Environmental risk assessment of GE plants under low-exposure conditions

The requirement for environmental risk assessment (ERA) of genetically engineered (GE) plants prior to large scale or commercial introduction into the environment is well established in national laws and regulations, as well as in international agreements. Since the first introductions of GE plants in commercial agriculture in the 1990s, a nearly universal paradigm has emerged for conducting these assessments based on a few guiding principles. These include the concept of case-by-case assessment, the use of comparative assessments, and a focus of the ERA on characteristics of the plant, the introduced trait, and the receiving environment as well as the intended use. In practice, however, ERAs for GE plants have frequently focused on achieving highly detailed characterizations of potential hazards at the expense of consideration of the relevant levels of exposure. This emphasis on exhaustive hazard characterization can lead to great difficulties when applied to ERA for GE plants under low-exposure conditions. This paper presents some relevant considerations for conducting an ERA for a GE plant in a low-exposure scenario in the context of the generalized ERA paradigm, building on discussions and case studies presented during a session at ISBGMO 12

Problem formulation in the environmental risk assessment for genetically modified plants

Problem formulation is the first step in environmental risk assessment (ERA) where policy goals, scope, assessment endpoints, and methodology are distilled to an explicitly stated problem and approach for analysis. The consistency and utility of ERAs for genetically modified (GM) plants can be improved through rigorous problem formulation (PF), producing an analysis plan that describes relevant exposure scenarios and the potential consequences of these scenarios. A properly executed PF assures the relevance of ERA outcomes for decision-making. Adopting a harmonized approach to problem formulation should bring about greater uniformity in the ERA process for GM plants among regulatory regimes globally. This paper is the product of an international expert group convened by the International Life Sciences Institute (ILSI) Research Foundation

“The future is unstable”: Exploring changing fertility intentions in the United Kingdom during the COVID-19 pandemic

Acknowledgement: The Economic and Social Research Council funded data collection as part of the lead author's PhD studentship.Objective: To understand whether reproductive decision-making among United Kingdom (UK) respondents had changed in light of the COVID-19 pandemic and, if so, why COVID-19 had led them to change their intentions. Methods: We conducted a cross-sectional online survey in January 2021. We asked survey participants if their fertility intentions had changed and to rate how aspects of their life had changed during COVID-19. We also included an open-ended question and asked participants to explain in their own words how COVID-19 had influenced their reproductive decision-making. We used descriptive and regression analyses to explore the quantitative data and thematically analyzed written responses. Results: Nine percent (n = 70) of our 789 UK respondents reported a change in fertility intention after the start of the pandemic. Changes in both pro-natal and anti-natal directions made the overall change in intentions small: there was a 2% increase across the sample in not intending a child between the two time points. Only increased financial insecurity was predictive of changing intentions. Responses to the open-ended question (n = 103) listed health concerns, indirect costs of the pandemic, and changing work-life priorities as reasons for changing their intentions. Conclusion: While studies conducted at the beginning of the pandemic found that fertility intentions became more anti-natal, we found little overall change in fertility intentions in January 2021. Our findings of small pro-natal and anti-natal changes in fertility intentions align with emerging UK birth rate data for 2021, which show minimal change in the total fertility rate in response to the pandemic.Economic and Social Research Council National Science Centre (Poland). Grant Number: 2018/30/E/HS4/0044

Human-machine scientific discovery

International audienceHumanity is facing existential, societal challenges related to food security, ecosystem conservation, antimicrobial resistance, etc, and Artificial Intelligence (AI) is already playing an important role in tackling these new challenges. Most current AI approaches are limited when it comes to ‘knowledge transfer’ with humans, i.e. it is difficult to incorporate existing human knowledge and also the output knowledge is not human comprehensible. In this chapter we demonstrate how a combination of comprehensible machine learning, text-mining and domain knowledge could enhance human-machine collaboration for the purpose of automated scientific discovery where humans and computers jointly develop and evaluate scientific theories. As a case study, we describe a combination of logic-based machine learning (which included human-encoded ecological background knowledge) and text-mining from scientific publications (to verify machine-learned hypotheses) for the purpose of automated discovery of ecological interaction networks (food-webs) to detect change in agricultural ecosystems using the Farm Scale Evaluations (FSEs) of genetically modified herbicide-tolerant (GMHT) crops dataset. The results included novel food-web hypotheses, some confirmed by subsequent experimental studies (e.g. DNA analysis) and published in scientific journals. These machine-leaned food-webs were also used as the basis of a recent study revealing resilience of agro-ecosystems to changes in farming management using GMHT crops