Global agricultural productivity is threatened by increasing pollinator dependence without a parallel increase in crop diversification

The global increase in the proportion of land cultivated with pollinator-dependent crops implies increased reliance on pollination services. Yet agricultural practices themselves can profoundly affect pollinator supply and pollination. Extensive monocultures are associated with a limited pollinator supply and reduced pollination, whereas agricultural diversification can enhance both. Therefore, areas where agricultural diversity has increased, or at least been maintained, may better sustain high and more stable productivity of pollinator-dependent crops. Given that >80% of all crops depend, to varying extents, on insect pollination, a global increase in agricultural pollinator dependence over recent decades might have led to a concomitant increase in agricultural diversification. We evaluated whether an increase in the area of pollinator-dependent crops has indeed been associated with an increase in agricultural diversity, measured here as crop diversity, at the global, regional, and country scales for the period 1961–2016. Globally, results show a relatively weak and decelerating rise in agricultural diversity over time that was largely decoupled from the strong and continually increasing trend in agricultural dependency on pollinators. At regional and country levels, there was no consistent relationship between temporal changes in pollinator dependence and crop diversification. Instead, our results show heterogeneous responses in which increasing pollinator dependence for some countries and regions has been associated with either an increase or a decrease in agricultural diversity. Particularly worrisome is a rapid expansion of pollinator-dependent oilseed crops in several countries of the Americas and Asia that has resulted in a decrease in agricultural diversity. In these regions, reliance on pollinators is increasing, yet agricultural practices that undermine pollination services are expanding. Our analysis has thereby identified world regions of particular concern where environmentally damaging practices associated with large-scale, industrial agriculture threaten key ecosystem services that underlie productivity, in addition to other benefits provided by biodiversity.Fil: Aizen, Marcelo Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Aguiar, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Biesmeijer, Jacobus C.. Leiden University; Países Bajos. Naturalis Biodiversity Center; Países BajosFil: Garibaldi, Lucas Alejandro. Universidad Nacional de Río Negro. Sede Andina. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; ArgentinaFil: Inouye, David W.. University of Maryland; Estados Unidos. Rocky Mountain Biological Laboratory; Estados UnidosFil: Jung, Chuleui. Andong National University; Corea del SurFil: Martins, Dino J.. University of Princeton; Estados UnidosFil: Medel, Rodrigo. Universidad de Chile; ChileFil: Morales, Carolina Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Ngo, Hien. UN Campus Platz der Vereinten Nationen. Intergovernmental Science Policy Platform on Biodiversity and Ecosystem Services; AlemaniaFil: Pauw, Anton. Stellenbosch University; SudáfricaFil: Paxton, Robert J. Martin Luther University Halle Wittenberg; Alemania. German Centre for Integrative Biodiversity Research; AlemaniaFil: Sáez, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Seymour, Colleen L.. South African National Biodiversity Institute; Sudáfrica. University of Cape Town; Sudáfric

The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library

Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July–2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA—we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020–2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data

The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library

Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July–2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA—we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020–2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data

RESULTADO NEONATAL ADVERSO EN LA ROTURA PREMATURA DE MEMBRANAS DE PRETÉRMINO SEGÚN EL MODO DEL PARTO

Objetivo: Evaluar el resultado neonatal adverso según modo de parto en la rotura prematura de membranas de pretérmino (RPMPT). Métodos: Participaron 135 embarazadas entre 24 y 34 semanas de gestación con diagnóstico de rotura prematura de membranas. Se excluyeron pacientes en trabajo de parto y condiciones maternas y fetales severas que pudiesen alterar el resultado perinatal. Todas las embarazadas tuvieron evaluación microbiológica del líquido amniótico y cérvicovaginal, y recibieron antibióticos, corticoesteroides y manejo expectante hasta las 35 semanas. Se definió resultado neonatal adverso (RA) compuesto, la variable que incluyó morbilidad neonatal severa, secuelas o muerte neonatal. Se definió invasión microbiana de la cavidad amniótica (IMCA) por cultivo positivo del líquido amniótico. Funisitis se diagnosticó por la presencia de leucocitos polimorfonucleares en la pared de los vasos umbilicales o gelatina de Warthon. La cesárea se realizó por indicaciones obstétricas o por urgencias. Para el análisis se usó curva ROC y chi cuadrado. Resultados: Se incluyeron 116 pacientes. Modo del parto: vaginal 50,1% y cesárea 49,9%. La IMCA fue 52,6% y el RA 17,2%. El RA no dependió del modo del parto (vaginal 13,6% vs. cesárea 21,1%). La vía del parto no influyó en el RA de los subgrupos donde este resultado fue más frecuente: Objective: To determine adverse neonatal outcome in patients with preterm PROM according to mode of delivery. Methods: 135 patients with preterm PROM between 24 and 34 weeks participated in this study. Exclusion criteria were labor, and additional fetal and maternal conditions that may influence perinatal outcome. Microbiologic assessment of amniotic cavity and the lower genital tract was performed. MIAC was defined as the presence of a positive amniotic fluid culture. Patients received antibiotics, steroids and were managed expectantly until 35 weeks. Cesarean section was performed if medically indicated or in urgency conditions. A composite variable including severe neonatal morbidity, sequelae or death was used. Funisitis was diagnosed in the presence of polymorphonuclear leukocyte infiltration into the umbilical vessel walls or Wharton jelly. Statistics were performed using ROC curve and chi square analysis. Results: 116 patients were included. Vaginal delivery occurred in 50.1% and cesarean delivery was performed in 49.9%. Frequency of MIAC was 52.6%. Adverse neonatal outcome (AO) was present in 17.2%.AO is not associated with mode of delivery (vaginal 13.6% or cesarean section 21.1%). Within the subgroups with birth weight less than 1500 g or 31 or less week gestation, AO was more frequent than good outcome, but there were no differences in the neonatal outcome between these subgroups with mode of delivery. AO was correlated with infectious variables: MIAC 25%, S agalactiae intraamniotic infection 71.4%, histological chorio-amnionitis 100% and funisitis 94%. The cut off point of the ROC curve for gestational age and risk neonatal outcome was 30 weeks at labor. Conclusions: In patients with preterm PROM and expectant management with antibiotics and corticosteroids, adverse neonatal outcome no depends on mode of deliver

Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores

Genetic discoveries of Alzheimer’s disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer’s disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer’s disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer’s disease