Francisco Javier Rojas

Gibepyrones: α-pyrones from Gibberella fujikuroi

Abstract Six α-pyrones, called gibepyrones A-F, were identified in three different culture media of G. fujikuroi (IMI 58289). Their structures were determined using both spectroscopic techniques and chemical synthesis. Their antimicrobial activity has been screened.

Response timing in the lunge and target change in elite versus medium-level fencers.

Abstract The aim of the present work is to examine the differences between two groups of fencers with different levels of competition, elite and medium level. The timing parameters of the response reaction have been compared together with the kinetic variables which determine the sequence of segmented participation used during the lunge with a change in target during movement. A total of 30 male sword fencers participated, 13 elite and 17 medium level. Two force platforms recorded the horizontal component of the force and the start of the movement. One system filmed the movement in 3D, recording the spatial positions of 11 markers, while another system projected a mobile target over a screen. For synchronisation, an electronic signal enabled all the systems to be started simultaneously. Among the timing parameters of the reaction response, the choice reaction time (CRT) to the target change during the lunge was measured. The results revealed differences between the groups regarding the flight time, horizontal velocity at the end of the acceleration phase, and the length of the lunge, these being higher for the elite group, as well as other variables related to the temporal sequence of movement. No significant differences have been found in the simple reaction time or in CRT. According to the literature, the CRT appears to improve with sports practice, although this factor did not differentiate the elite from medium-level fencers. The coordination of fencing movements, that is, the right technique, constitutes a factor that differentiates elite fencers from medium-level ones.

Prediction of distance in hammer throwing

The aim of this study was to determine how much the predicted distance of a hammer throw is affected by (1) ignoring air resistance and (2) assuming that the centre of mass of the hammer coincides with the centre of the ball. Three-dimensional data from actual throws (men: 72.82±7.43 m; women: 67.78±4.02 m) were used to calculate the kinematic conditions of the hammer at release. A mathematical model of the hammer was then used to simulate the three-dimensional airborne motion of the hammer and to predict the distance of the throw. The distance predicted for vacuum conditions and using the ball centre to represent the hammer centre of mass was 4.30±2.64 m longer than the official distance of the throw for the men and 8.82±3.20 m longer for the women. Predictions using the true centre of mass of the hammer reduced the discrepancy to 2.39±2.58 m for the men and 5.28±2.88 m for the women. Predictions using air resistance and the true centre of mass of the hammer further reduced the discrepancy to −0.46±2.63 m for the men and 1.16±2.31 m for the women. Approximately half the loss of distance produced by air resistance was due to forces made on the ball and the remainder to forces made on the cable and handle. Equations were derived for calculation of the effects of air resistance and of the assumption that the centre of mass of the hammer coincides with the centre of the ball, on the distance of the throw.

Intelligent Systems to Autonomously Classify Several Arrhythmia Using Information from ECG

This paper is focused on the development of intelligent classifiers in the area of biomedicine, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely, on the differentiation of several arrhythmia using a large data set, by an autonomous intelligent system which can be used as an expert system to support human experts in the diagnosis and, moreover, to autonomously display an alarm to the user in case of a dangerous situation. We will study and imitate the ECG treatment methodologies and the features extracted from the electrocardiograms used by the researchers, which obtained the best results in the PhysioNet Challenge. We will extract a great amount of features, partly those used by these researchers and some additional others we considered to be important for the distinction previously mentioned. A new method based on different paradigms of intelligent computation (such as extreme learning machine, support vector machine and feature selection) will be used to select the most relevant characteristics and to obtain a classifier capable of autonomously distinguishing the different types arrhythmia from the ECG signal. Finally, the behavior and performance of the classifier have been tested using data from several cardiac pathologies, obtaining good classification results.

Comparison of different computational intelligent classifier to autonomously detect cardiac pathologies diagnosed by ECG

The electrocardiogram (ECG) is a noninvasive technique used to reflect underlying heart conditions by measuring the electrical activity of the heart, and nowadays it is possible with just a few derivation (with just only two), obtain important information in order than an expert can recognize abnormal heart rhythms (the heart rate is very fast, very slow, or irregular) or a heart attack (myocardial infarction), and if it was recent or some time ago. In this paper, several intelligent classifiers are developed, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely, on the differentiation of several arrhythmia using a large data set. We will study and imitate the ECG treatment methodologies and the features extracted from the electrocardiograms used by the researchers, which obtained the best results in the PhysioNet Challenge (www.physionet.org/). We will extract a great amount of features, partly those used by these researchers and some additional others we considered to be important for the distinction previously mentioned. A new method based on different paradigms of intelligent computation (such as extreme learning machine, support vector machine, decision trees, genetic algorithms and feature selection) will be used to select the most relevant characteristics and to obtain a classifier capable of autonomously distinguishing the different types arrhythmia from the ECG signal.

INTELLIGENT SYSTEM BASED ON GENETIC PROGRAMMING FOR ATRIAL FIBRILLATION CLASSIFICATION

This article focuses on the development of intelligent classifiers in the area of biomedicine, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely, on the differentiation of the types of atrial fibrillations. First of all, we will study the ECG, and the treatment of the ECG in order to work with it with this specific pathology. In order to achieve this we will study different ways of elimination, in the best possible way, of any activity that is not caused by the auriculars. We will study and imitate the ECG treatment methodologies and the characteristics extracted from the electrocardiograms that were used by the researchers who obtained the best results in the Physionet Challenge, where the classification of ECG recordings according to the type of atrial fibrillation (AF) that they showed, was realized. We will extract a great amount of characteristics, partly those used by these researchers and additional characteristics that we consider to be important for the distinction previously mentioned. A new method based on evolutionary algorithms will be used to realize a selection of the most relevant characteristics and to obtain a classifier that will be capable of distinguishing the different types of this pathology.