Comparison of charge modulations in La1.875_{1.875}Ba0.125_{0.125}CuO4_4 and YBa2_2Cu3_3O6.6_{6.6}

A charge modulation has recently been reported in (Y,Nd)Ba$_2$Cu$_3$O$_{6+x}$ [Ghiringhelli {\em et al.} Science 337, 821 (2013)]. Here we report Cu $L_3$ edge soft x-ray scattering studies comparing the lattice modulation associated with the charge modulation in YBa$_2$Cu$_3$O$_{6.6}$ with that associated with the well known charge and spin stripe order in La$_{1.875}$Ba$_{0.125}$CuO$_4$. We find that the correlation length in the CuO$_2$ plane is isotropic in both cases, and is $259 \pm 9$ \AA for La$_{1.875}$Ba$_{0.125}$CuO$_4$ and $55 \pm 15$ \AA for YBa$_2$Cu$_3$O$_{6.6}$. Assuming weak inter-planar correlations of the charge ordering in both compounds, we conclude that the order parameters of the lattice modulations in La$_{1.875}$Ba$_{0.125}$CuO$_4$ and YBa$_2$Cu$_3$O$_{6.6}$ are of the same order of magnitude.Comment: 3 pages, 2 figure

Crystalline Structure and Vacancy Ordering across a Surface Phase Transition in Sn/Cu(001)

We report a surface X-ray diffraction study of the crystalline structure changes and critical behavior across the (3√2 × √2)R45° → (√2 × √2)R45° surface phase transition at 360 K for 0.5 monolayers of Sn on Cu(100). The phase transition is of the order-disorder type and is due to the disordering of the Cu atomic vacancies present in the low temperature phase. Two different atomic sites for Sn atoms, characterized by two different heights, are maintained across the surface phase transition.This work was funded by the Spanish MINECO under Grants FIS2011-23230 and MAT2014-52477-C5-5-P. E.G.M. and P.S. acknowledge financial support from MINECO through the “Maria de Maeztu” Programme for Units of Excellence in R&D (MDM-2014-0377).Peer Reviewe

WRF4SG: A scientific gateway for climate experiment workflows

Trabajo presentado a la European Geosciences Union General Assembly celebrada en Viena del 7 al 12 de abril de 2013.The Weather Research and Forecasting model (WRF) is a community-driven and public domain model widely used by the weather and climate communities. As opposite to other application-oriented models, WRF provides a flexible and computationally-efficient framework which allows solving a variety of problems for different time-scales, from weather forecast to climate change projection. Furthermore, WRF is also widely used as a research tool in modeling physics, dynamics, and data assimilation by the research community. Climate experiment workflows based on Weather Research and Forecasting (WRF) are nowadays among the one of the most cutting-edge applications. These workflows are complex due to both large storage and the huge number of simulations executed. In order to manage that, we have developed a scientific gateway (SG) called WRF for Scientific Gateway (WRF4SG) based on WS-PGRADE/gUSE and WRF4G frameworks to ease achieve WRF users needs (see [1] and [2]). WRF4SG provides services for different use cases that describe the different interactions between WRF users and the WRF4SG interface in order to show how to run a climate experiment. As WS-PGRADE/gUSE uses portlets (see [1]) to interact with users, its portlets will support these use cases. A typical experiment to be carried on by a WRF user will consist on a high-resolution regional re-forecast. These re-forecasts are common experiments used as input data form wind power energy and natural hazards (wind and precipitation fields). In the cases below, the user is able to access to different resources such as Grid due to the fact that WRF needs a huge amount of computing resources in order to generate useful simulations: - Resource configuration and user authentication: The first step is to authenticate on users’ Grid resources by virtual organizations. After login, the user is able to select which virtual organization is going to be used by the experiment. - Data assimilation: In order to assimilate the data sources, the user has to select them browsing through LFC Portlet. - Design Experiment workflow: In order to configure the experiment, the user will define the type of experiment (i.e. re-forecast), and its attributes to simulate. In this case the main attributes are: the field of interest (wind, precipitation, ...), the start and end date simulation and the requirements of the experiment. - Monitor workflow: In order to monitor the experiment the user will receive notification messages based on events and also the gateway will display the progress of the experiment. - Data storage: Like Data assimilation case, the user is able to browse and view the output data simulations using LFC Portlet. The objectives of WRF4SG can be described by considering two goals. The first goal is to show how WRF4SG facilitates to execute, monitor and manage climate workflows based on the WRF4G framework. And the second goal of WRF4SG is to help WRF users to execute their experiment workflows concurrently using heterogeneous computing resources such as HPC and Grid.Peer reviewe

Hypoxia conditions differentially modulate normal and osteoarthritic chondrocyte proteomes

Comunicaciones a congreso

Efficiency of the cerebroplacental ratio in Identifying high-risk late-term pregnancies

Background and Objectives: Over the last few years, great interest has arisen in the role of the cerebroplacental ratio (CPR) to identify low-risk pregnancies at higher risk of adverse pregnancy outcomes. This study aimed to assess the predictive capacity of the CPR for adverse perinatal outcomes in all uncomplicated singleton pregnancies attending an appointment at 40–42 weeks. Materials and Methods: This is a retrospective cohort study including all consecutive singleton pregnancies undergoing a routine prenatal care appointment after 40 weeks in three maternity units in Spain and the United Kingdom from January 2017 to December 2019. The primary outcome was adverse perinatal outcomes defined as stillbirth or neonatal death, cesarean section or instrumental delivery due to fetal distress during labor, umbilical arterial cord blood pH < 7.0, umbilical venous cord blood pH < 7.1, Apgar score at 5 min < 7, and admission to the neonatal unit. Logistic mixed models and ROC curve analyses were used to analyze the data. Results: A total of 3143 pregnancies were analyzed, including 537 (17.1%) with an adverse perinatal outcome. Maternal age (odds ratio (OR) 1.03, 95% confidence interval (CI) 1.01 to 1.04), body mass index (OR 1.04, 95% CI 1.03 to 1.06), racial origin (OR 2.80, 95% CI 1.90 to 4.12), parity (OR 0.36, 95% CI 0.29 to 0.45), and labor induction (OR 1.79, 95% CI 1.36 to 2.35) were significant predictors of adverse perinatal outcomes with an area under the ROC curve of 0.743 (95% CI 0.720 to 0.766). The addition of the CPR to the previous model did not improve performance. Additionally, the CPR alone achieved a detection rate of only 11.9% (95% CI 9.3 to 15) when using the 10th centile as the screen-positive cutoff. Conclusions: Our data on late-term unselected pregnancies suggest that the CPR is a poor predictor of adverse perinatal outcomes