{"id":947,"date":"2020-06-26T10:31:03","date_gmt":"2020-06-26T10:31:03","guid":{"rendered":"https:\/\/website.prod.unilu.spikeseed.cloud\/fr\/news\/le-supercalculateur-au-service-de-la-lutte-contre-la-covid-19\/"},"modified":"2020-06-26T10:31:03","modified_gmt":"2020-06-26T10:31:03","slug":"le-supercalculateur-au-service-de-la-lutte-contre-la-covid-19","status":"publish","type":"news","link":"https:\/\/www.uni.lu\/fr\/news\/le-supercalculateur-au-service-de-la-lutte-contre-la-covid-19\/","title":{"rendered":"Le supercalculateur au service de la lutte contre la COVID-19"},"content":{"rendered":"

L’installation informatique de haute performance (High-Performance Computing – HPC) \u00a0de l\u2019Universit\u00e9 du Luxembourg a contribu\u00e9 de mani\u00e8re significative \u00e0 la lutte contre la pand\u00e9mie COVID-19 depuis la mi-mars 2020.<\/p>

Gr\u00e2ce \u00e0 ses ressources de calcul, le \u00ab\u00a0supercalculateur\u00a0\u00bb et son \u00e9quipe, dirig\u00e9e par le professeur Pascal Bouvry<\/a> et le docteur S\u00e9bastien Varrette<\/a>, ont soutenu les chercheurs de l’Universit\u00e9 et les partenaires externes dans plus de sept projets.<\/p>

Les performances accrues du HPC<\/a> permettent la r\u00e9solution de probl\u00e8mes importants \u00e0 une vitesse plus \u00e9lev\u00e9e. Des t\u00e2ches qui n\u00e9cessiteraient normalement plusieurs ann\u00e9es de calcul sur un ordinateur de bureau typique peuvent se faire en quelques heures, jours ou semaines sur un syst\u00e8me HPC. En ce qui concerne les recherches men\u00e9es pour lutter contre la pand\u00e9mie COVID-19, le temps de r\u00e9solution acc\u00e9l\u00e9r\u00e9 est un crit\u00e8re essentiel pour une lutte efficace contre la propagation de la pand\u00e9mie.<\/p>

La grande puissance de calcul et les capacit\u00e9s de stockage du supercalculateur de l’Universit\u00e9 ont \u00e9t\u00e9 utilis\u00e9es pour activer et acc\u00e9l\u00e9rer la recherche COVID-19 dans les domaines des sciences biom\u00e9dicales et de la vie, des TIC et des sciences des mat\u00e9riaux. Il facilite entre autres les estimations de la biodisponibilit\u00e9 pulmonaire bas\u00e9es sur l’apprentissage machine et les techniques de mod\u00e9lisation et de simulation des \u00e9cosyst\u00e8mes commerciaux pour informer les d\u00e9cideurs \u00e9conomiques au Luxembourg et \u00e0 l’\u00e9tranger, tout en permettant de calculer les pr\u00e9visions futures de la visibilit\u00e9 du virus sur les surfaces.<\/p>

Les services HPC de l’Universit\u00e9 soutiennent quatre projets men\u00e9s par l’Universit\u00e9 et financ\u00e9s par l’appel \u00e0 propositions acc\u00e9l\u00e9r\u00e9 COVID-19 du FNR, un projet de la Task Force COVID-19 de Research Luxembourg et une collaboration entre le Luxembourg Centre for Systems Biomedicine de l’Universit\u00e9 du Luxembourg, TU Munich et l’Institut Flatiron.<\/p>

L’installation informatique de haute performance est un \u00e9l\u00e9ment de l’infrastructure de recherche num\u00e9rique et de l’expertise d\u00e9velopp\u00e9e par l’Universit\u00e9 au cours des derni\u00e8res ann\u00e9es. Elle soutient \u00e9galement l’ambitieuse strat\u00e9gie num\u00e9rique de l’Universit\u00e9 et en particulier la cr\u00e9ation d’un centre pour les donn\u00e9es et les sciences HPC. Ce centre vise \u00e0 fournir une infrastructure et des services num\u00e9riques de haut niveau ax\u00e9s sur l’utilisateur dans le but de favoriser le d\u00e9veloppement d’activit\u00e9s de collaboration li\u00e9es \u00e0 la recherche et \u00e0 l’enseignement de pointe dans les domaines des sciences informatiques et des sciences des donn\u00e9es, y compris le calcul haute performance, l’analyse des donn\u00e9es, les applications de donn\u00e9es \u00e0 grande \u00e9chelle, l’intelligence artificielle et l’apprentissage automatique.<\/p>

Plus de 1030 t\u00e2ches ont \u00e9t\u00e9 programm\u00e9es sur les r\u00e9serves d\u00e9di\u00e9es \u00e9tablies par l’\u00e9quipe du HPC (la plus longue t\u00e2che ayant dur\u00e9 58 jours). Le graphique 1 donne un aper\u00e7u de l’utilisation de la charge associ\u00e9e pendant la p\u00e9riode la plus critique de la pand\u00e9mie. L’\u00e9quipe HPC de l’Universit\u00e9 reste d\u00e9termin\u00e9e \u00e0 fournir des ressources et des conseils aux projets actuels et futurs li\u00e9s \u00e0 COVID-19.<\/p>

\n \n\"\"\n

\n Graphique 1 : Aper\u00e7u des ressources de calcul utilis\u00e9es par les projets COVID-19 de mi-mars \u00e0 fin avril. <\/p>\n <\/figure><\/p>

Le taux \u00e9lev\u00e9 d’utilisation des ressources pendant cette p\u00e9riode critique montre la forte implication et la collaboration de tous les partenaires de l\u2019Universit\u00e9 pour lutter contre cette pand\u00e9mie. Vous trouverez ci-dessous une liste des principaux projets li\u00e9s \u00e0 COVID-19 qui ont fait appel aux ressources informatiques du HPC de l’Universit\u00e9 :<\/p>

Combined In Silico Molecular Docking And In Vitro Experimental Assessment Of Drug Repurposing Candidates For Covid-19 (CovScreen)<\/p>

Currently, no vaccine or sufficiently validated pharmacological treatment is available for COVID-19. Drug-based strategies to reduce the viral load in patients with severe forms of COVID-19 include the repurposing of existing small molecule compounds that inhibit the activity of key viral proteins or human proteins involved in mediating viral entry or release from the host cell. The project proposes a combined computational and experimental approach to rank an alternative candidate known as drugs, antivirals, and natural compounds, which are commercially available, inexpensive, and safe in humans. The project screens and filters in silico x M~10k compounds using molecular docking and machine learning based lung bioavailability estimations and conducts molecular dynamics simulations for refined binding affinity estimation of the 100 top-ranked compounds. The project aims to provide a fast experimental validation of drug repurposing candidates for COVID-19 from a computational pre-selection of antivirals, drugs, and natural compounds that are inexpensive, have known safety properties, and high predicted bioavailability in the lung. The project is led by Enrico Glaab<\/a>.<\/p>

\n \n\"\"\n

\n Figure 2: A) Modeled SARS-CoV-2 Spike Glycoprotein overlaid with the SARS-CoV (PDB: 2GHV) unique amino acids are shown. Variable amino acid residue side chains are shown: Green: SARS-CoV Red: SARS-CoV-2. B) Minimised final structure of modeled SARS-CoV-2 spike glycoprotein. Source: https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC7152904\/figure\/fig1\/ <\/p>\n <\/figure><\/p>

Machine Learning To The Rescue: From Health Recovery To Economic Revival (REBORN)<\/p>

REBORN is a data science project that focuses on ensuring sustainable economic recovery in the face of COVID-19. The project team applies advanced Machine Learning, business ecosystem modelling (i.e., expert knowledge) and simulation techniques to yield recommendations of economic actions given different scenarios in which the lockdown is relaxed, partially or totally lifted. By interacting with other teams of the Luxembourg Task Force, this project targets high impact for the various sectors of the Luxembourg economy, by providing appropriate data-driven recommendations for political decision-makers. REBORN aims to answer important questions such as: What are the industrial sectors to help in priority? What are the sectors that should be restarted first? What are the possible changes in consumer habits and the impact of neighboring country decisions on commuters? etc. Ultimately, REBORN contributes towards reflections on initiatives to limit the spread of the future economic crisis due to COVID-19 as well as avoid worsening future waves of coronaviruses. The project is led by Jacques Klein<\/a>.<\/p>

\n \n\"\"\n

\n Figure 3: Tools to strengthen resilience for COVID-19. Source: https:\/\/council.science\/current\/blog\/setting-up-a-data-ecosystem-to-defeat-covid-19\/ <\/p>\n <\/figure><\/p>

Privacy Preserving Monitoring of Social Distancing In Public Environments Machine Learning, Computer Vision, Social Distancing, GDPR By Design (PEOPLE)<\/p>

The project aims to provide a platform to run a comprehensive analysis on the Social Distancing measures decided by the government in the context of the COVID-19 pandemic. It aims to analyse anonymised video data in the city of Luxembourg. The first step is to anonymise the video feed using well-known Artificial Intelligence (AI) models (face blurring). The next step uses other AI models to identify pedestrians and groups of individuals, calculate their relative distances and overall density. Those metrics can then be evaluated over time for different locations and provide valuable insights on the greater or lesser risks of infection spreading based on behaviour. The rules can be used to inform where the police need to focus their efforts on enforcing laws or informing and influencing the public\u2019s actions (or both). The project is led by Raphael Frank<\/a>.<\/p>

Virus-surface Interactions In Dynamic Environments (V-SIDE)<\/p>

The scientific community is trying to establish how long COVID-19 can survive on a given surface (e.g., paper, plastics, glass, and metals). Their properties (smooth v\/s rough) play a crucial role during the infection spreading phase. Using the Fourier-transform infrared spectroscopy (FTIR) helps to predict the surface-specific visibility of the coronavirus in the steady-state and dynamic state (e.g., changes in temperature and humidity). Later the obtained model will be mapped into the biophysical model system. Machine learning techniques are used for an optimistic and future prediction of the visibility of the virus on the surfaces. This is the part where the UL HPC software and hardware facilities will be used. The project is led by Anupam Sengupta<\/a>.<\/p>

\n \n\"\"\n

\n Figure 4: How long does the COVID-19 virus survive on surfaces?Source: New England Journal of Medicine <\/p>\n <\/figure>

Sars-CoV-2 protein structure prediction<\/p>

Bioinformaticians from Rostlab at TU Munich<\/strong>, the LCSB<\/strong> and the Flatiron institute joined forces to participate in a worldwide effort to predict 3D structures of Sars-CoV-2 proteins. The project was organised by the team behind CASP (Critical Assessment of protein Structure Prediction), who selected ten viral proteins for which no experimental structure is available, nor can homology-based modeling be used to infer structure. The goal is to obtain consensus structures, which would give insight into the molecular mechanisms of the virus, as well as aid vaccine development and the evaluation of possible drug targets.<\/p>

To this end, the team from TUM<\/strong> and LCSB<\/strong>, helped by collaborators at the Flatiron Institute<\/strong>, trained multiple Deep Learning systems, leveraging evolutionary information from multiple sequence alignments, to predict pairwise distances of amino acids in proteins. The hardest targets of previous CASP competitions were used to assess the reliability of predictions. Computer generated distance maps were then used as constraints to simulate protein folding and obtain 3D structures. Three nodes of the Iris HPC cluster with four Tesla GPUs each (in addition to three similar nodes at the Flatiron Institute and one node from the UCC at TUM) were used to prepare datasets, as well as to train DL models, enabling high quality submissions within the initiative’s short-term.<\/p>

\n \n\"\"\n

\n Figure 5: (left) Atomic-level structure of the spike protein of the virus that causes COVID-19 (Source: McLellan Lab, University of Texas at Austin); (right) Visualisations of predicted protein structures from this project. <\/p>\n <\/figure><\/p>

COVID-19 Task Force<\/p>

In response to the global COVID-19 pandemic, the COVID-19 Task Force was set up by Research Luxembourg at the early stage of the confinement. As part of Work Package 6 (statistical pandemic projections<\/i>), researchers from LCSB have developed a computational agent-based model that is essential in understanding the progression of the Covid-19 epidemic in Luxembourg. The statistical projections are required to avoid saturation of the healthcare system and can be used to aid in political decision-making by simulating the epidemic development and its associated outcomes under various future scenarios and strategies.<\/p>

In order to make informed decisions, hundreds of scenarios with several randomised replicates need to be considered. Parallelisation of the underlying computer code has been key to speed up the simulations and reduce the computing time to an absolute minimum. Thanks to the HPC platform of the University of Luxembourg and its team, it is now possible to launch hundreds of scenarios simultaneously, leading to results being made available to decision-makers within minutes.<\/p>

Medical image analysis of X-ray and CT of COVID19<\/p>

During the past weeks, the HPC infrastructure and in particular the GPUs were employed for the AICovIX project. This project focuses on the development of automated solutions for the analysis of medical image analysis of X-ray and CT of COVID19 pneumonia patients using computer vision and deep learning techniques. This work is conducted by researchers of the INS group.<\/p>

\n \n\"\"\n

\n Figure 6: Chest X-rays from a patient with COVID-19 pneumonia, original x-ray (left) and AI-for-pneumonia result (right) (Photo courtesy of UC San Diego Health). <\/p>\n <\/figure><\/div><\/section>","protected":false},"excerpt":{"rendered":"

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