In 'Ecodemocracy in the Wild: If existing democracies were to operationalize ecocentrism and animal ethics in policy-making, what would rewilding look like?' Helen Kopnina, Simon Leadbeater, Paul Cryer, Anja Heister, and Tamara Lewis present a democratic approach to considering the interests of entities and the correlation of rights of nature within it. According to the authors , ecodemocracy's overarching potential is to establish the baseline principles that dethrone single species domination and elevate multiple living beings as stakeholders in all decision-making. They provide insights on how ecodemocracy could become manifest and what it takes to achieve mult-species justice. A unique contribution in this chapter is the notion of ecodemocracy in rewilding , exemplified bij the controversial Dutch rewilding experiment in Oostvaardersplassen. The authors discuss the complexities of decision-making in the interest of different species and the challenges that arise when implementing such politics.
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Snelle technologische ontwikkelingen bieden kansen voor de maritieme sector. Zij maken de scheepvaart efficiënter, veiliger en schoner. De techniek heeft regelgeving en professionals nodig die ook klaar zijn voor de toekomst. Het lectoraat Maritime Law voert praktijkgericht onderzoek uit op de scheidslijn van recht en (maritieme) techniek samen met studenten, docenten, het bedrijfsleven en kennisinstellingen.
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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).
