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Country: Norway
117 Projects, page 1 of 24
  • Open Access mandate for Publications
    Funder: EC Project Code: 836355
    Overall Budget: 202,159 EURFunder Contribution: 202,159 EUR
    Partners: UiT

    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.

  • Open Access mandate for Publications and Research data
    Funder: EC Project Code: 886035
    Overall Budget: 214,159 EURFunder Contribution: 214,159 EUR
    Partners: UiT

    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.

  • Open Access mandate for Publications and Research data
    Funder: EC Project Code: 101062153
    Funder Contribution: 226,751 EUR
    Partners: UiT

    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.

  • Open Access mandate for Publications and Research data
    Funder: EC Project Code: 101086671
    Overall Budget: 2,161,250 EURFunder Contribution: 2,161,250 EUR
    Partners: UiT

    HiTime is a project about the adjustment of homeostatic set points in physiology. This is known as rheostasis and is of core importance for transitions between physiological states. I seek to develop the concept that a specialised cell type in the hypothalamus, the tanycyte, is the cellular embodiment of a rheostat, and that by understanding the function of the tanycyte, we will gain fundamental insights into hypothalamic control of rheostasis. Since rheostasis is weak in laboratory mice bred to live in constant laboratory environments, new paradigms must be developed. I will therefore investigate tanycyte function in the context of seasonal deep hibernation: the most dramatic example of rheostatic control of energy metabolism in mammals. I will use the golden hamster as the study organism because it has an excellent combination of attributes for pursuing the project aims, from experimental tractability to availability of molecular resources. The study approach will exploit advanced telemetry combined with transcriptomics, metabolomics and neuroimaging to develop an unprecedented “arrow of time” description of changes in tanycyte status in relation to peripheral and central changes in energy metabolites throughout the hibernation. This approach will encompass both long-term rheostatic control of seasonal entry and exit from hibernation, and short-term rheostatic control of torpor-arousal cycles during the hibernation phase. In parallel, I will develop neurogenetic and micro-infusion approaches to allow characterisation of the causal events in rheostatic hibernation control. My career track record gives me an unusual combination of in vivo physiology and bioinformatics skills, which, in combination with my pilot studies establishing new techniques, gives an excellent chance of success despite high ambition. This success will have reciprocal benefits for two major interest areas in physiology with impacts from human obesity research to interplanetary space travel.

  • Open Access mandate for Publications and Research data
    Funder: EC Project Code: 101123485
    Funder Contribution: 150,000 EUR
    Partners: UiT

    ERC starting grant 3D nanoMorph has achieved groundbreaking results that open an unprecedented opportunity for assisted reproduction technologies (ARTs). ARTs require selecting sperms with high fertilization potential - this is characterized by morphology, motility and kinematic parameters of sperms indicating their progressive motility. Current optical solutions for sperm selection can investigate only morphology, nature of motility and kinematics in terms of speed. Our groundbreaking solution enables studying the motion of sperm with unprecedented super-resolved details, which can be used to derive advanced kinematic features. The ART market is growing at an annual rate of 7.8%. Our population is aging, people are planning families at later ages, and fertility is deteriorating due to occupational, environmental and lifestyle factors. Thus, it is becoming a societal challenge to maintain population growth. This PoC grant aims to take the next step in transforming my groundbreaking research into innovation of commercial and societal value.