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.
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OBJECTIVE: This work aims to gain insight into the long-term impact of depression course on social network size and perceived loneliness in older people living in the community. METHODS: Within a large representative sample of older people in the community (Longitudinal Aging Study Amsterdam (LASA)), participants with clinically relevant levels of depressive symptoms (scores >16 on the Center for Epidemiological Studies Depression Scale) were followed up over a period of 13 years of the LASA study (five waves). General estimating equations were used to estimate the impact of depression course on network size and loneliness and the interaction with gender and age. RESULTS: An unfavorable course of depression was found to be associated with smaller network sizes and higher levels of loneliness over time, especially in men and older participants. CONCLUSIONS: The findings of this study stress the importance of clinical attention to the negative consequences of chronicity in depressed older people. Clinicians should assess possible erosion of the social network over time and be aware of increased feelings of loneliness in this patient group.
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The aim of this small explorative study was to get an impression of the participants’ views and understanding of the role of becoming a teacher in Swedish schools, realising the characteristic of pedagogy aimed for in the curriculum (in Lgr11 and Lgy), specifically the interaction patterns and student participation in learning processes. Main research questions addressed participants expectations of differences and challenges in the Swedish school context as compared to their experiences in Syria contexts, in specific the development of their understanding of student participation in interaction as characteristic of Swedish education and curriculum. From this, recommendations are formulated for curriculum and research for future Fast Track trajectories.
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Aanleiding Nieuwsuitgeverijen bevinden zich in zwaar weer. Economische malaise en toegenomen concurrentie in het pluriforme medialandschap dwingen uitgeverijen om enerzijds kosten te besparen en tegelijkertijd te investeren in innovatie. De verdere automatisering van de nieuwsredactie vormt hierbij een uitdaging. Buiten de branche ontstaan technieken die uitgeverijen hierbij zouden kunnen gebruiken. Deze zijn nog niet 'vertaald' naar gebruiksvriendelijke systemen voor redactieprocessen. De deelnemers aan het project formuleren voor dit braakliggend terrein een praktijkgericht onderzoek. Doelstelling Dit onderzoek wil antwoord geven op de vraag: Hoe kunnen bewezen en nieuw te ontwikkelen technieken uit het domein van 'natural language processing' een bijdrage leveren aan de automatisering van een nieuwsredactie en het journalistieke product? 'Natural language processing' - het automatisch genereren van taal - is het onderwerp van het onderzoek. In het werkveld staat deze ontwikkeling bekend als 'automated journalism' of 'robotjournalistiek'. Het onderzoek richt zich enerzijds op ontwikkeling van algoritmes ('robots') en anderzijds op de impact van deze technologische ontwikkelingen op het nieuwsveld. De impact wordt onderzocht uit zowel het perspectief van de journalist als de nieuwsconsument. De projectdeelnemers ontwikkelen binnen dit onderzoek twee prototypes die samen het automated-journalismsysteem vormen. Dit systeem gaat tijdens en na het project gebruikt worden door onderzoekers, journalisten, docenten en studenten. Beoogde resultaten Het concrete resultaat van het project is een prototype van een geautomatiseerd redactiesysteem. Verder levert het project inzicht op in de verankering van dit soort systemen binnen een nieuwsredactie. Het onderzoek biedt een nieuw perspectief op de manier waarop de nieuwsconsument de ontwikkeling van 'automated journalism' in Nederland waardeert. Het projectteam deelt de onderzoekresultaten door middel van presentaties voor de uitgeverijbranche, presentaties op wetenschappelijke conferenties, publicaties in (vak)tijdschriften, reflectiebijeenkomsten met collega-opleidingen en een samenvattende white paper.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
Introduction The research group Biobased Resources & Energy (BRE) of Avans focusses on recovery of valuable building blocks from low-value solid and liquid residual streams from agriculture, households and industries. For the valorisation of these residual streams, BRE looks into different biological, chemical and mechanical processes. One of the main issues in the utilisation of residual streams is economic feasibility and the recovery of multiple resources from one residual stream. Using membrane technologies in combination with biological, chemical and/or mechanical processes could offer great opportunities. Central Research Question What is the applicability of membrane technologies for valorisation of different residual streams and is it possible to integrate membrane technology in current and new biorefining projects of research group BRE: Set-up In order to reach the goal of this postdoc, 4 research questions will be answered using literature search, experimentation and modelling: 1) What membrane methods are currently (commercially) available to enhance the results of current projects in research group BRE? 2) What are the essential technical parameters for membrane separation and how can these be optimized? 3) What is the economic impact of using membrane technology in recovery of valuable building blocks from residual streams? 4) What are the effects of using membranes instead of or complementary to currently used methods on the sustainability of valorisation of residual streams? Cooperation The postdoc and the research group BRE want to extend the contact and research cooperation with (regional) businesses and (applied) universities and support and facilitate the introduction and further development of membrane technologies in the curriculum of different Avans study programmes. This will be done via internships, minor projects (together with businesses) and development of study material for courses and trainings.