Zoekresultaten

The application of natural language processing for the extraction of mechanistic information in toxicology

To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).

The waterharmonica

Does natural leadership exists in elite youth football?

This study assesses the evolutionary leadership theory and the natural leadership instrument of Van Vugt and Ahuja (2011) in the context of youth elite football. The Evolutionary Leadership Theory is a comprehensive new way of looking at leadership that suggests environmental pressures influence the choice of who becomes the leader. The results revealed that the concept of natural leadership, as measured using the six natural leaders questionnaire, cannot be applied to the context of youth football. The preliminary data showed that natural leadership in youth sport requires a more basic framework of leadership consisting of communication, resources and focus on competition.

Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies

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.

The art of the possible: an insider analysis of change processes within a Dutch teaching university

Insider ethnographic analysis is used to analyze change processes in an engineering department. Distributed leadership theory is used as conceptual framework.

Alternatives to natural gas

For centuries, natural gas has been one of humanity’s main energy sources. The gas sector is still heavily reliant on natural gas production; however, as natural gas fields contain only a finite quantity of gas, its continued extraction is leading to the resource’s depletion. Furthermore, natural gas production has become a subject of debate, with many considering continued utilisation incompatible with the achievement of international and European climate goals. The need for alternative gases that are less damaging to the environment is becoming increasingly evident. Biomethane has shown itself to be a reliable alternative to natural gas, and if sourced and manufactured responsibly results in no new CO2 emissions. Another alternative, hydrogen, can, through the process of methanisation, be converted into synthetic natural gas (SNG). This chapter discusses the legal aspects of the production and use of biomethane, hydrogen and SNG.