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Paixao R.C.,Federal University of Triangulo Mineiro | Paixao R.C.,Bahia State University | Da Mota G.R.,Federal University of Triangulo Mineiro | Marocolo M.,Federal University of Triangulo Mineiro
International Journal of Sports Medicine | Year: 2014

We verified the acute effect of ischemic preconditioning (IPC) in cyclists before high-intensity and short-duration activity. 15 amateur cyclists participated in a random crossover model on 2 different days [IPC or CONTROL (CON)]. Ischemic preconditioning consisted of 4 cycles of 5 min occlusion/5 min reperfusion in each thigh. After IPC or CON, volunteers performed a series of Wingate tests to evaluate anaerobic performance (maximal [Pmax] and medium [Pmed] power output, total anaerobic power, and fatigue index). Blood lactate concentrations were assessed at 6 min after each Wingate test. Ischemic preconditioning decreased Pmax (p<0.05), Pmed (p<0.01), and total anaerobic power (p<0.01) in the first Wingate, and decreased Pmed (p<0.01) and total anaerobic power (p<0.01) in the second Wingate (p<0.01). No significant differences were found in blood lactate or fatigue index between IPC and CON. In conclusion, our results indicate that IPC has a detrimental acute effect on anaerobic performance in amateur cyclists. Compared with positive results of previous studies, the effect of IPC seems to be dependent on the type of exercise. © Georg Thieme Verlag KG Stuttgart New York.


Dos Santos L.B.O.,Bahia State University | Infante C.M.C.,University of Sao Paulo | Masini J.C.,University of Sao Paulo
Analytical and Bioanalytical Chemistry | Year: 2010

This work describes the development and optimization of a sequential injection method to automate the determination of paraquat by square-wave voltammetry employing a hanging mercury drop electrode. Automation by sequential injection enhanced the sampling throughput, improving the sensitivity and precision of the measurements as a consequence of the highly reproducible and efficient conditions of mass transport of the analyte toward the electrode surface. For instance, 212 analyses can be made per hour if the sample/standard solution is prepared off-line and the sequential injection system is used just to inject the solution towards the flow cell. In-line sample conditioning reduces the sampling frequency to 44 h-1. Experiments were performed in 0.10 M NaCl, which was the carrier solution, using a frequency of 200 Hz, a pulse height of 25 mV, a potential step of 2 mV, and a flow rate of 100μL s-1. For a concentration range between 0.010 and 0.25 mg L -1, the current (ip, μA) read at the potential corresponding to the peak maximum fitted the following linear equation with the paraquat concentration (mg L-1): ip = (-20.5±0.3) Cparaquat - (0.02±0.03). The limits of detection and quantification were 2.0 and 7.0μg L-1, respectively. The accuracy of the method was evaluated by recovery studies using spiked water samples that were also analyzed by molecular absorption spectrophotometry after reduction of paraquat with sodium dithionite in an alkaline medium. No evidence of statistically significant differences between the two methods was observed at the 95% confidence level. © 2010 Springer-Verlag.


This paper sought to verify the perceptions of hypertensive people in relation to the risk factors and their experience with high blood pressure in a Reference Center for Cardiovascular Diseases in the city of Salvador. Interviews were staged with 33 hypertensive people. This is a descriptive study, of a qualitative nature, supported by the discourse analysis proposed by Foucault. It was observed that risk factors were confused with hypertension complications. Nevertheless, when the approach changed from "risk factors" to "factors that can increase blood pressure," it was seen that the answers were more coherent with the risk factors classified by the VI Brazilian Policies on Hypertension. Furthermore, it was also observed through the discourses that the perception of increase in blood pressure is directly related to experiences. Therefore, it is necessary that the guidance be transmitted as clearly as possible in order for the understanding to become an important facilitator for controlling the illness. This paper enabled the perception of the risk factors in the viewpoint of these people in such a manner as to supply clues for the interdisciplinary health team to promote healthcare based on the experiences and the socio-economic and cultural context in which these people are inserted.


Carels N.,Instituto Oswaldo Cruz IOC | Frias D.,Bahia State University
Bioinformatics and Biology Insights | Year: 2013

In this study, we investigated the modalities of coding open reading frame (cORF) classification of expressed sequence tags (EST) by using the universal feature method (UFM). The UFM algorithm is based on the scoring of purine bias (Rrr) and stop codon frequencies. UFM classifies ORFs as coding or non-coding through a score based on 5 factors: (i) stop codon frequency; (ii) the product of the probabilities of purines occurring in the three positions of nucleotide triplets; (iii) the product of the probabilities of Cytosine (C), Guanine (G), and Adenine (A) occurring in the 1st, 2nd, and 3rd positions of triplets, respectively; (iv) the probabilities of a G occurring in the 1st and 2nd positions of triplets; and (v) the probabilities of a T occurring in the 1st and an A in the 2nd position of triplets. Because UFM is based on primary determinants of coding sequences that are conserved throughout the biosphere, it is suitable for cORF classification of any sequence in eukaryote transcriptomes without prior knowledge. Considering the protein sequences of the Protein Data Bank (RCSB PDB or more simply PDB) as a reference, we found that UFM classifies cORFs of $200 bp (if the coding strand is known) and cORFs of $300 bp (if the coding strand is unknown), and releases them in their coding strand and coding frame, which allows their automatic translation into protein sequences with a success rate equal to or higher than 95%. We frst established the statistical parameters of UFM using ESTs from Plasmodium falciparum, Arabidopsis thaliana, Oryza sativa, Zea mays, Drosophila melanogaster, Homo sapiens and Chlamydomonas reinhardtii in reference to the protein sequences of PDB. Second, we showed that the success rate of cORF classification using UFM is expected to apply to approximately 95% of higher eukaryote genes that encode for proteins. Third, we used UFM in combination with CAP3 to assemble large EST samples into cORFs that we used to analyze transcriptome phenotypes in rice, maize, and humans. We discuss the error rate and the interference of noisy sequences such as pseudogenes, transposons, and retrotransposons. This method is suitable for rapid cORF extraction from transcriptome data and allows correct description of the genome phenotypes of plant genomes without prior knowledge. Additional care is necessary when addressing the human transcriptome due to the interference caused by large amounts of noisy sequences. UFM can be regarded as a low complexity tool for prior knowledge extraction concerning the coding fraction of the transcriptome of any eukaryote. Due to its low level of complexity, UFM is also very robust to variations of codon usage. © the author(s), publisher and licensee Libertas Academica Ltd.


Fernandez-Delgado M.,University of Santiago de Compostela | Cernadas E.,University of Santiago de Compostela | Barro S.,University of Santiago de Compostela | Amorim D.,Bahia State University
Journal of Machine Learning Research | Year: 2014

We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifi ers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearestneighbors, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R (with and without the caret package), C and Matlab, including all the relevant classifiers available today. We use 121 data sets, which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behavior, not dependent on the data set collection. The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package). The random forest is clearly the best family of classifiers (3 out of 5 bests classi ers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively). © 2014 Manuel Fernández-Delgado, Eva Cernadas, Senén Barro and Dinani Amorim.

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