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- Dareng, EO, et al.
(författare)
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Polygenic risk modeling for prediction of epithelial ovarian cancer risk
- 2022
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Ingår i: European journal of human genetics : EJHG. - : Springer Science and Business Media LLC. - 1476-5438 .- 1018-4813. ; 30:3, s. 349-362
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Tidskriftsartikel (refereegranskat)abstract
- Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.
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- Akiba, K., et al.
(författare)
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Measurement of thermal properties of the LHCb VELO detector using track-based software alignment
- 2023
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Ingår i: Journal of Instrumentation. - : Institute of Physics (IOP). - 1748-0221. ; 18:10
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Tidskriftsartikel (refereegranskat)abstract
- The thermal properties of the LHCb Vertex Locator (VELO) are studied using the real-time detector alignment procedure. The variation of the position and orientation of the detector elements as a function of the operating temperature of the VELO is presented. This study uses a dataset collected by the LHCb experiment during a VELO temperature scan performed at the end of LHC Run 2 (October 2018). Significant shrinkage of the VELO modules is observed at the operating temperature of -30(degrees)C compared to the laboratory measurements on a single module taken at a range of temperatures from +45(degrees)C to -25(degrees)C. The thermal shrinkage expected from the extrapolation of laboratory measurements to lower temperatures, and the results of this alignment study are in good agreement.
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- Akiba, K., et al.
(författare)
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The LHCb VELO Upgrade module construction
- 2024
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Ingår i: Journal of Instrumentation. - : Institute of Physics Publishing (IOPP). - 1748-0221. ; 19:6
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Tidskriftsartikel (refereegranskat)abstract
- The LHCb detector has undergone a major upgrade for LHC Run 3. This Upgrade I detector facilitates operation at higher luminosity and utilises full-detector information at the LHC collision rate, critically including the use of vertex information. A new vertex locator system, the VELO Upgrade, has been constructed. The core element of the new VELO are the double-sided pixelated hybrid silicon detector modules which operate in vacuum close to the LHC beam in a high radiation environment. The construction and quality assurance tests of these modules are described in this paper. The modules incorporate 200 mu m thick, n -on -p silicon sensors bump-bonded to 130 nm technology ASICs. These are attached with high precision to a silicon microchannel substrate that uses evaporative CO 2 cooling. The ASICs are controlled and read out with flexible printed circuits that are glued to the substrate and wire -bonded to the chips. The mechanical support of the module is given by a carbon fibre plate, two carbon fibre rods and an aluminium plate. The sensor attachment was achieved with an average precision of 21 mu m, more than 99.5% of all pixels are fully functional, and a thermal figure of merit of 3 Kcm 2 W - 1 was achieved. The production of the modules was successfully completed in 2021, with the final assembly and installation completed in time for data taking in 2022.
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