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- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 2. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 7. |
- El Kharraz, S., et al.
(författare)
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The androgen receptor depends on ligand-binding domain dimerization for transcriptional activation
- 2021
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Ingår i: Embo Reports. - : EMBO. - 1469-221X .- 1469-3178. ; 22:12
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Tidskriftsartikel (refereegranskat)abstract
- Whereas dimerization of the DNA-binding domain of the androgen receptor (AR) plays an evident role in recognizing bipartite response elements, the contribution of the dimerization of the ligand-binding domain (LBD) to the correct functioning of the AR remains unclear. Here, we describe a mouse model with disrupted dimerization of the AR LBD (AR(Lmon/Y)). The disruptive effect of the mutation is demonstrated by the feminized phenotype, absence of male accessory sex glands, and strongly affected spermatogenesis, despite high circulating levels of testosterone. Testosterone replacement studies in orchidectomized mice demonstrate that androgen-regulated transcriptomes in AR(Lmon/Y) mice are completely lost. The mutated AR still translocates to the nucleus and binds chromatin, but does not bind to specific AR binding sites. In vitro studies reveal that the mutation in the LBD dimer interface also affects other AR functions such as DNA binding, ligand binding, and co-regulator binding. In conclusion, LBD dimerization is crucial for the development of AR-dependent tissues through its role in transcriptional regulation in vivo. Our findings identify AR LBD dimerization as a possible target for AR inhibition.
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| 8. |
- Kraja, Aldi T., et al.
(författare)
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New Blood Pressure-Associated Loci Identified in Meta-Analyses of 475000 Individuals
- 2017
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Ingår i: Circulation. - : LIPPINCOTT WILLIAMS & WILKINS. - 1942-325X .- 1942-3268. ; 10:5
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Tidskriftsartikel (refereegranskat)abstract
- Background - Genome-wide association studies have recently identified >400 loci that harbor DNA sequence variants that influence blood pressure (BP). Our earlier studies identified and validated 56 single nucleotide variants (SNVs) associated with BP from meta-analyses of exome chip genotype data. An additional 100 variants yielded suggestive evidence of association.Methods and Results - Here, we augment the sample with 140886 European individuals from the UK Biobank, in whom 77 of the 100 suggestive SNVs were available for association analysis with systolic BP or diastolic BP or pulse pressure. We performed 2 meta-analyses, one in individuals of European, South Asian, African, and Hispanic descent (pan-ancestry, approximate to 475000), and the other in the subset of individuals of European descent (approximate to 423000). Twenty-one SNVs were genome-wide significant (P<5x10(-8) ) for BP, of which 4 are new BP loci: rs9678851 (missense, SLC4A1AP), rs7437940 (AFAP1), rs13303 (missense, STAB1), and rs1055144 (7p15.2). In addition, we identified a potentially independent novel BP-associated SNV, rs3416322 (missense, SYNPO2L) at a known locus, uncorrelated with the previously reported SNVs. Two SNVs are associated with expression levels of nearby genes, and SNVs at 3 loci are associated with other traits. One SNV with a minor allele frequency <0.01, (rs3025380 at DBH) was genome-wide significant.Conclusions - We report 4 novel loci associated with BP regulation, and 1 independent variant at an established BP locus. This analysis highlights several candidate genes with variation that alter protein function or gene expression for potential follow-up.
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| 9. |
- Goienetxea, Ainhoa, 1983-, et al.
(författare)
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Bringing together Lean and simulation : a comprehensive review
- 2020
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Ingår i: International Journal of Production Research. - : Taylor & Francis Group. - 0020-7543 .- 1366-588X. ; 58:1, s. 87-117
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Forskningsöversikt (refereegranskat)abstract
- Lean is and will still be one of the most popular management philosophies in the Industry 4.0 context and simulation is one of its key technologies. Many authors discuss about the benefits of combining Lean and simulation to better support decision makers in system design and improvement. However, there is a lack of reviews in the domain. Therefore, this paper presents a four-stage comprehensive review and analysis of existing literature on their combination. The aim is to identify the state of the art, existing methods and frameworks for combining Lean and simulation, while also identifying key research perspectives and challenges. The main trends identified are the increased interest in the combination of Lean and simulation in the Industry 4.0 context and in their combination with optimisation, Six Sigma, as well as sustainability. The number of articles in these areas is likely to continue to grow. On the other hand, we highlight six gaps found in the literature regarding the combination of Lean and simulation, which may induce new research opportunities. Existing technical, organisational, as well as people and culture related challenges on the combination of Lean and simulation are also discussed.
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| 10. |
- Goienetxea Uriarte, Ainhoa, 1983-
(författare)
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Bringing Together Lean, Simulation and Optimization : Defining a framework to support decision-making in system design and improvement
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The rapid changes in the market including globalization, the requirement for personalizedproducts and services by the customers, shorter product life-cycles, the exponential growthof technological advances, and the demographical changes, will demand organizations toeffectively improve and design their systems in order to survive. This is the actual paradigmcharacterizing the industrial and service sectors. This scenario presents a considerablechallenge to decision makers who will need to decide about how to design and improve amore than ever complex system without compromising the quality of the decision taken.Lean, being a widely applied management philosophy with very powerful principles, itsmethods and tools are static in nature and have some limitations when it comes to the designand improvement of complex and dynamic systems. Some authors have proposed thecombined use of simulation with Lean in order to overcome these limitations. Furthermore,optimization and post-optimization tools coupled to simulation, provide knowledge aboutoptimal or nearly optimal system configurations to choose from. However, even if Leanprinciples, methods and tools, as well as simulation and optimization, pursue the objectiveof supporting organizations regarding system design and improvement, a bilateral approachfor their combination and its benefits have barely been addressed in the literature.Many studies focus only on how specific Lean tools and simulation can be combined, treatingLean purely as a toolbox and not considering how Lean can support the simulation process.The aim of this research is to address this knowledge gap by analyzing the mutualbenefits and presenting a framework for combining Lean, simulation and optimization tobetter support decision makers in system design and improvement where the limitationsof Lean tools and simulation are overcome by their combination. This framework includesa conceptual framework explaining the relationships between the Lean philosophy, methodsand tools with simulation and optimization; the purposes for this combination and stepby step processes to achieve these purposes; the identification of the roles involved in eachprocess; a maturity model providing guidelines on how to implement the framework; existingbarriers for the implementation; and ethical considerations to take into account. Anindustrial handbook has also been written which explains how to deploy the framework.The research has been conducted in three main stages including an analysis of the literatureand the real-world needs, the definition and formulation of the framework, and finally, itsevaluation in real-world projects and with subject matter experts. The main contributionof this research is the reflection provided on the bilateral benefits of the combination, aswell as the defined and evaluated framework, which will support decision makers take qualitydecisions in system design and improvement even in complex scenarios.
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