A worldwide evaluation of trophic magnification of per‐ and polyfluoroalkyl substances in aquatic ecosystems

Phosphorus is one of the most important inorganic nutrients in aquatic ecosystems, the development and functioning of the phytoplankton communities being often correlated with the degree of availability in assimilable forms of this element. Alkaline phosphatase (AP) is an extracellular enzyme with nonspecific activity that catalyses the hydrolysis of a large variety of organic phosphate esters and release orthophosphates. During 2011-2013, AP Activity (APA) was assessed in the water column and sediments of several aquatic ecosystems from Danube Delta: Roșu Lake, Mândra Lake and their adjacent channels – Roșu-Împuțita and Roșu-Puiu. The intensity of APA widely fluctuated, ranging between 230-2578 nmol p-nitrophenol L-1h-1 in the water column and 2104-15631 nmol p-nitrophenol g-1h-1 in sediment. Along the entire period of the study, APA was the most intense in Roșu-Împuțita channel, for both water and sediment samples. Temporal dynamics revealed its highest values in summer for the water column and in autumn for sediment. Statistical analysis showed significant seasonal diferences of the APA dynamics in spring vs. summer and autumn for the water column, and any relevant diferences for sediment.

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Structure-Based Virtual Screening of Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) as Endocrine Disruptors of Androgen Receptor Activity Using Molecular Docking and Machine Learning

10.26434/chemrxiv.11886702.v1 ◽

2020 ◽

Author(s):

Azhagiya Singam Ettayapuram Ramaprasad ◽

Phum Tachachartvanich ◽

Denis Fourches ◽

Anatoly Soshilov ◽

Jennifer C.Y. Hsieh ◽

...

Keyword(s):

Machine Learning ◽

Molecular Docking ◽

Androgen Receptor ◽

Endocrine Disruptors ◽

Hormone Receptors ◽

Steroid Hormone Receptors ◽

Machine Learning Techniques ◽

Support Vector ◽

Polyfluoroalkyl Substances ◽

Perfluoroalkyl And Polyfluoroalkyl Substances

Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) pose a substantial threat as endocrine disruptors, and thus early identification of those that may interact with steroid hormone receptors, such as the androgen receptor (AR), is critical. In this study we screened 5,206 PFASs from the CompTox database against the different binding sites on the AR using both molecular docking and machine learning techniques. We developed support vector machine models trained on Tox21 data to classify the active and inactive PFASs for AR using different chemical fingerprints as features. The maximum accuracy was 95.01% and Matthew’s correlation coefficient (MCC) was 0.76 respectively, based on MACCS fingerprints (MACCSFP). The combination of docking-based screening and machine learning models identified 29 PFASs that have strong potential for activity against the AR and should be considered priority chemicals for biological toxicity testing.

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Faculty Opinions recommendation of Eutrophication of aquatic ecosystems: bistability and soil phosphorus.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1026974.327468 ◽

2005 ◽

Author(s):

Oswald Schmitz

Keyword(s):

Aquatic Ecosystems ◽

Soil Phosphorus

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The Calculation of Turbulent Diffusion Coefficients in Aquatic Ecosystems

Revista de Chimie ◽

10.37358/rc.19.11.7669 ◽

2019 ◽

Vol 70 (11) ◽

pp. 3903-3907

Author(s):

Galina Marusic ◽

Valeriu Panaitescu

Keyword(s):

Transport Equation ◽

Turbulent Diffusion ◽

Diffusion Coefficients ◽

Aquatic Ecosystems ◽

Specific Area ◽

Left Bank ◽

The Right ◽

Prut River

The paper deals with the issues related to the pollution of aquatic ecosystems. The influence of turbulence on the transport and dispersion of pollutants in the mentioned systems, as well as the calculation of the turbulent diffusion coefficients are studied. A case study on the determination of turbulent diffusion coefficients for some sectors of the Prut River is presented. A new method is proposed for the determination of the turbulent diffusion coefficients in the pollutant transport equation for specific sectors of a river, according to the associated number of P�clet, calculated for each specific area: the left bank, the right bank and the middle of the river.

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