The study of microplastic pollution involves multidisciplinary analyses on a large number of microplastics. Therefore, providing an overview of plastic pollution is time consuming and, despite high throughput analyses, remains a major challenge. The objective of this study is to propose a protocol to determine how many microplastics must be analyzed to give a representative view of the particle size distribution and chemical nature, and calculate the associated margin error. Based on microplastic data from Tara Mediterranean campaign, this approach is explained through different examples. In this particular case, the results show that only 3% of the collected microplastics need to be analyzed to give a precise view on the scale of the North West Mediterranean Basin (error <5%), and 17.7% to give an overview manta per manta (error <10%). This approach could be an important practical contribution to microplastic studies.

Wireless connections are more and more used in different applications and in public areas for services to consumers but also for handling (sometimes) sensitive communications (for instance, in railway systems or for remote video monitoring systems). Such systems can have to face different kind of attacks that target the behind service. Our article aims to detect, as soon as possible and online, attacks that can occur on wireless networks, to be able to react very quickly. In this article, we present some results of data analysis methods, on Wi-Fi signals, to differentiate the ones with attacks from the ones without. This study focuses on low-power jamming attacks with a slight or even no impact on Wi-Fi communications. This is more challenging than detecting high-power jamming attacks, which have already been addressed in the literature. Being able to detect a low-impact attack is a crucial issue in a global security strategy, making it possible to launch countermeasures before the interruption of the communication. The Wi-Fi bands are also in the Industrial, Scientific and Medical (ISM) frequencies, making the environment complicated to analyze. Clustering methods such as agglomerative hierarchical clustering are used to identify some clusters and then to map them to the real classes (with or without attacks). A deep analysis of the clusters obtained in a dataset acquired in uncontrolled conditions is carried out. This is done in order to understand what is responsible of the clustering assignment of the different points and to extract the clusters, which can be used to design a detection attack strategy.

5-Hydroxytryptamine subtype-4 (5-HT₄) receptors have stimulated considerable interest amongst scientists and clinicians owing to their importance in neurophysiology and potential as therapeutic targets. A comparative analysis of hierarchical methods applied to data from one thousand 5-HT₄ receptor–ligand binding interactions was carried out. The chemical structures were described as chemical and pharmacophore fingerprints. The definitions of indices, related to the quality of the hierarchies in being able to distinguish between active and inactive compounds, revealed two interesting hierarchies with the Unity (1 active cluster) and pharmacophore fingerprints (4 active clusters). The results of this study also showed the importance of correct choice of metrics as well as the effectiveness of a new alternative of the Ward clustering algorithm named Energy (Minimum E-Distance method). In parallel, the relationship between these classifications and a previously defined 3D 5-HT₄ antagonist pharmacophore was established.