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Country: Norway
117 Projects, page 1 of 24
  • Funder: EC Project Code: 836355
    Overall Budget: 202,159 EURFunder Contribution: 202,159 EUR

    Mitochondria play a vital role in the cellular machinery, hence it is little surprising that their dysfunction has been linked to many diseases, from diabetes to neurodegeneration. However, as many studies on the interplay of organelles and molecular dynamics often employ fluorescence microscopy, a continued worry overshadowing findings and deductions is the possibility that the transfection-induced overexpression of fluorescent proteins skews the obtained results. A recent approach, the gene editor CRISPR-CAS9, which modifies rather than adds DNA sequences, circumvents this issue, but in turn often reduces the available signal levels. To counter low signals and yet offer highest resolution and specificity, MitoQuant aims to image contextual mitochondrial information with label-free superresolution, while simultaneously enhance image quality of specific but sparse fluorescently labelled proteins of interest through recently presented de-noising routines based on machine learning. Therefore, the development of a novel instrument to provide adequate resolution and contrast, matching label-based live-cell superresolution techniques like structured illumination microscopy, is the first main goal of this project. The proposed microscope will work in the deep UV range and employ dedicated optics originally developed for material science to provide high numerical apertures at short wavelengths, thus enabling live-cell imaging in the 100nm range. Concurrently, a neural network will be compiled and trained to enhance signals under low-light conditions and to extract and classify cellular organelles based on their quantitative phase and autofluorescence information. Building on an excellent track record of developing application-tailored microscopes as well as advanced image reconstruction and processing algorithms particularly suited for live-cell superresolution, the researcher strives to start with first live-cell experiments in good time after establishing the technique.

  • Funder: EC Project Code: 886035
    Overall Budget: 214,159 EURFunder Contribution: 214,159 EUR

    Linguistic research and foreign language teaching have been drifting apart from each other. At a time of declining social cohesion in polyglot Europe, scientifically sound and effective foreign language teaching is vital for mutual understanding, in particular, teaching of German, which will likely gain in importance as European lingua franca. CLOSER attempts to bring foreign language research and teaching closer together. Adopting quantitative research methods from usage-based cognitive linguistics, I investigate the acquisition of German two-way prepositions (2WYP), which poses significant challenges to foreign language learners. I use regression modeling and large data samples to pin down the factors which determine native choices and nonnative errors in authentic language use. Based on the findings, I develop a semi-artificial grammar learning experiment which provides insight into the interplay of input-driven implicit practice and metalinguistic explicit knowledge, which is crucial and unique to foreign language learning but widely unexplored in usage-based research. CLOSER is unique and original in supplying quantitative evidence to the ongoing linguistic debate about 2WYP. In unprecedented ways, CLOSER looks at the intricate interplay of forces at the implicit-explicit learning interface to bridge the gap to instructed foreign language teaching. Needless to say, however, research findings do not straightforwardly extend to instructed learning in classroom settings. Therefore, I develop online applications which generate teaching materials for optimized construction learning based on prototypical usage contexts and effective metalinguistic instructions. The CLOSER-inspired materials are then field-tested in a classroom study. As MSCA fellow at UiT under the supervision of Laura Janda, I will be able receive advanced training in quantitative methods and programming to successfully deliver CLOSER and build up networks and skills for its future exploitation.

  • Funder: EC Project Code: 101062153
    Funder Contribution: 226,751 EUR

    MD GIG examines the transition to digital service provision in the public services by exploring the rise of the "online doctor" that provide consultations between doctor-patient via app-based mobile phone technology. The aim is to explore digitalization of healthcare from a worker perspective to highlight the preconditions that give rise to gig work in the healthcare sector and explore the potential consequences at different scales. Whereas before all patients had go to a primary care center on appointment during office hours, today patients can have a consult with a doctor on-demand at any time and from anywhere. Medical work's entry into the platform economy, where work is reshaped into "gigs" that workers perform where and when they want, is developing parallel to organizational and economic restructuring of the healthcare system towards more marketization and private-public partnerships. The project sets out to understand the individual motivations for doctors to take up work in digital doctor platforms through in-depth interviews to produce narratives based in the MDs own experiences, to explore the approach of the trade unions and medical associations to digital doctor platforms through expert-interviews with high level union employees to document their hopes, fears and strategies regarding changing labour markets and working conditions, and to analyse the role of digital doctor platforms in public healthcare restructuring to produce a political economy of digital healthcare in Europe.

  • Funder: EC Project Code: 101077496
    Overall Budget: 2,062,020 EURFunder Contribution: 2,062,020 EUR

    Arctic sea ice is diminishing with climate warming at a rate unmatched for 1000 years. As the receding ice pack changes rapidly and becomes increasingly mobile, the demand from academic and commercial stakeholders for accurate and timely sea ice forecasts is intensifying. Forecasting accuracy is enhanced with the assimilation of sea ice thickness observations from satellite altimetry. However, these data are currently unavailable during summer when they would be most valuable for stakeholders, owing to significant data processing challenges. This has been identified as a key observation gap for polar research by the IPCC. SI/3D will address this gap, harnessing deep machine learning, modelling of the radar altimeter response, and dedicated field campaigns, to overcome the processing barriers. I will integrate satellite data from multiple ESA, EU-Copernicus, and NASA altimetry missions to produce the first 15+ year high-accuracy record of pan-Arctic sea ice thickness without interruptions in the summer. With this unique dataset, I can achieve the following goals. (1) To close the Arctic sea ice volume budget, pinpointing the mechanisms driving seasonal decay and breakup of the ice pack and the feedbacks of sea ice loss on Arctic temperatures. (2) To upgrade seasonal sea ice forecasts from state-of-the-art modelling systems by assimilating summer ice thickness observations. SI/3D will create a new discipline of sea ice research by using altimetry to study the Arctic summer, which is risky. Having led the first published pilot research in this field, however, I am ideally placed to carry out the project and will leverage the expertise I have gained at three international Arctic research institutions to make it happen. On completion, this work will transform opportunities for Arctic system science, improving sea ice modelling, forecasting on timescales from weeks to years, mass budgeting, and biogeochemistry studies during the critical summer melting months.

  • Funder: EC Project Code: 101065670
    Funder Contribution: 210,911 EUR

    The objective of the Multilingual Individual Neurocognitive Differences in Middle Age Project (MIND-MAP) is to investigate how variation in multilingual engagement (e.g., context and intensity of L2 use) correlates to individual differences in neurocognitive adaptations in the mid-life age range. This age range is severely understudied in the relevant literatures, yet of critical importance as it is the immediate precursor period to when cognitive ageing (CA) becomes most prevalent. As such, MIND-MAP serves as a bridge between research mapping multilingual experience-related factors and individual differences in neurocognitive outcomes to research on multilingualism effects in CA. Herein we combine behavioral testing with brain recordings via electroencephalography (EEG). EEG is widely used in cognitive neuroscience to assess quality and quantity of overall brain wave oscillatory activity and the degree of communication between brain regions (functional connectivity) in response to stimuli and at rest. Combining behavior and neuroimaging modalities, MIND-MAP will examine specific domains of neurocognition adversely affected by CA that overlap with areas argued to be positively affected by multilingual experience precisely in the age range where revealing and understanding these correlations could be the most helpful, that is just prior to when CA progression is typically noted. Fine-tuning our understanding of the (potential) relationship between specific patterns of multilingualism and resulting neurocognitive adaptations is the first step to unlocking any future potential for capitalizing on it as a health initiative, which would have manifold societal repercussions.

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