Publications related to Noise and oscillations in biological systems: Multidisciplinary approach between experimental biology, theoretical modelling and synthetic biology

Graph generative deep learning models with an application to circuit topologies

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison.Managing their complexity and facilitating their design drove part of the co-evolution of moder ...

EPFL2024

Identification of typical district configurations: A two-step global sensitivity analysis framework

François Maréchal, Jonas Schnidrig, Cédric Terrier

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the ...

Pergamon-Elsevier Science Ltd2024

Inhalation of Microplastics—A Toxicological Complexity

Florian Frédéric Vincent Breider, Myriam Borgatta

Humans are chronically exposed to airborne microplastics (MPs) by inhalation. Various types of polymer particles have been detected in lung samples, which could pose a threat to human health. Inhalation toxicological studies are crucial for assessing the e ...

2024

Decoding Chromatin Ubiquitylation: A Chemical Biology Perspective

Beat Fierz, Pauline Franz

Since Strahl and Allis proposed the "language of covalent histone modifications", a host of experimental studies have shed light on the different facets of chromatin regulation by epigenetic mechanisms. Initially proposed as a concept for controlling gene ...

Academic Press Ltd- Elsevier Science Ltd2024

Reconstruction and analysis of genome-scale metabolic networks in single organisms and microbial communities

Evangelia Vagena

Microorganisms are a key component in the chain of life. They are essential for agriculture, produce a large proportion of oxygen, and play a central role in the cycle of elements. Microorganisms are widely used in the production of food and alcoholic beve ...

EPFL2024

Prolamins' 3D structure: A new insight into protein modeling using the language of numbers and shapes

Niloufar Sharif

This study combined protein modeling methods to generate the prolamins' fractions as precise as possible. Hence, gliadins, zeins, kafirins, hordeins, secalins, avenins and oryzins were generated based on their characteristics and disulfide mapping. Finding ...

Elsevier Sci Ltd2024

Efficient Continual Finite-Sum Minimization

Volkan Cevher, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

2024

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Aude Billard, Nadia Barbara Figueroa Fernandez, Mikhail Koptev

Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is construc ...

Sage Publications Ltd2024

Optimality principles for the analysis of biological processes across scales: From enzyme kinetics to microbiome dynamics

Asli Sahin

Microorganisms are essential for life on Earth, performing key roles in numerous biological processes. Their influence extends across a wide spectrum, from human health and ecological balance to advancements in biotechnology and industrial applications. Th ...

EPFL2024

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

Jean-Philippe Thiran, Tobias Kober, Bénédicte Marie Maréchal, Jonas Richiardi

With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have ...

Wiley2024

Noise and oscillations in biological systems: Multidisciplinary approach between experimental biology, theoretical modelling and synthetic biology

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Graph generative deep learning models with an application to circuit topologies

Identification of typical district configurations: A two-step global sensitivity analysis framework

Inhalation of Microplastics—A Toxicological Complexity

Decoding Chromatin Ubiquitylation: A Chemical Biology Perspective

Reconstruction and analysis of genome-scale metabolic networks in single organisms and microbial communities

Prolamins' 3D structure: A new insight into protein modeling using the language of numbers and shapes

Efficient Continual Finite-Sum Minimization

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Optimality principles for the analysis of biological processes across scales: From enzyme kinetics to microbiome dynamics

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

Graph generative deep learning models with an application to circuit topologies

Identification of typical district configurations: A two-step global sensitivity analysis framework

Inhalation of Microplastics—A Toxicological Complexity

Decoding Chromatin Ubiquitylation: A Chemical Biology Perspective

Efficient Continual Finite-Sum Minimization

Reconstruction and analysis of genome-scale metabolic networks in single organisms and microbial communities

Prolamins' 3D structure: A new insight into protein modeling using the language of numbers and shapes

Optimality principles for the analysis of biological processes across scales: From enzyme kinetics to microbiome dynamics

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency