Organoclorated and Organophosphorus Pesticides in the Pelotas River Sediment

Model, Kathleen Jeniffer; Sampaio, Sílvio César; Remor, Marcelo Bevilacqua; Mercante, Erivelto; Boas, Márcio Antônio Vilas

doi:10.1590/1809-4430-eng.agric.v38n1p124-134/2018

Cited by 4 publications

(3 citation statements)

References 48 publications

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“…The main threats in continental waters are changes in habitats arising from land uses and impoundments (Pelicice et al 2017) and these are also imminent in the Pelotas River basin (Model et al 2018). A growing number of hydroelectric projects are being established in the Upper Uruguay River basin in Brazil.…”

Section: Discussionmentioning

confidence: 99%

Fish fauna of the Pelotas River, Upper Uruguay River, southern Brazil

et al. 2019

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The fish fauna of the Pelotas River, in the Upper Uruguay ecoregion of southern Brazil was inventoried. Samplings were performed quarterly between August 2013 and May 2014 at 14 sites in the Pelotas River and its tributaries, using gill nets of different mesh sizes, sein nets, cast nets and electrofishing. In total, 7,745 specimens were recorded, comprising 46 species, belonging to 13 families and five orders. Approximately 80% of the species belonged to the orders Characiformes and Siluriformes, as well as the families Characidae and Loricariidae, which included a greater number of species. Of the total species, 24% were endemic to the Upper Uruguay River, four were identified only at the genus level and none was considered non-native. Approximately, half of the 98 species previously recorded for the Upper Uruguay basin were also detected in this study. The tributaries of the Pelotas River exhibited different faunas with five exclusive species. Thirty species were present in both the tributaries and the main channel. In addition, higher species richness, as well as the presence of larger-sized species were observed in the main channel. Many of the species not restricted to the Upper Uruguay River have already been reported as part of the Taquari-Antas River fauna. However, the local biodiversity of this region is under several threats, such as land use and installation of small hydroelectric plants. These results highlight the importance of the conservation of plateau/upland rivers, especially in the Upper Uruguay ecoregion, which shows a peculiar fish fauna and endemism.

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Section: Discussionmentioning

confidence: 99%

Fish fauna of the Pelotas River, Upper Uruguay River, southern Brazil

et al. 2019

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“…We proposed a consensus model combining the classification scores provided by five different ML approaches: SVM, random forest (RF), adaptive boosting (ADA), extreme gradient boosting (XGB), , and K-nearest neighbors (KNN) . It is worth noting that this is only one of the possible choices among many others.…”

Section: Methodsmentioning

confidence: 99%

“…We proposed a consensus model combining the classification scores provided by five different ML approaches: SVM, 64 random forest (RF), 65 adaptive boosting (ADA), 66 extreme gradient boosting (XGB), 67,68 and K-nearest neighbors (KNN). 69 It is worth noting that this is only one of the possible choices among many others. We opted for these five methods just because in our previous study, 47 we observed that they were the most accurate for modeling Dev Tox.…”

Section: ■ Materialsmentioning

confidence: 99%

TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity

Mastrolorito,

Togo,

Gambacorta

et al. 2024

Chem. Res. Toxicol.

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Despite being extremely relevant for the protection of prenatal and neonatal health, the developmental toxicity (Dev Tox) is a highly complex endpoint whose molecular rationale is still largely unknown. The lack of availability of high-quality data as well as robust nontesting methods makes its understanding even more difficult. Thus, the application of new explainable alternative methods is of utmost importance, with Dev Tox being one of the most animal-intensive research themes of regulatory toxicology. Descending from TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), the present work describes TISBE (TIRESIA Improved on Structure-Based Explainability), a new public web platform implementing four fundamental advancements for in silico analyses: a three times larger dataset, a transparent XAI (explainable artificial intelligence) framework employing a fragment-based fingerprint coding, a novel consensus classifier based on five independent machine learning models, and a new applicability domain (AD) method based on a double top-down approach for better estimating the prediction reliability. The training set (TS) includes as many as 1008 chemicals annotated with experimental toxicity values. Based on a 5-fold cross-validation, a median value of 0.410 for the Matthews correlation coefficient was calculated; TISBE was very effective, with a median value of sensitivity and specificity equal to 0.984 and 0.274, respectively. TISBE was applied on two external pools made of 1484 bioactive compounds and 85 pediatric drugs taken from ChEMBL (Chemical European Molecular Biology Laboratory) and TEDDY (Task-Force in Europe for Drug Development in the Young) repositories, respectively. Notably, TISBE gives users the option to clearly spot the molecular fragments responsible for the toxicity or the safety of a given chemical query and is available for free at .

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