Holiness and Patronage in the Hispanic World in the Modern Age

On March 23, 1630 the Congregation of rites issued a decree on the patron saints that limited their choice to the canonized. In this way direct control was granted to the Holy See. The choice of the cities of their patron saints in the modern age was determined by the interests of their ruling classes, the popular devotion and by the possession of the relics of the proposed saints. Concerning to the homeland of the Saints, martyrs, bishops and hermits, and the control of their relics were published many controversial writings that generated intense debates during the 17th century. In the election of the of the patron saints the Vatican regulation will not always be respected; in the case of Pedro Regalado and Rosa de Lima there were express authorization for the beginning of the canonization processes, his cult and their appointment

The membrane-spanning 4-domains, subfamily A (MS4A) gene cluster contains a common variant associated with Alzheimer's disease

Background\ud In order to identify novel loci associated with Alzheimer's disease (AD), we conducted a genome-wide association study (GWAS) in the Spanish population.\ud \ud Methods\ud We genotyped 1,128 individuals using the Affymetrix Nsp I 250K chip. A sample of 327 sporadic AD patients and 801 controls with unknown cognitive status from the Spanish general population were included in our initial study. To increase the power of the study, we combined our results with those of four other public GWAS datasets by applying identical quality control filters and the same imputation methods, which were then analyzed with a global meta-GWAS. A replication sample with 2,200 sporadic AD patients and 2,301 controls was genotyped to confirm our GWAS findings.\ud \ud Results\ud Meta-analysis of our data and independent replication datasets allowed us to confirm a novel genome-wide significant association of AD with the membrane-spanning 4-domains subfamily A (MS4A) gene cluster (rs1562990, P = 4.40E-11, odds ratio = 0.88, 95% confidence interval 0.85 to 0.91, n = 10,181 cases and 14,341 controls).\ud \ud Conclusions\ud Our results underscore the importance of international efforts combining GWAS datasets to isolate genetic loci for complex diseases

Forest Biomass Density across Large Climate Gradients in Northern South America is related to Water Availability but not with Temperature

Understanding and predicting the likely response of ecosystems to climate change are crucial challenges for ecology and for conservation biology. Nowhere is this challenge greater than in the tropics as these forests store more than half the total atmospheric carbon stock in their biomass. Biomass is determined by the balance between biomass inputs (i.e., growth) and outputs (mortality). We can expect therefore that conditions that favor high growth rates, such as abundant water supply, warmth, and nutrient-rich soils will tend to correlate with high biomass stocks. Our main objective is to describe the patterns of above ground biomass (AGB) stocks across major tropical forests across climatic gradients in Northwestern South America. We gathered data from 200 plots across the region, at elevations ranging between 0 to 3400 m. We estimated AGB based on allometric equations and values for stem density, basal area, and wood density weighted by basal area at the plot-level. We used two groups of climatic variables, namely mean annual temperature and actual evapotranspiration as surrogates of environmental energy, and annual precipitation, precipitation seasonality, and water availability as surrogates of water availability. We found that AGB is more closely related to water availability variables than to energy variables. In northwest South America, water availability influences carbon stocks principally by determining stand structure, i.e. basal area. When water deficits increase in tropical forests we can expect negative impact on biomass and hence carbon storage

The mediterranean dietary pattern and breast cancer risk in Greek-Cypriot women: a case-control study

Background: Diet has long been suspected to impact on breast cancer risk. In this study we evaluated whether the degree of adherence to a Mediterranean diet pattern modifies breast cancer risk amongst Greek-Cypriot women. Methods: Subjects included 935 cases and 817 controls, all participating in the MASTOS case-control study in Cyprus. The study was approved by the Cyprus National Bioethics Committee. Information on dietary intakes was collected using an interviewer administered 32-item Food Frequency Questionnaire. Information on demographic, anthropometric, lifestyle, and other confounding factors was also collected. Adherence to the Mediterranean Diet pattern was assessed using two a-priory defined diet scores. In addition, dietary patterns specific to our population were derived using Principal Component Analysis (PCA). Logistic regression models were used to assess the association between the dietary patters and breast cancer risk. Results: There was no association with breast cancer risk for either score, however, higher consumptions of vegetables, fish and olive oil, were independently associated with decreased risk. In addition, the PCA derived component which included vegetables, fruit, fish and legumes was shown to significantly reduce risk of breast cancer (ORs across quartiles of increasing levels of consumption: 0.89 95%CI: 0.65-1.22, 0.64 95%CI: 0.47-0.88, 0.67 95%CI: 0.49-0.92, P trend < 0.0001), even after adjustment for relevant confounders. Conclusions: Our results suggest that adherence to a diet pattern rich in vegetables, fish, legumes and olive oil may favorably influence the risk of breast cancer. This study is the first investigation of dietary effects on breast cancer risk in Cyprus, a country whose population has traditionally adhered to the Mediterranean diet

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

Mature Andean forests as globally important carbon sinks and future carbon refuges

It is largely unknown how South America’s Andean forests affect the global carbon cycle, and thus regulate climate change. Here, we measure aboveground carbon dynamics over the past two decades in 119 monitoring plots spanning a range of >3000 m elevation across the subtropical and tropical Andes. Our results show that Andean forests act as strong sinks for aboveground carbon (0.67 ± 0.08 Mg C ha−1 y−1) and have a high potential to serve as future carbon refuges. Aboveground carbon dynamics of Andean forests are driven by abiotic and biotic factors, such as climate and size-dependent mortality of trees. The increasing aboveground carbon stocks offset the estimated C emissions due to deforestation between 2003 and 2014, resulting in a net total uptake of 0.027 Pg C y−1. Reducing deforestation will increase Andean aboveground carbon stocks, facilitate upward species migrations, and allow for recovery of biomass losses due to climate change

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis