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Models with several levels of mixing (households, workplaces), as well as various corresponding formulations for R 0 , have been proposed in the literature. However, little attention has been paid to the impact of the distribution of the population size within social structures, effect that can help plan effective interventions. We focus on the influence on the model outcomes of teleworking strategies, consisting in reshaping the distribution of workplace sizes. We consider a stochastic SIR model with two levels of mixing, accounting for a uniformly mixing general population, each individual belonging also to a household and a workplace. The variance of the workplace size distribution appears to be a good proxy for the impact of this distribution on key outcomes of the epidemic, such as epidemic size and peak. In particular, our findings suggest that strategies where the proportion of individuals teleworking depends sublinearly on the size of the workplace outperform the strategy with linear dependence. Besides, one drawback of the model with multiple levels of mixing is its complexity, raising interest in a reduced model. We propose a homogeneously mixing SIR ODE-based model, whose infection rate is chosen as to observe the growth rate of the initial model. This reduced model yields a generally satisfying approximation of the epidemic. These results, robust to various changes in model structure, are very promising from the perspective of implementing effective strategies based on social distancing of specific contacts. Furthermore, they contribute to the effort of building relevant approximations of individual based models at intermediate scales., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Microscopy image observation is commonly performed on 2D screens, which limits human capacities to grasp volumetric, complex, and discrete biological dynamics. With the massive production of multidimensional images (3D + time, multi-channels) and derived images (e.g., restored images, segmentation maps, and object tracks), scientists need appropriate visualization and navigation methods to better apprehend the amount of information in their content. New modes of visualization have emerged, including virtual reality (VR)/augmented reality (AR) approaches which should allow more accurate analysis and exploration of large time series of volumetric images, such as those produced by the latest 3D + time fluorescence microscopy. They include integrated algorithms that allow researchers to interactively explore complex spatiotemporal objects at the scale of single cells or multicellular systems, almost in a real time manner. In practice, however, immersion of the user within 3D + time microscopy data represents both a paradigm shift in human-image interaction and an acculturation challenge, for the concerned community. To promote a broader adoption of these approaches by biologists, further dialogue is needed between the bioimaging community and the VR&AR developers., Competing Interests: J-BM is one of the cofounders, shareholder and Chief Scientific Officer (CSO) of AVATAR MEDICAL SAS, a startup that commercializes software for surgery planning in virtual reality. The DIVA software used in this perspective is not being commercialized by AVATAR MEDICAL SAS although the company’s technology is based on the same technology. The DIVA software used here is freely available and is reported in El Beheiry, et al. “DIVA: Natural Navigation Inside 3D Images Using Virtual Reality”. Journal of Molecular Biology 432, no. 16 (2020): 474. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Valades-Cruz, Leconte, Fouche, Blanc, Van Hille, Fournier, Laurent, Gallean, Deslandes, Hajj, Faure, Argelaguet, Trubuil, Isenberg, Masson, Salamero and Kervrann.)

Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives, such as: understanding epidemic drivers; forecasting epidemics; and prioritising control measures. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. Modelling challenges contribute to understanding the pros and cons of different approaches and to fostering technical dialogue between modellers. In this paper, we present the results of the first modelling challenge in animal health - the ASF Challenge - which focused on a synthetic epidemic of African swine fever (ASF) on an island. The modelling approaches proposed by five independent international teams were compared. We assessed their ability to predict temporal and spatial epidemic expansion at the interface between domestic pigs and wild boar, and to prioritise a limited number of alternative interventions. We also compared their qualitative and quantitative spatio-temporal predictions over the first two one-month projection phases of the challenge. Top-performing models in predicting the ASF epidemic differed according to the challenge phase, host species, and in predicting spatial or temporal dynamics. Ensemble models built using all team-predictions outperformed any individual model in at least one phase. The ASF Challenge demonstrated that accounting for the interface between livestock and wildlife is key to increasing our effectiveness in controlling emerging animal diseases, and contributed to improving the readiness of the scientific community to face future ASF epidemics. Finally, we discuss the lessons learnt from model comparison to guide decision making., Competing Interests: Declaration of interest The authors have no conflict of interest., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Several in vitro studies have previously demonstrated that the addition of TGF-beta to aortic smooth muscle cells or skin fibroblasts stimulates elastin synthesis. It is not clear however whether, in vivo, TGF-beta participates in the regulation of elastin synthesis, especially in physiological conditions. The aim of our study was to explore the localization of elastin mRNA and TGF-beta1 in the rat thoracic aorta (an elastic artery) and caudal artery (a muscular artery). Elastin mRNA was localized by in situ hybridization and quantified using Northern blot analysis. TGF-beta1 was detected using immunohistochemistry. The study was carried out as a function of age (rats of 3, 10, 20, and 30 months). We observed that TGF-beta1 immunoreactivity is present predominantly, but not exclusively, at the sites of elastin synthesis as determined by elastin mRNA detection: in smooth muscle cells in the aorta and in endothelial cells in the caudal artery. The ability of exogenously added TGF-beta1 (0.001-10 ng/ml) to modulate the steady-state levels of elastin mRNA in primary cultures of endothelial cells, smooth muscle cells, and fibroblasts isolated from the thoracic aorta was also studied. At the highest concentration used, elastin mRNA levels increased 5-fold in endothelial cells and 11-fold in smooth muscle cells. The demonstration that TGF-beta1 immunoreactivity is present at the sites of elastin synthesis in the thoracic aorta and in the caudal artery and the observation that TGF-beta1 induces an increase in elastin mRNA levels in cultured endothelial cells and smooth muscle cells suggest that TGF-beta1 may be implicated, at least in part, in the physiological regulation of elastin gene expression.