Now showing items 8465-8484 of 15070

    • Lydforhold i videregående skoler 

      Bårdsen, Marianne; Årvåg, Ida Beyer; Fjølstad, Jørgen (Bachelor thesis, 2019)
      “Lydforhold i videregående skoler” er oppgavens tittel. Å høre er en forutsetning for å lære. Gode akustiske forhold er viktig for læring, og spesielt i skolen er gode lydforhold svært viktig da innlæringen i stor grad ...
    • Lykkelig ulykkelig fødsel 

      Ditlevsen, Rikke Adelén (Bachelor thesis, 2020)
      Tittel: Lykkelig ulykkelig fødsel Problemstilling: Hvordan kan sykepleier, på barselavdeling, oppdage tidlige tegn på fødselsdepresjon og forebygge videre utvikling av den? Hensikt: Innhente informasjon rundt risikofaktorer ...
    • Lynch syndrome mutation spectrum in New South Wales, Australia, including 55 novel mutations 

      Sjursen, Wenche; McPhillips, Mary; Scott, Rodney J.; Talseth-Palmer, Bente (Peer reviewed; Journal article, 2016)
      Background Lynch syndrome, the most frequent hereditary colorectal cancer syndrome, is caused by defects in mismatch repair genes. Genetic testing is important in order to identify mutation carriers who can benefit from ...
    • LysoPC and PAF Trigger Arachidonic Acid Release by Divergent Signaling Mechanisms in Monocytes 

      Oestvang, Janne; Anthonsen, Marit Walbye; Johansen, Berit (Journal article; Peer reviewed, 2011)
      Oxidized low-density lipoproteins (LDLs) play an important role during the development of atherosclerosis characterized by intimal inflammation and macrophage accumulation. A key component of LDL is lysophosphatidylcholine ...
    • Læring og kompetanseutvikling i kommunehelsetenesta - ein intervjustudie av kreftsjukepleiarar 

      Hynne, Astrid Bjørnerheim; Kvangarsnes, Marit (Journal article; Peer reviewed, 2014)
      Learning and competence development in community health service – an interview study of cancer nurses The number of cancer patients is expected to increase 30% by 2020. To meet the needs of seriously ill and dying cancer ...
    • Lønnet arbeid som helsefremmende strategi 

      Hegvik, Hanne Herkedal; Rebne, Anna Viktoria; Sæther, Hilde (Bachelor thesis, 2022)
      Bakgrunn: Ergoterapeuters kompetanse kan benyttes til å få personer ut i arbeid. Lønnet arbeid er en viktig del av menneskers liv og har en positiv effekt på helsen. Allikevel er det registrert mange arbeidssøkere hos NAV. ...
    • M-ficolin: a valuable biomarker to identify leukaemia from juvenile idiopathic arthritis 

      Brix, Ninna; Glerup, Mia; Thiel, Steffen; Mistegaard, Clara Elbæk; Skals, Regitze Gyldenholm; Berntson, Lillemor; Fasth, Anders; Nielsen, Susan Mary; Nordal, Ellen Berit; Rygg, Marite; Hasle, Henrik; Albertsen, Birgitte Klug; Herlin, Troels (Peer reviewed; Journal article, 2021)
      Objective: Distinction on clinical grounds between acute lymphoblastic leukaemia presenting with arthropathy (ALLarthropathy) and juvenile idiopathic arthritis (JIA) is difficult, as the clinical and paraclinical signs of ...
    • M.H. Bjørk og medarbeidere svarer 

      Bjørk, Marte-Helene; Gerstner, Thorsten Alfons; Taubøll, Erik (Peer reviewed; Journal article, 2020)
    • Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury 

      Gravesteijn, BY; Nieboer, Daan; Ercole, Ari; Lingsma, Hester F; Nelson, David; Van Calster, Ben; Steyerberg, Ewout W; Andelic, Nada; Anke, Audny; Frisvold, Shirin; Helseth, Eirik; Røe, Cecilie; Røise, Olav; Skandsen, Toril; Vik, Anne (Peer reviewed; Journal article, 2020)
      Objective We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting We performed logistic regression ...
    • A Machine Learning Approach to Investigate Fronto-Parietal Neural Ensemble Dynamics During Complex 

      Gheorghiu, Medorian; Ciuparu, Andrei; Mimica, Bartul; Whitlock, Jonathan; Mureşan, Raul (Chapter, 2020)
      Brain circuits exhibit very complex dynamics, where individual neurons fire action potentials determining coordinated activity patterns. During behavior, a multitude of brain areas are engaged in planning and execution. A ...
    • A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study 

      Luzum, Geske; Thrane, Gyrd; Aam, Stina; Eldholm, Rannveig Sakshaug; Grambaite, Ramune; Munthe-Kaas, Ragnhild; Thingstad, Anne Pernille Mæhle; Saltvedt, Ingvild; Askim, Torunn (Journal article; Peer reviewed, 2014)
      Objective: This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data from the acute and sub-acute phases after stroke in a machine-learning set-up. Design: A prospective multicenter ...
    • Machine learning augmented reduced-order models for FFR-prediction 

      Fossan, Fredrik Eikeland; Müller, Lucas O.; Sturdy, Jacob; Bråten, Anders Tjellaug; Jørgensen, Arve; Wiseth, Rune; Hellevik, Leif Rune (Peer reviewed; Journal article, 2021)
      Computational predictions in cardiovascular medicine have largely relied on explicit models derived from physical and physiological principles. Recently, the application of artificial intelligence in cardiovascular medicine ...
    • A Machine Learning Classifier for Detection of Physical Activity Types and Postures During Free-Living 

      Bach, Kerstin; Kongsvold, Atle Austnes; Bårdstu, Hilde Bremseth; Bardal, Ellen Marie; Kjærnli, Håkon Slåtten; Herland, Sverre; Logacjov, Aleksej; Mork, Paul Jarle (Journal article; Peer reviewed, 2021)
      Accelerometer-based measurements of physical activity types are commonly used to replace self-reports. To advance the field, it is desirable that such measurements allow accurate detection of key daily physical activity ...
    • Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study 

      Ihlen, Espen Alexander F.; Støen, Ragnhild; Boswell, Lynn; de-Regnier, Raye-Ann; Fjørtoft, Toril Larsson; Gaebler-spira, Deborah; Labori, Cathrine; Loennecken, Marianne; Msall, Me; Møinicken, Unn inger; Peyton, Colleen; Schreiber, Me; Silberg, Inger Elisabeth; Songstad, Nils Thomas; Vaagen, Randi; Øberg, Gunn Kristin; Adde, Lars (Journal article; Peer reviewed, 2019)
      Background: Early identification of cerebral palsy (CP) during infancy will provide opportunities for early therapies and treatments. The aim of the present study was to present a novel machine-learning model, the ...
    • Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial 

      Rakaee, Mehrdad; Andersen, S.; Giannikou, K.; Paulsen, Erna-Elise; Kilvær, Thomas Karsten; Rasmussen Busund, Lill-Tove; Berg, Thomas; Richardsen, Elin; Lombardi, Ana Paola; Adib, E.; Pedersen, Mona Irene; Tafavvoghi, Masoud; Wahl, Sissel Gyrid Freim; Petersen, R.H.; Bondgaard, A.L.; Yde, C.W.; Baudet, C.; Licht, P.; Lund-Iversen, Marius; Grønberg, Bjørn Henning; Fjellbirkeland, Lars; Helland, Åslaug; Pøhl, M.; Kwiatkowski, D.J.; Dønnem, Tom (Peer reviewed; Journal article, 2023)
      Background We aim to implement an immune cell score model in routine clinical practice for resected non-small-cell lung cancer (NSCLC) patients (NCT03299478 ). Molecular and genomic features associated with immune ...
    • Machine prescription for chronic migraine 

      Stubberud, Anker; Gray, Robert; Tronvik, Erling Andreas; Matharu, Manjit Singh; Nachev, Parashkev (Peer reviewed; Journal article, 2022)
      Responsive to treatment individually, chronic migraine remains strikingly resistant collectively, incurring the second-highest population burden of disability worldwide. A heterogeneity of responsiveness, requiring ...
    • Machine prescription for chronic migraine 

      Stubberud, Anker; Gray, Robert; Tronvik, Erling Andreas; Matharu, Manjit; Nachev, Parashkev (Peer reviewed; Journal article, 2022)
      Responsive to treatment individually, chronic migraine remains strikingly resistant collectively, incurring the second-highest population burden of disability worldwide. A heterogeneity of responsiveness, requiring ...
    • Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts 

      Vänni, Petri; Tejesvi, Mysore V; Paalanne, Niko; Aagaard, Kjersti; Ackermann, Gail; Camargo Jr., Carlos A; Eggesbø, Merete Åse; Hasegawa, Kohei; Hoen, Anne G.; Karagas, Margaret R.; Kolho, Kaija-Leena; Laursen, Martin F.; Ludvigsson, Johnny; Madan, Juliette; Ownby, Dennis; Stanton, Catherine; Stokholm, Jakob; Tapiainen, Terhi (Peer reviewed; Journal article, 2023)
      Combining and comparing microbiome data from distinct infant cohorts has been challenging because such data are inherently multidimensional and complex. Here, we used an ensemble of machine-learning (ML) models and studied ...
    • MACPET: model-based analysis for ChIA-PET 

      Vardaxis, Ioannis; Drabløs, Finn; Rye, Morten Beck; Lindqvist, Bo Henry (Journal article; Peer reviewed, 2019)
      We present model-based analysis for ChIA-PET (MACPET), which analyzes paired-end read sequences provided by ChIA-PET for finding binding sites of a protein of interest. MACPET uses information from both tags of each PET ...
    • Macro-indentation testing of soft biological materials and assessment of hyper-elastic material models from inverse finite element analysis 

      Ayyalasomayajula, Venkat Siva Radha Krishna; Ervik, Øyvind; Sorger, Hanne; Skallerud, Bjørn Helge (Peer reviewed; Journal article, 2024)
      Mechanical characterization of hydrogels and ultra-soft tissues is a challenging task both from an experimental and material parameter estimation perspective because they are much softer than many biological materials, ...