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                  <text>Dominio científico: Coronavirus</text>
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                <text>Data Report: Golf-Related Engagement During COVID-19 Quarantine Restrictions (4–12th May 2020)</text>
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                <text>Graeme G. Sorbie, Alexander J. Beaumont, Ashley K. Richardson, Jonathan Glen, Scott M. Hardie, David Lavallee</text>
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                <text>coronavirus, Pandemic, SARS-CoV-2, physical activity, exercise, golf</text>
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                <text>Korean Society of Epidemiology</text>
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                <text>Sports</text>
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                  <text>Dominio científico: Coronavirus</text>
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                <text>Data Science Techniques for COVID-19 in Intensive Care Units</text>
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                <text>Sergio Muñoz Lezcano, Fernando Carlos López Hernández, Alberto Corbi Bellot</text>
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                <text>Data scientists aim to provide techniques and tools to the clinicians to manage the new coronavirus disease. Nowadays, new emerging tools based on Artificial Intelligence (AI), Image Processing (IP) and Machine Learning (ML) are contributing to the improvement of healthcare and treatments of different diseases. This paper reviews the most recent research efforts and approaches related to these new data driven techniques and tools in combination with the exploitation of the already available COVID-19 datasets. The tools can assist clinicians and nurses in efficient decision making with complex and heavily heterogeneous data, even in hectic and overburdened Intensive Care Units (ICU) scenarios. The datasets and techniques underlying these tools can help finding a more correct diagnosis. The paper also describes how these innovative AI+IP+ML-based methods (e.g., conventional X-ray imaging, clinical laboratory data, respiratory monitoring and automatic adjustments, etc.) can assist in the process of easing both the care of infected patients in ICUs and Emergency Rooms and the discovery of new treatments (drugs).</text>
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                <text>2021</text>
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                <text>Biomarkers, machine learning, data mining, x-ray, Image Processing, coronavirus (COVID-19)</text>
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            <name>Identifier</name>
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                <text>10.9781/ijimai.2020.11.008</text>
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                <text>International Journal of Interactive Multimedia and Artificial Intelligence</text>
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                <text>Universidad Internacional de La Rioja (UNIR)</text>
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                <text>Technology</text>
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                  <text>Agricultura sostenible</text>
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                  <text>Dominio científico: Agricultura sostenible</text>
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                <text>Data use by small scale horticultural producers in Wales, U.K.: directions for the provision of digital information in rural management</text>
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                <text>David Skydmore</text>
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            <description>An account of the resource</description>
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                <text>Food security for small-scale farmers requires resilient farming practice. Farms are affected by economic, market, regulatory and social factors and the farmer needs to be provided with relevant, reliable and up-to-date information on these factors. A case study is given of horticulture in Wales where land holdings tend to be small, being of less than 2ha. It showed the growers have an age profile predominating in the older ranges, such that inter-generational skills are likely to be lost. Trade is mainly through on-farm sales, local tourism outlets or local retailers. The majority of growers have access to digital technologies. The most useful to the small-scale farmer are communication devices and knowledge technologies supplying information including that on market prices, weather, soil conditions and best practice. This information increases its benefits if it provides scenarios and predictive models. To use the information, the farmers need an adequate agricultural education. The knowledge systems need to be developed and maintained and this often requires support from Government and Producer Organizations. Use of the knowledge technologies and the two-way supply of information between the farmers and the Producer Organizations and Government will help improve crop productivity, sustainability, rural economics and household well-being.</text>
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                <text>2018</text>
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                <text>food security, horticulture, precision agriculture, resilience, skills</text>
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                <text>Revista Eletrônica Competências Digitais para Agricultura Familiar</text>
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                <text>Universidade Estadual Paulista Júlio de Mesquita Filho</text>
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                <text>Agriculture (General)</text>
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                <text>&lt;a href="http://codaf.tupa.unesp.br:8082/index.php/recodaf/article/view/83" target="_blank" rel="noreferrer noopener"&gt;http://codaf.tupa.unesp.br:8082/index.php/recodaf/article/view/83&lt;/a&gt;</text>
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                  <text>Coronavirus</text>
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              <description>An account of the resource</description>
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                  <text>Dominio científico: Coronavirus</text>
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                <text>Data-based analysis, modelling and forecasting of the COVID-19 outbreak.</text>
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                <text>Cleo Anastassopoulou, Lucia Russo, Athanasios Tsakris, Constantinos Siettos</text>
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                <text>Since the first suspected case of coronavirus disease-2019 (COVID-19) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the publicly available epidemiological data for Hubei, China from January 11 to February 10, 2020, we provide estimates of the main epidemiological parameters. In particular, we provide an estimation of the case fatality and case recovery ratios, along with their 90% confidence intervals as the outbreak evolves. On the basis of a Susceptible-Infectious-Recovered-Dead (SIDR) model, we provide estimations of the basic reproduction number (R0), and the per day infection mortality and recovery rates. By calibrating the parameters of the SIRD model to the reported data, we also attempt to forecast the evolution of the outbreak at the epicenter three weeks ahead, i.e. until February 29. As the number of infected individuals, especially of those with asymptomatic or mild courses, is suspected to be much higher than the official numbers, which can be considered only as a subset of the actual numbers of infected and recovered cases in the total population, we have repeated the calculations under a second scenario that considers twenty times the number of confirmed infected cases and forty times the number of recovered, leaving the number of deaths unchanged. Based on the reported data, the expected value of R0 as computed considering the period from the 11th of January until the 18th of January, using the official counts of confirmed cases was found to be ∼4.6, while the one computed under the second scenario was found to be ∼3.2. Thus, based on the SIRD simulations, the estimated average value of R0 was found to be ∼2.6 based on confirmed cases and ∼2 based on the second scenario. Our forecasting flashes a note of caution for the presently unfolding outbreak in China. Based on the official counts for confirmed cases, the simulations suggest that the cumulative number of infected could reach 180,000 (with a lower bound of 45,000) by February 29. Regarding the number of deaths, simulations forecast that on the basis of the up to the 10th of February reported data, the death toll might exceed 2,700 (as a lower bound) by February 29. Our analysis further reveals a significant decline of the case fatality ratio from January 26 to which various factors may have contributed, such as the severe control measures taken in Hubei, China (e.g. quarantine and hospitalization of infected individuals), but mainly because of the fact that the actual cumulative numbers of infected and recovered cases in the population most likely are much higher than the reported ones. Thus, in a scenario where we have taken twenty times the confirmed number of infected and forty times the confirmed number of recovered cases, the case fatality ratio is around ∼0.15% in the total population. Importantly, based on this scenario, simulations suggest a slow down of the outbreak in Hubei at the end of February.</text>
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                <text>DOI: 10.1371/journal.pone.0230405</text>
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                <text>PLoS ONE</text>
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                <text>Science, Medicine</text>
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                <text>EN</text>
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                <text>Database use as a tool to assist the development of agricultural activities</text>
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                <text>João Victor Ayres de Almeida, Diogo de Lucca Sartori</text>
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                <text>The study aims to analyze the availability of a database that provided information focused on the agribusiness sector, and exemplify a form of use for this knowledge acquired during the analysis of data obtained during navigation. For it was chosen the EMBRAPA database where data were collected from the Brazilian cattle herd and by analyzing this we sought to understand the behavior of the cattle industry in the three main producing regions of Brazil: North, Southeast, and Midwest. The method used seeks to provide a simple way of accessing information, seeking to equate forces of those involved in the production chain, considering these large landowners as small producers, thus creating a parity among the producers of this important sector of the economy.</text>
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                <text>Agricultura Familiar, Agropensa, agronegocio, pecuária, tecnología</text>
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                <text>Revista Eletrônica Competências Digitais para Agricultura Familiar</text>
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                <text>Universidade Estadual Paulista Júlio de Mesquita Filho</text>
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                <text>Agriculture (General)</text>
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            <name>Relation</name>
            <description>A related resource</description>
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                <text>&lt;a href="http://codaf.tupa.unesp.br:8082/index.php/recodaf/article/view/59" target="_blank" rel="noreferrer noopener"&gt;http://codaf.tupa.unesp.br:8082/index.php/recodaf/article/view/59&lt;/a&gt;</text>
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                  <text>Dominio científico: Coronavirus</text>
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                <text>Dataset on SARS-CoV-2 non-pharmaceutical interventions in Brazilian municipalities</text>
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            <name>Creator</name>
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              <elementText elementTextId="42976">
                <text>Andreza Aruska de Souza Santos, Darlan da Silva Candido, William Marciel de Souza, Lewis Buss, Sabrina L. Li, Rafael H. M. Pereira, Chieh-Hsi Wu, Ester C. Sabino, Nuno R. Faria</text>
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                <text>Measurement(s) prevention and control intervention • SARS-CoV-2 non-pharmaceutical interventions Technology Type(s) Survey Sample Characteristic - Location Brazil Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13862108</text>
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                <text>2021</text>
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                <text>10.1038/s41597-021-00859-1</text>
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              <elementText elementTextId="42980">
                <text>Epidemiology and Health</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
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              <elementText elementTextId="42981">
                <text>Korean Society of Epidemiology</text>
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            <name>Coverage</name>
            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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                <text>Science</text>
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              <name>Title</name>
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                  <text>Coronavirus</text>
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              <name>Description</name>
              <description>An account of the resource</description>
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                  <text>Dominio científico: Coronavirus</text>
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      <name>Text</name>
      <description>A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.</description>
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        <name>Dublin Core</name>
        <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Date of origin of the SARS coronavirus strains</text>
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            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
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              <elementText elementTextId="27597">
                <text>Li Wei, Zhao Yi, Zhu Xiaopeng, Chen Runsheng, Wang Yuelan, Sun Shiwei, Bu Dongbo, Lu Hongchao, Cai Lun, Ling Lunjiang, Xu Jingyi, Zhang Jingfen</text>
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                <text>Abstract Background A new respiratory infectious epidemic, severe acute respiratory syndrome (SARS), broke out and spread throughout the world. By now the putative pathogen of SARS has been identified as a new coronavirus, a single positive-strand RNA virus. RNA viruses commonly have a high rate of genetic mutation. It is therefore important to know the mutation rate of the SARS coronavirus as it spreads through the population. Moreover, finding a date for the last common ancestor of SARS coronavirus strains would be useful for understanding the circumstances surrounding the emergence of the SARS pandemic and the rate at which SARS coronavirus diverge. Methods We propose a mathematical model to estimate the evolution rate of the SARS coronavirus genome and the time of the last common ancestor of the sequenced SARS strains. Under some common assumptions and justifiable simplifications, a few simple equations incorporating the evolution rate (K) and time of the last common ancestor of the strains (T0) can be deduced. We then implemented the least square method to estimate K and T0 from the dataset of sequences and corresponding times. Monte Carlo stimulation was employed to discuss the results. Results Based on 6 strains with accurate dates of host death, we estimated the time of the last common ancestor to be about August or September 2002, and the evolution rate to be about 0.16 base/day, that is, the SARS coronavirus would on average change a base every seven days. We validated our method by dividing the strains into two groups, which coincided with the results from comparative genomics. Conclusion The applied method is simple to implement and avoid the difficulty and subjectivity of choosing the root of phylogenetic tree. Based on 6 strains with accurate date of host death, we estimated a time of the last common ancestor, which is coincident with epidemic investigations, and an evolution rate in the same range as that reported for the HIV-1 virus.</text>
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            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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              <elementText elementTextId="27599">
                <text>2004</text>
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            <name>Identifier</name>
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                <text>DOI: 10.1186/1471-2334-4-3</text>
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            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
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              <elementText elementTextId="27601">
                <text>BMC Infectious Diseases</text>
              </elementText>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
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              <elementText elementTextId="27602">
                <text>BMC</text>
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            <name>Coverage</name>
            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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              <elementText elementTextId="27603">
                <text>Infectious and parasitic diseases</text>
              </elementText>
            </elementTextContainer>
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            <element elementId="50">
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              <description>A name given to the resource</description>
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                  <text>Agricultura sostenible</text>
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              <name>Description</name>
              <description>An account of the resource</description>
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                  <text>Dominio científico: Agricultura sostenible</text>
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      <name>Text</name>
      <description>A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.</description>
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        <name>Dublin Core</name>
        <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Datos geoespaciales: herramientas fundamentales para aumentar la competitividad del campo pampeano</text>
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          <element elementId="39">
            <name>Creator</name>
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                <text>María Soledad Mieza, Maria Soledad Meana</text>
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            <description>An account of the resource</description>
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                <text>Los datos geoespaciales, que comprenden a las imágenes satelitales, los datos GPS, los mapas de rendimiento, entre otros, se utilizan en las distintas etapas de los sistemas de Manejo de Sitio Específico o Agricultura de Precisión. Éstos están orientados al manejo diferenciado dentro de cada parcela agrícola (lote), permitiendo optimizar las aplicaciones de distintos insumos (semillas, fertilizantes, herbicidas, etc.) que, tradicionalmente, se aplicaban en dosis uniformes. Estos sistemas, no solo, apuntan a la obtención de beneficios económicos sino también ambientales permitiendo un uso racional de los recursos. El grupo de trabajo en conjunto a asesores técnicos de la zona, viene desarrollando desde hace 8 años estudios piloto a partir de herramientas geoespaciales, que incluyen la caracterización de ambientes productivos intra-lote, el monitoreo de la evolución de cultivos, la prescripción de siembras variables, etc. con resultados altamente satisfactorios. Por ello, el objetivo principal de este proyecto es validar a escala regional en la zona NE de la Pampa las metodologías que fueron desarrolladas en un número limitado de lotes y condiciones. Dada la difusión de estas prácticas, actualmente se dispone de información sobre un número significativo de lotes en distintos establecimientos, lo que posibilitará la concreción de este proyecto. Se incluirá un análisis económico de la relaciones costos/beneficios de implementación de dosis variables a escala regional, incluyendo también, consideraciones ambientales. Como resultado principal, se estima validar metodologías a nivel regional que puedan ser adoptadas en forma operativa por los productores aumentando la competitividad del sector.</text>
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            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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                <text>2018</text>
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              <elementText elementTextId="165006">
                <text>Ciencia Veterinaria</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
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                <text>Universidad Nacional de La Pampa</text>
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            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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                <text>Veterinary medicine, Animal culture, Zoology</text>
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                <text>&lt;a href="https://cerac.unlpam.edu.ar/index.php/veterinaria/article/view/2096" target="_blank" rel="noreferrer noopener"&gt;https://cerac.unlpam.edu.ar/index.php/veterinaria/article/view/2096&lt;/a&gt;</text>
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                <text>David Hare's fiery account of COVID-19.</text>
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                <text>Philip Ball</text>
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                <text>10.1016/S0140-6736(20)31974-7</text>
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                <text>Lancet (London, England)</text>
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                  <text>Agricultura sostenible</text>
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              <name>Description</name>
              <description>An account of the resource</description>
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                  <text>Dominio científico: Agricultura sostenible</text>
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            <name>Title</name>
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                <text>De América a Asia. El «otro viaje» de Alexander von Humboldt</text>
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                <text>Oliver Lubrich</text>
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                <text>En 1829 Alexander von Humboldt emprendió una expedición a Asia. Este segundo gran viaje del científico alemán fue complementario, en muchos sentidos, de su más conocido viaje a América, que realizó entre 1799 y 1804. En Rusia, Humboldt viajó y trabajó bajo una fuerte presión política. En su Asie centrale (1843) se sirve de técnicas sutiles y de una crítica indirecta. Humboldt concluyó que, en una economía de monopolios estatales, generar energía era poco eficiente y causa de la destrucción del medio ambiente y del cambio climático.</text>
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                <text>Alexander von Humboldt, Expedición, Orientalismo, Rusia, climatologia, política</text>
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                <text>10.3989/revindias.2019.015</text>
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                <text>Revista de Indias</text>
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                <text>Consejo Superior de Investigaciones Científicas</text>
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                <text>Latin America. Spanish America</text>
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                <text>&lt;a href="http://revistadeindias.revistas.csic.es/index.php/revistadeindias/article/view/1105" target="_blank" rel="noreferrer noopener"&gt;http://revistadeindias.revistas.csic.es/index.php/revistadeindias/article/view/1105&lt;/a&gt;</text>
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