Publications associées à Laboratoire de physique de la matiere vivante

Quantitative assessment of research data management practice - 2021

Eliane Ninfa Blumer, Chiara Gabella, Francesco Varrato

This survey aims to investigate research data management practices at EPFL and integrate their results into specific academic services. The previous two editions, in collaboration with TU Delft, Cambridge University and Illinois University, were carried out in 2017 and 2019. The objective of these surveys is to collect information on researchers' habits in terms of management of their research data, as well as to identify their needs for data curation services/support. For this edition of the survey, a particular focus has been given to the ways in which they disseminate data and code. You can find here a file corresponding to the report, in PDF, highlighting the findings of the survey, plus the file of the underlying data, in CSV, and a file with the graphical representation of such data, in PDF. For more information about this survey, a description on how the survey might be re-used by other institutions, and RDM services offered by the EPFL Library, please contact researchdata@epfl.ch.

2022

Mercury isotope evidence for Arctic summertime re-emission of mercury from the cryosphere

Jenny Thomas

During Arctic springtime, halogen radicals oxidize atmospheric elemental mercury (Hg0), which deposits to the cryosphere. This is followed by a summertime atmospheric Hg0 peak that is thought to resultmostly fromterrestrial Hg inputs to the ArcticOcean, followed by photoreduction and emission to air. The large terrestrial Hg contribution to the Arctic Ocean and global atmosphere has raised concern over the potential release of permafrost Hg, via rivers and coastal erosion,with Arctic warming. Here we investigateHg isotope variability of Arctic atmospheric,marine, and terrestrial Hg.We observe highly characteristic Hg isotope signatures during the summertime peak that reflect re-emission of Hg deposited to the cryosphere during spring. Air mass back trajectories support a cryospheric Hg emission source but nomajor terrestrial source. This implies that terrestrial Hg inputs to theArcticOcean remain in the marine ecosystem, without substantial loss to the global atmosphere, but with possible effects on food webs.

2022

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Carbon-carbon bond cleavage mechanisms play a key role in the selective deconstruction of alkanes and polyolefins. Here, we show that the product distribution, which encompasses carbon range and formation of unsaturated and isomerization products, serves as a distinctive feature that allows the reaction pathways of different catalysts to be classified. Co, Ni, or Ru nanoparticles immobilized on amorphous silica-alumina, Zeo-Y and ZSM-5, were evaluated as catalysts in the deconstruction of n-hexadecane model substrate with hydrogen to delineate between different mechanisms, i.e., monofunctional- (acid site dominated) or bifunctional-hydrocracking (acid site & metal site) versus hydrogenolysis (metal site dominated), established from the product distributions. The ZSM-5-based catalysts were further studied in the depolymerization of polyethylene. Based on these studies, the catalysts are plotted on an activity-mechanism map that functions as an expandable basis to benchmark catalytic activity and to identify optimal catalysts that afford specific product distributions. The systematic approach reported here should facilitate the acceleration of catalyst discovery for polyolefin depolymerization.

2022

Sleep And Shouts: The Intrinsically Aversive Nature Of Rough Sounds Is Preserved During Nrem Sleep

Dominique Grandjean

ELSEVIER2022

The Dynamics of Single-Cell Nanomotion Behaviour of Saccharomyces cerevisiae in a Microfluidic Chip for Rapid Antifungal Susceptibility Testing

Sandor Kasas, María Inés Villalba

The fast emergence of multi-resistant pathogenic yeasts is caused by the extensive-and sometimes unnecessary-use of broad-spectrum antimicrobial drugs. To rationalise the use of broad-spectrum antifungals, it is essential to have a rapid and sensitive system to identify the most appropriate drug. Here, we developed a microfluidic chip to apply the recently developed optical nanomotion detection (ONMD) method as a rapid antifungal susceptibility test. The microfluidic chip contains no-flow yeast imaging chambers in which the growth medium can be replaced by an antifungal solution without disturbing the nanomotion of the cells in the imaging chamber. This allows for recording the cellular nanomotion of the same cells at regular time intervals of a few minutes before and throughout the treatment with an antifungal. Hence, the real-time response of individual cells to a killing compound can be quantified. In this way, this killing rate provides a new measure to rapidly assess the susceptibility of a specific antifungal. It also permits the determination of the ratio of antifungal resistant versus sensitive cells in a population.

MDPI2022

Statistical potentials from the Gaussian scaling behaviour of chain fragments buried within protein globules

Stefano Zamuner

Knowledge-based approaches use the statistics collected from protein data-bank structures to estimate effective interaction potentials between amino acid pairs. Empirical relations are typically employed that are based on the crucial choice of a reference state associated to the null interaction case. Despite their significant effectiveness, the physical interpretation of knowledge-based potentials has been repeatedly questioned, with no consensus on the choice of the reference state. Here we use the fact that the Flory theorem, originally derived for chains in a dense polymer melt, holds also for chain fragments within the core of globular proteins, if the average over buried fragments collected from different non-redundant native structures is considered. After verifying that the ensuing Gaussian statistics, a hallmark of effectively non-interacting polymer chains, holds for a wide range of fragment lengths, although with significant deviations at short spatial scales, we use it to define a 'bona fide' reference state. Notably, despite the latter does depend on fragment length, deviations from it do not. This allows to estimate an effective interaction potential which is not biased by the presence of correlations due to the connectivity of the protein chain. We show how different sequence-independent effective statistical potentials can be derived using this approach by coarse-graining the protein representation at varying levels. The possibility of defining sequence-dependent potentials is explored.

PUBLIC LIBRARY SCIENCE2022

Managing the life cycle of a portfolio of open data resources at the SIB Swiss Institute of Bioinformatics

Chiara Gabella

Data resources are essential for the long-term preservation of scientific data and the reproducibility of science. The SIB Swiss Institute of Bioinformatics provides the life science community with a portfolio of openly accessible, high-quality databases and software platforms, which vary from expert-curated knowledgebases, such as UniProtKB/Swiss-Prot (part of the UniProt consortium) and STRING, to online platforms such as SWISS-MODEL and SwissDrugDesign. SIB's mission is to ensure that these resources are available in the long term, as long as their return on investment and their scientific impact are high. To this end, SIB provides its resources, in addition to stable financial support, with a range of high-quality, innovative services that are, to our knowledge, unique in the field. Through this first-class management framework with central services, such as user-centric consulting activities, legal support, open-science guidance, knowledge sharing and training efforts, SIB supports the promotion of excellence in resource development and operation. This review presents the ecosystem of data resources at SIB; the process used for the identification, evaluation and development of resources; and the support activities that SIB provides. A set of indicators has been put in place to select the resources and establish quality standards, reflecting their multifaceted nature and complexity. Through this paper, the reader will discover how SIB's leading tools and databases are fostered by the institute, leading them to be best-in-class resources able to tackle the burning matters that society faces from disease outbreaks and cancer to biodiversity and open science.

OXFORD UNIV PRESS2022

Arctic mercury cycling

Hélène Paule Angot

Anthropogenic mercury (Hg) emissions have driven marked increases in Arctic Hg levels, which are now being impacted by regional warming, with uncertain ecological consequences. This Review presents a comprehensive assessment of the present-day total Hg mass balance in the Arctic. Over 98% of atmospheric Hg is emitted outside the region and is transported to the Arctic via long-range air and ocean transport. Around two thirds of this Hg is deposited in terrestrial ecosystems, where it predominantly accumulates in soils via vegetation uptake. Rivers and coastal erosion transfer about 80 Mg year−1 of terrestrial Hg to the Arctic Ocean, in approximate balance with modelled net terrestrial Hg deposition in the region. The revised Arctic Ocean Hg mass balance suggests net atmospheric Hg deposition to the ocean and that Hg burial in inner-shelf sediments is underestimated (up to >100%), needing seasonal observations of sediment-ocean Hg exchange. Terrestrial Hg mobilization pathways from soils and the cryosphere (permafrost, ice, snow and glaciers) remain uncertain. Improved soil, snowpack and glacial Hg inventories, transfer mechanisms of riverine Hg releases under accelerated glacier and soil thaw, coupled atmosphere–terrestrial modelling and monitoring of Hg in sensitive ecosystems such as fjords can help to anticipate impacts on downstream Arctic ecosystems.

2022

Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning

Nahal Mansouri, Sahand Jamal Rahi

Background: Early prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation strategies, and thus reduce the prevalence of the disease. Such publicly accessible prediction models are lacking, however. Methods: Based on a comprehensive evaluation of existing machine learning (ML) methods, we created two models based solely on the age, gender, and medical histories of 23,749 hospital-confirmed COVID-19 patients from February to September 2020: a symptom prediction model (SPM) and a mortality prediction model (MPM). The SPM predicts 12 symptom groups for each patient: respiratory distress, consciousness disorders, chest pain, paresis or paralysis, cough, fever or chill, gastrointestinal symptoms, sore throat, headache, vertigo, loss of smell or taste, and muscular pain or fatigue. The MPM predicts the death of COVID-19-positive individuals. Results: The SPM yielded ROC-AUCs of 0.53-0.78 for symptoms. The most accurate prediction was for consciousness disorders at a sensitivity of 74% and a specificity of 70%. 2,440 deaths were observed in the study population. MPM had a ROC-AUC of 0.79 and could predict mortality with a sensitivity of 75% and a specificity of 70%. About 90% of deaths occurred in the top 21 percentile of risk groups. To allow patients and clinicians to use these models easily, we created a freely accessible online interface at www. aicovid.net. Conclusion: The ML models predict COVID-19-related symptoms and mortality using information that is readily available to patients as well as clinicians. Thus, both can rapidly estimate the severity of the disease, allowing shared and better healthcare decisions with regard to hospitalization, self-isolation strategy, and COVID-19 vaccine prioritization in the coming months.

FRONTIERS MEDIA SA2021

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EUROPEAN RESPIRATORY SOC JOURNALS LTD2021