Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
This research is commissioned by the professorship Novel Proteins: Insects and Fish, Healthy, Sustainable and Safe (INVIS) and conducted with the aim to investigate the constraints that hinder the uptake of insect-based feed in the Dutch finfish aquaculture branch and advise upon how to initiate a transition within the branch to adopt insect meal in fish feed widely. This is a underlying report of the webinar Insect culture in the Netherlands for feed and food on January 19, 2021.
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The scope of this thesis of Gerrit Bouwhuis, lecturer at Saxion Research Centre for Design and Technology in Enschede is the development of a new industrial applicable pre-treatment process for cotton based on catalysis. The pre-treatment generally consists of desizing, scouring and bleaching. These processes can be continuous or batch wise. Advances in the science of biocatalytic pre-treatment of cotton and catalytic bleaching formed the scientific basis for this work. The work of Agrawal on enzymes for bio-scouring and of Topalovic on catalytic bleaching led to the conclusion that reduced reaction temperatures for the pre-treatment processes of cotton are possible. A second reason for the present work is a persistent and strong pressure on the industry to implement ‘more sustainable’ and environmental friendlier processes. It was clear that for the industrial implementation of the newly developed process it would be necessary to ‘translate’ the academic knowledge based on the catalysts, into a process at conditions that are applicable in textile industry. Previous experiences learned that the transition from academic knowledge into industrial applicable processes often failed. This is caused by lack of experience of university researchers with industrial product and process development as well as a lack of awareness of industrial developers of academic research. This is especially evident for the so-called Small and Medium Enterprises (SME’s). To overcome this gap a first step was to organize collaboration between academic institutes and industries. The basis for the collaboration was the prospect of this work for benefits for all parties involved. A rational approach has been adopted by first gathering knowledge about the properties and morphology of cotton and the know how on the conventional pre-treatment process. To be able to understand the conventional processes it was necessary not only to explore the chemical and physical aspects but also to evaluate the process conditions and equipment that are used. This information has been the basis for the present lab research on combined bio-catalytic desizing and scouring as well as catalytic bleaching. For the measurement of the performance of the treatments and the process steps, the performance indicators have been evaluated and selected. Here the choice has been made to use industrially known and accepted performance indicators. For the new bio-catalytic pre-treatment an enzyme cocktail, consisting of amylase, cutinase and pectinase has been developed. The process conditions in the enzyme cocktail tests have been explored reflecting different pre-treatment equipment as they are used in practice and for their different operation conditions. The exploration showed that combined bio-catalytic desizing and scouring seemed attractive for industrial application, with major reduction of the reaction and the rinsing temperatures, leading to several advantages. The performance of this treatment, when compared with the existing industrial treatment showed that the quality of the treated fabric was comparable or better than the present industrial standard, while concentrations enzymes in the cocktail have not yet been fully optimized. To explore the application of a manganese catalyst in the bleaching step of the pre-treatment process the fabrics were treated with the enzyme cocktail prior to the bleaching. It has been decided not to use conventional pre-treatment processes because in that case the combined desizing and scouring step would not be integrated in the newly developed process. To explore catalytic bleaching it has been tried to mimic the existing industrial processes where possible. The use of the catalyst at 100°C, as occurs in a conventional steamer, leads to decomposition of the catalyst and thus no bleach activation occurs. This led to the conclusion that catalytic bleaching is not possible in present steamers nor at low temperatur
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