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- Din, Lennox, et al.
(författare)
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Genetic overlap between autoimmune diseases and non-Hodgkin lymphoma subtypes
- 2019
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Ingår i: Genetic Epidemiology. - : WILEY. - 0741-0395 .- 1098-2272. ; 43:7, s. 844-863
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Tidskriftsartikel (refereegranskat)abstract
- Epidemiologic studies show an increased risk of non-Hodgkin lymphoma (NHL) in patients with autoimmune disease (AD), due to a combination of shared environmental factors and/or genetic factors, or a causative cascade: chronic inflammation/antigen-stimulation in one disease leads to another. Here we assess shared genetic risk in genome-wide-association-studies (GWAS). Secondary analysis of GWAS of NHL subtypes (chronic lymphocytic leukemia, diffuse large B-cell lymphoma, follicular lymphoma, and marginal zone lymphoma) and ADs (rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis). Shared genetic risk was assessed by (a) description of regional genetic of overlap, (b) polygenic risk score (PRS), (c)"diseasome", (d)meta-analysis. Descriptive analysis revealed few shared genetic factors between each AD and each NHL subtype. The PRS of ADs were not increased in NHL patients (nor vice versa). In the diseasome, NHLs shared more genetic etiology with ADs than solid cancers (p =.0041). A meta-analysis (combing AD with NHL) implicated genes of apoptosis and telomere length. This GWAS-based analysis four NHL subtypes and three ADs revealed few weakly-associated shared loci, explaining little total risk. This suggests common genetic variation, as assessed by GWAS in these sample sizes, may not be the primary explanation for the link between these ADs and NHLs.
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| 5. |
- Lennox, Robert J., et al.
(författare)
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Electronic tagging and tracking aquatic animals to understand a world increasingly shaped by a changing climate and extreme weather events
- 2024
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Ingår i: Canadian Journal of Fisheries and Aquatic Sciences. - 0706-652X .- 1205-7533. ; 81:3, s. 326-339
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Tidskriftsartikel (refereegranskat)abstract
- Despite great promise for understanding the impacts and extent of climate change and extreme weather events on aquatic animals, their species, and ecological communities, it is surprising that electronic tagging and tracking tools, like biotelemetry and biologging, have not been extensively used to understand climate change or develop and evaluate potential interventions that may help adapt to its impacts. In this review, we provide an overview of methodologies and study designs that leverage available electronic tracking tools to investigate aspects of climate change and extreme weather events in aquatic ecosystems. Key interventions to protect aquatic life from the impacts of climate change, including habitat restoration, protected areas, conservation translocations, mitigations against interactive effects of climate change, and simulation of future scenarios, can all be greatly facilitated by using electronic tagging and tracking. We anticipate that adopting animal tracking to identify phenotypes, species, or ecosystems that are vulnerable or resilient to climate change will help in applying management interventions such as fisheries management, habitat restoration, invasive species control, or enhancement measures that prevent extinction and strengthen the resilience of communities against the most damaging effects of climate change. Given the scalability and increasing accessibility of animal tracking tools for researchers, tracking individual organisms will hopefully also facilitate research into effective solutions and interventions against the most extreme and acute impacts on species, populations, and ecosystems.
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| 6. |
- Sandberg, Erik, et al.
(författare)
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Multivariate process monitoring of EAFs
- 2005
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Ingår i: Ironmaking and Steelmaking. ; , s. 221-225
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Konferensbidrag (refereegranskat)abstract
- The application of a multivariate statistical process control (MSPC) to an EAF and the benefits that can be delivered was discussed. Several statistical methods for multivariate prediction were tested such as multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS). The results show that PLS was the most suitable of the tested methods and the prediction accuracy for tramp elements and alloying elements were satisfactory for online predictions and condition monitoring of scrap properties. Monitoring of short and long term variations in scrap quality was possible by analysis of the prediction errors and regression coefficients.
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