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Dwell time is crucial for railway operations corresponding to 20% of the total travel time in a mass transit context. With this, it is also a source of delays due to its stochastic nature. One way to ensure the robustness of a timetable is to add margins and much work has been done on run time margins. On the other hand, dwell time margins received little attention, except for a few heuristics. This paper aims to provide a novel method to define dwell time margins. To do so, we introduce the notion of tight dwell time (dwell without margins) in this work and compute it from high-resolution passenger flow data. Then, we propose two novel methods (the cluster method and the quantile method) to estimate it.Given the access to this data, the method enables the estimation of the tight dwell time for all the stops while existing heuristics are limited to late trains and/or few passengers. Our developed method highlights the propensity of existing heuristics to overestimate what they measure. The estimation, a posteriori, of dwell time margins thanks to the computed tight dwell time would help design future timetables.

The effects of hormone stimulation on the cell translational profile remain poorly understood. Here, using polysome profiling combined to RNA sequencing, we analyzed the translational response to follicle-stimulating hormone (FSH) of primary rat Sertoli cells, that exhibit an active anabolic activity regulated by reproductive hormones in the male gonad. We first established that mRNA distribution to polysomes follows a bimodal pattern, with 15% of mRNAs enriched in polysomes and exhibiting high expression. Critically, this basal polysomal enrichment had a major impact on FSH-induced mRNA recruitment to the polysomes, since FSH stimulation promoted the release of polysome-enriched mRNAs, while mRNAs that were the least associated to polysomes were preferentially recruited to polysomes upon stimulation. The FSH signal did not alter the core biological functions of Sertoli cells, but shifted the proteins involved in these functions, suggesting a molecular rewiring of the FSH-induced gene expression. These findings underscore how ribosomal reallocation dynamically adapts the cellular translatome to microenvironmental changes, enabling cells to fine-tune protein production in response to external stimuli.Competing Interest StatementThe authors have declared no competing interest.

Minimization of the (regularized) entropy of classification probabilities is a versatile class of discriminative clustering methods. The classification probabilities are usually defined through the use of some classical losses from supervised classification and the point is to avoid modelisation of the full data distribution by just optimizing the law of the labels conditioned on the observations. We give the first theoretical study of such methods, by specializing to logistic classification probabilities. We prove that if the observations are generated from a two-component isotropic Gaussian mixture, then minimizing the entropy risk over a Euclidean ball indeed allows to identify the separation vector of the mixture. Furthermore, if this separation vector is sparse, then penalizing the empirical risk by a \\(\\ell_{1}\\)-regularization term allows to infer the separation in a high-dimensional space and to recover its support, at standard rates of sparsity problems. Our approach is based on the local convexity of the logistic entropy risk, that occurs if the separation vector is large enough, with a condition on its norm that is independent from the space dimension. This local convexity property also guarantees fast rates in a classical, low-dimensional setting.