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Daphnia

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Microplastics but not natural particles induce multigenerational effects in Daphnia magna.

Environmental pollution (Barking, Essex : 1987)
Several studies have investigated the effects of nano- and microplastics on daphnids as key freshwater species. However, while information is abundant on the acute toxicity of plastic beads, little is known regarding the multigenerational effects of ...

Prediction of acute toxicity of emerging contaminants on the water flea Daphnia magna by Ant Colony Optimization-Support Vector Machine QSTR models.

Environmental science. Processes & impacts
According to the European REACH Directive, the acute toxicity towards Daphnia magna should be assessed for any industrial chemical with a market volume of more than 1 t/a. Therefore, it is highly recommended to determine the toxicity at a certain con...

Predicting the acute ecotoxicity of chemical substances by machine learning using graph theory.

Chemosphere
Accurate in silico predictions of chemical substance ecotoxicity has become an important issue in recent years. Most conventional methods, such as the Ecological Structure-Activity Relationship (ECOSAR) model, cluster chemical substances empirically ...

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna.

Small (Weinheim an der Bergstrasse, Germany)
This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (E...

Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.

Briefings in bioinformatics
Although a wide variety of machine learning (ML) algorithms have been utilized to learn quantitative structure-activity relationships (QSARs), there is no agreed single best algorithm for QSAR learning. Therefore, a comprehensive understanding of the...

New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments.

Molecules (Basel, Switzerland)
To assess the impact of chemicals on an aquatic environment, toxicological data for three trophic levels are needed to address the chronic and acute toxicities. The use of non-testing methods, such as predictive computational models, was proposed to ...

Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity.

Chemosphere
Engineered nanomaterials (ENMs) are ubiquitous nowadays, finding their application in different fields of technology and various consumer products. Virtually any chemical can be manipulated at the nano-scale to display unique characteristics which ma...

Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives.

International journal of molecular sciences
Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding af...

Application of deep learning for evaluation of the growth rate of Daphnia magna.

Journal of bioscience and bioengineering
For the safe use of chemicals widely used in human activities, it is crucial to assess their ecological impacts when released into the environment. Daphnia, a well-established environmental indicator species, is commonly used to evaluate the biologic...