University of Los Andes

About: University of Los Andes is a education organization based out in Bogotá, Colombia. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 17616 authors who have published 25555 publications receiving 413463 citations.

Polycystic Ovary Syndrome, Insulin Resistance, and Obesity: Navigating the Pathophysiologic Labyrinth

Abstract: Polycystic ovary syndrome (PCOS) is a highly prevalent endocrine-metabolic disorder that implies various severe consequences to female health, including alarming rates of infertility. Although its exact etiology remains elusive, it is known to feature several hormonal disturbances, including hyperandrogenemia, insulin resistance (IR), and hyperinsulinemia. Insulin appears to disrupt all components of the hypothalamus-hypophysis-ovary axis, and ovarian tissue insulin resistance results in impaired metabolic signaling but intact mitogenic and steroidogenic activity, favoring hyperandrogenemia, which appears to be the main culprit of the clinical picture in PCOS. In turn, androgens may lead back to IR by increasing levels of free fatty acids and modifying muscle tissue composition and functionality, perpetuating this IR-hyperinsulinemia-hyperandrogenemia cycle. Nonobese women with PCOS showcase several differential features, with unique biochemical and hormonal profiles. Nevertheless, lean and obese patients have chronic inflammation mediating the long term cardiometabolic complications and comorbidities observed in women with PCOS, including dyslipidemia, metabolic syndrome, type 2 diabetes mellitus, and cardiovascular disease. Given these severe implications, it is important to thoroughly understand the pathophysiologic interconnections underlying PCOS, in order to provide superior therapeutic strategies and warrant improved quality of life to women with this syndrome.

Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites

Abstract: Aim The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Location Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/

Searches for long-lived charged particles in pp collisions at sqrt(s) = 7 and 8 TeV

Abstract: Results of searches for heavy stable charged particles produced in pp collisions at sqrt(s) = 7 and 8 TeV are presented corresponding to an integrated luminosity of 5.0 inverse femtobarns and 18.8 inverse femtobarns, respectively. Data collected with the CMS detector are used to study the momentum, energy deposition, and time-of-flight of signal candidates. Leptons with an electric charge between e/3 and 8e, as well as bound states that can undergo charge exchange with the detector material, are studied. Analysis results are presented for various combinations of signatures in the inner tracker only, inner tracker and muon detector, and muon detector only. Detector signatures utilized are long time-of-flight to the outer muon system and anomalously high (or low) energy deposition in the inner tracker. The data are consistent with the expected background, and upper limits are set on the production cross section of long-lived gluinos, scalar top quarks, and scalar tau leptons, as well as pair produced long-lived leptons. Corresponding lower mass limits, ranging up to 1322 GeV for gluinos, are the most stringent to date.

Compositional data analysis for physical activity, sedentary time and sleep research

Abstract: The health effects of daily activity behaviours (physical activity, sedentary time and sleep) are widely studied. While previous research has largely examined activity behaviours in isolation, recent studies have adjusted for multiple behaviours. However, the inclusion of all activity behaviours in traditional multivariate analyses has not been possible due to the perfect multicollinearity of 24-h time budget data. The ensuing lack of adjustment for known effects on the outcome undermines the validity of study findings. We describe a statistical approach that enables the inclusion of all daily activity behaviours, based on the principles of compositional data analysis. Using data from the International Study of Childhood Obesity, Lifestyle and the Environment, we demonstrate the application of compositional multiple linear regression to estimate adiposity from children's daily activity behaviours expressed as isometric log-ratio coordinates. We present a novel method for predicting change in a continuous outcome based on relative changes within a composition, and for calculating associated confidence intervals to allow for statistical inference. The compositional data analysis presented overcomes the lack of adjustment that has plagued traditional statistical methods in the field, and provides robust and reliable insights into the health effects of daily activity behaviours.