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.
As I gaze out of my window, I am met with a totem. This totem is gray and windowless, nestled in between offices and academic buildings. Behind it is a park, and the longer I stare, the deeper it becomes embedded in the natural landscape, after a bit I forget it’s there. But in the corner of my eye I can see another one; another totem. This one intimidates me with its red glow. These buildings came to serve as mystical pillars of data flows to me, they became sites of reification, sites where the cloud finally condensed and data rained down. They assumed a posthuman status; high-tech facilities where humans are only needed to keep other humans out. I always imagined data as something abstract, as a floating entity, but as my encounters with these pillars started a process of materialization, it simultaneously sparked a desire to interrogate and to demystify.
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Screentime Airtime Facetime: Practicing Hybridity in the Cultural Field is the final publication of Going Hybrid, an applied research program on the future of hybridity in the cultural field. How could Covid-triggered innovations in digital cultural programming be turned into durable ways of high-level, participatory livecasting? How do you report on hybrid events? And how do you collect the results in a living and accessible archive? This publication gathers the findings of two years of hands-on experiments, introduces the developed prototypes, and gives insight into the research process.Because we believe in critical making, this book is itself a hybrid entity. It was originally a live-streamed online event and later turned into a print and a digital publication – each version a little different than what you would expect of a livestream, website, or print book. We encourage you to playfully explore the various versions of Screentime Airtime Facetime and hope that you will gain joy and insight from the form of this book as much as from its contents.Going Hybrid (2021-2023) was a research project of the Institute of Network Cultures, in collaboration with Willem de Kooning Academy, MU Hybrid Art House, Framer Framed, IMPAKT, Hackers & Designers, The Hmm, Varia, Anna Maria Michael, Ania Molenda, and Maria van der Togt.
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