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Collet, P.

Publications and source records attributed to Collet, P..

2 recordsLinked to original sources

Human-environment feedback and the consistency of proenvironmental behaviour

Addressing global environmental crises such as anthropogenic climate change requires the consistent adoption of proenvironmental behavior by a large part of a population. Here, we develop a mathematical model of a simple behavior-environment feedback loop to ask how the individual assessment of the environmental state combines with social interactions to influence the consistent adoption of proenvironmental behavior, and how this feeds back to the perceived environmental state. In this stochastic individual-based model, individuals can switch between two behaviors, active (or actively proenvironmental) and baseline, differing in their perceived cost (higher for the active behavior) and environmental impact (lower for the active behavior). We show that the deterministic dynamics and the stochastic fluctuations of the system can be approximated by ordinary differential equations and a Ornstein-Uhlenbeck type process. By definition, the proenvironmental behavior is adopted consistently when, at population stationary state, its frequency is high and random fluctuations in frequency are small. We find that the combination of social and environmental feedbacks can promote the spread of costly proenvironmental behavior when neither, operating in isolation, would. To be adopted consistently, strong social pressure for proenvironmental action is necessary but not sufficient - social interactions must occur on a faster timescale compared to individual assessment, and the difference in perceived environmental impact must be small. This simple model suggests a scenario to achieve large reductions in environmental impact, which involves incrementally more active and potentially more costly behavior being consistently adopted under increasing social pressure for proenvironmentalism.

ecology↗

IMPatienT: an integrated web application to digitize, process and explore multimodal patient data.

Medical acts, such as imaging, lead to the production of several medical text report that describes the relevant findings. This induces multimodality in patient data by linking image data to free-text and consequently, multimodal data have become central to drive research and improve diagnosis. However, the exploitation of patient data is challenging as the ecosystem of analysis tools is fragmented depending on the type of data (images, text, genetics), the task (processing, exploration) and domains of interest (clinical phenotype, histology). To address the challenges, we present IMPatienT (Integrated digital Multimodal PATIENt daTa), a simple, flexible and open-source web application to digitize, process and explore multimodal patient data. IMPatienT has a modular architecture to: (i) create a standard vocabulary for a domain, (ii) digitize and process free-text data, (iii) annotate images and perform image segmentation, and (iv) generate a visualization dashboard and perform diagnosis suggestions. We showcased IMPatienT on a corpus of 40 simulated muscle biopsy reports of congenital myopathy patients. As IMPatienT relies on a user-designed vocabulary, it can be adapted to any domain of research and can be used as a patient registry for exploratory data analysis (EDA). A demo instance of the application is available at https://impatient.lbgi.fr/.

bioinformatics↗