École Normale Supérieure Paris-Saclay

International audience

The emergence of online social platforms, such as social networks and social media, has drastically affected the way people apprehend the information flows to which they are exposed. In such platforms, various information cascades spreading among users is the main force creating complex dynamics of opinion formation, each user being characterized by their own behavior adoption mechanism. Moreover, the spread of multiple pieces of information or beliefs in a networked population is rarely uncorrelated. In this article, we introduce the mixture of interacting cascades (<monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MIC</monospace>), a model of marked multidimensional Hawkes processes with the capacity to model jointly non-trivial interaction between cascades and users. We emphasize on the interplay between information cascades and user activity, and use a mixture of temporal point processes to build a coupled user/cascade point process model. Experiments on synthetic and real data highlight the benefits of this approach and demonstrate that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MIC</monospace> achieves superior performance to existing methods in modeling the spread of information cascades. Finally, we demonstrate how <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MIC</monospace> can provide, through its learned parameters, insightful bi-layered visualizations of real social network activity data.

The widespread increase of antibiotic resistant bacteria (ARB) in agriculture has led to an interest in improving the competitive exclusion ability of the microbiota in livestock through the application of probiotics. In this study, we examined a collection of gram-negative bacteria isolated from healthy chickens for their ability to inhibit the growth of Salmonella enterica serovar Typhimurium. Twenty-five bacteria isolated from healthy chickens were mixed at a 1:1 or a 10:1 ratio with S. enterica ser. Typhimurium and grown overnight in co-culture on a solid media that contained bile salt. The S. enterica in each co-culture was quantified using on Salmonella Shigella Agar (SSA) to determine which isolates can inhibit the growth of S. enterica in vitro. Isolates that showed inhibitory action against S. enterica ser. Typhimurium were further analyzed by whole genome sequencing (WGS) to identify potential mechanisms. The WGS analysis, included using a variety of tools to identify potential mechanisms of S. enterica ser. Typhimurium antagonism. Three bacterial isolates: Alcaligenes faecalis Ae-14, Citrobacter braakii J-D0-20, and Escherichia ruysiae J-D0-44 significantly reduced the growth of S. enterica ser. Typhimurium in co-culture. Growth inhibition was most pronounced during the exponential growth phase. Sequence analysis revealed that the potential mechanisms of S. enterica ser. Typhimurium inhibition by C. braakii J-D0-20 and E.ruysiae J-D0-44 could be through T6SS-mediated competition, bacteriocin (carocin D) production, having a similar quorum sensing system, or by competition for similar nutrients. The potential antagonistic mechanisms for A. faecalis Ae-14 were unclear. Some bacteria, found naturally in the fecal material of healthy chickens, are capable inhibiting the growth of S. enterica ser. Typhimurium in co-culture and should be further investigated as potential probiotic prophylactics to provide protection against S. enterica ser. Typhimurium infections.

Antibiotic combinations aim to maximise drug treatment efficiency and minimise resistance evolution, but a full understanding of their effect on bacterial cells is lacking. The interaction between the DNA-damaging antibiotic ciprofloxacin and the translation inhibitor tetracycline is antagonistic, resulting in a weaker effect on bacterial growth than expected from each drug individually. While this antagonism has been analysed at the population level, it has not been investigated at the single-cell level. We used a microfluidic device to quantify the antagonism between ciprofloxacin and tetracycline in single bacterial cells under three nutrient conditions. Improved growth results from increased survival of cells under the drug combination compared to ciprofloxacin alone. This effect depends on the initial drug-free growth rate, with better suppression in nutrient-rich conditions. Quantifying the DNA damage response (SOS response) revealed two sub-populations among cells that died upon ciprofloxacin treatment. The larger low-SOS sub-population, which showed increased survival compared to high-SOS cells, explains the stronger antagonistic effect in nutrient-rich conditions. Our results underscore the importance of single-cell quantification in understanding bacterial responses to antibiotic combinations.

Discrete Element Method (DEM) simulations of granular media are computationally intensive, particularly during initialization phases dominated by large displacements and kinetic energy. This paper presents a novel generative pipeline based on 3D diffusion models that directly synthesizes arbitrarily large granular assemblies in mechanically realistic configurations. The approach employs a two-stage pipeline. First, an unconditional diffusion model generates independent 3D voxel grids representing granular media; second, a 3D inpainting model, adapted from 2D techniques using masked inputs and repainting strategies, seamlessly stitches these grids together. The inpainting model uses the outputs of the unconditional diffusion model to learn from the context of adjacent generations and creates new regions that blend smoothly into the context region. Both models are trained on binarized 3D occupancy grids derived from a database of small-scale DEM simulations, scaling linearly with the number of output voxels. Simulations that spanned over days can now run in hours, practically enabling simulations containing more than 200k ballast particles. The pipeline remains fully compatible with existing DEM workflows as it post-processes the diffusion generated voxel grids into DEM compatible particle meshes. Being mechanically consistent on key granulometry metrics with the original DEM simulations, the pipeline is also compatible with many other applications in the field of granular media, with capability of generating both convex and non-convex particles. Showcased on two examples (railway ballast and lunar regolith), the pipeline reimagines the way initialization of granular media simulations is performed, enabling scales of generation previously unattainable with traditional DEM simulations.