Hypertension Is Associated With Intestinal Microbiota Dysbiosis and Inflammation in a Brazilian Population

Hypertension is a major global health challenge, as it represents the main risk factor for stroke and cardiovascular disease. It is a multifactorial clinical condition characterized by high and sustained levels of blood pressure, likely resulting from a complex interplay of endogenous and environmental factors. The gut microbiota has been strongly supposed to be involved but its role in hypertension is still poorly understood. In an attempt to fill this gap, here we characterized the microbial composition of fecal samples from 48 hypertensive and 32 normotensive Brazilian individuals by next-generation sequencing of the 16S rRNA gene. In addition, the cytokine production of peripheral blood samples was investigated to build an immunological profile of these individuals. We identified a dysbiosis of the intestinal microbiota in hypertensive subjects, featured by reduced biodiversity and distinct bacterial signatures compared with the normotensive counterpart. Along with a reduction in Bacteroidetes members, hypertensive individuals were indeed mainly characterized by increased proportions of Lactobacillus and Akkermansia while decreased relative abundances of well-known butyrate-producing commensals, including Roseburia and Faecalibacterium within the Lachnospiraceae and Ruminococcaceae families. We also observed an inflamed immune profile in hypertensive individuals with an increase in TNF/IFN-\u3b3 ratio, and in TNF and IL-6 production when compared to normotensive ones. Our work provides the first evidence of association of hypertension with altered gut microbiota and inflammation in a Brazilian population. While lending support to the existence of potential microbial signatures of hypertension, likely to be robust to age and geography, our findings point to largely neglected bacteria as potential contributors to intestinal homeostasis loss and emphasize the high vulnerability of hypertensive individuals to inflammation-related disorders

Electronically excited states of formic acid investigated by theoretical and experimental methods

Absolute cross-section values are reported from high-resolution vacuum ultraviolet (VUV) photoabsorption measurements of formic acid in the photon energy range 4.7–10.8 eV (265–115 nm), together with quantum chemical calculations to provide vertical energies and oscillator strengths. The combination of experimental and theoretical methods has allowed a comprehensive assignment of the electronic transitions. The VUV spectrum reveals various vibronic features not previously reported in the literature, notably associated with (3pa'←10a'), (3p'a'←10a'), (3sa'←2a'') and (3pa' ←2a'') Rydberg transitions. The assignment of vibrational features in the absorption bands reveal that the C=O stretching, v'3(a'), the H′–O–′C deformation, v'5(a'), the C–O stretching, v'6(a'), and the O=C–O′ deformation, v'7(a') modes are mainly active. The measured absolute photoabsorption cross sections have also been used to estimate the photolysis lifetime of HCOOH in the upper stratosphere (30–50 km), showing that solar photolysis is an important sink at altitudes above 30 km but not in the troposphere. Potential energy curves for the lowest-lying electronic excited states, as a function of the C=O coordinate, are obtained employing time dependent density functional theory (TD-DFT). These calculations have shown the relevance of internal conversion from Rydberg to valence character governing the nuclear dynamics, yielding clear evidence of the rather complex multidimensional nature of the potential energy surfaces involved

Detection of inconsistencies in geospatial data with geostatistics

Almost every researcher has come through observations that “drift” from the rest of the sample, suggesting some inconsistency. The aim of this paper is to propose a new inconsistent data detection method for continuous geospatial data based in Geostatistics, independently from the generative cause (measuring and execution errors and inherent variability data). The choice of Geostatistics is based in its ideal characteristics, as avoiding systematic errors, for example. The importance of a new inconsistent detection method proposal is in the fact that some existing methods used in geospatial data consider theoretical assumptions hardly attended. Equally, the choice of the data set is related to the importance of the LiDAR technology (Light Detection and Ranging) in the production of Digital Elevation Models (DEM). Thus, with the new methodology it was possible to detect and map discrepant data. Comparing it to a much utilized detections method, BoxPlot, the importance and functionality of the new method was verified, since the BoxPlot did not detect any data classified as discrepant. The proposed method pointed that, in average, 1,2% of the data of possible regionalized inferior outliers and, in average, 1,4% of possible regionalized superior outliers, in relation to the set of data used in the study