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Transcultural validation in Soninke of a language assessment tool: The Avicenne ELAL <sup>©</sup>
No full textInternational audienceChildren of migrants are often exposed to more than one language from an early age. The Avicenne ELAL © test has been created to better assess language skills, avoid misdiagnosing learning disabilities, and inform early interventions. Plurilingual children aged 3.5 to 6.5 take the test in their mother tongues, with an interpreter's assistance. The test comprises three scales: Comprehension, Expression, and Storytelling. The objectives of this study were to describe steps of the transcultural validation of the Avicenne ELAL © for the Soninke culture and language, both for children living in a monolingual environment in Mauritania and for migrant children living in a multilingual environment in France; to compare the performance of these two groups; and to explore its qualitative use in studying language pathways among bilingual and plurilingual children. A total of 71 children participated in this study in Mauritania ( n = 25) and France ( n = 46). The Avicenne ELAL ©, a 30-minute plurilingual language assessment using objects, picture boards, and storytelling tasks, was administered in Soninke (and in French for children in France) to migrant children in France and to children in Mauritania, with standardized procedures to minimize distractions and ensure comfort. All sessions were recorded, de-identified, and supplemented with field notes and a logbook to capture contextual and qualitative elements of children's language use. The results of the two groups of children were analyzed and compared with quantitative and qualitative methods. Given the excellent results of the monolingual children, the ELAL in the Soninke language can be considered valid. Statistical analysis confirmed significant differences between the children's scores in Mauritania and France, for both the total score and each separate scale (Comprehension, Expression, and Storytelling). The results also showed that the quality of the narrative skills (storytelling) was strongly correlated with the child's age. Comparing the language assessments collected in these two settings highlights the variations and cultural specificities that should be considered when studying the language skills of Soninke-speaking migrant children
High spatiotemporal resolution traffic CO <sub>2</sub> emission maps derived from Floating Car Data (FCD) for 20 European cities (2023)
No full textInternational audienceAbstract. On-road transportation is a major contributor to CO2 emissions in cities, and high-resolution CO2 traffic emission maps are essential for analyzing emission patterns and characteristics. In this study, we developed new hourly on-road CO2 emission maps with a 100 × 100 m resolution for 20 major cities in France, Germany, and the Netherlands in 2023. We used commercial Floating Car Data (FCD) based on anonymized GPS signals periodically reported by individual vehicles, providing hourly information on mean speed and the number of GPS sample counts per street. Machine learning models were developed to fill FCD data gaps and convert sample counts into actual traffic volumes, and the COPERT model was used to estimate speed- and vehicle-type-dependent emission factors. These models were calibrated using independent traffic observations available for Paris and Berlin, and subsequently applied to the remaining 18 cities in an extrapolated manner due to data availability constraints. Hourly emissions, initially estimated at the street level, were aggregated to 100 × 100 m grid cells. Annual on-road CO2 emissions across the 20 European cities in 2023 ranged from 0.4 to 7.9 Mt CO2, with emissions strongly correlated with urban area (R2= 0.98) and, to a lesser extent, population size (R2= 0.74). Spatially, emissions are either highly concentrated along major highways in cities such as Paris and Amsterdam or more evenly distributed in cities such as Berlin and Bordeaux, highlighting the need for context-specific mitigation strategies. Temporally, this study shows the CO2 emission fluctuations due to holiday periods, weekly activity cycles, and distinct usage profiles of different vehicle types. Due to the low latency of FCD, this approach could support near-real-time traffic emission mapping in the future. Our approach enhances the spatial and temporal characterization of CO2 emissions in on-road transportation compared to the conventional method used in gridded inventories, indicating the potential of FCD data for near-real-time urban emission monitoring and timely policy-making. The datasets generated by this study are available on Zenodo https://doi.org/10.5281/zenodo.16600210 (Shi et al., 2025)
Urinary incontinence is common among people attending pulmonary rehabilitation, yet pulmonary rehabilitation has a small effect on urinary symptoms: A multicenter prospective cohort study
No full textInternational audienceUI is common among individuals attending PR, yet PR has a small effect on urinary symptoms. Despite this, individuals with UI may still achieve improvements from the program. Our findings suggest that UI should not delay PR initiation but should be screened and managed as part of the multidisciplinary care that defines PR
Impact of labor induction on cesarean risk and maternal–fetal outcomes by gestational age in primiparas with a previous cesarean delivery: LUSTrial secondary analysis
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Guidelines for the Design of Custom Affinity-Based Probes for Metalloproteases
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Decadal Shift in the Secondary Organic Aerosol Response to Winter Haze Mitigation in Eastern China: Insights from Aerosol Mass Spectrometry Measurements and Machine Learning
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Contribution of medical hypnosis via virtual reality in managing pain and anxiety in patients undergoing invasive sampling techniques for prenatal diagnosis: a prospective study
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Substantial contribution of trees outside forests to above-ground carbon across China
No full textInternational audienceAbstract Accurately quantifying canopy height and above-ground carbon across diverse land-cover types is crucial for understanding carbon storage dynamics and guiding climate-mitigation strategies. Yet existing maps often overlook non-forest ecosystems. Here we present a deep learning framework based on a U-Net architecture that combines radar, optical, elevation and slope data to produce a 10 m canopy height map across China. The model is trained with laser measurements from NASA's GEDI mission validated using unmanned aerial vehicle lidar data ( MAE = 2.39 m ). We then estimate the above-ground biomass and carbon from these heights using a Random Forest model ( MAE = 37.71 Mg ha-1 ). By deriving carbon from canopy height, we take advantage of U-Net's ability to capture trees in non-forest ecosystems such as croplands, grasslands and urban areas. Our nationwide 30 m carbon map reveals that trees outside forests contribute 20.8-32.9% of China's above-ground carbon in 2019 (3.62-5.72 Pg C), underscoring their importance
