University of Alcalá

About: University of Alcalá is a education organization based out in Alcalá de Henares, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 10795 authors who have published 20718 publications receiving 410089 citations. The organization is also known as: University of Alcala & University of Alcala de Henares.

Comparison of Ethinyl-Estradiol Plus Cyproterone Acetate Versus Metformin Effects on Classic Metabolic Cardiovascular Risk Factors in Women with the Polycystic Ovary Syndrome

Abstract: Context: Oral contraceptives may worsen the metabolic profile of patients with polycystic ovary syndrome (PCOS), favoring the use of insulin sensitizers in these patients. Objective: The aim of the study was to compare the effects of a contraceptive pill on metabolic classic cardiovascular risk factors with those of the insulin sensitizer metformin. Design: We conducted a randomized, parallel, open-label clinical trial. Setting: The study was conducted at an academic hospital. Patients: Thirty-four consecutive PCOS patients were studied. Interventions: Patients were randomized to oral treatment with metformin (850 mg twice daily) or with the Diane35 Diario pill (35 μg of ethinyl-estradiol plus 2 mg of cyproterone acetate) for 24 wk. Main Outcome Measures: Hyperandrogenism, lipid profiles, and indexes of glucose tolerance and insulin sensitivity were measured at baseline and after 12 and 24 wk of treatment. Results: Diane35 Diario resulted in higher reductions in hirsutism score and serum androgen levels c...

Psychosocial Factors and Health-Related Quality of Life in Hemodialysis Patients

Abstract: Several sociodemographic and clinical variables are known to influence the health-related quality of life (HRQOL) of patients with kidney disease, yet the relationship between psychological factors and the HRQOL measured by the Kidney Disease Quality of Life Short-Form (KDQOL-SF) is incompletely understood. The objective of this study was to examine the relationship between psychosocial status (depressive symptoms, trait anxiety, and social support) and KDQOL-SF scales in hemodialysis (HD) patients by controlling the effects of sociodemographic and clinical variables. The HRQOL of 194 patients from 43 dialysis centers in Spain was assessed by completing the KDQOL-SF, and evaluating depressive symptoms (Cognitive Depression Index), trait anxiety (Trait Anxiety Inventory) and degree of social support (Scale of Perceived Social Support). We also recorded several sociodemographic and clinical variables. Two regression models were estimated for each of the 19 scales in the KDQOL-SF. In the first model, we only included sociodemographic and clinical-factors, while the second model also took into consideration psychosocial variables. These last factors (trait anxiety and depressive symptoms, not social support) were found to increase the proportion of explained variability, with highest standardized regression coefficients observed for most KDQOL-SF scales. Depressive symptoms were related to a poor HRQOL when there was a strong physical component, while trait anxiety was mainly related to emotional upset and social relationships. We were able to conclude that trait anxiety and depressive symptoms are strongly associated with the HRQOL assessed by the KDQOL-SF in HD patients. The effects of these factors should therefore be considered when evaluating the quality of life of this type of patient.

Digital Filters for Fast Harmonic Sequence Component Separation of Unbalanced and Distorted Three-Phase Signals

Abstract: In this paper, two methods for determining the fundamental frequency and harmonic positive- and negative-sequence components of three-phase signals are investigated. Many aspects of the space-vector discrete Fourier transform and generalized delayed signal cancellation (GDSC) such as response time for different possible implementations, frequency adaptation schemes, stability of recursive implementation, and rounding error effects are discussed. A new design procedure for GDSC transformations is presented. New indices for characterizing three-phase unbalanced and distorted signals are proposed. Simulations and experiments are included in order to verify the performances and illustrate the theoretical conclusions.

Climate warming enhances snow avalanche risk in the Western Himalayas.

Abstract: Ongoing climate warming has been demonstrated to impact the cryosphere in the Indian Himalayas, with substantial consequences for the risk of disasters, human well-being, and terrestrial ecosystems. Here, we present evidence that the warming observed in recent decades has been accompanied by increased snow avalanche frequency in the Western Indian Himalayas. Using dendrogeomorphic techniques, we reconstruct the longest time series (150 y) of the occurrence and runout distances of snow avalanches that is currently available for the Himalayas. We apply a generalized linear autoregressive moving average model to demonstrate linkages between climate warming and the observed increase in the incidence of snow avalanches. Warming air temperatures in winter and early spring have indeed favored the wetting of snow and the formation of wet snow avalanches, which are now able to reach down to subalpine slopes, where they have high potential to cause damage. These findings contradict the intuitive notion that warming results in less snow, and thus lower avalanche activity, and have major implications for the Western Himalayan region, an area where human pressure is constantly increasing. Specifically, increasing traffic on a steadily expanding road network is calling for an immediate design of risk mitigation strategies and disaster risk policies to enhance climate change adaption in the wider study region.

Diversity buffers winegrowing regions from climate change losses

Abstract: Agrobiodiversity—the variation within agricultural plants, animals, and practices—is often suggested as a way to mitigate the negative impacts of climate change on crops [S. A. Wood et al., Trends Ecol. Evol. 30, 531–539 (2015)]. Recently, increasing research and attention has focused on exploiting the intraspecific genetic variation within a crop [Hajjar et al., Agric. Ecosyst. Environ. 123, 261–270 (2008)], despite few relevant tests of how this diversity modifies agricultural forecasts. Here, we quantify how intraspecific diversity, via cultivars, changes global projections of growing areas. We focus on a crop that spans diverse climates, has the necessary records, and is clearly impacted by climate change: winegrapes (predominantly Vitis vinifera subspecies vinifera). We draw on long-term French records to extrapolate globally for 11 cultivars (varieties) with high diversity in a key trait for climate change adaptation—phenology. We compared scenarios where growers shift to more climatically suitable cultivars as the climate warms or do not change cultivars. We find that cultivar diversity more than halved projected losses of current winegrowing areas under a 2 °C warming scenario, decreasing areas lost from 56 to 24%. These benefits are more muted at higher warming scenarios, reducing areas lost by a third at 4 °C (85% versus 58%). Our results support the potential of in situ shifting of cultivars to adapt agriculture to climate change—including in major winegrowing regions—as long as efforts to avoid higher warming scenarios are successful.