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    Character-Based Handwritten Text Recognition of Multilingual Documents

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    [EN] An effective approach to transcribe handwritten text documents is to follow a sequential interactive approach. During the supervision phase, user corrections are incorporated into the system through an ongoing retraining process. In the case of multilingual documents with a high percentage of out-of-vocabulary (OOV) words, two principal issues arise. On the one hand, a minor yet important matter for this interactive approach is to identify the language of the current text line image to be transcribed, as a language dependent recognisers typically performs better than a monolingual recogniser. On the other hand, word-based language models suffer from data scarcity in the presence of a large number of OOV words, degrading their estimation and affecting the performance of the transcription system. In this paper, we successfully tackle both issues deploying character-based language models combined with language identification techniques on an entire 764-page multilingual document. The results obtained significantly reduce previously reported results in terms of transcription error on the same task, but showed that a language dependent approach is not effective on top of character-based recognition of similar languages.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n◦ 287755. Also supported by the Spanish Government (MIPRCV ”Consolider Ingenio 2010”, iTrans2 TIN2009-14511, MITTRAL TIN2009-14633-C03-01 and FPU AP2007-0286) and the Generalitat Valenciana (Prometeo/2009/014).Del Agua Teba, MA.; Serrano Martinez Santos, N.; Civera Saiz, J.; Juan Císcar, A. (2012). Character-Based Handwritten Text Recognition of Multilingual Documents. Communications in Computer and Information Science. 328:187-196. https://doi.org/10.1007/978-3-642-35292-8_20S187196328Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(5), 855–868 (2009)Serrano, N., Tarazón, L., Pérez, D., Ramos-Terrades, O., Juan, A.: The GIDOC prototype. In: Proc. of the 10th Int. Workshop on Pattern Recognition in Information Systems (PRIS 2010), Funchal, Portugal, pp. 82–89 (2010)Serrano, N., Pérez, D., Sanchis, A., Juan, A.: Adaptation from Partially Supervised Handwritten Text Transcriptions. In: Proc. of the 11th Int. Conf. on Multimodal Interfaces and the 6th Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI 2009), Cambridge, MA, USA, pp. 289–292 (2009)Serrano, N., Sanchis, A., Juan, A.: Balancing error and supervision effort in interactive-predictive handwriting recognition. In: Proc. of the Int. Conf. on Intelligent User Interfaces (IUI 2010), Hong Kong, China, pp. 373–376 (2010)Serrano, N., Giménez, A., Sanchis, A., Juan, A.: Active learning strategies in handwritten text recognition. In: Proc. of the 12th Int. Conf. on Multimodal Interfaces and the 7th Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI 2010), Beijing, China, vol. (86) (November 2010)Pérez, D., Tarazón, L., Serrano, N., Castro, F., Ramos-Terrades, O., Juan, A.: The GERMANA database. In: Proc. of the 10th Int. 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    Topology analysis and visualization of Potyvirus protein-protein interaction network

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    Background: One of the central interests of Virology is the identification of host factors that contribute to virus infection. Despite tremendous efforts, the list of factors identified remains limited. With omics techniques, the focus has changed from identifying and thoroughly characterizing individual host factors to the simultaneous analysis of thousands of interactions, framing them on the context of protein-protein interaction networks and of transcriptional regulatory networks. This new perspective is allowing the identification of direct and indirect viral targets. Such information is available for several members of the Potyviridae family, one of the largest and more important families of plant viruses. Results: After collecting information on virus protein-protein interactions from different potyviruses, we have processed it and used it for inferring a protein-protein interaction network. All proteins are connected into a single network component. Some proteins show a high degree and are highly connected while others are much less connected, with the network showing a significant degree of dissortativeness. We have attempted to integrate this virus protein-protein interaction network into the largest protein-protein interaction network of Arabidopsis thaliana, a susceptible laboratory host. To make the interpretation of data and results easier, we have developed a new approach for visualizing and analyzing the dynamic spread on the host network of the local perturbations induced by viral proteins. We found that local perturbations can reach the entire host protein-protein interaction network, although the efficiency of this spread depends on the particular viral proteins. By comparing the spread dynamics among viral proteins, we found that some proteins spread their effects fast and efficiently by attacking hubs in the host network while other proteins exert more local effects. Conclusions: Our findings confirm that potyvirus protein-protein interaction networks are highly connected, with some proteins playing the role of hubs. Several topological parameters depend linearly on the protein degree. Some viral proteins focus their effect in only host hubs while others diversify its effect among several proteins at the first step. Future new data will help to refine our model and to improve our predictions.This work was supported by the Spanish Ministerio de Economia y Competitividad grants BFU2012-30805 (to SFE), DPI2011-28112-C04-02 (to AF) and DPI2011-28112-C04-01 (to JP). The first two authors are recipients of fellowships from the Spanish Ministerio de Economia y Competitividad: BES-2012-053772 (to GB) and BES-2012-057812 (to AF-F).Bosque, G.; Folch Fortuny, A.; Picó Marco, JA.; Ferrer, A.; Elena Fito, SF. (2014). Topology analysis and visualization of Potyvirus protein-protein interaction network. 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    Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

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    [EN] The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-tra_ c networks with multiple nodes/ sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G o_ ers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.Najm, IA.; Hamoud, AK.; Lloret, J.; Bosch Roig, I. (2019). Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment. Electronics. 8(6):1-23. https://doi.org/10.3390/electronics8060607S12386Rahem, A. A. T., Ismail, M., Najm, I. A., & Balfaqih, M. (2017). Topology sense and graph-based TSG: efficient wireless ad hoc routing protocol for WANET. 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Congestion Control Principles. doi:10.17487/rfc2914Qazi, I. A., & Znati, T. (2011). On the design of load factor based congestion control protocols for next-generation networks. Computer Networks, 55(1), 45-60. doi:10.1016/j.comnet.2010.07.010Katabi, D., Handley, M., & Rohrs, C. (2002). Congestion control for high bandwidth-delay product networks. ACM SIGCOMM Computer Communication Review, 32(4), 89-102. doi:10.1145/964725.633035Wang, Y., Rozhnova, N., Narayanan, A., Oran, D., & Rhee, I. (2013). An improved hop-by-hop interest shaper for congestion control in named data networking. ACM SIGCOMM Computer Communication Review, 43(4), 55-60. doi:10.1145/2534169.2491233Mirza, M., Sommers, J., Barford, P., & Zhu, X. (2010). A Machine Learning Approach to TCP Throughput Prediction. IEEE/ACM Transactions on Networking, 18(4), 1026-1039. doi:10.1109/tnet.2009.2037812Taherkhani, N., & Pierre, S. (2016). Centralized and Localized Data Congestion Control Strategy for Vehicular Ad Hoc Networks Using a Machine Learning Clustering Algorithm. IEEE Transactions on Intelligent Transportation Systems, 17(11), 3275-3285. doi:10.1109/tits.2016.2546555Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017). State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems. IEEE Communications Surveys & Tutorials, 19(4), 2432-2455. doi:10.1109/comst.2017.2707140Gonzalez-Landero, F., Garcia-Magarino, I., Lacuesta, R., & Lloret, J. (2018). PriorityNet App: A Mobile Application for Establishing Priorities in the Context of 5G Ultra-Dense Networks. IEEE Access, 6, 14141-14150. doi:10.1109/access.2018.2811900Lloret, J., Parra, L., Taha, M., & Tomás, J. (2017). An architecture and protocol for smart continuous eHealth monitoring using 5G. Computer Networks, 129, 340-351. doi:10.1016/j.comnet.2017.05.018Khan, I., Zafar, M., Jan, M., Lloret, J., Basheri, M., & Singh, D. (2018). Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems. Entropy, 20(2), 92. doi:10.3390/e20020092Elappila, M., Chinara, S., & Parhi, D. R. (2018). Survivable Path Routing in WSN for IoT applications. Pervasive and Mobile Computing, 43, 49-63. doi:10.1016/j.pmcj.2017.11.004Singh, K., Singh, K., Son, L. H., & Aziz, A. (2018). Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Computer Networks, 138, 90-107. doi:10.1016/j.comnet.2018.03.023Shelke, M., Malhotra, A., & Mahalle, P. N. (2017). Congestion-Aware Opportunistic Routing Protocol in Wireless Sensor Networks. Smart Innovation, Systems and Technologies, 63-72. doi:10.1007/978-981-10-5544-7_7Godoy, P. D., Cayssials, R. L., & García Garino, C. G. (2018). Communication channel occupation and congestion in wireless sensor networks. Computers & Electrical Engineering, 72, 846-858. doi:10.1016/j.compeleceng.2017.12.049Najm, I. A., Ismail, M., Lloret, J., Ghafoor, K. Z., Zaidan, B. B., & Rahem, A. A. T. (2015). Improvement of SCTP congestion control in the LTE-A network. Journal of Network and Computer Applications, 58, 119-129. doi:10.1016/j.jnca.2015.09.003Najm, I. A., Ismail, M., & Abed, G. A. (2014). High-Performance Mobile Technology LTE-A using the Stream Control Transmission Protocol: A Systematic Review and Hands-on Analysis. Journal of Applied Sciences, 14(19), 2194-2218. doi:10.3923/jas.2014.2194.2218Katuwal, R., Suganthan, P. N., & Zhang, L. (2018). An ensemble of decision trees with random vector functional link networks for multi-class classification. Applied Soft Computing, 70, 1146-1153. doi:10.1016/j.asoc.2017.09.020Gómez, S. E., Martínez, B. C., Sánchez-Esguevillas, A. J., & Hernández Callejo, L. (2017). Ensemble network traffic classification: Algorithm comparison and novel ensemble scheme proposal. Computer Networks, 127, 68-80. doi:10.1016/j.comnet.2017.07.018Hasan, M., Hossain, E., & Niyato, D. (2013). Random access for machine-to-machine communication in LTE-advanced networks: issues and approaches. IEEE Communications Magazine, 51(6), 86-93. doi:10.1109/mcom.2013.6525600Liang, D., Zhang, Z., & Peng, M. (2015). Access Point Reselection and Adaptive Cluster Splitting-Based Indoor Localization in Wireless Local Area Networks. IEEE Internet of Things Journal, 2(6), 573-585. doi:10.1109/jiot.2015.2453419Park, H., Haghani, A., Samuel, S., & Knodler, M. A. (2018). Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion. Accident Analysis & Prevention, 112, 39-49. doi:10.1016/j.aap.2017.11.025Shu, J., Liu, S., Liu, L., Zhan, L., & Hu, G. (2017). Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine. Chinese Journal of Electronics, 26(2), 377-384. doi:10.1049/cje.2017.01.013Riekstin, A. C., Januário, G. C., Rodrigues, B. B., Nascimento, V. T., Carvalho, T. C. M. B., & Meirosu, C. (2016). Orchestration of energy efficiency capabilities in networks. Journal of Network and Computer Applications, 59, 74-87. doi:10.1016/j.jnca.2015.06.015Adi, E., Baig, Z., & Hingston, P. (2017). Stealthy Denial of Service (DoS) attack modelling and detection for HTTP/2 services. Journal of Network and Computer Applications, 91, 1-13. doi:10.1016/j.jnca.2017.04.015Stimpfling, T., Bélanger, N., Cherkaoui, O., Béliveau, A., Béliveau, L., & Savaria, Y. (2017). Extensions to decision-tree based packet classification algorithms to address new classification paradigms. Computer Networks, 122, 83-95. doi:10.1016/j.comnet.2017.04.021Singh, D., Nigam, S. P., Agrawal, V. P., & Kumar, M. (2016). Vehicular traffic noise prediction using soft computing approach. 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E.-R., El-Gayyar, M., & Nassar, H. (2019). An adaptive framework for real-time data reduction in AMI. Journal of King Saud University - Computer and Information Sciences, 31(3), 392-402. doi:10.1016/j.jksuci.2018.02.012Louvieris, P., Clewley, N., & Liu, X. (2013). Effects-based feature identification for network intrusion detection. Neurocomputing, 121, 265-273. doi:10.1016/j.neucom.2013.04.038Verma, P. K., Verma, R., Prakash, A., Agrawal, A., Naik, K., Tripathi, R., … Abogharaf, A. (2016). Machine-to-Machine (M2M) communications: A survey. Journal of Network and Computer Applications, 66, 83-105. doi:10.1016/j.jnca.2016.02.016Hamoud, A. K., Hashim, A. S., & Awadh, W. A. (2018). Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 26. doi:10.9781/ijimai.2018.02.004Lavanya, D. (2012). Ensemble Decision Tree Classifier For Breast Cancer Data. 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    Legacy Network Integration with SDN-IP Implementation towards a Multi-Domain SoDIP6 Network Environment

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    [EN] The logical separation of the data plane and the control plane of the network device conceptually defined by software-defined networking (SDN) creates many opportunities to create smart networking with better efficiency for network management and operation. SDN implementation over telecommunications (Telcos) and Internet service provider (ISP) networks is a challenging issue due to the lack of a high maturity level of SDN-based standards and several other critical factors that are considered during the real-time migration of existing legacy IPv4 networks. Different migration approaches have been studied; however, none of them seem to be close to realizing implementation. This paper implements the SDN-IP and Open Network Operating System (ONOS) SDN controller to migrate legacy IPv4 networks to multi-domain software-defined IPv6 (SoDIP6) networks and experimentally evaluate the viability of joint network migration in the ISP networks. We present results using extensive simulations for the suitable placement of the master ONOS controller during network migration by considering minimum control path latency using optimal path routing and the breadth first router replacement (BFR) technique. Our empirical analysis and evaluations show that the identification of the median router to attach the master controller and router migration planning using BFR give better results for carrier-grade legacy networks' migration to SoDIP6 networks.This research was partially funded by the Norwegian University of Science and Technology, Trondhiem, Norway (NTNU) under Sustainable Engineering Education Project (SEEP) financed by EnPE, University Grant Commission (grant-ID: FRG7475Engg01), Bhaktapur, Nepal, Nepal academy of Science and Technology (NAST), Kathmandu, Nepal, and U.S. National Science Foundation (NSF). The work of Danda B. Rawat was partly supported by the U.S. National Science Foundation (NSF) under grants CNS 1650831 and HRD 1828811. Any opinions, findings, and conclusions or recommendations expressed in this article are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the NSF. We are thankful to the ERASMUS+ KA107 project and the GRC lab team members at Universitat Politècnica De València for the research support and facilitation.Dawadi, BR.; Rawat, DB.; Joshi, SR.; Manzoni, P. (2020). Legacy Network Integration with SDN-IP Implementation towards a Multi-Domain SoDIP6 Network Environment. Electronics. 9(9):1-22. https://doi.org/10.3390/electronics9091454S12299Dawadi, B. R., Rawat, D. B., & Joshi, S. R. (2019). Software Defined IPv6 Network: A New Paradigm for Future Networking. Journal of the Institute of Engineering, 15(2), 1-13. doi:10.3126/jie.v15i2.27636Dawadi, B. R., Rawat, D. B., Joshi, S. R., & Manzoni, P. (2020). Evolutionary gaming approach for decision making of Tier‐3 Internet service provider networks migration to SoDIP6 networks. International Journal of Communication Systems, 33(11). doi:10.1002/dac.4399Gu, D., Su, J., Xue, Y., Wang, D., Li, J., Luo, Z., & Yan, B. (2020). Modeling IPv6 adoption from biological evolution. Computer Communications, 158, 166-177. doi:10.1016/j.comcom.2020.02.081IPv6 Capability Measurement https://stats.labs.apnic.net/ipv6Dawadi, B. R., Rawat, D. B., Joshi, S. R., & Keitsch, M. M. (2018). Joint Cost Estimation Approach for Service Provider Legacy Network Migration to Unified Software Defined IPv6 Network. 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC). doi:10.1109/cic.2018.00056Csikor, L., Szalay, M., Retvari, G., Pongracz, G., Pezaros, D. P., & Toka, L. (2020). Transition to SDN is HARMLESS: Hybrid Architecture for Migrating Legacy Ethernet Switches to SDN. IEEE/ACM Transactions on Networking, 28(1), 275-288. doi:10.1109/tnet.2019.2958762Sandhya, Sinha, Y., & Haribabu, K. (2017). A survey: Hybrid SDN. Journal of Network and Computer Applications, 100, 35-55. doi:10.1016/j.jnca.2017.10.003Mostafaei, H., Lospoto, G., Di Lallo, R., Rimondini, M., & Di Battista, G. (2020). A framework for multi‐provider virtual private networks in software‐defined federated networks. International Journal of Network Management, 30(6). doi:10.1002/nem.2116Dawadi, B. R., Rawat, D. B., & Joshi, S. R. (2019). Evolutionary Dynamics of Service Provider Legacy Network Migration to Software Defined IPv6 Network. Advances in Intelligent Systems and Computing, 245-257. doi:10.1007/978-3-030-19861-9_24Salsano, S., Ventre, P. L., Lombardo, F., Siracusano, G., Gerola, M., Salvadori, E., … Prete, L. (2016). Hybrid IP/SDN Networking: Open Implementation and Experiment Management Tools. IEEE Transactions on Network and Service Management, 13(1), 138-153. doi:10.1109/tnsm.2015.2507622Vissicchio, S., Tilmans, O., Vanbever, L., & Rexford, J. (2015). Central Control Over Distributed Routing. Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication. doi:10.1145/2785956.2787497Rizvi, S. N., Raumer, D., Wohlfart, F., & Carle, G. (2015). Towards carrier grade SDNs. Computer Networks, 92, 218-226. doi:10.1016/j.comnet.2015.09.029Risdianto, A. C., Tsai, P.-W., Ling, T. C., Yang, C.-S., & Kim, J. (2017). Enhanced Onos Sdn Controllers Deployment For Federated Multi-Domain Sdn-Cloud With Sd-Routing-Exchange. Malaysian Journal of Computer Science, 30(2), 134-153. doi:10.22452/mjcs.vol30no2.5Ventre, P. L., Salsano, S., Gerola, M., Salvadori, E., Usman, M., Buscaglione, S., … Snow, W. (2017). SDN-Based IP and Layer 2 Services with an Open Networking Operating System in the GÉANT Service Provider Network. IEEE Communications Magazine, 55(4), 71-79. doi:10.1109/mcom.2017.1600194SDN-IP Arhitecture https://wiki.onosproject.org/display/ONOS/SDN-IP+ArchitectureLee, H.-L., Liu, T.-L., & Chen, M. (2019). Deploying SDN-IP over Transnational Network Testbed. 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). doi:10.1109/icce-tw46550.2019.8991776Das, T., Sridharan, V., & Gurusamy, M. (2020). A Survey on Controller Placement in SDN. IEEE Communications Surveys & Tutorials, 22(1), 472-503. doi:10.1109/comst.2019.2935453Chen, W., Chen, C., Jiang, X., & Liu, L. (2018). Multi-Controller Placement Towards SDN Based on Louvain Heuristic Algorithm. IEEE Access, 6, 49486-49497. doi:10.1109/access.2018.2867931Qi, Y., Wang, D., Yao, W., Li, H., & Cao, Y. (2019). Towards Multi-Controller Placement for SDN Based on Density Peaks Clustering. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). doi:10.1109/icc.2019.8761814Lu, J., Zhang, Z., Hu, T., Yi, P., & Lan, J. (2019). A Survey of Controller Placement Problem in Software-Defined Networking. IEEE Access, 7, 24290-24307. doi:10.1109/access.2019.2893283Singh, A. K., Maurya, S., Kumar, N., & Srivastava, S. (2019). Heuristic approaches for the reliable SDN controller placement problem. Transactions on Emerging Telecommunications Technologies, 31(2). doi:10.1002/ett.3761Das, T., & Gurusamy, M. (2018). Resilient Controller Placement in Hybrid SDN/Legacy Networks. 2018 IEEE Global Communications Conference (GLOBECOM). doi:10.1109/glocom.2018.8647566Heller, B., Sherwood, R., & McKeown, N. (2012). The controller placement problem. ACM SIGCOMM Computer Communication Review, 42(4), 473-478. doi:10.1145/2377677.2377767SDN Control Plane Performance: Raising the Bar on SDN Performance, Scalability, and High Availability https://wiki.onosproject.org/download/attachments/13994369/Whitepaper-%20ONOS%20Kingfisher%20release%20performance.pdf?version=

    Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

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    [EN] Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects.The authors wish to acknowledge the consortium of the MOSAIC project (funded by the European Commission, Grant No. FP7-ICT 600914) for their commitment during concept development, which led to the development of the research reported in this manuscriptMartinez-Millana, A.; Bayo-Monton, JL.; Argente-Pla, M.; Fernández Llatas, C.; Merino-Torres, JF.; Traver Salcedo, V. (2018). Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes. Sensors. 18 (1)(79):1-26. https://doi.org/10.3390/s18010079S12618 (1)79Thomas, C. C., & Philipson, L. H. (2015). Update on Diabetes Classification. Medical Clinics of North America, 99(1), 1-16. doi:10.1016/j.mcna.2014.08.015Kahn, S. E., Hull, R. L., & Utzschneider, K. M. (2006). Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature, 444(7121), 840-846. doi:10.1038/nature05482Guariguata, L., Whiting, D. R., Hambleton, I., Beagley, J., Linnenkamp, U., & Shaw, J. E. (2014). Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Research and Clinical Practice, 103(2), 137-149. doi:10.1016/j.diabres.2013.11.002Beagley, J., Guariguata, L., Weil, C., & Motala, A. A. (2014). Global estimates of undiagnosed diabetes in adults. Diabetes Research and Clinical Practice, 103(2), 150-160. doi:10.1016/j.diabres.2013.11.001Hippisley-Cox, J., Coupland, C., Robson, J., Sheikh, A., & Brindle, P. (2009). Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ, 338(mar17 2), b880-b880. doi:10.1136/bmj.b880Meigs, J. B., Shrader, P., Sullivan, L. M., McAteer, J. B., Fox, C. S., Dupuis, J., … Cupples, L. A. (2008). Genotype Score in Addition to Common Risk Factors for Prediction of Type 2 Diabetes. New England Journal of Medicine, 359(21), 2208-2219. doi:10.1056/nejmoa0804742Gillies, C. L., Abrams, K. R., Lambert, P. C., Cooper, N. J., Sutton, A. J., Hsu, R. T., & Khunti, K. (2007). Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. BMJ, 334(7588), 299. doi:10.1136/bmj.39063.689375.55Noble, D., Mathur, R., Dent, T., Meads, C., & Greenhalgh, T. (2011). Risk models and scores for type 2 diabetes: systematic review. BMJ, 343(nov28 1), d7163-d7163. doi:10.1136/bmj.d7163Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015). Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement. Annals of Internal Medicine, 162(1), 55. doi:10.7326/m14-0697Steyerberg, E. W., Moons, K. G. M., van der Windt, D. A., Hayden, J. A., Perel, P., … Schroter, S. (2013). Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research. PLoS Medicine, 10(2), e1001381. doi:10.1371/journal.pmed.1001381Collins, G. S., & Moons, K. G. M. (2012). Comparing risk prediction models. BMJ, 344(may24 2), e3186-e3186. doi:10.1136/bmj.e3186Riley, R. D., Ensor, J., Snell, K. I. E., Debray, T. P. A., Altman, D. G., Moons, K. G. M., & Collins, G. S. (2016). External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ, i3140. doi:10.1136/bmj.i3140Reilly, B. M., & Evans, A. T. (2006). Translating Clinical Research into Clinical Practice: Impact of Using Prediction Rules To Make Decisions. Annals of Internal Medicine, 144(3), 201. doi:10.7326/0003-4819-144-3-200602070-00009Altman, D. G., Vergouwe, Y., Royston, P., & Moons, K. G. M. (2009). Prognosis and prognostic research: validating a prognostic model. 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J., Kumari, M., … Humphries, S. E. (2010). Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ, 340(jan14 1), b4838-b4838. doi:10.1136/bmj.b4838Sackett, D. L. (1997). Evidence-based medicine. Seminars in Perinatology, 21(1), 3-5. doi:10.1016/s0146-0005(97)80013-4Segagni, D., Ferrazzi, F., Larizza, C., Tibollo, V., Napolitano, C., Priori, S. G., & Bellazzi, R. (2011). R Engine Cell: integrating R into the i2b2 software infrastructure. Journal of the American Medical Informatics Association, 18(3), 314-317. doi:10.1136/jamia.2010.007914Semantic Webhttp://www.w3.org/2001/sw/Murphy, S. N., Weber, G., Mendis, M., Gainer, V., Chueh, H. C., Churchill, S., & Kohane, I. (2010). Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association, 17(2), 124-130. doi:10.1136/jamia.2009.000893Murphy, S., Churchill, S., Bry, L., Chueh, H., Weiss, S., Lazarus, R., … Kohane, I. (2009). Instrumenting the health care enterprise for discovery research in the genomic era. Genome Research, 19(9), 1675-1681. doi:10.1101/gr.094615.109Lindstrom, J., & Tuomilehto, J. (2003). The Diabetes Risk Score: A practical tool to predict type 2 diabetes risk. Diabetes Care, 26(3), 725-731. doi:10.2337/diacare.26.3.725Alssema, M., Vistisen, D., Heymans, M. W., Nijpels, G., Glümer, C., … Dekker, J. M. (2010). The Evaluation of Screening and Early Detection Strategies for Type 2 Diabetes and Impaired Glucose Tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes. Diabetologia, 54(5), 1004-1012. doi:10.1007/s00125-010-1990-7Mann, D. M., Bertoni, A. G., Shimbo, D., Carnethon, M. R., Chen, H., Jenny, N. S., & Muntner, P. (2010). Comparative Validity of 3 Diabetes Mellitus Risk Prediction Scoring Models in a Multiethnic US Cohort: The Multi-Ethnic Study of Atherosclerosis. American Journal of Epidemiology, 171(9), 980-988. doi:10.1093/aje/kwq030Stern, M. P., Williams, K., & Haffner, S. M. (2002). Identification of Persons at High Risk for Type 2 Diabetes Mellitus: Do We Need the Oral Glucose Tolerance Test? Annals of Internal Medicine, 136(8), 575. doi:10.7326/0003-4819-136-8-200204160-00006Abdul-Ghani, M. A., Abdul-Ghani, T., Stern, M. P., Karavic, J., Tuomi, T., Bo, I., … Groop, L. (2011). Two-Step Approach for the Prediction of Future Type 2 Diabetes Risk. Diabetes Care, 34(9), 2108-2112. doi:10.2337/dc10-2201Rahman, M., Simmons, R. K., Harding, A.-H., Wareham, N. J., & Griffin, S. J. (2008). A simple risk score identifies individuals at high risk of developing Type 2 diabetes: a prospective cohort study. Family Practice, 25(3), 191-196. doi:10.1093/fampra/cmn024Guasch-Ferré, M., Bulló, M., Costa, B., Martínez-Gonzalez, M. Á., Ibarrola-Jurado, N., … Estruch, R. (2012). A Risk Score to Predict Type 2 Diabetes Mellitus in an Elderly Spanish Mediterranean Population at High Cardiovascular Risk. PLoS ONE, 7(3), e33437. doi:10.1371/journal.pone.0033437Wilson, P. W. F. (2007). Prediction of Incident Diabetes Mellitus in Middle-aged Adults. Archives of Internal Medicine, 167(10), 1068. doi:10.1001/archinte.167.10.1068Franzin, A., Sambo, F., & Di Camillo, B. (2016). bnstruct: an R package for Bayesian Network structure learning in the presence of missing data. Bioinformatics, btw807. doi:10.1093/bioinformatics/btw807Rood, B., & Lewis, M. J. (2009). Grid Resource Availability Prediction-Based Scheduling and Task Replication. Journal of Grid Computing, 7(4), 479-500. doi:10.1007/s10723-009-9135-2Ramakrishnan, L., & Reed, D. A. (2009). Predictable quality of service atop degradable distributed systems. Cluster Computing, 16(2), 321-334. doi:10.1007/s10586-009-0078-yKianpisheh, S., Kargahi, M., & Charkari, N. M. (2017). Resource Availability Prediction in Distributed Systems: An Approach for Modeling Non-Stationary Transition Probabilities. IEEE Transactions on Parallel and Distributed Systems, 28(8), 2357-2372. doi:10.1109/tpds.2017.2659746Weber, G. M., Murphy, S. N., McMurry, A. J., MacFadden, D., Nigrin, D. J., Churchill, S., & Kohane, I. S. (2009). The Shared Health Research Information Network (SHRINE): A Prototype Federated Query Tool for Clinical Data Repositories. Journal of the American Medical Informatics Association, 16(5), 624-630. doi:10.1197/jamia.m3191Martinez-Millana, A., Fico, G., Fernández-Llatas, C., & Traver, V. (2015). Performance assessment of a closed-loop system for diabetes management. 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    The effect of cross-linking on the molecular dynamics of the segmental and β Johari–Goldstein processes in polyvinylpyrrolidone-based copolymers

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    The effect of the cross-link density on the molecular dynamics of copolymers composed of vinylpyrrolidone (VP) and butyl acrylate (BA) was studied using differential scanning calorimetry (DSC) and dielectric relaxation spectroscopy (DRS). A single glass transition was detected by DSC measurements. The dielectric spectra exhibit conductive processes and three dipolar relaxations labeled as a, b and g in the decreasing order of temperatures. The cross-linker content affects both a and b processes, but the fastest g process is relatively unaffected. An increase of cross-linking produces a typical effect on the a process dynamics: (i) the glass transition temperature is increased, (ii) the dispersion is broadened, (iii) its strength is decreased and (iv) the relaxation times are increased. However, the b process, which possesses typical features of a pure Johari Goldstein relaxation, unexpectedly loses the intermolecular character for the highest cross-linker content.B.R.F., M.J.S., P.O.S. and M.C. gratefully acknowledge CICYT for grant MAT2012-33483. F.G. and J.M.G. acknowledge the Spanish Ministerio de Economia y Competitividad-FEDER (MAT2014-54137-R) and the Junta de Castilla y Leon (BU232U13).Redondo Foj, MB.; Sanchis Sánchez, MJ.; Ortiz Serna, MP.; Carsí Rosique, M.; García, JM.; García, FC. (2015). The effect of cross-linking on the molecular dynamics of the segmental and β Johari–Goldstein processes in polyvinylpyrrolidone-based copolymers. Soft Matter. 11:7171-7180. https://doi.org/10.1039/c5sm00714cS7171718011V. Bühler , Polyvinylpyrrolidone Excipients for Pharmaceuticals: Povidone, Crospovidone and Copovidone , Springer , Berlin , 2005Haaf, F., Sanner, A., & Straub, F. (1985). Polymers of N-Vinylpyrrolidone: Synthesis, Characterization and Uses. 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    Fluorogenic detection of Tetryl and TNT explosives using nanoscopic-capped mesoporous hybrid materials

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    [EN] A hybrid capped mesoporous material, which was selectively opened in the presence of Tetryl and TNT, has been synthesised and used for the fluorogenic recognition of these nitroaromatic explosives.Financial support from the Spanish Government (project MAT2012-38429-C04-01) and the Generalitat Valencia (project PROMETEO/2009/016) is gratefully acknowledged. Y.S. and E.P. are grateful to the Spanish Ministry of Science and Innovation for their grants. A. A. also thanks the Generalitat Valenciana for his Santiago Grisolia fellowship.Salinas Soler, Y.; Agostini, A.; Pérez Esteve, E.; Martínez Mañez, R.; Sancenón Galarza, F.; Marcos Martínez, MD.; Soto Camino, J.... (2013). Fluorogenic detection of Tetryl and TNT explosives using nanoscopic-capped mesoporous hybrid materials. Journal of Materials Chemistry. 1(11):3561-3564. https://doi.org/10.1039/C3TA01438JS35613564111Singh, S. (2007). Sensors—An effective approach for the detection of explosives. 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Instrument response measurements of ion mobility spectrometers in situ: maintaining optimal system performance of fielded systems. Chemical and Biological Sensing VI. doi:10.1117/12.609920Germain, M. E., & Knapp, M. J. (2009). Optical explosives detection: from color changes to fluorescence turn-on. Chemical Society Reviews, 38(9), 2543. doi:10.1039/b809631gForzani, E. S., Lu, D., Leright, M. J., Aguilar, A. D., Tsow, F., Iglesias, R. A., … Tao, N. (2009). A Hybrid Electrochemical−Colorimetric Sensing Platform for Detection of Explosives. Journal of the American Chemical Society, 131(4), 1390-1391. doi:10.1021/ja809104hSalinas, Y., Martínez-Máñez, R., Marcos, M. D., Sancenón, F., Costero, A. M., Parra, M., & Gil, S. (2012). Optical chemosensors and reagents to detect explosives. Chem. Soc. Rev., 41(3), 1261-1296. doi:10.1039/c1cs15173hThomas, S. W., Joly, G. D., & Swager, T. M. (2007). Chemical Sensors Based on Amplifying Fluorescent Conjugated Polymers. Chemical Reviews, 107(4), 1339-1386. doi:10.1021/cr0501339Gao, D., Wang, Z., Liu, B., Ni, L., Wu, M., & Zhang, Z. (2008). Resonance Energy Transfer-Amplifying Fluorescence Quenching at the Surface of Silica Nanoparticles toward Ultrasensitive Detection of TNT. Analytical Chemistry, 80(22), 8545-8553. doi:10.1021/ac8014356Zhang, S., Lü, F., Gao, L., Ding, L., & Fang, Y. (2007). Fluorescent Sensors for Nitroaromatic Compounds Based on Monolayer Assembly of Polycyclic Aromatics. Langmuir, 23(3), 1584-1590. doi:10.1021/la062773sHughes, A. D., Glenn, I. C., Patrick, A. D., Ellington, A., & Anslyn, E. V. (2008). A Pattern Recognition Based Fluorescence Quenching Assay for the Detection and Identification of Nitrated Explosive Analytes. Chemistry - A European Journal, 14(6), 1822-1827. doi:10.1002/chem.200701546Vijayakumar, C., Tobin, G., Schmitt, W., Kim, M.-J., & Takeuchi, M. (2010). Detection of explosive vapors with a charge transfer molecule: self-assembly assisted morphology tuning and enhancement in sensing efficiency. Chemical Communications, 46(6), 874. doi:10.1039/b921520dSalinas, Y., Climent, E., Martínez-Máñez, R., Sancenón, F., Marcos, M. D., Soto, J., … Pérez de Diego, A. (2011). Highly selective and sensitive chromo-fluorogenic detection of the Tetryl explosive using functional silica nanoparticles. Chemical Communications, 47(43), 11885. doi:10.1039/c1cc14877jCliment, E., Martínez-Máñez, R., Sancenón, F., Marcos, M. D., Soto, J., Maquieira, A., & Amorós, P. (2010). Controlled Delivery Using Oligonucleotide-Capped Mesoporous Silica Nanoparticles. Angewandte Chemie International Edition, 49(40), 7281-7283. doi:10.1002/anie.201001847Climent, E., Marcos, M. D., Martínez-Máñez, R., Sancenón, F., Soto, J., Rurack, K., & Amorós, P. (2009). 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    Mimicking tricks from nature with sensory organic-inorganic hybrid materials

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    Design strategies for (bio)chemical systems that are inspired by nature's accomplishments in system design and operation on various levels of complexity are increasingly gaining in importance. Within the broad field of biomimetic chemistry, this article highlights various attempts toward improved and sophisticated sensory materials that rely on the combination of supramolecular (bio)chemical recognition principles and nanoscopic solid structures. Examples range from more established concepts such as hybrid sensing ensembles with improved sensitivity and selectivity or for target analytes for which selectivity is hard to achieve by conventional methods, which were often inspired by protein binding pockets or ion channels in membranes, to very recent approaches relying on target-gated amplified signalling with functionalised mesoporous inorganic supports and the integration of native biological sensory species such as transmembrane proteins in spherically supported bilayer membranes. Besides obvious mimicry of recognition-based processes, selected approaches toward chemical transduction junctions utilizing artificially organized synapses, hybrid ensembles for improved antibody generation and uniquely colour changing systems are discussed. All of these strategies open up exciting new prospects for the development of sensing concepts and sensory devices at the interface of nanotechnology, smart materials and supramolecular (bio)chemistry. © 2011 The Royal Society of Chemistry.Martínez Mañez, R.; Sancenón Galarza, F.; Biyikal, M.; Hecht, M.; Rurack, K. (2011). Mimicking tricks from nature with sensory organic-inorganic hybrid materials. Journal of Materials Chemistry. 21(34):12588-12604. doi:10.1039/c1jm11210dS12588126042134Ma, M. (2007). Encoding Olfactory Signals via Multiple Chemosensory Systems. Critical Reviews in Biochemistry and Molecular Biology, 42(6), 463-480. doi:10.1080/10409230701693359Leinders-Zufall, T., Lane, A. P., Puche, A. C., Ma, W., Novotny, M. V., Shipley, M. T., & Zufall, F. (2000). Ultrasensitive pheromone detection by mammalian vomeronasal neurons. Nature, 405(6788), 792-796. doi:10.1038/35015572Serezani, C. H., Ballinger, M. N., Aronoff, D. M., & Peters-Golden, M. (2008). Cyclic AMP. 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    Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

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    [EN] Demand for 3D planning and guidance algorithms is increasing due, in part, to the increase in unmanned vehicle-based applications. Traditionally, two-dimensional (2D) trajectory planning algorithms address the problem by using the approach of maintaining a constant altitude. Addressing the problem of path planning in a three-dimensional (3D) space implies more complex scenarios where maintaining altitude is not a valid approach. The work presented here implements an architecture for the generation of 3D flight paths for fixed-wing unmanned aerial vehicles (UAVs). The aim is to determine the feasible flight path by minimizing the turning effort, starting from a set of control points in 3D space, including the initial and final point. The trajectory generated takes into account the rotation and elevation constraints of the UAV. From the defined control points and the movement constraints of the UAV, a path is generated that combines the union of the control points by means of a set of rectilinear segments and spherical curves. However, this design methodology means that the problem does not have a single solution; in other words, there are infinite solutions for the generation of the final path. For this reason, a multiobjective optimization problem (MOP) is proposed with the aim of independently maximizing each of the turning radii of the path. Finally, to produce a complete results visualization of the MOP and the final 3D trajectory, the architecture was implemented in a simulation with Matlab/Simulink/flightGear.The authors would like to acknowledge the Spanish Ministerio de Ciencia, Innovacion y Universidades for providing funding through the project RTI2018-096904-B-I00 and the local administration Generalitat Valenciana through projects GV/2017/029 and AICO/2019/055. Franklin Samaniego thanks IFTH (Instituto de Fomento al Talento Humano) Ecuador (2015-AR2Q9209), for its sponsorship of this work.Samaniego, F.; Sanchís Saez, J.; Garcia-Nieto, S.; Simarro Fernández, R. (2020). Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs. Electronics. 9(1):1-23. https://doi.org/10.3390/electronics9010051S12391Kyriakidis, M., Happee, R., & de Winter, J. C. F. (2015). Public opinion on automated driving: Results of an international questionnaire among 5000 respondents. Transportation Research Part F: Traffic Psychology and Behaviour, 32, 127-140. doi:10.1016/j.trf.2015.04.014Münzer, S., Zimmer, H. D., Schwalm, M., Baus, J., & Aslan, I. (2006). Computer-assisted navigation and the acquisition of route and survey knowledge. Journal of Environmental Psychology, 26(4), 300-308. doi:10.1016/j.jenvp.2006.08.001Morales, Y., Kallakuri, N., Shinozawa, K., Miyashita, T., & Hagita, N. (2013). 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    Performance of a Set of Eggplant (Solanum melongena) Lines With Introgressions From Its Wild Relative S. incanum Under Open Field and Screenhouse Conditions and Detection of QTLs

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    [EN] Introgression lines (ILs) of eggplant (Solanum melongena) represent a resource of high value for breeding and the genetic analysis of important traits. We have conducted a phenotypic evaluation in two environments (open field and screenhouse) of 16 ILs from the first set of eggplant ILs developed so far. Each of the ILs carries a single marker-defined chromosomal segment from the wild eggplant relative S. incanum (accession MM577) in the genetic background of S. melongena (accession AN-S-26). Seventeen agronomic traits were scored to test the performance of ILs compared to the recurrent parent and of identifying QTLs for the investigated traits. Significant morphological differences were found between parents, and the hybrid was heterotic for vigour-related traits. Despite the presence of large introgressed fragments from a wild exotic parent, individual ILs did not display differences with respect to the recipient parent for most traits, although significant genotype x environment interaction (G x E) was detected for most traits. Heritability values for the agronomic traits were generally low to moderate. A total of ten stable QTLs scattered across seven chromosomes was detected. For five QTLs, the S. incanum introgression was associated with higher mean values for plant- and flower-related traits, including vigour prickliness and stigma length. For one flower- and four fruit-related-trait QTLs, including flower peduncle and fruit pedicel lengths and fruit weight, the S. incanum introgression was associated with lower mean values for fruit-related traits. Evidence of synteny to other previously reported in eggplant populations was found for three of the fruit-related QTLs. The other seven stable QTLs are new, demonstrating that eggplant ILs are of great interest for eggplant breeding under different environments.This work was undertaken as part of the initiative "Adapting Agriculture to Climate Change: Collecting, Protecting, and Preparing Crop Wild Relatives", which is supported by the Government of Norway. The project is managed by the Global Crop Diversity Trust with the Millennium Seed Bank of the Royal Botanic Gardens, Kew and implemented in partnership with national and international gene banks and plant breeding institutes around the world. For further information, see the project website: http://www.cwrdiversity.org/.Funding was also received from Spanish Ministerio de Economia, Industria y Competitividad and Fondo Europeo de Desarrollo Regional (grant AGL2015-64755-R from MINECO/FEDER); from Ministerio de Ciencia, Innovacion y Universidades, Agencia Estatal de Investigacion and Fondo Europeo de Desarrollo Regional (grant RTI-2018-094592-B-100 from MCIU/AEI/FEDER, UE); from European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 677379 (G2P-SOL project: Linking genetic resources, genomes and phenotypes of Solanaceous crops); and from Vicerrectorado de Investigacion, Innovacion y Transferencia de la Universitat Politecnica de Valencia (Ayuda a Primeros Proyectos de Investigacion; PAID-06-18). Giulio Mangino is grateful to Generalitat Valenciana for a predoctoral grant within the Santiago Grisolia programme (GRISOLIAP/2016/012). Pietro Gramazio is grateful to Japan Society for the Promotion of Science for a postdoctoral grant (P19105, FY2019 JSPS Postdoctoral Fellowship for Research in Japan (Standard)).Mangino, G.; Plazas Ávila, MDLO.; Vilanova Navarro, S.; Prohens Tomás, J.; Gramazio, P. (2020). Performance of a Set of Eggplant (Solanum melongena) Lines With Introgressions From Its Wild Relative S. incanum Under Open Field and Screenhouse Conditions and Detection of QTLs. Agronomy. 10(4):1-15. https://doi.org/10.3390/agronomy10040467S115104FAOSTAThttp://www.fao.org/faostat/Gebhardt, C. (2016). The historical role of species from the Solanaceae plant family in genetic research. Theoretical and Applied Genetics, 129(12), 2281-2294. doi:10.1007/s00122-016-2804-1Hirakawa, H., Shirasawa, K., Miyatake, K., Nunome, T., Negoro, S., Ohyama, A., … Fukuoka, H. (2014). Draft Genome Sequence of Eggplant (Solanum melongena L.): the Representative Solanum Species Indigenous to the Old World. DNA Research, 21(6), 649-660. doi:10.1093/dnares/dsu027Barchi, L., Pietrella, M., Venturini, L., Minio, A., Toppino, L., Acquadro, A., … Rotino, G. L. (2019). A chromosome-anchored eggplant genome sequence reveals key events in Solanaceae evolution. Scientific Reports, 9(1). doi:10.1038/s41598-019-47985-wGramazio, P., Yan, H., Hasing, T., Vilanova, S., Prohens, J., & Bombarely, A. (2019). Whole-Genome Resequencing of Seven Eggplant (Solanum melongena) and One Wild Relative (S. incanum) Accessions Provides New Insights and Breeding Tools for Eggplant Enhancement. Frontiers in Plant Science, 10. doi:10.3389/fpls.2019.01220GRAMAZIO, P., PROHENS, J., PLAZAS, M., MANGINO, G., HERRAIZ, F. J., GARCÍA-FORTEA, E., & VILANOVA, S. (2018). Genomic Tools for the Enhancement of Vegetable Crops: A Case in Eggplant. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 46(1), 1-13. doi:10.15835/nbha46110936Frary, A., Frary, A., Daunay, M.-C., Huvenaars, K., Mank, R., & Doğanlar, S. (2014). 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