The Proteome of Biologically Active Membrane Vesicles from Piscirickettsia salmonis LF-89 Type Strain Identifies Plasmid-Encoded Putative Toxins

Indexación: Scopus.Piscirickettsia salmonis is the predominant bacterial pathogen affecting the Chilean salmonid industry. This bacterium is the etiological agent of piscirickettsiosis, a significant fish disease. Membrane vesicles (MVs) released by P. salmonis deliver several virulence factors to host cells. To improve on existing knowledge for the pathogenicity-associated functions of P. salmonis MVs, we studied the proteome of purified MVs from the P. salmonis LF-89 type strain using multidimensional protein identification technology. Initially, the cytotoxicity of different MV concentration purified from P. salmonis LF-89 was confirmed in an in vivo adult zebrafish infection model. The cumulative mortality of zebrafish injected with MVs showed a dose-dependent pattern. Analyses identified 452 proteins of different subcellular origins; most of them were associated with the cytoplasmic compartment and were mainly related to key functions for pathogen survival. Interestingly, previously unidentified putative virulence-related proteins were identified in P. salmonis MVs, such as outer membrane porin F and hemolysin. Additionally, five amino acid sequences corresponding to the Bordetella pertussis toxin subunit 1 and two amino acid sequences corresponding to the heat-labile enterotoxin alpha chain of Escherichia coli were located in the P. salmonis MV proteome. Curiously, these putative toxins were located in a plasmid region of P. salmonis LF-89. Based on the identified proteins, we propose that the protein composition of P. salmonis LF-89 MVs could reflect total protein characteristics of this P. salmonis type strain. © 2017 Oliver, Hernández, Tandberg, Valenzuela, Lagos, Haro, Sánchez, Ruiz, Sanhueza-Oyarzún, Cortés, Villar, Artigues, Winther-Larsen, Avendaño-Herrera and Yáñez.https://www.frontiersin.org/articles/10.3389/fcimb.2017.00420/ful

Deep learning for agricultural land use classification from Sentinel-2

[ES] En el campo de la teledetección se ha producido recientemente un incremento del uso de técnicas de aprendizaje profundo (deep learning). Estos algoritmos se utilizan con éxito principalmente en la estimación de parámetros y en la clasificación de imágenes. Sin embargo, se han realizado pocos esfuerzos encaminados a su comprensión, lo que lleva a ejecutarlos como si fueran “cajas negras”. Este trabajo pretende evaluar el rendimiento y acercarnos al entendimiento de un algoritmo de aprendizaje profundo, basado en una red recurrente bidireccional de memoria corta a largo plazo (2-BiLSTM), a través de un ejemplo de clasificación de usos de suelo agrícola de la Comunidad Valenciana dentro del marco de trabajo de la política agraria común (PAC) a partir de series temporales de imágenes Sentinel-2. En concreto, se ha comparado con otros algoritmos como los árboles de decisión (DT), los k-vecinos más cercanos (k-NN), redes neuronales (NN), máquinas de soporte vectorial (SVM) y bosques aleatorios (RF) para evaluar su precisión. Se comprueba que su precisión (98,6% de acierto global) es superior a la del resto en todos los casos. Por otra parte, se ha indagado cómo actúa el clasificador en función del tiempo y de los predictores utilizados. Este análisis pone de manifiesto que, sobre el área de estudio, la información espectral y espacial derivada de las bandas del rojo e infrarrojo cercano, y las imágenes correspondientes a las fechas del período de verano, son la fuente de información más relevante utilizada por la red en la clasificación. Estos resultados abren la puerta a nuevos estudios en el ámbito de la explicabilidad de los resultados proporcionados por los algoritmos de aprendizaje profundo en aplicaciones de teledetección.[EN] The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.Este trabajo ha sido subvencionado gracias al Convenio 2019 y 2020 de colaboración entre la Generalitat Valenciana, a través de la Conselleria d’Agricultura, Medi Ambient, Canvi Climàtic i Desenvolupament Rural, y la Universitat de València – Estudi General.Campos-Taberner, M.; García-Haro, F.; Martínez, B.; Gilabert, M. (2020). Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2. Revista de Teledetección. 0(56):35-48. https://doi.org/10.4995/raet.2020.13337OJS3548056Baraldi, A., Parmiggiani, F. 1995. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 293-304. https://doi.org/10.1109/36.377929Bengio, Y., Simard, P., Frasconi, P. 1994. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157-166. https://doi.org/10.1109/72.279181Breiman, L., Friedman, J., Olshen, R.A., Stone, C.J. 1984. Classification and regression trees. Taylor & Francis: London, UK.Breiman, L. 2001. Random forests. Machine learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324Campos-Taberner, M., Romero-Soriano, A., Gatta, C., Camps-Valls, G., Lagrange, A., Le Saux, B., Beaupère, A., Boulch, A., Chan-Hon-Tong, A., Herbin, S., Randrianarivo, H., Ferecatu, M., Shimoni, M., Moser, G., Tuia, D. 2016. Processing of extremely high-resolution Lidar and RGB data: outcome of the 2015 IEEE GRSS data fusion contest-part a: 2-D contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(12), 5547-5559. https://doi.org/10.1109/JSTARS.2016.2569162Campos-Taberner, M., García-Haro, F.J., Martínez, B., Sánchez-Ruíz, S., Gilabert, M.A. 2019a. A Copernicus Sentinel-1 and Sentinel-2 Classification Framework for the 2020+ European Common Agricultural Policy: A Case Study in València (Spain). Agronomy, 9(9), 556. https://doi.org/10.3390/agronomy9090556Campos-Taberner, M., García-Haro, F.J., Martínez, B., Sánchez-Ruiz, S., Gilabert, M.A. 2019b. Evaluación del potencial de Sentinel-2 para actualizar el SIGPAC de la Comunitat Valenciana. En: XVIII Congreso de la Asociación Española de Teledetección. Valladolid, España, 24-27, septiembre. pp 11-14.Camps-Valls, G., Tuia, D., Bruzzone, L., Benediktsson, J.A. 2013. Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE Signal Processing Magazine, 31(1), 45-54. https://doi.org/10.1109/MSP.2013.2279179Chuvieco, E. 2008. Teledetección Ambiental. La observación de la Tierra desde el espacio. Madrid: Ariel.Cover, T., Hart, P. 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. https://doi.org/10.1109/TIT.1967.1053964Estrada, J., Sánchez, H., Hernanz, L., Checa, M.J., Roman, D. 2017. Enabling the Use of Sentinel-2 and LiDAR Data for Common Agriculture Policy Funds Assignment. ISPRS International Journal of Geo-Information, 6(8), 255. https://doi.org/10.3390/ijgi6080255Friedl, M.A., McIver, D.K., Hodges, J.C., Zhang, X.Y., Muchoney, D., Strahler, A.H., Baccini, A. 2002. Global land cover mapping from MODIS: algorithms and early results. Remote sensing of Environment, 83(1-2), 287-302. https://doi.org/10.1016/S0034-4257(02)00078-0Graves, A., Mohamed, A.R., Hinton, G. 2013. Speech recognition with deep recurrent neural networks. En 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645-6649. https://doi.org/10.1109/ICASSP.2013.6638947Gilabert, M.A., González-Piqueras, J., García-Haro, J., 1997. Acerca de los índices de vegetación. Revista de teledetección, 8, 1-10. Disponible en http://www.aet.org.es/?q=revista8-4González-Guerrero, O., y Pons, X., 2020. The 2017 Land Use/Land Cover Map of Catalonia based on Sentinel-2 images and auxiliary data. Revista de Teledetección, 55, 81-92. https://doi.org/10.4995/raet.2020.13112Gregrio, A., Jansen, J. 2000. Land cover classification system (LCCS); Classification concepts and user manual for software version 2. Roma: FAO.Griffiths, P., Nendel, C., Hostert, P. 2019. Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sensing of Environment, 220, 135-151. https://doi.org/10.1016/j.rse.2018.10.031Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.Z. 2019. XAI-Explainable artificial intelligence. Science Robotics, 4(37). https://doi.org/10.1126/scirobotics.aay7120Haralick, R.M., Shanmugam, K., Dinstein, I.H. 1973. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, 3(6), 610-621. https://doi.org/10.1109/TSMC.1973.4309314Haykin, S. 1994. Neural networks: a comprehensive foundation. River: Prentice Hall.Immitzer, M., Vuolo, F., Atzberger, C. 2016. First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sensing, 8(3), 166. https://doi.org/10.3390/rs8030166Hochreiter, S., Schmidhuber, J. 1997. Long short-term memory. Neural computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A. 2017. Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778-782. https://doi.org/10.1109/LGRS.2017.2681128Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., Johnson, B.A. 2019. Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166-177. https://doi.org/10.1016/j.isprsjprs.2019.04.015Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P. 2016. Convolutional neural networks for large-scale remotesensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 645-657. https://doi.org/10.1109/TGRS.2016.2612821Mikolov, T., Kombrink, S., Burget, L., Černocký, J., Khudanpur, S. 2011. Extensions of recurrent neural network language model. En 2011 IEEE International Conference on acoustics, speech and signal processing (ICASSP). Praga, República Checa, 22-27 Mayo. pp. 5528-5531. https://doi.org/10.1109/ICASSP.2011.5947611Montavon, G., Samek, W., Müller, K.R. 2018. Methods for interpreting and understanding deep neural networks. Digital Signal Processing, 73, 1-15. https://doi.org/10.1016/j.dsp.2017.10.011Mou, L., Ghamisi, P., Zhu, X.X. 2017. Deep recurrent neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7), 3639-3655. https://doi.org/10.1109/TGRS.2016.2636241Mountrakis, G., Im, J., Ogole, C. 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247- 259. https://doi.org/10.1016/j.isprsjprs.2010.11.001Ndikumana, E., Ho Tong Minh, D., Baghdadi, N., Courault, D., Hossard, L. 2018. Deep recurrent neural network for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sensing, 10(8), 1217. https://doi.org/10.3390/rs10081217Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N. 2019. Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204. https://doi.org/10.1038/s41586-019-0912-1Ruiz, L.A., Almonacid-Caballer, J., Crespo-Peremarch, P., Recio, J.A., Pardo-Pascual, J.E., SánchezGarcía, E. 2020. Automated classification of crop types and condition in a mediterranean area using a fine-tuned convolutional neural network. En The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. Niza, Francia, 31 Agosto - 2 Septiembre (en línea). pp. 1061-1068. https://doi.org/10.5194/isprs-archivesXLIII-B3-2020-1061-2020Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., Muller, K.R. (Eds.). 2020. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Cham: Springer Nature. https://doi.org/10.1007/978-3-030-28954-6Schmedtmann, J., Campagnolo, M.L. 2015. Reliable crop identification with satellite imagery in the context of common agriculture policy subsidy control. Remote Sensing, 7(7), 9325-9346. https://doi.org/10.3390/rs70709325Schuster, M., Paliwal, K.K. 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673-2681. https://doi.org/10.1109/78.650093Sitokonstantinou, V., Papoutsis, I., Kontoes, C., Lafarga Arnal, A., Armesto Andrés, A.P., Garraza Zurbano, J.A. 2018. Scalable parcel-based crop identification scheme using sentinel-2 data time-series for the monitoring of the common agricultural policy. Remote Sensing, 10(6), 911. https://doi.org/10.3390/rs10060911Story, M., Congalton, R.G. 1986. Accuracy assessment: a user's perspective. Photogrammetric Engineering and Remote Sensing, 52(3), 397-399.Tapsall, B., Milenov, P., Tasdemir, K. 2010. Analysis of RapidEye imagery for annual landcover mapping as an aid to European Union (EU) common agricultural polic. En ISPRS Technical Commission VII Symposium - 100 Years ISPRS. Viena, Austria, 5-7 Julio. pp. 568-573.Van Den Oord, A., Kalchbrenner, N., Kavukcuoglu, K. 2016. Pixel recurrent neural networks. En Proceedings of the 33rd International Conference on International Conference on Machine Learning. New York, EEUU., 20-22 Junio. pp. 1747-1756.Vuolo, F., Neuwirth, M., Immitzer, M., Atzberger, C., Ng, W.T. 2018. How much does multi-temporal Sentinel-2 data improve crop type classification? International Journal of Applied Earth Observation and Geoinformation, 72, 122-130. https://doi.org/10.1016/j.jag.2018.06.007Wardlow, B.D., Egbert, S.L., Kastens, J.H. 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sensing of Environment, 108(3), 290-310. https://doi.org/10.1016/j.rse.2006.11.021Watson, R.T., Noble, I.R., Bolin, B., Ravindranath, N.H., Verardo, D.J., Dokken, D.J. 2000. Land use, land-use change and forestry: a special report of the Intergovernmental Panel on Climate Change. 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Corrigendum: Intrathecal drug delivery: Advances and applications in the management of chronic pain patient.

post-print4149 K

Number of particle creation and decoherence in the nonideal dynamical Casimir effect at finite temperature

In this work we investigate the dynamical Casimir effect in a nonideal cavity by deriving an effective Hamiltonian. We first compute a general expression for the average number of particle creation, applicable for any law of motion of the cavity boundary. We also compute a general expression for the linear entropy of an arbitrary state prepared in a selected mode, also applicable for any law of motion of the cavity boundary. As an application of our results we have analyzed both the average number of particle creation and linear entropy within a particular oscillatory motion of the cavity boundary. On the basis of these expressions we develop a comprehensive analysis of the resonances in the number of particle creation in the nonideal dynamical Casimir effect. We also demonstrate the occurrence of resonances in the loss of purity of the initial state and estimate the decoherence times associated with these resonances.Comment: comments are welcom

On-line tools to improve the presentation skills of scientific results

[EN] In experimental sciences and engineering it is essential to communicate and present the results effectively. The authors have participated in several educational innovation projects since 2016, aimed at developing of materials to improve the communication skills of scientific results. An exhaustive and updated compilation of the international rules that constitute the basis for the writted and oral scientific presentations was carried out. The good teaching practices in these fields were also identified. The results of those previous projects have shown the need to incorporate web questionnaires and other interactive content into the educational program. These are adapted to the demands of the students and provide a training feeback. In this contribution, the new materials that are being developed within the innovation project UV-SFPIE_PID19-1096780, funded by the University of Valencia, are presented. They are devoted to facilitate the acquisition of communication skills of scientific results. In particular, these tools combine ICT self-learning environments with traditional classroom teaching (blended learning). The project methodology includes educational data mining aimed at identifying the most effective materials and activities to achieve its objectives. The aim of these mixed learning tools is to facilitate the acquisition by the students of the necessary skills of oral and written communication, improve their presentation skills and, consequently, also their employability as university graduates.This work has been supported by the University of Valencia through project SFPIE_PID19-1096780.Campos-Taberner, M.; Gilabert, M.; Manzanares, J.; Mafé, S.; Cervera, J.; García-Haro, F.; Martínez, B.... (2020). On-line tools to improve the presentation skills of scientific results. IATED. 4907-4910. https://doi.org/10.21125/inted.2020.1342S4907491

Altered glucose homeostasis and hepatic function in obese mice deficient for both kinin receptor genes

The Kallikrein-Kinin System (KKS) has been implicated in several aspects of metabolism, including the regulation of glucose homeostasis and adiposity. Kinins and des-Arg-kinins are the major effectors of this system and promote their effects by binding to two different receptors, the kinin B2 and B1 receptors, respectively. To understand the influence of the KKS on the pathophysiology of obesity and type 2 diabetes (T2DM), we generated an animal model deficient for both kinin receptor genes and leptin (obB1B2KO). Six-month-old obB1B2KO mice showed increased blood glucose levels. Isolated islets of the transgenic animals were more responsive to glucose stimulation releasing greater amounts of insulin, mainly in 3-month-old mice, which was corroborated by elevated serum C-peptide concentrations. Furthermore, they presented hepatomegaly, pronounced steatosis, and increased levels of circulating transaminases. This mouse also demonstrated exacerbated gluconeogenesis during the pyruvate challenge test. The hepatic abnormalities were accompanied by changes in the gene expression of factors linked to glucose and lipid metabolisms in the liver. Thus, we conclude that kinin receptors are important for modulation of insulin secretion and for the preservation of normal glucose levels and hepatic functions in obese mice, suggesting a protective role of the KKS regarding complications associated with obesity and T2DM

The Physics of turbulent and dynamically unstable Herbig-Haro jets

The overall properties of the Herbig-Haro objects such as centerline velocity, transversal profile of velocity, flow of mass and energy are explained adopting two models for the turbulent jet. The complex shapes of the Herbig-Haro objects, such as the arc in HH34 can be explained introducing the combination of different kinematic effects such as velocity behavior along the main direction of the jet and the velocity of the star in the interstellar medium. The behavior of the intensity or brightness of the line of emission is explored in three different cases : transversal 1D cut, longitudinal 1D cut and 2D map. An analytical explanation for the enhancement in intensity or brightness such as usually modeled by the bow shock is given by a careful analysis of the geometrical properties of the torus.Comment: 17 pages, 10 figures. Accepted for publication in Astrophysics & Spac

Vegetation vulnerability to drought in Spain

Revista oficial de la Asociación Española de Teledetección[EN] Frequency of climatic extremes like long duration droughts has increased in Spain over the last century.The use of remote sensing observations for monitoring and detecting drought is justified on the basis that vegetation vigor is closely related to moisture condition. We derive satellite estimates of bio-physical variables such as fractional vegetation cover (FVC) from MODIS/EOS and SEVIRI/MSG time series. The study evaluates the strength of temporal relationships between precipitation and vegetation condition at time-lag and cumulative rainfall intervals. From this analysis, it was observed that the climatic disturbances affected both the growing season and the total amount of vegetation. However, the impact of climate variability on the vegetation dynamics has shown to be highly dependent on the regional climate, vegetation community and growth stages. In general, they were more significant in arid and semiarid areas, since water availability most strongly limits vegetation growth in these environments.[EN] Los extremos climáticos se han incrementado en España a los largo del último siglo; por ello, su análisis se ha convertido en una línea prioritaria de conocimiento con objeto fundamental de diseñar planes para la gestión y mitigación de sus efectos. Los datos de satélite permiten analizar las variaciones en la actividad de la vegetación a varias escalas temporales y su respuesta a la variabilidad climática. En este trabajo se pone de manifiesto la vulnera-bilidad de la vegetación en España ante condiciones ambientales extremas a través de las correlaciones entre índices meteorológicos de sequía (SPI) y variables biofísicas extraídas de datos MODIS/EOS y SEVIRI/MSG. Las anomalías en la vegetación, como indicadores de las condiciones de humedad de la misma, pueden ayudar a cuantificar y gestionar episodios meteorológicos extremos y hacer un seguimiento de la misma. Las mayores correlaciones se han obtenido en las regiones áridas y semiáridas y durante los meses de máxima actividad de la vegetación, generalmente entre mayo y junio.Este trabajo se enmarca en los proyectos DULCINEA (CGL2005–04202), RESET CLIMATE (CGL2012–35831), LSA SAF (EUMETSAT) y ERMES (FP7-SPACE-2013, Contract 606983).García-Haro, F.; Campos-Taberner, M.; Sabater, N.; Belda, F.; Moreno, A.; Gilabert, M.; Martínez, B.... (2014). Vulnerabilidad de la vegetación a la sequía en España. Revista de Teledetección. (42):29-38. https://doi.org/10.4995/raet.2014.2283SWORD29384

(Sub)mm Interferometry Applications in Star Formation Research

This contribution gives an overview about various applications of (sub)mm interferometry in star formation research. The topics covered are molecular outflows, accretion disks, fragmentation and chemical properties of low- and high-mass star-forming regions. A short outlook on the capabilities of ALMA is given as well.Comment: 20 pages, 7 figures, in proceedings to "2nd European School on Jets from Young Star: High Angular Resolution Observations". A high-resolution version of the paper can be found at http://www.mpia.de/homes/beuther/papers.htm