Effect of inspiratory threshold loading on ventilatory kinetics during constant-load exercise.

Humoral factors play an important role in the control of exercise hyperpnea. The role of neuromechanical ventilatory factors, however, is still being investigated. We tested the hypothesis that the afferents of the thoracopulmonary system, and consequently of the neuromechanical ventilatory loop, have an influence on the kinetics of oxygen consumption (VO2), carbon dioxide output (VCO2), and ventilation (VE) during moderate intensity exercise. We did this by comparing the ventilatory time constants (tau) of exercise with and without an inspiratory load. Fourteen healthy, trained men (age 22.6 +/- 3.2 yr) performed a continuous incremental cycle exercise test to determine maximal oxygen uptake (VO2max = 55.2 +/- 5.8 ml x min(-1) x kg(-1)). On another day, after unloaded warm-up they performed randomized constant-load tests at 40% of their VO2max for 8 min, one with and the other without an inspiratory threshold load of 15 cmH2O. Ventilatory variables were obtained breath by breath. Phase 2 ventilatory kinetics (VO2, VCO2, and VE) could be described in all cases by a monoexponential function. The bootstrap method revealed small coefficients of variation for the model parameters, indicating an accurate determination for all parameters. Paired Student's t-tests showed that the addition of the inspiratory resistance significantly increased the tau during phase 2 of VO2 (43.1 +/- 8.6 vs. 60.9 +/- 14.1 s; P < 0.001), VCO2 (60.3 +/- 17.6 vs. 84.5 +/- 18.1 s; P < 0.001) and VE (59.4 +/- 16.1 vs. 85.9 +/- 17.1 s; P < 0.001). The average rise in tau was 41.3% for VO2, 40.1% for VCO2, and 44.6% for VE. The tau changes indicated that neuromechanical ventilatory factors play a role in the ventilatory response to moderate exercise.

Conditioning of stochastic 3-D fracture networks to hydrological and geophysical data

The geometry and connectivity of fractures exert a strong influence on the flow and transport properties of fracture networks. We present a novel approach to stochastically generate three-dimensional discrete networks of connected fractures that are conditioned to hydrological and geophysical data. A hierarchical rejection sampling algorithm is used to draw realizations from the posterior probability density function at different conditioning levels. The method is applied to a well-studied granitic formation using data acquired within two boreholes located 6 m apart. The prior models include 27 fractures with their geometry (position and orientation) bounded by information derived from single-hole ground-penetrating radar (GPR) data acquired during saline tracer tests and optical televiewer logs. Eleven cross-hole hydraulic connections between fractures in neighboring boreholes and the order in which the tracer arrives at different fractures are used for conditioning. Furthermore, the networks are conditioned to the observed relative hydraulic importance of the different hydraulic connections by numerically simulating the flow response. Among the conditioning data considered, constraints on the relative flow contributions were the most effective in determining the variability among the network realizations. Nevertheless, we find that the posterior model space is strongly determined by the imposed prior bounds. Strong prior bounds were derived from GPR measurements and helped to make the approach computationally feasible. We analyze a set of 230 posterior realizations that reproduce all data given their uncertainties assuming the same uniform transmissivity in all fractures. The posterior models provide valuable statistics on length scales and density of connected fractures, as well as their connectivity. In an additional analysis, effective transmissivity estimates of the posterior realizations indicate a strong influence of the DFN structure, in that it induces large variations of equivalent transmissivities between realizations. The transmissivity estimates agree well with previous estimates at the site based on pumping, flowmeter and temperature data.

Current epidemiological trends for Chagas disease in Latin America and future challenges in epidemiology, surveillance and health policy

Chagas disease, named after Carlos Chagas, who first described it in 1909, exists only on the American Continent. It is caused by a parasite, Trypanosoma cruzi, which is transmitted to humans by blood-sucking triatomine bugs and via blood transfusion. Chagas disease has two successive phases: acute and chronic. The acute phase lasts six-eight weeks. Several years after entering the chronic phase, 20-35% of infected individuals, depending on the geographical area, will develop irreversible lesions of the autonomous nervous system in the heart, oesophagus and colon, and of the peripheral nervous system. Data on the prevalence and distribution of Chagas disease improved in quality during the 1980s as a result of the demographically representative cross-sectional studies in countries where accurate information was not previously available. A group of experts met in Brasilia in 1979 and devised standard protocols to carry out countrywide prevalence studies on human T. cruzi infection and triatomine house infestation. Thanks to a coordinated multi-country programme in the Southern Cone countries, the transmission of Chagas disease by vectors and via blood transfusion was interrupted in Uruguay in 1997, in Chile in 1999 and in Brazil in 2006; thus, the incidence of new infections by T. cruzi across the South American continent has decreased by 70%. Similar multi-country initiatives have been launched in the Andean countries and in Central America and rapid progress has been reported towards the goal of interrupting the transmission of Chagas disease, as requested by a 1998 Resolution of the World Health Assembly. The cost-benefit analysis of investment in the vector control programme in Brazil indicates that there are savings of US$17 in medical care and disabilities for each dollar spent on prevention, showing that the programme is a health investment with very high return. Many well-known research institutions in Latin America were key elements of a worldwide network of laboratories that carried out basic and applied research supporting the planning and evaluation of national Chagas disease control programmes. The present article reviews the current epidemiological trends for Chagas disease in Latin America and the future challenges in terms of epidemiology, surveillance and health policy.

Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking

This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task