Emré Anakok’s research while affiliated with Sorbonne University and other places

Pollinators play a crucial role for plant reproduction, either in natural ecosystem or in human-modified landscape. Global change drivers,including climate change or land use modifications, can alter the plant-pollinator interactions. To assert the potential influence of global change drivers on pollination, large-scale interactions, climate and land use data are required. While recent machine learning methods, such as graph neural networks (GNNs), allow the analysis of such datasets, interpreting their results can be challenging. We explore existing methods for interpreting GNNs in order to highlight the effects of various environmental covariates on pollination network connectivity. A large simulation study is performed to confirm whether these methods can detect the interactive effect between a covariate and a genus of plant on connectivity, and whether the application of debiasing techniques influences the estimation of these effects. An application on the Spipoll dataset, with and without accounting for sampling effects, highlights the potential impact of land use on network connectivity and shows that accounting for sampling effects partially alters the estimation of these effects.

The structure of a bipartite interaction network can be described by providing a clustering for each of the two types of nodes. Such clusterings are outputted by fitting a Latent Block Model (LBM) on an observed network that comes from a sampling of species interactions in the field. However, the sampling is limited and possibly uneven. This may jeopardize the fit of the LBM and then the description of the structure of the network by detecting structures which result from the sampling and not from actual underlying ecological phenomena. If the observed interaction network consists of a weighted bipartite network where the number of observed interactions between two species is available, the sampling efforts for all species can be estimated and used to correct the LBM fit. We propose to combine an observation model that accounts for sampling and an LBM for describing the structure of underlying possible ecological interactions. We develop an original inference procedure for this model, the efficiency of which is demonstrated on simulation studies. The pratical interest in ecology of our model is highlighted on a large dataset of plant-pollinator network.

Background: Surgery is performed in 50-70% of Crohn's disease (CD) patients, and its main risk is surgical site infection (SSI). The microbiota has been extensively assessed in CD but not as a potential risk factor for septic morbidity. The objective of this study was to assess the impact of the gut microbiota on SSI in CD. Methods: We used the multicentric REMIND prospective cohort to identify all patients who experienced SSI after ileocolonic resection for CD, defined as any postoperative local septic complication within 90 days after surgery: wound abscess, intra-abdominal collection, anastomotic leakage, enterocutaneous fistula. The mucosa-associated microbiota of the ileal resection specimen was analyzed by 16S sequencing in 149 patients. The variable selection and prediction were performed with random forests (R package VSURF) on clinical and microbiotal data. The criterion of performance that we considered was the area under the Receiver Operating Characteristic (ROC) curve (AUC). Results: SSI occurred in 24 patients (16.1%), including 15 patients (10.1%) with major morbidity. There were no significant differences between patients with or without SSI regarding alpha and beta diversity. The top selected variables for the prediction of SSI were all microbiota-related. The maximum AUC (0.796) was obtained with a model including 14 genera, but an AUC of 0.78 was already obtained with a model including only six genera (Hungatella, Epulopiscium, Fusobacterium, Ruminococcaceae_ucg_009, Actinomyces, and Ralstonia). Conclusion: The gut microbiota has the potential to predict SSI after ileocolonic resection for CD. It might play a role in this frequent postoperative complication.