Análisis de la uniformidad de riego en sistemas de aspersión semiportátil con aspersores de gran tamaño

La sostenibilidad en la agricultura de regadío depende en gran medida de conseguir una alta eficiencia de aplicación en el riego. Es muy importante conocer los factores que afectan a la uniformidad de riego, especialmente en aspersores semi-portátiles de gran tamaño, que son un sistema muy común en áreas áridas y semiáridas, como Irán. Hasta el momento, la uniformidad de distribución del agua aplicada no ha sido considerada cuantitativamente en la mayoría de las combinaciones de variables hidráulicas y meteorológicas con aspersores portátiles de gran tamaño. En este trabajo, se ha caracterizado el coeficiente de uniformidad (CU) analizando la influencia de los principales factores que le afectan, como la velocidad del viento (W), la presión de trabajo (P) y el marco de riego. Los ensayos de campo se realizaron con un solo aspersor al aire libre. Se aprecia un efecto significativo del viento, como parámetro meteorológico, sobre el CU bajo diferentes condiciones climáticas, en relación a la presión y la separación entre aspersores. Este comportamiento es muy similar al obtenido con aspersores de tamaño medio. Los criterios técnicos propuestos en los resultados se pueden utilizar para optimizar la gestión del riego por aspersión de acuerdo con factores de diseño adecuados para una amplia gama de condiciones climáticas y presión (es decir 450 y 500 kPa). Así, la relación entre la separación entre aspersores y el radio mojado no debe superar los 0,45 con el fin de alcanzar el coeficiente de diseño de uniformidad aceptable (80%) bajo condiciones de viento (>2 m s -1 ) en el sistema de riego

Laser-induced breakdown spectroscopy of cyanobacteria in carbonate matrices under simulated Martian environment

The finding on the Martian surface of hydrated salt minerals, like carbonates and sulphates, and their interpretation as deriving from the desiccation of old bodies of water, has provided an evidence of liquid water activity on the surface of Mars [1]. These evaporite environments and their saline deposits are now a chief goal for planetary missions devoted to the search for fossil Martian life. Such minerals have the possibility of trapping and preserving over geologic times a biological record made up of halophilic extremophiles [1]. The existence of species of cyanobacteria that inhabit rock substrates on Earth, capable of growing in environments considered extreme, makes them ideal organisms for studying biological responses in different environmental conditions [2]. One possible organism detection strategy consists in the study of the most relevant emission lines and molecular bands attributed to presence of life by laser-induced breakdown spectroscopy (LIBS). However, the detection of these species can be complex as LIBS is sensitive to environmental conditions, such as the atmosphere composition and pressure, and could contribute to this signal [3]. In the present study, several species of cyanobacteria with dissimilar extremophilic characteristics [4] (tolerance to desiccation and salinity) were examined by LIBS. The identification and discrimination of cyanobacteria on carbonate substrates was based on organic signal emissions (C, C2, CN...) and the presence of other microelements (Fe, Si, Cu, K…). For this purpose, and to evaluate the influence of the surrounding atmosphere on the plasma composition and its contribution on LIBS signal, a set of samples including Arthrospira platensis (commercial), Microcystys aeruginosa (cultured) and Chroococcidiopsis sp. (natural samples) was analyzed under i) Mars-analogue atmosphere and ii) low air vacuum (7mbar)Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

EoE CONNECT, the European registry of clinical, environmental, and genetic determinants in eosinophilic esophagitis: rationale, design, and study protocol of a large-scale epidemiological study in Europe

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si lo hubiere, y los autores pertenecientes a la UAMThe growing prevalence of eosinophilic esophagitis (EoE) represents a considerable burden to patients and health care systems. Optimizing cost-effective management and identifying mechanisms for disease onset and progression are required. However, the paucity of large patient cohorts and heterogeneity of practice hinder the defining of optimal management of EoE. Methods: EoE CONNECT is an ongoing, prospective registry study initiated in 2016 and currently managed by EUREOS, the European Consortium for Eosinophilic Diseases of the Gastrointestinal Tract. Patients are managed and treated by their responsible specialists independently. Data recorded using a web-based system include demographic and clinical variables; patient allergies; environmental, intrapartum, and early life exposures; and family background. Symptoms are structurally assessed at every visit; endoscopic features and histological findings are recorded for each examination. Prospective treatment data are registered sequentially, with new sequences created each time a different treatment (active principle, formulation, or dose) is administered to a patient. EoE CONNECT database is actively monitored to ensure the highest data accuracy and the highest scientific and ethical standards. Results: EoE CONNECT is currently being conducted at 39 centers in Europe and enrolls patients of all ages with EoE. In its aim to increase knowledge, to date EoE CONNECT has provided evidence on the effectiveness of first- and second-line therapies for EoE in clinical practice, the ability of proton pump inhibitors to induce disease remission, and factors associated with improved response. Drug effects to reverse fibrous remodeling and endoscopic features of fibrosis in EoE have also been assessed. Conclusion: This prospective registry study will provide important information on the epidemiological and clinical aspects of EoE and evidence as to the real-world and long-term effectiveness and safety of therapy. These data will potentially be a vital benchmark for planning future EoE health care services in EuropeThe authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The establishment and design of the EoE CONNECT registry was developed with a grant from the United European Gastroenterology through the National Societies Link Award program. The maintenance of the database is financed by EUREOS (European Society of Eosinophilic Oesophagitis). Funding agencies had no role in the study design, in the writing of this manuscript, or the decision to submit for publicatio

Data-driven nonparametric Li-ion battery ageing model aiming at learningfrom real operation data - Part B: Cycling operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates.This investigation work was financially supported by ELKARTEK (CICe2018 - Desarrollo de actividades de investigacion fundamental estrategica en almacenamiento de energia electroquimica y termica para sistemas de almacenamiento hibridos, KK-2018/00098) and EMAITEK Strategic Programs of the Basque Government. In addition, the research was undertaken as a part of ELEVATE project (EP/M009394/1) funded by the Engineering and Physical Sciences Research Council (EPSRC) and partnership with the WMG High Value Manufacturing (HVM) Catapult. Authors would like to thank the FP7 European project Batteries 2020 consortium (grant agreement No. 608936) for the valuable battery ageing data provided during the project

Data-driven nonparametric Li-ion battery ageing model aiming at learningfrom real operation data – Part A: Storage operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. This first paper focusses on the systematic modelling and experimental verification of cell degradation through calendar ageing. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 32 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 18 tested cells achieves an overall mean-absolute-error of 0.53% in the capacity curves prediction, after being validated under a broad window of both dynamic and static temperature and SOC storage conditions.This investigation work was financially supported by ELKARTEK (CICe2018 -Desarrollo de actividades de investigacion fundamental estrategica en almacenamiento de energia electroquimica y termica para sistemas de almacenamiento hibridos, KK-2018/00098) and EMAITEK Strategic Programs of the Basque Government. In addition, the research was undertaken as a part of ELEVATE project (EP/M009394/1) funded by the Engineering and Physical Sciences Research Council (EPSRC) and partnership with the WMG High Value Manufacturing (HVM) Catapult. Authors would like to thank the FP7 European project Batteries 2020 consortium (grant agreement No. 608936) for the valuable battery ageing data provided during the course of the project

Thermal Vision for Soil Assessment in a Multipurpose Environmental Chamber under Martian Conditions towards Robot Navigation

Soil assessment is important for mobile robot planning and navigation on natural and planetary environments. Terramechanic characteristics can be inferred from the thermal behaviour of soils under the influence of sunlight using remote sensors such as Long-Wave Infrared cameras. However, this behaviour is greatly affected by the low atmospheric pressures of planets such as Mars, so practical models are needed to relate robot remote sensing data on Earth to target planetary exploration conditions. This article proposes a general framework based on multipurpose environmental chambers to generate representative diurnal cycle dataset pairs that can be useful to relate the thermal behaviour of a soil on Earth to the corresponding behaviour under planetary pressure conditions using remote sensing. Furthermore, we present an application of the proposed framework to generate datasets using the UMA-Laserlab chamber, which can replicate the atmospheric \ch{CO2} composition of Mars. In particular, we analyze the thermal behaviour of four soil samples of different granularity by comparing replicated Martian surface conditions and their Earth's diurnal cycle equivalent. Results indicate a correlation between granularity and thermal inertia that is consistent with available Mars surface measurements recorded by rovers. The resulting dataset pairs, consisting of representative diurnal cycle thermal images with heater, air, and subsurface temperatures, have been made available for the scientific community.Comment: 10 pages, 13 figure

Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences

Shotgun metagenomic sequencing reveals the potential in microbial communities. However, lower-cost 16S ribosomal RNA (rRNA) gene sequencing provides taxonomic, not functional, observations. To remedy this, we previously introduced Piphillin, a software package that predicts functional metagenomic content based on the frequency of detected 16S rRNA gene sequences corresponding to genomes in regularly updated, functionally annotated genome databases. Piphillin (and similar tools) have previously been evaluated on 16S rRNA data processed by the clustering of sequences into operational taxonomic units (OTUs). New techniques such as amplicon sequence variant error correction are in increased use, but it is unknown if these techniques perform better in metagenomic content prediction pipelines, or if they should be treated the same as OTU data in respect to optimal pipeline parameters

Poor sensitivity of fecal gluten immunogenic peptides and serum antibodies to detect duodenal mucosal damage in celiac disease monitoring

A lifelong gluten-free diet (GFD) is the only current treatment for celiac disease (CD), but strict compliance is complicated. Duodenal biopsies are the “gold standard” method for diagnosing CD, but they are not generally recommended for disease monitoring. We evaluated the sensitivity and specificity of fecal gluten immunogenic peptides (GIPs) to detect duodenal lesions in CD patients on a GFD and compared them with serum anti-tissue transglutaminase (tTG) IgA antibodies. A prospective study was conducted at two tertiary centers in Spain on a consecutive series of adolescents and adults with CD who maintained a long-lasting GFD. Adherence to a GFD and health-related quality of life were scored with validated questionnaires. Mucosal damage graded according to the Marsh–Oberhüber classification (Marsh 1/2/3) was used as the reference standard. Of the 97 patients included, 27 presented duodenal mucosal damage and 70 had normal biopsies (Marsh 0). The sensitivity (33%) and specificity (81%) of GIPs were similar to those provided by the two assays used to measure anti-tTG antibodies. Scores in questionnaires showed no association with GIP, but an association between GIPs and patients’ self-reported gluten consumption was found (p = 0.003). GIP displayed low sensitivity but acceptable specificity for the detection of mucosal damage in CD.This research was funded by a grant from Asociación Castellana de Aparato Digestivo (year 2018) to A.J.L

Evaluation of a New Monoclonal Chemiluminescent Immunoassay Stool Antigen Test for the Diagnosis of Helicobacter pylori Infection: A Spanish Multicentre Study

The stool antigen test (SAT) represents an attractive alternative for detection of Helicobacter pylori. The aim of this study was to assess the accuracy of a new SAT, the automated LIAISON(R) Meridian H. pylori SA based on monoclonal antibodies, compared to the defined gold standard C-13-urea breath test (UBT). This prospective multicentre study (nine Spanish centres) enrolled patients >= 18 years of age with clinical indication to perform UBT for the initial diagnosis and for confirmation of bacterial eradication. Two UBT methods were used: mass spectrometry (MS) including citric acid (CA) or infrared spectrophotometry (IRS) without CA. Overall, 307 patients (145 naive, 162 with confirmation of eradication) were analysed. Using recommended cut-off values (negative SAT = 1.10) the sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 67%, 97%, 86%, 92% and 91%, respectively, obtaining an area under the receiver operating characteristic (ROC) curve (AUC) of 0.85. Twenty-eight patients, including seven false positives and 21 false negatives, presented a discordant result between SAT and UBT. Among the 21 false negatives, four of six tested with MS and 11 of 15 tested with IRS presented a borderline UBT delta value. In 25 discordant samples, PCR targeting H. pylori DNA was performed to re-assess positivity and SAT accuracy was re-analysed: sensitivity, specificity, positive predictive value, negative predictive value, accuracy and AUC were 94%, 97%, 86%, 99%, 97% and 0.96, respectively. The new LIAISON(R) Meridian H. pylori SA SAT showed a good accuracy for diagnosis of H. pylori infection