Genomics accelerated isolation of a new stem rust avirulence gene - wheat resistance gene pair

Stem rust caused by the fungus Puccinia graminis f. sp. tritici (Pgt) is a devastating disease of the global staple crop wheat. Although this disease was largely controlled by genetic resistance in the latter half of the 20th century, new strains of Pgt with increased virulence, such as Ug99, have evolved by somatic hybridisation and mutation. These newly emerged strains have caused significant losses in Africa and other regions and their continued spread threatens global wheat production. Breeding for disease resistance provides the most cost-effective control of wheat rust diseases. A number of race-specific rust resistance genes have been characterised in wheat and most encode immune receptors of the nucleotide-binding leucine-rich repeat (NLR) class. These receptors recognize pathogen effector proteins often known as avirulence (Avr) proteins. However, only two Avr genes have been identified in Pgt to date, AvrSr35 and AvrSr50 and none in other cereal rusts, which hinders efforts to understand the evolution of virulence in rust populations. The Sr27 resistance gene was first identified in a wheat line carrying an introgression of the 3R chromosome from Imperial rye. Although not deployed widely in wheat, Sr27 is widespread in the artificial crop species Triticosecale (triticale) which is a wheat-rye hybrid and is a host for Pgt. Sr27 is effective against Ug99 and other recently emerged Pgt strains. Here we identify both the Sr27 gene in wheat and the corresponding AvrSr27 gene in Pgt and show that virulence to Sr27 can arise experimentally and in the field through deletion mutations, copy number variation and expression level polymorphisms at the AvrSr27 locus

All-mass n-gon integrals in n dimensions

We explore the correspondence between one-loop Feynman integrals and (hyperbolic) simplicial geometry to describe the "all-mass" case: integrals with generic external and internal masses. Specifically, we focus on $n$-particle integrals in exactly $n$ space-time dimensions, as these integrals have particularly nice geometric properties and respect a dual conformal symmetry. In four dimensions, we leverage this geometric connection to give a concise dilogarithmic expression for the all-mass box in terms of the Murakami-Yano formula. In five dimensions, we use a generalized Gauss-Bonnet theorem to derive a similar dilogarithmic expression for the all-mass pentagon. We also use the Schl\"afli formula to write down the symbol of these integrals for all $n$. Finally, we discuss how the geometry behind these formulas depends on space-time signature, and we gather together many results related to these integrals from the mathematics and physics literature.Comment: 49 pages, 8 figure

Multi-Trait and Multi-Environment QTL Analyses for Resistance to Wheat Diseases

BACKGROUND: Stripe rust, leaf rust, tan spot, and Karnal bunt are economically significant diseases impacting wheat production. The objectives of this study were to identify quantitative trait loci for resistance to these diseases in a recombinant inbred line (RIL) from a cross HD29/WH542, and to evaluate the evidence for the presence loci on chromosome region conferring multiple disease resistance. METHODOLOGY/PRINCIPAL FINDINGS: The RIL population was evaluated for four diseases and genotyped with DNA markers. Multi-trait (MT) analysis revealed thirteen QTLs on nine chromosomes, significantly associated with resistance. Phenotypic variation explained by all significant QTLs for KB, TS, Yr, Lr diseases were 57%, 55%, 38% and 22%, respectively. Marginal trait analysis identified the most significant QTLs for resistance to KB on chromosomes 1BS, 2DS, 3BS, 4BL, 5BL, and 5DL. Chromosomes 3AS and 4BL showed significant association with TS resistance. Significant QTLs for Yr resistance were identified on chromosomes 2AS, 4BL and 5BL, while Lr was significant on 6DS. MT analysis revealed that all the QTLs except 3BL significantly reduce KB and was contributed from parent HD29 while all resistant QTLs for TS except on chromosomes 2DS.1, 2DS.2 and 3BL came from WH542. Five resistant QTLs for Yr and six for Lr were contributed from parents WH542 and HD29 respectively. Chromosome region on 4BL showed significant association to KB, TS, and Yr in the population. The multi environment analysis for KB identified three putative QTLs of which two new QTLs, mapped on chromosomes 3BS and 5DL explained 10 and 20% of the phenotypic variation, respectively. CONCLUSIONS/SIGNIFICANCE: This study revealed that MT analysis is an effective tool for detection of multi-trait QTLs for disease resistance. This approach is a more effective and practical than individual QTL mapping analyses. MT analysis identified RILs that combine resistance to multiple diseases from parents WH542 and/or HD29

Extending MAM5 Meta-Model and JaCalIVE Framework to Integrate Smart Devices from Real Environments

[EN] This paper presents the extension of a meta-model (MAM5) and a framework based on the model (JaCalIVE) for developing intelligent virtual environments. The goal of this extension is to develop augmented mirror worlds that represent a real and virtual world coupled, so that the virtual world not only reflects the real one, but also complements it. A new component called a smart resource artifact, that enables modelling and developing devices to access the real physical world, and a human in the loop agent to place a human in the system have been included in the meta-model and framework. The proposed extension of MAM5 has been tested by simulating a light control system where agents can access both virtual and real sensor/actuators through the smart resources developed. The results show that the use of real environment interactive elements (smart resource artifacts) in agent-based simulations allows to minimize the error between simulated and real system.This work is partially supported by the TIN2009-13839-C03-01, TIN2011-27652-C03-01, 547CSD2007-00022, COST Action IC0801, FP7-294931 and the FPI grant AP2013-01276 548 awarded to Jaime-Andres Rincon.Rincón Arango, JA.; Poza Luján, JL.; Julian Inglada, VJ.; Posadas Yagüe, JL.; Carrascosa Casamayor, C. (2016). Extending MAM5 Meta-Model and JaCalIVE Framework to Integrate Smart Devices from Real Environments. PLoS ONE. 11(2):1-27. https://doi.org/10.1371/journal.pone.0149665S127112Luck, M., & Aylett, R. (2000). Applying artificial intelligence to virtual reality: Intelligent virtual environments. Applied Artificial Intelligence, 14(1), 3-32. doi:10.1080/088395100117142Barella A, Ricci A, Boissier O, Carrascosa C. MAM5: Multi-Agent Model For Intelligent Virtual Environments. In: 10th European Workshop on Multi-Agent Systems (EUMAS 2012); 2012. p. 16–30.Omicini, A., Ricci, A., & Viroli, M. (2008). Artifacts in the A&A meta-model for multi-agent systems. Autonomous Agents and Multi-Agent Systems, 17(3), 432-456. doi:10.1007/s10458-008-9053-xYu Ch, Nagpal R. Distributed Consensus and Self-Adapting Modular Robots. In: IROS-2008 workshop on Self-Reconfigurable Robots and Applications; 2008. Available from: http://www.isi.edu/robots/iros08wksp/Papers/iros08-wksp-paper.pdfLidoris G, Buss M. A Multi-Agent System Architecture for Modular Robotic Mobility Aids. In: European Robotics Symposium 2006; 2006. p. 15–26. Available from: http://link.springer.com/chapter/10.1007/11681120_2Yu, C.-H., & Nagpal, R. (2010). A Self-adaptive Framework for Modular Robots in a Dynamic Environment: Theory and Applications. The International Journal of Robotics Research, 30(8), 1015-1036. doi:10.1177/0278364910384753Barbero A, González-Rodríguez MS, de Lara J, Alfonseca M. Multi-Agent Simulation of an Educational Collaborative Web System. In: European Simulation and Modelling Conference; 2007. Available from: http://sistemas-humano-computacionais.wikidot.com/local--files/capitulo:colaboracao-auxiliada-por-computador/%5BBarbero%202007%5D%20Multi-Agent%20Simulation%20of%20an%20Educational%20Collaborative%20Web%20System.pdfRanathunga S, Cranefield S, Purvis MK. Interfacing a cognitive agent platform with a virtual world: a case study using Second Life. In: AAMAS; 2011. p. 1181–1182. Available from: http://www.aamas-conference.org/Proceedings/aamas2011/papers/B20.pdfAndreoli R, De Chiara R, Erra U, Scarano V. Interactive 3d environments by using videogame engines. In: Information Visualisation, 2005. Proceedings. Ninth International Conference on. IEEE; 2005. p. 515–520. Available from: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1509124Dignum, F. (2011). Agents for games and simulations. Autonomous Agents and Multi-Agent Systems, 24(2), 217-220. doi:10.1007/s10458-011-9169-2dos Santos C, Osorio F. AdapTIVE: an intelligent virtual environment and its application in e-commerce. In: Computer Software and Applications Conference, 2004. COMPSAC 2004. Proceedings of the 28th Annual International; 2004. p. 468–473 vol.1.Kazemi, A., Fazel Zarandi, M. H., & Moattar Husseini, S. M. (2008). A multi-agent system to solve the production–distribution planning problem for a supply chain: a genetic algorithm approach. The International Journal of Advanced Manufacturing Technology, 44(1-2), 180-193. doi:10.1007/s00170-008-1826-5Dimuro GP, Costa ACdR, Gonçalves LV, Hubner A. Interval-valued Hidden Markov Models for recognizing personality traits in social exchanges in open multiagent systems. Repositório Institucional da Universidade Federal do Rio Grande. 2008;.Woźniak, M., Graña, M., & Corchado, E. (2014). A survey of multiple classifier systems as hybrid systems. Information Fusion, 16, 3-17. doi:10.1016/j.inffus.2013.04.006Jia L, Zhenjiang M. Entertainment Oriented Intelligent Virtual Environment with Agent and Neural Networks. In: IEEE International Workshop on Haptic, Audio and Visual Environments and Games, 2007. HAVE 2007; 2007. p. 90–95.Corchado, E., Woźniak, M., Abraham, A., de Carvalho, A. C. P. L. F., & Snášel, V. (2014). Recent trends in intelligent data analysis. Neurocomputing, 126, 1-2. doi:10.1016/j.neucom.2013.07.001Ricci A, Viroli M, Omicini A. Give agents their artifacts: the A&A approach for engineering working environments in MAS. In: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems; 2007. p. 150. Available from: http://dl.acm.org/citation.cfm?id=1329308Barella, A., Valero, S., & Carrascosa, C. (2009). JGOMAS: New Approach to AI Teaching. IEEE Transactions on Education, 52(2), 228-235. doi:10.1109/te.2008.925764Behrens, T. M., Hindriks, K. V., & Dix, J. (2010). Towards an environment interface standard for agent platforms. Annals of Mathematics and Artificial Intelligence, 61(4), 261-295. doi:10.1007/s10472-010-9215-9Ricci A, Viroli M, Omicini A. A general purpose programming model & technology for developing working environments in MAS. In: 5th International Workshop Programming Multi-Agent Systems(PROMAS 2007); 2007. p. 54–69. Available from: http://lia.deis.unibo.it/~ao/pubs/pdf/2007/promas.pdfChee-Yee Chong, & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247-1256. doi:10.1109/jproc.2003.814918Kushner D. The making of arduino. IEEE Spectrum. 2011;26.Schmidt, A., & van Laerhoven, K. (2001). How to build smart appliances? IEEE Personal Communications, 8(4), 66-71. doi:10.1109/98.944006Salzmann C, Gillet D. Smart device paradigm standardization for online labs. In: 4th IEEE Global Engineering Education Conference (EDUCON); 2013.Gonzalez-Jorge, H., Riveiro, B., Vazquez-Fernandez, E., Martínez-Sánchez, J., & Arias, P. (2013). Metrological evaluation of Microsoft Kinect and Asus Xtion sensors. Measurement, 46(6), 1800-1806. doi:10.1016/j.measurement.2013.01.011Cook, D. J., & Das, S. K. (2007). How smart are our environments? An updated look at the state of the art. Pervasive and Mobile Computing, 3(2), 53-73. doi:10.1016/j.pmcj.2006.12.001Compton, M., Barnaghi, P., Bermudez, L., García-Castro, R., Corcho, O., Cox, S., … Taylor, K. (2012). The SSN ontology of the W3C semantic sensor network incubator group. Journal of Web Semantics, 17, 25-32. doi:10.1016/j.websem.2012.05.003Munera, E., Poza-Lujan, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E., & Noguera, J. (2015). Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems. Sensors, 15(8), 18080-18101. doi:10.3390/s150818080Castrillón-Santan, M., Lorenzo-Navarro, J., & Hernández-Sosa, D. (2014). Conteo de personas con un sensor RGBD comercial. Revista Iberoamericana de Automática e Informática Industrial RIAI, 11(3), 348-357. doi:10.1016/j.riai.2014.05.006Rincon JA, Julian V, Carrascosa C. An Emotional-based Hybrid Application for Human-Agent Societies. In: 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. vol. 368; 2015. p. 203–214.Rincon JA, Julian V, Carrascosa C. Applying a Social Emotional Model in Human-Agent Societies. In: Workshop WIHAS’15. Intelligent Human-Agent Societies‥ vol. 524 of CCIS; 2015. p. 377–388.Leccese, F., Cagnetti, M., & Trinca, D. (2014). A Smart City Application: A Fully Controlled Street Lighting Isle Based on Raspberry-Pi Card, a ZigBee Sensor Network and WiMAX. Sensors, 14(12), 24408-24424. doi:10.3390/s141224408Mateevitsi V, Haggadone B, Leigh J, Kunzer B, Kenyon RV. Sensing the environment through SpiderSense. In: Proceedings of the 4th Augmented Human International Conference. ACM; 2013. p. 51–57.Kavitha R, Thiyagarajan N. Distributed Intelligent Street Lamp Monitoring and Control System Based on Zigbee. International Journal of Science and Research (IJSR) PP; p. 2319–7064.Pan, M.-S., Yeh, L.-W., Chen, Y.-A., Lin, Y.-H., & Tseng, Y.-C. (2008). A WSN-Based Intelligent Light Control System Considering User Activities and Profiles. IEEE Sensors Journal, 8(10), 1710-1721. doi:10.1109/jsen.2008.2004294Villarrubia, G., De Paz, J., Bajo, J., & Corchado, J. (2014). Ambient Agents: Embedded Agents for Remote Control and Monitoring Using the PANGEA Platform. Sensors, 14(8), 13955-13979. doi:10.3390/s14081395