Overcoming data scarcity in earth science.

The Data Scarcity problem is repeatedly encountered in environmental research. This may induce an inadequate representation of the response?s complexity in any environmental system to any input/change (natural and human-induced). In such a case, before getting engaged with new expensive studies to gather and analyze additional data, it is reasonable first to understand what enhancement in estimates of system performance would result if all the available data could be well exploited. The purpose of this Special Issue, "Overcoming Data Scarcity in Earth Science" in the Data journal, is to draw attention to the body of knowledge that leads at improving the capacity of exploiting the available data to better represent, understand, predict, and manage the behavior of environmental systems at meaningful space-time scales. This Special Issue contains six publications (three research articles, one review, and two data descriptors) covering a wide range of environmental fields: geophysics, meteorology/climatology, ecology, water quality, and hydrology

Water-quality data imputation with a high percentage of missing values : A machine learning approach

Publicación producida a partir de un Proyecto financiado por la ANIIThe monitoring of surface-water quality followed by water-quality modeling and analysis are essential for generating effective strategies in surface-water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. In this context, several statistical and machine-learning models were assessed for imputing water-quality data at six monitoring stations located in the Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. The challenge of this study is represented by the high percentage of missing data (between 50% and 70%) and the high temporal and spatial variability that characterizes the water-quality variables. The competing algorithms implement univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Hubber Regressor (HR), Support Vector Regressor (SVR) and K-nearest neighbors Regressor (KNNR)). According to the results, more than 76% of the imputation outcomes are considered “satisfactory” (NSE > 0.45). The imputation performance shows better results at the monitoring stations located inside the reservoir than those positioned along the mainstream. IDW was the model with the best imputation results, followed by RFR, HR and SVR. The approach proposed in this study is expected to aid water-resource researchers and managers in augmenting water-quality datasets and overcoming the missing data issue to increase the number of future studies related to the water-quality matter

Spatial patterns in phage-Rhizobium coevolutionary interactions across regions of common bean domestication

Bacteriophages play significant roles in the composition, diversity, and evolution of bacterial communities. Despite their importance, it remains unclear how phage diversity and phage-host interactions are spatially structured. Local adaptation may play a key role. Nitrogen-fixing symbiotic bacteria, known as rhizobia, have been shown to locally adapt to domesticated common bean at its Mesoamerican and Andean sites of origin. This may affect phage-rhizobium interactions. However, knowledge about the diversity and coevolution of phages with their respective Rhizobium populations is lacking. Here, through the study of four phage-Rhizobium communities in Mexico and Argentina, we show that both phage and host diversity is spatially structured. Cross-infection experiments demonstrated that phage infection rates were higher overall in sympatric rhizobia than in allopatric rhizobia except for one Argentinean community, indicating phage local adaptation and host maladaptation. Phage-host interactions were shaped by the genetic identity and geographic origin of both the phage and the host. The phages ranged from specialists to generalists, revealing a nested network of interactions. Our results suggest a key role of local adaptation to resident host bacterial communities in shaping the phage genetic and phenotypic composition, following a similar spatial pattern of diversity and coevolution to that in the host.Fil: Van Cauwenberghe, Jannick. University of California at Berkeley; Estados Unidos. Universidad Nacional Autónoma de México; MéxicoFil: Santamaría, Rosa I.. Universidad Nacional Autónoma de México; MéxicoFil: Bustos, Patricia. Universidad Nacional Autónoma de México; MéxicoFil: Juárez, Soledad. Universidad Nacional Autónoma de México; MéxicoFil: Ducci, Maria Antonella. Universidad Nacional de Salta; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta; ArgentinaFil: Figueroa Fleming, Trinidad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta; Argentina. Universidad Nacional de Salta; ArgentinaFil: Etcheverry, Angela Virginia. Universidad Nacional de Salta; ArgentinaFil: González, Víctor. Universidad Nacional Autónoma de México; Méxic

Informe final del proyecto: Evaluación temporal y espacial del impacto del cambio de cobertura del suelo sobre la calidad del agua: cuenca del río Santa Lucía como cuenca piloto

En las últimas décadas, en Uruguay, se han producido cambios significativos de uso del suelo como resultado de la intensificación y expansión de las actividades agropecuarias e industriales. Estas actividades, muchas veces realizadas sin considerar la protección del medio ambiente, han generado severos daños a la conservación de los ecosistemas acuáticos del país en general, y a la calidad del agua en particular. La cuenca del río Santa Lucía constituye uno de los sistemas hidrográficos más importantes del país porque representa la fuente de agua potable para más de la mitad de la población nacional, además de ser una fuente de agua de riego para la zona de actividad agroindustrial más intensa del país. Desde 2004, año de comienzo del registro de información sistemático de calidad de agua, el río Santa Lucía sufre una progresiva eutrofización, alcanzando niveles elevados de fósforo total. El desafío es por lo tanto desarrollar en la cuenca actividades productivas relevantes para el desarrollo económico del país preservando la calidad de los cuerpos de agua y evitando la afectación de otras actividades como la potabilización de aguas o la preservación de ecosistemas relevantes como los humedales del río Santa Lucía. Basándonos en lo anterior, este proyecto propone utilizar algoritmos de aprendizaje automático no supervisados para investigar las correlaciones entre los cambios en el uso del suelo y/o cobertura del suelo, y los parámetros físico-químicos de calidad del agua. Como resultado, se crearán conocimientos fundamentales para diseñar estrategias efectivas para disminuir la contaminación del agua debido al cambio en el uso del suelo a lo largo del tiempo. El enfoque metodológico desarrollado por este trabajo no será específico para el lugar de estudio, sino que será aplicable en otras cuencas donde se aborden problemáticas similares a las aquí planteadas.Agencia Nacional de Investigación e Innovació

Informe final del proyecto: Evaluación temporal y espacial del impacto del cambio de cobertura del suelo sobre la calidad del agua: cuenca del río Santa Lucía como cuenca piloto

En las últimas décadas, en Uruguay, se han producido cambios significativos de uso del suelo como resultado de la intensificación y expansión de las actividades agropecuarias e industriales. Estas actividades, muchas veces realizadas sin considerar la protección del medio ambiente, han generado severos daños a la conservación de los ecosistemas acuáticos del país en general, y a la calidad del agua en particular. La cuenca del río Santa Lucía constituye uno de los sistemas hidrográficos más importantes del país porque representa la fuente de agua potable para más de la mitad de la población nacional, además de ser una fuente de agua de riego para la zona de actividad agroindustrial más intensa del país. Desde 2004, año de comienzo del registro de información sistemático de calidad de agua, el río Santa Lucía sufre una progresiva eutrofización, alcanzando niveles elevados de fósforo total. El desafío es por lo tanto desarrollar en la cuenca actividades productivas relevantes para el desarrollo económico del país preservando la calidad de los cuerpos de agua y evitando la afectación de otras actividades como la potabilización de aguas o la preservación de ecosistemas relevantes como los humedales del río Santa Lucía. Basándonos en lo anterior, este proyecto propone utilizar algoritmos de aprendizaje automático no supervisados para investigar las correlaciones entre los cambios en el uso del suelo y/o cobertura del suelo, y los parámetros físico-químicos de calidad del agua. Como resultado, se crearán conocimientos fundamentales para diseñar estrategias efectivas para disminuir la contaminación del agua debido al cambio en el uso del suelo a lo largo del tiempo. El enfoque metodológico desarrollado por este trabajo no será específico para el lugar de estudio, sino que será aplicable en otras cuencas donde se aborden problemáticas similares a las aquí planteadas.Agencia Nacional de Investigación e Innovació

Genetic and Physiological Analysis of Iron Biofortification in Maize Kernels

BACKGROUND: Maize is a major cereal crop widely consumed in developing countries, which have a high prevalence of iron (Fe) deficiency anemia. The major cause of Fe deficiency in these countries is inadequate intake of bioavailable Fe, where poverty is a major factor. Therefore, biofortification of maize by increasing Fe concentration and or bioavailability has great potential to alleviate this deficiency. Maize is also a model system for genomic research and thus allows the opportunity for gene discovery. Here we describe an integrated genetic and physiological analysis of Fe nutrition in maize kernels, to identify loci that influence grain Fe concentration and bioavailability. METHODOLOGY: Quantitative trait locus (QTL) analysis was used to dissect grain Fe concentration (FeGC) and Fe bioavailability (FeGB) from the Intermated B73 × Mo17 (IBM) recombinant inbred (RI) population. FeGC was determined by ion coupled argon plasma emission spectroscopy (ICP). FeGB was determined by an in vitro digestion/Caco-2 cell line bioassay. CONCLUSIONS: Three modest QTL for FeGC were detected, in spite of high heritability. This suggests that FeGC is controlled by many small QTL, which may make it a challenging trait to improve by marker assisted breeding. Ten QTL for FeGB were identified and explained 54% of the variance observed in samples from a single year/location. Three of the largest FeGB QTL were isolated in sister derived lines and their effect was observed in three subsequent seasons in New York. Single season evaluations were also made at six other sites around North America, suggesting the enhancement of FeGB was not specific to our farm site. FeGB was not correlated with FeGC or phytic acid, suggesting that novel regulators of Fe nutrition are responsible for the differences observed. Our results indicate that iron biofortification of maize grain is achievable using specialized phenotyping tools and conventional plant breeding techniques

Multicentre observational study on multisystem inflammatory syndrome related to COVID-19 in Argentina

Background: The impact of the pediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 (PIMS-TS) in low- and middle-income countries remains poorly understood. Our aim was to understand the characteristics and outcomes of PIMS-TS in Argentina. Methods: This observational, prospective, and retrospective multicenter study enrolled patients younger than 18 years-old manifesting PIMS-TS, Kawasaki disease (KD) or Kawasaki shock syndrome (KSS) between March 2020 and May 2021. Patients were followed-up until hospital discharge or death (one case). The primary outcome was pediatric intensive care unit (PICU) admission. Multiple logistic regression was used to identify variables predicting PICU admission. Results: Eighty-one percent, 82%, and 14% of the 176 enrolled patients fulfilled the suspect case criteria for PIMS-TS, KD, and KSS, respectively. Temporal association with SARS-CoV-2 was confirmed in 85% of the patients and 38% were admitted to the PICU. The more common clinical manifestations were fever, abdominal pain, rash, and conjunctival injection. Lymphopenia was more common among PICU-admitted patients (87% vs. 51%, p < 0.0001), who also showed a lower platelet count and higher plasmatic levels of inflammatory and cardiac markers. Mitral valve insufficiency, left ventricular wall motion alterations, pericardial effusion, and coronary artery alterations were observed in 30%, 30%, 19.8%, and 18.6% of the patients, respectively. Days to initiation of treatment, rash, lymphopenia, and low platelet count were significant independent contributions to PICU admission. Conclusion: Rates of severe outcomes of PIMS-TS in the present study agreed with those observed in high-income countries. Together with other published studies, this work helps clinicians to better understand this novel clinical entity.Fil: Vainstein, Eduardo. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Baleani, Silvia. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Urrutia, Luis. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Affranchino, Nicolás. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Ackerman, Judith. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños Pedro Elizalde (ex Casa Cuna); ArgentinaFil: Cazalas, Mariana. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Goldsman, Alejandro. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Sardella, Angela. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Tolin, Ana Laura. Gobierno de la Provincia de Mendoza. Hospital Pediátrico Humberto Notti; ArgentinaFil: Goldaracena, Pablo. Provincia de Buenos Aires. Ministerio de Salud. Hospital de Niños "Sor María Ludovica" de La Plata; ArgentinaFil: Fabi, Mariana. Provincia de Buenos Aires. Ministerio de Salud. Hospital de Niños "Sor María Ludovica" de La Plata; ArgentinaFil: Cosentino, Mariana. Hospital Británico de Buenos Aires; ArgentinaFil: Magliola, Ricardo. Hospital Británico de Buenos Aires; ArgentinaFil: Roggiero, Gustavo. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; ArgentinaFil: Manso, Paula. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; ArgentinaFil: Triguy, Jésica. Gobierno de la Provincia de Mendoza. Hospital Pediátrico Humberto Notti; ArgentinaFil: Ballester, Celeste. Gobierno de la Provincia de Mendoza. Hospital Pediátrico Humberto Notti; ArgentinaFil: Cervetto, Vanesa. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños Pedro Elizalde (ex Casa Cuna); ArgentinaFil: Vaccarello, María. Sanatorio de la Trinidad; ArgentinaFil: De Carli, Domingo Norberto. Clínica del Niño de Quilmes; ArgentinaFil: De Carli, Maria Estela. Clínica del Niño de Quilmes; ArgentinaFil: Ciotti, Ana Laura. Hospital Nacional Profesor Alejandro Posadas; ArgentinaFil: Sicurello, María Irene. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Rios Leiva, Cecilia. Provincia de Buenos Aires. Ministerio de Salud. Hospital Interzonal de Agudos "Eva Perón"; ArgentinaFil: Villalba, Claudia. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Hortas, María. Sanatorio de la Trinidad; ArgentinaFil: Peña, Sonia. Gobierno de la Provincia de Mendoza. Hospital Pediátrico Humberto Notti; ArgentinaFil: González, Gabriela. Gobierno de la Provincia de Mendoza. Hospital Pediátrico Humberto Notti; ArgentinaFil: Zold, Camila Lidia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Murer, Mario Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Grippo, M.. No especifíca;Fil: Vázquez, H.. No especifíca;Fil: Morós, C.. No especifíca;Fil: Di Santo, M.. No especifíca;Fil: Villa, A.. No especifíca;Fil: Lazota, P.. No especifíca;Fil: Foti, M.. No especifíca;Fil: Napoli, N.. No especifíca;Fil: Katsikas, M. M.. No especifíca;Fil: Tonello, L.. No especifíca;Fil: Peña, J.. No especifíca;Fil: Etcheverry, M.. No especifíca;Fil: Iglesias, D.. No especifíca;Fil: Alcalde, A. L.. No especifíca;Fil: Bruera, M.J.. No especifíca;Fil: Bruzzo, V.. No especifíca;Fil: Giordano, P.. No especifíca;Fil: Pena Acero, F.. No especifíca;Fil: Netri Pelandi, G.. No especifíca;Fil: Pastaro, D.. No especifíca;Fil: Bleiz, J.. No especifíca;Fil: Rodríguez, M. F.. No especifíca;Fil: Laghezza, L.. No especifíca;Fil: Molina, M. B.. No especifíca;Fil: Patynok, N.. No especifíca;Fil: Chatelain, M. S.. No especifíca;Fil: Aguilar, M. J.. No especifíca;Fil: Gamboa, J.. No especifíca;Fil: Cervan, M.. No especifíca;Fil: Ruggeri, A.. No especifíca;Fil: Marinelli, I.. No especifíca;Fil: Checcacci, E.. No especifíca;Fil: Meregalli, C.. No especifíca;Fil: Damksy Barbosa, J.. No especifíca;Fil: Fernie, L.. No especifíca;Fil: Fernández, M. J.. No especifíca;Fil: Saenz Tejeira, M.M.. No especifíca;Fil: Cereigido, C.. No especifíca;Fil: Nunell, A.. No especifíca;Fil: Villar, D.. No especifíca;Fil: Mansilla, A. D.. No especifíca;Fil: Darduin, M. D.. No especifíca

The Athena X-ray Integral Field Unit: a consolidated design for the system requirement review of the preliminary definition phase

The Athena X-ray Integral Unit (X-IFU) is the high resolution X-ray spectrometer, studied since 2015 for flying in the mid-30s on the Athena space X-ray Observatory, a versatile observatory designed to address the Hot and Energetic Universe science theme, selected in November 2013 by the Survey Science Committee. Based on a large format array of Transition Edge Sensors (TES), it aims to provide spatially resolved X-ray spectroscopy, with a spectral resolution of 2.5 eV (up to 7 keV) over an hexagonal field of view of 5 arc minutes (equivalent diameter). The X-IFU entered its System Requirement Review (SRR) in June 2022, at about the same time when ESA called for an overall X-IFU redesign (including the X-IFU cryostat and the cooling chain), due to an unanticipated cost overrun of Athena. In this paper, after illustrating the breakthrough capabilities of the X-IFU, we describe the instrument as presented at its SRR, browsing through all the subsystems and associated requirements. We then show the instrument budgets, with a particular emphasis on the anticipated budgets of some of its key performance parameters. Finally we briefly discuss on the ongoing key technology demonstration activities, the calibration and the activities foreseen in the X-IFU Instrument Science Center, and touch on communication and outreach activities, the consortium organisation, and finally on the life cycle assessment of X-IFU aiming at minimising the environmental footprint, associated with the development of the instrument. Thanks to the studies conducted so far on X-IFU, it is expected that along the design-to-cost exercise requested by ESA, the X-IFU will maintain flagship capabilities in spatially resolved high resolution X-ray spectroscopy, enabling most of the original X-IFU related scientific objectives of the Athena mission to be retained. (abridged).Comment: 48 pages, 29 figures, Accepted for publication in Experimental Astronomy with minor editin

The Athena X-ray Integral Field Unit: a consolidated design for the system requirement review of the preliminary definition phase

The Athena X-ray Integral Unit (X-IFU) is the high resolution X-ray spectrometer studied since 2015 for flying in the mid-30s on the Athena space X-ray Observatory. Athena is a versatile observatory designed to address the Hot and Energetic Universe science theme, as selected in November 2013 by the Survey Science Committee. Based on a large format array of Transition Edge Sensors (TES), X-IFU aims to provide spatially resolved X-ray spectroscopy, with a spectral resolution of 2.5 eV (up to 7 keV) over a hexagonal field of view of 5 arc minutes (equivalent diameter). The X-IFU entered its System Requirement Review (SRR) in June 2022, at about the same time when ESA called for an overall X-IFU redesign (including the X-IFU cryostat and the cooling chain), due to an unanticipated cost overrun of Athena. In this paper, after illustrating the breakthrough capabilities of the X-IFU, we describe the instrument as presented at its SRR (i.e. in the course of its preliminary definition phase, so-called B1), browsing through all the subsystems and associated requirements. We then show the instrument budgets, with a particular emphasis on the anticipated budgets of some of its key performance parameters, such as the instrument efficiency, spectral resolution, energy scale knowledge, count rate capability, non X-ray background and target of opportunity efficiency. Finally, we briefly discuss the ongoing key technology demonstration activities, the calibration and the activities foreseen in the X-IFU Instrument Science Center, touch on communication and outreach activities, the consortium organisation and the life cycle assessment of X-IFU aiming at minimising the environmental footprint, associated with the development of the instrument. Thanks to the studies conducted so far on X-IFU, it is expected that along the design-to-cost exercise requested by ESA, the X-IFU will maintain flagship capabilities in spatially resolved high resolution X-ray spectroscopy, enabling most of the original X-IFU related scientific objectives of the Athena mission to be retained. The X-IFU will be provided by an international consortium led by France, The Netherlands and Italy, with ESA member state contributions from Belgium, Czech Republic, Finland, Germany, Poland, Spain, Switzerland, with additional contributions from the United States and Japan.The French contribution to X-IFU is funded by CNES, CNRS and CEA. This work has been also supported by ASI (Italian Space Agency) through the Contract 2019-27-HH.0, and by the ESA (European Space Agency) Core Technology Program (CTP) Contract No. 4000114932/15/NL/BW and the AREMBES - ESA CTP No.4000116655/16/NL/BW. This publication is part of grant RTI2018-096686-B-C21 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. This publication is part of grant RTI2018-096686-B-C21 and PID2020-115325GB-C31 funded by MCIN/AEI/10.13039/501100011033