Trew@sig: spatial workflow. Workflow y SIG para la I-Administación

Trew@SIG nace para cubrir la necesidad de gestión de información espacial asociada a los flujos de trabajo en la tramitación de expedientes de la administración electrónica. Es un completo framework de desarrollo que proporciona la total integración de los Sistemas de Información Geográfica con datos de gestión, como un módulo complementario "al sistema de tramitación". Esto es, un entorno de desarrollo multifuncional, aplicable a diferentes ámbitos de la gestión pública, como el urbanismo, recursos hídricos, inventarios georreferenciados, participación pública, etc. Trew@SIG se fundamenta en dos conceptos básicos. Por un lado, el “Momento de Tramitación”, definido por el estado en el que se encuentra el proceso de trabajo y las necesidades de información espacial y herramientas, que apoyarán la toma de decisiones para la continuación del flujo. Por otro, el “GeoPerfil”, como conjunto de capas y herramientas de gestión de la información espacial ligados al momento de tramitación y a las necesidades del usuario. Estas capacidades se han desarrollado en entornos diferentes: web, escritorio y móvil, todos ellos según normas OGC y usando componentes desarrollados en software libre, entre los que se encuentran: • Núcleo de integración con el workflow, que ofrece las funcionalidades adecuadas para la integración del sistema con el momento de tramitación. • Núcleo SIG., aporta todas las funcionalidades para la gestión de la información espacial, basado en estándares OGC: WMS, WFS, WFS-G, WPS. • Trew@SIG cliente web, aporta las funcionalidades de visualización de capas y herramientas de gestión mediante un framework de desarrollo basado en tecnologías OpenSource. • Aplicación de movilidad basada en Enebro Framework, que incorpora las funcionalidades necesarias para las operaciones de campo. • Aplicación de escritorio basada en gvSIG y empleada para aprovechar toda la potencia de esta herramienta de escritorio. Trew@SIG puede ser usado por cualquier entidad pública o privada en procesos trabajo, mejorando la gestión de datos y ofreciendo todos los instrumentos requeridos en la toma de decisiones que requieran consultar, capturar, modificar o analizar elementos territoriales. El resultado es un entorno de desarrollo que permite modelar procesos de gestión con dimensión espacial, de fácil implantación, implementado con tecnologías de fuentes abiertas y bajo estándares OGC. Ofrece a las distintas organizaciones y usuarios la posibilidad de integrar en sus procesos de trabajo datos y funcionalidades espaciales, hasta ahora, tratadas con herramientas y en entornos diferentes.Trew@SIG is born to cover the need of spatial data management in workflow-processing. Trew@SIG is a complete framework to develop professional workflow solutions providing total GIS-Data integration,as an add-on module to the pure-workflow-system. It's a multipurpose framework, being used in different workflow enviroments, as urbanism, hydrological processes, multipurposal inventaries, urban tasks, contingency management, etc. Trew@SIG is based on two basic concepts “Workflow momentum” and “GeoProfile”. Workflow momentum is an unique combination of workflow status with specific needs of spatial information and tools,in order to decide which way the workflow will go on. GeoProfile is defined as a set of layers and tools to manage spatial information. This way, Trew@SIG deals with workflow and spatial data associating a geoprofile to a certain workflow momentum, providing the user with all spatial information and capabilities needed to go ahead with the next step of the workflow process. These capabilities are offered in different enviroments: web, desktop and mobile, all of them according to OGC standards and using open source components, which are detailed as follows: •Workflow integration core, which offers full integration with workflow processes. It has been integrated with Trew@ workflow system, based on WFMC (WorkFlowManagementCoalition) standards, implementing XPDL and WSDL standards. •GIS core,this module contains all the GIS functionallities based on OGC standards: WMS,WFS,WFS-G,WPS. •Trew@SIG webclient,open for different client mapping frameworks.Full configuration of a geoprofile (layers&tools) implemented for OpenLayers and Mapfish. •Desktop application,based on gvSIG, provides advanced users full and highest performance managing spatial data. •Fieldwork application,based on Enebro, gives full capabilities for fieldwork operations,GPS-capturing,editing,etc. Trew@SIG can be used for any public or private entity for their own workflow processes,improving the management of data,offering all the tools required to make a decision in a process. To sum up,Trew@SIG makes workflow processing much easier,faster and simpler to deploy,joining the effort of leading opensource projects in a framework and offering the final users GIS data and functionalities completely integrated in workflow processing

Yield estimation using machine learning from satellite imagery

Accurate and early yield estimation (from pea size) allows 1.- Make decisions at field level: green harvesting, irrigation management. 2.- Advance or organise the purchase of grapes from suppliers. 3.- Forecast the volume of wine produced in the campaign that has not yet begun. 4.- Define the quality of the vintage: regular and detailed monitoring of whether, or not, the heterogeneity of the leaf surface, photosynthetic activity or soil moisture observed in the vineyards is as expected at this time, compared with historical values. 5.- Precise control of each vine in production, knowing which vines are no longer productive or should be grubbed up. The Sentinel-2 satellite has generated a time series of images spanning more than six years, which is a great help in analysing the state of permanent crops such as vineyards, where grapes are produced every year. The weekly comparison of what is happening in the current season with what has happened in the previous six seasons is information that is in line with agricultural practices: Winegrowers make the mental exercise of comparing how the vines are developing today with how they developed in previous seasons, with the aim of repeating the years of good yields. In addition, several commercial satellites can now capture images of 50 centimetres pixel resolution or even better, making it possible to check the health of each vine every year. Since 2020, GMV and Pago de Carraovejas have been working together to develop a yield estimation service based on field information and satellite images that feed machine learning algorithms. This paper describes the path followed from the beginning and the steps taken, summarising as follows: 1. - Machine learning algorithm trained with cluster counting and satellite data. 2. - Adjustment of the number of vines in production in each vineyard using very high-resolution imagery. 3. - Machine learning algorithm trained on real production from past campaigns and historical Sentinel-2 time series. The results obtained by comparing the actual grape intake in the winery with the yield estimation range from 91% accuracy in 2020 to 95% accuracy in 2022

Microbial Contribution to Wine Aroma and Its Intended Use for Wine Quality Improvement

Wine is a complex matrix that includes components with different chemical natures, the volatile compounds being responsible for wine aroma quality. The microbial ecosystem of grapes and wine, including Saccharomyces and non-Saccharomyces yeasts, as well as lactic acid bacteria, is considered by winemakers and oenologists as a decisive factor influencing wine aroma and consumer’s preferences. The challenges and opportunities emanating from the contribution of wine microbiome to the production of high quality wines are astounding. This review focuses on the current knowledge about the impact of microorganisms in wine aroma and flavour, and the biochemical reactions and pathways in which they participate, therefore contributing to both the quality and acceptability of wine. In this context, an overview of genetic and transcriptional studies to explain and interpret these effects is included, and new directions are proposed. It also considers the contribution of human oral microbiota to wine aroma conversion and perception during wine consumption. The potential use of wine yeasts and lactic acid bacteria as biological tools to enhance wine quality and the advent of promising advice allowed by pioneering -omics technologies on wine research are also discussed

Worldwide Effects of Coronavirus Disease Pandemic on Tuberculosis Services, January–April 2020

Coronavirus disease has disrupted tuberculosis services globally. Data from 33 centers in 16 countries on 5 continents showed that attendance at tuberculosis centers was lower during the first 4 months of the pandemic in 2020 than for the same period in 2019. Resources are needed to ensure tuberculosis care continuity during the pandemic

Brettanomyces/Dekkera: control y detección en bodegas

Las levaduras del género Brettanomyces/Dekkera ocasionan uno de los problemas más graves en el ámbito de la enología actual. A ellas se debe la génesis en vinos de determinados fenoles volátiles que se traducen, a partir de cierta concentración, en sensaciones olfativas muy negativas, descritas como "olor animal", "cuero mal curado" o "sudor de caballo

Enzymatic analysis normalization in vine soils: determination of quality and biological productivity

Dehydrogenase activity enzymatic determination and the measurement of Substrate Induced Respiration (SIR) are methods that allow microbial processes analysis produced in the soil. Enzymes such as phosphatases and β-galactosidase, can be considered as specific parameters, integrated in the cycle of phosphorus and carbon. Measurement of all these activities allows to know soil activity metabolic and behavior of all. Our research team has collaborated on the standardization project: ISO/CD20130 "Measurement of enzyme activity patterns in soil samples using colorimetric substrates in micro-well plates”, led by Nathalie Cheviron, technical director of BiochemENV platform (INRA, Versailles). Since 2014, we have been managing an integral microbiota study associated with singular organic vineyards (CDTI IDI 20140448) in Ribera de Duero. Our objective is to determine fertility indexes based on the microbial groups present and their most representative metabolic activities. Due to the robustness, repeatability and specificity of the ISO/CD20130 technique, we have implemented the measurement standard of soil enzymatic activities in our laboratory. Being interesting for technical applications in viticulture to guarantee a sustainable end product with unique characteristic

Outlining the influence of non-conventional yeasts in wine ageing over lees

During the last decade, the use of innovative yeast cultures of both Saccharomyces cerevisiae and non-Saccharomyces yeasts as alternative tools to manage the winemaking process have turned the enology industry. Although the contribution of different yeast species to wine quality during fermentation is increasingly understood, information about their role in wine ageing over lees is really scarce. This work aims to analyse the incidence of three non-Saccharomyces yeast species of oenological interest (Torulaspora delbrueckii, Lachancea thermotolerans and Metschnikowia pulcherrima) and of a commercial mannoprotein-overproducer S. cerevisiae strain compared with a conventional industrial yeast strain during wine ageing over lees. To evaluate their incidence in mouthfeel properties of wine after 4 months of ageing, the mannoprotein content of wines was evaluated, together with other wine analytic parameters, such as colour and aroma, biogenic amines and amino acids profile. Some differences among the studied parameters were observed during the study, especially regarding the mannoprotein concentration of wines. Our results suggest that the use of T. delbrueckii lees in wine ageing is a useful tool for the improvement of overall wine quality by notably increasing mannoproteins, reaching values higher than obtained using a S. cerevisiae overproducer strain

Autochthonous Oenococcus oeni Strain to Avoid Histamine Formation in Red Wines: A Study in Real Winemaking Conditions

The production of wines with low biogenic amine (BA) concentrations is a current concern in the wine sector, and strategies to avoid the formation of BAs during winemaking are of particular interest. The aim of this work was to determine the influence of selected autochthonous Oenococcus oeni lactic acid bacteria (LAB) on the BA content in red wines and their prevalence against the indigenous microbiota to avoid BA formation. Sixty-seven red wines were produced at industrial scale under real winemaking conditions for three consecutive vintages. For each wine, we determined LAB implantation and the BA concentrations at various stages of the winemaking process. The results clearly indicated that the use of selected O. oeni strains that are unable to produce BA, in combination with adapted biomass production, is a good strategy to control histamine production in wines. These practices, carried out over three consecutive years, were also observed to ensure the persistence of the selected autochthonous O. oeni strain (CECT 9749) against other indigenous microbiota in the entire winery. Furthermore, analysis of BA content during wine aging in barrels indicated that low BA content was maintained, resulting in healthier wines for the consumer

Yield estimation using machine learning from satellite imagery

Accurate and early yield estimation (from pea size) allows 1.- Make decisions at field level: green harvesting, irrigation management. 2.- Advance or organise the purchase of grapes from suppliers. 3.- Forecast the volume of wine produced in the campaign that has not yet begun. 4.- Define the quality of the vintage: regular and detailed monitoring of whether, or not, the heterogeneity of the leaf surface, photosynthetic activity or soil moisture observed in the vineyards is as expected at this time, compared with historical values. 5.- Precise control of each vine in production, knowing which vines are no longer productive or should be grubbed up. The Sentinel-2 satellite has generated a time series of images spanning more than six years, which is a great help in analysing the state of permanent crops such as vineyards, where grapes are produced every year. The weekly comparison of what is happening in the current season with what has happened in the previous six seasons is information that is in line with agricultural practices: Winegrowers make the mental exercise of comparing how the vines are developing today with how they developed in previous seasons, with the aim of repeating the years of good yields. In addition, several commercial satellites can now capture images of 50 centimetres pixel resolution or even better, making it possible to check the health of each vine every year. Since 2020, GMV and Pago de Carraovejas have been working together to develop a yield estimation service based on field information and satellite images that feed machine learning algorithms. This paper describes the path followed from the beginning and the steps taken, summarising as follows: 1. - Machine learning algorithm trained with cluster counting and satellite data. 2. - Adjustment of the number of vines in production in each vineyard using very high-resolution imagery. 3. - Machine learning algorithm trained on real production from past campaigns and historical Sentinel-2 time series. The results obtained by comparing the actual grape intake in the winery with the yield estimation range from 91% accuracy in 2020 to 95% accuracy in 2022

Microsatellite typing of Lachancea thermotolerans for wine fermentation monitoring

Climate change is causing a lack of acidity during winemaking and oenologists use several solutions to cope with such a problem. Lachancea thermotolerans, which has the potential to tolerate the harsh physicochemical conditions of wine, has emerged as a promising alternative for pH management during winemaking and, currently, it is the most valuable yeast used for acidity control in wine. In this work a manageable method for L. thermotolerans genotyping based on a multiplexed microsatellite amplification in 6 different loci was developed. The proposed method was used to distinguish between 103 collection strains obtained from different geographical and isolation sources, and then challenged against a 429 L. thermotolerans isolates from several wineries and harvests. The procedure was also tested for fermentation monitoring and strain implantation. This approach was conceived to simplify the methodology available for L. thermotolerans genotyping, making it easy for applying in wine-related laboratories. This method can be applied to distinguish between L. thermotolerans strains in selection programs and to follow implantation of inoculated strains during winemaking with optimal results.Ministerio de Ciencia e Innovación (MICIN)Depto. de Genética, Fisiología y MicrobiologíaFac. de Ciencias BiológicasTRUEpu