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- Altmann, Patrick, et al.
(författare)
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Serum neurofilament light chain withstands delayed freezing and repeated thawing.
- 2020
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Serum neurofilament light chain (sNfL) and its ability to expose axonal damage in neurologic disorders have solicited a considerable amount of attention in blood biomarker research. Hence, with the proliferation of high-throughput assay technology, there is an imminent need to study the pre-analytical stability of this biomarker. We recruited 20 patients with common neurological diagnoses and 10 controls (i.e. patients without structural neurological disease). We investigated whether a variation in pre-analytical variables (delayed freezing up to 24h and repeated thawing/freezing for up to three cycles) affects the measured sNfL concentrations using state of the art Simoa technology. Advanced statistical methods were applied to expose any relevant changes in sNfL concentration due to different storing and processing conditions. We found that sNfL concentrations remained stable when samples were frozen within 24h (mean absolute difference 0.2pg/ml; intraindividual variation below 0.1%). Repeated thawing and re-freezing up to three times did not change measured sNfL concentration significantly, either (mean absolute difference 0.7pg/ml; intraindividual variation below 0.2%). We conclude that the soluble sNfL concentration is unaffected at 4-8°C when samples are frozen within 24h and single aliquots can be used up to three times. These observations should be considered for planning future studies.
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- Docherty, Anna R, et al.
(författare)
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GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.
- 2023
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Ingår i: The American journal of psychiatry. - : American Psychiatric Association Publishing. - 1535-7228 .- 0002-953X. ; 180:10, s. 723-738
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Tidskriftsartikel (refereegranskat)abstract
- Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures.This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses.Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors.This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.
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- Helgadottir, Anna, et al.
(författare)
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Apolipoprotein(a) Genetic Sequence Variants Associated With Systemic Atherosclerosis and Coronary Atherosclerotic Burden But Not With Venous Thromboembolism
- 2012
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Ingår i: Journal of the American College of Cardiology. - : Elsevier BV. - 0735-1097 .- 1558-3597. ; 60:8, s. 722-729
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Tidskriftsartikel (refereegranskat)abstract
- Objectives The purpose of this study is investigate the effects of variants in the apolipoprotein(a) gene (LPA) on vascular diseases with different atherosclerotic and thrombotic components. Background It is unclear whether the LPA variants rs10455872 and rs3798220, which correlate with lipoprotein(a) levels and coronary artery disease (CAD), confer susceptibility predominantly via atherosclerosis or thrombosis. Methods The 2 LPA variants were combined and examined as LPA scores for the association with ischemic stroke (and TOAST [Trial of Org 10172 in Acute Stroke Treatment] subtypes) (effective sample size [n(e)] = 9,396); peripheral arterial disease (n(e) = 5,215); abdominal aortic aneurysm (ne = 4,572); venous thromboembolism (ne = 4,607); intracranial aneurysm (ne = 1,328); CAD (n(e) = 12,716), carotid intima-media thickness (n = 3,714), and angiographic CAD severity (n = 5,588). Results LPA score was associated with ischemic stroke subtype large artery atherosclerosis (odds ratio [OR]: 1.27; p = 6.7 X 10(-4)), peripheral artery disease (OR: 1.47; p = 2.9 x 10(-14)), and abdominal aortic aneurysm (OR: 1.23; p = 6.0 x 10(-5)), but not with the ischemic stroke subtypes cardioembolism (OR: 1.03; p = 0.69) or small vessel disease (OR: 1.06; p = 0.52). Although the LPA variants were not associated with carotid intima-media thickness, they were associated with the number of obstructed coronary vessels (p = 4.8 x 10(-12)). Furthermore, CAD cases carrying LPA risk variants had increased susceptibility to atherosclerotic manifestations outside of the coronary tree (OR: 1.26; p = 0.0010) and had earlier onset of CAD (-1.58 years/allele; p = 8.2 x 10(-8)) than CAD cases not carrying the risk variants. There was no association of LPA score with venous thromboembolism (OR: 0.97; p = 0.63) or intracranial aneurysm (OR: 0.85; p = 0.15). Conclusions LPA sequence variants were associated with atherosclerotic burden, but not with primarily thrombotic phenotypes. (J Am Coll Cardiol 2012; 60: 722-9) (C) 2012 by the American College of Cardiology Foundation
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- Henriksson, Jens, et al.
(författare)
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Evaluation of Out-of-Distribution Detection Performance on Autonomous Driving Datasets
- 2023
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Ingår i: Proceedings - 5th IEEE International Conference on Artificial Intelligence Testing, AITest 2023. - 9798350336290
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Konferensbidrag (refereegranskat)abstract
- Safety measures need to be systemically investigated to what extent they evaluate the intended performance of Deep Neural Networks (DNNs) for critical applications. Due to a lack of verification methods for high-dimensional DNNs, a trade-off is needed between accepted performance and handling of out-of-distribution (OOD) samples.This work evaluates rejecting outputs from semantic segmentation DNNs by applying a Mahalanobis distance (MD) based on the most probable class-conditional Gaussian distribution for the predicted class as an OOD score. The evaluation follows three DNNs trained on the Cityscapes dataset and tested on four automotive datasets and finds that classification risk can drastically be reduced at the cost of pixel coverage, even when applied on unseen datasets. The applicability of our findings will support legitimizing safety measures and motivate their usage when arguing for safe usage of DNNs in automotive perception.
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- Henriksson, Jens, 1991, et al.
(författare)
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Performance analysis of out-of-distribution detection on trained neural networks
- 2020
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Ingår i: Information and Software Technology. - : Elsevier B.V.. - 0950-5849 .- 1873-6025.
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Tidskriftsartikel (refereegranskat)abstract
- Context: Deep Neural Networks (DNN) have shown great promise in various domains, for example to support pattern recognition in medical imagery. However, DNNs need to be tested for robustness before being deployed in safety critical applications. One common challenge occurs when the model is exposed to data samples outside of the training data domain, which can yield to outputs with high confidence despite no prior knowledge of the given input. Objective: The aim of this paper is to investigate how the performance of detecting out-of-distribution (OOD) samples changes for outlier detection methods (e.g., supervisors) when DNNs become better on training samples. Method: Supervisors are components aiming at detecting out-of-distribution samples for a DNN. The experimental setup in this work compares the performance of supervisors using metrics and datasets that reflect the most common setups in related works. Four different DNNs with three different supervisors are compared during different stages of training, to detect at what point during training the performance of the supervisors begins to deteriorate. Results: Found that the outlier detection performance of the supervisors increased as the accuracy of the underlying DNN improved. However, all supervisors showed a large variation in performance, even for variations of network parameters that marginally changed the model accuracy. The results showed that understanding the relationship between training results and supervisor performance is crucial to improve a model's robustness. Conclusion: Analyzing DNNs for robustness is a challenging task. Results showed that variations in model parameters that have small variations on model predictions can have a large impact on the out-of-distribution detection performance. This kind of behavior needs to be addressed when DNNs are part of a safety critical application and hence, the necessary safety argumentation for such systems need be structured accordingly.
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