Contrat de prestations Ifremer 2017 . Contrôle de surveillance 2017 DCE de la faune benthique de substrat meuble des masses d’eau côtière « FRFC01 Côte Nord-Est île d’Oléron » et « FRFC02 Pertuis Charentais » : rapport final

L’objet de ce document est d’exposer les résultats des suivis stationnels des invertébrés benthiques de substrats meubles subtidaux et intertidaux réalisé en avril 2017 conformément au protocoles DCE de 2014 (Garcia et al., 2014) sur : - la station subtidale Malconche, - la station subtidale d’appui Boyardville, et - les deux stations intertidales Bellevue et Les Dou

Association rule mining to help detect plant phenolic compounds putatively involved in decreased ruminal methane production in vitro: In the virgin forest of unknown bioactive plant phenolic compounds, association rules work like bushcutters clearing pathways to help discover active compounds

International audienceIntroduction In the search for natural alternatives to synthetic chemicals able to mitigate methane emission by ruminants, bioactive plant secondary metabolites are valuable candidates. However, these phytochemicals come in myriad chemical structures, and any one plant may contain hundreds of them. Even in plant extracts containing tannins, saponins or essential oils, it is difficult to link the presence of a compound or combination to the plant's activity. Here we focus on low-molecular-weight (0.5) were discarded before data mining to avoid false-positives. With the minimum thresholds of 5 for S and 0.5 for C, there were 205 candidate peaks. In a first strategy, results were filtered via the constraints of a) co-occurrence of the peak in the 280 and 320 nm matrices and b) C > 0.65, which narrowed the candidate peaks down to 28. In a second strategy, the constraints were that the peaks had to be major (i.e. more than 10 times the area of the median peak) and present in the plants that showed high antimethanogenic effect (outliers), which narrowed the candidate peaks down to 24. Combining the two strategies resulted in 7 candidate peaks. One peak was easily identified as gallic acid. Based on absorbance spectrum between 200 and 400 nm, three others were cinnamic acid derivatives and two were flavonols. Conclusion Association rules mining was able to select a compact number of peaks making identification feasible. The effect of these pure compounds now has to be verified for proof of the concept. While the algorithm works with qualitative data, using strategy which selects among the major peaks of the profiles serves to integrate the quantitative aspect. Acknowledgements We thank ethnobotanist G. Lalière and the Conservatoire Botanique National du Massif Central for plant collection

Association rule mining to help detect plant phenolic compounds putatively involved in decreased ruminal methane production in vitro: In the virgin forest of unknown bioactive plant phenolic compounds, association rules work like bushcutters clearing pathways to help discover active compounds

International audienceIntroduction In the search for natural alternatives to synthetic chemicals able to mitigate methane emission by ruminants, bioactive plant secondary metabolites are valuable candidates. However, these phytochemicals come in myriad chemical structures, and any one plant may contain hundreds of them. Even in plant extracts containing tannins, saponins or essential oils, it is difficult to link the presence of a compound or combination to the plant's activity. Here we focus on low-molecular-weight (0.5) were discarded before data mining to avoid false-positives. With the minimum thresholds of 5 for S and 0.5 for C, there were 205 candidate peaks. In a first strategy, results were filtered via the constraints of a) co-occurrence of the peak in the 280 and 320 nm matrices and b) C > 0.65, which narrowed the candidate peaks down to 28. In a second strategy, the constraints were that the peaks had to be major (i.e. more than 10 times the area of the median peak) and present in the plants that showed high antimethanogenic effect (outliers), which narrowed the candidate peaks down to 24. Combining the two strategies resulted in 7 candidate peaks. One peak was easily identified as gallic acid. Based on absorbance spectrum between 200 and 400 nm, three others were cinnamic acid derivatives and two were flavonols. Conclusion Association rules mining was able to select a compact number of peaks making identification feasible. The effect of these pure compounds now has to be verified for proof of the concept. While the algorithm works with qualitative data, using strategy which selects among the major peaks of the profiles serves to integrate the quantitative aspect. Acknowledgements We thank ethnobotanist G. Lalière and the Conservatoire Botanique National du Massif Central for plant collection