Publications by authors named "F Schmidt"

Genotoxicity is a critical determinant for assessing the safety of pharmaceutical drugs, their metabolites, and impurities. Among genotoxicity tests, mechanistic assays such as the MultiFlow® DNA damage assay (MFA) allows the investigations on mode of action (MoA) of DNA damage through four mechanistic markers recorded at two time points. Previous studies have shown that machine learning (ML) can enhance precision on classifying the MoA of genotoxicants.

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The cytosolic nucleic acid sensors RIG-I and cGAS induce type-I interferon (IFN)-mediated immune responses to RNA and DNA viruses, respectively. So far no connection between the two cytosolic pathways upstream of IKK-like kinase activation has been investigated. Here, we identify heterogeneous nuclear ribonucleoprotein M (hnRNPM) as a positive regulator of IRF3 phosphorylation and type-I IFN induction downstream of both cGAS and RIG-I.

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Optical pooled screening offers a broader-scale alternative to enrichment-based perturbation screening, using fluorescence microscopy to correlate phenotypes and perturbations across single cells. Previous methods work well in large, transcriptionally active cell lines, because they rely on cytosolic detection of endogenously expressed barcoded transcripts; however, they are limited by reliable cell segmentation, cytosol size, transcriptional activity and cell density. Nuclear In-Situ Sequencing (NIS-Seq) expands this technology by creating bright sequencing signals directly from nuclear genomic DNA to screen nucleated cells at high density and high library complexity.

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This dataset provides a numerical simulation of pavement mechanical behavior under Traffic Speed Deflectometer (TSD) measurements. It consists of simulated deflection slope data for various pavement structures and subgrade properties, generated using the Alizé-LCPC software, a standard tool in French pavement engineering. The dataset addresses limitations in traditional Falling Weight Deflectometer (FWD) methods, offering a more accurate and computationally efficient approach for estimating the Subgrade Resilient Modulus (M) using machine learning models.

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We present optimal Bayesian field-level cosmological constraints from nonlinear tracers of cosmic large-scale structure, specifically the amplitude σ_{8} of linear matter fluctuations inferred from rest-frame simulated dark matter halos in a comoving volume of 8  (h^{-1} Gpc)^{3}. Our constraint on σ_{8} is entirely due to nonlinear information, and obtained by explicitly sampling the initial conditions along with tracer bias and noise parameters via a Lagrangian effective field theory-based forward model, leftfield. The comparison with a simulation-based inference of the power spectrum and bispectrum-likewise using the leftfield forward model-shows that, when including precisely the same modes of the same data up to k_{max}=0.

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