Low dose of dichloroacetate infusion reduces blood lactate after submaximal exercise in horses

The acute administration of an indirect activator of the enzyme pyruvate dehydroge-nase (PDH) in human athletes causes a reduction in blood lactate level during and after exercise. A single IV dose (2.5m.kg-1) of dichloroacetate (DCA) was administered before a submaximal incremental exercise test (IET) with five velocity steps, from 5.0 m.s-1 for 1 min to 6.0, 6.5, 7.0 and 7.5m.s-1 every 30s in four untrained mares. The blood collections were done in the period after exercise, at times 1, 3, 5, 10, 15 and 20 min. Blood lactate and glucose (mM) were determined electro-enzymatically utilizing a YSI 2300 automated analyzer. There was a 15.3% decrease in mean total blood lactate determined from the values obtained at all assessment times in both trials after the exercise. There was a decrease in blood lactate 1, 3, 5, 10, 15 and 20 min after exercise for the mares that received prior DCA treatment, with respective mean values of 6.31±0.90 vs 5.81±0.50, 6.45±1.19 vs 5.58±1.06, 6.07±1.56 vs 5.26±1.12, 4.88±1.61 vs 3.95±1.00, 3.66±1.41 vs 2.86±0.75 and 2.75±0.51 vs 2.04±0.30. There was no difference in glucose concentrations. By means of linear regression analysis, V140, V160, V180 and V200 were determined (velocity at which the rate heart is 140, 160, 180, and 200 beats/minute, respectively). The velocities related to heart rate did not differ, indicating that there was no ergogenic effect, but prior administration of a relatively low dose of DCA in mares reduced lactatemia after an IET.Laboratório de Farmacologia e Fisiologia do Exercício Equino (LAFEQ) Faculdade de Ciências Agrárias e Veterinárias (FCAV) Universidade Estadual Paulista, Jaboticabal, SP 14884-900Laboratório de Farmacologia e Fisiologia do Exercício Equino (LAFEQ) Faculdade de Ciências Agrárias e Veterinárias (FCAV) Universidade Estadual Paulista, Jaboticabal, SP 14884-90

Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements

A preliminary appeared as INRIA RR-5093, January 2004.International audienceIn medical image analysis and high level computer vision, there is an intensive use of geometric features like orientations, lines, and geometric transformations ranging from simple ones (orientations, lines, rigid body or affine transformations, etc.) to very complex ones like curves, surfaces, or general diffeomorphic transformations. The measurement of such geometric primitives is generally noisy in real applications and we need to use statistics either to reduce the uncertainty (estimation), to compare observations, or to test hypotheses. Unfortunately, even simple geometric primitives often belong to manifolds that are not vector spaces. In previous works [1, 2], we investigated invariance requirements to build some statistical tools on transformation groups and homogeneous manifolds that avoids paradoxes. In this paper, we consider finite dimensional manifolds with a Riemannian metric as the basic structure. Based on this metric, we develop the notions of mean value and covariance matrix of a random element, normal law, Mahalanobis distance and X² law. We provide a new proof of the characterization of Riemannian centers of mass and an original gradient descent algorithm to efficiently compute them. The notion of Normal law we propose is based on the maximization of the entropy knowing the mean and covariance of the distribution. The resulting family of pdfs spans the whole range from uniform (on compact manifolds) to the point mass distribution. Moreover, we were able to provide tractable approximations (with their limits) for small variances which show that we can effectively implement and work with these definitions