Présence de chimiorécepteurs sur l'aile des tsé-tsé (Diptera : Glossinidae)

Cette note signale pour la première fois l'existence de chimiorécepteurs sur les ailes des mouches tsé-tsé. Ceux-ci sont principalement localisés sur le milieu de la nervure costale. Leur morphologie est comparable à celle des chimiorécepteurs observés sur les pattes. Leur nombre ne différe pas entre les sexes comme pour les pattes, mais entre les espèces. Ceci suggère un rôle dans la perception chimique proche de l'environnement, par rapport aux chimiorécepteurs des pattes qui semblent impliqués dans le comportement sexuel. L'étude a été conduite sur six espèces ou sous-espèces de glossines. (Résumé d'auteur

MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks

Predicting multiple real-world tasks in a single model often requires a particularly diverse feature space. Multimodal (MM) models aim to extract the synergistic predictive potential of multiple data types to create a shared feature space with aligned semantic meaning across inputs of drastically varying sizes (i.e. images, text, sound). Most current MM architectures fuse these representations in parallel, which not only limits their interpretability but also creates a dependency on modality availability. We present MultiModN, a multimodal, modular network that fuses latent representations in a sequence of any number, combination, or type of modality while providing granular real-time predictive feedback on any number or combination of predictive tasks. MultiModN's composable pipeline is interpretable-by-design, as well as innately multi-task and robust to the fundamental issue of biased missingness. We perform four experiments on several benchmark MM datasets across 10 real-world tasks (predicting medical diagnoses, academic performance, and weather), and show that MultiModN's sequential MM fusion does not compromise performance compared with a baseline of parallel fusion. By simulating the challenging bias of missing not-at-random (MNAR), this work shows that, contrary to MultiModN, parallel fusion baselines erroneously learn MNAR and suffer catastrophic failure when faced with different patterns of MNAR at inference. To the best of our knowledge, this is the first inherently MNAR-resistant approach to MM modeling. In conclusion, MultiModN provides granular insights, robustness, and flexibility without compromising performance.Comment: Accepted as a full paper at NeurIPS 2023 in New Orleans, US

Adaptive Mitigation: Identification of the Dynamic Drivers of Effective Policy during the COVID-19 Pandemic

Introduction: The unprecedented speed and scale of the COVID-19 pandemic necessitated the rapid implementation of untested public health measures to mitigate the consequences of viral spread. In the 8 months that have passed since the first recognized case, an enormous amount of research has been published evaluating the efficacy of the various policies implemented by different countries, however the majority of these studies focus on a specific region or time, over-representing high-income countries during periods of extreme transmission or “peak events”. The aim of this study is to provide a more general analysis that considers a global scope of the pandemic and the dynamic drivers that build effective policies to mitigate human interactions and slow the spread of disease. Methods: We collected a range of information regarding epidemic trends, weather, demographics, government response and mobility reports across the globe. We then built an hybrid Neural Network that combined an LSTM layer with a multilayer perceptron to infer the reproduction number from various non-epidemiological factors. The model was designed to predict the reproduction number (R-value) on 93 countries with available data and compare it to our ground truth estimate obtained from officially reported epidemiological data. Finally, we used an alternative model to assess the impact of public health measures on the epidemic. Findings: From the available features, we obtained the best performances using demographics combined with mobility features. The sanitary indices (beds/thousand, diabetes prevalence, ...) did not help the prediction and, more interestingly, the pressure indicator of historical weather forecast improved the prediction of the reproduction number by about 4.5%. This optimized model predicted the reproduction number with a mean absolute error of 0.254 across the 93 countries over the time of the epidemic. For many countries (Switzerland, United Kingdom, South Africa, ...) this error passed below 0.17. An alternative version of the model allowed us to estimate the impact of policies in terms of average reduction in reproduction number, and more importantly, allowed us to compare these trends between countries. For instance, we observe that the model showed that no policy had a positive impact in India as opposed to Switzerland, where most of them are associated to improved epidemic control. Conclusion: Understanding these complex interactions may allow individuals and policy makers to better adapt mitigation strategies to optimize the efficacy of the implemented policies

Final report on NLP analysis and normalization

This document describes the ALVIS NLP line that has been developed.This document describes the ALVIS NLP line that has been developed. In ALVIS, it is used to analyze the crawled documents that are then indexed on semantic and domain-specific grounds. It is also to process training corpora in order to acquire the specialized linguistic and domain resources (in WP6) that are then exploited for the semantic analysis of larger document collections. Four languages are addressed in ALVIS: English, French, Slovene and Chinese. Some specific NLP modules have been developed and integrated for each language. The resulting NLP lines have been tested trough several experiments on different domains, on document collections of various size and with various degrees of NLP analysis

Florilege : a database gathering microbial phenotypes of food interest

Food fermentation and biopreservation processes involve the use of various species and strains of bacteria and yeast. These strains are responsible for the targeted qualities of the food products that are sanitary, organoleptic (aroma and texture) and healthy qualities. The Florilege database project aims at (i) gathering bacterial and yeast phenotypes of food product of interest that are automatically extracted from PubMed-referenced full-text litterature by using a text mining approach (ii) managing the information in a relational database (iii) enabling multi-criteria requests via a Web user-friendly interface. To date 368 phenotypes, 260 synthetised or degraded molecules, 1076 medium or food products, 1181 bacterial taxons have been acquired by a combinaison of automatic and manual annotations of text, used for training the text-mining method.Food products are automatically categorized in Florilege according to the OntoBiotope ontology that we have extended with dairy and bakery products definitions. Taxa are categorized by the NCBI taxonomy. An ontology of microbial characteristics has been specifically enriched by the Florilege project. This ontology defines microbial phenotypes (Ontobiotope-Phenotype), including intracellular characteristics of cells (such as shape, antibiotic resistance...) and microbial uses (Ontobiotope-Use) that express the microbial alteration of the external environment, food or matrix, such as aroma, vitamin or other molecule production, degradation or food coloring.A preliminary Web interface is available for querying taxa, culture medium and food products at http://genome.jouy.inra.fr/Florilege/. The public availability of Florilege database is planned for the end of 2017 with a user-friendly interface for multi-criteria requests and access to various phenotypes.Florilege will be a highly valuable tool to (i) assess phenotypic biodiversity of food microbes (ii) assign biochemical functions to each strain/species from fermented or biopreserved food products (iii) help into the development of innovative food products in particular those that involve fermentation or biopreservation processes

Florilège: a database gathering microbial phenotypes of food interest

International audienceFlorilège: a database gathering microbial phenotypes of food interest. 2017 Scientific MEM days: Journées scientifiques MEM (Métaomiques et écosystèmes microbiens

Florilège: a database gathering microbial phenotypes of food interest

International audienceFlorilège: a database gathering microbial phenotypes of food interest. 2017 Scientific MEM days: Journées scientifiques MEM (Métaomiques et écosystèmes microbiens