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Big Data in Predictive Toxicology
The rate at which toxicological data is generated is continually becoming more rapid and the volume of data generated is growing dramatically. This is due in part to advances in software solutions and cheminformatics approaches which increase the availability of open data from chemical, biological and toxicological and high throughput screening resources. However, the amplified pace and capacity of data generation achieved by these novel techniques presents challenges for organising and analysing data output. Big Data in Predictive Toxicology discusses these challenges as well as the opportunities of new techniques encountered in data science. It addresses the nature of toxicological big data, their storage, analysis and interpretation. It also details how these data can be applied in toxicity prediction, modelling and risk assessment. This title is of particular relevance to researchers and postgraduates working and studying in the fields of computational methods, applied and physical chemistry, cheminformatics, biological sciences, predictive toxicology and safety and hazard assessment.
Computational Nanotoxicology
The development of computational methods that support human health and environmental risk assessment of engineered nanomaterials (ENMs) has attracted great interest because the application of these methods enables us to fill existing experimental data gaps. However, considering the high degree of complexity and multifunctionality of ENMs, computational methods originally developed for regular chemicals cannot always be applied explicitly in nanotoxicology. This book discusses the current state of the art and future needs in the development of computational modeling techniques for nanotoxicology. It focuses on (i) computational chemistry (quantum mechanics, semi-empirical methods, density fun...
Modelling the Toxicity of Nanoparticles
In today’s nanotechnology and pharmaceutical research, alternative toxicology testing methods are crucial for ethically and commercially sound practice. This book provides practical guidelines on how to develop and validate quantitative nanostructure-toxicity relationship (QNTR) models, which are ideal for rapidly exploring the effects of a large number of variables in complex scenarios. Through contributions by academic, industrial, and governmental experts, Modelling the Toxicity of Nanoparticles delivers clear instruction on these methods and their integration and use in risk assessment. Specific topics include the physico-chemical characteristics of engineered nanoparticles, nanoparticle interactions, in vivo nanoparticle processing, and more. A much-needed practical guide, Modelling the Toxicity of Nanoparticles is a key text for researchers as well as government and industry regulators.
DUZ Magazin
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In Silico Toxicology
This book defines the use of computational approaches to predict the environmental toxicity and human health effects of organic chemicals.
Bioanalytical Tools in Water Quality Assessment
Part of Water Quality Set - Buy all four books and save over 30% on buying separately! Bioanalytical Tools in Water Quality Assessment reviews the application of bioanalytical tools to the assessment of water quality including surveillance monitoring. The types of water included range from wastewater to drinking water, including recycled water, as well as treatment processes and advanced water treatment. Bioanalytical Tools in Water Quality Assessment not only demonstrates applications but also fills in the background knowledge in toxicology/ecotoxicology needed to appreciate these applications. Each chapter summarises fundamental material in a targeted way so that information can be applied...
Mitteilungsblatt
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Mixture Toxicity
In the last decade and a half, great progress has been made in the development of concepts and models for mixture toxicity, both in human and environmental toxicology. However, due to their different protection goals, developments have often progressed in parallel but with little integration. Arguably the first book to clearly link ecotoxicology an
