Role of estrogen and other sex hormones in brain aging: Neuroprotection and DNA repair

Aging is an inevitable biological process characterized by a progressive decline in physiological function and increased susceptibility to disease. The detrimental effects of aging are observed in all tissues, the brain being the most important one due to its main role in the homeostasis of the organism. As our knowledge about the underlying mechanisms of brain aging increases, potential approaches to preserve brain function rise significantly. Accumulating evidence suggests that loss of genomic maintenance may contribute to aging, especially in the central nervous system (CNS) owing to its low DNA repair capacity. Sex hormones, particularly estrogens, possess potent antioxidant properties and play important roles in maintaining normal reproductive and non-reproductive functions. They exert neuroprotective actions and their loss during aging and natural or surgical menopause is associated with mitochondrial dysfunction, neuroinflammation, synaptic decline, cognitive impairment and increased risk of age-related disorders. Moreover, loss of sex hormones has been suggested to promote an accelerated aging phenotype eventually leading to the development of brain hypometabolism, a feature often observed in menopausal women and prodromal Alzheimer's disease (AD). Although data on the relation between sex hormones and DNA repair mechanisms in the brain is still limited, various investigations have linked sex hormone levels with different DNA repair enzymes. Here, we review estrogen anti-aging and neuroprotective mechanisms, which are currently an area of intense study, together with the effect they may have on the DNA repair capacity in the brain.Fil: Zarate, Sandra Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: Stevnsner, Tinna. University of Aarhus; DinamarcaFil: Gredilla, Ricardo. Universidad Complutense de Madrid; Españ

Historia empresarial española: Ruiz-Mateos y el caso Rumasa

El empresario José María Ruiz-Mateos es una figura empresarial que rompe todos los esquemas. El objetivo de este trabado es ofrecer una visión general sobre la trayectoria del empresario y especialmente sobre el caso de Rumasa, el poderoso holding del empresario gaditano que fue expropiado por el Gobierno en 1983. Así mismo, se intenta reflejar la historia de España durante la segunda mitad del siglo XX, desde un punto de vista social, político y económico.Departamento de Fundamentos del Análisis Económico e Historia e Instituciones EconómicasGrado en Administración y Dirección de Empresa

DNA Damage and Base Excision Repair in Mitochondria and Their Role in Aging

During the last decades, our knowledge about the processes involved in the aging process has exponentially increased. However, further investigation will be still required to globally understand the complexity of aging. Aging is a multifactorial phenomenon characterized by increased susceptibility to cellular loss and functional decline, where mitochondrial DNA mutations and mitochondrial DNA damage response are thought to play important roles. Due to the proximity of mitochondrial DNA to the main sites of mitochondrial-free radical generation, oxidative stress is a major source of mitochondrial DNA mutations. Mitochondrial DNA repair mechanisms, in particular the base excision repair pathway, constitute an important mechanism for maintenance of mitochondrial DNA integrity. The results reviewed here support that mitochondrial DNA damage plays an important role in aging

Laplace Approximation for Divisive Gaussian Processes for Nonstationary Regression

The standard Gaussian Process regression (GP) is usually formulated under stationary hypotheses: The noise power is considered constant throughout the input space and the covariance of the prior distribution is typically modeled as depending only on the difference between input samples. These assumptions can be too restrictive and unrealistic for many real-world problems. Although nonstationarity can be achieved using specific covariance functions, they require a prior knowledge of the kind of nonstationarity, not available for most applications. In this paper we propose to use the Laplace approximation to make inference in a divisive GP model to perform nonstationary regression, including heteroscedastic noise cases. The log-concavity of the likelihood ensures a unimodal posterior and makes that the Laplace approximation converges to a unique maximum. The characteristics of the likelihood also allow to obtain accurate posterior approximations when compared to the Expectation Propagation (EP) approximations and the asymptotically exact posterior provided by a Markov Chain Monte Carlo implementation with Elliptical Slice Sampling (ESS), but at a reduced computational load with respect to both, EP and ESS

Efficiency, profitability and carbon footprint of different management programs under no-till to control herbicide resistant Papaver rhoeas

The present work examines the effects of different integrated weed management (IWM) programs on multiple herbicide-resistant Papaver rhoeas populations in terms of effectiveness, profitability and carbon footprint. With this aim a trial was established in a winter cereal field under no-till in North-Eastern Spain during three consecutive seasons. Four IWM programs with different intensification levels, from less (crop rotation, mechanical control, and no herbicides) to more intense (wheat monoculture with high chemical inputs), were established. The different strategies integrated in the four programs were efficient in managing the weed after three years, with increased effectiveness after management program intensification. Whereas low input program (which includes fallow season) represented less economic cost than the other programs, on average, no differences were observed on carbon foot print, considered as kg CO2eq kg−1 product, between the different programs, except in the crop rotation program due to the low pea yield obtained. The results from this study show that in the search for a balance between crop profitability and reduction of the carbon footprint while controlling an herbicide resistant population is challenging, and particularly under notill. In this scenario the short term priority should be to reduce the presence of multiple herbicide resistant biotypes integrating the different available chemical, cultural, and physical strategies.This work has been supported by the Agencia Estatal de Investigación (AEI) and the European Regional Development Fund (ERDF) with project AGL2014-52465-C4-2-R. Dr. J. Torra obtained a Ramon y Cajal contract from the Spanish Ministry of Science, Innovation and Universities (RYC2018-023866-I). Mr. F. Valencia-Gredilla obtained a PhD grant from the University of Lleida

Pseudospectral Model Predictive Control under Partially Learned Dynamics

Trajectory optimization of a controlled dynamical system is an essential part of autonomy, however many trajectory optimization techniques are limited by the fidelity of the underlying parametric model. In the field of robotics, a lack of model knowledge can be overcome with machine learning techniques, utilizing measurements to build a dynamical model from the data. This paper aims to take the middle ground between these two approaches by introducing a semi-parametric representation of the underlying system dynamics. Our goal is to leverage the considerable information contained in a traditional physics based model and combine it with a data-driven, non-parametric regression technique known as a Gaussian Process. Integrating this semi-parametric model with model predictive pseudospectral control, we demonstrate this technique on both a cart pole and quadrotor simulation with unmodeled damping and parametric error. In order to manage parametric uncertainty, we introduce an algorithm that utilizes Sparse Spectrum Gaussian Processes (SSGP) for online learning after each rollout. We implement this online learning technique on a cart pole and quadrator, then demonstrate the use of online learning and obstacle avoidance for the dubin vehicle dynamics.Comment: Accepted but withdrawn from AIAA Scitech 201