HAL-Université de Bretagne Occidentale
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Imitation of Business Models: The Case of Online News Industry in France
No full textInternational audienceIn the rapidly evolving landscape of online business models within the news industry, the phenomenon of business model imitation hasemerged as a prominent yet understudied aspect. This paper investigates the forms and implications of strategic imitation on competitiondynamics and economic sustainability, focusing on French news producers from 2015 to 2020. Leveraging an extensive original datasetand methodology, our findings reveal a prevalent trend of imitation, particularly driven by rivalry-based dynamics. However, this copycatstrategy poses long-term risks to industry performance and societal systems. To ensure economic viability and foster innovation, industrystakeholders must prioritize the development of original and forward-thinking strategies in response to the challenges posed by imitation
L'acceptabilité des pratiques de sobriété par les consommateurs : une étude exploratoire dans le domaine alimentaire
No full textInternational audienc
Estimation of the time-varying probability density function from ensemble simulations and observations using Analogs
No full textInternational audienceUnderstanding the role of ocean-atmosphere interactions is crucial in determining the drivers of ocean variability. Indeed, a part of this variability is not driven by the atmosphere but spontaneously and randomly generated by the ocean through non-linear processes. This internal variability is associated with multiple spatial and temporal scales, and may complicate the detection and attribution of climate change signals. Hence, quantifying the relative importance of atmospherically-forced and chaotic intrinsic variability is necessary to understand the mechanisms of climate change in the ocean-atmosphere system. However, both atmospherically-forced and intrinsic variability cannot be estimated with a single model experiment alone : an ensemble simulation approach is required. The ensemble mean approximates the component of the oceanic variability that is due to the influence of the atmosphere, while the spread represents the range of the estimated intrinsic variability. This work investigates the possibility of describing and predicting the random part of the ocean's variability from observations using an ensemble of ocean simulations in the North Atlantic ocean. An analog-based method is developed, and applied to Sea Surface Height data, with the aim of obtaining a less-computationally expensive method of estimating the time-varying probability function (PDF) that is normally obtained through ensemble simulation. The ensemble is supplied by the multi-decadal (1960-2015) global ocean/sea-ice eddy-permitting (1/4° resolution) large (50-member) ensemble simulation (OCCIPUT Experiment). The ensemble of SSH data as a whole provides the target PDF that we seek to estimate in a regions representative of the diversity of flows in the North Atlantic (e.g. at the centre of the North Atlantic gyre and in the Gulfstream current). The individual members are used to form the catalog of simulations in order to find analogs situations on which the estimate of the target PDF is based at time t. First results are promising and show that we are able to estimate the ensemble mean, but the variance is still a subject of active work due to the complexity of the shape of the PDF. The method greatly reduces the time and resources of computation by producing mean and variance of time-varying PDF for the entire time series in generally a few tens of minutes.keywords : Internal variability, detection and attribution, model uncertainty, ocean-atmosphere interaction, predictabilit
Learning-based calibration of ocean carbon models to tackle physical forcing uncertainties and observation sparsity
No full textBiogeochemical (BGC) ocean models are simplified representations of complex coupled processes, usually resulting in a large number of parameters, that need to be calibrated. In general, these parameters are constrained relying on incomplete and very heterogeneous sets of data. In addition, as biogeochemical tracers strongly depend on ocean circulation, the spatio-temporal uncertainties in the physical forcing can bias the circulation, which makes challenging the calibration of ocean carbon models.This study addresses the calibration of ocean biogeochemical models when dealing with imperfect physical forcings and sparse observations. We design a numerical testbed based on a simple 0{\sc d}+t BGC model. It comprises different uncertainty scenarios for the physical forcing as well as different observation configurations of the considered NPZD (nutrient, phytoplankton, zooplankton, detritus) dynamics.We propose and benchmark a learning-based scheme against a variational data assimilation (DA) approach. The former frames the calibration as learning a neural operator between observations and model parameters. The experiments revealed that the DA-based calibration is highly sensitive to imperfect physical forcing and limited observations, often leading to significant estimation errors in BGC parameters. Conversely, the learning-based approach demonstrated a greater robustness in parameter estimation and simulated BGC patterns. We discuss further how these results could transfer to more realistic BGC models and real observing systems
Cis-Regulation of the CFTR Gene in Pancreatic Cells
No full textInternational audienceGenome organization is essential for precise spatial and temporal gene expression and relies on interactions between promoters and distal cis-regulatory elements (CREs), which constitute ~8% of the human genome. For the cystic fibrosis transmembrane conductance regulator (CFTR) gene, tissue-specific expression, especially in the pancreas, remains poorly understood. Unraveling its regulation could clarify the clinical heterogeneity observed in cystic fibrosis and CFTR-related disorders. To understand the role of 3D chromatin architecture in establishing tissue-specific expression of the CFTR gene, we mapped chromatin interactions and epigenomic regulation in Capan-1 pancreatic cells. Candidate CREs are validated by luciferase reporter assay and CRISPR knock-out. We identified active CREs not only around the CFTR gene but also outside the topologically associating domain (TAD). We demonstrate the involvement of multiple CREs upstream and downstream of the CFTR gene and reveal a cooperative effect of the -44 kb, -35 kb, +15.6 kb, and +37.7 kb regions, which share common predicted transcription factor (TF) motifs. We also extend our analysis to compare 3D chromatin conformation in intestinal and pancreatic cells, providing valuable insights into the tissue specificity of CREs in regulating CFTR gene expression.</div
Predictive models of clinical outcome of endovascular treatment for anterior circulation stroke using machine learning
No full textInternational audienceBackground and purpose: Mechanical Thrombectomy (MT) has recently become the standard of care for anterior circulation stroke with large vessel occlusion, but predictive factors of successful MT are still not clearly defined. To tailor treatment individually for each patient, the aim of this study was to evaluate the performances of Machine Learning to predict clinical outcome (mRS) at 3 months after MT. Material and methods: From the ETIS French prospective multicenter registry, data from patients who underwent MT for anterior circulation stroke with large vessel occlusion between January 2018 and December 2020 were extracted. Three machine learning models (Support Vector Machine, Random Forest and XGBoost) have been trained with clinical, biological and brain imaging data available in emergency conditions from the cohort of patients treated from 2018 to 2019. Models' performances to predict good outcome (3-months mRS <3) were evaluated on patients treated in 2020. Performances were evaluated with AUC, accuracy, sensitivity and specificity, then ROC curves AUC were compared with the best performing model. Results: 4297 patients were included, 1737 (40 %) with good outcome and 2560 (60 %) with bad outcome were used to train models and 599 patients treated in 2020 were used to evaluate their performances. The best model was obtained with XGBoost: AUC = 0.77, accuracy = 69.3 % but no statistically significant difference existed between models. Conclusion:Our study shows satisfying performances of machine learning to predict clinical outcome after MT using data easily available at initial diagnosis and before the decision to treat.</div
Characterization of the Cystic Phenotype Associated with Monoallelic ALG8 and ALG9 Pathogenic Variants
No full textInternational audienceKey Points Loss-of-function ALG8 and ALG9 variants were enriched in polycystic kidney/liver groups and International Classification of Diseases–coded cystic individuals in population cohorts. The ALG8 and ALG9 kidney phenotypes were usually mild to moderate, and lower eGFR or kidney failure was rare. ALG8 pathogenic variants sometimes resulted in severe polycystic liver disease. Background Autosomal dominant polycystic kidney disease (ADPKD) is a common, inherited nephropathy often resulting in kidney failure. It is genetically heterogeneous; along with the major genes, PKD1 and PKD2 , at least eight others have been suggested. ALG8 pathogenic variants have been associated with autosomal dominant polycystic liver disease and implicated in ADPKD, while ALG9 has been suggested as an ADPKD gene, but details of the phenotypes and penetrance are unclear. Methods We screened >3900 families with cystic kidneys and/or livers using global approaches to detect ALG8 or ALG9 pathogenic variants. In addition, population cohorts with sequence data (Genomics England 100K Genomics Project, UK Biobank, and Mayo Clinic Biobank [MCBB]) were screened for ALG8 / ALG9 pathogenic variants. Results Multicenter screening of individuals with polycystic kidney and/or liver disease identified 51 (1.3%) ALG8 (7 multiplex) and 23 (0.6%) ALG9 (5 multiplex) families—frequencies that were approximately 10× and approximately 24× greater than nonpolycystic kidney disease controls. Analysis of individuals with polycystic kidney disease phenotypes in 100K Genomics Project, UK Biobank, and MCBB identified nine ALG8 (0.39%) and nine ALG9 (0.39%) families, an enriched frequency over controls. Two individuals had PKD1 and ALG8 pathogenic changes. Eighty-nine percent of individuals with ALG8 mutations with imaging in the entire MCBB had kidney cysts (50%, >10 cysts), with greater median kidney and liver cyst numbers than controls. For ALG9, 78% had kidney cysts (27%, >10 cysts). Individuals with ALG8 mutations typically had mild cystic kidneys with limited enlargement. Liver cysts were common (71%), with enlarged livers (>2L) found in 11 of 62 patients, although surgical intervention was rare. The ALG9 kidney phenotype was also of mild cystic kidneys, but enlarged livers were rare; for both genes, CKD or kidney failure were rare. Conclusions ALG8 and ALG9 are defined as cystic kidney/liver genes but with limited penetrance for lower eGFR
Presentation of the Citizens’ Observatory Pilots
No full textInternational audienceThe AGEO project aims to implement five pilots of citizen observatories to improve the monitoring and management of natural hazards in the Atlantic Arc Area, through active participation of stakeholders, local communities and citizens in multiple aspects of risk assessment and prevention. These will be referred to as “pilots” in the following
Thriving Through Synergy: Fostering a SOLAS Science Community Built on Equity, International Connections, and the Integration of Early Career Scientists
No full textInternational audienceThe Surface Ocean-Lower Atmosphere Study (SOLAS) is a global research network dedicated to advancing coupled oceanographic and atmospheric science, a field that requires both interdisciplinary and globally distributed expertise. Since 2004, SOLAS has fostered an international interdisciplinary scientific community through coordinated science and capacity sharing activities. This paper outlines how SOLAS 3.0 (2026–2035) will build on this legacy by further prioritizing diversity, equity, and inclusion, and expanding and strengthening research at the ocean-atmosphere interface. SOLAS 3.0 new initiatives include a mentorship program, skill enhancement workshops, increasing access to resources, and a network of observation and training centers. By learning from past successes and challenges, SOLAS 3.0 aims to inspire scientists from around the world, as well as the next generation, to address complex transdisciplinary research and tackle present and future societal challenges in a truly global way