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6,685 results on '"Cross A. J."'

1. The VMC Survey : LI. Classifying extragalactic sources using a probabilistic random forest supervised machine learning algorithm

Author

Pennock, Clara M., van Loon, Jacco Th., Cioni, Maria-Rosa L., Maitra, Chandreyee, Oliveira, Joana M., Craig, Jessica E. M., Ivanov, Valentin D., Aird, James, Anih, Joy. O., Cross, Nicholas J. G., Dresbach, Francesca, de Grijs, Richard, and Groenewegen, Martin A. T.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We used a supervised machine learning algorithm (probabilistic random forest) to classify ~130 million sources in the VISTA Survey of the Magellanic Clouds (VMC). We used multi-wavelength photometry from optical to far-infrared as features to be trained on, and spectra of Active Galactic Nuclei (AGN), galaxies and a range of stellar classes including from new observations with the Southern African Large Telescope (SALT) and SAAO 1.9m telescope. We also retain a label for sources that remain unknown. This yielded average classifier accuracies of ~79% (SMC) and ~87% (LMC). Restricting to the 56,696,719 sources with class probabilities (P$_{class}$) > 80% yields accuracies of ~90% (SMC) and ~98% (LMC). After removing sources classed as 'Unknown', we classify a total of 707,939 (SMC) and 397,899 (LMC) sources, including > 77,600 extragalactic sources behind the Magellanic Clouds. The extragalactic sources are distributed evenly across the field, whereas the Magellanic sources concentrate at the centres of the Clouds, and both concentrate in optical/IR colour-colour/magnitude diagrams as expected. We also test these classifications using independent datasets, finding that, as expected, the majority of X-ray sources are classified as AGN (554/883) and the majority of radio sources are classed as AGN (1756/2694) or galaxies (659/2694), where the relative AGN-galaxy proportions vary substantially with radio flux density. We have found: > 49,500 hitherto unknown AGN candidates, likely including more AGN dust dominated sources which are in a critical phase of their evolution; > 26,500 new galaxy candidates and > 2800 new Young Stellar Object (YSO) candidates., Comment: MNRAS accepted

Published
2025

2. Active learning for regression in engineering populations: A risk-informed approach

Author

Clarkson, Daniel R., Bull, Lawrence A., Wickramarachchi, Chandula T., Cross, Elizabeth J., Rogers, Timothy J., Worden, Keith, Dervilis, Nikolaos, and Hughes, Aidan J.

Subjects
Computer Science - Machine Learning
Abstract

Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitoring), feature-label pairs used to learn such mappings are of limited availability which hinders the effectiveness of traditional supervised machine learning approaches. The current paper proposes a methodology for overcoming the issue of data scarcity by combining active learning with hierarchical Bayesian modelling. Active learning is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g.\ inspection and maintenance). Hierarchical Bayesian modelling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modelling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modelling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost -- maintaining predictive performance while reducing the number of inspections required., Comment: 19 pages, 12 figures, 3 tables, submitted to Data-Centric Engineering

Published
2024

3. The VISTA Variables in the V\'ia L\'actea eXtended (VVVX) ESO public survey: Completion of the observations and legacy

Author

Saito, R. K., Hempel, M., Alonso-García, J., Lucas, P. W., Minniti, D., Alonso, S., Baravalle, L., Borissova, J., Caceres, C., Chené, A. N., Cross, N. J. G., Duplancic, F., Garro, E. R., Gómez, M., Ivanov, V. D., Kurtev, R., Luna, A., Majaess, D., Navarro, M. G., Pullen, J. B., Rejkuba, M., Sanders, J. L., Smith, L. C., Albino, P. H. C., Alonso, M. V., Amôres, E. B., Angeloni, R., Arias, J. I., Arnaboldi, M., Barbuy, B., Bayo, A., Beamin, J. C., Bedin, L. R., Bellini, A., Benjamin, R. A., Bica, E., Bonatto, C. J., Botan, E., Braga, V. F., Brown, D. A., Cabral, J. B., Camargo, D., Garatti, A. Caratti o, Carballo-Bello, J. A., Catelan, M., Chavero, C., Chijani, M. A., Clariá, J. J., Coldwell, G. V., Peña, C. Contreras, Ramos, R. Contreras, Corral-Santana, J. M., Cortés, C. C., Cortés-Contreras, M., Cruz, P., Daza-Perilla, I. V., Debattista, V. P., Dias, B., Donoso, L., D'Souza, R., Emerson, J. P., Federle, S., Fermiano, V., Fernandez, J., Fernández-Trincado, J. G., Ferreira, T., Lopes, C. E. Ferreira, Firpo, V., Flores-Quintana, C., Fraga, L., Froebrich, D., Galdeano, D., Gavignaud, I., Geisler, D., Gerhard, O. E., Gieren, W., Gonzalez, O. A., Gramajo, L. V., Gran, F., Granitto, P. M., Griggio, M., Guo, Z., Gurovich, S., Hilker, M., Jones, H. R. A., Kammers, R., Kuhn, M. A., Kumar, M. S . N., Kundu, R., Lares, M., Libralato, M., Lima, E., Maccarone, T. J., Cortés, P. Marchant, Martin, E. L., Masetti, N., Matsunaga, N., Mauro, F., McDonald, I., Mejías, A., Mesa, V., Milla-Castro, F. P., Minniti, J. H., Bidin, C. Moni, Montenegro, K., Morris, C., Motta, V., Navarete, F., Molina, C. Navarro, Nikzat, F., Castellón, J. L. Nilo, Obasi, C., Ortigoza-Urdaneta, M., Palma, T., Parisi, C., Ramírez, K. Pena, Pereyra, L., Perez, N., Petralia, I., Pichel, A., Pignata, G., Alegría, S. Ramírez, Rojas, A. F., Rojas, D., Roman-Lopes, A., Rovero, A. C., Saroon, S., Schmidt, E. O., Schröder, A. C., Schultheis, M., Sgró, M. A., Solano, E., Soto, M., Stecklum, B., Steeghs, D., Tamura, M., Tissera, P., Valcarce, A. A. R., Valotto, C. A., Vasquez, S., Villalon, C., Villanova, S., Cádiz, F. Vivanco, Bacigalupo, R. Zelada, Zijlstra, A., and Zoccali, M.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics
Abstract

The ESO public survey VISTA Variables in the V\'ia L\'actea (VVV) surveyed the inner Galactic bulge and the adjacent southern Galactic disk from $2009-2015$. Upon its conclusion, the complementary VVV eXtended (VVVX) survey has expanded both the temporal as well as spatial coverage of the original VVV area, widening it from $562$ to $1700$ sq. deg., as well as providing additional epochs in $JHK_{\rm s}$ filters from $2016-2023$. With the completion of VVVX observations during the first semester of 2023, we present here the observing strategy, a description of data quality and access, and the legacy of VVVX. VVVX took $\sim 2000$ hours, covering about 4% of the sky in the bulge and southern disk. VVVX covered most of the gaps left between the VVV and the VISTA Hemisphere Survey (VHS) areas and extended the VVV time baseline in the obscured regions affected by high extinction and hence hidden from optical observations. VVVX provides a deep $JHK_{\rm s}$ catalogue of $\gtrsim 1.5\times10^9$ point sources, as well as a $K_{\rm s}$ band catalogue of $\sim 10^7$ variable sources. Within the existing VVV area, we produced a $5D$ map of the surveyed region by combining positions, distances, and proper motions of well-understood distance indicators such as red clump stars, RR Lyrae, and Cepheid variables. In March 2023 we successfully finished the VVVX survey observations that started in 2016, an accomplishment for ESO Paranal Observatory upon 4200 hours of observations for VVV+VVVX. The VVV+VVVX catalogues complement those from the Gaia mission at low Galactic latitudes and provide spectroscopic targets for the forthcoming ESO high-multiplex spectrographs MOONS and 4MOST., Comment: 17 pages, 11 figures (+ appendix). Accepted for publication in Astronomy and Astrophysics in section 14: Catalogs and data

Published
2024
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8. Evidence-based Medication knowledge Brokers in Residential Aged CarE (EMBRACE): protocol for a helix-counterbalanced randomised controlled trial

Author

Bell, J. Simon, La Caze, Adam, Steeper, Michelle, Haines, Terry P., Hilmer, Sarah N., Troeung, Lakkhina, Quirke, Lyntara, Wesson, Jacqueline, Pond, Constance Dimity, Buys, Laurie, Ghahreman-Falconer, Nazanin, Lawless, Michael T., Shrestha, Shakti, Martini, Angelita, Ochieng, Nancy, Glamorgan, Francesca, Lagasca, Carmela, Walton, Rebecca, Cenin, Dayna, Kitson, Alison, Jung, Monica, Bennett, Alexandra, and Cross, Amanda J.

Published
2024
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10. On the monotonicity of $Q^2$ spectral element method for Laplacian on quasi-uniform rectangular meshes

Author

Cross, Logan J. and Zhang, Xiangxiong

Subjects
Mathematics - Numerical Analysis, 65N30, 65N06, 65N12
Abstract

The monotonicity of discrete Laplacian implies discrete maximum principle, which in general does not hold for high order schemes. The $Q^2$ spectral element method has been proven monotone on a uniform rectangular mesh. In this paper we prove the monotonicity of the $Q^2$ spectral element method on quasi-uniform rectangular meshes under certain mesh constraints. In particular, we propose a relaxed Lorenz's condition for proving monotonicity., Comment: arXiv admin note: substantial text overlap with arXiv:2010.07282

Published
2023

11. Population-based wind farm monitoring based on a spatial autoregressive approach

Author

Lin, W., Worden, K., and Cross, E. J.

Subjects
Computer Science - Machine Learning, Physics - Fluid Dynamics
Abstract

An important challenge faced by wind farm operators is to reduce operation and maintenance cost. Structural health monitoring provides a means of cost reduction through minimising unnecessary maintenance trips as well as prolonging turbine service life. Population-based structural health monitoring can further reduce the cost of health monitoring systems by implementing one system for multiple structures (i.e.~turbines). At the same time, shared data within a population of structures may improve the predictions of structural behaviour. To monitor turbine performance at a population/farm level, an important initial step is to construct a model that describes the behaviour of all turbines under normal conditions. This paper proposes a population-level model that explicitly captures the spatial and temporal correlations (between turbines) induced by the wake effect. The proposed model is a Gaussian process-based spatial autoregressive model, named here a GP-SPARX model. This approach is developed since (a) it reflects our physical understanding of the wake effect, and (b) it benefits from a stochastic data-based learner. A case study is provided to demonstrate the capability of the GP-SPARX model in capturing spatial and temporal variations as well as its potential applicability in a health monitoring system., Comment: 8 pages, 4 figures, submitted to the Modern Practice in Stress and Vibration Analysis (MPSVA) 2022 Conference Proceedings

Published
2023

12. Sharing Information Between Machine Tools to Improve Surface Finish Forecasting

Author

Clarkson, Daniel R., Bull, Lawrence A., Dardeno, Tina A., Wickramarachchi, Chandula T., Cross, Elizabeth J., Rogers, Timothy J., Worden, Keith, Dervilis, Nikolaos, and Hughes, Aidan J.

Subjects
Computer Science - Machine Learning, Computer Science - Computational Engineering, Finance, and Science
Abstract

At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental costs. To counter this, the authors propose a Bayesian hierarchical model to predict surface-roughness measurements for a turning machining process. The hierarchical model is compared to multiple independent Bayesian linear regression models to showcase the benefits of partial pooling in a machining setting with respect to prediction accuracy and uncertainty quantification., Comment: Submitted to International Workshop on Structural Health Monitoring 2023, Stanford University, California, USA

Published
2023

13. A spectrum of physics-informed Gaussian processes for regression in engineering

Author

Cross, Elizabeth J, Rogers, Timothy J, Pitchforth, Daniel J, Gibson, Samuel J, and Jones, Matthew R

Subjects
Computer Science - Machine Learning
Abstract

Despite the growing availability of sensing and data in general, we remain unable to fully characterise many in-service engineering systems and structures from a purely data-driven approach. The vast data and resources available to capture human activity are unmatched in our engineered world, and, even in cases where data could be referred to as ``big,'' they will rarely hold information across operational windows or life spans. This paper pursues the combination of machine learning technology and physics-based reasoning to enhance our ability to make predictive models with limited data. By explicitly linking the physics-based view of stochastic processes with a data-based regression approach, a spectrum of possible Gaussian process models are introduced that enable the incorporation of different levels of expert knowledge of a system. Examples illustrate how these approaches can significantly reduce reliance on data collection whilst also increasing the interpretability of the model, another important consideration in this context.

Published
2023

15. Meta-models for transfer learning in source localisation

Author

Bull, Lawrence A., Jones, Matthew R., Cross, Elizabeth J., Duncan, Andrew, and Girolami, Mark

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Applications
Abstract

In practice, non-destructive testing (NDT) procedures tend to consider experiments (and their respective models) as distinct, conducted in isolation and associated with independent data. In contrast, this work looks to capture the interdependencies between acoustic emission (AE) experiments (as meta-models) and then use the resulting functions to predict the model hyperparameters for previously unobserved systems. We utilise a Bayesian multilevel approach (similar to deep Gaussian Processes) where a higher level meta-model captures the inter-task relationships. Our key contribution is how knowledge of the experimental campaign can be encoded between tasks as well as within tasks. We present an example of AE time-of-arrival mapping for source localisation, to illustrate how multilevel models naturally lend themselves to representing aggregate systems in engineering. We constrain the meta-model based on domain knowledge, then use the inter-task functions for transfer learning, predicting hyperparameters for models of previously unobserved experiments (for a specific design).

Published
2023

16. A probabilistic approach for acoustic emission based monitoring techniques: with application to structural health monitoring

Author

Lindley, C. A., Jones, M. R., Rogers, T. J., Cross, E. J., Dwyer-Joyce, R. S., Dervilis, N., and Worden, K.

Subjects
Statistics - Applications
Abstract

It has been demonstrated that acoustic-emission (AE), inspection of structures can offer advantages over other types of monitoring techniques in the detection of damage; namely, an increased sensitivity to damage, as well as an ability to localise its source. There are, however, numerous challenges associated with the analysis of AE data. One issue is the high sampling frequencies required to capture AE activity. In just a few seconds, a recording can generate very high volumes of data, of which a significant portion may be of little interest for analysis. Identifying the individual AE events in a recorded time-series is therefore a necessary procedure to reduce the size of the dataset. Another challenge that is also generally encountered in practice, is determining the sources of AE, which is an important exercise if one wishes to enhance the quality of the diagnostic scheme. In this paper, a state-of-the-art technique is presented that can automatically identify AE events, and simultaneously help in their characterisation from a probabilistic perspective. A nonparametric Bayesian approach, based on the Dirichlet process (DP), is employed to overcome some of the challenges associated with these tasks. Two main sets of AE data are considered in this work: (1) from a journal bearing in operation, and (2) from an Airbus A320 main landing gear subjected to fatigue testing.

Published
2023
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23. Prevalence of strong anticholinergic use in residents with and without cognitive impairment and frailty: Analysis from 106 nursing homes in 12 Asia-Pacific and European countries

Author

Cross, Amanda J., Villani, Emanuele R., Jadczak, Agathe D., Pitkälä, Kaisu, Hamada, Shota, Zhao, Meng, Gutiérrez-Valencia, Marta, Aalto, Ulla, Dowd, Laura A., Li, Li, Liau, Shin J., Liperoti, Rosa, Martínez-Velilla, Nicolás, Ooi, Choon Ean, Onder, Graziano, Petrie, Kate, Roitto, Hanna M., Roncal-Belzunce, Victoria, Saarela, Riitta, Sakata, Nobuo, Visvanathan, Renuka, Zhang, Tiange G., and Bell, J. Simon

Published
2025
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24. Missed Opportunity for Diversity in Engineering: Black Women and Undergraduate Engineering Degree Attainment

Author

Fletcher, Trina L., Jefferson, Jay P., Boyd, Brittany N., and Cross, Kelly J.

Abstract

Efforts dedicated towards broadening participation for Black and other underrepresented groups in engineering at post-secondary institutions has intensified in recent decades. However, Black women have not yet reached parity in undergraduate engineering degree attainment. To elucidate this trend, data from the U.S. Department of Education was analyzed to investigate postsecondary completion for Black women in engineering. Results indicate that the percentage of degrees awarded to Black women has slightly decreased during the last five years when compared to women of all races. However, the percentage of Black women obtaining engineering degrees has increased when compared to the general Black engineering population, with a larger percentage of Black women obtaining engineering degrees compared to their male counterparts than any other ethnicity. Thus, the purpose of this paper is to provide recommendations for research avenues that may strengthen knowledge around the enrollment and retention of Black women in engineering at post-secondary institutions.

Published
2023
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25. Physics-informed machine learning for Structural Health Monitoring

Author

Cross, Elizabeth J, Gibson, Samuel J, Jones, Matthew R, Pitchforth, Daniel J, Zhang, Sikai, and Rogers, Timothy J

Subjects
Computer Science - Machine Learning
Abstract

The use of machine learning in Structural Health Monitoring is becoming more common, as many of the inherent tasks (such as regression and classification) in developing condition-based assessment fall naturally into its remit. This chapter introduces the concept of physics-informed machine learning, where one adapts ML algorithms to account for the physical insight an engineer will often have of the structure they are attempting to model or assess. The chapter will demonstrate how grey-box models, that combine simple physics-based models with data-driven ones, can improve predictive capability in an SHM setting. A particular strength of the approach demonstrated here is the capacity of the models to generalise, with enhanced predictive capability in different regimes. This is a key issue when life-time assessment is a requirement, or when monitoring data do not span the operational conditions a structure will undergo. The chapter will provide an overview of physics-informed ML, introducing a number of new approaches for grey-box modelling in a Bayesian setting. The main ML tool discussed will be Gaussian process regression, we will demonstrate how physical assumptions/models can be incorporated through constraints, through the mean function and kernel design, and finally in a state-space setting. A range of SHM applications will be demonstrated, from loads monitoring tasks for off-shore and aerospace structures, through to performance monitoring for long-span bridges.

Published
2022
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26. Constraining Gaussian processes for physics-informed acoustic emission mapping

Author

Jones, Matthew R, Rogers, Timothy J, and Cross, Elizabeth J

Subjects
Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

The automated localisation of damage in structures is a challenging but critical ingredient in the path towards predictive or condition-based maintenance of high value structures. The use of acoustic emission time of arrival mapping is a promising approach to this challenge, but is severely hindered by the need to collect a dense set of artificial acoustic emission measurements across the structure, resulting in a lengthy and often impractical data acquisition process. In this paper, we consider the use of physics-informed Gaussian processes for learning these maps to alleviate this problem. In the approach, the Gaussian process is constrained to the physical domain such that information relating to the geometry and boundary conditions of the structure are embedded directly into the learning process, returning a model that guarantees that any predictions made satisfy physically-consistent behaviour at the boundary. A number of scenarios that arise when training measurement acquisition is limited, including where training data are sparse, and also of limited coverage over the structure of interest. Using a complex plate-like structure as an experimental case study, we show that our approach significantly reduces the burden of data collection, where it is seen that incorporation of boundary condition knowledge significantly improves predictive accuracy as training observations are reduced, particularly when training measurements are not available across all parts of the structure.

Published
2022
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27. Lifestyle changes in middle age and risk of cancer: evidence from the European Prospective Investigation into Cancer and Nutrition

Author

Botteri, Edoardo, Peveri, Giulia, Berstad, Paula, Bagnardi, Vincenzo, Hoff, Geir, Heath, Alicia K., Cross, Amanda J., Vineis, Paolo, Dossus, Laure, Johansson, Mattias, Freisling, Heinz, Matta, Komodo, Huybrechts, Inge, Chen, Sairah L. F., B. Borch, Kristin, Sandanger, Torkjel M., H. Nøst, Therese, Dahm, Christina C., Antoniussen, Christian S., Tin Tin, Sandar, Fournier, Agnès, Marques, Chloé, Artaud, Fanny, Sánchez, Maria-José, Guevara, Marcela, Santiuste, Carmen, Agudo, Antonio, Bajracharya, Rashmita, Katzke, Verena, Ricceri, Fulvio, Agnoli, Claudia, Bergmann, Manuela M., Schulze, Matthias B., Panico, Salvatore, Masala, Giovanna, Tjønneland, Anne, Olsen, Anja, Stocks, Tanja, Manjer, Jonas, Aizpurua-Atxega, Amaia, Weiderpass, Elisabete, Riboli, Elio, Gunter, Marc J., and Ferrari, Pietro

Published
2024
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29. The 2024 report of the Lancet Countdown on health and climate change: facing record-breaking threats from delayed action

Author

Romanello, Marina, Walawender, Maria, Hsu, Shih-Che, Moskeland, Annalyse, Palmeiro-Silva, Yasna, Scamman, Daniel, Ali, Zakari, Ameli, Nadia, Angelova, Denitsa, Ayeb-Karlsson, Sonja, Basart, Sara, Beagley, Jessica, Beggs, Paul J, Blanco-Villafuerte, Luciana, Cai, Wenjia, Callaghan, Max, Campbell-Lendrum, Diarmid, Chambers, Jonathan D, Chicmana-Zapata, Victoria, Chu, Lingzhi, Cross, Troy J, van Daalen, Kim R, Dalin, Carole, Dasandi, Niheer, Dasgupta, Shouro, Davies, Michael, Dubrow, Robert, Eckelman, Matthew J, Ford, James D, Freyberg, Chris, Gasparyan, Olga, Gordon-Strachan, Georgiana, Grubb, Michael, Gunther, Samuel H, Hamilton, Ian, Hang, Yun, Hänninen, Risto, Hartinger, Stella, He, Kehan, Heidecke, Julian, Hess, Jeremy J, Jamart, Louis, Jankin, Slava, Jatkar, Harshavardhan, Jay, Ollie, Kelman, Ilan, Kennard, Harry, Kiesewetter, Gregor, Kinney, Patrick, Kniveton, Dominic, Kouznetsov, Rostislav, Lampard, Pete, Lee, Jason K W, Lemke, Bruno, Li, Bo, Liu, Yang, Liu, Zhao, Llabrés-Brustenga, Alba, Lott, Melissa, Lowe, Rachel, Martinez-Urtaza, Jaime, Maslin, Mark, McAllister, Lucy, McMichael, Celia, Mi, Zhifu, Milner, James, Minor, Kelton, Minx, Jan, Mohajeri, Nahid, Momen, Natalie C, Moradi-Lakeh, Maziar, Morrisey, Karyn, Munzert, Simon, Murray, Kris A, Obradovich, Nick, O'Hare, Megan B, Oliveira, Camile, Oreszczyn, Tadj, Otto, Matthias, Owfi, Fereidoon, Pearman, Olivia L, Pega, Frank, Perishing, Andrew J, Pinho-Gomes, Ana-Catarina, Ponmattam, Jamie, Rabbaniha, Mahnaz, Rickman, Jamie, Robinson, Elizabeth, Rocklöv, Joacim, Rojas-Rueda, David, Salas, Renee N, Semenza, Jan C, Sherman, Jodi D, Shumake-Guillemot, Joy, Singh, Pratik, Sjödin, Henrik, Slater, Jessica, Sofiev, Mikhail, Sorensen, Cecilia, Springmann, Marco, Stalhandske, Zélie, Stowell, Jennifer D, Tabatabaei, Meisam, Taylor, Jonathon, Tong, Daniel, Tonne, Cathryn, Treskova, Marina, Trinanes, Joaquin A, Uppstu, Andreas, Wagner, Fabian, Warnecke, Laura, Whitcombe, Hannah, Xian, Peng, Zavaleta-Cortijo, Carol, Zhang, Chi, Zhang, Ran, Zhang, Shihui, Zhang, Ying, Zhu, Qiao, Gong, Peng, Montgomery, Hugh, and Costello, Anthony

Published
2024
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30. Food biodiversity and gastrointestinal cancer risk in nine European countries: Analysis within a prospective cohort study

Author

Huybrechts, Inge, Chimera, Bernadette, Hanley-Cook, Giles T., Biessy, Carine, Deschasaux-Tanguy, Mélanie, Touvier, Mathilde, Kesse-Guyot, Emmanuelle, Srour, Bernard, Baudry, Julia, Berlivet, Justine, Casagrande, Corinne, Nicolas, Geneviève, Lopez, Jessica Blanco, Millett, Christopher J., Cakmak, Emine Koc, Robinson, Oliver J.K., Murray, Kris A., Schulze, Matthias B., Masala, Giovanna, Guevara, Marcela, Bodén, Stina, Cross, Amanda J., Tsilidis, Kostas, Heath, Alicia K., Panico, Salvatore, Amiano, Pilar, Huerta, José Ma, Key, Tim, Ericson, Ulrika, Stocks, Tanja, Lundblad, Marie Wasmuth, Skeie, Guri, Sacerdote, Carlotta, Katzke, Verena, Playdon, Mary C., Ferrari, Pietro, Vineis, Paolo, Lachat, Carl, and Gunter, Marc J.

Published
2024
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32. Individual variation in the emergence of anterior-to-posterior neural fates from human pluripotent stem cells

Author

Kim, Suel-Kee, Seo, Seungmae, Stein-O’Brien, Genevieve, Jaishankar, Amritha, Ogawa, Kazuya, Micali, Nicola, Luria, Victor, Karger, Amir, Wang, Yanhong, Kim, Hyojin, Hyde, Thomas M., Kleinman, Joel E., Voss, Ty, Fertig, Elana J., Shin, Joo-Heon, Bürli, Roland, Cross, Alan J., Brandon, Nicholas J., Weinberger, Daniel R., Chenoweth, Joshua G., Hoeppner, Daniel J., Sestan, Nenad, Colantuoni, Carlo, and McKay, Ronald D.

Published
2024
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34. Top 10 Signs and Symptoms of Psychotropic Adverse Drug Events to Monitor in Residents of Long-Term Care Facilities

Author

McInerney, Brigid E., Cross, Amanda J., Alderman, Christopher P., Bhat, Ravi, Boyd, Cynthia M., Brandt, Nicole, Cossette, Benoit, Desforges, Katherine, Dowd, Laura A., Frank, Chris, Hartikainen, Sirpa, Herrmann, Nathan, Hilmer, Sarah N., Jack, Leanne, Jordan, Sue, Kitamura, Christopher R., Koujiya, Eriko, Lampela, Pasi, Macfarlane, Stephen, Manias, Elizabeth, Martin, Christine, Martínez-Velilla, Nicolás, Moriarty, Frank, Onder, Graziano, Quirke, Tara, Silvius, James L., Soulsby, Natalie, Stafford, Andrew C., Steinman, Michael A., Sun, Winnie, Taguchi, Reina, Todd, Adam, Trenaman, Shanna C., Yap, Kai Zhen, Zhao, Meng, Bell, J. Simon, and Turner, Justin P.

Published
2024
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35. Bayesian Modelling of Multivalued Power Curves from an Operational Wind Farm

Author

Bull, L. A., Gardner, P. A., Rogers, T. J., Dervilis, N., Cross, E. J., Papatheou, E., Maguire, A. E., Campos, C., and Worden, K.

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Power curves capture the relationship between wind speed and output power for a specific wind turbine. Accurate regression models of this function prove useful in monitoring, maintenance, design, and planning. In practice, however, the measurements do not always correspond to the ideal curve: power curtailments will appear as (additional) functional components. Such multivalued relationships cannot be modelled by conventional regression, and the associated data are usually removed during pre-processing. The current work suggests an alternative method to infer multivalued relationships in curtailed power data. Using a population-based approach, an overlapping mixture of probabilistic regression models is applied to signals recorded from turbines within an operational wind farm. The model is shown to provide an accurate representation of practical power data across the population.

Published
2021
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38. Undergraduate Student Perceptions of Stress and Mental Health in Engineering Culture

Author

Jensen, Karin J., Mirabelli, Joseph F., Kunze, Andrea J., Romanchek, Thomas E., and Cross, Kelly J.

Abstract

Background: Mental health for engineering undergraduates is an urgent topic for engineering educators. Narratives of engineering education requiring suffering may create or exacerbate problematic perceptions around stress and mental health in engineering. This study explored the roles of stress and mental health in engineering culture. We sought to explore: (1) how engineering students describe their experiences related to stress and mental health and (2) norms and expectations engineering students share about stress and mental health. Qualitative interview data were collected from 30 students who had previously responded to a college-wide survey. Results: Codes related to experiences with stress and mental health in engineering were organized in a bioecological systems model and analyzed for emergent themes depicting engineering culture. The study identified three themes related to stress and mental health in engineering culture: (1) engineering workload as a defining stressor, (2) specific barriers that prevent engineering students from seeking help for mental health concerns, and (3) reliance on peers to cope with stress and mental health distress. Conclusions: Our analysis provided insight into how engineering students perceive norms around stress and mental health in engineering and how this impacts help-seeking for mental health challenges. These findings have important implications for developing interventions and positive cultures that support student mental health.

Published
2023
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39. General and abdominal adiposity and hypertension in eight world regions: a pooled analysis of 837 population-based studies with 7·5 million participants

Author

Zhou, Bin, Bennett, James E, Wickham, Aidan P, Singleton, Rosie K, Mishra, Anu, Carrillo-Larco, Rodrigo M, Ikeda, Nayu, Jain, Lakshya, Barradas-Pires, Ana, Heap, Rachel A, Lhoste, Victor PF, Sheffer, Kate E, Phelps, Nowell H, Rayner, Archie W, Gregg, Edward W, Woodward, Mark, Stevens, Gretchen A, Iurilli, Maria LC, Danaei, Goodarz, Di Cesare, Mariachiara, Aguilar-Salinas, Carlos A, Ahmad, Noor Ani, Bovet, Pascal, Chen, Zhengming, Damasceno, Albertino, Filippi, Sarah L, Janszky, Imre, Kengne, Andre P, Khang, Young-Ho, Khunti, Kamlesh, Laxmaiah, Avula, Lim, Lee-Ling, Lissner, Lauren, Margozzini, Paula, Mbanya, Jean Claude N, McGarvey, Stephen T, Shaw, Jonathan E, Söderberg, Stefan, Soto-Mota, Luis Adrián, Wang, Junyang, Zaccardi, Francesco, Abarca-Gómez, Leandra, Abbasi-Kangevari, Mohsen, Abdrakhmanova, Shynar, Abdul Ghaffar, Suhaila, Abdul Rahim, Hanan F, Abdurrahmonova, Zulfiya, Abu-Rmeileh, Niveen M, Acosta-Cazares, Benjamin, Adam, Ishag, Adamczyk, Marzena, Aekplakorn, Wichai, Agdeppa, Imelda A, Aghazadeh-Attari, Javad, Agyemang, Charles, Ahmad, Mohamad Hasnan, Ahmadi, Ali, Ahmadi, Naser, Ahmadi, Nastaran, Ahmed, Soheir H, Ahrens, Wolfgang, Aitmurzaeva, Gulmira, Ajlouni, Kamel, Al-Hazzaa, Hazzaa M, Al-Hinai, Halima, Al-Lawati, Jawad A, Al-Raddadi, Rajaa, Al Asfoor, Deena, Al Hourani, Huda M, Alarouj, Monira, AlBuhairan, Fadia, AlDhukair, Shahla, Ali, Mohamed M, Alieva, Anna V, Alkandari, Abdullah, Alkhatib, Buthaina M, Aly, Eman, Amarapurkar, Deepak N, Amiano Etxezarreta, Pilar, Amougou, Norbert, Andersen, Lars Bo, Anderssen, Sigmund A, Androutsos, Odysseas, Anjana, Ranjit Mohan, Ansari-Moghaddam, Alireza, Anufrieva, Elena, Aounallah-Skhiri, Hajer, Aris, Tahir, Arku, Raphael E, Arlappa, Nimmathota, Aryal, Krishna K, Assah, Felix K, Assembekov, Batyrbek, Assunção, Maria Cecília F, Auvinen, Juha, Avdičová, Mária, Azad, Kishwar, Azevedo, Ana, Azimi-Nezhad, Mohsen, Azizi, Fereidoun, Bacopoulou, Flora, Bahijri, Suhad, Bajramovic, Izet, Balakrishna, Nagalla, Bamoshmoosh, Mohamed, Banach, Maciej, Bandosz, Piotr, Banegas, José R, Baran, Rafał, Barbagallo, Carlo M, Barbosa Filho, Valter, Barceló, Alberto, Baretić, Maja, Barnoya, Joaquin, Barrera, Lena, Barros, Aluisio JD, Barros, Mauro Virgílio Gomes, Basit, Abdul, Bastos, Joao Luiz, Batieha, Anwar M, Batista, Aline P, Batista, Rosangela L, Battakova, Zhamilya, Baur, Louise A, Bayauli, Pascal M, Bel-Serrat, Silvia, Belavendra, Antonisamy, Ben Romdhane, Habiba, Benedek, Theodora, Benedics, Judith, Benet, Mikhail, Benitez Rolandi, Gilda Estela, Benzeval, Michaela, Bere, Elling, Berger, Nicolas, Bergh, Ingunn Holden, Berkinbayev, Salim, Bernabe-Ortiz, Antonio, Bettiol, Heloísa, Beybey, Augustin F, Bezerra, Jorge, Bhagyalaxmi, Aroor, Bhargava, Santosh K, Bika Lele, Elysée Claude, Bikbov, Mukharram M, Bista, Bihungum, Bjelica, Dusko J, Bjerregaard, Peter, Bjertness, Espen, Bjertness, Marius B, Björkelund, Cecilia, Bloch, Katia V, Blokstra, Anneke, Bo, Simona, Bobak, Martin, Boddy, Lynne M, Boehm, Bernhard O, Boggia, Jose G, Bogova, Elena, Bonaccio, Marialaura, Bonilla-Vargas, Alice, Borghs, Herman, Botomba, Steve, Bourne, Rupert, Boymatova, Khadichamo, Braeckman, Lutgart, Braithwaite, Tasanee, Brajkovich, Imperia, Branca, Francesco, Brenner, Hermann, Brewster, Lizzy M, Briceño, Yajaira, Brinduse, Lacramioara, Bringolf-Isler, Bettina, Brito, Miguel, Brug, Johannes, Bugge, Anna, Buntinx, Frank, Buoncristiano, Marta, Burns, Con, Cabrera de León, Antonio, Caixeta, Roberta B, Cama, Tilema, Can, Günay, Cândido, Ana Paula C, Cañete, Felicia, Capanzana, Mario V, Čapková, Naděžda, Capuano, Eduardo, Capuano, Rocco, Capuano, Vincenzo, Cardoso, Viviane C, Carlsson, Axel C, Casanueva, Felipe F, Casas, Maribel, Censi, Laura, Cervantes‐Loaiza, Marvin, Chamnan, Parinya, Chamukuttan, Snehalatha, Chan, Queenie, Chaturvedi, Nish, Chen, Fangfang, Chen, Huashuai, Chen, Long-Sheng, Cheng, Yiling J, Cheraghian, Bahman, Chetrit, Angela, Chikova-Iscener, Ekaterina, Chinapaw, Mai JM, Chinnock, Anne, Chiolero, Arnaud, Chirita-Emandi, Adela, Chirlaque, María-Dolores, Chong, Chean Lin, Christofaro, Diego G, Chudek, Jerzy, Cifkova, Renata, Cirillo, Massimo, Claessens, Frank, Clare, Philip, Cohen, Emmanuel, Confortin, Susana C, Coppinger, Tara C, Cortés, Lilia Yadira, Cosmin, Cojocaru R, Costanzo, Simona, Cowan, Melanie J, Cowell, Chris, Crampin, Amelia C, Cross, Amanda J, Crujeiras, Ana B, Cruz, Juan J, Cucu, Alexandra M, Cureau, Felipe V, Cuschieri, Sarah, D'Arrigo, Graziella, d'Orsi, Eleonora, da Silva-Ferreira, Haroldo, Dahm, Christina C, Dallongeville, Jean, Dankner, Rachel, Davletov, Kairat, de Assis Guedes de Vasconcelos, Francisco, de Assis, Maria Alice Altenburg, De Bacquer, Dirk, De Bacquer, Jaco, de Bont, Jeroen, De Curtis, Amalia, de Fragas Hinnig, Patrícia, de Gaetano, Giovanni, De Henauw, Stefaan, De Miguel-Etayo, Pilar, de Oliveira, Paula Duarte, de Paiva, Karina Mary, De Ridder, Karin, de Valois Correia Júnior, Marco Aurélio, Deepa, Mohan, DeGennaro, Vincent Jr, Demarest, Stefaan, Dennison, Elaine, Deschamps, Valérie, Dhimal, Meghnath, Díez Ripollés, María Pilar, Dika, Zivka, Djalalinia, Shirin, Dominguez, Liria, Donati, Maria Benedetta, Donfrancesco, Chiara, Dong, Guanghui, Donoso, Silvana P, Dorobantu, Maria, Dörr, Marcus, Dragano, Nico, Drygas, Wojciech, Du, Shufa, Duante, Charmaine A, Duboz, Priscilla, Duda, Rosemary B, Duleva, Vesselka L, Dushpanova, Anar, Dyussupova, Azhar, Dziankowska-Zaborszczyk, Elzbieta, Ebrahimi, Narges, Echeverría, Guadalupe, Eddie, Ricky, Eftekhar, Ebrahim, Efthymiou, Vasiliki, Egbagbe, Eruke E, Eghtesad, Sareh, Ekelund, Ulf, El-Khateeb, Mohammad, El Ati, Jalila, Elosua, Roberto, Enang, Ofem, Erasmus, Rajiv T, Erem, Cihangir, Ergor, Gul, Eriksen, Louise, Eriksson, Johan G, Escobedo-de la Peña, Jorge, Esmaeili, Ali, Evans, Roger G, Fakhradiyev, Ildar, Fakhretdinova, Albina A, Fall, Caroline H, Faramarzi, Elnaz, Farjam, Mojtaba, Farzadfar, Farshad, Farzi, Yosef, Fattahi, Mohammad Reza, Fawwad, Asher, Felix-Redondo, Francisco J, Ferguson, Trevor S, Fernandes, Romulo A, Fernández-Bergés, Daniel, Fernando, Desha R, Ferrante, Daniel, Ferrari, Gerson, Ferrari, Marika, Ferreccio, Catterina, Ferrer, Eldridge, Figueiró, Thamara Hubler, Fijalkowska, Anna, Fink, Günther, Fisberg, Mauro, Forsner, Maria, Fottrell, Edward F, Fouad, Heba M, Francis, Damian K, Frontera, Guillermo, Fuchs, Flavio D, Fuchs, Sandra C, Furdela, Viktoriya, Furusawa, Takuro, Gabriela, Stefan Adela, Gaciong, Zbigniew, Galán Cuesta, Manuel, Galbarczyk, Andrzej, Galcheva, Sonya V, Galfo, Myriam, Garcia-de-la-Hera, Manoli, Garcia, Pablo, Garnett, Sarah P, Gasull, Magda, Gazzinelli, Andrea, Gehring, Ulrike, Gerdts, Eva, Ghaderi, Ebrahim, Ghamari, Seyyed-Hadi, Ghanbari, Ali, Ghasemi, Erfan, Gheorghe-Fronea, Oana-Florentina, Ghimire, Anup, Gialluisi, Alessandro, Giampaoli, Simona, Gianfagna, Francesco, Gironella, Glen, Giwercman, Aleksander, Gkiouras, Konstantinos, Glushkova, Natalya, Godara, Ramesh, Godos, Justyna, Goldberg, Marcel, Gómez, Georgina, Gómez Gómez, Jesús Humberto, Gomez, Luis F, Gómez, Santiago F, Gomula, Aleksandra, Gonçalves Cordeiro da Silva, Bruna, Gonçalves, Helen, Gonçalves, Mauer, González-Alvarez, Ana D, Gonzalez-Chica, David A, González-Gil, Esther M, Gonzalez-Gross, Marcela, González-Rivas, Juan P, Gonzalez, Angel R, Gottrand, Frederic, Grafnetter, Dušan, Grajda, Aneta, Grammatikopoulou, Maria G, Grodzicki, Tomasz, Grøholt, Else Karin, Grøntved, Anders, Guajardo, Viviana, Guallar-Castillón, Pilar, Guerchet, Maëlenn, Guerrero, Ramiro, Guimaraes, Andre L, Gujral, Unjali P, Gulliford, Martin C, Gunter, Marc J, Gupta, Rajeev, Gureje, Oye, Gurinović, Mirjana A, Gurzkowska, Beata, Gutierrez, Laura, Gwee, Xinyi, Haghshenas, Rosa, Hakimi, Hamid, Halkjær, Jytte, Hambleton, Ian R, Hamzeh, Behrooz, Hanekom, Willem A, Hange, Dominique, Hanif, Abu AM, Hantunen, Sari, Hao, Jie, Hardman, Carla Menêses, Hardy, Louise, Hari Kumar, Rachakulla, Harooni, Javad, Hashemi-Shahri, Seyed Mohammad, Hassapidou, Maria, Hata, Jun, Haugsgjerd, Teresa, Heinen, Mirjam, Hendriks, Marleen Elisabeth, Henrique, Rafael dos Santos, Henriques, Ana, Hernandez Cadena, Leticia, Herrala, Sauli, Herrera-Cuenca, Marianella, Herrera, Victor M, Herter-Aeberli, Isabelle, Herzig, Karl-Heinz, Heshmat, Ramin, Hill, Allan G, Ho, Sai Yin, Holdsworth, Michelle, Homayounfar, Reza, Homs, Clara, Hoogendijk, Emiel O, Horimoto, Andrea RVR, Hormiga, Claudia M, Horta, Bernardo L, Houti, Leila, Howitt, Christina, Htay, Thein Thein, Htet, Aung Soe, Htike, Maung Maung Than, Huerta, José María, Huhtaniemi, Ilpo Tapani, Huiart, Laetitia, Huidumac Petrescu, Constanta, Huisman, Martijn, Husseini, Abdullatif, Huybrechts, Inge, Hwalla, Nahla, Iacoviello, Licia, Iakupova, Ellina M, Iannone, Anna G, Igland, Jannicke, Ijoma, Chinwuba, Iotova, Violeta, Irazola, Vilma E, Ishida, Takafumi, Isiguzo, Godsent C, Islam, Muhammad, Islam, Sheikh Mohammed Shariful, Islek, Duygu, Ittermann, Till, Ivanova-Pandourska, Ivaila Y, Iwasaki, Masanori, Jääskeläinen, Tuija, Jackson, Rod T, Jaddou, Hashem Y, Jadoul, Michel, Jafar, Tazeen, Jan, Nataša, Janus, Edward, Jarani, Juel, Jarnig, Gerald, Jarvelin, Marjo-Riitta, Jasienska, Grazyna, Jelaković, Ana, Jelaković, Bojan, Jha, Anjani Kumar, Jimenez, Ramon O, Jöckel, Karl-Heinz, Joffres, Michel, Jokelainen, Jari J, Jonas, Jost B, Joshi, Pradeep, Joshi, Rohina, Josipović, Josipa, Joukar, Farahnaz, Jóźwiak, Jacek J, Juolevi, Anne, Juresa, Vesna, Jureša, Vesna, Kaaks, Rudolf, Kaducu, Felix O, Kadvan, Agnes L, Kafatos, Anthony, Kajantie, Eero O, Kakutia, Natia, Kállayová, Daniela, Kalmatayeva, Zhanna, Kalter-Leibovici, Ofra, Kannan, Srinivasan, Kapantais, Efthymios, Karaglani, Eva, Karakosta, Argyro, Karki, Khem B, Kassi Anicet, Adoubi, Katibeh, Marzieh, Katulanda, Prasad, Katzmarzyk, Peter T, Kauhanen, Jussi, Kazakbaeva, Gyulli M, Kaze, François F, Ke, Calvin, Keinänen-Kiukaanniemi, Sirkka, Kelishadi, Roya, Kelleher, Cecily, Kemper, Han CG, Keramati, Maryam, Kersting, Mathilde, Khader, Yousef Saleh, Khaledifar, Arsalan, Khalili, Davood, Kheiri, Bahareh, Kheradmand, Motahareh, Khosravi, Alireza, Kiechl-Kohlendorfer, Ursula, Kiechl, Sophia J, Kiechl, Stefan, Kim, Hyeon Chang, Klakk, Heidi, Klanarong, Suntara, Klanova, Jana, Klimek, Magdalena, Knoflach, Michael, Kobel, Susanne, Koirala, Bhawesh, Kolle, Elin, Kolsteren, Patrick, König, Jürgen, Korpelainen, Raija, Korrovits, Paul, Korzycka, Magdalena, Kos, Jelena, Koskinen, Seppo, Koussoh Simone, Malik, Kovács, Éva, Kovalskys, Irina, Kowlessur, Sudhir, Koziel, Slawomir, Kratenova, Jana, Kratzer, Wolfgang, Kriemler, Susi, Kristensen, Peter Lund, Krizan, Helena, Kroker-Lobos, Maria F, Krokstad, Steinar, Kruger, Herculina S, Kruger, Ruan, Kryst, Łukasz, Kubinova, Ruzena, Kujala, Urho M, Kujundzic, Enisa, Kulaga, Zbigniew, Kulimbet, Mukhtar, Kumari, Meena, Kunešová, Marie, Kurjata, Pawel, Kyobutungi, Catherine, La, Quang Ngoc, Labadarios, Demetre, Lachat, Carl, Lai, Daphne, Laid, Youcef, Lall, Lachmie, Landaeta Jimenez, Maritza, Landais, Edwige, Lankila, Tiina, Lanska, Vera, Lappas, Georg, Larijani, Bagher, Lateva, Mina P, Latt, Tint Swe, Laurenzi, Martino, Lazo-Porras, Maria, Le Coroller, Gwenaëlle, Le Nguyen Bao, Khanh, Lehtimäki, Terho, Lemogoum, Daniel, Leong, Elvynna, Leszczak, Justyna, Leung, Gabriel M, Li, Yanping, Liivak, Merike, Lim, Charlie, Lim, Wei-Yen, Lima-Costa, M Fernanda, Lin, Hsien-Ho, Lind, Lars, Litwin, Mieczyslaw, Liu, Liping, Liu, Xiaotian, Longo Abril, Guadalupe, Lopes, Oscar, Lopez-Garcia, Esther, López-Gil, José Francisco, Lopez, Tania, Lozano, José Eugenio, Lukrafka, Janice L, Luksiene, Dalia, Lundqvist, Annamari, Lunet, Nuno, Lunogelo, Charles, Lustigová, Michala, M'Buyamba-Kabangu, Jean-René, Machado-Coelho, George LL, Machado-Rodrigues, Aristides M, Macia, Enguerran, Madar, Ahmed A, Maestre, Gladys E, Maggi, Stefania, Magliano, Dianna J, Magnacca, Sara, Magriplis, Emmanuella, Mahasampath, Gowri, Maire, Bernard, Makdisse, Marcia, Malekpour, Mohammad-Reza, Malekzadeh, Fatemeh, Malekzadeh, Reza, Mallikharjuna Rao, Kodavanti, Malyutina, Sofia, Maniego, Lynell V, Manios, Yannis, Mann, Jim I, Mansour-Ghanaei, Fariborz, Manzato, Enzo, Mapatano, Mala Ali, Maria-Magdalena, Rosu, Mariño, Joany, Markaki, Anastasia, Marques, Larissa Pruner, Marrugat, Jaume, Martorell, Reynaldo, Maruszczak, Katharina, Masala, Giovanna, Mascarenhas, Luis P, Masimango Imani, Mannix, Masinaei, Masoud, Mathiesen, Ellisiv B, Matijasevich, Alicia, Matłosz, Piotr, Matsha, Tandi E, Matsudo, Victor, Matteo, Giletta, Maulik, Pallab K, Mavrogianni, Christina, Mc Donald Posso, Anselmo J, McFarlane, Shelly R, McLean, Rachael M, Mediene Benchekor, Sounnia, Mehlig, Kirsten, Mehrparvar, Amir Houshang, Melgarejo, Jesus D, Méndez, Fabián, Mendivil, Carlos O, Mendoza Montano, Carlos, Menezes, Ana Maria B, Mensink, Gert BM, Mereke, Alibek, Meshram, Indrapal I, Meto, Diane T, Meyer, Haakon E, Mi, Jie, Miłkowska, Karolina, Miller, Jody C, Milushkina, Olga, Minderico, Cláudia S, Mini, GK, Miquel, Juan Francisco, Miranda, J Jaime, Mirjalili, Mohammad Reza, Mišigoj-Duraković, Marjeta, Mistretta, Antonio, Mocanu, Veronica, Modesti, Pietro A, Moghaddam, Sahar Saeedi, Mohammad, Kazem, Mohammadi, Mohammad Reza, Mohammadi, Zahra, Mohammadifard, Noushin, Mohammadpourhodki, Reza, Mohan, Viswanathan, Mohd Yusoff, Muhammad Fadhli, Mohebbi, Iraj, Møller, Niels C, Molnár, Dénes, Momenan, Amirabbas, Mondo, Charles K, Monroy-Valle, Michele M, Montenegro Mendoza, Roger A, Monterrubio-Flores, Eric, Monyeki, Kotsedi Daniel K, Moon, Jin Soo, Moosazadeh, Mahmood, Moradpour, Farhad, Moreira, Leila B, Morejon, Alain, Moreno, Luis A, Morgan, Karen, Moschonis, George, Moslem, Alireza, Mosquera, Mildrey, Mossakowska, Malgorzata, Mostafa, Aya, Mostafavi, Seyed-Ali, Motlagh, Mohammad Esmaeel, Motta, Jorge, Moura-dos-Santos, Marcos André, Mridha, Malay K, Msyamboza, Kelias P, Mu, Thet Thet, Muca, Florian, Mugoša, Boban, Munroe, Patricia B, Mursu, Jaakko, Musa, Kamarul Imran, Musić Milanović, Sanja, Musil, Vera, 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Rodriguez-Perez, María del Cristo, Rodríguez-Villamizar, Laura A, Rodríguez, Andrea Y, Roggenbuck, Ulla, Rohloff, Peter, Rojas-Martinez, Rosalba, Romeo, Elisabetta L, Rosario, Rafaela V, Rosengren, Annika, Rouse, Ian, Rubinstein, Adolfo, Ruiz-Betancourt, Blanca Sandra, Ruiz-Castell, Maria, Ruiz Moreno, Emma, Rusakova, Iuliia A, Rusek, Wojciech, Rust, Petra, Rutkowski, Marcin, Saamel, Marge, Sabbaghi, Hamideh, Sachdev, Harshpal S, Sadjadi, Alireza, Safarpour, Ali Reza, Safi, Sare, Saghi, Mohammad Hossien, Saidi, Olfa, Saieva, Calogero, Sakata, Satoko, Saki, Nader, Šalaj, Sanja, Salazar Martinez, Eduardo, Salkhanova, Akkumis, Salonen, Jukka T, Samoutian, Margarita, Sánchez-Abanto, Jose, Sánchez Rodríguez, Inés, Santos, Diana A, Santos, Ina S, Santos, Maria Paula, Santos, Tamara R, Saramies, Jouko L, Sardinha, Luis B, Sarganas, Giselle, Sarrafzadegan, Nizal, Saum, Kai-Uwe, Savin, Stefan, Sbaraini, Mariana, Scazufca, Marcia, Schaan, Beatriz D, Schienkiewitz, Anja, Schindler, Karin, Schipf, Sabine, Schmidt, Amand Floriaan, Schmidt, Börge, Schmidt, Carsten O, Schöttker, Ben, Schramm, Sara, Schramm, Stine, Schröder, Helmut, Schultsz, Constance, Schutte, Aletta E, Sebert, Sylvain, Sedaghattalab, Moslem, Sein, Aye Aye, Sen, Abhijit, Sepanlou, Sadaf G, Sequera, Guillermo, Ševčíková, Ľudmila, Sewpaul, Ronel, Shamah-Levy, Teresa, Shamshirgaran, Seyed Morteza, Sharafkhah, Maryam, Sharma, Sanjib K, Sharman, Almaz, Shayanrad, Amaneh, Shayesteh, Ali Akbar, Shengelia, Lela, Shibuya, Kenji, Shimizu-Furusawa, Hana, Shiri, Rahman, Shoranov, Marat, Shrestha, Namuna, Si-Ramlee, Khairil, Sibai, Abla M, Sidossis, Labros S, Silva, Antonio M, Silva, Caroline Ramos de Moura, Silva, Diego Augusto Santos, Silva, Kelly Samara, Sim, Xueling, Simon, Mary, Sjöström, Michael, Skoblina, Natalia A, Slowikowska-Hilczer, Jolanta, Slusarczyk, Przemysław, Smeeth, Liam, Smith, Lee, Soares, Fernanda Cunha, Sobek, Grzegorz, Sobngwi, Eugène, Sodemann, Morten, Soemantri, Agustinus, Solfrizzi, Vincenzo, Somi, Mohammad Hossein, Sørgjerd, Elin P, Sorić, Maroje, Soto-Rojas, Victoria E, Soumaré, Aïcha, Sousa-Poza, Alfonso, Spiroski, Igor, Staessen, Jan A, Stang, Andreas, Steene-Johannessen, Jostein, Stehle, Peter, Stein, Aryeh D, Stergiou, George S, Stokwiszewski, Jakub, Stoyanova, Ekaterina, Stratton, Gareth, Stronks, Karien, Sturua, Lela, Suarez-Ortegón, Milton F, Suebsamran, Phalakorn, Sulo, Gerhard, Sundström, Johan, Suriyawongpaisal, Paibul, Swinburn, Boyd A, Sylva, René Charles, Szponar, Lucjan, Tai, E Shyong, Tambalis, Konstantinos D, Tamosiunas, Abdonas, Tanabayev, Baimakhan, Tanrygulyyeva, Maya, Tarawneh, Mohammed Rasoul, Tarp, Jakob, Tarqui-Mamani, Carolina B, Taxová Braunerová, Radka, Te Velde, Saskia, Tebar, William R, Tell, Grethe S, Tello, Tania, Thankappan, KR, Theodoridis, Xenophon, Thirunavukkarasu, Sathish, Thomas, Nihal, Thrift, Amanda G, Tichá, Ľubica, Timmermans, Erik J, Tjandrarini, Dwi Hapsari, Tjonneland, Anne, Tolstrup, Janne S, Topbas, Murat, Torres-Collado, Laura, Touloumi, Giota, Traissac, Pierre, Triantafyllou, Areti, Trivedi, Atul, Tshepo, Lechaba, Tsintavis, Panagiotis, Tuitele, John, Tuliakova, Azaliia M, Tulloch-Reid, Marshall K, Tullu, Fikru, Tuomainen, Tomi-Pekka, Turley, Maria L, Tzala, Evangelia, Tzotzas, Themistoklis, Tzourio, Christophe, Ueda, Peter, Ugel, Eunice, Ukoli, Flora AM, Usupova, Zhamyila, Uusitalo, Hannu MT, Uysal, Nalan, Valdivia, Gonzalo, Valvi, Damaskini, van Dam, Rob M, van den Born, Bert-Jan, Van der Heyden, Johan, van der Schouw, Yvonne T, Van Lippevelde, Wendy, Van Minh, Hoang, Van Schoor, Natasja M, van Valkengoed, Irene GM, Vanderschueren, Dirk, Vanuzzo, Diego, Varela-Moreiras, Gregorio, Vargas, Luz Nayibe, Vasan, Senthil K, Vasques, Daniel G, Vega, Tomas, Velasquez-Melendez, Gustavo, Velika, Biruta, Verdot, Charlotte, Verloigne, Maïté, Veronesi, Giovanni, Verschuren, WM Monique, Verstraeten, Roosmarijn, Viet, Lucie, Vik, Frøydis N, Vilar, Monica, Villalpando, Salvador, Vioque, Jesus, Virtanen, Jyrki K, Visser, Marjolein, Viswanathan, Bharathi, Vladulescu, Mihaela, Völzke, Henry, Voutilainen, Ari, Vrijheid, Martine, Wade, Alisha N, Wan Bebakar, Wan Mohamad, Wan Mohamud, Wan Nazaimoon, Wanderley Júnior, Rildo de Souza, Wang, Chongjian, Wang, Huijun, Wang, Ningli, Wang, Qian, Wang, Xiangjun, Wang, Ya Xing, Wang, Ying-Wei, Wannamethee, S Goya, Wareham, Nicholas, Wartha, Olivia, Weber, Adelheid, Webster-Kerr, Karen, Wedderkopp, Niels, Weghuber, Daniel, Wei, Wenbin, Westbury, Leo, Whincup, Peter H, Wickramasinghe, Kremlin, Widhalm, Kurt, Widyahening, Indah S, Więcek, Andrzej, Wilks, Rainford J, Willeit, Karin, Willeit, Peter, Williams, Julianne, Wilsgaard, Tom, Wojtyniak, Bogdan, Wong-McClure, Roy A, Wong, Andrew, Wong, Emily B, Wu, Frederick C, Wyszyńska, Justyna, Xu, Haiquan, Xu, Liang, Yaacob, Nor Azwany, Yan, Li, Yan, Weili, Yang, Yang, Yépez García, Martha, Yoosefi, Moein, Yoshihara, Akihiro, Younger-Coleman, Novie O, Yu, Yu-Ling, Yu, Yunjiang, Yusoff, Ahmad Faudzi, Zafiropulos, Vassilis, Zainuddin, Ahmad A, Zamani, Farhad, Zambon, Sabina, Zampelas, Antonis, Zapata, Maria Elisa, Zaw, Ko Ko, Zdrojewski, Tomasz, Żegleń, Magdalena, Zejglicova, Kristyna, Zeljkovic Vrkic, Tajana, Zhang, Bing, Zhang, Zhen-Yu, Zhecheva, Yanitsa V, Zholdin, Bekbolat, Zimmet, Paul, Zins, Marie, Zuñiga Cisneros, Julio, Zuziak, Monika, and Ezzati, Majid

Published
2024
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41. Diet and lifestyle in relation to small intestinal cancer risk: findings from the European Prospective Investigation into Cancer and Nutrition (EPIC)

Author

Ersoy Guller, Zeynep, Harewood, Rhea N., Weiderpass, Elisabete, Huybrechts, Inge, Jenab, Mazda, Huerta, José María, Sánchez, Maria-Jose, Jakszyn, Paula, Amiano, Pilar, Ardanaz, Eva, Agnoli, Claudia, Tumino, Rosario, Palli, Domenico, Skeie, Guri, Manjer, Jonas, Papier, Keren, Tjønneland, Anne, Eriksen, Anne Kirstine, Schulze, Matthias B., Kaaks, Rudolf, Katzke, Verena, Bergmann, Manuela M., Riboli, Elio, Gunter, Marc J., and Cross, Amanda J.

Published
2023
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44. Interrogating the possibilities and problems of YouTube sex edutainment content : an Actor-Network Theory approach

Author

Garwood-Cross, Lisa J.

Abstract

British Relationships and Sex Education (RSE) has gained public attention in recent years due to several reports highlighting gaps in provision and a change of statutory guidance for RSE in 2019. Historically RSE has linked sex with risk and shame, stifling communication and leaving gaps in RSE provision around pleasure, LGBTQ+ education, and other intersectional needs. This can lead to unfulfilled information needs that many young people try to fulfil by seeking information online. However, the rise of social media influencers has created micro-celebrities with influence on their followers. Some influencers specialise in talking openly about sex education topics. This research focuses on YouTube content created by these influencers, referred to as YouTube sex edutainment. Using an Actor-Network Theory approach, the research interrogates the possibilities and problems of this form of disseminating sex, relationships and sexual health information to understand if and how it can be utilised on a broader level to fill the gaps in provision for 13-24-year-olds. Using a three-phase mixed methods approach, this research focuses on each of the key actors in the YouTube sex edutainment assemblage: YouTube, Influencers and Young People. Phase one interrogates YouTube using Walkthrough analysis to observe opportunities and limitations of utilising YouTube for sexual health learning. Phase two develops a health influencer framework, before identifying the role of influencers within the assemblage through email interviews with YouTube sex edutainment influencers and analysis of public audience comments on their videos. The third phase uses online surveys with 13-24-year-olds to identify how British young people seek and share sex, relationships and sexual health information, and the role of digital media within this. The perspectives of each of these actors are then amalgamated to identify 3 key possibilities for YouTube sex edutainment: existing use, peer education potential, and accessibility; and 3 key problems: funding, misinformation, and platform governance; and how these could be addressed in future interventions.

Published
2022

46. Grey-box models for wave loading prediction

Author

Pitchforth, Daniel J, Rogers, Timothy J, Tygesen, Ulf T, and Cross, Elizabeth J

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Physics - Fluid Dynamics
Abstract

The quantification of wave loading on offshore structures and components is a crucial element in the assessment of their useful remaining life. In many applications the well-known Morison's equation is employed to estimate the forcing from waves with assumed particle velocities and accelerations. This paper develops a grey-box modelling approach to improve the predictions of the force on structural members. A grey-box model intends to exploit the enhanced predictive capabilities of data-based modelling whilst retaining physical insight into the behaviour of the system; in the context of the work carried out here, this can be considered as physics-informed machine learning. There are a number of possible approaches to establish a grey-box model. This paper demonstrates two means of combining physics (white box) and data-based (black box) components; one where the model is a simple summation of the two components, the second where the white-box prediction is fed into the black box as an additional input. Here Morison's equation is used as the physics-based component in combination with a data-based Gaussian process NARX - a dynamic variant of the more well-known Gaussian process regression. Two key challenges with employing the GP-NARX formulation that are addressed here are the selection of appropriate lag terms and the proper treatment of uncertainty propagation within the dynamic GP. The best performing grey-box model, the residual modelling GP-NARX, was able to achieve a 29.13\% and 5.48\% relative reduction in NMSE over Morison's Equation and a black-box GP-NARX respectively, alongside significant benefits in extrapolative capabilities of the model, in circumstances of low dataset coverage.

Published
2021
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47. Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data

Author

Bull, Lawrence A., Gardner, Paul, Rogers, Timothy J., Cross, Elizabeth J., Dervilis, Nikolaos, and Worden, Keith

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

In data-driven SHM, the signals recorded from systems in operation can be noisy and incomplete. Data corresponding to each of the operational, environmental, and damage states are rarely available a priori; furthermore, labelling to describe the measurements is often unavailable. In consequence, the algorithms used to implement SHM should be robust and adaptive, while accommodating for missing information in the training-data -- such that new information can be included if it becomes available. By reviewing novel techniques for statistical learning (introduced in previous work), it is argued that probabilistic algorithms offer a natural solution to the modelling of SHM data in practice. In three case-studies, probabilistic methods are adapted for applications to SHM signals -- including semi-supervised learning, active learning, and multi-task learning., Comment: This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://doi.org/10.1061/AJRUA6.0001106

Published
2021
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49. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Author

Phelps, Nowell H, Singleton, Rosie K, Zhou, Bin, Heap, Rachel A, Mishra, Anu, Bennett, James E, Paciorek, Christopher J, Lhoste, Victor PF, Carrillo-Larco, Rodrigo M, Stevens, Gretchen A, Rodriguez-Martinez, Andrea, Bixby, Honor, Bentham, James, Di Cesare, Mariachiara, Danaei, Goodarz, Rayner, Archie W, Barradas-Pires, Ana, Cowan, Melanie J, Savin, Stefan, Riley, Leanne M, Aguilar-Salinas, Carlos A, Baker, Jennifer L, Barkat, Amina, Bhutta, Zulfiqar A, Branca, Francesco, Caixeta, Roberta B, Cuschieri, Sarah, Farzadfar, Farshad, Ganapathy, Shubash, Ikeda, Nayu, Iotova, Violeta, Kengne, Andre P, Khang, Young-Ho, Laxmaiah, Avula, Lin, Hsien-Ho, Ma, Jun, Mbanya, Jean Claude N, Miranda, J Jaime, Pradeepa, Rajendra, Rodríguez-Artalejo, Fernando, Sorić, Maroje, Turley, Maria, Wang, Limin, Webster-Kerr, Karen, Aarestrup, Julie, Abarca-Gómez, Leandra, Abbasi-Kangevari, Mohsen, Abdeen, Ziad A, Abdrakhmanova, Shynar, Abdul Ghaffar, Suhaila, Abdul Rahim, Hanan F, Abdurrahmonova, Zulfiya, Abu-Rmeileh, Niveen M, Abubakar Garba, Jamila, Acosta-Cazares, Benjamin, Adam, Ishag, Adamczyk, Marzena, Adams, Robert J, Adu-Afarwuah, Seth, Aekplakorn, Wichai, Afsana, Kaosar, Afzal, Shoaib, Agbor, Valirie N, Agdeppa, Imelda A, Aghazadeh-Attari, Javad, Ågren, Åsa, Aguenaou, Hassan, Agyemang, Charles, Ahmad, Mohamad Hasnan, Ahmad, Noor Ani, Ahmadi, Ali, Ahmadi, Naser, Ahmadi, Nastaran, Ahmed, Imran, Ahmed, Soheir H, Ahrens, Wolfgang, Aitmurzaeva, Gulmira, Ajlouni, Kamel, Al-Hazzaa, Hazzaa M, Al-Hinai, Halima, Al-Lahou, Badreya, Al-Lawati, Jawad A, Al-Raddadi, Rajaa, Al Asfoor, Deena, Al Hourani, Huda M, Al Qaoud, Nawal M, Alarouj, Monira, AlBuhairan, Fadia, AlDhukair, Shahla, Aldwairji, Maryam A, Alexius, Sylvia, Ali, Mohamed M, Alieva, Anna V, Alkandari, Abdullah, Alkerwi, Ala'a, Alkhatib, Buthaina M, Allin, Kristine, Alomary, Shaker A, Alomirah, Husam F, Alshangiti, Arwa M, Alvarez-Pedrerol, Mar, Aly, Eman, Amarapurkar, Deepak N, Amiano Etxezarreta, Pilar, Amoah, John, Amougou, Norbert, Amouyel, Philippe, Andersen, Lars Bo, Anderssen, Sigmund A, Androutsos, Odysseas, Ängquist, Lars, Anjana, Ranjit Mohan, Ansari-Moghaddam, Alireza, Anufrieva, Elena, Aounallah-Skhiri, Hajer, Araújo, Joana, Ariansen, Inger, Aris, Tahir, Arku, Raphael E, Arlappa, Nimmathota, Aryal, Krishna K, Assefa, Nega, Aspelund, Thor, Assah, Felix K, Assembekov, Batyrbek, Assunção, Maria Cecília F, Aung, May Soe, Aurélio de Valois, Correia Júnior Marco, Auvinen, Juha, Avdičová, Mária, Avi, Shina, Azad, Kishwar, Azevedo, Ana, Azimi-Nezhad, Mohsen, Azizi, Fereidoun, Babu, Bontha V, Bacopoulou, Flora, Bæksgaard Jørgensen, Maja, Baharudin, Azli, Bahijri, Suhad, Bajramovic, Izet, Bakacs, Marta, Balakrishna, Nagalla, Balanova, Yulia, Bamoshmoosh, Mohamed, Banach, Maciej, Banegas, José R, Baran, Joanna, Baran, Rafał, Barbagallo, Carlo M, Barbosa Filho, Valter, Barceló, Alberto, Baretić, Maja, Barnoya, Joaquin, Barrera, Lena, Barreto, Marta, Barros, Aluisio JD, Barros, Mauro Virgílio Gomes, Bartosiewicz, Anna, Basit, Abdul, Bastos, Joao Luiz, Bata, Iqbal, Batieha, Anwar M, Batista, Aline P, Batista, Rosangela L, Battakova, Zhamilya, Baur, Louise A, Bayauli, Pascal M, Beaglehole, Robert, Bel-Serrat, Silvia, Belavendra, Antonisamy, Ben Romdhane, Habiba, Benedek, Theodora, Benedics, Judith, Benet, Mikhail, Benitez Rolandi, Gilda Estela, Benzeval, Michaela, Bere, Elling, Berger, Nicolas, Bergh, Ingunn Holden, Berhane, Yemane, Berkinbayev, Salim, Bernabe-Ortiz, Antonio, Bernotiene, Gailute, Berrios Carrasola, Ximena, Bettiol, Heloísa, Beutel, Manfred E, Beybey, Augustin F, Bezerra, Jorge, Bhagyalaxmi, Aroor, Bharadwaj, Sumit, Bhargava, Santosh K, Bi, Hongsheng, Bi, Yufang, Bia, Daniel, Biasch, Katia, Bika Lele, Elysée Claude, Bikbov, Mukharram M, Bista, Bihungum, Bjelica, Dusko J, Bjerregaard, Anne A, Bjerregaard, Peter, Bjertness, Espen, Bjertness, Marius B, Björkelund, Cecilia, Bloch, Katia V, Blokstra, Anneke, Blychfeld Magnazu, Moran, Bo, Simona, Bobak, Martin, Boddy, Lynne M, Boehm, Bernhard O, Boer, Jolanda MA, Boggia, Jose G, Bogova, Elena, Boissonnet, Carlos P, Bojesen, Stig E, Bonaccio, Marialaura, Bongard, Vanina, Bonilla-Vargas, Alice, Bopp, Matthias, Borghs, Herman, Botomba, Steve, Bourne, Rupert RA, Bovet, Pascal, Boymatova, Khadichamo, Braeckevelt, Lien, Braeckman, Lutgart, Bragt, Marjolijn CE, Braithwaite, Tasanee, Brajkovich, Imperia, Breckenkamp, Juergen, Breda, João, Brenner, Hermann, Brewster, Lizzy M, Brian, Garry R, Briceño, Yajaira, Brinduse, Lacramioara, Bringolf-Isler, Bettina, Brito, Miguel, Brophy, Sinead, Brug, Johannes, Bruno, Graziella, Bugge, Anna, Buoncristiano, Marta, Burazeri, Genc, Burns, Con, Cabrera de León, Antonio, Cacciottolo, Joseph, Cai, Hui, Cama, Tilema, Cameron, Christine, Camolas, José, Can, Günay, Cândido, Ana Paula c, Cañete, Felicia, Capanzana, Mario V, Čapková, Naděžda, Capuano, Eduardo, Capuano, Rocco, Capuano, Vincenzo, Cardol, Marloes, Cardoso, Viviane C, Carlsson, Axel C, Carmuega, Esteban, Carvalho, Joana, Casajús, José A, Casanueva, Felipe F, Casas, Maribel, Celikcan, Ertugrul, Censi, Laura, Cervantes-Loaiza, Marvin, Cesar, Juraci A, Chamnan, Parinya, Chamukuttan, Snehalatha, Chan, Angelique, Chan, Queenie, Charchar, Fadi J, Charles, Marie-Aline, Chaturvedi, Himanshu K, Chaturvedi, Nish, Che Abdul Rahim, Norsyamlina, Chee, Miao Li, Chen, Chien-Jen, Chen, Fangfang, Chen, Huashuai, Chen, Long-Sheng, Chen, Shuohua, Chen, Zhengming, Cheng, Ching-Yu, Cheng, Yiling J, Cheraghian, Bahman, Chetrit, Angela, Chikova-Iscener, Ekaterina, Chinapaw, Mai JM, Chinnock, Anne, Chiolero, Arnaud, Chiou, Shu-Ti, Chirita-Emandi, Adela, Chirlaque, María-Dolores, Cho, Belong, Christensen, Kaare, Christofaro, Diego G, Chudek, Jerzy, Cifkova, Renata, Cilia, Michelle, Cinteza, Eliza, Cirillo, Massimo, Claessens, Frank, Clare, Philip, Clarke, Janine, Clays, Els, Cohen, Emmanuel, Cojocaru, Cosmin R, Colorado-Yohar, Sandra, Compañ-Gabucio, Laura-María, Concin, Hans, Confortin, Susana C, Cooper, Cyrus, Coppinger, Tara C, Corpeleijn, Eva, Cortés, Lilia Yadira, Costanzo, Simona, Cottel, Dominique, Cowell, Chris, Craig, Cora L, Crampin, Amelia C, Cross, Amanda J, Crujeiras, Ana B, Cruz, Juan J, Csányi, Tamás, Csilla, Semánová, Cucu, Alexandra M, Cui, Liufu, Cureau, Felipe V, Czenczek-Lewandowska, Ewelina, D'Arrigo, Graziella, d'Orsi, Eleonora, da Silva, Alanna G, Dacica, Liliana, Dahm, Christina C, Dallongeville, Jean, Damasceno, Albertino, Damsgaard, Camilla T, Dankner, Rachel, Dantoft, Thomas M, Dasgupta, Parasmani, Dastgiri, Saeed, Dauchet, Luc, Davletov, Kairat, de Assis Guedes de Vasconcelos, Francisco, de Assis, Maria Alice Altenburg, De Backer, Guy, De Bacquer, Dirk, De Bacquer, Jaco, de Bont, Jeroen, De Curtis, Amalia, de Fragas Hinnig, Patrícia, de Gaetano, Giovanni, De Henauw, Stefaan, De Miguel-Etayo, Pilar, De Neve, Jan-Walter, Duarte de Oliveira, Paula, De Ridder, David, De Ridder, Karin, de Rooij, Susanne R, de Sá, Ana Carolina MGN, De Smedt, Delphine, Deepa, Mohan, Deev, Alexander D, DeGennaro, Vincent Jr, Delisle, Hélène, Delpeuch, Francis, Demarest, Stefaan, Dennison, Elaine, Dereń, Katarzyna, Deschamps, Valérie, Devrishov, Ruslan D, Dhimal, Meghnath, Di Castelnuovo, Augusto, Dias-da-Costa, Juvenal Soares, Díaz-Sánchez, María Elena, Diaz, Alejandro, Díaz Fernández, Pedro, Díez Ripollés, María Pilar, Dika, Zivka, Djalalinia, Shirin, Djordjic, Visnja, Do, Ha TP, Dobson, Annette J, Dominguez, Liria, Donati, Maria Benedetta, Donfrancesco, Chiara, Dong, Guanghui, Dong, Yanhui, Donoso, Silvana P, Döring, Angela, Dorobantu, Maria, Dorosty, Ahmad Reza, Dörr, Marcus, Doua, Kouamelan, Dragano, Nico, Drygas, Wojciech, Du, Shufa, Duan, Jia Li, Duante, Charmaine A, Duboz, Priscilla, Duleva, Vesselka L, Dulskiene, Virginija, Dumith, Samuel C, Dushpanova, Anar, Dwyer, Terence, Dyussupova, Azhar, Dzerve, Vilnis, Dziankowska-Zaborszczyk, Elzbieta, Ebrahimi, Narges, Echeverría, Guadalupe, Eddie, Ricky, Eftekhar, Ebrahim, Efthymiou, Vasiliki, Egbagbe, Eruke E, Eggertsen, Robert, Eghtesad, Sareh, Eiben, Gabriele, Ekelund, Ulf, El-Khateeb, Mohammad, El Ammari, Laila, El Ati, Jalila, Eldemire-Shearer, Denise, Elliott, Paul, Enang, Ofem, Endevelt, Ronit, Engle-Stone, Reina, Erasmus, Rajiv T, Erem, Cihangir, Ergor, Gul, Eriksen, Louise, Eriksson, Johan G, Escobedo-de la Peña, Jorge, Eslami, Saeid, Esmaeili, Ali, Evans, Alun, Evans, Roger G, Faeh, David, Fagherazzi, Guy, Fakhradiyev, Ildar, Fakhretdinova, Albina A, Fall, Caroline H, Faramarzi, Elnaz, Farjam, Mojtaba, Farrugia Sant'Angelo, Victoria, Farzi, Yosef, Fattahi, Mohammad Reza, Fawwad, Asher, Fawzi, Wafaie W, Felix-Redondo, Francisco J, Ferguson, Trevor S, Fernandes, Romulo A, Fernández-Bergés, Daniel, Ferrante, Daniel, Ferrao, Thomas, Ferrari, Gerson, Ferrari, Marika, Ferrario, Marco M, Ferreccio, Catterina, Ferreira, Haroldo S, Ferrer, Eldridge, Ferrieres, Jean, Figueiró, Thamara Hubler, Fijalkowska, Anna, Fink, Günther, Fisberg, Mauro, Fischer, Krista, Foo, Leng Huat, Forsner, Maria, 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2024
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50. Association between pre-diagnostic circulating lipid metabolites and colorectal cancer risk: a nested case–control study in the European Prospective Investigation into Cancer and Nutrition (EPIC)

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Harewood, Rhea, Rothwell, Joseph A., Bešević, Jelena, Viallon, Vivian, Achaintre, David, Gicquiau, Audrey, Rinaldi, Sabina, Wedekind, Roland, Prehn, Cornelia, Adamski, Jerzy, Schmidt, Julie A., Jacobs, Inarie, Tjønneland, Anne, Olsen, Anja, Severi, Gianluca, Kaaks, Rudolf, Katzke, Verena, Schulze, Matthias B., Prada, Marcela, Masala, Giovanna, Agnoli, Claudia, Panico, Salvatore, Sacerdote, Carlotta, Jakszyn, Paula Gabriela, Sánchez, Maria-Jose, Castilla, Jesús, Chirlaque, María-Dolores, Atxega, Amaia Aizpurua, van Guelpen, Bethany, Heath, Alicia K., Papier, Keren, Tong, Tammy Y.N., Summers, Scott A., Playdon, Mary, Cross, Amanda J., Keski-Rahkonen, Pekka, Chajès, Véronique, Murphy, Neil, and Gunter, Marc J.

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2024
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