Cardiac output and leg and arm blood flow during incremental exercise to exhaustion on the cycle ergometer

[EN] To determine central and peripheral hemodynamic responses to upright leg cycling exercise, nine physically active men underwent measurements of arterial blood pressure and gases, as well as femoral and subclavian vein blood flows and gases during incremental exercise to exhaustion (Wmax). Cardiac output (CO) and leg blood flow (BF) increased in parallel with exercise intensity. In contrast, arm BF remained at 0.8 l/min during submaximal exercise, increasing to 1.2 +/- 0.2 l/min at maximal exercise (P < 0.05) when arm O(2) extraction reached 73 +/- 3%. The leg received a greater percentage of the CO with exercise intensity, reaching a value close to 70% at 64% of Wmax, which was maintained until exhaustion. The percentage of CO perfusing the trunk decreased with exercise intensity to 21% at Wmax, i.e., to approximately 5.5 l/min. For a given local Vo(2), leg vascular conductance (VC) was five- to sixfold higher than arm VC, despite marked hemoglobin deoxygenation in the subclavian vein. At peak exercise, arm VC was not significantly different than at rest. Leg Vo(2) represented approximately 84% of the whole body Vo(2) at intensities ranging from 38 to 100% of Wmax. Arm Vo(2) contributed between 7 and 10% to the whole body Vo(2). From 20 to 100% of Wmax, the trunk Vo(2) (including the gluteus muscles) represented between 14 and 15% of the whole body Vo(2). In summary, vasoconstrictor signals efficiently oppose the vasodilatory metabolites in the arms, suggesting that during whole body exercise in the upright position blood flow is differentially regulated in the upper and lower extremities.

Ecucational data mining (EDM) para la determinación de comportamientos en estudiantes de ingeniería en la modalidad virtual de UDE

[ES]En este artículo se describe la experiencia de la aplicación de técnicas de EDM (clustering) a un curso disponible en la plataforma Ude@ de la Universidad de Antioquia. El objetivo es clasificar los patrones de interacción de los estudiantes a partir de la información almacenada en la base de datos de la plataforma Moodle. Para ello, se generan informes sobre el uso de los recursos y la autoevaluación que permiten analizar el comportamiento y los patrones de navegación de los estudiantes durante el uso del LMS (Learning Management System).