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1. Design and Construction of the MicroBooNE Detector

Author

MicroBooNE Collaboration, Acciarri, R., Adams, C., An, R., Aparicio, A., Aponte, S., Asaadi, J., Auger, M., Ayoub, N., Bagby, L., Baller, B., Barger, R., Barr, G., Bass, M., Bay, F., Biery, K., Bishai, M., Blake, A., Bocean, V., Boehnlein, D., Bogert, V. D., Bolton, T., Bugel, L., Callahan, C., Camilleri, L., Caratelli, D., Carls, B., Fernandez, R. Castillo, Cavanna, F., Chappa, S., Chen, H., Chen, K., Chi, C. Y., Chiu, C. S., Church, E., Cianci, D., Collin, G. H., Conrad, J. M., Convery, M., Cornele, J., Cowan, P., Crespo-Anadon, J. I., Crutcher, G., Darve, C., Davis, R., Del Tutto, M., Devitt, D., Duffin, S., Dytman, S., Eberly, B., Ereditato, A., Erickson, D., Sanchez, L. Escudero, Esquivel, J., Farooq, S., Farrell, J., Featherston, D., Fleming, B. T., Foreman, W., Furmanski, A. P., Genty, V., Geynisman, M., Goeldi, D., Goff, B., Gollapinni, S., Graf, N., Gramellini, E., Green, J., Greene, A., Greenlee, H., Griffin, T., Grosso, R., Guenette, R., Hackenburg, A., Haenni, R., Hamilton, P., Healey, P., Hen, O., Henderson, E., Hewes, V, Hill, C., Hill, K., Himes, L., Ho, J., Horton-Smith, G., Huffman, D., Ignarra, C. M., James, C., James, E., de Vries, J. Jan, Jaskierny, W., Jen, C. M., Jiang, L., Johnson, B., Johnson, M., Johnson, R. A., Jones, B. J. P., Joshi, J., Jostlein, H., Kaleko, D., Kalousis, L. N., Karagiorgi, G., Katori, T., Kellogg, P., Ketchum, W., Kilmer, J., King, B., Kirby, B., Kirby, M., Klein, E., Kobilarcik, T., Kreslo, I., Krull, R., Kubinski, R., Lange, G., Lanni, F., Lathrop, A., Laube, A., Lee, W. M., Li, Y., Lissauer, D., Lister, A., Littlejohn, B. R., Lockwitz, S., Lorca, D., Louis, W. C., Lukhanin, G., Luethi, M., Lundberg, B., Luo, X., Mahler, G., Majoros, I., Makowiecki, D., Marchionni, A., Mariani, C., Markley, D., Marshall, J., Caicedo, D. A. Martinez, McDonald, K. T., McKee, D., McLean, A., Mead, J., Meddage, V., Miceli, T., Mills, G. B., Miner, W., Moon, J., Mooney, M., Moore, C. D., Moss, Z., Mousseau, J., Murrells, R., Naples, D., Nienaber, P., Norris, B., Norton, N., Nowak, J., OBoyle, M., Olszanowski, T., Palamara, O., Paolone, V., Papavassiliou, V., Pate, S. F., Pavlovic, Z., Pelkey, R., Phipps, M., Pordes, S., Porzio, D., Pulliam, G., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Rameika, R. A, Rebel, B., Rechenmacher, R., Rescia, S., Rochester, L., von Rohr, C. Rudolf, Ruga, A., Russell, B., Sanders, R., Sands III, W. R., Sarychev, M., Schmitz, D. W., Schukraft, A., Scott, R., Seligman, W., Shaevitz, M. H., Shoun, M., Sinclair, J., Sippach, W., Smidt, T., Smith, A., Snider, E. L., Soderberg, M., Solano-Gonzalez, M., Soldner-Rembold, S., Soleti, S. R., Sondericker, J., Spentzouris, P., Spitz, J., John, J. St., Strauss, T., Sutton, K., Szelc, A. M., Taheri, K., Tagg, N., Tatum, K., Teng, J., Terao, K., Thomson, M., Thorn, C., Tillman, J., Toups, M., Tsai, Y. T., Tufanli, S., Usher, T., Utes, M., Van de Water, R. G., Vendetta, C., Vergani, S., Voirin, E., Voirin, J., Viren, B., Watkins, P., Weber, M., Wester, T., Weston, J., Wickremasinghe, D. A., Wolbers, S., Wongjirad, T., Woodruff, K., Wu, K. C., Yang, T., Yu, B., Zeller, G. P., Zennamo, J., Zhang, C., and Zuckerbrot, M.

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

This paper describes the design and construction of the MicroBooNE liquid argon time projection chamber and associated systems. MicroBooNE is the first phase of the Short Baseline Neutrino program, located at Fermilab, and will utilize the capabilities of liquid argon detectors to examine a rich assortment of physics topics. In this document details of design specifications, assembly procedures, and acceptance tests are reported.

Published
2016
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2. Design and construction of the MicroBooNE detector

Author

Acciarri, R, Adams, C, An, R, Aparicio, A, Aponte, S, Asaadi, J, Auger, M, Ayoub, N, Bagby, L, Baller, B, Barger, R, Barr, G, Bass, M, Bay, F, Biery, K, Bishai, M, Blake, A, Bocean, V, Boehnlein, D, Bogert, VD, Bolton, T, Bugel, L, Callahan, C, Camilleri, L, Caratelli, D, Carls, B, Castillo Fernandez, R, Cavanna, F, Chappa, S, Chen, H, Chen, K, Chi, CY, Chiu, CS, Church, E, Cianci, D, Collin, GH, Conrad, JM, Convery, M, Cornele, J, Cowan, P, Crespo-Anadon, JI, Crutcher, G, Darve, C, Davis, R, Del Tutto, M, Devitt, D, Duffin, S, Dytman, S, Eberly, B, Ereditato, A, Erickson, D, Escudero Sanchez, L, Esquivel, J, Farooq, S, Farrell, J, Featherston, D, Fleming, BT, Foreman, W, Furmanski, AP, Genty, V, Geynisman, M, Goeldi, D, Goff, B, Gollapinni, S, Graf, N, Gramellini, E, Green, J, Greene, A, Greenlee, H, Griffin, T, Grosso, R, Guenette, R, Hackenburg, A, Haenni, R, Hamilton, P, Healey, P, Hen, O, Henderson, E, Hewes, J, Hill, C, Hill, K, Himes, L, Ho, J, Horton-Smith, G, Huffman, D, Ignarra, CM, James, C, James, E, Jan De Vries, J, Jaskierny, W, Jen, CM, Jiang, L, Johnson, B, Johnson, M, Johnson, RA, Jones, BJP, Joshi, J, Jostlein, H, Kaleko, D, and Kalousis, LN

Subjects
Neutrino detectors, Noble liquid detectors, Time projection chambers, physics.ins-det, hep-ex, Nuclear & Particles Physics, Other Physical Sciences, Physical Sciences, Engineering
Abstract

This paper describes the design and construction of the MicroBooNE liquid argon time projection chamber and associated systems. MicroBooNE is the first phase of the Short Baseline Neutrino program, located at Fermilab, and will utilize the capabilities of liquid argon detectors to examine a rich assortment of physics topics. In this document details of design specifications, assembly procedures, and acceptance tests are reported.

Published
2017

6. A synthesis of carbon dioxide emissions from fossil-fuel combustion

Author

Andres, R J, Boden, T A, Breon, F -M, Ciais, P, Davis, S, Erickson, D, Gregg, J S, Jacobson, A, Marland, G, Miller, J, Oda, T, Oliver, J G. J., Raupach, M R, Rayner, P, and Treanton, K

Subjects
input-output-analysis, co2 emissions, dioxide emissions, land-use, consumption, models, growth, footprint, impact, flows
Abstract

This synthesis discusses the emissions of carbon dioxide from fossil-fuel combustion and cement production. While much is known about these emissions, there is still much that is unknown about the details surrounding these emissions. This synthesis explores our knowledge of these emissions in terms of why there is concern about them; how they are calculated; the major global efforts on inventorying them; their global, regional, and national totals at different spatial and temporal scales; how they are distributed on global grids (i.e., maps); how they are transported in models; and the uncertainties associated with these different aspects of the emissions. The magnitude of emissions from the combustion of fossil fuels has been almost continuously increasing with time since fossil fuels were first used by humans. Despite events in some nations specifically designed to reduce emissions, or which have had emissions reduction as a byproduct of other events, global total emissions continue their general increase with time. Global total fossil-fuel carbon dioxide emissions are known to within 10 % uncertainty (95 % confidence interval). Uncertainty on individual national total fossil-fuel carbon dioxide emissions range from a few percent to more than 50 %. This manuscript concludes that carbon dioxide emissions from fossil-fuel combustion continue to increase with time and that while much is known about the overall characteristics of these emissions, much is still to be learned about the detailed characteristics of these emissions.

Published
2012

13. Optimization Tools Based on Metaheuristics for Performance Enhancement in a Gaussian Adaptive PID Controller

Author

Erickson D. P. Puchta, Mauricio dos Santos Kaster, and Hugo Siqueira

Subjects
Steady state, Computer science, Algebraic solution, Gaussian, 020208 electrical & electronic engineering, MathematicsofComputing_NUMERICALANALYSIS, PID controller, Particle swarm optimization, 02 engineering and technology, Function (mathematics), Computer Science Applications, Human-Computer Interaction, symbols.namesake, Control and Systems Engineering, Control theory, Genetic algorithm, 0202 electrical engineering, electronic engineering, information engineering, Gaussian function, symbols, 020201 artificial intelligence & image processing, Electrical and Electronic Engineering, Metaheuristic, Software, Information Systems
Abstract

This paper presents the proposal of using two bio-inspired metaheuristics-genetic algorithms (GAs) and particle swarm optimization (PSO)-to adjust the free coefficients of a Gaussian adaptive proportional-integral-derivative (GAPID) controller. When a specific adaptation rule is imposed to a conventional proportional-integral-derivative (PID) controller, either by means of a hyperbolic tangent function or a Gaussian function, the solution is left exposed to the function restrictions/impositions. Finding the correct proportionality between the parameters is an arduous task, which often does not have an algebraic solution. Each Gaussian function of each control action has three parameters, resulting in a total of nine parameters to be defined. This paper proposes making the parameters linked to the linear PID gains, in order to keep the GAPID the same design requirements as for the PID. Then, two metaheuristics (GA and PSO) were employed in order to find the best parameters for the GAPID. A comparison between these two strategies is presented. In this investigation, a well-known plant of a step-down dc-dc converter is used, which represents a typical second-order system, where the absence of significant nonlinearities helps focus the study on the control behavior. Simulation and experimentation were performed, and both have been successful, but PSO stood out due to its simplicity and low-computational effort.

Published
2020

14. Unorganized Machines to Estimate the Number of Hospital Admissions Due to Respiratory Diseases Caused by PM10 Concentration

Author

Yara de Souza Tadano, Cássia Maria Lie Ugaya, Eduardo Tadeu Bacalhau, Luciana Casacio, Thiago Antonini Alves, Hugo Siqueira, Erickson D. P. Puchta, and Thomas Siqueira Pereira

Subjects
Estimation, Atmospheric Science, Artificial neural network, Computer science, Generalization, echo state network, health risks, Environmental Science (miscellaneous), Volterra filters, neural networks, extreme learning machine, PM10, Meteorology. Climatology, Statistics, Echo state network, QC851-999, Extreme learning machine
Abstract

The particulate matter PM10 concentrations have been impacting hospital admissions due to respiratory diseases. The air pollution studies seek to understand how this pollutant affects the health system. Since prediction involves several variables, any disparity causes a disturbance in the overall system, increasing the difficulty of the models’ development. Due to the complex nonlinear behavior of the problem and their influencing factors, Artificial Neural Networks are attractive approaches for solving estimations problems. This paper explores two neural network architectures denoted unorganized machines: the echo state networks and the extreme learning machines. Beyond the standard forms, models variations are also proposed: the regularization parameter (RP) to increase the generalization capability, and the Volterra filter to explore nonlinear patterns of the hidden layers. To evaluate the proposed models’ performance for the hospital admissions estimation by respiratory diseases, three cities of São Paulo state, Brazil: Cubatão, Campinas and São Paulo, are investigated. Numerical results show the standard models’ superior performance for most scenarios. Nevertheless, considering divergent intensity in hospital admissions, the RP models present the best results in terms of data dispersion. Finally, an overall analysis highlights the models’ efficiency to assist the hospital admissions management during high air pollution episodes.

Published
2021
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18. Metaheuristic-based optimization applied to GAPID controller

Author

Priscilla Bassetto, Erickson D. P. Puchta, Mauricio dos Santos Kaster, Hugo Siqueira, Lucas H. Biuk, and Marco A. Itaborahy Filho

Subjects
Mathematical optimization, symbols.namesake, Optimization algorithm, Control theory, Computer science, Approximation error, Gaussian, symbols, Benchmark (computing), PID controller, Particle swarm optimization, Metaheuristic
Abstract

This work presents a comparative study of the performance of two bioinspired metaheuristics - Whale Optimization Algorithm (WOA) and Artificial Bee Colony (ABC) to find the parameters of an Adaptive Gaussian PID controller (GAPID), comparing the results with a known benchmark, the Particle Swarm Optimization (PSO). The GAPID was tested with a Buck step-down converter, and the overall system exhibits multimodal characteristics where different solutions that can achieve similar performances. The GAPID gains are defined to be relative to the PID gains in order to inherit the same design requiremets. This results a total of six parameters to be defined using the optimization algorithms. To determine the quality of the results obtained by the optimizers, evaluation metrics based on the Integral of Absolute Error (IAE) were used to determine the fitness of the solutions found. The computational results were obtained through simulations using julia® (V1.5.3), which follows that all the methods used are capable of solving the proposed problem. In the overall evaluation, the PSO obtained the best results and comparing the two bioinspired metaheuristics, the WOA obtained the best result.

Published
2021

22. Previsão do Preço do Açúcar: Uma Análise Comparativa entre Modelo Linear e Redes Neurais

Author

Yslene Rocha Kachba, Mauren Louise Sguario Coelho de Andrade, Fernanda Cristina Corrêa, Lucas H. Biuk, Priscila Bassetto, Erickson D. P. Puchta, Tathiana Mikamura Barchi, Hugo Siqueira, and Lucas F. P. Costa

Abstract

O Brasil desempenha um papel importante na produção mundial de subprodutos gerados a partir da cana-de-açúcar, principalmente açúcar e etanol. O açúcar ́e uma das mais antigas commodities comercializadas pelo país. Realizar uma previsão adequada dos preços de tal produto tem impacto direto no sistema econômico, pois auxilia os gestores nos planejamentos estratégicos e minimiza os riscos futuros com uma avaliação mais precisa de suas tendências de mercado. Assim, o presente estudo tem como objetivo realizar a previsão do preço do açúcar, usando os modelos autorregressivo (AR), Perceptron de Multiplas Camadas (MLP), Máquinas de Aprendizado Extremo(ELM) e Redes Neurais com Estados de Eco (ESN). Os resultados experimentais evidenciaram que os modelos que apresentaram melhores desempenhos para um passo a frente nas bases CEPEA e NY No. 11, foram ELM e MLP, respectivamente.

Published
2021

23. Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Author

Marco A. Itaborahy Filho, Attilio Converti, Lucas H. Biuk, Mauricio dos Santos Kaster, Erickson D. P. Puchta, Priscilla Bassetto, and Hugo Siqueira

Subjects
Technology, Control and Optimization, Computer science, 020209 energy, Gaussian, Computer Science::Neural and Evolutionary Computation, GAPID controller, PSO, ABC, WOA, optimization, MathematicsofComputing_NUMERICALANALYSIS, Energy Engineering and Power Technology, PID controller, 02 engineering and technology, symbols.namesake, Control theory, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Engineering (miscellaneous), Metaheuristic, Renewable Energy, Sustainability and the Environment, Algebraic solution, WOA, GAPID controller, PSO, Swarm behaviour, Particle swarm optimization, Nonlinear system, symbols, 020201 artificial intelligence & image processing, Algorithm, ABC, optimization, Energy (miscellaneous)
Abstract

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

Published
2021

28. Constantino revisited

Author

Erickson D. Calata

Subjects
media_common.quotation_subject, Political science, Ancient history, Diplomacy, media_common
Published
2020

41. Implementation and dissemination of an African American popular opinion model to improve lupus awareness: an academic–community partnership

Author

Phillip, C R, primary, Mancera-Cuevas, K, additional, Leatherwood, C, additional, Chmiel, J S, additional, Erickson, D L, additional, Freeman, E, additional, Granville, G, additional, Dollear, M, additional, Walker, K, additional, McNeil, R, additional, Correia, C, additional, Canessa, P, additional, Ramsey-Goldman, R, additional, and Feldman, C H, additional

Published
2019
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47. Identification of symbol digit modality test score extremes in Huntington's disease

Author

Braisch, U, Muche, R, Rothenbacher, D, Landwehrmeyer, GB, Long, JD, Bentivoglio, AR, Biunno, I, Bonelli, RM, Dunnett, SB, Illmann, T, Levey, J, Ramos-Arroyo, M, Nielsen, JE, Paivarinta, M, Sebastian, AR, Tabrizi, SJ, Vandenberghe, W, Uhrova, T, Come, A, Garde, MB, Betz, S, Capodarca, S, Wildson, SC, da Silva, V, Di Renzo, M, Finisterra, M, Genoves, C, Gilling, M, Handley, OJ, Hvalstedt, C, Koppers, K, Lamanna, C, Laura, M, Descals, AM, Monza, D, Mutze, L, Oehmen, M, Padieu, H, Paterski, L, Koivisto, SP, Rindal, B, Roren, N, Sasinkova, P, Seliverstov, Y, Timewell, E, Cubillo, PT, van Walsem, MR, Witjes-Ane, MN, Yudina, E, Zielonka, E, Zinzi, P, Braunwarth, EM, Brugger, F, Buratti, L, Hametner, EM, Hepperger, C, Holas, C, Hotter, A, Hussl, A, Larcher, B, Mahlknecht, P, Muller, C, Pinter, B, Poewe, W, Seppi, K, Sprenger, F, Wenning, G, Dupuis, M, Minet, C, Ribai, P, Van Paemel, D, Verellen-Dumoulin, C, Klempir, J, Majerova, V, Roth, J, Babiloni, B, Debruxelles, S, Duche, C, Goizet, C, Jameau, L, Lafoucriere, D, Spampinato, U, Bachoud-Levi, AC, Boisse, MF, de Langavant, LC, Lemoine, L, Morgado, G, Youssov, K, Annic, A, Barthelemy, R, De Bruycker, C, Cabaret, M, Carette, AS, Carriere, N, Decorte, E, Defebvre, L, Delliaux, M, Delval, A, Depelchin, A, Destee, A, Dewulf-Pasz, N, Dondaine, T, Dugauquier, F, Dujardin, K, Lemaire, MH, Manouvrier, S, Peter, M, Plomhause, L, Sablonniere, B, Simonin, C, Tard, C, Thibault-Tanchou, S, Vuillaume, I, Bellonet, M, Benoit, A, Blin, S, Courtin, F, Duru, C, Fasquel, V, Godefroy, O, Krystkowiak, P, Mantaux, B, Roussel, M, Tir, M, Schuler, B, Wannepain, S, Azulay, JP, Chabot, C, Delfini, M, Eusebio, A, Fluchere, F, Grosjean, H, Mundler, L, Nowak, M, Bioux, S, Bliaux, E, Girard, C, Guyant-Marechal, L, Hannequin, D, Hannier, V, Jourdain, S, Maltete, D, Pouliquen, D, Blondeau, L, Calvas, F, Cheriet, S, Delabaere, H, Demonet, JF, Pariente, J, Pierre, M, Beuth, M, Gelderblom, H, Priller, J, Pruss, H, Spruth, E, Thiel, S, Ellrichmannberlin, G, Herrmann, L, Hoffmann, R, Kaminski, B, Saft, C, Bosredon, C, Hunger, U, Lohle, M, Maass, A, Ossig, C, Schmidt, S, Storch, A, Wolz, A, Wolz, M, Kohl, Z, Kozay, C, Ullah, J, Winkler, J, Bergmann, U, Boringer, R, Capetian, P, Kammel, G, Lambeck, J, Meier, S, Rijntjes, M, Zucker, B, Boelmans, K, Ganos, C, Goerendt, I, Heinicke, W, Hidding, U, Munchau, A, Schmalfeld, J, Stubbe, L, Zittel, S, Diercks, G, Dressler, D, Francis, F, Gayde-Stephan, S, Gorzolla, H, Kramer, B, Minschke, R, Schrader, C, Tacik, P, Longinus, B, Lusebrink, A, Muhlau, M, Peinemann, A, Stadtler, M, Weindl, A, Winkelmann, J, Ziegler, C, Bechtel, N, Beckmann, H, Bohlen, S, Gopfert, N, Holzner, E, Lange, H, Reilmann, R, Rohm, S, Rumpf, S, Sass, C, Schepers, S, Weber, N, Barth, K, Buck, A, Connemann, J, Ecker, D, Geitner, C, Held, C, Kesse, A, Landwehrmeyer, B, Lezius, F, Lewerenz, J, Nepper, S, Niess, A, Orth, M, Schneider, A, Schwenk, D, Sussmuth, S, Trautmann, S, Weydt, P, Klebe, S, Musacchio, T, Leypold, C, Noth, K, Cormio, C, de Tommaso, M, Franco, G, Sciruicchio, V, Serpino, C, Calandra-Buonaura, G, Capellari, S, Cortelli, P, Gallassi, R, Poda, R, Sambati, L, Scaglione, C, Maserati, MS, Agosti, C, Barlati, S, Compostella, S, Marchina, E, Padovani, A, Bertini, E, Ghelli, E, Ginestroni, A, Mechi, C, Paganini, M, Piacentini, S, Pradella, S, Romoli, AM, Sorbi, S, Abbruzzese, G, di Poggio, MB, Ferrandes, G, Mandich, P, Marchese, R, Tamburini, T, Baake, V, van den Bogaard, SJA, Bos, R, Dumas, EM, t'Hart, EP, Kampstra, A, Roos, RAC, Schoonderbeek, A, Aaserud, O, Bjorgo, K, Borgeod, N, Dramstad, E, Fannemel, M, Frich, JC, Gorvell, PF, Heiberg, A, Lorentzen, E, Retterstol, L, Rosby, O, Sikiric, A, Stokke, B, van Walsem, M, Wehus, R, Bjornevoll, I, Sando, SB, Haug, MG, Storseth, HH, Arntsen, V, Dziadkiewicz, A, Konkel, A, Narozanska, E, Robowski, P, Sitek, E, Slawek, J, Soltan, W, Szinwelski, M, Arkuszewski, M, Blaszczyk, M, Boczarska-Jedynak, M, Ciach-Wysocka, E, Gorzkowska, A, Nska-Myga, BJ, Kaczmarczyk, A, Klodowska-Duda, G, Opala, G, Stompel, D, Banaszkiewicz, K, Bocwinska, D, Bojakowska-Jaremek, K, Dec, M, Grabska, N, Krawczyk, GM, Kubowicz, E, Malec-Litwinowicz, M, Rudzinska, M, Stenwak, A, Szczudlik, A, Szczygiel, E, Wojcik, M, Wasielewska, A, Bryl, JAA, Ciesielska, A, Klimberg, A, Marcinkowski, J, Samara, H, Sempolowicz, J, Sniewski, BW, Zielonka, D, Gogol, A, Janik, P, Jamrozik, Z, Kaminska, A, Kwiecinski, H, Antczak, J, Jachinska, K, Krysa, W, Rakowicz, M, Richter, P, Rola, R, Ryglewicz, D, Sienkiewicz-Jarosz, H, Stepniak, I, Sulek, A, Witkowski, G, Zaremba, J, Zdzienicka, E, Ziora-Jakutowicz, K, Januario, C, Julio, F, Guedes, LC, Coelho, M, Finisterra, AM, Ferreira, JJ, Mestre, T, Mendes, T, Rosa, MM, Valadas, A, Kopishinskaya, S, Korotysh, M, Herrera, CD, Moreno, PG, Bas, J, Busquets, N, Calopa, M, Classen, SJ, Dedicha, NR, Buongiorno, MT, Maria, ADS, Munoz, E, Santacruz, P, Barbera, MA, Pardo, SA, Guia, DB, Calzado, N, Hernanz, LC, Diaz-Zorita, JPT, Catena, JL, Ferrer, PQ, Carruesco, GT, Robert, MF, Viladrich, CM, Roca, E, Idiago, JMR, Riballo, AV, Campolongo, A, de Bobadilla, RF, Bojarsky, JK, Martinez-Horta, S, Pagonabarraga, J, Perez, JP, Ribosa, R, Villa, C, Gil, MAA, Corrales, KB, Esteban, JCG, Gonzalez, A, Merino, BT, Cubo, E, Polo, CG, Mariscal, N, Romero, SG, Arbelo, JM, de Molina, RM, Martin, I, Perianez, JM, Udaeta, B, Alonso-Frech, F, Frades, B, Villanueva, MA, Sevilla, MAZ, Frech, FA, Fenollar, MD, Garcia, RGR, Villanueva, C, Bascunana, M, Ventura, MF, Ribas, GG, de Yebenes, JG, Moreno, JLLS, Barral, VM, Ruiz, PJG, Garcia, A, Lopez, RG, Barcenas, AH, Martinez-Descals, A, Martin, VP, Martinez, NR, Artiga, MJS, Sanchez, V, Pueyo, A, Gonzalez, S, Guisasola, LM, Ribacoba, MPPR, Salvador, C, Lozano, PS, Caldentey, JG, Ramirez, IL, Arques, PN, Lopera, MR, Pastor, BV, Gaston, I, Garcia-Amigot, F, Martinez-Jaurrieta, MD, Ramos-Arroyo, MA, Carrillo, F, Redondo, MTC, Mir, P, Gonzalez, LV, Moreno, JMG, Lucena, CM, Pena, JC, Redondo, L, Sanchez, VS, Fernandez, CM, Mata, MP, Lemos, MDR, Bosca, M, Burguera, JA, Vilaplana, FCBCP, Solis, P, Figuerola, BJ, Palanca, PM, Berglund, P, Constantinescu, R, Fredlund, G, Hosterey-Ugander, U, Linnsand, P, Neleborn-Lingefjard, L, Wahlstrom, J, Palhagen, S, Svenningsson, P, Paucar, M, Wallden, T, Ekwall, C, Goller, ML, Sundblom, J, Stebler, Y, Kaelin, A, Romero, I, Schupbach, M, Zaugg, SW, Jung, H, Petersen, J, Auer, M, Mihaylova, V, Vernon, N, Akhtar, S, Crooks, J, Curtis, A, de Souza, J, Piedad, J, Rickards, H, Wright, J, Pallett, A, Coulthard, E, Gethin, L, Hayward, B, Sieradzan, K, Wright, A, Busse, M, Butcher, C, Dunnett, S, Clenaghan, C, Hunt, S, Jones, L, Jones, U, Khalil, H, Minster, S, Owen, M, Price, K, Townhill, J, Rosser, A, Edwards, M, Ho, C, McGill, M, Porteous, M, Pearson, P, Harrower, T, Irvine, S, Brockie, P, Foster, J, Johns, N, McKenzie, S, Rothery, J, Thomas, G, Yates, S, Deith, C, Ireland, J, Ritchie, S, Andrew, A, Frost, J, Noad, R, Cosgrove, J, Gallantree, D, Hamer, S, Hobson, E, Jamieson, S, Kraus, A, Longthorpe, M, Markova, I, Musgrave, H, Peacy, C, Raman, A, Rowett, L, Toscano, J, Wild, S, Yardumian, P, Clayton, C, Dipple, H, Freire-Patino, D, Hallam, C, Middleton, J, Alusi, S, Davies, R, Foy, K, Gerrans, E, Leggett, H, Pate, L, Anjum, U, Coebergh, J, Eddy, C, McEntagart, M, Patton, M, Peterson, M, Rose, S, Andrews, T, Brown, S, Bruno, S, Doherty, K, Golding, C, Haider, S, Hensman, D, Lahiri, N, Lewis, M, Novak, M, Patel, A, Robertson, N, Rosser, E, Tabrizi, S, Taylor, R, Warner, T, Wild, E, Arran, N, Bek, J, Callaghan, J, Craufurd, D, Fullam, R, Howard, L, Huson, S, Johnson, L, Jones, M, Krishnamoorthy, A, Murphy, H, Oughton, E, Partington-Jones, L, Rogers, D, Sollom, A, Snowden, J, Stopford, C, Thompson, J, Tinkler, P, Trender-Gerhard, I, Verstraelen, N, Westmoreland, L, Cass, G, Davidson, L, Davison, J, Fullerton, N, Holmes, K, Komati, S, McDonnell, S, Mohammed, Z, Morgan, K, Savage, L, Singh, B, Wood, J, Chu, E, Knight, C, O'Neill, M, Das Purkayastha, D, Nemeth, AH, Siuda, G, Valentine, R, Dixon, K, Armstrong, R, Harrison, D, Hughes, M, Large, S, Donovan, JO, Palmer, A, Parkinson, A, Soltysiak, B, Timings, L, Williams, J, Burn, J, Weekes, R, Craven, J, Bailey, W, Coleman, C, Haig-Brown, D, Simpson, S, Hare, M, Majeed, T, Bandmann, O, Bradbury, A, Fairtlough, H, Fillingham, K, Foustanos, I, Gill, P, Kazoka, M, Nevitt, L, Peppa, N, Quarrell, O, Taylor, C, Tidswell, K, O'Donovan, K, Agarwal, V, Anderson, M, Gunner, K, Harris, K, Hayward, E, Heywood, M, Keys, L, Kipps, C, MacKinnon, L, Smalley, S, Gowers, L, Powell, K, Bethwaite, P, Edwards, R, Fuller, K, Phillips, M, Tan, L, Burgunder, JM, Lau, PN, Pica, E, Shoulson, I, Gusella, JG, Antonijevic, I, vankammen, D, Foroud, T, Warner, J, Giuliano, J, Vetter, L, Marshall, F, Marder, K, Frucht, S, Moskowitz, C, Clouse, R, Wasserman, P, Shannon, K, Jaglin, J, Jankovic, J, Palao, A, Harrison, M, Singer, C, Quesada, M, Hersch, S, Rosas, D, Tanev, K, Malarick, K, Colcher, A, 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S, Sperin, E, Testa, C, Thiede, F, Zauber, SE, McInnis, R, Welsh, C, Wesson, M, Coleman, A, and European Commission

Subjects
Adult, Male, congenital, hereditary, and neonatal diseases and abnormalities, COHORT, Cox hazard model, quantile regression, REGISTRY, symbol digit modalities test, Genotype, Neuropsychological Tests, Cohort Studies, 03 medical and health sciences, Cellular and Molecular Neuroscience, Cognition, 0302 clinical medicine, Huntington's disease, Rating scale, mental disorders, medicine, Humans, Verbal fluency test, Longitudinal Studies, Genetics (clinical), Proportional Hazards Models, 030304 developmental biology, 0303 health sciences, Proportional hazards model, business.industry, Reproducibility of Results, Middle Aged, medicine.disease, nervous system diseases, Psychiatry and Mental health, Huntington Disease, Phenotype, Test score, Cohort, Disease Progression, Female, Observational study, business, 030217 neurology & neurosurgery, Stroop effect, Clinical psychology
Abstract

REGISTRY Investigators of the European Huntington's Disease Network and COHORT Investigators of the Huntington Study Group., Studying individuals with extreme phenotypes could facilitate the understanding of disease modification by genetic or environmental factors. Our aim was to identify Huntington's disease (HD) patients with extreme symbol digit modality test (SDMT) scores. We first examined in HD the contribution of cognitive measures of the Unified Huntington's Disease Rating Scale (UHDRS) in predicting clinical endpoints. The language-independent SDMT was used to identify patients performing very well or very poorly relative to their CAG and age cohort. We used data from REGISTRY and COHORT observational study participants (5,603 HD participants with CAG repeats above 39 with 13,868 visits) and of 1,006 healthy volunteers (with 2,241 visits), included to identify natural aging and education effects on cognitive measures. Separate Cox proportional hazards models with CAG, age at study entry, education, sex, UHDRS total motor score and cognitive (SDMT, verbal fluency, Stroop tests) scores as covariates were used to predict clinical endpoints. Quantile regression for longitudinal language-independent SDMT data was used for boundary (2.5% and 97.5% quantiles) estimation and extreme score analyses stratified by age, education, and CAG repeat length. Ten percent of HD participants had an extreme SDMT phenotype for at least one visit. In contrast, only about 3% of participants were consistent SDMT extremes at two or more visits. The thresholds for the one-visit and two-visit extremes can be used to classify existing and new individuals. The identification of these phenotype extremes can be useful in the search for disease modifiers., This work was in part funded by a grant from the EuropeanCommission under the 7th framework programme (RD-Connect, grantagreement number 305444).

Published
2019

49. Evaluating the Capacity of Global CO2 Flux and Atmospheric Transport Models to Incorporate New Satellite Observations

Author

Kawa, S. R, Collatz, G. J, Erickson, D. J, Denning, A. S, Wofsy, S. C, and Andrews, A. E

Subjects
Meteorology And Climatology
Abstract

As we enter the new era of satellite remote sensing for CO2 and other carbon cyclerelated quantities, advanced modeling and analysis capabilities are required to fully capitalize on the new observations. Model estimates of CO2 surface flux and atmospheric transport are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, and ultimately for future projections of carbon-climate interactions. For application to current, planned, and future remotely sensed CO2 data, it is desirable that these models are accurate and unbiased at time scales from less than daily to multi-annual and at spatial scales from several kilometers or finer to global. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 1998 through 2006. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at lxi degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-2), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to in situ observations at sites in Northern mid latitudes and the continental tropics. The influence of key process representations is inferred. We find that the model can resolve much of the hourly to synoptic variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The seasonal cycle and its interannual variations generally respond adequately, but discrepancies in the tropics suggest the need for a refinement of the soil moisture dependence of the respiration flux in CASA. Examples and inferences for interpretation of satellite data will be discussed. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in the terrestrial CO2 sink and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time.

Published
2007

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