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5. ITRF2020 Plate Motion Model

Author

Z. Altamimi, L. Métivier, P. Rebischung, X. Collilieux, K. Chanard, and J. Barnéoud

Subjects
plate motion model, reference frames, ITRF, Geophysics. Cosmic physics, QC801-809
Abstract

Abstract A tectonic Plate Motion Model (PMM) is essential for geodetic applications, while contributing to the understanding of geodynamic processes affecting the Earth's surface. We introduce a PMM derived from the horizontal velocities of 518 sites extracted from the ITRF2020 solution. These sites were chosen away from plate boundaries, Glacial Isostatic Adjustment regions, and other deforming zones. Unlike the ITRF2014‐PMM, which showed no significant Origin Rate Bias (ORB), velocities used to determine the ITRF2020‐PMM exhibit a statistically significant ORB (0.74 ± 0.09 mm/yr along the Z‐component). Users are advised to add the estimated ORB to the horizontal velocities predicted by the ITRF2020‐PMM rotation poles for full consistency with the ITRF2020. However, the predicted vertical velocities resulting from the addition of the ORB should be discarded. The overall precision with which the ITRF2020 velocity field is represented by the rigid ITRF2020‐PMM is at the level of 0.25 mm/yr WRMS.

Published
2023
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7. The ULR-repro3 GPS data reanalysis and its estimates of vertical land motion at tide gauges for sea level science

Author

M. Gravelle, G. Wöppelmann, K. Gobron, Z. Altamimi, M. Guichard, T. Herring, and P. Rebischung

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

A new reanalysis of Global Navigation Satellite System (GNSS) data at or near tide gauges worldwide was produced by the University of La Rochelle (ULR) group within the third International GNSS Service (IGS) reprocessing campaign (repro3). The new solution, called ULR-repro3, complies with the IGS standards adopted for repro3, implementing advances in data modelling and corrections since the previous reanalysis campaign and extending the average record length by about 7 years. The results presented here focus on the main products of interest for sea level science: the station position time series and associated velocities on the vertical component at tide gauges. These products are useful to estimate accurate vertical land motion at the coast and supplement data from satellite altimetry or tide gauges for an improved understanding of sea level changes and their impacts along coastal areas. To provide realistic velocity uncertainty estimates, the noise content in the position time series was investigated considering the impact of non-tidal atmospheric loading. Overall, the ULR-repro3 position time series show reduced white noise and power-law amplitudes and lower station velocity uncertainties compared with the previous reanalysis. The products are available via SONEL (https://doi.org/10.26166/sonel_ulr7a; Gravelle et al., 2022).

Published
2023
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16. ITRF2020 Plate Motion Model.

Author

Altamimi, Z., Métivier, L., Rebischung, P., Collilieux, X., Chanard, K., and Barnéoud, J.

Subjects
GLACIAL isostasy, SATELLITE geodesy, SURFACE of the earth, RELATIVE motion, GEODETIC observations, PLATE tectonics, MOTION
Abstract

A tectonic Plate Motion Model (PMM) is essential for geodetic applications, while contributing to the understanding of geodynamic processes affecting the Earth's surface. We introduce a PMM derived from the horizontal velocities of 518 sites extracted from the ITRF2020 solution. These sites were chosen away from plate boundaries, Glacial Isostatic Adjustment regions, and other deforming zones. Unlike the ITRF2014‐PMM, which showed no significant Origin Rate Bias (ORB), velocities used to determine the ITRF2020‐PMM exhibit a statistically significant ORB (0.74 ± 0.09 mm/yr along the Z‐component). Users are advised to add the estimated ORB to the horizontal velocities predicted by the ITRF2020‐PMM rotation poles for full consistency with the ITRF2020. However, the predicted vertical velocities resulting from the addition of the ORB should be discarded. The overall precision with which the ITRF2020 velocity field is represented by the rigid ITRF2020‐PMM is at the level of 0.25 mm/yr WRMS. Plain Language Summary: The Earth's surface is divided in large and small tectonic plates, which evolve and move slowly over time, resulting in lateral displacements of the ground surface typically of the order of a few cm/yr. Because of the relative motion between tectonic plates, plate boundaries can be either divergent (when two plates move away from each other), convergent (when two plates collide) or transform (when two plates slide past each other). Plate motion models are used to quantify the relative motions of the plates with respect to each other, and are determined using geological data or observations collected by space geodesy instruments distributed over different plates at the Earth's surface. In the latter case, space geodesy observations from the four space geodetic techniques covering more than 40 years of data are analyzed to estimate the long‐term displacements (or velocities) of each instrument in a well defined and self‐consistent global reference frame. The derived velocity field is then used to estimate a comprehensive plate motion model (PMM). This article presents a PMM for 13 tectonic plates based on a subset of the velocity field from the recently released International Terrestrial Reference Frame 2020 (ITRF2020); see https://itrf.ign.fr/en/solutions/ITRF2020. Key Points: We derive a plate motion model for 13 tectonic plates from the ITRF2020 horizontal velocity fieldBuilt under the rigid‐plate motion hypothesis, the model represents the ITRF2020 velocity field with a precision of 0.25 mm/yr WRMSThe residual velocities would show a global northward motion if a translation rate was not included in the inversion model [ABSTRACT FROM AUTHOR]

Published
2023
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19. IGS polar motion measurement accuracy

Author

Jim Ray, Paul Rebischung, and Jake Griffiths

Subjects
PM (Polar motion), GPS (Global Positioning System), EOPs (Earth orientation parameters), Accuracy, IGS (International GNSS Service), Geodesy, QB275-343, Geophysics. Cosmic physics, QC801-809
Abstract

We elaborate an error budget for the long-term accuracy of IGS (International Global Navigation Satellite System Service) polar motion estimates, concluding that it is probably about 25–30 μas (1-sigma) overall, although it is not possible to quantify possible contributions (mainly annual) that might transfer directly from aliases of subdaily rotational tide errors. The leading sources are biases arising from the need to align daily, observed terrestrial frames, within which the pole coordinates are expressed and which are continuously deforming, to the secular, linear international reference frame. Such biases are largest over spans longer than about a year. Thanks to the very large number of IGS tracking stations, the formal covariance errors are much smaller, around 5 to 10 μas. Large networks also permit the systematic frame-related errors to be more effectively minimized but not eliminated. A number of periodic errors probably also influence polar motion results, mainly at annual, GPS (Global Positioning System) draconitic, and fortnightly periods, but their impact on the overall error budget is unlikely to be significant except possibly for annual tidal aliases. Nevertheless, caution should be exercised in interpreting geophysical excitations near any of the suspect periods.

Published
2017
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20. Is the processing of DORIS measurements affected by multicollinearity effects?

Author

Nahmani, S., Pollet, A., and Rebischung, P.

Abstract

The DORIS technique (Doppler Orbitography by Radiopositioning Integrated on Satellite) is a French satellite system used for the determination of satellite orbits and for positioning. With orbits estimated with a precision better than a centimeter on the radial component, DORIS is important for space altimetry to monitor sea level rise, one of the key indicators of climate change. The processing of spatial geodesy measurements in general requires modeling involving the estimation of many parameters by least squares or by an equivalent estimation method. A limiting factor in performing the estimation, regardless of the physical validity of the chosen model, is to ensure that no parameter of the model can be written as a linear combination of the other parameters, otherwise the model matrix is singular and the normal matrix non-invertible. The situation becomes more complicated when the model matrix is non-singular but close to being singular, leading to possible multicollinearity problems: the estimates of a parameter can thus be questioned if it is close to a linear combination of other parameters. These multicollinearity effects explain, among other things, why estimating the origin of the terrestrial reference frame via GNSS is impossible. In this poster, we evaluate to what extent the DORIS data processing can be affected by multicollinearity effects. We will analyze the estimated parameters for single satellite calculations with different satellites and in a multi-satellite computation in order to test mathematically whether the parameter estimates are reasonable. In particular, we will review the frame parameters (origin and TRF scale)., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Published
2023
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21. Impact of RAS Mutations in Metastatic Colorectal Cancer After Potentially Curative Resection: Does Site of Metastases Matter?

Author

Passot, Guillaume, Kim, Bradford J., Glehen, Olivier, Mehran, Reza J., Kopetz, Scott E., Goere, Diane, Overman, Michael J., Pocard, Marc, Marchal, Frédéric, Conrad, Claudius, Aloia, Thomas A., Vauthey, Jean-Nicolas, Chun, Yun Shin, Abba, Julio, Abboud, Karine, Alyami, Mohammad, Arvieux, Catherine, Asnacios, Amani, Averous, Gerlinde, Bakrin, Naoual, Bardier, Armelle, Rejeb, Houda Ben, Bereder, Jean-Marc, Bernard, Jean-Louis, Bibeau, Frédéric, Bonnefoy, Isabelle, Borg, Christophe, Boschetti, Gilles, Bouarioua, Nadia, Bouche, Olivier, Bouzard, Dominique, Brigand, Cécile, Cacheux, Wulfran, Capitain, Olivier, Carrère, Sébastien, Carretier, Michel, Castel, Benjamin, Chauvenet, Marion, Chevallier, Anne, Coffin, Benoît, Costan, Cristina, Cotte, Eddy, Courvoisier, Thomas, Dahan, Laetitia, Dartigues, Peggy, De Chaisemartin, Cécile, Dechelotte, Pierre, Deguelte-Lardière, Sophie, Delpero, Jean-Robert, Del Grande, Jean, Demarchi, Martin, Desandes, Emmanuel, Desolneux, Grégoire, Dufour, Patrick, Dumont, Frédéric, Durand-Fontanier, Sylvaine, Eveno, Clarisse, Evrard, Serge, Facy, Olivier, Farkhondeh, Fereshteh, Fontaine, Juliette, Gagniere, Johan, Galais, Marie-Pierre, Ghouti, Laurent, Gilly, François-Noël, Goasguen, Nicolas, Gornet, Jean-Marc, Gourdiole, Pierre, Guerin-Meyer, Véronique, Guilloit, Jean-Marc, Guimbaud, Rosine, Guyon, Frédéric, Heyd, Bruno, Heymann, Marie-Françoise, Isaac, Sylvie, Jouet, Pauline, Jourdan-Enfer, Peggy, Jouve, Jean-Louis, Kaci, Rachid, Karoui, Mehdi, Kianmanesh, Reza, Kirzin, Sylvain, Kurtz, Jean-Emmanuel, Labrousse, François, Lebrun-Ly, Valérie, Lefevre, Jérémie, Lelong, Bernard, Lemaistre, Anne-Elisabeth, Leroux-Broussier, Agnès, Levillain, Pierre, Linot, Benjamin, Dico, Réa Lo, Loi, Valéria, Lorimier, Gérard, Mahammat, Abakar, Malka, David, Marchal, Frédéric, Mariani, Antoine, Mariani, Pascale, Mariette, Christophe, Martin, Laurent, Meeus, Pierre, Meffert, Jean-Luc, Messager, Mathieu, Michel, Pierre, Msika, Simon, Muron, Thierry, Noel, Georges, Ortega-Deballon, Pablo, Paquette, Brice, Peoc’h, Michel, Petorin, Caroline, Peyrat, Patrice, Pezet, Denis, Piessen, Guillaume, Pirro, Nicolas, Pocard, Marc, Poizat, Flora, Porcheron, Jack, Poulet, Anaïs, Portales, Fabienne, la Denise, Justine Prost a, Quenet, François, Rat, Patrick, Rebischung, Christine, Regimbeau, Jean-Marc, Rivoirard, Romain, Rohr, Serge, Rougier, Philippe, Rousselot, Pierre, Saban-Roche, Léa, Sabate, Jean-Marc, Sabbagh, Charles, Sabourin, Jean-Christophe, Samalin-Scalzi, Emmanuelle, Seitz, Jean-François, Selves, Janick, Senellart, Helène, Serrano, Martine, Sgabura, Olivia, Sielezneff, Igor, Svrcek, Magali, Taibi, Abdelkader, Taieb, Julien, Thibaudeau, Emilie, Tougeron, David, Tuech, Jean-Jacques, Valmary-Degano, Séverine, Vaudoyer, Delphine, Verriele-Beurrier, Véronique, Villeneuve, Laurent, Volet, Julien, Wernert, Romuald, Ychou, Marc, You, Benoît, Zaanan, Aziz, Zinzindohoue, Franck, and on behalf of the BIG-RENAPE Working Group

Published
2017
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26. A Randomized Phase II Trial of Three Intensified Chemotherapy Regimens in First-Line Treatment of Colorectal Cancer Patients with Initially Unresectable or Not Optimally Resectable Liver Metastases. The METHEP Trial

Author

Ychou, Marc, Rivoire, Michel, Thezenas, Simon, Quenet, François, Delpero, Jean-Robert, Rebischung, Christine, Letoublon, Christian, Guimbaud, Rosine, Francois, Eric, Ducreux, Michel, Desseigne, Françoise, Fabre, Jean-Michel, and Assenat, Eric

Published
2013
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30. Randomized Phase II Trial Evaluating Two Sequential Treatments in First Line of Metastatic Pancreatic Cancer: Results of the PANOPTIMOX-PRODIGE 35 Trial.

Author

Dahan, Laetitia, Williet, Nicolas, Le Malicot, Karine, Phelip, Jean-Marc, Desrame, Jérôme, Bouché, Olivier, Petorin, Caroline, Malka, David, Rebischung, Christine, Aparicio, Thomas, Lecaille, Cédric, Rinaldi, Yves, Turpin, Anthony, Bignon, Anne-Laure, Bachet, Jean-Baptiste, Seitz, Jean-François, Lepage, Come, François, Eric, and PRODIGE 35 Investigators/Collaborators

Published
2021
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32. IGS14/igs14.atx: a new framework for the IGS products

Author

Rebischung P., Schmid R. and Deutsches Geodätisches Forschungsinstitut (DGFI-TUM)

Subjects
ddc
Abstract

The International GNSS Service (IGS) is about to switch to a new reference frame (IGS14), based on the latest release of the International Terrestrial Reference Frame (ITRF2014), as the basis for its products. An updated set of satellite and ground antenna calibrations (igs14.atx) will become effective at the same time. IGS14 and igs14.atx will then replace the previous IGS08/igs08.atx framework in use since GPS week 1632 (17 April 2011) and in the second IGS reprocessing campaign (repro2). Despite the negligible scale difference between ITRF2008 and ITRF2014 (0.02 ppb), the radial components of all GPS and GLONASS satellite antenna phase center offsets (z-PCOs) had to be updated in igs14.atx, because of modeling changes recently introduced within the IGS that affect the scale of the IGS products. This was achieved by deriving and averaging time series of satellite z-PCO estimates, consistent with the ITRF2014 scale, from the daily repro2 and latest operational SINEX solutions of seven IGS Analysis Centers (ACs). Compared to igs08.atx, igs14.atx includes robot calibrations for 16 additional ground antenna types, so that the percentage of stations with absolute calibrations in the IGS network will reach ~90% after the switch. 19 type-mean robot calibrations were also updated thanks to the availability of calibration results for additional antenna samples. IGS14 is basically an extract of well-suited reference frame stations (i.e., with long and stable position time series) from ITRF2014. However, to make the IGS14 station coordinates consistent with the new igs14.atx ground antenna calibrations, position offsets due to the switch from igs08.atx to igs14.atx were derived for all IGS14 stations affected by ground antenna calibration updates and applied to their ITRF2014 coordinates. This presentation will first detail the different steps of the elaboration of IGS14 and igs14.atx. The impact of the switch on GNSS-derived geodetic parameter time series will then be assessed by re-aligning the daily repro2 and latest operational IGS combined SINEX solutions to IGS14/igs14.atx. A particular focus will finally be given to the biases and trends present in the satellite z-PCO time series derived from the daily AC SINEX solutions, and to their interpretation in terms of scale and scale rate of the terrestrial frame.

Published
2015

36. Prospective, randomized, multicenter, phase III study of fluorouracil, leucovorin, and irinotecan versus epirubicin, cisplatin, and capecitabine in advanced gastric adenocarcinoma: a French intergroup (Fédération Francophone de Cancé...

Author

Guimbaud, Rosine, Louvet, Christophe, Ries, Pauline, Ychou, Marc, Maillard, Emilie, André, Thierry, Gornet, Jean-Marc, Aparicio, Thomas, Nguyen, Suzanne, Azzedine, Ahmed, Etienne, Pierre-Luc, Boucher, Eveline, Rebischung, Christine, Hammel, Pascal, Rougier, Philippe, Bedenne, Laurent, and Bouché, Olivier

Published
2014
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37. Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast–ovarian cancer susceptibility locus

Author

Lawrenson, Kate, Kar, Siddhartha, McCue, Karen, Kuchenbaeker, Karoline, Michailidou, Kyriaki, Tyrer, Jonathan, Beesley, Jonathan, Ramus, Susan J., Li, Qiyuan, Delgado, Melissa K., Lee, Janet M., Aittomäki, Kristiina, Andrulis, Irene L., Anton-Culver, Hoda, Arndt, Volker, Arun, Banu K., Arver, Brita, Bandera, Elisa V., Barile, Monica, Barkardottir, Rosa B., Barrowdale, Daniel, Beckmann, Matthias W., Benitez, Javier, Berchuck, Andrew, Bisogna, Maria, Bjorge, Line, Blomqvist, Carl, Blot, William, Bogdanova, Natalia, Bojesen, Anders, Bojesen, Stig E., Bolla, Manjeet K., Bonanni, Bernardo, Børresen-Dale, Anne-Lise, Brauch, Hiltrud, Brennan, Paul, Brenner, Hermann, Bruinsma, Fiona, Brunet, Joan, Buhari, Shaik Ahmad, Burwinkel, Barbara, Butzow, Ralf, Buys, Saundra S., Cai, Qiuyin, Caldes, Trinidad, Campbell, Ian, Canniotto, Rikki, Chang-Claude, Jenny, Chiquette, Jocelyne, Choi, Ji-Yeob, Claes, Kathleen B. M., Collonge-Rame, Marie- Agnès, Damette, Alexandre, Barouk-Simonet, Emmanuelle, Bonnet, Françoise, Bubien, Virginie, Sevenet, Nicolas, Longy, Michel, Berthet, Pascaline, Vaur, Dominique, Castera, Laurent, Ferrer, Sandra Fert, Bignon, Yves-Jean, Uhrhammer, Nancy, Coron, Fanny, Faivre, Laurence, Baurand, Amandine, Jacquot, Caroline, Bertolone, Geoffrey, Lizard, Sarab, Leroux, Dominique, Dreyfus, Hélène, Rebischung, Christine, Peysselon, Magalie, Peyrat, Jean-Philippe, Fournier, Joëlle, Révillion, Françoise, Adenis, Claude, Vénat-Bouvet, Laurence, Léone, Mélanie, Boutry-Kryza, Nadia, Calender, Alain, Giraud, Sophie, Verny-Pierre, Carole, Lasset, Christine, Bonadona, Valérie, Barjhoux, Laure, Sobol, Hagay, Bourdon, Violaine, Noguchi, Tetsuro, Remenieras, Audrey, Coupier, Isabelle, Pujol, Pascal, Sokolowska, Johanna, Bronner, Myriam, Delnatte, Capucine, Bézieau, Stéphane, Mari, Véronique, Gauthier-Villars, Marion, Buecher, Bruno, Rouleau, Etienne, Golmard, Lisa, Moncoutier, Virginie, Belotti, Muriel, de Pauw, Antoine, Elan, Camille, Fourme, Emmanuelle, Birot, Anne-Marie, Saule, Claire, Laurent, Maïté, Houdayer, Claude, Lesueur, Fabienne, Mebirouk, Noura, Coulet, Florence, Colas, Chrystelle, Soubrier, Florent, Warcoin, Mathilde, Prieur, Fabienne, Lebrun, Marine, Kientz, Caroline, Muller, Danièle, Fricker, Jean-Pierre, Toulas, Christine, Guimbaud, Rosine, Gladieff, Laurence, Feillel, Viviane, Mortemousque, Isabelle, Bressac-de-Paillerets, Brigitte, Caron, Olivier, Guillaud-Bataille, Marine, Cook, Linda S., Cox, Angela, Cramer, Daniel W., Cross, Simon S., Cybulski, Cezary, Czene, Kamila, Daly, Mary B., Damiola, Francesca, Dansonka-Mieszkowska, Agnieszka, Darabi, Hatef, Dennis, Joe, Devilee, Peter, Diez, Orland, Doherty, Jennifer A., Domchek, Susan M., Dorfling, Cecilia M., Dörk, Thilo, Dumont, Martine, Ehrencrona, Hans, Ejlertsen, Bent, Ellis, Steve, Gregory, Helen, Miedzybrodzka, Zosia, Morrison, Patrick J., Donaldson, Alan, Rogers, Mark T., Kennedy, M. John, Porteous, Mary E., Brady, Angela, Barwell, Julian, Foo, Claire, Lalloo, Fiona, Side, Lucy E., Eason, Jacqueline, Henderson, Alex, Walker, Lisa, Cook, Jackie, Snape, Katie, Murray, Alex, McCann, Emma, Engel, Christoph, Lee, Eunjung, Evans, D. Gareth, Fasching, Peter A., Feliubadalo, Lidia, Figueroa, Jonine, Flesch-Janys, Dieter, Fletcher, Olivia, Flyger, Henrik, Foretova, Lenka, Fostira, Florentia, Foulkes, William D., Fridley, Brooke L., Friedman, Eitan, Frost, Debra, Gambino, Gaetana, Ganz, Patricia A., Garber, Judy, García-Closas, Montserrat, Gentry-Maharaj, Aleksandra, Ghoussaini, Maya, Giles, Graham G., Glasspool, Rosalind, Godwin, Andrew K., Goldberg, Mark S., Goldgar, David E., González-Neira, Anna, Goode, Ellen L., Goodman, Marc T., Greene, Mark H., Gronwald, Jacek, Guénel, Pascal, Haiman, Christopher A., Hall, Per, Hallberg, Emily, Hamann, Ute, Hansen, Thomas V. O., Harrington, Patricia A., Hartman, Mikael, Hassan, Norhashimah, Healey, Sue, Rookus, M. A., van Leeuwen, F. E., van der Kolk, L. E., Schmidt, M. K., Russell, N. S., de Lange, J. L., Wijnands, R., Collée, J. M., Hooning, M. J., Seynaeve, C., van Deurzen, C. H. M., Obdeijn, I. M., van Asperen, C. J., Tollenaar, R. A. E. M., van Cronenburg, T. C. T. E. F., Kets, C. M., Ausems, M. G. E. M., van der Pol, C. C., van Os, T. A. M., Waisfisz, Q., Meijers-Heijboer, H. E. J., Gómez-Garcia, E. B., Oosterwijk, J. C., Mourits, M. J., de Bock, G. H., Vasen, H. F., Siesling, S., Verloop, J., Overbeek, L. I. H., Heitz, Florian, Herzog, Josef, Høgdall, Estrid, Høgdall, Claus K., Hogervorst, Frans B. L., Hollestelle, Antoinette, Hopper, John L., Hulick, Peter J., Huzarski, Tomasz, Imyanitov, Evgeny N., Fox, Stephen, Kirk, Judy, Lindeman, Geoff, Price, Melanie, Bowtell, David, deFazio, Anna, Webb, Penny, Isaacs, Claudine, Ito, Hidemi, Jakubowska, Anna, Janavicius, Ramunas, Jensen, Allan, John, Esther M., Johnson, Nichola, Kabisch, Maria, Kang, Daehee, Kapuscinski, Miroslav, Karlan, Beth Y., Khan, Sofia, Kiemeney, Lambertus A., Kjaer, Susanne Kruger, Knight, Julia A., Konstantopoulou, Irene, Kosma, Veli-Matti, Kristensen, Vessela, Kupryjanczyk, Jolanta, Kwong, Ava, de la Hoya, Miguel, Laitman, Yael, Lambrechts, Diether, Le, Nhu, De Leeneer, Kim, Lester, Jenny, Levine, Douglas A., Li, Jingmei, Lindblom, Annika, Long, Jirong, Lophatananon, Artitaya, Loud, Jennifer T., Lu, Karen, Lubinski, Jan, Mannermaa, Arto, Manoukian, Siranoush, Le Marchand, Loic, Margolin, Sara, Marme, Frederik, Massuger, Leon F. A. G., Matsuo, Keitaro, Mazoyer, Sylvie, McGuffog, Lesley, McLean, Catriona, McNeish, Iain, Meindl, Alfons, Menon, Usha, Mensenkamp, Arjen R., Milne, Roger L., Montagna, Marco, Moysich, Kirsten B., Muir, Kenneth, Mulligan, Anna Marie, Nathanson, Katherine L., Ness, Roberta B., Neuhausen, Susan L., Nevanlinna, Heli, Nord, Silje, Nussbaum, Robert L., Odunsi, Kunle, Offit, Kenneth, Olah, Edith, Olopade, Olufunmilayo I., Olson, Janet E., Olswold, Curtis, O'Malley, David, Orlow, Irene, Orr, Nick, Osorio, Ana, Park, Sue Kyung, Pearce, Celeste L., Pejovic, Tanja, Peterlongo, Paolo, Pfeiler, Georg, Phelan, Catherine M., Poole, Elizabeth M., Pylkäs, Katri, Radice, Paolo, Rantala, Johanna, Rashid, Muhammad Usman, Rennert, Gad, Rhenius, Valerie, Rhiem, Kerstin, Risch, Harvey A., Rodriguez, Gus, Rossing, Mary Anne, Rudolph, Anja, Salvesen, Helga B., Sangrajrang, Suleeporn, Sawyer, Elinor J., Schildkraut, Joellen M., Schmidt, Marjanka K., Schmutzler, Rita K., Sellers, Thomas A., Seynaeve, Caroline, Shah, Mitul, Shen, Chen-Yang, Shu, Xiao-Ou, Sieh, Weiva, Singer, Christian F., Sinilnikova, Olga M., Slager, Susan, Song, Honglin, Soucy, Penny, Southey, Melissa C., Stenmark-Askmalm, Marie, Stoppa-Lyonnet, Dominique, Sutter, Christian, Swerdlow, Anthony, Tchatchou, Sandrine, Teixeira, Manuel R., Teo, Soo H., Terry, Kathryn L., Terry, Mary Beth, Thomassen, Mads, Tibiletti, Maria Grazia, Tihomirova, Laima, Tognazzo, Silvia, Toland, Amanda Ewart, Tomlinson, Ian, Torres, Diana, Truong, Thérèse, Tseng, Chiu-chen, Tung, Nadine, Tworoger, Shelley S., Vachon, Celine, van den Ouweland, Ans M. W., van Doorn, Helena C., van Rensburg, Elizabeth J., Van't Veer, Laura J., Vanderstichele, Adriaan, Vergote, Ignace, Vijai, Joseph, Wang, Qin, Wang-Gohrke, Shan, Weitzel, Jeffrey N., Wentzensen, Nicolas, Whittemore, Alice S., Wildiers, Hans, Winqvist, Robert, Wu, Anna H., Yannoukakos, Drakoulis, Yoon, Sook-Yee, Yu, Jyh-Cherng, Zheng, Wei, Zheng, Ying, Khanna, Kum Kum, Simard, Jacques, Monteiro, Alvaro N., French, Juliet D., Couch, Fergus J., Freedman, Matthew L., Easton, Douglas F., Dunning, Alison M., Pharoah, Paul D., Edwards, Stacey L., Chenevix-Trench, Georgia, Antoniou, Antonis C., and Gayther, Simon A.

Abstract

A locus at 19p13 is associated with breast cancer (BC) and ovarian cancer (OC) risk. Here we analyse 438 SNPs in this region in 46,451 BC and 15,438 OC cases, 15,252 BRCA1 mutation carriers and 73,444 controls and identify 13 candidate causal SNPs associated with serous OC (P=9.2 × 10−20), ER-negative BC (P=1.1 × 10−13), BRCA1-associated BC (P=7.7 × 10−16) and triple negative BC (P-diff=2 × 10−5). Genotype-gene expression associations are identified for candidate target genes ANKLE1 (P=2 × 10−3) and ABHD8 (P<2 × 10−3). Chromosome conformation capture identifies interactions between four candidate SNPs and ABHD8, and luciferase assays indicate six risk alleles increased transactivation of the ADHD8 promoter. Targeted deletion of a region containing risk SNP rs56069439 in a putative enhancer induces ANKLE1 downregulation; and mRNA stability assays indicate functional effects for an ANKLE1 3′-UTR SNP. Altogether, these data suggest that multiple SNPs at 19p13 regulate ABHD8 and perhaps ANKLE1 expression, and indicate common mechanisms underlying breast and ovarian cancer risk.

Published
2016
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39. Accounting for spatiotemporal correlations of GNSS coordinate time series to estimate station velocities.

Author

Benoist, C., Collilieux, X., Rebischung, P., Altamimi, Z., Jamet, O., Métivier, L., Chanard, K., and Bel, L.

Subjects
*TIME series analysis, *SURFACE waves (Seismic waves), *SPATIAL filters, *EXPONENTIAL functions, *VELOCITY, *FORECASTING, *WHITE noise
Abstract

GNSS coordinate time series provided by the International GNSS Service are spatially correlated. Spatiotemporal covariance models of the GNSS coordinate time series can be used to improve the estimation of station velocities. Modeling the spatiotemporal correlations by exponential functions and adding spatial white noise is shown to improve the velocity determination of short GNSS coordinate series for a tested subset of 21 stations. The new method performs similarly to standard spatial filtering of the time series but can be improved in the future by a more reliable modeling of the coordinate spatiotemporal correlations. It is well known that GNSS permanent station coordinate time series exhibit time-correlated noise. Spatial correlations between coordinate time series of nearby stations are also long-established and generally handled by means of spatial filtering techniques. Accounting for both the temporal and spatial correlations of the noise via a spatiotemporal covariance model is however not yet a common practice. We demonstrate in this paper the interest of using such a spatiotemporal covariance model of the stochastic variations in GNSS time series in order to estimate long-term station coordinates and especially velocities. We provide a methodology to rigorously assess the covariances between horizontal coordinate variations and use it to derive a simple exponential spatiotemporal covariance model for the stochastic variations in the IGS repro2 station coordinate time series. We then use this model to estimate station velocities for two selected datasets of 10 time series in Europe and 11 time series in the USA. We show that coordinate prediction as well as velocity determination from short time series are improved when using this spatiotemporal model, as compared with the case where spatiotemporal correlations are ignored. [ABSTRACT FROM AUTHOR]

Published
2020
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40. Singular spectrum analysis for modeling seasonal signals from GPS time series.

Author

Chen, Q., van Dam, T., Sneeuw, N., Collilieux, X., Weigelt, M., and Rebischung, P.

Subjects
*SPECTRUM analysis, *SIGNAL processing, *GLOBAL Positioning System, *TIME series analysis, *GEODESY, *GEODYNAMICS
Abstract

Abstract: Seasonal signals in GPS time series are of great importance for understanding the evolution of regional mass fluctuations, i.e., ice, hydrology, and ocean mass. Conventionally these signals (quasi-annual and semi-annual signals) are modeled by least-squares fitting harmonic terms with a constant amplitude and phase. In reality, however, such seasonal signals are modulated, i.e., they will have a time-variable amplitude and phase. Recently, Davis et al. (2012) proposed a Kalman filter based approach to capture the stochastic seasonal behavior of geodetic time series. Singular Spectrum Analysis (SSA) is a non-parametric method, which uses time domain data to extract information from short and noisy time series without a priori knowledge of the dynamics affecting the time series. A prominent benefit is that trends obtained in this way are not necessarily linear. Further, true oscillations can be amplitude and phase modulated. In this work, we will assess the value of SSA for extracting time-variable seasonal signals from GPS time series. We compare our SSA-based results to those obtained using (1) least-squares analysis and (2) Kalman filtering. Our results demonstrate that SSA is a viable and complementary tool for extracting modulated oscillations from GPS time series. [Copyright &y& Elsevier]

Published
2013
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