Time series causal relationships discovery through feature importance and ensemble models.

Inferring causal relationships from observational data is a key challenge in understanding the interpretability of Machine Learning models. Given the ever-increasing amount of observational data available in many areas, Machine Learning algorithms used for forecasting have become more complex, leading to a less understandable path of how a decision is made by the model. To address this issue, we propose leveraging ensemble models, e.g., Random Forest, to assess which input features the trained model prioritizes when making a forecast and, in this way, establish causal relationships between the variables. The advantage of these algorithms lies in their ability to provide feature importance, which allows us to build the causal network. We present our methodology to estimate causality in time series from oil field production. As it is difficult to extract causal relations from a real field, we also included a synthetic oil production dataset and a weather dataset, which is also synthetic, to provide the ground truth. We aim to perform causal discovery, i.e., establish the existing connections between the variables in each dataset. Through an iterative process of improving the forecasting of a target's value, we evaluate whether the forecasting improves by adding information from a new potential driver; if so, we state that the driver causally affects the target. On the oil field-related datasets, our causal analysis results agree with the interwell connections already confirmed by tracer information; whenever the tracer data are available, we used it as our ground truth. This consistency between both estimated and confirmed connections provides us the confidence about the effectiveness of our proposed methodology. To our knowledge, this is the first time causal analysis using solely production data is employed to discover interwell connections in an oil field dataset.

Antiparasitic properties of 4-nerolidylcatechol from Pothomorphe umbellata (L.) Miq. (Piperaceae) in vitro and in mice models with either prepatent or patent Schistosoma mansoni infections.

ETHNOPHARMACOLOGICAL RELEVANCE: Roots of Pothomorphe umbellata (L.) Miq. are used in traditional medicine of Africa and South America for the treatment of malaria and helminthiasis. However, neither P. umbellata nor its isolated compounds have been evaluated against Schistosoma species. AIMS OF THIS STUDY: To investigate the antischistosomal effects of P. umbellata root extracts and the isolated compound 4-nerolidylcatechol (4-NC) against Schistosoma mansoni ex vivo and in murine models of schistosomiasis. MATERIALS AND METHODS: The crude hydroalcoholic (PuE) and hexane (PuH) extracts of P. umbellata roots were prepared and initially submitted to an ex vivo phenotypic screening against adult S. mansoni. PuH was analyzed by HPLC-DAD, characterized by UHPLC-HRMS/MS, and submitted to chromatographic fractionation, leading to the isolation of 4-NC. The anthelmintic properties of 4-NC were assayed ex vivo against adult schistosomes and in murine models of schistosomiasis for both patent and prepatent S. mansoni infections. Praziquantel (PZQ) was used as a reference compound. RESULTS: PuE (EC50: 18.7 µg/mL) and PuH (EC50: 9.2 µg/mL) kill adult schistosomes ex vivo. The UHPLC-HRMS/MS analysis of PuH, the most active extract, revealed the presence of 4-NC, peltatol A, and peltatol B or C. After isolation from PuH, 4-NC presented remarkable in vitro schistosomicidal activity with EC50 of 2.9 µM (0.91 µg/mL) and a selectivity index higher than 68 against Vero mammalian cells, without affecting viability of nematode Caenorhabditis elegans. In patent S. mansoni infection, the oral treatment with 4-NC decreased worm burden and egg production in 52.1% and 52.3%, respectively, also reducing splenomegaly and hepatomegaly. 4-NC, unlike PZQ, showed in vivo efficacy against juvenile S. mansoni, decreasing worm burden in 52.4%. CONCLUSIONS: This study demonstrates that P. umbellata roots possess antischistosomal activity, giving support for the medicinal use of this plant against parasites. 4-NC was identified from P. umbellata roots as one of the effective in vitro and in vivo antischistosomal compound and as a potential lead for the development of novel anthelmintics.