A benchmark dataset for machine learning in ecotoxicology

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Evaluation of genetic demultiplexing of single-cell sequencing data from model species

Single-cell sequencing (sc-seq) provides a species agnostic tool to study cellular processes. However, these technologies are expensive and require sufficient cell quantities and biological replicates to avoid artifactual results. An option to address thes ...

LIFE SCIENCE ALLIANCE LLC2023

Different responses of mice and rats hippocampus CA1 pyramidal neurons to in vitro and in vivo-like inputs

Ying Shi, Armando Romani, Maurizio Ferdinando Pezzoli, Michele Migliore, Rosanna Migliore

The fundamental role of any neuron within a network is to transform complex spatiotemporal synaptic input patterns into individual output spikes. These spikes, in turn, act as inputs for other neurons in the network. Neurons must execute this function acro ...

2023

Chronic warming and dry soils limit carbon uptake and growth despite a longer growing season in beech and oak

Charlotte Grossiord, Yann Vitasse, Margaux Clara Lou Didion-Gency

Progressively warmer and drier climatic conditions impact tree phenology and carbon cycling with large consequences for forest carbon balance. However, it remains unclear how individual impacts of warming and drier soils differ from their combined effects ...

Oxford Univ Press Inc2023

Using continuous electrical conductivity measurements to derive major solute concentrations in karst systems

Paolo Benettin

Hydrochemical data of karst springs provide valuable insights into the internal hydrodynamical functioning of karst systems and support model structure identification. However, the collection of high-frequency time series of major solute species is limited ...

WILEY2023

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Emilio Maddalena

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

EPFL2023

Sulfurospirillum diekertiae sp. nov., a tetrachloroethene-respiring bacterium isolated from contaminated soil

Christof Holliger, Julien Maillard, Junxiong Wang

Two anaerobic, tetrachloroethene- (PCE-) respiring bacterial isolates, designated strain ACSDCE T and strain ACSTCE, were characterized using a polyphasic approach. Cells were Gram-stain-negative, motile, non-spore-forming and shared a vibrioid- to spirill ...

2023

Aiming beyond slight increases in accuracy

Daniel Probst

Owing to the diminishing returns of deep learning and the focus on model accuracy, machine learning for chemistry might become an endeavour exclusive to well-funded institutions and industry. Extending the focus to model efficiency and interpretability wil ...

NATURE PORTFOLIO2023

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

Christian Leinenbach, Sergey Shevchik, Rafal Wróbel

Metal-based Laser Powder Bed Fusion (LPBF) has made fabricating intricate components easier. Yet, assessing part quality is inefficient, relying on costly Computed Tomography (CT) scans or time-consuming destructive tests. Also, intermittent inspection of ...

Elsevier Science Sa2023

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Rita Drissi Daoudi

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

EPFL2023

Data-driven approaches for non-invasive cuffless blood pressure monitoring

Clémentine Léa Aguet

Blood pressure (BP) is a crucial indicator of cardiovascular health. Hypertension is a common life-threatening condition and a key factor of cardiovascular diseases (CVDs). Identifying abnormal BP fluctuations can allow for early detection and management o ...

EPFL2023

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Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Rita Drissi Daoudi

EPFL2023

Data-driven approaches for non-invasive cuffless blood pressure monitoring

Clémentine Léa Aguet

EPFL2023

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

Christian Leinenbach, Sergey Shevchik, Rafal Wróbel

Elsevier Science Sa2023