5,782 research outputs found

    Effect of bentonite fining on polyfunctional mercaptans and other volatile compounds in Sauvignon blanc wines

    Get PDF
    Bentonite fining is the most common process used in the wine industry to remove proteins from wine. Herein, the influence of fermentative and post-fermentative fining on aroma compounds found in Sauvignon blanc wines was studied. Sauvignon blanc musts from different vintages were fined using bentonite. Conventional enological parameters, together with more than 60 volatile compounds, including varietal thiols, were determined in the bottled wines. The results showed that bentonite fining was more effective in removing proteins from wine when carried out on finished wines. Several volatile compounds were influenced by bentonite fining depending on the tim­ing of addition and the vintage. Varietal thiols, key compounds of Sauvignon blanc wine aroma, were significantly reduced when the wines were fined with bentonite, particularly when fining took place during fermentation. Results suggest that bentonite fining of musts could damage the organoleptic quality and varietal character of Sauvignon blanc wines because of its impact on polyfunctional mercaptans

    Comprehensive analysis of design principles in the context of Industry 4.0

    Full text link
    [ES] Los sistemas de producción han evolucionado los últimos años gracias a avances tecnológicos recientes e innovaciones en el proceso de manufactura. El termino Industria 4.0 se ha convertido en prioridad y objeto de estudio para empresas, centros de investigación y universidades, sin existir un consenso generalmente aceptado del término. Como resultado es difícil diseñar e implementar soluciones de Industria 4.0 a nivel académico, científico o empresarial. La contribución de este documento se centra en proporcionar un análisis del significado e implicaciones de Industria 4.0 y exponer de forma detallada 17 principios de diseño fundamentales obtenidos a través de un estudio de mapeo sistemático. Estos principios son eficiencia, integración, flexibilidad, descentralización, personalización, virtualización, seguridad, es holística, orientada a servicios, ubicua, colaborativa, modular, robusta, utiliza información en tiempo real, toma decisiones optimizadas por datos, equilibra la vida laboral y es autónoma e inteligente. A través de estos principios, ingenieros e investigadores están capacitados para investigar e implementar escenarios apropiados de Industria 4.0.[EN] Production systems have evolved in the last years thanks to the recent technological advances and innovations in the manufacturing process. The Industry 4.0 term has become a priority and object of study for companies, research centers and universities, but there is not a generally accepted consensus for the term. As a result, is difficult design and implementation appropriate Industry 4.0 solutions at academic, scientific or business level. The contribution of this paper focuses on providing an analysis of Industry 4.0 meaning and implications and exposes in detail 17 fundamental design principles obtained by a systematic mapping study method. These principles are efficiency, integration, flexibility, decentralization, personalization, virtualization, security, is holistic, ubiquitous, collaborative, modular, robust, use information in real time, makes optimized decisions driven by data, is service-oriented, work life balance and is autonomous and intelligent. With these design principles, engineers and researchers have the capacity to research and implement appropriate Industry 4.0 scenarios.Belman-Lopez, CE.; Jiménez-García, JA.; Hernández-González, S. (2020). Análisis exhaustivo de los principios de diseño en el contexto de Industria 4.0. Revista Iberoamericana de Automática e Informática industrial. 17(4):432-447. https://doi.org/10.4995/riai.2020.12579OJS432447174Ahmad, A., & Babar, M. (2016). Software architectures for robotic systems: A systematic mapping study. The Journal of Systems and Software, 16-39. https://doi.org/10.1016/j.jss.2016.08.039Alexopoulos, K., Sipsas, K., Xanthakis, E., Makris, S., & Mourtzis, D. (2018). An industrial Internet of things based platform for context-aware information services in manufacturing. International Journal of Computer Integrated Manufacturing, 1-14. https://doi.org/10.1080/0951192X.2018.1500716Almada-Lobo, F. (2015). The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES). Journal of Innovation Management, 16-21. https://doi.org/10.24840/2183-0606_003.004_0003Angulo, P., Guzmán, C., Jiménez, G., & Romero, D. (2016). A service-oriented architecture and its ICT infrastructure to support eco-efficiency performance monitoring in manufacturing enterprises. International Journal of Computer Integrated Manufacturing, 202-214. https://doi.org/10.1080/0951192X.2016.1145810Babiceanua, R., & Seker, R. (2016). Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Computers in Industry, 128-137. https://doi.org/10.1016/j.compind.2016.02.004Bagheri, B., Yang, S., Kao, H.-A., & Lee, J. (2015). Cyber-physical Systems Architecture for Self-Aware Machines in Industry 4.0 Environment. IFAC- PapersOnLine, 1622 - 1627. https://doi.org/10.1016/j.ifacol.2015.06.318Beysolow II, T. (2017). Introduction to Deep Learning Using R. San Francisco, California, USA: Apress. https://doi.org/10.1007/978-1-4842-2734-3Bibby, L., & Dehe, B. (2018). Defining and assessing industry 4.0 maturity levels - case of the defence sector. Production Planning & Control, 1-15. https://doi.org/10.1080/09537287.2018.1503355Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective. International Journal of Information and Communication Engineering, 1-8.Caggiano, A. (2018). Cloud-based manufacturing process monitoring for smart diagnosis services. International Journal of Computer Integrated Manufacturing, 31(7), 612-623. https://doi.org/10.1080/0951192X.2018.1425552Cervantes Maceda, H., Velasco-Elizondo, P., & Castro Careaga, L. (2016). Arquitectura de Software. Conceptos y ciclo de desarrollo. Ciudad de México, México: CENGAGE Learning.Charro, A., & Schaefer, D. (2018). Cloud Manufacturing as a new type of Product- Service System. International Journal of Computer Integrated Manufacturing, 1018-1033. https://doi.org/10.1080/0951192X.2018.1493228Chen, T., & Tsai, H.-R. (2016). Ubiquitous manufacturing: Current practices, challenges, and opportunities. Robotics and Computer-Integrated Manufacturing, 1-7. https://doi.org/10.1016/j.rcim.2016.01.001Chen, X.-W., & Lin, X. (2014). Big Data Deep Learning: Challenges and Perspectives. IEEE Xplore, 514 - 525. https://doi.org/10.1109/ACCESS.2014.2325029Chen, Y. (2017). Integrated and Intelligent Manufacturing: Perspectives and Enablers. Engineering, 588-595. https://doi.org/10.1016/J.ENG.2017.04.009Chiu, Y.-C., Cheng, F.-T., & Huang, H.-C. (2017). Developing a factory-wide intelligent predictive maintenance system based on Industry 4.0. Journal of the Chinese Institute of Engineers, 1-11. https://doi.org/10.1080/02533839.2017.1362357Ciffolilli, A., & Muscio, A. (2018). Industry 4.0: national and regional comparative advantages in key enabling technologies. European Planning Studies, 1-22. https://doi.org/10.1080/09654313.2018.1529145Clusterplattform Deutschland . (2019). Clusterplattform Deutschland. Obtenido de Clusterplattform Deutschland: https://www.clusterplattform.de/CLUSTER/Navigation/DE/Home/home.htmlCobo, M., Jürgens, B., Herrero-Solana, V., Herrera-Viedma, E., & Martínez, M. (2018). Industry 4.0: a perspective based on bibliometric analysis. Procedia Computer Science, 364-371. https://doi.org/10.1016/j.procs.2018.10.278Crawford, M., & ASME.org. (01 de Julio de 2018). How Industry 4.0 Impacts Engineering Design. Obtenido de ASME: https://www.asme.org/engineering- topics/articles/manufacturing-design/industry-40-impacts-engineering-designdefinicionde.org. (27 de Diciembre de 2016). Definición de ubicuo - Que es según la RAE? Obtenido de Definición de las palabras: http://definicionde.org/ubicuo/Delaram, J., & Valilai, O. (2016). Development of a Novel Solution to Enable Integration and Interoperability for Cloud Manufacturing. Procedia CIRP, 6-11. https://doi.org/10.1016/j.procir.2016.07.056Delicato, F., Al-Anbuky, A., & Wang, K.-K. (2019). Editorial: Smart Cyber-Physical Systems: Toward Pervasive Intelligence systems. Future Generation Computer Systems, 1-6. https://doi.org/10.1016/j.future.2019.06.031Deloitte. (05 de 10 de 2018). ¿Qué es la Industria 4.0? Obtenido de Deloite.: https://www2.deloitte.com/es/es/pages/manufacturing/articles/que-es-la- industria-4.0.htmlDilberoglua, U., Bahar, G., Yaman, U., & Dolen, M. (2017). The role of additive manufacturing in the era of Industry 4.0. International Conference on Flexible Automation and Intelligent Manufacturing (págs. 1-10). Italia: Procedia Manufacturing. https://doi.org/10.1016/j.promfg.2017.07.148European Secretariat for Cluster Analysis. (2017). Quality audit: Gold Label of the European Cluster Excellence Initiative (ECEI). Obtenido de ESCA: https://www.cluster-analysis.org/gold-label-newEvans, P., & Annunziata, M. (26 de Noviembre de 2012). Industrial Internet: Pushing the Boundaries of Minds and Machines. Obtenido de GE: https://www.ge.com/docs/chapters/Industrial_Internet.pdfFatorachian, H., & Kazemi, H. (2018). A critical investigation of Industry 4.0 in manufacturing: theoretical operationalisation framework. Production Planning & Control, 633-644. https://doi.org/10.1080/09537287.2018.1424960Federal Minister of Education and Research. (2013). Deutschlands Spitzencluster Germany's Leading-Edge Clusters. Obtenido de Federal Ministry of Education and Research (BMBF): https://www.hamburg.de/contentblob/2593364/3113df3e6f569c97b937bd8747 5564db/data/deutschlands-spitzencluster.pdfFerreira,, J., Sarraipa, J., Ferro-Beca, M., Agostinho, C., Costa, R., & Jardim-Goncalves, R. (2016). End-to-end manufacturing in factories of the future. International Journal of Computer Integrated Manufacturing, 1-14. https://doi.org/10.1080/0951192X.2016.1185155Fettermann, D., Cavalcante, C., Domingues de Almeida, T., & Tortorella, G. (2018). How does Industry 4.0 contribute to operations management? Journal of Industrial and Production Engineering, 1-15. https://doi.org/10.1080/21681015.2018.1462863Francalanza, E., Borg, J., & Constantinescu, C. (2018). Approaches for handling wicked manufacturing system design problems. Procedia CIRP, 67, 134-139. https://doi.org/10.1016/j.procir.2017.12.189García, M., Irisarri, E., Pérez, F., Estévez, E., & Marcos, M. (2017). Arquitectura de Automatización basada en Sistemas Ciberfísicos para la Fabricación Flexible en la Industria de Petróleo y Gas. Revista Iberoamericana de Automática e Informática Industrial, 1-11. https://doi.org/10.4995/riai.2017.8823Germany Trade & Invest (GTAI). (1 de Julio de 2014). Industrie 4.0 Smart Manufacturing for the future. Obtenido de Germany Trade & Invest (GTAI): https://www.gtai.de/GTAI/Content/CN/Invest/_SharedDocs/Downloads/GTAI/ Brochures/Industries/industrie4.0-smart-manufacturing-for-the-future-en.pdfGhobakhloo, M. (2019). Determinants of information and digital technology implementation for smart manufacturing. International Journal of Production Research, 1-23. https://doi.org/10.1080/00207543.2019.1630775Götz, M., & Jankowska, B. (2017). Clusters and Industry 4.0 - do they fit together? European Planning Studies, 1633-1653. https://doi.org/10.1080/09654313.2017.1327037Gregor, S. (2002). A Theory of Theories in Information Systems. Information Systems Foundations. Building the Theoretical, 1 - 20.Gregor, S. (2009). Building Theory in the Sciences of the Artificial. Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology (págs. 1- 10). Philadelphia, Pennsylvania, USA: ACM Digital Library. https://doi.org/10.1145/1555619.1555625Henzel, R., & Herzwurm, G. (2018). Cloud Manufacturing: A state-of-the-art survey of current issues. CIRP, 947-952. https://doi.org/10.1016/j.procir.2018.03.055Hermann, M., Otto, B., & Pentek, T. (2015). Design Principles for Industrie 4.0 Scenarios: A Literature Review. ResearchGate, 1-16. https://doi.org/10.13140/RG.2.2.29269.22248Hernández A., A., Figueroa F., V., & Jiménez G., J. (2018). Propuesta de una metodología de diagnóstico para identificar los requerimientos tecnológicos de una empresa tradicional de manufactura para evolucionar a Industria 4.0. Celaya, Guanajuato, México: Tecnológico Nacional de México en Celaya.Huang, S., & Yan, Y. (2019). Design of delayed reconfigurable manufacturing system based on part family grouping and machine selection. International Journal of Production Research, 1-19. https://doi.org/10.1080/00207543.2019.1654631Ibarra, D., Ganzarain, J., & Igartua, J. (2017). Business model innovation through Industry 4.0: A review. Procedia Manufacturing, 4-10. https://doi.org/10.1016/j.promfg.2018.03.002Jardim-Goncalves, R., Romero, D., & Grilo, A. (2017). Factories of the future: challenges and leading innovations in intelligent manufacturing. International Journal of Computer Integrated Manufacturing, 30, 4-14.Jazdi, N. (17 de Jolio de 2014). Cyber Physical Systems in the Context of Industry 4.0. IEEE International Conference on Automation, Quality and Testing, Robotics. (págs. 1-3). Cluj-Napoca, Romania: IEEE. https://doi.org/10.1109/AQTR.2014.6857843Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final report of the Industrie 4.0 Working Group. National Academy of Science and Engineering (acatech)., 1-82.Kamble, S., Gunasekaran, A., & Gawankar, S. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 408-425. https://doi.org/10.1016/j.psep.2018.05.009Khan, K., Kunz, R., Kleijnen, J., & Antes, G. (2003). Five steps to conducting a systematic review. Journal of the royal society of medicine, 118-121. https://doi.org/10.1177/014107680309600304Kipper, L., Furstenau, L., Hoppe, D., Frozza, R., & Iespen, S. (2019). Scopus scientific mapping production in industry 4.0 (2011-2018): a bibliometric analysis. International Journal of Production Research, 1-24. doi:https://doi.org/10.1080/00207543.2019.1671625Klingenberg, C. (2017). Industry 4.0: what makes it a revolution? EurOMA (págs. 1-11). ResearchGate.Kusiak, A. (2017). Smart manufacturing. International Journal of Production Research, 508-517. https://doi.org/10.1080/00207543.2017.1351644Laudante, E. (2017). Industry 4.0, Innovation and Design. A new approach for ergonomic analysis in manufacturing system. An International Journal for All Aspects of Design, 1-12. https://doi.org/10.1080/14606925.2017.1352784Lee, J., Ardakani, H., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia CIRP, 3-7. https://doi.org/10.1016/j.procir.2015.08.026Lee, J., Bagheri, B., & Kao, H.-A. (2014). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Society of Manufacturing Engineers (SME), 18- 23. https://doi.org/10.1016/j.mfglet.2014.12.001Lee, J., Kao, H.-A., & Yang, S. (2014). Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment. Procedia CIRP, 16, 3-8. https://doi.org/10.1016/j.procir.2014.02.001Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 1-10. https://doi.org/10.1016/j.jii.2017.04.005Luque, A., Peralta, E., De las Heras, A., & Córdoba, A. (2017). State of Industry 4.0 in the Andalusian food sector. Procedia Manufacturing, 1199-1205. https://doi.org/10.1016/j.promfg.2017.09.195Macchi, D., & Solari, M. (2012). Mapeo sistemático de la literatura sobre la Adopción de Inspecciones de Software. Universidad ORT de Uruguay, 1 - 8.MIT Technology Review. (31 de Octubre de 2018). "Digital twin", un gemelo virtual para aconsejar a la Industria 4.0. Obtenido de MIT Technology Review: https://www.technologyreview.es/s/10696/digital-twin-un-gemelo-virtual-para- aconsejar-la-industria-40Moghaddam, S., Houshmand, M., Saitou, K., & Valilai, O. (2019). Configuration design of scalable reconfigurable manufacturing systems for part family. International Journal of Production Research, 1-24. https://doi.org/10.1080/00207543.2019.1620365Moktadir, M., Ali, S., Kusi-Sarpong, S., & Ali Shaikh, M. (2018). Assessing challenges for implementing Industry 4.0: Implications for process safety and environmental protection. Process Safety and Environmental Protection, 730- 741. https://doi.org/10.1016/j.psep.2018.04.020Muhuri, P., Shukla, A., & Abraham, A. (2019). Industry 4.0: A bibliometric analysis and detailed overview. Engineering Applications of Artificial Intelligence, 218- 235. https://doi.org/10.1016/j.engappai.2018.11.007Nassehi, A., Schaefer, D., Wu, D., Xu, X., & Zaeh, M. (2018). Special issue on 'Cyber-physical product creation for Industry 4.0'. International Journal of Computer Integrated Manufacturing, 611-611. https://doi.org/10.1080/0951192X.2018.1482106Netzwerk Smart Production. (01 de Enero de 2019). Smart Production. Obtenido de Netzwerk Smart Production: https://www.smartproduction.de/Neugebauer, R., Hippmann, S., Leis, M., & Landherr, M. (2016). Industrie 4.0 - From the Perspective of Applied Research. Procedia CIRP, 57, 2-7. https://doi.org/10.1016/j.procir.2016.11.002NIST. (16 de Abril de 2018). Framework for Improving Critical Infrastructure Cybersecurity. Obtenido de National Institute of Standards and Technology: https://nvlpubs.nist.gov/nistpubs/CSWP/NIST.CSWP.04162018.pdfNodehi, T., Jardim-Goncalves, R., Zutshi, A., & Grilo, A. (2015). ICIF: an intercloud interoperability framework for computing resource cloud providers in factories of the future. International Journal of Computer Integrated Manufacturing, 1-12. https://doi.org/10.1080/0951192X.2015.1067921Nunes, M., Pereira, A., & Alves, A. (2017). Smart products development approches for Industry 4.0. Manufacturing Engineering Society International Conference (págs. 1215-1222). Vigo, España: Procedia Manufacturing. https://doi.org/10.1016/j.promfg.2017.09.035Oesterreich, T., & Teuteberg, F. (2016). Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 121-139. https://doi.org/10.1016/j.compind.2016.09.006Packianathera, M., Davies, A., Harraden, S., Soman, S., & White, J. (2017). Data mining techniques applied to a manufacturing SME. Data mining techniques applied to a manufacturing SME, 123 - 128. https://doi.org/10.1016/j.procir.2016.06.120Pereira, A., & Romero, F. (2017). A review of the meaning and the implications of the Industry 4.0 concept. En P. Manufacturing (Ed.), Manufacturing Engineering Society International Conference (págs. 1206-1214). Vigo, España: Elsevier. https://doi.org/10.1016/j.promfg.2017.09.032Pereira, T., Barreto, L., & Amaral, A. (2017). Network and information security challenges within Industry 4.0 paradigm. Procedia Manufacturing, 1253-1260. https://doi.org/10.1016/j.promfg.2017.09.047Piedrahita, A., & Vélez Ángel, P. (2017). Control de calidad en sistemas crowdsourcing: un mapeo sistemático. Scientia et Technica, 1 - 10. https://doi.org/10.22517/23447214.13541Plattform Industrie 4.0. (2019). Plattform Industrie 4.0. Obtenido de Plattform Industrie 4.0: https://www.plattform- i40.de/PI40/Navigation/EN/ThePlatform/Background/background.htmlPorter, M. (2000). Location, Competition, and Economic Development: Local Clusters in a Global Economy. Economic Development Quarterly, 15-34. https://doi.org/10.1177/089124240001400105PWC. (01 de 01 de 2016). Industry 4.0: Building the Digital Enterprise. Obtenido de PWC: https://www.pwc.com/gx/en/industries/industries-4.0/landing- page/industry-4.0-building-your-digital-enterprise-april-2016.pdfQin, J., Liu, Y., & Grosvenor, R. (2016). A Categorical Framework of Manufacturing for Industry 4.0 and Beyond. Procedia CIRP, 173-178. https://doi.org/10.1016/j.procir.2016.08.005Quintana, G., & Solari, M. (2012). Estudio de Mapeo Sistemático sobre Experimentos de Generación Automática de Casos de Prueba Estructurales. Universidad ORT de Uruguay, 1-10.Radziwon, A., Bilberg, A., Bogers, M., & Madsen, E. (2014). The Smart Factory: Exploring Adaptive and Flexible Manufacturing Solutions. Procedia Engineering, 1184 - 1190. https://doi.org/10.1016/j.proeng.2014.03.108Roblek, V., Meško, M., & Krapež, A. (2016). A Complex View of Industry 4.0. SAGE, 1-11. https://doi.org/10.1177/2158244016653987Rojko, A. (2017). Industry 4.0 Concept: Background and Overview. International Journal of Innovation Management, 1-14. https://doi.org/10.3991/ijim.v11i5.7072Román-Ibáñez, V., Jimeno-Morenilla, A., & Pujol-López, F. (2018). Distributed monitoring of heterogeneous robotic cells. A proposal for the footwear industry 4.0. International Journal of Computer Integrated Manufacturing, 1-16. https://doi.org/10.1080/0951192X.2018.1529432Rosin, F., Forget, P., Lamouri, S., & Pellerin, R. (2019). Impacts of Industry 4.0 technologies on Lean principles. International Journal of Production Research, 1-19. https://doi.org/10.1080/00207543.2019.1672902Rossit, D., Tohmé, F., & Frutos, M. (2018). Industry 4.0: Smart Scheduling. International Journal of Production Research. https://doi.org/10.1080/00207543.2018.1504248Russo, J., & Solari, M. (2017). Estudio de Mapeo Sistemático sobre Arquitecturas de Software para Big Data. Conferencia Iberoamericana en Software Engineering (págs. 1 - 14). Buenos Aires, Argentina: ResearchGate.Schmidt, R., Möhring, M., Härting, R.-C., Reichstein, C., Neumaier, P., & Jozinović, P. (2015). Industry 4.0 - Potentials for Creating Smart Products: Empirical Research Results. Business Information Systems, 16-27. https://doi.org/10.1007/978-3-319-19027-3_2Schuh, G., Potente, T., Wesch-Potente, C., Weber, A., & Prote, J.-P. (2014). Collaboration Mechanisms to increase Productivity in the Context of Industrie 4.0. Procedia CIRP, 51 - 56. https://doi.org/10.1016/j.procir.2014.05.016Schumacher, A., Erol, S., & Sihn, W. (2016). A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP, 161 - 166. https://doi.org/10.1016/j.procir.2016.07.040Shafiq, S., Sanin, C., Toro, C., & Szczerbicki, E. (2015). Virtual Engineering Object (VEO): Toward Experience-Based Design and Manufacturing for Industry 4.0. Cybernetics and Systems: An International Journal, 1-17. https://doi.org/10.1080/01969722.2015.1007734Shariatzadeh, N., Lundholm, T., Lindberg, L., & Sivard, G. (2016). Integration of digital factory with smart fa

    Flavonoids: Important Biocompounds in Food

    Get PDF
    Flavonoids are secondary metabolites in plants that show some desirable characteristics. These compounds can be grouped in different classes on the basis of their basic structure. It has been reported that flavonoids are important for human health because of their antioxidant, antibacterial, antiviral, and anti‐inflammatory activities and because they act as free radical scavengers as they are potential reducing agents that protect from oxidative damage, which are conferred by the content of hydroxyl groups. In recent years, flavonoids have been investigated based on their ability to reduce the incidence of many diseases, to inhibit cell damage, to repair DNA process and to reduce oxidative stress. Besides, flavonoids have been demonstrated to have cardioprotective effects, have potential to improve coronary vasodilatation and prevent LDLs from oxidizing and also showed potential neuroprotective effects. Moreover, flavonoids have been used in the food industry due to their ability to preserve foods, to provide colour and flavour and to make dietary supplements, among other important industrial applications

    Pigmentation and production of vitamins in mango (Mangifera indica L.)

    Get PDF
    Mango (Mangifera indica L.) is the fifth most cultivated vegetable in the world. One way to classify the mango is according to the color of the peel, they are classified as green, yellow and red. Color is a visual attribute that defines consumer preference in some countries. This diversity of pigmentation is defined by families of genes that code for the production of proteins, which lead to biosynthetic pathways responsible for the production of vitamins and their precursors. In Mexico there is a wide range of colors in the native mango germplasm, which could represent an important source of antioxidants, pigments and would bring benefits for the human health of Mexicans, through the consumption of fresh fruit, or commercial / industrial exploitation of these. According to the literature, this diversity of colors represents a genetic wealth that could be exploited in the genetic improvement programs of the species in the country, to generate new varieties with desirable characteristics in the national and international market. In order to gather and discuss information that contributes to understanding the biochemical and genetic processes that determine said pigmentation and the production of vitamins in mango, this review makes a description of the main genes involved and the biosynthetic pathways of the most common pigments, considering the impact on human health when consuming them, and highlighting the challenges and opportunities that could arise from the use of pigments from Mexican germplasm.Mango (Mangifera indica L.) is the fifth most cultivated plant in the world. One way to classify mango is according to the color of the skin; mangoes are classified as green,  yellow and red. Color is a visual attribute that defines consumer preference in some countries. This pigmentation diversity is defined by families of genes that encode for protein production, which lead to biosynthetic pathways responsible for the production of vitamins and vitamin precursors. In Mexico there is a wide range of colors in the native mango germplasm, which could represent an important source of antioxidants, pigments and would bring benefits to the human health of Mexicans, through the consumption of the fresh fruits, or the commercial/industrial exploitationof these. According to the literature, this diversity of colors represents a genetic richness that could be exploited in the genetic breeding programs of the species in the country, to generate new varieties with desirable characteristics in the national and international market. In order to gather and discuss information that contributes to the understanding of the biochemical and genetic processes that determine such pigmentation and the production of vitamins in mango, this review describes the main genes involved and the biosynthetic pathways of the most common pigments, considering the impact on human health when they are consumed, and highlighting the challenges and opportunities that could be derived from the utilization of pigmentsfrom the Mexican germplasm

    On weak r-Helix submanifolds

    Full text link
    In this paper, we investigate special curves on a weak r-helix submanifold in Euclidean n-space E^{n}. Also, we give the important relations between weak r-helix submanifolds and the special curves such as line of curvature, asymptotic curve and helix line.Comment: arXiv admin note: text overlap with arXiv:1203.160

    Acoustically Levitated Whispering-Gallery Mode Microlasers

    Get PDF
    Acoustic levitation has become a crucial technique for contactless manipulation in several fields, particularly in biological applications. However, its application in the photonics field remains largely unexplored. In this study, we implement an affordable and innovative phased-array levitator that enables stable trapping in the air of micrometer dye-doped droplets, thereby enabling the creation of microlasers. For the first time, this paper presents a detailed performance of the levitated microlaser cavity, supported by theoretical analysis concerning the hybrid technology based on the combination of whispering-gallery modes and acoustic fields. The pressure field distribution inside the acoustic cavity is numerically solved and qualitatively matched with the schlieren deflectometry technique. The optical lasing features of the levitated microlasers are highly comparable with those devices based On-a-Chip registering maximum Q-factors of ~ 105, and minimum lasing thresholds ~ 150 nJ cm−2. The emission comb is explained as a sum of multiple individual-supported whispering-gallery modes. The use of novel touchless micrometric lasers, produced with an acoustic levitator brings new technological opportunities based on photonic-acoustic technological platforms

    Feminist Pedagogy in the STEM Research Laboratory: an Intersectional Approach

    Get PDF
    The research laboratory is a crucial and indispensable classroom for STEM education. It is where we practice science as a craft and test the ideas that awaken our curiosity, allowing us to create knowledge. It is also a space where challenges await and struggles are imminent. Thus, supporting mentees through their traineeship in a research lab requires an intersectional approach and lens to provide equitable mentorship and guidance. The concept of intersectionality, initially devised by Black feminist professor Kimberlé W. Crenshaw, can be employed to generate practices and frameworks that democratize laboratory culture and provide trainees with a space in which they shape the laboratory culture while helping them recognize their positionality. This critical commentary provides insights and experiences when incorporating feminist frameworks to sustain equitable working environments in the research lab setting, specifically in leading a research group composed predominantly of Puerto Rican women

    A primordial star in the heart of the Lion

    Full text link
    Context: The discovery and chemical analysis of extremely metal-poor stars permit a better understanding of the star formation of the first generation of stars and of the Universe emerging from the Big Bang. aims: We report the study of a primordial star situated in the centre of the constellation Leo (SDSS J102915+172027). method: The star, selected from the low resolution-spectrum of the Sloan Digital Sky Survey, was observed at intermediate (with X-Shooter at VLT) and at high spectral resolution (with UVES at VLT). The stellar parameters were derived from the photometry. The standard spectroscopic analysis based on 1D ATLAS models was completed by applying 3D and non-LTE corrections. results: An iron abundance of [Fe/H]=--4.89 makes SDSS J102915+172927 one of the lowest [Fe/H] stars known. However, the absence of measurable C and N enhancements indicates that it has the lowest metallicity, Z<= 7.40x10^{-7} (metal-mass fraction), ever detected. No oxygen measurement was possible. conclusions: The discovery of SDSS J102915+172927 highlights that low-mass star formation occurred at metallicities lower than previously assumed. Even lower metallicity stars may yet be discovered, with a chemical composition closer to the composition of the primordial gas and of the first supernovae.Comment: To be published in A&

    Efficacy of lisdexamfetamine dimesylate throughout the day in children and adolescents with attention-deficit/hyperactivity disorder:results from a randomized, controlled trial

    Get PDF
    Lisdexamfetamine dimesylate (LDX) is a long-acting, prodrug stimulant therapy for patients with attention-deficit/hyperactivity disorder (ADHD). This randomized placebo-controlled trial of an optimized daily dose of LDX (30, 50 or 70 mg) was conducted in children and adolescents (aged 6–17 years) with ADHD. To evaluate the efficacy of LDX throughout the day, symptoms and behaviors of ADHD were evaluated using an abbreviated version of the Conners’ Parent Rating Scale-Revised (CPRS-R) at 1000, 1400 and 1800 hours following early morning dosing (0700 hours). Osmotic-release oral system methylphenidate (OROS-MPH) was included as a reference treatment, but the study was not designed to support a statistical comparison between LDX and OROS-MPH. The full analysis set comprised 317 patients (LDX, n = 104; placebo, n = 106; OROS-MPH, n = 107). At baseline, CPRS-R total scores were similar across treatment groups. At endpoint, differences (active treatment − placebo) in least squares (LS) mean change from baseline CPRS-R total scores were statistically significant (P < 0.001) throughout the day for LDX (effect sizes: 1000 hours, 1.42; 1400 hours, 1.41; 1800 hours, 1.30) and OROS-MPH (effect sizes: 1000 hours, 1.04; 1400 hours, 0.98; 1800 hours, 0.92). Differences in LS mean change from baseline to endpoint were statistically significant (P < 0.001) for both active treatments in all four subscales of the CPRS-R (ADHD index, oppositional, hyperactivity and cognitive). In conclusion, improvements relative to placebo in ADHD-related symptoms and behaviors in children and adolescents receiving a single morning dose of LDX or OROS-MPH were maintained throughout the day and were ongoing at the last measurement in the evening (1800 hours)
    corecore