Reassessment of the 2010-2011 Haiti cholera outbreak and rainfall-driven multiseason projections

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Dynamics of CLIMP-63 S-acylation control ER morphology

Vassily Hatzimanikatis, Françoise Gisou van der Goot Grunberg, Graham Knott, Kathryn Hess Bellwald, Laurence Gouzi Abrami, Béatrice Kunz, Luciano Andres Abriata, Sylvia Ho, Catherine Maclachlan, Patrick Alain Sandoz

A key player in the formation of endoplasmic reticulum sheets is CLIMP-63, but mechanistic details remained elusive. Here authors combined cellular experiments and mathematical modelling to show that S-acylation of CLIMP-63 regulates its function by mediat ...

Nature Portfolio2023

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Colin Neil Jones, Roland Schwan, Melanie Nicole Zeilinger, Xuan Truong Nghiem

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

IEEE2023

Beyond undulation! Body morphology and sensing components of elongated animals and robots reveal skills to maintain competent locomotion

Laura Isabel Paez Coy

Locomotion is an essential evolutive innovation of living beings that allows them to colonize and dominate the planet. As diverse as animal morphologies are (living) and were (extinct), their locomotion modalities are also diverse. In particular, animal mo ...

EPFL2023

Deep Learning Meets Sparse Regularization

Rahul Parhi

Deep learning (DL) has been wildly successful in practice, and most of the state-of-the-art machine learning methods are based on neural networks (NNs). Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of ...

2023

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Jesus Sanchez Rodriguez

Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical and less intuitive for students than classical model-based methods. This is particularly the case for reinforcement learning, an approach that does n ...

PUBLIC LIBRARY SCIENCE2023

Spatially explicit effective reproduction numbers from incidence and mobility data

Andrea Rinaldo, Cristiano Trevisin, Enrico Bertuzzo, Lorenzo Mari, Damiano Pasetto, Marino Gatto

Current methods for near real-time estimation of effective reproduction numbers from surveillance data overlook mobility fluxes of infectors and susceptible individuals within a spatially connected network (the metapopulation). Exchanges of infections amon ...

2023

Landscapes of DNA Mechanics and Genomes

Thomas Antonin Zwahlen

The local physical properties - such as shape and flexibility - of the DNA double-helix is today widely believed to be influenced by nucleic acid sequence in a non-trivial way. Furthermore, there is strong evidence that these properties play a role in many ...

EPFL2023

lifex-ep: a robust and efficient software for cardiac electrophysiology simulations

Alfio Quarteroni, Francesco Regazzoni, Stefano Pagani, Marco Fedele

Background: Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors ...

BMC2023

Back-to-Africa introductions of Mycobacterium tuberculosis as the main cause of tuberculosis in Dar es Salaam, Tanzania

Jacques Fellay, Zhi Ming Xu

In settings with high tuberculosis (TB) endemicity, distinct genotypes of the Mycobacterium tuberculosis complex (MTBC) often differ in prevalence. However, the factors leading to these differences remain poorly understood. Here we studied the MTBC populat ...

PUBLIC LIBRARY SCIENCE2023

Anomalous Dissipation and Lack of Selection in the Obukhov-Corrsin Theory of Scalar Turbulence

Maria Colombo, Massimo Sorella

The Obukhov-Corrsin theory of scalar turbulence [21, 54] advances quantitative predictions on passive-scalar advection in a turbulent regime and can be regarded as the analogue for passive scalars of Kolmogorov's K41 theory of fully developed turbulence [4 ...

Springernature2023

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Dynamics of CLIMP-63 S-acylation control ER morphology

Nature Portfolio2023

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Colin Neil Jones, Roland Schwan, Melanie Nicole Zeilinger, Xuan Truong Nghiem

IEEE2023

Beyond undulation! Body morphology and sensing components of elongated animals and robots reveal skills to maintain competent locomotion

Laura Isabel Paez Coy

EPFL2023