Efficient Linear

This thesis use the extreme powers of the GPU for linear algebra. Selected linear algebra algorithms, more specifically the LU and the conjugate gradient algorithm for solving linear systems, has been ported to execute its main computational load on the graphics processing unit available on most computers. The main contributions in the thesis is more efficient pivoting in the LU-algorithm, where a minimum of data is copied, and gathering of the inner products for simultainous readback and reduction on the GPU

Lokalhistorien i søkelyset

Dybdahl, Vagn: Lad os afskaffe lokalhistorien Hesselager, Lise: Danske småtryk i Det kgl. Bibliotek Jensen, Bernhardt: Storkommunen og det lokalhistoriske arbejde  Malmberg, Per: Belgierne i Hellebæk Prange, Knud:- Fra »frucktbar Herlighed« til herlig frugtbarhed  - Lokalhistorie og undervisning  - Slægt - miljø - samfund &nbsp

Cerebrospinal fluid markers in Creutzfeldt-Jakob disease

<p>Abstract</p> <p>Background</p> <p>The objective was to assess the utility of total tau protein (tTau), the ratio of (tTau)/181 phosphorylated tau protein (P-Tau) and 14-3-3 protein, as diagnostic markers in cerebrospinal fluid (CSF) for Creutzfeldt-Jakob disease (CJD).</p> <p>Methods</p> <p>CSF samples received from Norwegian hospitals between August 2005 and August 2007 were retrospectively selected from consecutive patients with tTau values > 1200 ng/L (n = 38). The samples from patients clinically diagnosed with CJD (n = 12) were compared to those from patients with other degenerative neurological diseases: Alzheimer's/vascular dementia (AD/VaD, n = 21), other neurological diseases (OND, n = 5). Total Tau, P-Tau, and β-Amyloid (Aβ₄₂) were measured with commercial kits. Additionally, 14-3-3 protein was measured semi-quantitatively by immunoblot.</p> <p>Results</p> <p>The minimum cut-off limits for diagnosis of CJD were chosen from the test results. For tTau the lower limit was fixed at 3000 ng/L, for the tTau/P-Tau ratio it was 60, and for 14-3-3 protein it was 0.75 arbitrary units. For tTau and tTau/P-Tau ratio, all but three CJD patients had levels above the minimum, whereas almost all of the other patients were below. For the 14-3-3 protein, two CJD patients were below the minimum and five were above. Only one of the other patients was higher than the limit. The sensitivities, specificities and diagnostic efficiencies were: tTau 75%, 92%, and 87%; tTau/P-Tau 75%, 96%, and 89%; and 14-3-3 protein 80%, 96%, and 91%.</p> <p>Conclusion</p> <p>The results suggest that 14-3-3 protein may be the better marker for CJD, tTau/P-Tau ratio and tTau are also efficient markers, but showed slightly inferior diagnostic properties in this study, with tTau/P-Tau marginally better than tTau.</p

A Knowledge Graph Framework for Dementia Research Data

Dementia disease research encompasses diverse data modalities, including advanced imaging, deep phenotyping, and multi-omics analysis. However, integrating these disparate data sources has historically posed a significant challenge, obstructing the unification and comprehensive analysis of collected information. In recent years, knowledge graphs have emerged as a powerful tool to address such integration issues by enabling the consolidation of heterogeneous data sources into a structured, interconnected network of knowledge. In this context, we introduce DemKG, an open-source framework designed to facilitate the construction of a knowledge graph integrating dementia research data, comprising three core components: a KG-builder that integrates diverse domain ontologies and data annotations, an extensions ontology providing necessary terms tailored for dementia research, and a versatile transformation module for incorporating study data. In contrast with other current solutions, our framework provides a stable foundation by leveraging established ontologies and community standards and simplifies study data integration while delivering solid ontology design patterns, broadening its usability. Furthermore, the modular approach of its components enhances flexibility and scalability. We showcase how DemKG might aid and improve multi-modal data investigations through a series of proof-of-concept scenarios focused on relevant Alzheimer’s disease biomarkers

Improving validity of the trail making test with alphabet support

The Trail Making Test (TMT) is commonly used worldwide to evaluate cognitive decline and car driving ability. However, it has received critique for its dependence on the Latin alphabet and thus, the risk of misclassifying some participants. Alphabet support potentially increases test validity by avoiding misclassification of executive dysfunction in participants with dyslexia and those with insufficient automatization of the Latin alphabet. However, Alphabet support might render the test less sensitive to set-shifting, thus compromising the validity of the test. This study compares two versions of the TMT: with and without alphabet support. Methods: We compared the TMT-A, TMT-B, and TMT-B:A ratios in two independent normative samples with (n = 220) and without (n = 64) alphabet support using multiple regression analysis adjusted for age and education. The sample comprised Scandinavians aged 70–84 years. Alphabet support was included by adding the Latin alphabet A–L on top of the page on the TMT-B. We hypothesized that alphabet support would not change the TMT-B:A ratio. Results: After adjusting for age and years of education, there were no significant differences between the two samples in the TMT-A, TMT-B, or the ratio score (TMT-B:A). Conclusion: Our results suggest that the inclusion of alphabet support does not alter TMT’s ability to measure set-shifting in a sample of older Scandinavian adults

Segmenting white matter hyperintensities on isotropic three-dimensional Fluid Attenuated Inversion Recovery magnetic resonance images: Assessing deep learning tools on a Norwegian imaging database

An important step in the analysis of magnetic resonance imaging (MRI) data for neuroimaging is the automated segmentation of white matter hyperintensities (WMHs). Fluid Attenuated Inversion Recovery (FLAIR-weighted) is an MRI contrast that is particularly useful to visualize and quantify WMHs, a hallmark of cerebral small vessel disease and Alzheimer's disease (AD). In order to achieve high spatial resolution in each of the three voxel dimensions, clinical MRI protocols are evolving to a three-dimensional (3D) FLAIR-weighted acquisition. The current study details the deployment of deep learning tools to enable automated WMH segmentation and characterization from 3D FLAIR-weighted images acquired as part of a national AD imaging initiative. Based on data from the ongoing Norwegian Disease Dementia Initiation (DDI) multicenter study, two 3D models-one off-the-shelf from the NVIDIA nnU-Net framework and the other internally developed-were trained, validated, and tested. A third cutting-edge Deep Bayesian network model (HyperMapp3r) was implemented without any de-novo tuning to serve as a comparison architecture. The 2.5D in-house developed and 3D nnU-Net models were trained and validated in-house across five national collection sites among 441 participants from the DDI study, of whom 194 were men and whose average age was (64.91 +/- 9.32) years. Both an external dataset with 29 cases from a global collaborator and a held-out subset of the internal data from the 441 participants were used to test all three models. These test sets were evaluated independently. The ground truth human-in-the-loop segmentation was compared against five established WMH performance metrics. The 3D nnU-Net had the highest performance out of the three tested networks, outperforming both the internally developed 2.5D model and the SOTA Deep Bayesian network with an average dice similarity coefficient score of 0.76 +/- 0.16. Our findings demonstrate that WMH segmentation models can achieve high performance when trained exclusively on FLAIR input volumes that are 3D volumetric acquisitions. Single image input models are desirable for ease of deployment, as reflected in the current embedded clinical research project. The 3D nnU-Net had the highest performance, which suggests a way forward for our need to automate WMH segmentation while also evaluating performance metrics during on-going data collection and model retraining

Hva påvirker selgers prestasjoner?

I vår undersøkelse har vi forsket på hvilke faktorer som påvirker en selgers prestasjoner i Orkla House Care, som er ledende leverandør av kjente merkevarer til faghandelen. Modellens hovedpunkter støttes av flere forskningsartikler og fagbøker for å få en dypere teoretisk innsikt og forståelse. Vi har gjennomført en kvalitativ metode gjennom et casedesign og har utført seks dybdeintervjuer med erfarne selgere innen faghandelen. Empirien skal besvare vår problemstilling: «Hva påvirker selgers prestasjoner?». For å besvare og avgrense forskningen har vi ulike forskningsspørsmål som skal hjelpe oss å svare på problemstillingen. Faktorene vi undersøker er: empati, ego-drive, produktkunnskap og aktiv lytting. Videre vil vi presentere funnene gjennom analysering og drøfting av empiri. Her vil vi kartlegge dybdeintervjuene opp mot teorien vi har brukt i oppgaven. Dette vil gi oss forståelse og svar til problemstilling og forskningsspørsmålene vi har foretatt innledningsvis. Formålet er å se sammenhengen mellom selgers erfaringer, meninger og oppfatning på hvilke faktorer som påvirker deres prestasjoner. Alle faktorene viser å gi en positiv påvirkning på prestasjoner, bare i ulik grad. Ved utdypningen av undersøkelsen fant vi ut at produktkunnskap viste til stor variasjon i svarene fra respondentene. Gjennom empiri viste det seg at aktiv lytting har en sterk indirekte sammenheng med prestasjoner, mens empati har i større grad en direkte sammenheng. Funnene tilsier at ego-drive er den viktigste faktoren en selger bør ha for å oppnå prestasjoner i Orkla House Care. Som en avsluttende del i forskningen ser vi at utvalget av respondenter er for lite til å generalisere funnene. Empirien indikerer sterkt hvilke faktorer som påvirker selgers prestasjoner. Forskningen kan overføres til andre lignende bedrifter eller brukes som utgangspunkt til videre forskning. Ved hjelp av reliabilitet og validitet har vi kvalitetssikret forskningens funn

Regression-based norms for the FAS phonemic fluency test for ages 40–84 based on a Norwegian sample

The FAS phonemic fluency test is a commonly used neuropsychological test of executive function and processing speed. Although Norwegian discrete norms have been developed for the FAS test, American regression-based norms are frequently used by clinicians in Norway. However, language and cultural differences impact performance on the FAS test, and using foreign norms may not be appropriate. Moreover, while discrete norming relies on stratified subgroups of demographics, regression-based norming uses the entire sample to estimate the influence of demographics on performance and may thus improve normative estimates. Here we develop regression-based norms for the FAS phonemic fluency test based on n = 204 healthy Norwegian controls between the ages 40−84 from the Norwegian Dementia Disease Initiation cohort (DDI). We compare the proposed regression norms to published Norwegian discrete norms and American regression-based norms in an independent sample of n = 182 cognitively healthy adults reporting subjective cognitive decline (SCD). We found that years of education was the only significant predictor of FAS performance in our normative sample, accounting for 14.9% of the variance. Both the proposed regression-based norms and previously published discrete norms adequately adjusted for demographics in the independent sample. In contrast, the American norms underestimated the effect of education and overestimated the effect of age. While both the proposed Norwegian regression norms and the previously published discrete norms are suitable for use in Norway, the proposed regression norms may be less vulnerable to sub-stratification sample characteristics posed by discrete norming procedures, and thereby improve normative estimation

Regression-based norms for the FAS phonemic fluency test for ages 40–84 based on a Norwegian sample

The FAS phonemic fluency test is a commonly used neuropsychological test of executive function and processing speed. Although Norwegian discrete norms have been developed for the FAS test, American regression-based norms are frequently used by clinicians in Norway. However, language and cultural differences impact performance on the FAS test, and using foreign norms may not be appropriate. Moreover, while discrete norming relies on stratified subgroups of demographics, regression-based norming uses the entire sample to estimate the influence of demographics on performance and may thus improve normative estimates. Here we develop regression-based norms for the FAS phonemic fluency test based on n = 204 healthy Norwegian controls between the ages 40−84 from the Norwegian Dementia Disease Initiation cohort (DDI). We compare the proposed regression norms to published Norwegian discrete norms and American regression-based norms in an independent sample of n = 182 cognitively healthy adults reporting subjective cognitive decline (SCD). We found that years of education was the only significant predictor of FAS performance in our normative sample, accounting for 14.9% of the variance. Both the proposed regression-based norms and previously published discrete norms adequately adjusted for demographics in the independent sample. In contrast, the American norms underestimated the effect of education and overestimated the effect of age. While both the proposed Norwegian regression norms and the previously published discrete norms are suitable for use in Norway, the proposed regression norms may be less vulnerable to sub-stratification sample characteristics posed by discrete norming procedures, and thereby improve normative estimation