| 1. |
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
|
|
| 9. |
- Adcox, K, et al.
(författare)
-
PHENIX detector overview
- 2003
-
Ingår i: Nuclear Instruments & Methods in Physics Research. Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment. - 0167-5087. ; 499:2-3, s. 469-479
-
Tidskriftsartikel (refereegranskat)abstract
- The PHENIX detector is designed to perform a broad study of A-A, p-A, and p-p collisions to investigate nuclear matter under extreme conditions. A wide variety of probes, sensitive to all timescales, are used to study systematic variations with species and energy as well as to measure the spin structure of the nucleon. Designing for the needs of the heavy-ion and polarized-proton programs has produced a detector with unparalleled capabilities. PHENIX measures electron and muon pairs, photons, and hadrons with excellent energy and momentum resolution. The detector consists of a large number of subsystems that are discussed in other papers in this volume. The overall design parameters of the detector are presented. (C) 2002 Elsevier Science B.V. All rights reserved.
|
|
| 10. |
|
|
| 11. |
- Driscoll, T, et al.
(författare)
-
Global and regional burden of disease and injury in 2016 arising from occupational exposures: a systematic analysis for the Global Burden of Disease Study 2016
- 2020
-
Ingår i: Occupational and environmental medicine. - : BMJ. - 1470-7926 .- 1351-0711. ; 77:3, s. 133-141
-
Tidskriftsartikel (refereegranskat)abstract
- This study provides an overview of the influence of occupational risk factors on the global burden of disease as estimated by the occupational component of the Global Burden of Disease (GBD) 2016 study.MethodsThe GBD 2016 study estimated the burden in terms of deaths and disability-adjusted life years (DALYs) arising from the effects of occupational risk factors (carcinogens; asthmagens; particulate matter, gases and fumes (PMGF); secondhand smoke (SHS); noise; ergonomic risk factors for low back pain; risk factors for injury). A population attributable fraction (PAF) approach was used for most risk factors.ResultsIn 2016, globally, an estimated 1.53 (95% uncertainty interval 1.39–1.68) million deaths and 76.1 (66.3–86.3) million DALYs were attributable to the included occupational risk factors, accounting for 2.8% of deaths and 3.2% of DALYs from all causes. Most deaths were attributable to PMGF, carcinogens (particularly asbestos), injury risk factors and SHS. Most DALYs were attributable to injury risk factors and ergonomic exposures. Men and persons 55 years or older were most affected. PAFs ranged from 26.8% for low back pain from ergonomic risk factors and 19.6% for hearing loss from noise to 3.4% for carcinogens. DALYs per capita were highest in Oceania, Southeast Asia and Central sub-Saharan Africa. On a per capita basis, between 1990 and 2016 there was an overall decrease of about 31% in deaths and 25% in DALYs.ConclusionsOccupational exposures continue to cause an important health burden worldwide, justifying the need for ongoing prevention and control initiatives.
|
|
| 12. |
|
|
| 13. |
|
|
| 14. |
- Romagnoni, A, et al.
(författare)
-
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
-
Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
-
Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
|
|
| 15. |
- Glasbey, JC, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
| 16. |
- Kanai, M, et al.
(författare)
-
- 2023
-
swepub:Mat__t
|
|
