Association of Acculturation and Country of Origin with Self-Reported Hypertension and Diabetes in a Heterogeneous Hispanic Population

Background: Hispanics are the fasting growing population in the U.S. and disproportionately suffer from chronic diseases such as hypertension and diabetes. Little is known about the complex interplay between acculturation and chronic disease prevalence in the growing and increasingly diverse Hispanic population. We explored the association between diabetes and hypertension prevalence among distinct U.S. Hispanic subgroups by country of origin and by degree of acculturation. Methods: We examined the adult participants in the 2001, 2003, 2005, and 2007 California Health Interview Survey (CHIS). Using weighted logistic regression stratified by nativity, we measured the association between country of origin and self-reported hypertension and diabetes adjusting for participants’ demographics, insurance status, socio-economic status and degree of acculturation measured by citizenship, English language proficiency and the number of years of residence in the U.S. Results: There were 33,633 self-identified Hispanics (foreign-born: 19,988; U.S.-born: 13,645). After multivariable adjustment, we found significant heterogeneity in self-reported hypertension and diabetes prevalence among Hispanic subgroups. Increasing years of U.S. residence was associated with increased disease prevalence. Among all foreign-born subgroups, only Mexicans reported lower odds of hypertension after adjustment for socioeconomic and acculturation factors. Both U.S.-born and foreign-born Mexicans had higher rates of diabetes as compared to non-Hispanic whites. Conclusions: We found significant heterogeneity among Hispanics in self-reported rates of hypertension and diabetes by acculturation and country of origin. Our findings highlight the importance of disaggregation of Hispanics by country of origin and acculturation factors whenever possible

Investigations using data from Earth Resources Technology Satellite in the fields of agriculture/geography. Timber inventory (land use) in the Province of Huelva, Spain

A test site was chosen for the purpose of elaborating the patterns for the future total use of the satellite photographs. The election of the test site was made with the following criteria in mind: (1) a flat terrain for eliminating the dangers of shadows produced by a difficult topography; and (2) searching of well defined natural limits for the test site. Due to the lack of satellite photographs from the study area, a number of photos from the northern area of Spain have been studied from the point of view of obtaining answers from the spectra of the vegetation masses

Technological and social networks of a pastoralist artificial society: agent-based modeling of mobility patterns

This paper explores the advantages of simulation to raise the question of how digital and social networks affect the mobility in a pastoralist artificial society in the context of environmental degradation. We aim to explore mechanisms and develop scenarios, which are going to be validated through further research. We use a model of a simple pastoralist society in a world without borders to migration by adding the possibility of experiencing the effects of social structures (such as family and friends) and technological networks (e.g., social media). It appears obvious that pastoralist mobility depends on other dimensions as land tenure and traditional knowledge; however, isolating these two effects and experimenting in a simple society allow us to filter the multidimensionality of mobility decisions and concentrate on comparing scenarios in several different social structures and technological network combinations. The results show an expected behavior of more connection and more mobility, and a non-linear emergent behavior where pastoralists wait for a longer amount of time to mobilize when they interact using powerful social and technological networks. This occurs until they decide to move, and then, they mobilize more quickly and strongly than they did when communication was non-existent between them. The literature on migration explains this emergent non-linear behavior

High-energy Neutrinos from the Inner Circumnuclear Region of NGC 1068

High-energy neutrinos are detected by the IceCube Observatory in the direction of NGC 1068, the archetypical type II Seyfert galaxy. The neutrino flux, surprisingly, is more than an order of magnitude higher than the $\gamma$-ray upper limits at measured TeV energy, posing tight constraints on the physical conditions of a neutrino production site. We report an analysis of the sub-millimeter, mid-infrared, and ultraviolet observations of the central $50$ pc of NGC 1068 and suggest that the inner dusty torus and the region where the jet interacts with the surrounding interstellar medium (ISM) may be a potential neutrino production site. Based on radiation and magnetic field properties derived from observations, we calculate the electromagnetic cascade of the $\gamma$-rays accompanying the neutrinos. Our model may explain the observed neutrino flux above $\sim 10$ TeV and contribute to 20% of the neutrino flux at 3 TeV. It predicts a unique sub-TeV $\gamma$-ray component, which could be identified by a future observation. Jet-ISM interactions are commonly observed in the proximity of jets of both supermassive and stellar-mass black holes. Our results imply that such interaction regions could be $\gamma$-ray obscured neutrino production sites, which are needed to explain the IceCube diffuse neutrino flux.Comment: 8 pages, 4 figures, 1 tabl

ALMA polarimetry measures magnetically aligned dust grains in the torus of NGC 1068

The obscuring structure surrounding active galactic nuclei (AGN) can be explained as a dust and gas flow cycle that fundamentally connects the AGN with their host galaxies. This structure is believed to be associated with dusty winds driven by radiation pressure. However, the role of magnetic fields, which are invoked in almost all models for accretion onto a supermassive black hole and outflows, is not thoroughly studied. Here we report the first detection of polarized thermal emission by means of magnetically aligned dust grains in the dusty torus of NGC 1068 using ALMA Cycle 4 polarimetric dust continuum observations ($0.07"$, $4.2$ pc; 348.5 GHz, $860$ $\mu$m). The polarized torus has an asymmetric variation across the equatorial axis with a peak polarization of $3.7\pm0.5$\% and position angle of $109\pm2^{\circ}$ (B-vector) at $\sim8$ pc east from the core. We compute synthetic polarimetric observations of magnetically aligned dust grains assuming a toroidal magnetic field and homogeneous grain alignment. We conclude that the measured 860 $\mu$m continuum polarization arises from magnetically aligned dust grains in an optically thin region of the torus. The asymmetric polarization across the equatorial axis of the torus arises from 1) an inhomogeneous optical depth, and 2) a variation of the velocity dispersion, i.e. variation of the magnetic field turbulence at sub-pc scales, from the eastern to the western region of the torus. These observations and modeling constrain the torus properties beyond spectral energy distribution results. This study strongly supports that magnetic fields up to a few pc contribute to the accretion flow onto the active nuclei.Comment: 19 pages, 11 figures (Accepted for Publication to ApJ

A GEANT4 Study of a Gamma-ray Collimation Array

Proton beam therapy uses high-energy protons to destroy cancer cells which are still uncertain about where in the body they hit. A possible way to answer this question is to detect the gamma rays produced during the irradiation and determine where in the body they are produced. This work investigates the use of collimators to determine where the proton interactions occur. GEANT4 is used to simulate the gamma production of a source interacting with a collimator. Each event simulates a number of gammas obtained as a function of the position along the detector. Repeating for different collimator configurations can thus help determine the best characteristics of a detector device

3D reconstruction of medical images from slices automatically landmarked with growing neural models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid

Real time motion estimation using a neural architecture implemented on GPUs

This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis

Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis

The advent and progressive deployment of the so-called Smart Grid has unleashed a profitable portfolio of new possibilities for an efficient management of the low-voltage distribution network supported by the introduction of information and communication technologies to exploit its digitalization. Among all such possibilities this work focuses on the detection of anomalous energy consumption traces: disregarding whether they are due to malfunctioning metering equipment or fraudulent purposes, strong efforts are invested by utilities to detect such outlying events and address them to optimize the power distribution and avoid significant income costs. In this context this manuscript introduce a novel algorithmic approach for the identification of consumption outliers in Smart Grids that relies on concepts from probabilistic data mining and time series analysis. A key ingredient of the proposed technique is its ability to accommodate time irregularities – shifts and warps – in the consumption habits of the user by concentrating on the shape of the consumption rather than on its temporal properties. Simulation results over real data from a Spanish utility are presented and discussed, from where it is concluded that the proposed approach excels at detecting different outlier cases emulated on the aforementioned consumption traces.Ministerio de Energía y Competitividad under the RETOS program (OSIRIS project, grant ref. RTC-2014-1556-3)