, uh, protein surface,
like we use, uh, gemage recalculation to render pixel perfect, uh, resolution of all the shapes
of a proteins. Um, and it's quite fast. And, uh, it's main strength, uh, lies in it's, uh,
lightweight. Um, it's in memory, uh, I mean, because RAM is quite expensive this day. And,
uh, if we reduce the memory footprint, we could, uh, we could, um, display a way bigger,
uh, more, um, systems and before up to, uh, two million items for this, uh, for, uh,
eight gigabytes, uh, video card. Oh, that's fast. So, take away, it's already available. You can
do a little bit, uh, following this QR code, uh, it's open source, obviously. And, uh, yeah,
it's designed around, uh, performance and, uh, usability. Thank you, thank you, thank you, thank you.
Oh, okay. Sorry. Sorry.
Oh, sorry. Okay.
Oh, okay.
Play, everyone. My name is Fabian. I'm a PhD in a research assistant in Timishora, Romania.
Alongside my colleagues and our supervisor, we wanted to create a web application for an
ecological clinic. But in order to do this, we hit different problems. One of the problems was,
okay, decont data, which is more important there. They want to see different modules or something like that.
Okay, this coming into the, we have to make it 3D. And, after we want to see an evolution in time.
So, go with 4D. In order to do that, we created, um, we found a way to go from 2D to 3D.
Afterwards, we just added the slides and the way of getting it in time. For the discharge letters,
which usually have the data regarding the, uh, blood samples and everything else,
there was a little bit more complicated. And on top of this, we also wanted to automate
kind of automate it and edit our all-based access. So, we can split it between
radiologists, oncologists, and the front desk. So, the patient just go with the front desk. And,
afterwards, everything is kind of automated. And now, I'm going to just go with each of them.
For the natural visualization, most of it is based on precision, and this in toolbox, which
helped us a lot for getting the data from 2D to 3D via the NRD conversion. And, after all,
we just used PyroDomics to get different characteristics like not your volume, not your
area. We also are trying to get the fractal dimension. We are still working on something,
regarding that in, and you want to see if that's related to more into the oncology research.
For the data visualization, here was a little bit complicated, because the discharge letters
don't have a pattern. They are just written by hand in the word, even the tables are done with tabs,
and it's very hard to get all the data. But we still managed to get it. We have an evolution in
time for a graph or even a table. And in order to do that, we created an ensemble method,
because first our deterministic model wasn't good enough. So, we kind of screwed up. But,
with an ensemble method, we managed to get two, two, I models that work pretty good, combine
with our DNA estimator. We have a voting algorithm, and the best output is selected, and added to the,
database, in order to show it for the users. The database is among, most of the UI is done in
React, and for the, and already conversion, and everything regarding the visualization,
everything is done in a flash cap, in Python. And that's it. Thank you.
I hope it is in time.
So, yeah, I've been doing my PhD research and discipline students, my
degree of study in past life. I'm a software hardware engineer, I come to the scientists,
and the problem I'm trying to solve is as a hobbyist as well. I like working on the HDD SSDs.
So, you can see the problem statement. Data is growing. So, currently I'm also working as an
engineer, but I have a lot of synthetic data as well. So, what I'm trying to, with this
lighting talk, trying to target is that, you know, as a computer scientist, I started working
on the DNA storage, but the problem is the DNA storage has, does not have resources,
accumulated resources for computer scientists. So, what I started, what doing was that I
started collecting the open source DNA storage simulators, projects, so that, you know,
I can make the life of the software hardware engineers, easier, who wants to basically proceed
with this open source project. So, this is the seven steps of DNA storage. You have a source
file where you basically have a binary format. You can work that into DNA sequences, then you do
synthesis, and as, you know, in the synthesis, the DNA is basically broken down into sequences.
Though, you have to cluster, you have to use clustering algorithms, so these are the seven steps
of DNA storage. This is the report I'm basically trying to maintain now for the engineers,
mostly working on the storage side, and this is something, you know, so-called awesome
DNA storage get-up. So, basically, a curated list of all that projects, which was missing
in the community. So, just please follow that, or if you have anything to fall to find in this
report, please do that. And if you have any time tomorrow, tomorrow is a buff, which is
photonics optics, so please drop by, we can discuss also something else storage. Thank you.
Yeah.
Okay, I'm the last one, and for our meeting, so, I'm Peyton. I'm
a lecturer in health, and have some other science, but I'm here to present my work,
observe, can I? So, in biology, isn't about if something happened, is about when and why
where happened, something else. So, but in the risk comes from the continuous variables,
the two is often forced us to pretend biology is binary, which is like in here,
you can see just have high and low, but in the data here, you can see the gradients, which is
a hidden messages, and also this is some of our researchers, you can see each live represent,
maybe in patient, there will be a die, and the right will be alive. Okay, so, because there's
limitations to think about categories, and just only high and low in the software,
existing in our package at the moment. So, I developed the package, which is able to categorize
in different and more than one. So, there's a free steps. On the first steps, there will be
use an information criteria, a, i, c, or a, i, i, c, or b, i, c, and c, two, think about.
Thus, the data is self suitable for four groups, or two is enough. And then, if that's two,
even enough, or four enough, there will be use a genetic algorithms to more precise to know
what the exact locations of the data. So, there will be, I avoid the problems of non-linear
or use shape data. Okay, so once we note the cut off, of course, we need to do the
validations, because in biology, having a risk for unknown would be a, there's also not,
in a, sorry, about that. It's not in the data. So, which is why I needed to do the
real and boosted up with sample links. So, in here, and the backgrounds of the three different
pits, if we're presenting, so we're repeating 200 times, and you can see the cut off from the
previous steps, because verify closely to the pits. So, which is the free step process for the
project, a free step project for the package. So, right now, the package is in the, and is under
reveal, in officer of cut out, if you would like to try, give a try, please scan in this
Calcoc here, and thank you so much.
That was the last one, right? Yes, yeah. I will let it open the fire close and work, it's, is it different
to what was on this point? Because I could just plug in here. Yeah, okay. So, the last laptop switch
of the day, it's a Mac, let's see what happens.
Nope, thankfully. Well, yeah, we'll see. What does it do?
Okay. So, oh, is it doing that as a, uh, good grief. How many different ways can they break?
It's start mirroring, that's what I needed. There we go.
Okay, right. Wow. So, um, yeah, I, I, yeah. This is it. This is it.
Yeah, we do. Yeah, no, no, it will be. Was it fun?
That was kind of a, yeah, we're smiling. We're smiling.
I have to thank you both for being so freaking amazing just sitting there and being, like, timing
demons, stream demons. We should have, like, rotated a bit more, I think that's something I would
like, but no, no, I was trying, you know. I just want to sit at the back, you know.
Ask or questions. So, we have a way for you to tell us whether you had fun, whether you
didn't have fun, scan the QR code, go to our effort pad, make some notes, talk to us afterwards
as well, because I was just saying before, actually, I don't have the pin for, but we will be posting
where we'll be heading to the Brue Log, sorry, the Brue Dog in central, um, in the matrix trap.
So, we'll be going, we'll put it in both, but one of the complicated things about phosdom is that
because you have these phosdom rooms for the devrooms, um, but they kind of disappear. So, we have our
permanent space, which you can also join. It really doesn't want to join. There are things
that you might want to ask. So,