Phenolic Characterization of Cabernet Sauvignon Wines From Different Geographical Indications of Mendoza, Argentina: Effects of Plant Material and Environment

The chemical and sensory characteristics of the wines are related to the geographical origin of the grape, as a result of the interplay between the plant material (G), its acclimatization to the environment (E) and the human factor that influences both the vineyard and the winery. The range of phenotypes that a single genotype can express depending on its environment is known as phenotypic plasticity and is the result of G × E interaction. The present study evaluated the independent and interactive effects of Cabernet Sauvignon plant materials (G: Clone 7 and Mount Eden) implanted in different geographical indications of Mendoza, Argentina (E: Agrelo, Pampa El Cepillo, Altamira and Gualtallary) according to fruit yield and phenolic profiles of wines. The experiment was carried out during 2018 and 2019 vintages using a multifactorial design. When berries reached 24 °Brix, the clusters were harvested, analyzed and wines elaborated by a standardized procedure. Then, the anthocyanin and non-anthocyanin phenolic profiles of wines were determined by high-performance liquid chromatography with diode array and fluorescence detection (HPLC-DAD–FLD). The results revealed significant G × E interactions for yield traits, including the number of clusters per plant. Differential chemical composition and quality parameters of the resulting wines, markedly affected by E, were observed; that is the geographical location of the vineyards. There were similarities in the phenolic composition between Pampa El Cepillo and Altamira, while larger differences between Agrelo and Gualtallary were observed. Gualtallary presented the highest levels of anthocyanins, quercetin and trans-resveratrol. The increased amount of these compounds in Gualtallary was associated with an increased UV-B exposure of plants at this high altitude environment. This is the first report that characterizes the effects of plant material and environment for Cabernet Sauvignon. These results are of oenological and viticulture interest for the wine industry demonstrating that the selection of the plant material and the vineyard location for Cabernet Sauvignon can considerably affect the quality attributes of wines.Fil: Muñoz, Flavio Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; ArgentinaFil: Urvieta, Roy Alexander. Catena Institute Of Wine.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; ArgentinaFil: Buscema, Fernando. Catena Institute Of Wine.; ArgentinaFil: Rasse, Manuel. School of Agriculture Ingenieers; FranciaFil: Fontana, Ariel Ramón. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; ArgentinaFil: Berli, Federico Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; Argentin

Caracterización fenólica de bayas y vinos Cabernet Franc de distintos terroir de Mendoza

Los viñedos de Mendoza se encuentran en cinco zonas que corresponden a los distintos oasis productivos y en ellas existen Indicaciones Geográficas (IG), para identificar los productos originarios de cada localidad, con diferente reputación, calidad, y características.El Cabernet Franc es un cultivar que en Argentina se encuentra presente en todas las provincias vitivinícolas con 929 ha, representando el 0,4% del total de superficie con vid del país. Dentro de la provincia los viñedos de Cabernet Franc se concentran en el Valle de Uco (45%) y en el departamento de Luján de Cuyo (33%). Cabernet Franc viene creciendo, pasó de ser una variedad utilizada para vinos de corte a vinificarse como varietal de gama media alta, principalmente por su potencialidad para producir vinos con alto valor agregado y a las características distintivas del producto. Las particularidades de la vid y de los vinos dependen del terroir, concepto que en el sentido más simple se refiere a los efectos del material vegetativo (genotipo), combinado con los factores ambientales y con las condiciones del manejo del cultivo. Asimismo, la significación de la calidad integra varios aspectos, pero para la elaboración de vinos tintos, tienen correlación con altos contenidos de compuestos fenólicos. El presente estudio tuvo por objetivo caracterizar los perfiles y concentración de compuestos fenólicos de uvas y vinos cv. Cabernet Franc provenientes de viñedos ubicados en distintas IG del Valle de Uco (Gualtallary, El Cepillo, Altamira) y Luján de Cuyo (Agrelo). En cada IG se cosecharon uvas con una madurez comercial (24Brix) y los vinos se elaboraron por triplicado bajo condiciones estandarizadas en vasijas. Se determinaron los compuestos fenólicos de bayas y vinos por cromatografía líquida con detector de arreglo de diodos (HPLC-DAD).Los resultados revelan que las muestras de distintas IG tienen una composición química diferente, y que muchos compuestos se correlacionan con variables como la altitud en la que se ubican los viñedos. La concentración de antocianos y de polifenoles totales aumenta con la altura. Existen similitudes con respecto a la composición polifenólica entre las IG El Cepillo y Altamira, ya que se encuentran cercanas entre sí, mientras que las diferencias son mayores si comparamos las IG Agrelo con Gualtallary.Fil: Muñoz, Flavio Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; ArgentinaFil: Rasse, Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; ArgentinaFil: Fontana, Ariel Ramón. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; ArgentinaFil: Urvieta, Roy Alexander. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; ArgentinaFil: Buscema, Fernando. No especifíca;Fil: Berli, Federico Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; ArgentinaXXVII Jornadas de Jovens PesquisadoresSão CarlosBrasilAsociación Universidades Grupo MontevideanoUniversidade Federal de São Carlo

Ciencias de la Biología y Agronomía

Este volumen I contiene 17 capítulos arbitrados que se ocupan de estos asuntos en Tópicos Selectos de Ciencias de la Biología y Agronomía, elegidos de entre las contribuciones, reunimos algunos investigadores y estudiantes. Se presenta un Estudio Comparativo de los Recursos Hidrológico-Forestales de la Microcuenca de la Laguna de Epatlan, Pue. (1993 a 2014); la Situación Actual de la Mancha de Asfalto en Maíz (Zea mays L.) en los Municipios de Jiquipilas y Ocozocoautla, Chiapas, México; las poblaciones sobresalientes de maíz de la raza Zapalote Chico, en la Región Istmeña de Oaxaca; Se indica el índice de área foliar de cultivo de Chile Poblano mediante dos métodos en condiciones protegidas; Esquivel, Urzúa y Ramírez exploran el efecto de la biofertilización con Azospirillum en el crecimiento y producción de Jitomate; esbozan su artículo sobre la determinación del nivel de Heterosis en híbridos de Maíz para la Comarca Lagunera; una investigación sobre la estabilización de semilla de Solanum lycopersicum durante el almacenamiento y estimulación de la germinación; acotan sobre el CTAB como una nueva opción para la detección de Huanglongbing en cítricos, plantean su evaluación sobre el aluminio y cómo afecta la vida de florero de Heliconia psittacorum; indican sobre el impacto del H-564C, como un híbrido de maíz con alta calidad de proteina para el trópico húmedo de México; presetan su investigación sobre la producción de Piña Cayena Lisa y MD2 (Ananas comosus L.) en condiciones de Loma Bonita, en Oaxaca; acotan sobre el efecto de coberteras como control biológico por conservación contra áfidos en Nogal Pecanero; esbozan sobre la caracterización de cuatro genotipos de Frijol Negro en Martínez de la Torre, Veracruz, México; presentan una caracterización hidroecológica de la microcuenca de Arroyo Prieto, Yuriría, Gto., y alternativas para su restauración ambiental; presentan su investigación sobre el efecto del hongo Beauveria bassiana sobre solubilización de fosfatos y la disponibilidad de fósforo en el suelo; plantean su investigación sobre la Germinación y regeneración in vitro de Epidendrum falcatum LINDL; esbozan su artículo sobre genotipos de frijol negro y su tolerancia a sequía terminal en Veracruz, México

Contributions of mean and shape of blood pressure distribution to worldwide trends and variations in raised blood pressure: A pooled analysis of 1018 population-based measurement studies with 88.6 million participants

© The Author(s) 2018. Background: Change in the prevalence of raised blood pressure could be due to both shifts in the entire distribution of blood pressure (representing the combined effects of public health interventions and secular trends) and changes in its high-blood-pressure tail (representing successful clinical interventions to control blood pressure in the hypertensive population). Our aim was to quantify the contributions of these two phenomena to the worldwide trends in the prevalence of raised blood pressure. Methods: We pooled 1018 population-based studies with blood pressure measurements on 88.6 million participants from 1985 to 2016. We first calculated mean systolic blood pressure (SBP), mean diastolic blood pressure (DBP) and prevalence of raised blood pressure by sex and 10-year age group from 20-29 years to 70-79 years in each study, taking into account complex survey design and survey sample weights, where relevant. We used a linear mixed effect model to quantify the association between (probittransformed) prevalence of raised blood pressure and age-group- and sex-specific mean blood pressure. We calculated the contributions of change in mean SBP and DBP, and of change in the prevalence-mean association, to the change in prevalence of raised blood pressure. Results: In 2005-16, at the same level of population mean SBP and DBP, men and women in South Asia and in Central Asia, the Middle East and North Africa would have the highest prevalence of raised blood pressure, and men and women in the highincome Asia Pacific and high-income Western regions would have the lowest. In most region-sex-age groups where the prevalence of raised blood pressure declined, one half or more of the decline was due to the decline in mean blood pressure. Where prevalence of raised blood pressure has increased, the change was entirely driven by increasing mean blood pressure, offset partly by the change in the prevalence-mean association. Conclusions: Change in mean blood pressure is the main driver of the worldwide change in the prevalence of raised blood pressure, but change in the high-blood-pressure tail of the distribution has also contributed to the change in prevalence, especially in older age groups

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

5G-PPP Technology Board:AI and ML – Enablers for Beyond 5G Networks

This white paper on AI and ML as enablers of beyond 5G (B5G) networks is based on contributions from 5G PPP projects that research, implement and validate 5G and B5G network systems. The white paper introduces the main relevant mechanisms in Artificial Intelligence (AI) and Machine Learning (ML), currently investigated and exploited for 5G and B5G networks. A family of neural networks is presented which are, generally speaking, non-linear statistical data modelling and decision-making tools. They are typically used to model complex relationships between input and output parameters of a system or to find patterns in data. Feed-forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks belong to this family. Reinforcement learning is concerned about how intelligent agents must take actions in order to maximize a collective reward, e.g., to improve a property of the system. Deep reinforcement learning combines deep neural networks and has the benefit that is can operate on non-structured data. Hybrid solutions are presented such as combined analytical and machine learning modelling as well as expert knowledge aided machine learning. Finally, other specific methods are presented, such as generative adversarial networks and unsupervised learning and clustering. In the sequel the white paper elaborates on use case and optimisation problems that are being tackled with AI/ML, partitioned in three major areas namely, i) Network Planning, ii) Network Diagnostics/Insights, and iii) Network Optimisation and Control. In Network Planning, attention is given to AI/ML assisted approaches to guide planning solutions. As B5G networks become increasingly complex and multi-dimensional, parallel layers of connectivity are considered a trend towards disaggregated deployments in which a base station is distributed over a set of separate physical network elements which ends up in the growing number of services and network slices that need to be operated. This climbing complexity renders traditional approaches in network planning obsolete and calls for their replacement with automated methods that can use AI/ML to guide planning decisions. In this respect two solutions are discussed, first the network element placement problem is introduced which aims at improvements in the identification of optimum constellation of base stations each located to provide best network performance taking into account various parameters, e.g. coverage, user equipment (UE) density and mobility patterns (estimates), required hardware and cabling, and overall cost. The second problem considered in this regard is the dimensioning considerations for C-RAN clusters, in which employing ML-based algorithms to provide optimal allocation of baseband unit (BBU) functions (to the appropriate servers hosted by the central unit (CU)) to provide the expected gains is addressed. In Network Diagnostics, attention is given to the tools that can autonomously inspect the network state and trigger alarms when necessary. The contributions are divided into network characteristics forecasts solutions, precise user localizations methods, and security incident identification and forecast. The application of AI/ML methods in high-resolution synthesising and efficient forecasting of mobile traffic; QoE inference and QoS improvement by forecasting techniques; service level agreement (SLA) prediction in multi-tenant environments; and complex event recognition and forecasting are among network characteristics forecasts methods discussed. On high-precision user localization, AI-assisted sensor fusion and line-of-sight (LoS)/non-line-of-sight (NLoS) discrimination, and 5G localization based on soft information and sequential autoencoding are introduced. And finally, on forecasting security incidents, after a short introduction on modern attacks in mobile networks, ML-based network traffic inspection and real-time detection of distributed denial-of-service (DDoS) attacks are briefly examined. In regard to the Network Optimisation and Control, attention is given to the different network segments, including radio access, transport/fronthaul (FH)/backhaul (BH), virtualisation infrastructure, end-to-end 5G PPP Technology Board AI/ML for Networks 3 (E2E) network slicing, security, and application functions. Among application of AI/ML in radio access, the slicing in multi-tenant networks, radio resource provisioning and traffic steering, user association, demand-driven power allocation, joint MAC scheduling (across several gNBs), and propagation channel estimation and modelling are discussed. Moreover, these solutions are categorised (based on the application time-scale) into real-time, near-real-time, and non-real-time groups. On transport and FH/BH networks, AI/ML algorithms on triggering path computations, traffic management (using programmable switches), dynamic load balancing, efficient per-flow scheduling, and optimal FH/BH functional splitting are introduced. Moreover, federated learning across MEC and NFV orchestrators, resource allocation for service function chaining, and dynamic resource allocation in NFV infrastructure are among introduced AI/ML applications for virtualisation infrastructure. In the context of E2E slicing, several applications such as automated E2E service assurance, resource reservation (proactively in E2E slice) and resource allocation (jointly with slice-based demand prediction), slice isolation, and slice optimisation are presented. In regard to the network security, the application of AI/ML techniques in responding to the attack incidents are discussed for two cases, i.e. in moving target defence for network slice protection, and in self-protection against app-layer DDoS attacks. And finally, on the AI/ML applications in optimisation of application functions, the dash prefetching optimization and Q-learning applications in federated scenarios are presented.The white paper continues with the discussions on the application of AI/ML in the 5G and B5G network architectures. In this context the AI/ML based solutions pertaining to autonomous slice management, control and orchestration, cross-layer optimisation framework, anomaly detection, and management analytics, as well as aspects in AI/ML-as-a-service in network management and orchestration, and enablement of ML for the verticals' domain are presented. This is followed by topics on management of ML models and functions, namely the ML model lifecycle management, e.g., training, monitoring, evaluation, configuration and interface management of ML models. Furthermore, the white paper investigates the standardisation activities on the enablement of AI/ML in networks, including the definition of network data analytics function (NDAF) by 3GPP, the definition of an architecture that helps address challenges in network automation and optimization using AI and the categories of use cases where AI may benefit network operation and management by ETSI ENI, and finally the O-RAN definition of non-real-time and near-real-time RAN controllers to support ML-based management and intelligent RAN optimisation. Additionally, the white paper identifies the challenges in view of privacy and trust in AI/ML-based networks and potential solutions by introducing privacy preserving mechanisms and the zero-trust management approach are introduced. The availability of reliable data-sets as a crucial prerequisite to efficiency of AI/ML algorithms is discussed and the white paper concludes with a brief overview of AI/ML-based KPI validation and system troubleshooting. In summary the findings of this white paper conclude with the identification of several areas (research and development work) for further attention in order to enhance future network return-on-investment (ROI): (a) building standardized interfaces to access relevant and actionable data, (b) exploring ways of using AI to optimize customer experience, (c) running early trials with new customer segments to identify AI opportunities, (d) examining use of AI and automation for network operations, including planning and optimization, (e) ensuring early adoption of new solutions for AI and automation to facilitate introduction of new use cases, and (f) establish/launch an open repository for network data-sets that can be used for training and benchmarking algorithms by all

Search for prompt production of pentaquarks in charm hadron final states

International audienceA search for hidden-charm pentaquark states decaying to a range of $\Sigma_{c}\bar{D}$ and $\Lambda_{c}\bar{D}$ final states, as well as doubly-charmed pentaquark states to $\Sigma_{c}D$ and $\Lambda_{c}^{+}D$, is made using samples of proton-proton collision data corresponding to an integrated luminosity of $5.7fb^{-1}$ recorded by the LHCb detector at $\sqrt{s} = 13Te\kern -0.1em V$. Since no significant signals are found, upper limits are set on the pentaquark yields relative to that of the $\Lambda_{c}^{+}$ baryon in the $\Lambda_{c}^{+}\to pK^{-}\pi^{+}$ decay mode. The known pentaquark states are also investigated, and their signal yields are found to be consistent with zero in all cases

A century of trends in adult human height

International audienc