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2,180 results on '"Rubio F"'

1. On harmonic oscillator hazard functions

Author

Christen, J. A. and Rubio, F. J.

Subjects
Statistics - Methodology
Abstract

We propose a parametric hazard model obtained by enforcing positivity in the damped harmonic oscillator. The resulting model has closed-form hazard and cumulative hazard functions, facilitating likelihood and Bayesian inference on the parameters. We show that this model can capture a range of hazard shapes, such as increasing, decreasing, unimodal, bathtub, and oscillatory patterns, and characterize the tails of the corresponding survival function. We illustrate the use of this model in survival analysis using real data., Comment: R code and data are available at: https://github.com/FJRubio67/HOH

Published
2024

2. Shock Wave Formation in Radiative Plasmas

Author

Garcia-Rubio, F., Tranchant, V., Hansen, E. C., Tabassum, R., Reyes, A., Rahman, H. U., Ney, P., Ruskov, E., and Tzeferacos, P.

Subjects
Physics - Plasma Physics, High Energy Physics - Theory, Physics - Fluid Dynamics
Abstract

The temporal evolution of weak shocks in radiative media is theoretically investigated in this work. The structure of radiative shocks has traditionally been studied in a stationary framework. Their systematic classification is complex because layers of optically thin and thick regions alternate to form a radiatively-driven precursor and a temperature-relaxation layer, between which the hydrodynamic shock is embedded. In this work, we analyze the formation of weak shocks when two radiative plasmas with different pressures are put in contact. Applying a reductive perturbative method yields a Burgers-type equation that governs the temporal evolution of the perturbed variables including the radiation field. The conditions upon which optically thin and thick solutions exist have been derived and expressed as a function of the shock strength and Boltzmann number. Below a certain Boltzmann number threshold, weak shocks always become optically thick asymptotically in time, while thin solutions appear as transitory structures. The existence of an optically thin regime is related to the presence of an overdense layer in the compressed material. Scaling laws for the characteristic formation time and shock width are provided for each regime. The theoretical analysis is supported by FLASH simulations, and a comprehensive test-case has been designed to benchmark radiative hydrodynamic codes., Comment: Approved in Phys. Rev. E

Published
2024

3. Optimal control analysis and Practical NMPC applied to refrigeration systems

Author

Bejarano, G., Ortega, M. G., Normey-Rico, J. E., and Rubio, F. R

Subjects
Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Systems and Control
Abstract

This work is focused on optimal control of mechanical compression refrigeration systems. A reduced-order state-space model based on the moving boundary approach is proposed for the canonical cycle, which eases the controller design. The optimal cycle (that satisfying the cooling demand while maximizing efficiency) is defined by three variables, but only two inputs are available, therefore the controllability of the proposed model is studied. It is shown through optimization simulations how optimal cycles for a range of the cooling demand turn out not to be achieved by keeping the degree of superheating to a minimum. The Practical NMPC and a well-known feedback-plus-feedforward strategy from the literature are compared in simulation, both showing trouble in reaching the optimal cycle, which agrees with the controllability study., Comment: 39 pages, 14 figures. Postprint of the final published work

Published
2024
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4. Bayesian variable selection in sample selection models using spike-and-slab priors

Author

Iqbal, Adam, Ogundimu, Emmanuel, and Rubio, F. Javier

Subjects
Statistics - Computation, Statistics - Methodology
Abstract

Sample selection models represent a common methodology for correcting bias induced by data missing not at random. It is well known that these models are not empirically identifiable without exclusion restrictions. In other words, some variables predictive of missingness do not affect the outcome model of interest. The drive to establish this requirement often leads to the inclusion of irrelevant variables in the model. A recent proposal uses adaptive LASSO to circumvent this problem, but its performance depends on the so-called covariance assumption, which can be violated in small to moderate samples. Additionally, there are no tools yet for post-selection inference for this model. To address these challenges, we propose two families of spike-and-slab priors to conduct Bayesian variable selection in sample selection models. These prior structures allow for constructing a Gibbs sampler with tractable conditionals, which is scalable to the dimensions of practical interest. We illustrate the performance of the proposed methodology through a simulation study and present a comparison against adaptive LASSO and stepwise selection. We also provide two applications using publicly available real data. An implementation and code to reproduce the results in this paper can be found at https://github.com/adam-iqbal/selection-spike-slab, Comment: An implementation and code used to reproduce simulation studies and the real data applications can be found at https://github.com/adam-iqbal/selection-spike-slab

Published
2023

5. Dynamic survival analysis: modelling the hazard function via ordinary differential equations

Author

Christen, J. A. and Rubio, F. J.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

The hazard function represents one of the main quantities of interest in the analysis of survival data. We propose a general approach for parametrically modelling the dynamics of the hazard function using systems of autonomous ordinary differential equations (ODEs). This modelling approach can be used to provide qualitative and quantitative analyses of the evolution of the hazard function over time. Our proposal capitalises on the extensive literature of ODEs which, in particular, allow for establishing basic rules or laws on the dynamics of the hazard function via the use of autonomous ODEs. We show how to implement the proposed modelling framework in cases where there is an analytic solution to the system of ODEs or where an ODE solver is required to obtain a numerical solution. We focus on the use of a Bayesian modelling approach, but the proposed methodology can also be coupled with maximum likelihood estimation. A simulation study is presented to illustrate the performance of these models and the interplay of sample size and censoring. Two case studies using real data are presented to illustrate the use of the proposed approach and to highlight the interpretability of the corresponding models. We conclude with a discussion on potential extensions of our work and strategies to include covariates into our framework. Although we focus on examples on Medical Statistics, the proposed framework is applicable in any context where the interest lies on estimating and interpreting the dynamics hazard function., Comment: R and Python code available at: https://github.com/FJRubio67/ODESurv. To appear in Statistical Methods in Medical Research

Published
2023

6. On near-redundancy and identifiability of parametric hazard regression models under censoring

Author

Rubio, F. J., Espindola, J. A., and Montoya, J. A.

Subjects
Statistics - Methodology
Abstract

We study parametric inference on a rich class of hazard regression models in the presence of right-censoring. Previous literature has reported some inferential challenges, such as multimodal or flat likelihood surfaces, in this class of models for some particular data sets. We formalize the study of these inferential problems by linking them to the concepts of near-redundancy and practical non-identifiability of parameters. We show that the maximum likelihood estimators of the parameters in this class of models are consistent and asymptotically normal. Thus, the inferential problems in this class of models are related to the finite-sample scenario, where it is difficult to distinguish between the fitted model and a nested non-identifiable (i.e., parameter-redundant) model. We propose a method for detecting near-redundancy, based on distances between probability distributions. We also employ methods used in other areas for detecting practical non-identifiability and near-redundancy, including the inspection of the profile likelihood function and the Hessian method. For cases where inferential problems are detected, we discuss alternatives such as using model selection tools to identify simpler models that do not exhibit these inferential problems, increasing the sample size, or extending the follow-up time. We illustrate the performance of the proposed methods through a simulation study. Our simulation study reveals a link between the presence of near-redundancy and practical non-identifiability. Two illustrative applications using real data, with and without inferential problems, are presented., Comment: To appear in Biometrical Journal

Published
2023

7. Individual frailty excess hazard models in cancer epidemiology

Author

Rubio, F. J., Putter, H., and Belot, A.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Unobserved individual heterogeneity is a common challenge in population cancer survival studies. This heterogeneity is usually associated with the combination of model misspecification and the failure to record truly relevant variables. We investigate the effects of unobserved individual heterogeneity in the context of excess hazard models, one of the main tools in cancer epidemiology. We propose an individual excess hazard frailty model to account for individual heterogeneity. This represents an extension of frailty modelling to the relative survival framework. In order to facilitate the inference on the parameters of the proposed model, we select frailty distributions which produce closed-form expressions of the marginal hazard and survival functions. The resulting model allows for an intuitive interpretation, in which the frailties induce a selection of the healthier individuals among survivors. We model the excess hazard using a flexible parametric model with a general hazard structure which facilitates the inclusion of time-dependent effects. We illustrate the performance of the proposed methodology through a simulation study. We present a real-data example using data from lung cancer patients diagnosed in England, and discuss the impact of not accounting for unobserved heterogeneity on the estimation of net survival. The methodology is implemented in the R package IFNS., Comment: Accepted for publication in Statistics in Medicine. Codes and Examples can be found at: https://github.com/FJRubio67/IFNS

Published
2023

9. A Unifying Framework for Flexible Excess Hazard Modeling with Applications in Cancer Epidemiology

Author

Eletti, A., Marra, G., Quaresma, M., Radice, R., and Rubio, F. J.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Excess hazard modeling is one of the main tools in population-based cancer survival research. Indeed, this setting allows for direct modeling of the survival due to cancer even in the absence of reliable information on the cause of death, which is common in population-based cancer epidemiology studies. We propose a unifying link-based additive modeling framework for the excess hazard that allows for the inclusion of many types of covariate effects, including spatial and time-dependent effects, using any type of smoother, such as thin plate, cubic splines, tensor products and Markov random fields. In addition, this framework accounts for all types of censoring as well as left-truncation. Estimation is conducted by using an efficient and stable penalized likelihood-based algorithm whose empirical performance is evaluated through extensive simulation studies. Some theoretical and asymptotic results are discussed. Two case studies are presented using population-based cancer data from patients diagnosed with breast (female), colon and lung cancers in England. The results support the presence of non-linear and time-dependent effects as well as spatial variation. The proposed approach is available in the R package GJRM., Comment: To appear in the Journal of the Royal Statistical Society: Series C Code, simulated data and supplementary material available at: https://github.com/FJRubio67/LBANS

Published
2022

10. Diagnosing Magnetic Fields in Cylindrical Implosions with Oblique Proton Radiography

Author

Heuer, P. V., Leal, L. S., Davies, J. R., Hansen, E. C., Barnak, D. H., Peebles, J. L., García-Rubio, F., Pollock, B., Moody, J., Birkel, A., and Seguin, F. H.

Subjects
Physics - Plasma Physics
Abstract

Two experiments on the OMEGA Laser System used oblique proton radiography to measure magnetic fields in cylindrical implosions with and without an applied axial magnetic field. Although the goal of both experiments was to measure the magnitude of the compressed axial magnetic field in the core of the implosion, this field was obfuscated by two features in the coronal plasma produced by the compression beams: an azimuthal self-generated magnetic field and small length scale, high-amplitude structures attributed to collisionless effects. In order to understand these features, synthetic radiographs are generated using fields produced by 3-D HYDRA simulations. These synthetic radiographs reproduce the features of the experimental radiographs with the exception of the small-scale structures. A direct inversion algorithm is successfully applied to a synthetic radiograph, but is only partially able to invert the experimental radiographs in part because some protons are blocked by the field coils. The origins of the radiograph features and their dependence on various experimental parameters are explored. The results of this analysis should inform future measurements of compressed axial magnetic fields in cylindrical implosions.

Published
2022
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11. On a prior based on the Wasserstein information matrix

Author

Li, W. and Rubio, F. J.

Subjects
Mathematics - Statistics Theory, Statistics - Methodology
Abstract

We introduce a prior for the parameters of univariate continuous distributions, based on the Wasserstein information matrix, which is invariant under reparameterisations. We discuss the links between the proposed prior with information geometry. We present sufficient conditions for the propriety of the posterior distribution for general classes of models. We present a simulation study that shows that the induced posteriors have good frequentist properties., Comment: To appear in Statistics and Probability Letters

Published
2022

12. Design, construction and operation of the ProtoDUNE-SP Liquid Argon TPC

Author

DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adams, D., Adinolfi, M., Aduszkiewicz, A., Aguilar, J., Ahmad, Z., Ahmed, J., Ali-Mohammadzadeh, B., Alion, T., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alt, C., Alton, A., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antoniassi, M., Antonova, M., Antoshkin, A., Antusch, S., Aranda-Fernandez, A., Ariga, A., Arnold, L. O., Arroyave, M. A., Asaadi, J., Asquith, L., Aurisano, A., Aushev, V., Autiero, D., Ayala-Torres, M., Azfar, F., Back, A., Back, H., Back, J. J., Backhouse, C., Baesso, P., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baller, B., Bambah, B., Barao, F., Barenboim, G., Barker, G. J., Barkhouse, W., Barnes, C., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Belchior, E., Battat, J. B. R., Battisti, F., Bay, F., Alba, J. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Belver, D., Benekos, N., Montiel, C. Benitez, Neves, F. Bento, Berger, J., Berkman, S., Bernardini, P., Berner, R. M., Berns, H., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezerra, T. J. C., Bhattacharjee, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biassoni, M., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D. M., Blazey, G. C., Blucher, E., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bongrand, M., Bonini, F., Booth, A., Booth, C., Boran, F., Bordoni, S., Borkum, A., Boschi, T., Bostan, N., Bour, P., Bourgeois, C., Boyd, S. B., Boyden, D., Bracinik, J., Braga, D., Brailsford, D., Branca, A., Brandt, A., Bremer, J., Brew, C., Brianne, E., Brice, S. J., Brizzolari, C., Bromberg, C., Brooijmans, G., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Butorov, I., Cagnoli, I., Caiulo, D., Calabrese, R., Calafiura, P., Calcutt, J., Calin, M., Calvez, S., Calvo, E., Caminata, A., Campanelli, M., Cankocak, K., Caratelli, D., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carroll, T., Casta, J. F., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cervelli, A., Villanueva, A. Cervera, Chalifour, M., Chappell, A., Chardonnet, E., Charitonidis, N., Chatterjee, A., Chattopadhyay, S., Chen, H., Chen, K., Chen, M., Chen, Y., Chen, Z., Cheon, Y., Cherdack, D., Chi, C., Childress, S., Chiriacescu, A., Chisnall, G., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Choubey, S., Christensen, A., Christian, D., Christodoulou, G., Chukanov, A., Chung, M., Church, E., Cicero, V., Clarke, P., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Conley, E., Conley, R., Conrad, J. M., Convery, M., Copello, S., Corwin, L., Valentim, R., Cremaldi, L., Cremonesi, L., Crespo-Anadon, J. I., Crisler, M., Cristaldo, E., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Cussans, D., Dabrowski, M., Dalager, O., da Motta, H., Peres, L. Da Silva, David, C., David, Q., Davies, G. S., Davini, S., Dawson, J., De, K., De Almeida, R. M., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvea, A., De Holanda, P. C., Astiz, I. L. De Icaza, Deisting, A., De Jong, P., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., De Lurgio, P., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Deptuch, G. W., De Roeck, A., De Romeri, V., De Souza, G., Devi, R., Dharmapalan, R., Dias, M., Diaz, F., Diaz, J. S., Di Domizio, S., Di Giulio, L., Ding, P., Di Noto, L., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domine, L., Douglas, D., Douillet, D., Drake, G., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dunne, P., Durkin, T., Duyang, H., Dvornikov, O., Dwyer, D. A., Dyshkant, A. S., Eads, M., Earle, A., Edmunds, D., Eisch, J., Emberger, L., Emery, S., Ereditato, A., Erjavec, T., Escobar, C. O., Eurin, G., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Falcone, A., Faní, M., Farnese, C., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Menendez, P. Fernandez, Morales, M. Fernandez, Ferraro, F., Fields, L., Filip, P., Filthaut, F., Fiorentini, A., Fiorini, M., Fitzpatrick, R. S., Flanagan, W., Fleming, B., Flight, R., Forero, D. V., Fowler, J., Fox, W., Franc, J., Francis, K., Franco, D., Freeman, J., Freestone, J., Fried, J., Friedland, A., Robayo, F. Fuentes, Fuess, S., Furic, I., Furmanski, A. P., Gabrielli, A., Gago, A., Gallagher, H., Gallas, A., Gallego-Ros, A., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Gandrajula, R., Gao, F., Gao, S., B., A. C. Garcia, Garcia-Gamez, D., García-Peris, M. A., Gardiner, S., Gastler, D., Gauvreau, J., Ge, G., Gelli, B., Gendotti, A., Gent, S., Ghorbani-Moghaddam, Z., Giammaria, P., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Girerd, C., Giri, A. K., Gnani, D., Gogota, O., Gold, M., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonnella, F., Gonzalez-Cuevas, J. A., Diaz, D. Gonzalez, Gonzalez-Lopez, M., Goodman, M. C., Goodwin, O., Goswami, S., Gotti, C., Goudzovski, E., Grace, C., Graham, M., Gran, R., Granados, E., Granger, P., Grant, A., Grant, C., Gratieri, D., Green, P., Greenler, L., Greer, J., Grenard, J., Griffith, W. C., Groh, M., Grudzinski, J., Grzelak, K., Gu, W., Guardincerri, E., Guarino, V., Guarise, M., Guenette, R., Guerard, E., Guerzoni, M., Guglielmi, A., Guo, B., Guthikonda, K. K., Gutierrez, R., Guzowski, P., Guzzo, M. M., Gwon, S., Ha, C., Habig, A., Hadavand, H., Haenni, R., Hahn, A., Haiston, J., Hamacher-Baumann, P., Hamernik, T., Hamilton, P., Han, J., Harris, D. A., Hartnell, J., Harton, J., Hasegawa, T., Hasnip, C., Hatcher, R., Hatfield, K. W., Hatzikoutelis, A., Hayes, C., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Hennessy, K., Henry, S., Morquecho, M. A. Hernandez, Herner, K., Hertel, L., Hewes, V, Higuera, A., Hill, T., Hillier, S. J., Himmel, A., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Hoppe, E., Horton-Smith, G. A., Hostert, M., Hourlier, A., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Hugon, J., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Isenhower, L., Itay, R., Izmaylov, A., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jediny, F., Jena, D., Jeong, Y. S., Jesus-Valls, C., Ji, X., Jiang, L., Jimenez, S., Jipa, A., Johnson, R., Johnston, N., Jones, B., Jones, S. B., Judah, M., Jung, C. K., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A., Kadenko, I., Kalra, D., Kakorin, I., Kalitkina, A., Kamiya, F., Kaneshige, N., Karagiorgi, G., Karaman, G., Karcher, A., Karolak, M., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Kazaryan, N., Kearns, E., Keener, P., Kelly, K. J., Kemp, E., Kemularia, O., Ketchum, W., Kettell, S. H., Khabibullin, M., Khotjantsev, A., Khvedelidze, A., Kim, D., King, B., Kirby, B., Kirby, M., Klein, J., Koehler, K., Koerner, L. W., Kohn, S., Koller, P. P., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecky, V. A., Kothekar, K., Krennrich, F., Kreslo, I., Kropp, W., Kudenko, Y., Kudryavtsev, V. A., Kulagin, S., Kumar, J., Kumar, P., Kunze, P., Kuruppu, C., Kus, V., Kutter, T., Kvasnicka, J., Kwak, D., Lambert, A., Land, B. J., Lande, K., Lane, C. E., Lang, K., Langford, T., Langstaff, M., Larkin, J., Lasorak, P., Last, D., Lastoria, C., Laundrie, A., Laurenti, G., Lawrence, A., Lazanu, I., LaZur, R., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeBrun, P., LeCompte, T., Lee, C., Lee, S. Y., Miotto, G. Lehmann, Lehnert, R., de Oliveira, M. A. Leigui, Leitner, M., Lepin, L. M., Li, L., Li, S. W., Li, T., Li, Y., Liao, H., Lin, C. S., Lin, Q., Lin, S., Ling, J., Lister, A., Littlejohn, B. R., Liu, J., Lockwitz, S., Loew, T., Lokajicek, M., Lomidze, I., Long, K., Loo, K., Lord, T., LoSecco, J. M., Louis, W. C., Lu, X. -G., Luk, K. B., Luo, X., Luppi, E., Lurkin, N., Lux, T., Luzio, V. P., MacFarlane, D., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., Maddalena, A., Madera, A., Madigan, P., Magill, S., Mahn, K., Maio, A., Major, A., Maloney, J. A., Mandrioli, G., Mandujano, R. C., Maneira, J., Manenti, L., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Manyam, V. N., Manzanillas, L., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marie, R., Marinho, F., Marino, A. D., Marsden, D., Marshak, M., Marshall, C. M., Marshall, J., Marteau, J., Martin-Albo, J., Martinez, N., Caicedo, D. A. Martinez, Martynenko, S., Mascagna, V., Mason, K., Mastbaum, A., Masud, M., Matichard, F., Matsuno, S., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., Mazzacane, A., Mazzucato, E., McAskill, T., McCluskey, E., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Mefodiev, A., Mehta, P., Melas, P., Mena, O., Menary, S., Mendez, H., Mendez, P., M, D. P., Menegolli, A., Meng, G., Messier, M. D., Metcalf, W., Mettler, T., Mewes, M., Meyer, H., Miao, T., Michna, G., Miedema, T., Mikola, V., Milincic, R., Miller, G., Miller, W., Mills, J., Milne, C., Mineev, O., Miranda, O. G., Miryala, S., Mishra, C. S., Mishra, S. R., Mislivec, A., Mladenov, D., Mocioiu, I., Moffat, K., Moggi, N., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Zetina, L. M. Montano, Moon, J., Moon, S. H., Mooney, M., Moor, A. F., Moreno, D., Morris, C., Mossey, C., Motuk, E., Moura, C. A., Mousseau, J., Mouster, G., Mu, W., Mualem, L., Mueller, J., Muether, M., Mufson, S., Muheim, F., Muir, A., Mulhearn, M., Munford, D., Muramatsu, H., Murphy, S., Musser, J., Nachtman, J., Nagu, S., Nalbandyan, M., Nandakumar, R., Naples, D., Narita, S., Nath, A., Navas-Nicolás, D., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Negishi, K., Nelson, J. K., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Newhart, D., Newton, H., Nichol, R., Nicolas-Arnaldos, F., Niner, E., Nishimura, K., Norman, A., Norrick, A., Northrop, R., Novella, P., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Del Campo, A. Olivares, Olivier, A., Olshevskiy, A., Onel, Y., Onishchuk, Y., Ott, J., Pagani, L., Pakvasa, S., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Palomino-Gallo, J. L., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papadimitriou, V., Papaleo, R., Papanestis, A., Paramesvaran, S., Parke, S., Parozzi, E., Parsa, Z., Parvu, M., Pascoli, S., Pasqualini, L., Pasternak, J., Pater, J., Patrick, C., Patrizii, L., Patterson, R. B., Patton, S. J., Patzak, T., Paudel, A., Paulos, B., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Pec, V., Peeters, S. J. M., Pennacchio, E., Penzo, A., Peres, O. L. G., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Piastra, F., Pickering, L., Pietropaolo, F., Plunkett, R., Poling, R., Pons, X., Poonthottathil, N., Poppi, F., Pordes, S., Porter, J., Potekhin, M., Potenza, R., Potukuchi, B. V. K. 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P., Zennamo, J., Zeug, K., Zhang, C., Zhao, M., Zhivun, E., Zhu, G., Zilberman, P., Zimmerman, E. D., Zito, M., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, USA. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of $7\times 6\times 7.2$~m$^3$. The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP's successful operation between 2018 and 2020 demonstrates the effectiveness of the single-phase far detector design. This paper describes the design, construction, assembly and operation of the detector components.

Published
2021

13. Searching for solar KDAR with DUNE

Author

DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adams, D., Adinolfi, M., Aduszkiewicz, A., Aguilar, J., Ahmad, Z., Ahmed, J., Ali-Mohammadzadeh, B., Alion, T., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alt, C., Alton, A., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antoniassi, M., Antonova, M., Antoshkin, A., Antusch, S., Aranda-Fernandez, A., Ariga, A., Arnold, L. O., Arroyave, M. A., Asaadi, J., Asquith, L., Aurisano, A., Aushev, V., Autiero, D., Ayala-Torres, M., Azfar, F., Back, A., Back, H., Back, J. J., Backhouse, C., Baesso, P., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baller, B., Bambah, B., Barao, F., Barenboim, G., Barker, G. J., Barkhouse, W., Barnes, C., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Belchior, E., Battat, J. B. R., Battisti, F., Bay, F., Alba, J. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Belver, D., Benekos, N., Montiel, C. Benitez, Neves, F. Bento, Berger, J., Berkman, S., Bernardini, P., Berner, R. M., Berns, H., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezerra, T. J. C., Bhatnagar, V., Bhattacharjee, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biassoni, M., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D. M., Blazey, G. C., Blucher, E., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bongrand, M., Bonini, F., Booth, A., Booth, C., Boran, F., Bordoni, S., Borkum, A., Boschi, T., Bostan, N., Bour, P., Bourgeois, C., Boyd, S. B., Boyden, D., Bracinik, J., Braga, D., Brailsford, D., Branca, A., Brandt, A., Bremer, J., Brew, C., Brianne, E., Brice, S. 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P., Zennamo, J., Zeug, K., Zhang, C., Zhao, M., Zhivun, E., Zhu, G., Zilberman, P., Zimmerman, E. D., Zito, M., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.

Subjects
High Energy Physics - Experiment, Astrophysics - Cosmology and Nongalactic Astrophysics, High Energy Physics - Phenomenology
Abstract

The observation of 236 MeV muon neutrinos from kaon-decay-at-rest (KDAR) originating in the core of the Sun would provide a unique signature of dark matter annihilation. Since excellent angle and energy reconstruction are necessary to detect this monoenergetic, directional neutrino flux, DUNE with its vast volume and reconstruction capabilities, is a promising candidate for a KDAR neutrino search. In this work, we evaluate the proposed KDAR neutrino search strategies by realistically modeling both neutrino-nucleus interactions and the response of DUNE. We find that, although reconstruction of the neutrino energy and direction is difficult with current techniques in the relevant energy range, the superb energy resolution, angular resolution, and particle identification offered by DUNE can still permit great signal/background discrimination. Moreover, there are non-standard scenarios in which searches at DUNE for KDAR in the Sun can probe dark matter interactions., Comment: 19 pages, 13 figures

Published
2021
Full Text
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14. Deep Underground Neutrino Experiment (DUNE) Near Detector Conceptual Design Report

Author

Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adamov, G., Adams, D., Adinolfi, M., Aduszkiewicz, A., Ahmad, Z., Ahmed, J., Alion, T., Monsalve, S. Alonso, Alrashed, M., Alt, C., Alton, A., Amedo, P., Anderson, J., Andreopoulos, C., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antonova, M., Antusch, S., Aranda-Fernandez, A., Ariga, A., Arnold, L. O., Arroyave, M. A., Asaadi, J., Aurisano, A., Aushev, V., Autiero, D., Ayala-Torres, M., Azfar, F., Back, H., Back, J. J., Backhouse, C., Baesso, P., Bagaturia, I., Bagby, L., Balasubramanian, S., Baldi, P., Baller, B., Bambah, B., Barao, F., Barenboim, G., Barker, G. J., Barkhouse, W., Barnes, C., Barr, G., Monarca, J. Barranco, Barros, N., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Belchior, E., Battat, J. B. R., Battisti, F., Bay, F., Alba, J. L. Bazo, Beacom, J. 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K., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Newhart, D., Newton, H., Nichol, R., Nicolas-Arnaldos, F., Niner, E., Nishimura, K., Norman, A., Norrick, A., Northrop, R., Novella, P., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Del Campo, A. Olivares, Olivier, A., Olshevskiy, A., Onel, Y., Onishchuk, Y., Ott, J., Pagani, L., Pakvasa, S., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Palomino-Gallo, J. L., Pantic, E., Paolone, V., Papadimitriou, V., Papaleo, R., Papanestis, A., Paramesvaran, S., Parke, S., Parsa, Z., Parvu, M., Pascoli, S., Pasqualini, L., Pasternak, J., Pater, J., Patrick, C., Patrizii, L., Patterson, R. B., Patton, S. J., Patzak, T., Paudel, A., Paulos, B., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Pec, V., Peeters, S. J. M., Pennacchio, E., Penzo, A., Peres, O. L. G., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Piastra, F., Pickering, L., Pietropaolo, F., Plunkett, R., Poling, R., Pons, X., Poonthottathil, N., Pordes, S., Porter, J., Potekhin, M., Potenza, R., Potukuchi, B. V. K. S., Pozimski, J., Pozzato, M., Prakash, S., Prakash, T., Prince, S., Pugnere, D., Qian, X., Bazetto, M. C. Queiroga, Raaf, J. L., Radeka, V., Rademacker, J., Radics, B., Rafique, A., Raguzin, E., Rai, M., Rajaoalisoa, M., Rakhno, I., Rakotonandrasana, A., Rakotondravohitra, L., Ramachers, Y. A., Rameika, R., Delgado, M. A. Ramirez, Ramson, B., Rappoldi, A., Raselli, G., Ratoff, P., Raut, S., Razakamiandra, R. F., Real, J. S., Rebel, B., Reggiani-Guzzo, M., Rehak, T., Reichenbacher, J., Reitzner, S. D., Sfar, H. Rejeb, Renshaw, A., Rescia, S., Resnati, F., Reynolds, A., Riccio, C., Riccobene, G., Rice, L. C. J., Ricol, J., Rigamonti, A., Rigaut, Y., Rivera, D., Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Bonilla, E. Rodriguez, Rondon, J. Rodriguez, Rosauro-Alcaraz, S., Rosenberg, M., Rosier, P., Roskovec, B., Rossella, M., Rout, J., Roy, P., Roy, S., Rubbia, A., Rubbia, C., Rubio, F. C., Russell, B., Ruterbories, D., Saakyan, R., Sacerdoti, S., Safford, T., Sahay, R., Sahu, N., Sala, P., Samios, N., Samoylov, O., Sanchez, M. C., Sanders, D. A., Sankey, D., Santana, S., Santos-Maldonado, M., Saoulidou, N., Sapienza, P., Sarasty, C., Sarcevic, I., Savage, G., Savinov, V., Scaramelli, A., Scarff, A., Scarpelli, A., Schaffer, T., Schellman, H., Schlabach, P., Schmitz, D., Scholberg, K., Schukraft, A., Segreto, E., Sensenig, J., Seong, I., Sergi, A., Sgalaberna, D., Shaevitz, M. H., Shafaq, S., Shamma, M., Sharankova, R., Sharma, H. R., Sharma, R., Kumar, R., Shaw, T., Shepherd-Themistocleous, C., Shin, S., Shooltz, D., Shrock, R., Simard, L., Simon, F., Simos, N., Sinclair, J., Sinev, G., Singh, J., Singh, V., Sipos, R., Sippach, F. W., Sirri, G., Sitraka, A., Siyeon, K., VIII, K. Skarpaas, Smith, A., Smith, E., Smith, P., Smolik, J., Smy, M., Snider, E. L., Snopok, P., Nunes, M. Soares, Sobel, H., Soderberg, M., Salinas, C. J. Solano, Söldner-Rembold, S., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Soto-Oton, J., Sousa, A., Soustruznik, K., Spagliardi, F., Spanu, M., Spitz, J., Spooner, N. J. C., Spurgeon, K., Staley, R., Stancari, M., Stanco, L., Stanley, R., Stein, R., Steiner, H. M., Stewart, J., Stillwell, B., Stock, J., Stocker, F., Stokes, T., Strait, M., Strauss, T., Striganov, S., Stuart, A., Suarez, J. G., Sullivan, H., Summers, D., Surdo, A., Susic, V., Suter, L., Sutera, C. M., Svoboda, R., Szczerbinska, B., Szelc, A. M., Talaga, R., Tanaka, H. A., Oregui, B. Tapia, Tapper, A., Tariq, S., Tatar, E., Tayloe, R., Teklu, A. M., Tenti, M., Terao, K., Ternes, C. A., Terranova, F., Testera, G., Thea, A., Thompson, J. L., Thorn, C., Timm, S. C., Todd, J., Tonazzo, A., Torbunov, D., Torti, M., Tortola, M., Tortorici, F., Totani, D., Toups, M., Touramanis, C., Trevor, J., Trilov, S., Trzaska, W. H., Tsai, Y. T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tufanli, S., Tull, C., Tyley, E., Tzanov, M., Uchida, M. A., Urheim, J., Usher, T., Uzunyan, S., Vagins, M. R., Vahle, P., Valdiviesso, G. A., Valencia, E., Vallari, Z., Valle, J. W. F., Vallecorsa, S., Van Berg, R., Van de Water, R. G., Varanini, F., Vargas, D., Varner, G., Vasel, J., Vasina, S., Vasseur, G., Vaughan, N., Vaziri, K., Ventura, S., Verdugo, A., Vergani, S., Vermeulen, M. A., Verzocchi, M., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Viren, B., Vrba, T., Wachala, T., Waldron, A. V., Wallbank, M., Wang, H., Wang, J., Wang, M. H. L. S., Wang, Y., Warburton, K., Warner, D., Wascko, M., Waters, D., Watson, A., Weatherly, P., Weber, A., Weber, M., Wei, H., Weinstein, A., Wenman, D., Wetstein, M., White, A., Whitehead, L. H., Whittington, D., Wilking, M. J., Wilkinson, C., Williams, Z., Wilson, F., Wilson, R. J., Wolcott, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wret, C., Wu, W., Xiao, Y., Yandel, E., Yang, G., Yang, K., Yang, S., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zazueta, L., Zeit, G., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhao, M., Zhivun, E., Zhu, G., Zilberman, P., Zimmerman, E. D., Zito, M., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

This report describes the conceptual design of the DUNE near detector, Comment: 314 pages, 185 figures

Published
2021

16. Shock wave formation in radiative plasmas

Author

Garcia-Rubio, F., primary, Tranchant, V., additional, Hansen, E. C., additional, Reyes, A., additional, Tabassum, R., additional, Rahman, H. U., additional, Ney, P., additional, Ruskov, E., additional, and Tzeferacos, P., additional

Published
2024
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17. Mass diffusion and liner material effect in a MagLIF fusion-like plasma

Author

García-Rubio, F. and Sanz, J.

Subjects
Physics - Plasma Physics
Abstract

In this paper, liner - fuel mass diffusion and the effect of the liner material on mass ablation, energy and magnetic flux losses are studied in a MagLIF fusion-like plasma. The analysis performed in [Garc\'ia-Rubio and Sanz, Phys. Plasmas 24, 072710 (2017)] is extended to liner materials of an arbitrary atomic number. The liner ablates and penetrates into the hot spot, compressing and cooling down the fuel. The magnetic flux in the fuel is lost by Nernst convection through the ablated liner - fuel interface, called ablated border. Increasing the liner atomic number leads to a reduction of both energy and magnetic flux losses in the fuel for a small and moderate magnetization values. Mass diffusion is confined within a thin layer at the ablated border. Concentration gradient and baro-diffusion are the predominant mechanisms leading to liner - fuel mixing. The width of the diffusion layer may be comparable to the turbulent mixing layer resulting from the Rayleigh-Taylor instability at the ablated border. An asymptotic analysis performed for large liner atomic number Z_{2} shows that mass ablation, energy and magnetic flux losses and liner - fuel mass diffusion scale as 1/\sqrt{Z_{2}}., Comment: 16 figures

Published
2018
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21. Flexible linear mixed models with improper priors for longitudinal and survival data

Author

Rubio, F. J. and Steel, M. F. J.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

We propose a Bayesian approach using improper priors for hierarchical linear mixed models with flexible random effects and residual error distributions. The error distribution is modelled using scale mixtures of normals, which can capture tails heavier than those of the normal distribution. This generalisation is useful to produce models that are robust to the presence of outliers. The case of asymmetric residual errors is also studied. We present general results for the propriety of the posterior that also cover cases with censored observations, allowing for the use of these models in the contexts of popular longitudinal and survival analyses. We consider the use of copulas with flexible marginals for modelling the dependence between the random effects, but our results cover the use of any random effects distribution. Thus, our paper provides a formal justification for Bayesian inference in a very wide class of models (covering virtually all of the literature) under attractive prior structures that limit the amount of required user elicitation., Comment: To appear in the Electronic Journal of Statistics. Additional R codes can be found at: http://rpubs.com/FJRubio

Published
2016

22. Flexible objective Bayesian linear regression with applications in survival analysis

Author

Rubio, F. J. and Yu, K.

Subjects
Statistics - Applications
Abstract

We study objective Bayesian inference for linear regression models with residual errors distributed according to the class of two-piece scale mixtures of normal distributions. These models allow for capturing departures from the usual assumption of normality of the errors in terms of heavy tails, asymmetry, and certain types of heteroscedasticity. We propose a general noninformative, scale-invariant, prior structure and provide sufficient conditions for the propriety of the posterior distribution of the model parameters, which cover cases when the response variables are censored. These results allow us to apply the proposed models in the context of survival analysis. This paper represents an extension to the Bayesian framework of the models proposed in Rubio and Hong (2015). We present a simulation study that shows good frequentist properties of the posterior credible intervals as well as point estimators associated to the proposed priors. We illustrate the performance of these models with real data in the context of survival analysis of cancer patients.

Published
2016
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23. Resultado de la implantación de consultas telemáticas en cirugía ortopédica y traumatología durante la pandemia COVID-19

Author

Zamora Navas, P., Montañez Heredia, E., Nieto Orellana, J., Gónzalez García, C., Cano Obando, L., Cárdenas Rebollo, L., Abderrachid Al Chaid, Y., Saadouli Arco, M., Rodríguez Delourme, I., Díez Izquierdo, M., Prado Martín de Lucía, L.F., Durán Garrido, J., Fernández Hijano, M., Víquez da Silva, R., Olofsson Suárez-Bárcena, C., Jiménez Garrido, C., García Vera, J.J., Pérez Cardeña, J., Irízar Jiménez, S., Esteban del Castillo, J.L., García Píriz, M., Gómez Palomo, J.M., Fernández Martín, F.J.A., Huertas Segador, P., Santos de la Fuente, J., Serrano Fernández, J., Estades Rubio, F., Fernández de Rota, A., García Herrera, J.M., Leiva Gea, A., Lombardo Torre, M., Mariscal Lara, J., Martín Castilla, B., Urbano Labajos, V., Sánchez García, A., Quirante Sánchez, V., Cañada Oya, S., Rodríguez Jiménez, M., Delgado Rufino, B., Queipo de Llano Temboury, A., Barón Romero, M., López Hermoso, M.D., Benavente Casajús, M., Loring Caffarena, T., Chaparro Villar, M.A., and de la Torre Solís, F.

Published
2021
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24. Soluble HLA-G in heart transplantation: their relationship to rejection episodes and immunosuppressive therapy

Author

Luque, Jordi, Torres, María Isabel, Aumente, María Dolores, Marín, J, García-Jurado, G, González, R, Pascual, Domingo, Guerra Pérez, Natalia, López-Rubio, F, Alvarez-López, M. Rocío, Arizón, José, Peña, J., Luque, Jordi, Torres, María Isabel, Aumente, María Dolores, Marín, J, García-Jurado, G, González, R, Pascual, Domingo, Guerra Pérez, Natalia, López-Rubio, F, Alvarez-López, M. Rocío, Arizón, José, and Peña, J.

Abstract

Depto. de Genética, Fisiología y Microbiología, Fac. de Ciencias Biológicas, TRUE, pub

Published
2024

25. “Candidatus Siderophilus nitratireducens”: a putative nap-dependent nitrate-reducing iron oxidizer within the new order Siderophiliales

Author

Corbera Rubio, F. (author), Stouten, G.R. (author), Bruins, Jantinus (author), Dost, Simon F. (author), Merkel, Alexander Y. (author), Müller, S. (author), van Loosdrecht, Mark C.M. (author), van Halem, D. (author), Laureni, M. (author), Corbera Rubio, F. (author), Stouten, G.R. (author), Bruins, Jantinus (author), Dost, Simon F. (author), Merkel, Alexander Y. (author), Müller, S. (author), van Loosdrecht, Mark C.M. (author), van Halem, D. (author), and Laureni, M. (author)

Abstract

Nitrate leaching from agricultural soils is increasingly found in groundwater, a primary source of drinking water worldwide. This nitrate influx can potentially stimulate the biological oxidation of iron in anoxic groundwater reservoirs. Nitrate-dependent iron-oxidizing (NDFO) bacteria have been extensively studied in laboratory settings, yet their ecophysiology in natural environments remains largely unknown. To this end, we established a pilot-scale filter on nitrate-rich groundwater to elucidate the structure and metabolism of nitrate-reducing iron-oxidizing microbiomes under oligotrophic conditions mimicking natural groundwaters. The enriched community stoichiometrically removed iron and nitrate consistently with the NDFO metabolism. Genome-resolved metagenomics revealed the underlying metabolic network between the dominant iron-dependent denitrifying autotrophs and the less abundant organoheterotrophs. The most abundant genome belonged to a new Candidate order, named Siderophiliales. This new species, “Candidatus Siderophilus nitratireducens,” carries genes central genes to iron oxidation (cytochrome c cyc2), carbon fixation (rbc), and for the sole periplasmic nitrate reductase (nap). Using thermodynamics, we demonstrate that iron oxidation coupled to nap based dissimilatory reduction of nitrate to nitrite is energetically favorable under realistic Fe3+/Fe2+ and NO3−/NO2− concentration ratios. Ultimately, by bridging the gap between laboratory investigations and nitrate real-world conditions, this study provides insights into the intricate interplay between nitrate and iron in groundwater ecosystems, and expands our understanding of NDFOs taxonomic diversity and ecological role., BT/Environmental Biotechnology, Sanitary Engineering

Published
2024
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26. Single‐extracellular vesicle (EV) analyses validate the use of L1 Cell Adhesion Molecule (L1CAM) as a reliable biomarker of neuron‐derived EVs.

Author

Nogueras‐Ortiz, Carlos J, Eren, Erden, Yao, Pamela, Calzada, Elizabeth, Dunn, Christopher, Volpert, Olga, Delgado‐Peraza, Francheska, Mustapic, Maja, Lyashkov, Alexey, Rubio, F Javier, Vreones, Michael, Cheng, Lesley, You, Yang, Hill, Andrew F, Ikezu, Tsuneya, Eitan, Erez, Goetzl, Edward J, and Kapogiannis, Dimitrios

Subjects
CELL adhesion molecules, EXTRACELLULAR vesicles, BIOMARKERS
Abstract

Isolation of neuron‐derived extracellular vesicles (NDEVs) with L1 Cell Adhesion Molecule (L1CAM)‐specific antibodies has been widely used to identify blood biomarkers of CNS disorders. However, full methodological validation requires demonstration of L1CAM in individual NDEVs and lower levels or absence of L1CAM in individual EVs from other cells. Here, we used multiple single‐EV techniques to establish the neuronal origin and determine the abundance of L1CAM‐positive EVs in human blood. L1CAM epitopes of the ectodomain are shown to be co‐expressed on single‐EVs with the neuronal proteins β‐III‐tubulin, GAP43, and VAMP2, the levels of which increase in parallel with the enrichment of L1CAM‐positive EVs. Levels of L1CAM‐positive EVs carrying the neuronal proteins VAMP2 and β‐III‐tubulin range from 30% to 63%, in contrast to 0.8%–3.9% of L1CAM‐negative EVs. Plasma fluid‐phase L1CAM does not bind to single‐EVs. Our findings support the use of L1CAM as a target for isolating plasma NDEVs and leveraging their cargo to identify biomarkers reflecting neuronal function. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Bayesian modelling of skewness and kurtosis with two-piece scale and shape distributions

Author

Rubio, F. J. and Steel, M. F. J.

Subjects
Statistics - Methodology
Abstract

We formalise and generalise the definition of the family of univariate double two--piece distributions, obtained by using a density--based transformation of unimodal symmetric continuous distributions with a shape parameter. The resulting distributions contain five interpretable parameters that control the mode, as well as the scale and shape in each direction. Four-parameter subfamilies of this class of distributions that capture different types of asymmetry are discussed. We propose interpretable scale and location-invariant benchmark priors and derive conditions for the propriety of the corresponding posterior distribution. The prior structures used allow for meaningful comparisons through Bayes factors within flexible families of distributions. These distributions are applied to data from finance, internet traffic and medicine, comparing them with appropriate competitors.

Published
2014

31. On the Independence Jeffreys prior for skew--symmetric models with applications

Author

Rubio, F. J. and Liseo, B.

Subjects
Statistics - Methodology
Abstract

We study the Jeffreys prior of the skewness parameter of a general class of scalar skew--symmetric models. It is shown that this prior is symmetric about 0, proper, and with tails $O(\lambda^{-3/2})$ under mild regularity conditions. We also calculate the independence Jeffreys prior for the case with unknown location and scale parameters. Sufficient conditions for the existence of the corresponding posterior distribution are investigated for the case when the sampling model belongs to the family of skew--symmetric scale mixtures of normal distributions. The usefulness of these results is illustrated using the skew--logistic model and two applications with real data.

Published
2013

32. On modelling asymmetric data using two-piece sinh-arcsinh distributions

Author

Rubio, F. J., Ogundimu, E. O., and Hutton, J. L.

Subjects
Statistics - Applications
Abstract

We introduce the univariate two--piece sinh-arcsinh distribution, which contains two shape parameters that separately control skewness and kurtosis. We show that this new model can capture higher levels of asymmetry than the original sinh-arcsinh distribution (Jones and Pewsey, 2009), in terms of some asymmetry measures, while keeping flexibility of the tails and tractability. We illustrate the performance of the proposed model with real data, and compare it to appropriate alternatives. Although we focus on the study of the univariate versions of the proposed distributions, we point out some multivariate extensions., Comment: Previous title: Robust modelling using two-piece sinh-arcsinh distributions

Published
2013

33. A Simple Approach to Maximum Intractable Likelihood Estimation

Author

Rubio, F. J. and Johansen, Adam M.

Subjects
Statistics - Methodology
Abstract

Approximate Bayesian Computation (ABC) can be viewed as an analytic approximation of an intractable likelihood coupled with an elementary simulation step. Such a view, combined with a suitable instrumental prior distribution permits maximum-likelihood (or maximum-a-posteriori) inference to be conducted, approximately, using essentially the same techniques. An elementary approach to this problem which simply obtains a nonparametric approximation of the likelihood surface which is then used as a smooth proxy for the likelihood in a subsequent maximisation step is developed here and the convergence of this class of algorithms is characterised theoretically. The use of non-sufficient summary statistics in this context is considered. Applying the proposed method to four problems demonstrates good performance. The proposed approach provides an alternative for approximating the maximum likelihood estimator (MLE) in complex scenarios.

Published
2013

48. Penumbral imaging and functional outcome in patients with anterior circulation ischaemic stroke treated with endovascular thrombectomy versus medical therapy: a meta-analysis of individual patient-level data

Author

Berkhemer, Olvert A, Fransen, Puck SS, Beumer, Debbie, van den Berg, Lucie A, Lingsma, Hester F, Yoo, Albert J, Schonewille, Wouter J, Vos, Jan Albert, Nederkoorn, Paul J, Wermer, Marieke JH, van Walderveen, Marianne AA, Staals, Julie, Hofmeijer, Jeannette, van Oostayen, Jacques A., Lycklama à Nijeholt, Geert J., Boiten, Jelis, Brouwer, Patrick A., Emmer, Bart J., de Bruijn, Sebastiaan F., van Dijk, Lukas C., Kappelle, Jaap, Lo, Rob H, van Dijk, Ewoud J., de Vries, Joost, de Kort, Paul L.M., van Rooij, Willem Jan J., van den Berg, Jan S.P., van Hasselt, Boudewijn A.A.M., Aerden, Leo A.M., Dallinga, René J., Visser, Marieke C., Bot, Joseph C.J., Vroomen, Patrick C., Eshghi, Omid, Schreuder, Tobien H.C.M.L., Heijboer, Roel J.J., Keizer, Koos, Tielbeek, Alexander V., den Hertog, Heleen M., Gerrits, Dick G., van den Berg-Vos, Renske M., Karas, Giorgos B., Steyerberg, Ewout W., Flach, Zwenneke, Marquering, Henk A., Sprengers, Marieke E.S., Jenniskens, Sjoerd F.M., Beenen, Ludo F.M., van den Berg, René, Koudstaal, Peter J., van Zwam, Wim H., Roos, Yvo B.W.E.M., van der Lugt, Aad, van Oostenbrugge, Robert J., Majoie, Charles B.L.M., Dippel, Diederik W.J., Brown, Martin M., Liebig, Thomas, Stijnen, Theo, Andersson, Tommy, Mattle, Heinrich, Wahlgren, Nils, van der Heijden, Esther, Ghannouti, Naziha, Fleitour, Nadine, Hooijenga, Imke, Puppels, Corina, Pellikaan, Wilma, Geerling, Annet, Lindl-Velema, Annemieke, van Vemde, Gina, de Ridder, Ans, Greebe, Paut, de Bont-Stikkelbroeck, José, de Meris, Joke, Janssen, Kirsten, Struijk, Willy, Licher, Silvan, Boodt, Nikki, Ros, Adriaan, Venema, Esmee, Slokkers, Ilse, Ganpat, Raymie-Jayce, Mulder, Maxim, Saiedie, Nawid, Heshmatollah, Alis, Schipperen, Stefanie, Vinken, Stefan, van Boxtel, Tiemen, Koets, Jeroen, Boers, Merel, Santos, Emilie, Borst, Jordi, Jansen, Ivo, Kappelhof, Manon, Lucas, Marit, Geuskens, Ralph, Barros, Renan Sales, Dobbe, Roeland, Csizmadia, Marloes, Hill, MD, Goyal, M, Demchuk, AM, Menon, BK, Eesa, M, Ryckborst, KJ, Wright, MR, Kamal, NR, Andersen, L, Randhawa, PA, Stewart, T, Patil, S, Minhas, P, Almekhlafi, M, Mishra, S, Clement, F, Sajobi, T, Shuaib, A, Montanera, WJ, Roy, D, Silver, FL, Jovin, TG, Frei, DF, Sapkota, B, Rempel, JL, Thornton, J, Williams, D, Tampieri, D, Poppe, AY, Dowlatshahi, D, Wong, JH, Mitha, AP, Subramaniam, S, Hull, G, Lowerison, MW, Salluzzi, M, Maxwell, M, Lacusta, S, Drupals, E, Armitage, K, Barber, PA, Smith, EE, Morrish, WF, Coutts, SB, Derdeyn, C, Demaerschalk, B, Yavagal, D, Martin, R, Brant, R, Yu, Y, Willinsky, RA, Weill, A, Kenney, C, Aram, H, Stys, PK, Watson, TW, Klein, G, Pearson, D, Couillard, P, Trivedi, A, Singh, D, Klourfeld, E, Imoukhuede, O, Nikneshan, D, Blayney, S, Reddy, R, Choi, P, Horton, M, Musuka, T, Dubuc, V, Field, TS, Desai, J, Adatia, S, Alseraya, A, Nambiar, V, van Dijk, R, Newcommon, NJ, Schwindt, B, Butcher, KS, Jeerakathil, T, Buck, B, Khan, K, Naik, SS, Emery, DJ, Owen, RJ, Kotylak, TB, Ashforth, RA, Yeo, TA, McNally, D, Siddiqui, M, Saqqur, M, Hussain, D, Kalashyan, H, Manosalva, A, Kate, M, Gioia, L, Hasan, S, Mohammad, A, Muratoglu, M, Cullen, A, Brennan, P, O'Hare, A, Looby, S, Hyland, D, Duff, S, McCusker, M, Hallinan, B, Lee, S, McCormack, J, Moore, A, O'Connor, M, Donegan, C, Brewer, L, Martin, A, Murphy, S, O'Rourke, K, Smyth, S, Kelly, P, Lynch, T, Daly, T, O'Brien, P, O'Driscoll, A, Martin, M, Collins, R, Coughlan, T, McCabe, D, O'Neill, D, Mulroy, M, Lynch, O, Walsh, T, O'Donnell, M, Galvin, T, Harbison, J, McElwaine, P, Mulpeter, K, McLoughlin, C, Reardon, M, Harkin, E, Dolan, E, Watts, M, Cunningham, N, Fallon, C, Gallagher, S, Cotter, P, Crowe, M, Doyle, R, Noone, I, Lapierre, M, Coté, VA, Lanthier, S, Odier, C, Durocher, A, Raymond, J, Daneault, N, Deschaintre, Y, Jankowitz, B, Baxendell, L, Massaro, L, Jackson-Graves, C, Decesare, S, Porter, P, Armbruster, K, Adams, A, Billigan, J, Oakley, J, Ducruet, A, Jadhav, A, Giurgiutiu, D-V, Aghaebrahim, A, Reddy, V, Hammer, M, Starr, M, Totoraitis, V, Wechsler, L, Streib, S, Rangaraju, S, Campbell, D, Rocha, M, Gulati, D, Krings, T, Kalman, L, Cayley, A, Williams, J, Wiegner, R, Casaubon, LK, Jaigobin, C, del Campo, JM, Elamin, E, Schaafsma, JD, Agid, R, Farb, R, ter Brugge, K, Sapkoda, BL, Baxter, BW, Barton, K, Knox, A, Porter, A, Sirelkhatim, A, Devlin, T, Dellinger, C, Pitiyanuvath, N, Patterson, J, Nichols, J, Quarfordt, S, Calvert, J, Hawk, H, Fanale, C, Bitner, A, Novak, A, Huddle, D, Bellon, R, Loy, D, Wagner, J, Chang, I, Lampe, E, Spencer, B, Pratt, R, Bartt, R, Shine, S, Dooley, G, Nguyen, T, Whaley, M, McCarthy, K, Teitelbaum, J, Poon, W, Campbell, N, Cortes, M, Lum, C, Shamloul, R, Robert, S, Stotts, G, Shamy, M, Steffenhagen, N, Blacquiere, D, Hogan, M, AlHazzaa, M, Basir, G, Lesiuk, H, Iancu, D, Santos, M, Choe, H, Weisman, DC, Jonczak, K, Blue-Schaller, A, Shah, Q, MacKenzie, L, Klein, B, Kulandaivel, K, Kozak, O, Gzesh, DJ, Harris, LJ, Khoury, JS, Mandzia, J, Pelz, D, Crann, S, Fleming, L, Hesser, K, Beauchamp, B, 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2019
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50. A note on the infinite divisibility of a class of transformations of normal variables

Author

Murillo-Salas, A. and Rubio, F. J.

Subjects
Mathematics - Statistics Theory, 60E05, 60E07
Abstract

This note examines the infinite divisibility of density-based transformations of normal random variables. We characterize a class of density-based transformations of normal variables which produces non-infinitely divisible distributions. We relate our result with some known skewing mechanisms.

Published
2011

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