Over ecotoxicologische grenzen:de choreografie van stoffen in de natuur

Oratie uitgesproken door Prof.dr. Martina G. Vijver bij de aanvaarding van het ambt van hoogleraar in de Ecotoxicologie aan de Universiteit Leiden op vrijdag 16 november 2018Environmental Biolog

The Ins and Outs of Bioaccumulation: Metal Bioaccumulation Kinetics in Soil Invertebrates in Relation to Availability and Physiology

Bioaccumulation is the nett result of a metal influx influenced by the environment and an outflux driven by the animal. In this thesis two physiological different invertebrate species, the earthworm and isopod are studied. The focus of the research was on the route of uptake, the quantification of uptake and elimination kinetics, and the way organisms deal with accumulated metals so-called internal metal sequestration. Transfer functions for both organisms were derived in which metal availability in soil was related to animal physiology. These transfer functions can be used to estimate the degree of metal bioaccumulation under changing environmental conditions.Straalen, N.M. van [Promotor]Gestel, C.A.M. van [Copromotor]Vink, J.P.M. [Copromotor

Snel nieuwe plek voor Levend Lab

Environmental Biolog

Bestrijden van de Trosbosbes in de Peel - overzicht van de mogelijkheden voor het inzetten van het chemische bestrijdingsmiddel glyfosaat en biologische bestrijding

Conservation Biology - ol

Bestrijdingsmiddelen en waterkwaliteit

Het aantal bestrijdingsmiddelen dat op de Nederlandse markt is toegelaten neemt de laatste jaren toe en ook het gebruik van de bestrijdingsmiddelen in de land- en tuinbouw stijgt. Dit staat haaks op de beleidsvoornemens uit het verleden waar werd gepleit voor een vermindering van het gebruik en een vermindering van de afhankelijkheid van bestrijdingsmiddelen. Desondanks lijkt het de laatste jaren toch mogelijk om een intensieve landbouw te hebben naast een redelijke waterkwaliteit. Vijftig jaar na Dode Lente drijven er zelden meer dode vissen in het water en zijn grote problemen met bestrijdingsmiddelen in het oppervlaktewater waaruit drinkwater wordt gemaakt verleden tijd

Virus brengt pesticide in bodem

Environmental Biolog

Bestrijdingsmiddelen en Waterkwaliteit: 50 jaar na Silent Spring

Conservation Biology - ol

Development of a quasi-quantitative structure-activity relationship model for prediction of the immobilization response of Daphnia magna exposed to metal-based nanomaterials

The conventional Hill equation model is suitable to fit dose-response data obtained from performing (eco)toxicity assays. Models based on quasi-quantitative structure-activity relationships (QSARs) to estimate the Hill coefficient ( n H ) ${n}_{{\rm{H}}})$ were developed with the aim of predicting the response of the invertebrate species Daphnia magna to exposure to metal-based nanomaterials. Descriptors representing the pristine properties of nanoparticles and media conditions were coded to a quasi-simplified molecular input line entry system and correlated to experimentally derived values of n H ${n}_{{\rm{H}}}$. Monte Carlo optimization was used to model the set of n H ${n}_{{\rm{H}}}$ values, and the model was trained on the basis of reported dose-response relationships of 60 data sets (n = 367 individual response observations) of 11 metal-based nanomaterials as obtained from 20 literature reports. The model simulates the training data well, with only 2.3% deviation between experimental and modeled response data. The technique was employed to predict the dose-response relationships of 15 additional data sets (n = 72 individual observations) not included in model development of seven metal-based nanomaterials from 10 literature reports, with an average error of 3.5%. Combining the model output with either the median effective concentration value or any other known effect level as obtained from experimental data allows the prediction of full dose-response curves of D. magna immobilization. This model is an accurate screening tool that allows the determination of the shape and slope of dose-response curves, thereby greatly reducing experimental effort in case of novel advanced metal-based nanomaterials or the prediction of responses in altered exposure media. This screening model is compliant with the 3Rs (replacement, reduction, and refinement) principle, which is embraced by the scientific and regulatory communities dealing with nano-safety. Environ Toxicol Chem 2022;00:1-12. (c) 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.Environmental Biolog

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

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 makes them appealing over larger sized materials. As the production and development of ENMs have increased considerably over time, so too have concerns regarding their adverse effects and environmental impacts. It is unfeasible to assess the risks associated with every single ENM through in vivo or in vitro experiments. As an alternative, in silico methods can be employed to evaluate ENMs. To perform such an evaluation, we collected data from databases and literature to create classification models based on machine learning algorithms in accordance with the principles laid out by the OECD for the creation of QSARs. The aim was to investigate the performance of various machine learning algorithms towards predicting a well-defined in vivo toxicity endpoint (Daphnia magna immobilization) and also to identify which features are important drivers of D. magna in vivo nanotoxicity. Results indicated highly comparable model performance between all algorithms and predictive performance exceeding ∼0.7 for all evaluated metrics (e.g. accuracy, sensitivity, specificity, balanced accuracy, Matthews correlation coefficient, area under the receiver operator characteristic curve). The random forest, artificial neural network, and k-nearest neighbor models displayed the best performance but this was only marginally better compared to the other models. Furthermore, the variable importance analysis indicated that molecular descriptors and physicochemical properties were generally important within most models, while features related to the exposure conditions produced slightly conflicting results. Lastly, results also indicate that reliable and robust machine learning models can be generated for in vivo endpoints with smaller datasets. Horizon 2020(H2020)814426Environmental Biolog