Zoekresultaten

The probable future of toxicology - probabilistic risk assessment

Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.

Geographical dimensions of risk management

Land use or land-use changes can trigger or generate hazards and affect the potential consequences of these hazards. Deforestation can trigger land slides, for example, and land reclamation or levee construction can increase flood hazards downstream. New dwellings in or near forests can trigger wildfires, especially if home owners fail to prioritise fire safety measures. In addition, if land is used for industrial activities, new technological hazards, such as the risks resulting from the storage or production of hazardous materials, can be introduced into the environment. Moreover, land-use changes can increase damage potential. Residential developments in hazard-prone areas, such as areas prone to flooding or earthquakes, can negatively affect the number of properties and people exposed to hazards. Consequently, spatial planning activities that are concerned with influencing land use by locating physical structures and activities such as agriculture, recreation or industry within a territory (Couclelis, 2005; Tewdwr-Jones, 2001) can result in new or increased safety risks in a particular area.

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.

From individual sustainability orientations to collective sustainability innovation and sustainable entrepreneurial ecosystems

Sustainability and economic growth—the integration and balance of social, environmental, and economic needs—is a salient concern for sustainable development and social well-being. By focusing on a sustainable innovation project, we explore how entrepreneurial ecosystems become sustainable entrepreneurial ecosystems and investigate the interactions of entrepreneurial actors. We conducted an inductive, single-case study of a specific collaborative innovation project in the denim industry specialized in a specific geographic location. From our data, we show that the presence of four conditional aspects foster sustainable entrepreneurial ecosystems. These include sustainability orientation of actors, recognition of sustainable opportunities and resource mobilization, collaborative innovation of sustainability opportunities, and markets for sustainable products. We make two observations that contribute to the literature. First, we see that in a sustainable entrepreneurial ecosystem, entrepreneurial experimentation is a highly interdependent and interactive process. Second, we see that recognition of sustainable opportunities is distributed among different actors in the ecosystem. Our findings also have implications for practitioners and policy-makers.

Integrated flood disaster management and spatial information

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 147-154, 2014www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-8/147/2014/doi:10.5194/isprsarchives-XL-8-147-2014Integrated flood disaster management and spatial information: Case studies ofNetherlands and IndiaS. Zlatanova1, T. Ghawana2, A. Kaur2, and J. M. M. Neuvel31Faculty of Architecture, Jullianalaan, TU Delft, 134, 2628BL Delft, the Netherlands2Centre for Disaster Management Studies, Guru Gobind Singh Indraprastha University, Sector-16C, Dwarka, New Delhi, P.O. Box-110078, India3Saxion University of Applied Sciences, Risk management, Handelskade 75, 7417 DH Deventer, the NetherlandsKeywords: Floods, Spatial Information Infrastructure, GIS, Risk Management, Emergency Management Abstract. Spatial Information is an integral part of flood management practices which include risk management &emergency response processes. Although risk & emergency management activities have their own characteristics, forexample, related to the time scales, time pressure, activities & actors involved, it is still possible to identify at least onecommon challenge that constrains the ability of risk & emergency management to plan for & manage emergencieseffectively and efficiently i.e. the need for better information. Considering this aspect, this paper explores flood managementin Netherlands& India with an emphasis on spatial information requirements of each system. The paper examines theactivities, actors & information needs related to flood management. Changing perspectives on flood management inNetherlands are studied where additional attention is being paid to the organization and preparation of flood emergencymanagement. Role of different key actors involved in risk management is explored. Indian Flood management guidelines, byNational Disaster Management Authority, are analyzed in context of their history, institutional framework, achievements andgaps. Flood Forecasting System of Central Water Commission of India is also analyzed in context of spatial dimensions.Further, information overlap between risk & emergency management from the perspectives of spatial planners & emergencyresponders and role of GIS based modelling / simulation is analyzed. Finally, the need for an integrated spatial informationstructure is explained & discussed in detail. This examination of flood management practices in the Netherlands and Indiawith an emphasis on the required spatial information in these practices has revealed an increased recognition of the stronginterdependence between risk management and emergency response processes. Consequently, the importance of anintegrated spatial information infrastructure that facilitates the process of both risk and emergency management isaddressed.Conference Paper (PDF, 1063 KB)   Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 147-154, 2014www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-8/147/2014/doi:10.5194/isprsarchives-XL-8-147-2014Integrated flood disaster management and spatial information: Case studies ofNetherlands and IndiaS. Zlatanova1, T. Ghawana2, A. Kaur2, and J. M. M. Neuvel31Faculty of Architecture, Jullianalaan, TU Delft, 134, 2628BL Delft, the Netherlands2Centre for Disaster Management Studies, Guru Gobind Singh Indraprastha University, Sector-16C, Dwarka, New Delhi, P.O. Box-110078, India3Saxion University of Applied Sciences, Risk management, Handelskade 75, 7417 DH Deventer, the NetherlandsKeywords: Floods, Spatial Information Infrastructure, GIS, Risk Management, Emergency ManagementAbstract. Spatial Information is an integral part of flood management practices which include risk management &emergency response processes. Although risk & emergency management activities have their own characteristics, forexample, related to the time scales, time pressure, activities & actors involved, it is still possible to identify at least onecommon&

Documentation of assumptions and system vulnerability monitoring