EPFL: Interactive visualization of the Fusion Reactor Tokamak plasma

In my dedicated efforts to advance nuclear fusion, I embarked on a journey to translate conceptual ideas into tangible actions. This mission commenced with the creation of a test dataset, meticulously crafted with insights derived from engaging discussions within our team. To expedite the visualization and exploration of this data, I turned to the remarkable open-source application known as BioExplorer.

BioExplorer, originally developed for biological datasets, proved to be an invaluable asset in our venture. While its roots lay in the realm of biology, its adaptability and versatility allowed it to seamlessly integrate with our nuclear fusion research. This application, once primarily designed for biological data, had evolved to accommodate and process a wide range of scientific datasets, transcending its initial domain.

In the context of nuclear fusion, the core challenge lies in modeling the behavior of superheated plasma, the fuel for this revolutionary energy source. To visually represent this complex phenomenon, I envisioned the plasma as an array of particles, with each particle intricately characterized by its position and direction. The direction vector of each particle not only represented its position but also served as a direct indicator of the particle's charge, a crucial factor in the fusion reaction.

Efficiency was paramount in this ambitious undertaking, and BioExplorer played a pivotal role. This open-source application, known for its adaptability, provided a user-friendly platform to visually explore and analyze the scientific datasets, irrespective of their origin. It allowed us to gain valuable insights and a deeper understanding of the plasma's behavior, further propelling our research forward.

To ensure real-time visualization of the plasma's behavior, I harnessed the formidable processing power of Graphics Processing Units (GPUs). These GPUs were thoughtfully optimized with a tree-based acceleration structure, making them an ideal choice for handling the intricate calculations required for real-time visual representation of the plasma's behavior.

Perhaps the most astonishing aspect of this project was its efficiency. What might seem like a Herculean task was accomplished in no more than two days of relentless work. This achievement not only underscores the power of modern computational tools but also highlights the immense potential of nuclear fusion as a clean and virtually limitless energy source, hinting at a future where our energy needs may be met sustainably and efficiently, with the invaluable assistance of open-source applications like the versatile and adaptable BioExplorer.

Check out the BioExplorer source code and the Python Jupyter Notebook used to create the image above.

PASC 2023: MS4E - Scientific Visualization of Big Data

Data acquisition and computing systems have evolved to produce large amounts of data, but most of these datasets contain small scale features that cannot be aggregated to be understood. While these small features are usually very hard to detect automatically, the human eye can do it almost instantly, provided that the data is presented in a visually efficient way. Visualizing data in a human-readable way not only helps with data comprehensibility, but serves both data analysis and science communication. However, visualization tools and techniques need to keep pace with both increasing data sizes and the diverse needs of the scientific community. This is why a special effort towards developing automatic data visualization has been used since for as long as computing has existed, and it is getting more and more important in the present years. The minisymposium aims at gathering people working in the field of scientific big data visualization and researchers to discuss current needs and available technologies to initiate new collaborations and ideas. It will probably arise that across different fields, similar problems have to be solved, especially regarding performance and interface design. The minisymposium will motivate the mutualization of efforts needed to tackle those problems.

Link: https://pasc23.pasc-conference.org/presentation/?id=msa164&sess=sess168

CERN: Particles Composition and Interactions Using the Nuon Model

Drawing inspiration from René Brun's remarkable publication titled "Particles Composition and Interactions Using the Nuon Model" I embarked on a venture to visualize the magnetic fields engendered by sub-particles within the realm of collisions.

 

Although the data I employed differs from that presented in the paper, I found the concept of moving away from the conventional depiction of particles as simple spheres and instead illustrating their genuine magnetic fields to be captivating.

 

 

With the computational capabilities readily accessible on ordinary consumer PCs today, it is now possible to calculate these fields in real-time, opening up novel avenues for visualizing and exploring the intricacies of the sub-atomic world.

 

Try it for yourself!! https://particle-physics.netlify.app

Full source code: https://github.com/favreau/NuonModelVisualizer

HARVARD: The Harvard Brain

So proud to appear in the Harvard Brain Spring 2023 issue.

Studies In Silico: An Interview With Cyrille Favreau On EPFL’s Blue Brain Project, by Lara Ota, Buse Toksöz, and Kei Hayashi

http://www.theharvardbrain.com/spring-2023-8203lara-ota-buse-toksoumlz-and-kei-hayashi.html

EPFL: Blue Brain BioExplorer goes RTX!

Exciting news! I just released the version 1.6.0 of the Blue Brain BioExplorer. It now goes #NVDIARTX with OptiX backend compatibility, #AI denoiser, and new stereo camera! High-quality rendering of scientific datasets and #VR use-cases with pure ray-tracing.

Open source code: https://github.com/BlueBrain/BioExplorer

EPFL: Machine Learning and understanding the role of blood glucose levels in the severity of COVID-19

On the third anniversary of the first lock-down, we look back at how Machine Learning helped reveal the role of blood glucose levels in the severity of COVID-19.

With access to enough open data, imagine what other problems could be tackled.

So proud we made it! 😀

For the 3D visualization part of the movie, I developed the open-source Blue Brain Explorer, in collaboration with Emmanuelle Logette for the scientific part. Check it out:

https://github.com/BlueBrain/BioExplorer

Watch the documentary:

Read more: https://lnkd.in/dW7m6Ea

#scivis #ScientificVisualization #ScientificExploration #RayTracing #covid19 #sarscov2 #covid19pandemic

 

CERN : Visual scientific exploration at Blue Brain, and beyond

by Cyrille Favreau (EPFL), Evelyne Schmid Osborne (EPFL)

 

Friday Feb 17, 2023, 11:00 AM → 12:00 PM Europe/Zurich

503/1-001 - Council Chamber (CERN)

Scientific exploration relies on building software that combines data integration, analysis and interactive visualization to build, modify and navigate through large scientific datasets. For this, Blue Brain built and open-sourced the Blue Brain BioExplorer. The Blue Brain BioExplorer was originally developed to answer key scientific questions related to the Coronavirus as a use case and to deliver a visualization tool. Today, the BioExplorer allows reconstructing, visualizing, exploring and describing in detail the structure and function of highly-detailed biological structures such as molecular systems, neurons, astrocytes, blood vessels, and more.

 

The BioExplorer is built as an extension of Brayns, the official underlying and generic rendering platform that was designed to easily adapt to all fields of science.

 

In this very visual talk, we will present EPFL's Blue Brain Project, and explain how we could architecture and build the application that is now being used to produce high quality and high fidelity media, as well as interactive and immersive experiences of the digital reconstruction of the mouse brain.

We will finally discuss the impact of using real raw data for science communication and dissemination.

CERN reference: https://indico.cern.ch/event/1253917/

Happy New Year 2023

Dear followers,

Another year has passed, and I was once again lucky enough to work on some outstanding projects and share amazing times with very precious people. Kind, smart and sensitive people who touched my heart, and brought me back to life. I was simply given the chance to be in good company, and that counts more than anything else.

The project I am most proud of if the one that was used to generate the wishing card: Quantum of Sol-R. A good old (but still fast!) ray-tracer that makes use of real photons to improve the quality of the rendering. By using a random generator device based on quantum properties of real photons, the ray-tracer is now capable of using pure randomness to process light transport. God does play dice, and that’s good news to me.

I am wishing you all a wonderful 2023. Take care of yourselves, and be kind to each other. We’re only humans after all.

Quantum of Sol-R: A singular graphics engine making use of quantum physics

Ten years ago I wrote a graphics engine as a personal project, perhaps one of the first to want to access the Holy Grail of computer-generated imagery: real-time ray tracing.

To learn this extraordinary news that is the awarding of the Nobel Prize in Physics to Mr Alain Aspect sounded like a reminder. It's been a while that I wanted to integrate in one way or another a little quantum in my work, the opportunity to get started was too good.

Quantum coincidence or not, I work in Geneva and the company ID Quantique happens to have its premises in Carouge, a 30-minute walk from my home. This company designs, produces and markets a random number generator based on the quantum properties of photons, and my graphics engine is in dire need of random numbers.

Better, it is the quality of the randomness which determines the beauty of the final image. What could be better than asking the best experts, the photons themselves, to help me transport light correctly in this virtual world?

The complete source code of the graphics engine is online, and available to everyone but I am still waiting for the Quantum device provider to allow me to make the code public. In the meantime, run it, modify it and improve it. The beauty of science is endless, it's up to you to bring it to light! (As if light could play a role in this world 😉 )

https://github.com/favreau/Sol-R

If it is still impossible for me to visualize the quantum world, perhaps it is given me now to be able to contemplate the random it creates.

EPFL: Scientific Exploration

 

My work at the Blue Brain Project is all about scientific exploration.

Exploration relies on building software that combines data integration, analysis and interactive visualization to build, modify and navigate through large scientific datasets. For this, Blue Brain built and open-sourced the Blue Brain BioExplorer.

The Blue Brain BioExplorer (BBBE), which started as an internal project, is now open source. It was originally developed to answer key scientific questions related to the Coronavirus as a use case and to deliver a visualization tool.

 

Today, the BioExplorer allows to reconstruct, visualize, explore and describe in detail the structure and function of highly-detailed biological structures such as molecular systems, neurons, astrocytes, blood vessels, and more. You can see the first application of the BioExplorer in ‘A Machine-Generated View of the Role of Blood Glucose Levels in the Severity of COVID-19’ study.

More information here.

OUTREACH: Brain(s) - CCCB - Barcelona (27 July — 11 December 2022)

The human brain is the most complex object we know of and the one that raises most questions in the fields of both science and philosophy. Brain(s) looks at how, throughout history, art, science, and philosophy have studied and represented this fascinating organ

 

https://www.cccb.org/en/exhibitions/file/brains/237851

I am proud to contribute to that project by creating an open-source software (Blue Brain BioExplorer) that could generate the images that stand aside of Cajal's originals.

 

EPFL: A Machine-Generated View of the Role of Blood Glucose Levels in the Severity of COVID-19

Abstract

SARS-CoV-2 started spreading toward the end of 2019 causing COVID-19, a disease that reached pandemic proportions among the human population within months. The reasons for the spectrum of differences in the severity of the disease across the population, and in particular why the disease affects more severely the aging population and those with specific preconditions are unclear. We developed machine learning models to mine 240,000 scientific articles openly accessible in the CORD-19 database, and constructed knowledge graphs to synthesize the extracted information and navigate the collective knowledge in an attempt to search for a potential common underlying reason for disease severity. The machine-driven framework we developed repeatedly pointed to elevated blood glucose as a key facilitator in the progression of COVID-19. Indeed, when we systematically retraced the steps of the SARS-CoV-2 infection, we found evidence linking elevated glucose to each major step of the life-cycle of the virus, progression of the disease, and presentation of symptoms. Specifically, elevations of glucose provide ideal conditions for the virus to evade and weaken the first level of the immune defense system in the lungs, gain access to deep alveolar cells, bind to the ACE2 receptor and enter the pulmonary cells, accelerate replication of the virus within cells increasing cell death and inducing an pulmonary inflammatory response, which overwhelms an already weakened innate immune system to trigger an avalanche of systemic infections, inflammation and cell damage, a cytokine storm and thrombotic events. We tested the feasibility of the hypothesis by manually reviewing the literature referenced by the machine-generated synthesis, reconstructing atomistically the virus at the surface of the pulmonary airways, and performing quantitative computational modeling of the effects of glucose levels on the infection process. We conclude that elevation in glucose levels can facilitate the progression of the disease through multiple mechanisms and can explain much of the differences in disease severity seen across the population. The study provides diagnostic considerations, new areas of research and potential treatments, and cautions on treatment strategies and critical care conditions that induce elevations in blood glucose levels.

 

Full paper: https://www.frontiersin.org/articles/10.3389/fpubh.2021.695139/full?utm_source=fweb&utm_medium=nblog&utm_campaign=ba-sci-fpubh-covid-19-elevated-blood-glucose-blue-brain

 

Checkout the Blue Brain Portal for details on how the movie was made:

https://portal.bluebrain.epfl.ch/resources/software/exploration/

 

 

EPFL: Blue Brain BioExplorer

I have just released the Blue Brain BioExplorer, a tool for scientists to extract and analyse scientific data from visualization. BBBE is built on top of Blue Brain Brayns, the Blue Brain rendering platform.

 

 

Source code -> https://github.com/BlueBrain/BioExplorer

Architecture

The BBBE application is built on top of Brayns, the Blue Brain rendering platform. The role of the application is to use the underlying technical capabilities of the rendering platform to create large scale and accurate 3D scenes from Jupyter notebooks.

General components

Assemblies

Assemblies are groups of biological elements, such as proteins, membranes, glycans, etc. As an example, a virion is made of a lipid membrane, spikes proteins, an RNA sequence, etc, and all those elements belong to the same object. That’s why they need to belong to the same container, the assembly. Assemblies can have different shapes: Sphere, Cube, etc, that are automatically generated according to the parameters of individual components.

Proteins

Proteins are loaded from PDB files. Atoms, non-polymer chemicals and bonds can be loaded and displayed in various colour schemes: chain id, atom, residue, etc. Proteins also contain the amino acid sequences of the individual chains. Sequences that can be used to query glycosylation sites, or functional regions of the protein.

Glycans

Glycans are small proteins that are attached to an existing protein of the assembly. Individual glycan trees are loaded from PDB files and attached to the glycosylation sites of the specified protein. By default, glycans are attached to all available glycosylation sites, but a set of specific sites can be specified.

RNA sequence

An RNA sequence can be loaded from a text sequence of codons. Various shapes can be selected to represent the RNA sequence: Trefoil knot, torus, star, etc. This allows the sequence to be efficiently packed into a given volume. A different color is assigned per type of codon.

Mesh-based membranes

Mesh-based membranes create membranes based on 3D meshes. This allows the construction of complex membranes where mesh faces are filled with proteins.

Python SDK

A simple API if exposed via the BBBE python library. The API allows scientists to easily create and modify assemblies, according the biological parameters. The BBBE programming language is not necessarily reflecting the underlying implementation, but is meant to be as simple as close as possible to the language used by the scientists to describe biological assemblies.

Documentation

See here for detailed documentation of the source code.

Deployment

BBBE binaries are publicaly available as docker images. BBE is designed to run in distributed mode, and is composed of 3 modules: A server, a python SDK, and a web user interface. This means that there are 3 docker images to be downloaded on run. Those images can of course run on different machines.

In this example, we will expose the server on port 5000, the python SDK jupyter notebooks on port 5001, and the user inferface on port 5002. One is free to change those ports at will.

Server

docker run -ti --rm -p 5000:8200 bluebrain/bioexplorer

Python SDK

docker run -ti --rm -p 5001:8888 bluebrain/bioexplorer-python-sdk

Web User Interface

docker run -ti --rm -p 5002:8080 bluebrain/bioexplorer-ui