| 61. |
- Purrington, Kristen S., et al.
(författare)
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Genome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple-negative breast cancer
- 2014
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Ingår i: Carcinogenesis. - : Oxford University Press (OUP). - 0143-3334 .- 1460-2180. ; 35:5, s. 1012-1019
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Tidskriftsartikel (refereegranskat)abstract
- In a genome-wide scan, we show that 30 variants in 25 genomic regions are associated with risk of TN breast cancer. Women carrying many of the risk variants may have 4-fold increased risk relative to women with few variants.Triple-negative (TN) breast cancer is an aggressive subtype of breast cancer associated with a unique set of epidemiologic and genetic risk factors. We conducted a two-stage genome-wide association study of TN breast cancer (stage 1: 1529 TN cases, 3399 controls; stage 2: 2148 cases, 1309 controls) to identify loci that influence TN breast cancer risk. Variants in the 19p13.1 and PTHLH loci showed genome-wide significant associations (P < 5 x 10(-) (8)) in stage 1 and 2 combined. Results also suggested a substantial enrichment of significantly associated variants among the single nucleotide polymorphisms (SNPs) analyzed in stage 2. Variants from 25 of 74 known breast cancer susceptibility loci were also associated with risk of TN breast cancer (P < 0.05). Associations with TN breast cancer were confirmed for 10 loci (LGR6, MDM4, CASP8, 2q35, 2p24.1, TERT-rs10069690, ESR1, TOX3, 19p13.1, RALY), and we identified associations with TN breast cancer for 15 additional breast cancer loci (P < 0.05: PEX14, 2q24.1, 2q31.1, ADAM29, EBF1, TCF7L2, 11q13.1, 11q24.3, 12p13.1, PTHLH, NTN4, 12q24, BRCA2, RAD51L1-rs2588809, MKL1). Further, two SNPs independent of previously reported signals in ESR1 [rs12525163 odds ratio (OR) = 1.15, P = 4.9 x 10(-) (4)] and 19p13.1 (rs1864112 OR = 0.84, P = 1.8 x 10(-) (9)) were associated with TN breast cancer. A polygenic risk score (PRS) for TN breast cancer based on known breast cancer risk variants showed a 4-fold difference in risk between the highest and lowest PRS quintiles (OR = 4.03, 95% confidence interval 3.46-4.70, P = 4.8 x 10(-) (69)). This translates to an absolute risk for TN breast cancer ranging from 0.8% to 3.4%, suggesting that genetic variation may be used for TN breast cancer risk prediction.
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| 62. |
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| 63. |
- Roy, Alan, et al.
(författare)
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VLBI at APEX: First Fringes
- 2012
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Ingår i: Proceedings of Science. - 1824-8039. ; 2012-October
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Konferensbidrag (refereegranskat)
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| 64. |
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| 65. |
- Strandberg, Joakim, 1991, et al.
(författare)
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Coastal Sea Ice Detection Using Ground-Based GNSS-R
- 2017
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - 1558-0571 .- 1545-598X. ; 14:9, s. 1552-1556
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Tidskriftsartikel (refereegranskat)abstract
- Determination of sea ice extent is important both for climate modeling and transportation planning. Detection and monitoring of ice are often done by synthetic aperture radar imagery, but mostly without any ground truth. For the latter purpose, robust and continuously operating sensors are required. We demonstrate that signals recorded by ground-based Global Navigation Satellite System (GNSS) receivers can detect coastal ice coverage on nearby water surfaces. Beside a description of the retrieval approach, we discuss why GNSS reflectometry is sensitive to the presence of sea ice. It is shown that during winter seasons with freezing periods, the GNSS-R analysis of data recorded with a coastal GNSS installation clearly shows the occurrence of ice in the bay where this installation is located. Thus, coastal GNSS installations could be promising sources of ground truth for sea ice extent measurements.
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| 66. |
- Strandberg, Joakim, 1991, et al.
(författare)
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Improving GNSS-R sea level determination through inverse modeling of SNR data
- 2016
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Ingår i: Radio Science. - 0048-6604 .- 1944-799X. ; 51:8, s. 1286-1296
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a new method for retrieving sea surface heights from Global Navigation Satellite Systems reflectometry (GNSS-R) data by inverse modeling of SNR observations from a single geodetic receiver. The method relies on a B-spline representation of the temporal sea level variations in order to account for its continuity. The corresponding B-spline coefficients are determined through a nonlinear least squares fit to the SNR data, and a consistent choice of model parameters enables the combination of multiple GNSS in a single inversion process. This leads to a clear increase in precision of the sea level retrievals which can be attributed to a better spatial and temporal sampling of the reflecting surface. Tests with data from two different coastal GNSS sites and comparison with colocated tide gauges show a significant increase in precision when compared to previously used methods, reaching standard deviations of 1.4 cm at Onsala, Sweden, and 3.1 cm at Spring Bay, Tasmania.
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| 67. |
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| 68. |
- Strandberg, Joakim, 1991, et al.
(författare)
-
Inverse modelling of GNSS multipath for sea level measurements - initial results
- 2016
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Ingår i: Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS) Volume 2016-November, 1 November 2016, Article number 7729479, Pages 1867-1869 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016; Beijing; China; 10 - 15 July 2016. - 9781509033324 ; 2016-November, s. 1867-1869
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Konferensbidrag (refereegranskat)abstract
- We present a new method to retrieve sea level from GNSS SNRdata that relies upon inverse modelling of the detrended SNR. This method can simultaneously use data from both GPS and GLONASS, and both L1 and L2 frequencies, to improve thesolution with respect to prior studies. Results from the GNSS-R installation at Onsala Space Observatory are presented and the retrieved sea level heights are compared with a co-located pressure mareograph. The method is found to give an RMS error of 1.8 cm. The results are also compared against previous implementations of GNSS tide gauges and found to have lower RMS than both the earlier SNR algorithm and also the dual receiver, phase delay method.
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| 69. |
- Strandberg, Joakim, 1991, et al.
(författare)
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Real-time sea-level monitoring using Kalman filtering of GNSS-R data
- 2019
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Ingår i: GPS Solutions. - : Springer Science and Business Media LLC. - 1080-5370 .- 1521-1886. ; 23:3
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Tidskriftsartikel (refereegranskat)abstract
- Current GNSS-R (GNSS reflectometry) techniques for sea surface measurements require data collection over longer periods, limiting their usability for real-time applications. In this work, we present a new, alternative GNSS-R approach based on the unscented Kalman filter and the so-called inverse modeling approach. The new method makes use of a mathematical description that relates SNR (signal-to-noise ratio) variations to multipath effects and uses a B-spline formalism to obtain time series of reflector height. The presented algorithm can provide results in real time with a precision that is significantly better than spectral inversion methods and almost comparable to results from inverse modeling in post-processing mode. To verify the performance, the method has been tested at station GTGU at the Onsala Space Observatory, Sweden, and at the station SPBY in Spring Bay, Australia. The RMS (root mean square) error with respect to nearby tide gauge data was found to be 2.0 cm at GTGU and 4.8 cm at SPBY when evaluating the output corresponding to real-time analysis. The method can also be applied in post-processing, resulting in RMS errors of 1.5 cm and 3.3 cm for GTGU and SPBY, respectively. Finally, based on SNR data from GTGU, it is also shown that the Kalman filter approach is able to detect the presence of sea ice with a higher temporal resolution than the previous methods and traditional remote sensing techniques which monitor ice in coastal regions.
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| 70. |
- Strandberg, Joakim, 1991, et al.
(författare)
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Towards Real-Time GNSS Reflectometry Using Kalman Filtering
- 2018
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Ingår i: International Geoscience and Remote Sensing Symposium (IGARSS). ; 2018-July, s. 2043-2046
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Konferensbidrag (refereegranskat)abstract
- GNSS-R has emerged as an attractive way of using a signal of opportunity that is collected by GNSS stations all around the world to measure a wide variety of properties of the surroundings of the stations. Current state-of-the-art algorithms based on the inversion of SNR values rely on off-line processing, causing a significant delay before results are available. We present a new approach for ground-based GNSS-R that uses Kalman filtering with a realistic physical model that allows close to real-time inversion of SNR oscillations into sea-surface height with high precision. From the analysis of test measurements from the GTGU GNSS installation at the Onsala Space Observatory, Sweden, we conclude that the new method provides better estimates than single-arc retrievals from spectral analysis and that the final precision is close to that of post-processing inversion algorithms.
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