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392 results on '"Navarro, J.A."'

16. Development of a reference material for analysing naturally occurring radioactive material from the steel industry

Author

Braysher, E., Russell, B., Collins, S.M., van Es, E.M., Shearman, R., Molin, F. Dal, Read, D., Anagnostakis, M., Arndt, R., Bednár, A., Bituh, T., Bolivar, J.P., Cobb, J., Dehbi, N., Di Pasquale, S., Gascó, C., Gilligan, C., Jovanovič, P., Lawton, A., Lees, A.M.J., Lencsés, A., Mitchell, L., Mitsios, I., Petrinec, B., Rawcliffe, J., Shyti, M., Suárez-Navarro, J.A., Suursoo, S., Tóth-Bodrogi, E., Vaasma, T., Verheyen, L., Westmoreland, J., and de With, G.

Published
2021
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37. Supervised Machine Learning Algorithms for Ground Motion Time Series Classification from InSAR Data

Author

Mirmazloumi S.M., Gambin A.F., Palamà R., Crosetto M., Wassie Y., Navarro J.A., Barra A., and Monserrat O.

Subjects
Ground-motion, Time series, Classification (of information), Decision trees, Times series, Differential interferometric SAR, Time series analysis, Synthetic aperture radar, Learning algorithms, Machine learning algorithms, Deformation, Interferometric SAR, Machine learning models, Ground deformations, Supervised machine learning, Time series classifications, Machine-learning, Supervised learning
Abstract

The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the generation of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deformation identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS. © 2022 by the authors.

Published
2022
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38. Classification of ground deformation using sentinel-1 persistent scatterer interferometry time series

Author

Mirmazloumi S.M., Wassie Y., Navarro J.A., Palamà R., Krishnakumar V., Barra A., Cuevas-González M., Crosetto M., and Monserrat O.

Subjects
remote sensing, Catalonia, classification, spatial distribution, Spain, deformation, Barcelona [Catalonia], Granada [Andalucia], time series, Sentinel, Andalucia, ground movement, synthetic aperture radar
Abstract

Displacement time series (TS) provides temporal and spatial information related to ground deformation. This study aims to investigate temporal behavior of ground deformation TS, including classification of displacement trends and periodicity evaluation, which ease the interpretation of movements. To this end, we propose several modifications to an existing automatic classification workflow of Persistent Scatterers Interferometry (PSI) TS using new tests to classify ground deformations into seven main trends: Stable, Linear, Quadratic, Bilinear, Phase Unwrapping Errors (PUE), Discontinuous with constant and different velocities. We illustrate our approach over 1500 km2 of the Granada region and the metropolitan area of Barcelona, which were monitored using Sentinel-1 images and a PSI technique. This study provided the spatial distribution of different ground movement types and was useful to detect several TS anomalies due to PUE. The proposed approach also identified stable targets, which were wrongly classified as moving scatterers by the existing classification method. A periodicity analysis was finally performed using the Welch’s power spectral density estimator to investigate seasonal and yearly fluctuations. The method was validated using simulated data, where the classified TSs characterized by probable phase unwrapping errors were verified by PSI experts. The overall classification accuracy was 77.8%, indicating that the proposed method has a considerable TS classification potential. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Published
2022
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44. Enfermedad neumocócica invasiva en niños de la Región de Murcia

Author

Espín M.I., Sandoval A., Ruiz J., Navarro J.A., García J., and Pérez Flores D.

Subjects
Enfermedad neumocócica invasiva, Vacuna neumocócica conjugada, Public aspects of medicine, RA1-1270
Abstract

Objetivo: La disponibilidad de una vacuna neumocócica conjugada plantea la necesidad de conocer la incidencia y características de la enfermedad neumocócica invasiva en niños de la Región de Murcia, con la finalidad de obtener información que pueda ser de utilidad para establecer adecuadamente las indicaciones de vacunación. Métodos: Se ha realizado una búsqueda retrospectiva de casos de enfermedad invasiva por Streptococcus pneumoniae en menores de 15 años de edad atendidos en hospitales de la Región de Murcia durante el período 1991-2000. Las fuentes de datos utilizadas han sido las bases de datos de los Servicios de Microbiología, el Conjunto Mínimo de Datos Básicos, registro de altas pediátricas y Registro EDO. Resultados: La tasa de incidencia para el período 1996-2000 fue, para los menores de 1 año, de 18,25 por 10(5) personas-año en el caso de enfermedad invasiva (10,6 para meningitis); para los menores de 2 años, de 13,6 para enfermedad invasiva (6 para meningitis); para menores de 5 años, de 8,9 (1,35 para meningitis), y para los menores de 15 años, de 3,7 (1,3 para meningitis). El 28% de los casos presentaba factores de riesgo. Las complicaciones alcanzaron el 35,2% y las secuelas el 5%. La letalidad fue del 11,8%. Los serogrupos prevalentes fueron el 19, el 6, el 18, el 5, el 14 y el 23. Conclusiones: El alto porcentaje de casos con factores de riesgo de enfermedad neumocócica invasiva aconseja la implantación de programas de vacunación dirigidos a todos los niños con factores de riesgo. La incidencia de enfermedad neumocócica invasiva encontrada en la Región de Murcia difiere de la de otras zonas geográficas; sin embargo, la incidencia de meningitis es similar a la de otros estudios. La gravedad de la enfermedad justifica la realización de estudios coste-efectividad para valorar la posible incorporación de la vacuna en el calendario vacunal.

Published
2002

45. Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales

Author

Kuhn J.H., Adkins S., Agwanda B.R., Al Kubrusli R., Alkhovsky S.V., Amarasinghe G.K., Avsic-Zupanc T., Ayllon M.A., Bahl J., Balkema-Buschmann A., Ballinger M.J., Basler C.F., Bavari S., Beer M., Bejerman N., Bennett A.J., Bente D.A., Bergeron E., Bird B.H., Blair C.D., Blasdell K.R., Blystad D.-R., Bojko J., Borth W.B., Bradfute S., Breyta R., Briese T., Brown P.A., Brown J.K., Buchholz U.J., Buchmeier M.J., Bukreyev A., Burt F., Buttner C., Calisher C.H., Cao M., Casas I., Chandran K., Charrel R.N., Cheng Q., Chiaki Y., Chiapello M., Choi I.-R., Ciuffo M., Clegg J.C.S., Crozier I., Dal Bo E., de la Torre J.C., de Lamballerie X., de Swart R.L., Debat H., Dheilly N.M., Di Cicco E., Di Paola N., Di Serio F., Dietzgen R.G., Digiaro M., Dolnik O., Drebot M.A., Drexler J.F., Dundon W.G., Duprex W.P., Durrwald R., Dye J.M., Easton A.J., Ebihara H., Elbeaino T., Ergunay K., Ferguson H.W., Fooks A.R., Forgia M., Formenty P.B.H., Franova J., Freitas-Astua J., Fu J., Furl S., Gago-Zachert S., Gao G.F., Garcia M.L., Garcia-Sastre A., Garrison A.R., Gaskin T., Gonzalez J.-P.J., Griffiths A., Goldberg T.L., Groschup M.H., Gunther S., Hall R.A., Hammond J., Han T., Hepojoki J., Hewson R., Hong J., Hong N., Hongo S., Horie M., Hu J.S., Hu T., Hughes H.R., Huttner F., Hyndman T.H., Ilyas M., Jalkanen R., Jiang D., Jonson G.B., Junglen S., Kadono F., Kaukinen K.H., Kawate M., Klempa B., Klingstrom J., Kobinger G., Koloniuk I., Kondo H., Koonin E.V., Krupovic M., Kubota K., Kurath G., Laenen L., Lambert A.J., Langevin S.L., Lee B., Lefkowitz E.J., Leroy E.M., Li S., Li L., Li J., Liu H., Lukashevich I.S., Maes P., de Souza W.M., Marklewitz M., Marshall S.H., Marzano S.-Y.L., Massart S., McCauley J.W., Melzer M., Mielke-Ehret N., Miller K.M., Ming T.J., Mirazimi A., Mordecai G.J., Muhlbach H.-P., Muhlberger E., Naidu R., Natsuaki T., Navarro J.A., Netesov S.V., Neumann G., Nowotny N., Nunes M.R.T., Olmedo-Velarde A., Palacios G., Pallas V., Palyi B., Papa A., Paraskevopoulou S., Park A.C., Parrish C.R., Patterson D.A., Pauvolid-Correa A., Paweska J.T., Payne S., Peracchio C., Perez D.R., Postler T.S., Qi L., Radoshitzky S.R., Resende R.O., Reyes C.A., Rima B.K., Luna G.R., Romanowski V., Rota P., Rubbenstroth D., Rubino L., Runstadler J.A., Sabanadzovic S., Sall A.A., Salvato M.S., Sang R., Sasaya T., Schulze A.D., Schwemmle M., Shi M., Shi X., Shi Z., Shimomoto Y., Shirako Y., Siddell S.G., Simmonds P., Sironi M., Smagghe G., Smither S., Song J.-W., Spann K., Spengler J.R., Stenglein M.D., Stone D.M., Sugano J., Suttle C.A., Tabata A., Takada A., Takeuchi S., Tchouassi D.P., Teffer A., Tesh R.B., Thornburg N.J., Tomitaka Y., Tomonaga K., Tordo N., Torto B., Towner J.S., Tsuda S., Tu C., Turina M., Tzanetakis I.E., Uchida J., Usugi T., Vaira A.M., Vallino M., van den Hoogen B., Varsani A., Vasilakis N., Verbeek M., von Bargen S., Wada J., Wahl V., Walker P.J., Wang L.-F., Wang G., Wang Y., Waqas M., Wei T., Wen S., Whitfield A.E., Williams J.V., Wolf Y.I., Wu J., Xu L., Yanagisawa H., Yang C., Yang Z., Zerbini F.M., Zhai L., Zhang Y.-Z., Zhang S., Zhang J., Zhang Z., and Zhou X.

Subjects
Virus classification, Negative-stranded RNA viruses
Abstract

In March 2021, following the annual International Committee on Taxonomy of Viruses (ICTV) ratifcation vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by four families (Aliusviridae, Crepuscuviridae, Myriaviridae, and Natareviridae), three subfamilies (Alpharhabdovirinae, Betarhabdovirinae, and Gammarhabdovirinae), 42 genera, and 200 species. Thirty-nine species were renamed and/ or moved and seven species were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV

Published
2021
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46. Combining satellite insar, slope units and finite element modeling for stability analysis in mining waste disposal areas

Author

López-Vinielles J., Fernández-Merodo J.A., Ezquerro P., García-Davalillo J.C., Sarro R., Reyes-Carmona C., Barra A., Navarro J.A., Krishnakumar V., Alvioli M., and Herrera G.

Subjects
Application programs, Finite element method, Remote sensing data, Radar interferometry, Slope stability analysis, Stability analysis, Open pit mining, Remote sensing, Validation analysis, Waste disposal, Interferometry, Space-based radar, Mitigation measures, Slope instability, Slope protection, Safety factor, Landslides, Satellite radar interferometry
Abstract

Slope failures pose a substantial threat to mining activity due to their destructive potential and high probability of occurrence on steep slopes close to limit equilibrium conditions, which are often found both in open pits and in waste and tailing disposal facilities. The development of slope monitoring and modeling programs usually entails the exploitation of in situ and remote sensing data, together with the application of numerical modeling, and it plays an important role in the definition of prevention and mitigation measures aimed at minimizing the impact of slope failures in mining areas. In this paper, a new methodology is presented; one that combines satellite radar interferometry and 2D finite element modeling for slope stability analysis at a regional scale, and applied within slope unit polygons. Although the literature includes many studies applying radar interferometry and modeling for slope stability analysis, the addition of slope units as input data for radar interferometry and modeling purposes has, to our knowledge, not previously been reported. A former mining area in southeast Spain was studied, and the method proved useful for detecting and characterizing a large number of unstable slopes. Out of the 1959 slope units used for the spatial analysis of the radar interferometry data, 43 were unstable, with varying values of safety factor and landslide size. Out of the 43 active slope units, 21 exhibited line of sight velocities greater than the maximum error obtained through validation analysis (2.5 cm/year). Finally, this work discusses the possibility of using the results of the proposed approach to devise a proxy for landslide hazard. The proposed methodology can help to provide non-expert final users with intelligible, clear, and easily comparable information to analyze slope instabilities in different settings, and not limited to mining areas. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Published
2021

47. 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales

Author

Kuhn, J.H., Adkins, S., Agwanda, B.R., Al Kubrusli, R., Alkhovsky, S.V., Amarasinghe, G.K., Avšič-Županc, T., Ayllón, M.A., Bahl, J., Balkema-Buschmann, A., Ballinger, M.J., Basler, C.F., Bavari, S., Beer, M., Bejerman, N., Bennett, A.J., Bente, D.A., Bergeron, É., Bird, B.H., Blair, C.D., Blasdell, K.R., Blystad, D-R, Bojko, J., Borth, W.B., Bradfute, S., Breyta, R., Briese, T., Brown, P.A., Brown, J.K., Buchholz, U.J., Buchmeier, M.J., Bukreyev, A., Burt, F., Büttner, C., Calisher, C.H., Cao, M., Casas, I., Chandran, K., Charrel, R.N., Cheng, Q., Chiaki, Y., Chiapello, M., Choi, I-R, Ciuffo, M., Clegg, J.C.S., Crozier, I., Dal Bó, E., de la Torre, J.C., de Lamballerie, X., de Swart, R.L., Debat, H., Dheilly, N.M., Di Cicco, E., Di Paola, N., Di Serio, F., Dietzgen, R.G., Digiaro, M., Dolnik, O., Drebot, M.A., Drexler, J.F., Dundon, W.G., Duprex, W.P., Dürrwald, R., Dye, J.M., Easton, A.J., Ebihara, H., Elbeaino, T., Ergünay, K., Ferguson, H.W., Fooks, A.R., Forgia, M., Formenty, P.B.H., Fránová, J., Freitas-Astúa, J., Fu, J., Fürl, S., Gago-Zachert, S., Gāo, G.F., García, M.L., García-Sastre, A., Garrison, A.R., Gaskin, T., Gonzalez, J-P.J., Griffiths, A., Goldberg, T.L., Groschup, M.H., Günther, S., Hall, R.A., Hammond, J., Han, T., Hepojoki, J., Hewson, R., Hong, J., Hong, N., Hongo, S., Horie, M., Hu, J.S., Hu, T., Hughes, H.R., Hüttner, F., Hyndman, T.H., Ilyas, M., Jalkanen, R., Jiāng, D., Jonson, G.B., Junglen, S., Kadono, F., Kaukinen, K.H., Kawate, M., Klempa, B., Klingström, J., Kobinger, G., Koloniuk, I., Kondo, H., Koonin, E.V., Krupovic, M., Kubota, K., Kurath, G., Laenen, L., Lambert, A.J., Langevin, S.L., Lee, B., Lefkowitz, E.J., Leroy, E.M., Li, S., Li, L., Lǐ, J., Liu, H., Lukashevich, I.S., Maes, P., de Souza, W.M., Marklewitz, M., Marshall, S.H., Marzano, S-Y.L., Massart, S., McCauley, J.W., Melzer, M., Mielke-Ehret, N., Miller, K.M., Ming, T.J., Mirazimi, A., Mordecai, G.J., Mühlbach, H-P, Mühlberger, E., Naidu, R., Natsuaki, T., Navarro, J.A., Netesov, S.V., Neumann, G., Nowotny, N., Nunes, M.R.T., Olmedo-Velarde, A., Palacios, G., Pallas, V., Pályi, B., Papa, A., Paraskevopoulou, S., Park, A.C., Parrish, C.R., Patterson, D.A., Pauvolid-Corrêa, A., Pawęska, J.T., Payne, S., Peracchio, C., Pérez, D.R., Postler, T.S., Qi, L., Radoshitzky, S.R., Resende, R.O., Reyes, C.A., Rima, B.K., Luna, G.R., Romanowski, V., Rota, P., Rubbenstroth, D., Rubino, L., Runstadler, J.A., Sabanadzovic, S., Sall, A.A., Salvato, M.S., Sang, R., Sasaya, T., Schulze, A.D., Schwemmle, M., Shi, M., Shi, X., Shí, Z., Shimomoto, Y., Shirako, Y., Siddell, S.G., Simmonds, P., Sironi, M., Smagghe, G., Smither, S., Song, J-W, Spann, K., Spengler, J.R., Stenglein, M.D., Stone, D.M., Sugano, J., Suttle, C.A., Tabata, A., Takada, A., Takeuchi, S., Tchouassi, D.P., Teffer, A., Tesh, R.B., Thornburg, N.J., Tomitaka, Y., Tomonaga, K., Tordo, N., Torto, B., Towner, J.S., Tsuda, S., Tu, C., Turina, M., Tzanetakis, I.E., Uchida, J., Usugi, T., Vaira, A.M., Vallino, M., van den Hoogen, B., Varsani, A., Vasilakis, N., Verbeek, M., von Bargen, S., Wada, J., Wahl, V., Walker, P.J., Wang, L-F, Wang, G., Wang, Y., Waqas, M., Wèi, T., Wen, S., Whitfield, A.E., Williams, J.V., Wolf, Y.I., Wu, J., Xu, L., Yanagisawa, H., Yang, C., Yang, Z., Zerbini, F.M., Zhai, L., Zhang, Y-Z, Zhang, S., Zhang, J., Zhang, Z., Zhou, X., Kuhn, J.H., Adkins, S., Agwanda, B.R., Al Kubrusli, R., Alkhovsky, S.V., Amarasinghe, G.K., Avšič-Županc, T., Ayllón, M.A., Bahl, J., Balkema-Buschmann, A., Ballinger, M.J., Basler, C.F., Bavari, S., Beer, M., Bejerman, N., Bennett, A.J., Bente, D.A., Bergeron, É., Bird, B.H., Blair, C.D., Blasdell, K.R., Blystad, D-R, Bojko, J., Borth, W.B., Bradfute, S., Breyta, R., Briese, T., Brown, P.A., Brown, J.K., Buchholz, U.J., Buchmeier, M.J., Bukreyev, A., Burt, F., Büttner, C., Calisher, C.H., Cao, M., Casas, I., Chandran, K., Charrel, R.N., Cheng, Q., Chiaki, Y., Chiapello, M., Choi, I-R, Ciuffo, M., Clegg, J.C.S., Crozier, I., Dal Bó, E., de la Torre, J.C., de Lamballerie, X., de Swart, R.L., Debat, H., Dheilly, N.M., Di Cicco, E., Di Paola, N., Di Serio, F., Dietzgen, R.G., Digiaro, M., Dolnik, O., Drebot, M.A., Drexler, J.F., Dundon, W.G., Duprex, W.P., Dürrwald, R., Dye, J.M., Easton, A.J., Ebihara, H., Elbeaino, T., Ergünay, K., Ferguson, H.W., Fooks, A.R., Forgia, M., Formenty, P.B.H., Fránová, J., Freitas-Astúa, J., Fu, J., Fürl, S., Gago-Zachert, S., Gāo, G.F., García, M.L., García-Sastre, A., Garrison, A.R., Gaskin, T., Gonzalez, J-P.J., Griffiths, A., Goldberg, T.L., Groschup, M.H., Günther, S., Hall, R.A., Hammond, J., Han, T., Hepojoki, J., Hewson, R., Hong, J., Hong, N., Hongo, S., Horie, M., Hu, J.S., Hu, T., Hughes, H.R., Hüttner, F., Hyndman, T.H., Ilyas, M., Jalkanen, R., Jiāng, D., Jonson, G.B., Junglen, S., Kadono, F., Kaukinen, K.H., Kawate, M., Klempa, B., Klingström, J., Kobinger, G., Koloniuk, I., Kondo, H., Koonin, E.V., Krupovic, M., Kubota, K., Kurath, G., Laenen, L., Lambert, A.J., Langevin, S.L., Lee, B., Lefkowitz, E.J., Leroy, E.M., Li, S., Li, L., Lǐ, J., Liu, H., Lukashevich, I.S., Maes, P., de Souza, W.M., Marklewitz, M., Marshall, S.H., Marzano, S-Y.L., Massart, S., McCauley, J.W., Melzer, M., Mielke-Ehret, N., Miller, K.M., Ming, T.J., Mirazimi, A., Mordecai, G.J., Mühlbach, H-P, Mühlberger, E., Naidu, R., Natsuaki, T., Navarro, J.A., Netesov, S.V., Neumann, G., Nowotny, N., Nunes, M.R.T., Olmedo-Velarde, A., Palacios, G., Pallas, V., Pályi, B., Papa, A., Paraskevopoulou, S., Park, A.C., Parrish, C.R., Patterson, D.A., Pauvolid-Corrêa, A., Pawęska, J.T., Payne, S., Peracchio, C., Pérez, D.R., Postler, T.S., Qi, L., Radoshitzky, S.R., Resende, R.O., Reyes, C.A., Rima, B.K., Luna, G.R., Romanowski, V., Rota, P., Rubbenstroth, D., Rubino, L., Runstadler, J.A., Sabanadzovic, S., Sall, A.A., Salvato, M.S., Sang, R., Sasaya, T., Schulze, A.D., Schwemmle, M., Shi, M., Shi, X., Shí, Z., Shimomoto, Y., Shirako, Y., Siddell, S.G., Simmonds, P., Sironi, M., Smagghe, G., Smither, S., Song, J-W, Spann, K., Spengler, J.R., Stenglein, M.D., Stone, D.M., Sugano, J., Suttle, C.A., Tabata, A., Takada, A., Takeuchi, S., Tchouassi, D.P., Teffer, A., Tesh, R.B., Thornburg, N.J., Tomitaka, Y., Tomonaga, K., Tordo, N., Torto, B., Towner, J.S., Tsuda, S., Tu, C., Turina, M., Tzanetakis, I.E., Uchida, J., Usugi, T., Vaira, A.M., Vallino, M., van den Hoogen, B., Varsani, A., Vasilakis, N., Verbeek, M., von Bargen, S., Wada, J., Wahl, V., Walker, P.J., Wang, L-F, Wang, G., Wang, Y., Waqas, M., Wèi, T., Wen, S., Whitfield, A.E., Williams, J.V., Wolf, Y.I., Wu, J., Xu, L., Yanagisawa, H., Yang, C., Yang, Z., Zerbini, F.M., Zhai, L., Zhang, Y-Z, Zhang, S., Zhang, J., Zhang, Z., and Zhou, X.

Abstract

In March 2021, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by four families (Aliusviridae, Crepuscuviridae, Myriaviridae, and Natareviridae), three subfamilies (Alpharhabdovirinae, Betarhabdovirinae, and Gammarhabdovirinae), 42 genera, and 200 species. Thirty-nine species were renamed and/or moved and seven species were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.

Published
2021

48. Correction to: 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales

Author

Kuhn, J.H., Adkins, S., Agwanda, B.R., Al Kubrusli, R., Alkhovsky, S.V., Amarasinghe, G.K., Avšič-Županc, T., Ayllón, M.A., Bahl, J., Balkema-Buschmann, A., Ballinger, M.J., Basler, C.F., Bavari, S., Beer, M., Bejerman, N., Bennett, A.J., Bente, D.A., Bergeron, É., Bird, B.H., Blair, C.D., Blasdell, K.R., Blystad, D-R, Bojko, J., Borth, W.B., Bradfute, S., Breyta, R., Briese, T., Brown, P.A., Brown, J.K., Buchholz, U.J., Buchmeier, M.J., Bukreyev, A., Burt, F., Büttner, C., Calisher, C.H., Cao, M., Casas, I., Chandran, K., Charrel, R.N., Cheng, Q., Chiaki, Y., Chiapello, M., Choi, I-R, Ciuffo, M., Clegg, J.C.S., Crozier, I., Dal Bó, E., de la Torre, J.C., de Lamballerie, X., de Swart, R.L., Debat, H., Dheilly, N.M., Di Cicco, E., Di Paola, N., Di Serio, F., Dietzgen, R.G., Digiaro, M., Dolnik, O., Drebot, M.A., Drexler, J.F., Dundon, W.G., Duprex, W.P., Dürrwald, R., Dye, J.M., Easton, A.J., Ebihara, H., Elbeaino, T., Ergünay, K., Ferguson, H.W., Fooks, A.R., Forgia, M., Formenty, P.B.H., Fránová, J., Freitas-Astúa, J., Fu, J., Fürl, S., Gago-Zachert, S., Gāo, G.F., García, M.L., García-Sastre, A., Garrison, A.R., Gaskin, T., Gonzalez, J-P.J., Griffiths, A., Goldberg, T.L., Groschup, M.H., Günther, S., Hall, R.A., Hammond, J., Han, T., Hepojoki, J., Hewson, R., Hong, J., Hong, N., Hongo, S., Horie, M., Hu, J.S., Hu, T., Hughes, H.R., Hüttner, F., Hyndman, T.H., Ilyas, M., Jalkanen, R., Jiāng, D., Jonson, G.B., Junglen, S., Kadono, F., Kaukinen, K.H., Kawate, M., Klempa, B., Klingström, J., Kobinger, G., Koloniuk, I., Kondo, H., Koonin, E.V., Krupovic, M., Kubota, K., Kurath, G., Laenen, L., Lambert, A.J., Langevin, S.L., Lee, B., Lefkowitz, E.J., Leroy, E.M., Li, S., Li, L., Lǐ, J., Liu, H., Lukashevich, I.S., Maes, P., de Souza, W.M., Marklewitz, M., Marshall, S.H., Marzano, S-Y.L., Massart, S., McCauley, J.W., Melzer, M., Mielke-Ehret, N., Miller, K.M., Ming, T.J., Mirazimi, A., Mordecai, G.J., Mühlbach, H-P, Mühlberger, E., Naidu, R., Natsuaki, T., Navarro, J.A., Netesov, S.V., Neumann, G., Nowotny, N., Nunes, M.R.T., Olmedo-Velarde, A., Palacios, G., Pallás, V., Pályi, B., Papa, A., Paraskevopoulou, S., Park, A.C., Parrish, C.R., Patterson, D.A., Pauvolid-Corrêa, A., Pawęska, J.T., Payne, S., Peracchio, C., Pérez, D.R., Postler, T.S., Qi, L., Radoshitzky, S.R., Resende, R.O., Reyes, C.A., Rima, B.K., Luna, G.R., Romanowski, V., Rota, P., Rubbenstroth, D., Rubino, L., Runstadler, J.A., Sabanadzovic, S., Sall, A.A., Salvato, M.S., Sang, R., Sasaya, T., Schulze, A.D., Schwemmle, M., Shi, M., Shí, X., Shí, Z., Shimomoto, Y., Shirako, Y., Siddell, S.G., Simmonds, P., Sironi, M., Smagghe, G., Smither, S., Song, J-W, Spann, K., Spengler, J.R., Stenglein, M.D., Stone, D.M., Sugano, J., Suttle, C.A., Tabata, A., Takada, A., Takeuchi, S., Tchouassi, D.P., Teffer, A., Tesh, R.B., Thornburg, N. J., Tomitaka, Y., Tomonaga, K., Tordo, N., Torto, B., Towner, J.S., Tsuda, S., Tu, C., Turina, M., Tzanetakis, I.E., Uchida, J., Usugi, T., Vaira, A.M., Vallino, M., van den Hoogen, B., Varsani, A., Vasilakis, N., Verbeek, M., von Bargen, S., Wada, J., Wahl, V., Walker, P.J., Wang, L-F, Wang, G., Wang, Y., Waqas, M., Wèi, T., Wen, S., Whitfield, A.E., Williams, J.V., Wolf, Y.I., Wu, J., Xu, L., Yanagisawa, H., Yang, C., Yang, Z., Zerbini, F.M., Zhai, L., Zhang, Y-Z, Zhang, S., Zhang, J., Zhang, Z., Zhou, X., Kuhn, J.H., Adkins, S., Agwanda, B.R., Al Kubrusli, R., Alkhovsky, S.V., Amarasinghe, G.K., Avšič-Županc, T., Ayllón, M.A., Bahl, J., Balkema-Buschmann, A., Ballinger, M.J., Basler, C.F., Bavari, S., Beer, M., Bejerman, N., Bennett, A.J., Bente, D.A., Bergeron, É., Bird, B.H., Blair, C.D., Blasdell, K.R., Blystad, D-R, Bojko, J., Borth, W.B., Bradfute, S., Breyta, R., Briese, T., Brown, P.A., Brown, J.K., Buchholz, U.J., Buchmeier, M.J., Bukreyev, A., Burt, F., Büttner, C., Calisher, C.H., Cao, M., Casas, I., Chandran, K., Charrel, R.N., Cheng, Q., Chiaki, Y., Chiapello, M., Choi, I-R, Ciuffo, M., Clegg, J.C.S., Crozier, I., Dal Bó, E., de la Torre, J.C., de Lamballerie, X., de Swart, R.L., Debat, H., Dheilly, N.M., Di Cicco, E., Di Paola, N., Di Serio, F., Dietzgen, R.G., Digiaro, M., Dolnik, O., Drebot, M.A., Drexler, J.F., Dundon, W.G., Duprex, W.P., Dürrwald, R., Dye, J.M., Easton, A.J., Ebihara, H., Elbeaino, T., Ergünay, K., Ferguson, H.W., Fooks, A.R., Forgia, M., Formenty, P.B.H., Fránová, J., Freitas-Astúa, J., Fu, J., Fürl, S., Gago-Zachert, S., Gāo, G.F., García, M.L., García-Sastre, A., Garrison, A.R., Gaskin, T., Gonzalez, J-P.J., Griffiths, A., Goldberg, T.L., Groschup, M.H., Günther, S., Hall, R.A., Hammond, J., Han, T., Hepojoki, J., Hewson, R., Hong, J., Hong, N., Hongo, S., Horie, M., Hu, J.S., Hu, T., Hughes, H.R., Hüttner, F., Hyndman, T.H., Ilyas, M., Jalkanen, R., Jiāng, D., Jonson, G.B., Junglen, S., Kadono, F., Kaukinen, K.H., Kawate, M., Klempa, B., Klingström, J., Kobinger, G., Koloniuk, I., Kondo, H., Koonin, E.V., Krupovic, M., Kubota, K., Kurath, G., Laenen, L., Lambert, A.J., Langevin, S.L., Lee, B., Lefkowitz, E.J., Leroy, E.M., Li, S., Li, L., Lǐ, J., Liu, H., Lukashevich, I.S., Maes, P., de Souza, W.M., Marklewitz, M., Marshall, S.H., Marzano, S-Y.L., Massart, S., McCauley, J.W., Melzer, M., Mielke-Ehret, N., Miller, K.M., Ming, T.J., Mirazimi, A., Mordecai, G.J., Mühlbach, H-P, Mühlberger, E., Naidu, R., Natsuaki, T., Navarro, J.A., Netesov, S.V., Neumann, G., Nowotny, N., Nunes, M.R.T., Olmedo-Velarde, A., Palacios, G., Pallás, V., Pályi, B., Papa, A., Paraskevopoulou, S., Park, A.C., Parrish, C.R., Patterson, D.A., Pauvolid-Corrêa, A., Pawęska, J.T., Payne, S., Peracchio, C., Pérez, D.R., Postler, T.S., Qi, L., Radoshitzky, S.R., Resende, R.O., Reyes, C.A., Rima, B.K., Luna, G.R., Romanowski, V., Rota, P., Rubbenstroth, D., Rubino, L., Runstadler, J.A., Sabanadzovic, S., Sall, A.A., Salvato, M.S., Sang, R., Sasaya, T., Schulze, A.D., Schwemmle, M., Shi, M., Shí, X., Shí, Z., Shimomoto, Y., Shirako, Y., Siddell, S.G., Simmonds, P., Sironi, M., Smagghe, G., Smither, S., Song, J-W, Spann, K., Spengler, J.R., Stenglein, M.D., Stone, D.M., Sugano, J., Suttle, C.A., Tabata, A., Takada, A., Takeuchi, S., Tchouassi, D.P., Teffer, A., Tesh, R.B., Thornburg, N. J., Tomitaka, Y., Tomonaga, K., Tordo, N., Torto, B., Towner, J.S., Tsuda, S., Tu, C., Turina, M., Tzanetakis, I.E., Uchida, J., Usugi, T., Vaira, A.M., Vallino, M., van den Hoogen, B., Varsani, A., Vasilakis, N., Verbeek, M., von Bargen, S., Wada, J., Wahl, V., Walker, P.J., Wang, L-F, Wang, G., Wang, Y., Waqas, M., Wèi, T., Wen, S., Whitfield, A.E., Williams, J.V., Wolf, Y.I., Wu, J., Xu, L., Yanagisawa, H., Yang, C., Yang, Z., Zerbini, F.M., Zhai, L., Zhang, Y-Z, Zhang, S., Zhang, J., Zhang, Z., and Zhou, X.

Abstract

Unfortunately, the inclusion of original names (in non-Latin script) of the following authors caused problems with author name indexing in PubMed. Therefore, these original names were removed from XML data to correct the PubMed record...

Published
2021

49. Unravelling the involvement of cilevirus p32 protein in the viral transport

Author

Leastro, M.O., Freitas-Astúa, Juliana, Kitajima, E.W., Pallás, Vicente, Sánchez-Navarro, J.A., Leastro, M.O., Freitas-Astúa, Juliana, Kitajima, E.W., Pallás, Vicente, and Sánchez-Navarro, J.A.

Abstract

Citrus leprosis (CL) is a severe disease that affects citrus orchards mainly in Latin America. It is caused by Brevipalpus-transmitted viruses from genera Cilevirus and Dichorhavirus. Currently, no reports have explored the movement machinery for the cilevirus. Here, we have performed a detailed functional study of the p32 movement protein (MP) of two cileviruses. Citrus leprosis-associated viruses are not able to move systemically in neither their natural nor experimental host plants. However, here we show that cilevirus MPs are able to allow the cell-to-cell and long-distance transport of movement-defective alfalfa mosaic virus (AMV). Several features related with the viral transport were explored, including: (i) the ability of cilevirus MPs to facilitate virus movement on a nucleocapsid assembly independent-manner; (ii) the generation of tubular structures from transient expression in protoplast; (iii) the capability of the N- and C- terminus of MP to interact with the cognate capsid protein (p29) and; (iv) the role of the C-terminus of p32 in the cell-to-cell and long-distance transport, tubule formation and the MP-plasmodesmata co-localization. The MP was able to direct the p29 to the plasmodesmata, whereby the C-terminus of MP is independently responsible to recruit the p29 to the cell periphery. Furthermore, we report that MP possess the capacity to enter the nucleolus and to bind to a major nucleolar protein, the fibrillarin. Based on our findings, we provide a model for the role of the p32 in the intra- and intercellular viral spread.

Published
2021

50. Solar Radiation Estimation Using Data Mining Techniques for Remote Areas-A Case Study in Ethiopia

Author

Abebe Worke, B., Bludszuweit, H., and Dominguez-Navarro, J.A.

Abstract

High quality of solar radiation data is essential for solar resource assessment. For remote areas this is a challenge, as often only satellite data with low spatial resolution are available. This paper presents an interpolation method based on topographic data in digital elevation model format to improve the resolution of solar radiation maps. The refinement is performed with a data mining method based on first-order Sugeno type Adaptive Neuro-Fuzzy Inference System. The training set contains topographic characteristics such as terrain aspect, slope and elevation which may influence the solar radiation distribution. An efficient sampling method is proposed to obtain representative training sets from digital elevation model data. The proposed geographic information system based approach makes this method reproducible and adaptable for any region. A case study is presented on the remote Amhara region in North Shewa, Ethiopia. Results are shown for interpolation of solar radiation data from 10 km x 10 km to a resolution of 1 km x 1 km and are validated with data from the PVGIS and SWERA projects.

Published
2020

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