This article aims to supplement the three “golden rules” of rewilding – or three Cs – the Cores, Carnivores, and Corridors – by a fourth C – Compassion, in discussing the case of Oostvaardeplassen in The Netherlands. The cores refer to large, strictly protected ecologically intact areas, carnivores refer to natural predators, and corridors connect passages for fauna movements. We propose a fourth requirement: Compassion. This fourth C would ensure that any active (re)introduction must be in the interests of the individual animals involved. This article briefly explains the history of the Oostvaardeplassen project and leads into a discussion of the scientific (biological requirements of the species, area, and species fit, etc. ) and ethical (animal welfare, ecocentrism, etc.) constraints and opportunities for rewilding. All four Cs, we argue, are absent from Oostvaardeplassen, which can be considered an example of how rewilding should not be undertaken. Against this background, we propose an alternative way forward. https://www.ecos.org.uk/ecos-406-the-golden-rules-of-rewilding-examining-the-case-of-oostvaardersplassen/ LinkedIn: https://www.linkedin.com/in/helenkopnina/
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Abstract-Architecture Compliance Checking (ACC) is an approach to verify the conformance of implemented program code to high-level models of architectural design. ACC is used to prevent architectural erosion during the development and evolution of a software system. Static ACC, based on static software analysis techniques, focuses on the modular architecture and especially on rules constraining the modular elements. A semantically rich modular architecture (SRMA) is expressive and may contain modules with different semantics, like layers and subsystems, constrained by rules of different types. To check the conformance to an SRMA, ACC-tools should support the module and rule types used by the architect. This paper presents requirements regarding SRMA support and an inventory of common module and rule types, on which basis eight commercial and non-commercial tools were tested. The test results show large differences between the tools, but all could improve their support of SRMA, what might contribute to the adoption of ACC in practice.
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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).
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