Influence of substrate density and cropping conditions on the cultivation of sun mushroom

Aim of the study: To evaluate agronomical features demanded by the sun mushroom (Agaricus subrufescens) in order to optimise the commercial cultivation of this worldwide demanded medicinal mushroom.Area of study: The study was carried out in Castilla-La Mancha (Spain), the second most productive region of cultivated mushrooms in Spain.Material and methods: In this work we summarise the results obtained while evaluating the performance of sun mushroom crops (A. subrufescens). Two agronomical traits have been evaluated, the effect on the productive outputs of applying five different compost filling rates of high N substrate (yield and BE of the compost), and the influence of implementing two different conditions for the induction to fructification on the analytical properties of the harvested mushrooms. Besides, two commercial compost formulations (CM and VC) obtained from local providers have been used.Main results: The number of sporophores harvested and the yield per unit area increased with rising density of compost load, although the biological efficiency was not significantly modified. Compost fill rate of 70 kg m-2 provided an average yield of 13.33 kg m-2 and BE=55.45 kg dt-1, generally higher than those values reported in the literature. The proposed moderate slow induction provides better yields, particularly in the last flushes, and larger sporophores. Proximate analysis of harvested sporophores has not shown significant differences between treatments or factors.Research highlights: As guidance for growers, compost fill weight between 65 and 70 kg per m2 of productive area with a moderate slow induction to fructification is presented as the best option for commercial production under controlled environmental conditions

Proteases in parasite organisms

Las proteasas tienen gran importancia en la biología y Fisiología de los seres vivos, por lo que no resulta extraño que estas enzimas hayan sido encontradas en diferentes organismos parásitos implicadas en una gran variedad de procesos propios del parásito, con las procesos de nutrición o de muda y desarrollo así como en las interacciones con el hospedador al invadir sus tejidos digerir sus proteínas o evadir su respuesta inmune (Mc Kerrow 1989) los últimos trabajos apuntan a la posibilidad de utilizar estas enzimas en el control de las parasitosis tanto como fuente de antígenos útiles para la realización de técnicas inmunodiagnósticas como material inmunógeno aplicable en inmunoprofilaxis como blancos de acción de nuevos fármacos antiparasitarios (Song y Chapeil 1993 Williams y Cooms 1995 Armas et al 1995a). Por todo ellos ya que posiblemente el mayor conocimiento de estas enzimas contribuirá a un mejor control de las parasitosis a continuación pasaremos a realizar una revisión bibliográfica de lo realizado al respecto en las últimos años siguiendo una ordenación en cuanto a su estructura química.Proteases have been implicated in the most important biological and physiological processes of the living organisms although is not rare thing to find them implicated in a variety of activities of the parasite (nutrition or exchysement and growth) as interactions with the host (invading their tissues degrading their proteins or avoiding its immune response) (Mc Kerrow 1989). Recent works propose the possibility of using these enzymes in the control of the parasites, as antigens for making immunodiagnosis or immunoprophylaxis as well as new targets from alternative antiparasitic treatment (Song and Chapeil 1993 Williams and Cooms 1995 Armas et al 1995a). The present revision was made according to the chemical structure of the enzymes.Facultad de Ciencias Veterinaria

Proteases in parasite organisms

Las proteasas tienen gran importancia en la biología y Fisiología de los seres vivos, por lo que no resulta extraño que estas enzimas hayan sido encontradas en diferentes organismos parásitos implicadas en una gran variedad de procesos propios del parásito, con las procesos de nutrición o de muda y desarrollo así como en las interacciones con el hospedador al invadir sus tejidos digerir sus proteínas o evadir su respuesta inmune (Mc Kerrow 1989) los últimos trabajos apuntan a la posibilidad de utilizar estas enzimas en el control de las parasitosis tanto como fuente de antígenos útiles para la realización de técnicas inmunodiagnósticas como material inmunógeno aplicable en inmunoprofilaxis como blancos de acción de nuevos fármacos antiparasitarios (Song y Chapeil 1993 Williams y Cooms 1995 Armas et al 1995a). Por todo ellos ya que posiblemente el mayor conocimiento de estas enzimas contribuirá a un mejor control de las parasitosis a continuación pasaremos a realizar una revisión bibliográfica de lo realizado al respecto en las últimos años siguiendo una ordenación en cuanto a su estructura química.Proteases have been implicated in the most important biological and physiological processes of the living organisms although is not rare thing to find them implicated in a variety of activities of the parasite (nutrition or exchysement and growth) as interactions with the host (invading their tissues degrading their proteins or avoiding its immune response) (Mc Kerrow 1989). Recent works propose the possibility of using these enzymes in the control of the parasites, as antigens for making immunodiagnosis or immunoprophylaxis as well as new targets from alternative antiparasitic treatment (Song and Chapeil 1993 Williams and Cooms 1995 Armas et al 1995a). The present revision was made according to the chemical structure of the enzymes.Facultad de Ciencias Veterinaria

Geostatistical methods to map the probability of hydrogeotoxic risk by high As concentrations in groundwater. Case study in Ávila province ( Spain)

[EN] The presence of As in groundwater is a priority public health issue because it imposes serious restrictions on drinking water. Mapping probabilities of exceedance of the threshold permitted by the World Health Organization, WHO (10 μg/L) allow delimiting the most vulnerable areas. The existing geostatistical techniques are a common tool for the evaluation of these maps, though, there is no agreement on which of the methods is the best. In this study different comparison criteria are illustrated. Seven non-parametric kriging methods are used to estimate the map of probability of exceeding the As concentration the limit of 10 mg/L in groundwater at the province of Ávila. Performed validation reveals that one the best results correspond to the simplicial indicator kriging, never before compared in studies of presence of geogenic As in groundwater.[ES] La presencia de As en las aguas subterráneas es un problema prioritario de salud pública e impone serias restricciones en el agua de consumo. Los mapas de probabilidad de superar el umbral permitido por la Organización Mundial de la Salud, OMS (10 μg/L) permiten delimitar las áreas que más riesgo presentan en relación con este parámetro. Las técnicas geoestadísticas constituyen una herramienta de uso común para elaborar estos mapas, aunque lamentablemente no hay un acuerdo sobre qué técnica es la más adecuada. El presente estudio recopila distintos criterios para decidir qué método presenta resultados más robustos. Se utilizan siete métodos de kriging no paramétrico en la estimación del mapa de probabilidad de que la concentración de As en manantiales de la provincia de Ávila supere el límite de 10 μg/L. La validación revela que uno de los mejores resultados es del simplicial indicator kriging, nunca antes tenido en cuenta en estudios sobre presencia de As geogénico en aguas subterráneas.Los autores agradecen a la Obra Social de Caja de Ávila el apoyo a la investigación, al financiar el proyecto “Manantiales de la provincia de Ávila (2006-2007)” y a los revisores anónimos por los comentarios realizados.Guardiola-Albert, C.; Pardo-Igúzquiza, E.; Giménez-Forcada, E. (2017). Métodos geoestadísticos para la elaboración de mapas de probabilidad de riesgo hidrogeotóxico (HGT) por altas concentraciones de As en las aguas subterráneas. Aplicación a la distribución de HGT en la provincia de Ávila (España). Ingeniería del Agua. 21(1):71-85. doi:10.4995/ia.2017.6798.SWORD7185211Aragonés Sanz, N., Palacios Diez, M., Avello de Miguel, A., Gómez Rodríguez, P., Martínez Cortés, M., Rodríguez Bernabeu, M.J. 2001. Nivel de arsénico en abastecimientos de agua de consumo de origen subterráneo en la Comunidad de Madrid. Revista Española de Salud Pública, 75, 421-432.Barroso, J.L., Lillo, J., Sahún, B., Tenajas, J. 2002. Caracterización del contenido de arsénico en las aguas subterráneas de la zona comprendida entre el río Duero, el río Cega y el Sistema Central. In: Presente y Futuro del agua subterránea en España y la Directiva Marco Europea. Zaragoza, Spain, 77-84.Brus, D.J., Gruijter, J.J., Walvoort, D.J.J., de Vries, F., Bronswijk, J.J.B., Römkens, P.F.A.M., de Vries, W. 2002. Mapping the probability of exceeding critical thresholds for cadmium concentrations in soils in the Netherlands. Journal of Environmental Quality, 31, 1875-1884. doi:10.2134/jeq2002.1875Cattle, J.A., McBratney, A.B., Minasny, B. 2002. Kriging method evaluation for assessing the spatial distribution of urban soil lead contamination. Journal of Environmental Quality, 31, 1576-1588. doi:10.2134/jeq2002.1576Delgado, J., Medina, J., Vega, M., Carretero, C., Pardo, R. 2009. Los minerales de la arcilla y el arsénico en los acuíferos de la Tierra de Pinares. Revista de la Sociedad Española de Mineralogía, 11, 75-76.D'Or, D., Demougeot-Renard, H., Garcia, M. 2008. Geostatistics for contaminated sites and soils: some pending questions. geoENV VI - Geosatistics for Environmental Applications, 15, 409-420. doi:10.1007/978-1-4020-6448-7_34ESRI. 2011. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute.Falivene, O., Cabrera, L., Tolosana-Delgado, R., Sáez, A. 2010. Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example. Computers & Geociences, 36(4), 512-519.García-Sánchez, A., Alvarez-Ayuso, E. 2003. Arsenic in soils waters its relation to geology mining activities, (Salamanca Province, Spain). Journal of Geochemical Exploration 80, 69-79. doi:10.1016/S0375-6742(03)00183-3García-Sánchez, A., Moyano, A., Mayorga, P. 2005. High arsenic in groundwater of central Spain. Environmental Geology, 47(6), 847-854. doi:10.1007/s00254-004-1216-8Giménez-Forcada, E., Smedley, P.L. 2014. Geological factors controlling occurrence and distribution of arsenic in groundwaters from the southern margin of the Duero Basin, Spain. Environmental Geochemistry and Health, 36(6), 1029-1047. doi:10.1007/s10653-014-9599-2Gómez, J.J., Lillo, F.J., Sahún, B. 2006. Naturally occurring arsenic in groundwater identification of the geochemical sources in the Duero Cenozoic Basin, Spain. Environmental Geology, 50, 1151-1170. doi:10.1007/s00254-006-0288-zGómez Hernández, J.J. 1991. Geoestadística, para el análisis de riesgos: Una introducción a la geoestadística no paramétrica. Publicación Técnica 04/91, ENRESA.Goovaerts, P. 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press, New York, USA.Goovaerts, P., AvRuskin, G., Meliker, J., Slotnick, M., Jacquez, G., Nriagu, J. 2005. Geostatistical modeling of the spatial variability of arsenic in groundwater of southeast Michigan. Water Resources Research, 41(7), W07013. doi:10.1029/2004WR003705Goovaerts, P. 2009. AUTO-IK: A 2D indicator kriging program for the automated non-parametric modeling of local uncertainty in earth sciences. Computers & Geoscienes, 35(6), 1255-1270. doi:10.1016/j.cageo.2008.08.014Guardiola-Albert, C., Pardo-Igúzquiza, E. 2011. Compositional Bayesian indicator estimation. Stochastic Environmental Research and Risk Assessment, 25(6), 835-849. doi:10.1007/s00477-011-0455-yIsaaks, E.H., Srivastava, R.M. 1989. An Introduction to Applied Geostatistics. Oxford University Press, New York, USA.Journel, A.G. 1983. Non-parametric estimation of spatial distributions. Mathematical Geology, 15(3), 445-468. doi:10.1007/BF01031292Journel, A., Kyriakidis, P.C., Mao, S. 2000. Correcting the smoothing effect of estimators: a spectral postprocessor. Mathematical Geology, 32(7), 787-813. doi:10.1023/A:1007544406740Juang, K.W., Chen, Y.S., Lee, D.Y. 2004. Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils. Environmental Pollution, 127(2), 229-238. doi:10.1016/j.envpol.2003.07.001Kitanidis, P.K. 1991. Orthonormal Residuals in Geostatistics: Model Criticism and Parameter Estimation. Mathematical Geology, 23(5), 741-758. doi:10.1007/BF02082534Lark, R.M., Ferguson, R.B. 2004. Mapping risk of soil nutrient deficiency or excess by disjunctive and indicator kriging. Geoderma, 118(1-2), 39-53. doi:10.1016/S0016-7061(03)00168-XMayorga, P., Moyano, A., Anawar, H.M., García-Sánchez, A. 2013. Temporal variation of arsenic and nitrate content in groundwater of the Duero River Basin (Spain). Physics and Chemistry of the Earth, 58-60, 22-27. doi:10.1016/j.pce.2013.04.001Olea, R., Pawlowsky, V. 1996. Compensating for estimation smoothing in kriging. Mathematical Geology, 28(4), 407-417. doi:10.1007/BF02083653Papritz, A., Dubois, J.P. 1999. Mapping heavy metals in soil by (non-)linear kriging: an empirical validation. In: geoENV II: Geostatistics for Environmental Applications. Quantitative Geology and Geostatistics, vol. 10. Gomez-Hernandez, J. et al. (eds.), Kluwer Academic Publishing, Dordrecht, 429-440.Remy, N., Boucher, A., Wu, J. 2009. Applied Geostatistics with SGeMS: A User's Guide. Cambridge University Press. doi:10.1017/CBO9781139150019Rey-Moral, C., Gómez Ortíz, D., Giménez-Forcada, E., López-Bahut, M.T. 2016. Modelización gravimétrica y aeromagnética en el SE de la Cuenca del Duero (provincias de Ávila y Segovia). Factores geológicos que controlan la distribución de As (Arsénico) y otros ETGPT (Elementos Traza Geogénicos Potencialmente Tóxicos) en las aguas subterráneas. IX Congreso Geológico de España. Huelva 2016.Rivoirard, J. 1994. Introduction to Disjunctive Kriging and Non-Linear Geostatistics. Oxford Univ. Press, Oxford, UK.Rousseau, D. 1980. Contrôle des previsions, II, Vérification des prévisions de l'occurrence d'un phénomène : Application aux prévisions de précipitations, report. Étab. d'Études et Rech. de la Météorol./Météo France, Paris, France.Ryker, S.J. 2001. Mapping arsenic in ground water: A real need, but a hard problem. Geotimes, 46(11), 34-36.Sahún, B., Gómez Fernández, J.J., Lillo, J., Olmo, P.D. 2004. Arsénico en aguas subterráneas e interacción agua-roca: un ejemplo en la cuenca terciaria del Duero (Castilla y León, España). Revista de la Sociedad Geológica de España, 17(1-2), 137-155.Smedley, P.L., Kinniburg, D.G. 2002 A review of the source, behaviour and distribution of arsenic in natural waters. Applied Geochemistry, 17(5), 517-568. doi:10.1016/S0883-2927(02)00018-5Tolosana-Delgado, R., Pawlowsky-Glahn, V., Egozcue, J.J., van der Boogaart, K.G. 2005. A compositional approach to indicator kriging. In: 2005 annual conference of the IAMG (Cheng, Q., Bonham-Carter, G.,eds.), Toronto, Canada: 651-656.Tolosana-Delgado, R., Pawlowsky-Glahn, V., Egozcue, J.J. 2008. Indicator kriging without order relation violations. Mathematical Geosciences, 40(3), 327-347. doi:10.1007/s11004-008-9146-8WHO. 2009. Chemicals Safety - Activity Report 2009. http://www.who.int/ipcs/about_ipcs/activity_report_2009.pdf. Last access: 24.10.2016

A Seventeen-Year Epidemiological Surveillance Study of Borrelia burgdorferi Infections in Two Provinces of Northern Spain

This paper reports a 17-year seroepidemiological surveillance study of Borrelia burgdorferi infection, performed with the aim of improving our knowledge of the epidemiology of this pathogen. Serum samples (1,179) from patients (623, stratified with respect to age, sex, season, area of residence and occupation) bitten by ticks in two regions of northern Spain were IFA-tested for B. burgdorferi antibodies. Positive results were confirmed by western blotting. Antibodies specific for B. burgdorferi were found in 13.3% of the patients; 7.8% were IgM positive, 9.6% were IgG positive, and 4.33% were both IgM and IgG positive. Five species of ticks were identified in the seropositive patients: Dermacentor marginatus (41.17% of such patients) Dermacentor reticulatus (11.76%), Rhiphicephalus sanguineus (17.64%), Rhiphicephalus turanicus (5.88%) and Ixodes ricinus (23.52%). B. burgdorferi DNA was sought by PCR in ticks when available. One tick, a D. reticulatus male, was found carrying the pathogen. The seroprevalence found was similar to the previously demonstrated in similar studies in Spain and other European countries

Cognitive impairment induced by delta9-tetrahydrocannabinol occurs through heteromers between cannabinoid CB1 and serotonin 5-HT2A receptors

Delta-9-tetrahydrocannabinol (THC), the main psychoactive compound of marijuana, induces numerous undesirable effects, including memory impairments, anxiety, and dependence. Conversely, THC also has potentially therapeutic effects, including analgesia, muscle relaxation, and neuroprotection. However, the mechanisms that dissociate these responses are still not known. Using mice lacking the serotonin receptor 5-HT2A, we revealed that the analgesic and amnesic effects of THC are independent of each other: while amnesia induced by THC disappears in the mutant mice, THC can still promote analgesia in these animals. In subsequent molecular studies, we showed that in specific brain regions involved in memory formation, the receptors for THC and the 5-HT2A receptors work together by physically interacting with each other. Experimentally interfering with this interaction prevented the memory deficits induced by THC, but not its analgesic properties. Our results highlight a novel mechanism by which the beneficial analgesic properties of THC can be dissociated from its cognitive side effects

Gaia Early Data Release 3: The Gaia Catalogue of Nearby Stars

Smart, R. L., et al. (Gaia Collaboration)[Aims] We produce a clean and well-characterised catalogue of objects within 100 pc of the Sun from the Gaia Early Data Release 3. We characterise the catalogue through comparisons to the full data release, external catalogues, and simulations. We carry out a first analysis of the science that is possible with this sample to demonstrate its potential and best practices for its use. [Methods] Theselection of objects within 100 pc from the full catalogue used selected training sets, machine-learning procedures, astrometric quantities, and solution quality indicators to determine a probability that the astrometric solution is reliable. The training set construction exploited the astrometric data, quality flags, and external photometry. For all candidates we calculated distance posterior probability densities using Bayesian procedures and mock catalogues to define priors. Any object with reliable astrometry and a non-zero probability of being within 100 pc is included in the catalogue. [Results] We have produced a catalogue of 331 312 objects that we estimate contains at least 92% of stars of stellar type M9 within 100 pc of the Sun. We estimate that 9% of the stars in this catalogue probably lie outside 100 pc, but when the distance probability function is used, a correct treatment of this contamination is possible. We produced luminosity functions with a high signal-to-noise ratio for the main-sequence stars, giants, and white dwarfs. We examined in detail the Hyades cluster, the white dwarf population, and wide-binary systems and produced candidate lists for all three samples. We detected local manifestations of several streams, superclusters, and halo objects, in which we identified 12 members of Gaia Enceladus. We present the first direct parallaxes of five objects in multiple systems within 10 pc of the Sun. [Conclusions] We provide the community with a large, well-characterised catalogue of objects in the solar neighbourhood. This is a primary benchmark for measuring and understanding fundamental parameters and descriptive functions in astronomy.The Gaia mission and data processing have financially been supported by, in alphabetical order by country: the Algerian Centre de Recherche en Astronomie, Astrophysique et Géophysique of Bouzareah Observatory; the Austrian Fonds zur Förderung der wissenschaftlichen Forschung (FWF) Hertha Firnberg Programme through grants T359, P20046, and P23737; the BELgian federal Science Policy Office (BELSPO) through various PROgramme de Développement d’Expériences scientifiques (PRODEX) grants and the Polish Academy of Sciences – Fonds Wetenschappelijk Onderzoek through grant VS.091.16N, and the Fonds de la Recherche Scientifique (FNRS); the Brazil-France exchange programmes Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Coordenação de Aperfeicoamento de Pessoal de Nível Superior (CAPES) – Comité Français d’Evaluation de la Coopération Universitaire et Scientifique avec le Brésil (COFECUB); the National Science Foundation of China (NSFC) through grants 11573054 and 11703065 and the China Scholarship Council through grant 201806040200; the Tenure Track Pilot Programme of the Croatian Science Foundation and the École Polytechnique Fédérale de Lausanne and the project TTP-2018-07-1171 “Mining the Variable Sky”, with the funds of the Croatian-Swiss Research Programme; the Czech-Republic Ministry of Education, Youth, and Sports through grant LG 15010 and INTER-EXCELLENCE grant LTAUSA18093, and the Czech Space Office through ESA PECS contract 98058; the Danish Ministry of Science; the Estonian Ministry of Education and Research through grant IUT40-1; the European Commission’s Sixth Framework Programme through the European Leadership in Space Astrometry (ELSA) Marie Curie Research Training Network (MRTN-CT-2006-033481), through Marie Curie project PIOF-GA-2009-255267 (Space AsteroSeismology & RR Lyrae stars, SAS-RRL), and through a Marie Curie Transfer-of-Knowledge (ToK) fellowship (MTKD-CT-2004-014188); the European Commission’s Seventh Framework Programme through grant FP7-606740 (FP7-SPACE-2013-1) for the Gaia European Network for Improved data User Services (GENIUS) and through grant 264895 for the Gaia Research for European Astronomy Training (GREAT-ITN) network; the European Research Council (ERC) through grants 320360 and 647208 and through the European Union’s Horizon 2020 research and innovation and excellent science programmes through Marie Skłodowska-Curie grant 745617 as well as grants 670519 (Mixing and Angular Momentum tranSport of massIvE stars – MAMSIE), 687378 (Small Bodies: Near and Far), 682115 (Using the Magellanic Clouds to Understand the Interaction of Galaxies), and 695099 (A sub-percent distance scale from binaries and Cepheids – CepBin); the European Science Foundation (ESF), in the framework of the Gaia Research for European Astronomy Training Research Network Programme (GREAT-ESF); the European Space Agency (ESA) in the framework of the Gaia project, through the Plan for European Cooperating States (PECS) programme through grants for Slovenia, through contracts C98090 and 4000106398/12/NL/KML for Hungary, and through contract 4000115263/15/NL/IB for Germany; the Academy of Finland and the Magnus Ehrnrooth Foundation; the French Centre National d’Etudes Spatiales (CNES), the Agence Nationale de la Recherche (ANR) through grant ANR-10-IDEX-0001-02 for the “Investissements d’avenir” programme, through grant ANR-15-CE31-0007 for project “Modelling the Milky Way in the Gaia era” (MOD4Gaia), through grant ANR-14-CE33-0014-01 for project “The Milky Way disc formation in the Gaia era” (ARCHEOGAL), and through grant ANR-15-CE31-0012-01 for project “Unlocking the potential of Cepheids as primary distance calibrators” (UnlockCepheids), the Centre National de la Recherche Scientifique (CNRS) and its SNO Gaia of the Institut des Sciences de l’Univers (INSU), the “Action Fédératrice Gaia” of the Observatoire de Paris, the Région de Franche-Comté, and the Programme National de Gravitation, Références, Astronomie,et Métrologie (GRAM) of CNRS/INSU with the Institut National Polytechnique (INP) and the Institut National de Physique nucléaire et de Physique des Particules (IN2P3) co-funded by CNES; the German Aerospace Agency (Deutsches Zentrum für Luft- und Raumfahrt e.V., DLR) through grants 50QG0501, 50QG0601, 50QG0602, 50QG0701, 50QG0901, 50QG1001, 50QG1101, 50QG1401, 50QG1402, 50QG1403, 50QG1404, and 50QG1904 and the Centre for Information Services and High Performance Computing (ZIH) at the Technische Universität (TU) Dresden for generous allocations of computer time; the Hungarian Academy of Sciences through the Lendület Programme grants LP2014-17 and LP2018-7 and through the Premium Postdoctoral Research Programme (L. Molnár), and the Hungarian National Research, Development, and Innovation Office (NKFIH) through grant KH_18-130405; the Science Foundation Ireland (SFI) through a Royal Society - SFI University Research Fellowship (M. Fraser); the Israel Science Foundation (ISF) through grant 848/16; the Agenzia Spaziale Italiana (ASI) through contracts I/037/08/0, I/058/10/0, 2014-025-R.0, 2014-025-R.1.2015, and 2018-24-HH.0 to the Italian Istituto Nazionale di Astrofisica (INAF), contract 2014-049-R.0/1/2 to INAF for the Space Science Data Centre (SSDC, formerly known as the ASI Science Data Center, ASDC), contracts I/008/10/0, 2013/030/I.0, 2013-030-I.0.1-2015, and 2016-17-I.0 to the Aerospace Logistics Technology Engineering Company (ALTEC S.p.A.), INAF, and the Italian Ministry of Education, University, and Research (Ministero dell’Istruzione, dell’Università e della Ricerca) through the Premiale project “MIning The Cosmos Big Data and Innovative Italian Technology for Frontier Astrophysics and Cosmology” (MITiC); the Netherlands Organisation for Scientific Research (NWO) through grant NWO-M-614.061.414, through a VICI grant (A. Helmi), and through a Spinoza prize (A. Helmi), and the Netherlands Research School for Astronomy (NOVA); the Polish National Science Centre through HARMONIA grant 2018/06/M/ST9/00311, DAINA grant 2017/27/L/ST9/03221, and PRELUDIUM grant 2017/25/N/ST9/01253, and the Ministry of Science and Higher Education (MNiSW) through grant DIR/WK/2018/12; the Portugese Fundação para a Ciência e a Tecnologia (FCT) through grants SFRH/BPD/74697/2010 and SFRH/BD/128840/2017 and the Strategic Programme UID/FIS/00099/2019 for CENTRA; the Slovenian Research Agency through grant P1-0188; the Spanish Ministry of Economy (MINECO/FEDER, UE) through grants ESP2016-80079-C2-1-R, ESP2016-80079-C2-2-R, RTI2018-095076-B-C21, RTI2018-095076-B-C22, BES-2016-078499, and BES-2017-083126 and the Juan de la Cierva formación 2015 grant FJCI-2015-2671, the Spanish Ministry of Education, Culture, and Sports through grant FPU16/03827, the Spanish Ministry of Science and Innovation (MICINN) through grant AYA2017-89841P for project “Estudio de las propiedades de los fósiles estelares en el entorno del Grupo Local” and through grant TIN2015-65316-P for project “Computación de Altas Prestaciones VII”, the Severo Ochoa Centre of Excellence Programme of the Spanish Government through grant SEV2015-0493, the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia “María de Maeztu”) through grants MDM-2014-0369 and CEX2019-000918-M, the University of Barcelona’s official doctoral programme for the development of an R+D+i project through an Ajuts de Personal Investigador en Formació (APIF) grant, the Spanish Virtual Observatory through project AyA2017-84089, the Galician Regional Government, Xunta de Galicia, through grants ED431B-2018/42 and ED481A-2019/155, support received from the Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC) funded by the Xunta de Galicia, the Xunta de Galicia and the Centros Singulares de Investigación de Galicia for the period 2016-2019 through CITIC, the European Union through the European Regional Development Fund (ERDF) / Fondo Europeo de Desenvolvemento Rexional (FEDER) for the Galicia 2014-2020 Programme through grant ED431G-2019/01, the Red Española de Supercomputación (RES) computer resources at MareNostrum, the Barcelona Supercomputing Centre – Centro Nacional de Supercomputación (BSC-CNS) through activities AECT-2016-1-0006, AECT-2016-2-0013, AECT-2016-3-0011, and AECT-2017-1-0020, the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya through grant 2014-SGR-1051 for project “Models de Programació i Entorns d’Execució Parallels” (MPEXPAR), and Ramon y Cajal Fellowship RYC2018-025968-I; the Swedish National Space Agency (SNSA/Rymdstyrelsen); the Swiss State Secretariat for Education, Research, and Innovation through the Mesures d’Accompagnement, the Swiss Activités Nationales Complémentaires, and the Swiss National Science Foundation; the United Kingdom Particle Physics and Astronomy Research Council (PPARC), the United Kingdom Science and Technology Facilities Council (STFC), and the United Kingdom Space Agency (UKSA) through the following grants to the University of Bristol, the University of Cambridge, the University of Edinburgh, the University of Leicester, the Mullard Space Sciences Laboratory of University College London, and the United Kingdom Rutherford Appleton Laboratory (RAL): PP/D006511/1, PP/D006546/1, PP/D006570/1, ST/I000852/1, ST/J005045/1, ST/K00056X/1, ST/K000209/1, ST/K000756/1, ST/L006561/1, ST/N000595/1, ST/N000641/1, ST/N000978/1, ST/N001117/1, ST/S000089/1, ST/S000976/1, ST/S001123/1, ST/S001948/1, ST/S002103/1, and ST/V000969/1

New insights into the neolithisation process in southwest Europe according to spatial density analysis from calibrated radiocarbon dates

The agricultural way of life spreads throughout Europe via two main routes: the Danube corridor and the Mediterranean basin. Current archaeological literature describes the arrival to the Western Mediterranean as a rapid process which involves both demic and cultural models, and in this regard, the dispersal movement has been investigated using mathematical models, where the key factors are time and space. In this work, we have created a compilation of all available radiocarbon dates for the whole of Iberia, in order to draw a chronological series of maps to illustrate temporal and spatial patterns in the neolithisation process. The maps were prepared by calculating the calibrated 14C date probability density curves, as a proxy to show the spatial dynamics of the last hunter-gatherers and first farmers. Several scholars have pointed out problems linked with the variability of samples, such as the overrepresentation of some sites, the degree of regional research, the nature of the dated samples and above all the archaeological context, but we are confident that the selected dates, after applying some filters and statistical protocols, constitute a good way to approach settlement spatial patterns in Iberia at the time of the neolithisation process

Cut-offs and response criteria for the Hospital Universitario la Princesa Index (HUPI) and their comparison to widely-used indices of disease activity in rheumatoid arthritis

Objective To estimate cut-off points and to establish response criteria for the Hospital Universitario La Princesa Index (HUPI) in patients with chronic polyarthritis. Methods Two cohorts, one of early arthritis (Princesa Early Arthritis Register Longitudinal PEARL] study) and other of long-term rheumatoid arthritis (Estudio de la Morbilidad y Expresión Clínica de la Artritis Reumatoide EMECAR]) including altogether 1200 patients were used to determine cut-off values for remission, and for low, moderate and high activity through receiver operating curve (ROC) analysis. The areas under ROC (AUC) were compared to those of validated indexes (SDAI, CDAI, DAS28). ROC analysis was also applied to establish minimal and relevant clinical improvement for HUPI. Results The best cut-off points for HUPI are 2, 5 and 9, classifying RA activity as remission if =2, low disease activity if >2 and =5), moderate if >5 and <9 and high if =9. HUPI''s AUC to discriminate between low-moderate activity was 0.909 and between moderate-high activity 0.887. DAS28''s AUCs were 0.887 and 0.846, respectively; both indices had higher accuracy than SDAI (AUCs: 0.832 and 0.756) and CDAI (AUCs: 0.789 and 0.728). HUPI discriminates remission better than DAS28-ESR in early arthritis, but similarly to SDAI. The HUPI cut-off for minimal clinical improvement was established at 2 and for relevant clinical improvement at 4. Response criteria were established based on these cut-off values. Conclusions The cut-offs proposed for HUPI perform adequately in patients with either early or long term arthritis