PHYSIOLOGICAL and NEUROMUSCULAR PROFILE DURING A BODYPUMP SESSION: ACUTE RESPONSES DURING A HIGH-RESISTANCE TRAINING SESSION

Oliveira, AS, Greco, CC, Pereira, MP, Figueira, TR, de Araujo Ruas, VD, Goncalves, M, and Denadai, BS. Physiological and neuromuscular profile during a Bodypump session: acute responses during a high-resistance training session. J Strength Cond Res 23(2): 579-586, 2009-The main purposes of this study were 1) to describe and to compare blood lactate ([La]), heart rate (HR), and electromyographic (EMG) parameters during high-repetition training sessions (HRTSs), 2) to analyze the influence of physical fitness levels in these parameters, and, 3) to analyze the relationship between metabolic ([La]) and neuromuscular (EMG) responses during the HRTS. Fifteen healthy untrained women (21.7 +/- 2.1 years) performed an HRTS called Bodypump for 1 hour, which incorporated the use of variable free weights and high repetitions in a group setting. This session involved 10 music selections (M1-M10) containing resistive exercises for different muscle groups. After music selections 2 (M2), 4 (M4), 6 (M6), 7 (M7), and 9 (M9), [La], HR, and EMG (vastus medialis [VM], vastus lateralis [VL], iliocostalis lumborum [IC], and longissimus thoracis <) were determined. The [La] (M2, 4.00 +/- 1.45 mM; M7, 5.02 +/- 1.73 mM) and HR (M2, 153.64 +/- 18.89 bpm; M7, 16.14 +/- 20.14 bpm) obtained at M2 and M7 were similar but were significantly higher than the other moments of the session. However, EMG (root mean square [RMS]) at M2 (VL, VM, and LT) was lower than at M7. There was no significant correlation of strength and aerobic physical fitness with [La], RMS. In the same way, there was no significant correlation of [La] with RMS at M2 and M7. on the basis of our data, we can conclude that metabolic, cardiovascular, and EMG variables present different and independent behavior during an HRTS. Accordingly, for neuromuscular conditions during HRTS, it seems to be enough to induce improvement in the muscular strength of inferior limbs in untrained subjects.

Differential coronary resistance microvessel remodeling between type 1 and type 2 diabetic mice: Impact of exercise training

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

L-carnitine as an ergogenic aid for patients with chronic obstructive pulmonary disease submitted to whole-body and respiratory muscle training programs

The effects of adding L-carnitine to a whole-body and respiratory training program were determined in moderate-to-severe chronic obstructive pulmonary disease (COPD) patients. Sixteen COPD patients (66 ± 7 years) were randomly assigned to L-carnitine (CG) or placebo group (PG) that received either L-carnitine or saline solution (2 g/day, orally) for 6 weeks (forced expiratory volume on first second was 38 ± 16 and 36 ± 12%, respectively). Both groups participated in three weekly 30-min treadmill and threshold inspiratory muscle training sessions, with 3 sets of 10 loaded inspirations (40%) at maximal inspiratory pressure. Nutritional status, exercise tolerance on a treadmill and six-minute walking test, blood lactate, heart rate, blood pressure, and respiratory muscle strength were determined as baseline and on day 42. Maximal capacity in the incremental exercise test was significantly improved in both groups (P < 0.05). Blood lactate, blood pressure, oxygen saturation, and heart rate at identical exercise levels were lower in CG after training (P < 0.05). Inspiratory muscle strength and walking test tolerance were significantly improved in both groups, but the gains of CG were significantly higher than those of PG (40 ± 14 vs 14 ± 5 cmH2O, and 87 ± 30 vs 34 ± 29 m, respectively; P < 0.05). Blood lactate concentration was significantly lower in CG than in PG (1.6 ± 0.7 vs 2.3 ± 0.7 mM, P < 0.05). The present data suggest that carnitine can improve exercise tolerance and inspiratory muscle strength in COPD patients, as well as reduce lactate production.

Screening Brazilian C gasoline quality: Application of the SIMCA chemometric method to gas chromatographic data

A total of 2400 samples of commercial Brazilian C gasoline were collected over a 6-month period from different gas stations in the São Paulo state, Brazil, and analysed with respect to 12 physicochemical parameters according to regulation 309 of the Brazilian Government Petroleum, Natural Gas and Biofuels Agency (ANP). The percentages (v/v) of hydrocarbons (olefins, aromatics and saturated) were also determined. Hierarchical cluster analysis (HCA) was employed to select 150 representative samples that exhibited least similarity on the basis of their physicochemical parameters and hydrocarbon compositions. The chromatographic profiles of the selected samples were measured by gas chromatography with flame ionisation detection and analysed using soft independent modelling of class analogy (SIMCA) method in order to create a classification scheme to identify conform gasolines according to ANP 309 regulation. Following the optimisation of the SIMCA algorithm, it was possible to classify correctly 96% of the commercial gasoline samples present in the training set of 100. In order to check the quality of the model, an external group of 50 gasoline samples (the prediction set) were analysed and the developed SIMCA model classified 94% of these correctly. The developed chemometric method is recommended for screening commercial gasoline quality and detection of potential adulteration. (c) 2007 Elsevier B.V. All rights reserved.

International recommendations for training future toxicologic pathologists participating in regulatory-type, nonclinical toxicity studies

The International Federation of Societies of Toxicologic Pathologists (IFSTP) proposes a common global framework for training future toxicologic pathologists who will support regulatory-type - nonclinical toxicology studies. Trainees optimally should undertake a scientific curriculum of at least 5 years at an accredited institution leading to a clinical degree (veterinary medicine or medicine). Trainees should then obtain 4 or more years of intensive pathology practice during a residency and/or on-the-job "apprenticeship," at least 2 years of which must be focused on regulatory-type toxicologic pathology topics. Possession of a recognized pathology qualification (i.e., certification) is highly recommended. A nonclinical pathway (e.g., a graduate degree in medical biology or pathology) may be possible if medically trained pathologists are scarce, but this option is not optimal. Regular, lifelong continuing education (peer review of nonclinical studies, professional meetings, reading, short courses) will be necessary to maintain and enhance one's understanding of current toxicologic pathology knowledge, skills, and tools. This framework should provide a rigorous yet flexible way to reliably train future toxicologic pathologists to generate, interpret, integrate, and communicate data in regulatory-type, nonclinical toxicology studies. (C) 2010 Reprinted by Permission of SAGE Publications Inc. Published by Elsevier GmbH. All rights reserved.

Swimming training exacerbates pathological cardiac hypertrophy in kinin B(2) receptor-deficient mice

Kallikrein-kinin system exerts cardioprotective effects against pathological hypertrophy. These effects are modulated mainly via B(2) receptor activation. Chronic physical exercise can induce physiological cardiac hypertrophy characterized by normal organization of cardiac structure. Therefore, the aim of this work was to verify the influence of kinin B(2) receptor deletion on physiological hypertrophy to exercise stimulus. Animals were submitted to swimming practice for 5 min or for 60 min, 5 days a week, during 1 month and several cardiac parameters were evaluated. Results showed no significantly difference in heart weight between both groups, however an increased left ventricle weight and myocyte diameter were observed after the 60 min swimming protocol, which was more pronounced in B(2)(-/-) mice. In addition, sedentary B(2)(-/-) animals presented higher left ventricle mass when compared to wild-type (WT) mice. An increase in capillary density was observed in exercised animals, however the effect was less pronounced in B(2)(-/-) mice. Collagen, a marker of pathological hypertrophy, was increased in B(2)(-/-) mice submitted to swimming protocol, as well as left ventricular thickness, suggesting that these animals do not respond with physiological hypertrophy for this kind of exercise. In conclusion, our data suggest an important role for the kinin B(2) receptor in physiological cardiac hypertrophy. (c) 2007 Elsevier B.V. All rights reserved.

Cardiac benefits of exercise training in aging spontaneously hypertensive rats

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The workload and plasma ion concentration in a training match session of high-goal (elite) polo ponies

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Improving accuracy and speed of optimum-path forest classifier using combination of disjoint training subsets

The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more. © 2011 Springer-Verlag.

Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.

Adapted Judo training on bone-variables in postmenopausal women in pharmacological treatment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Speeding up optimum-path forest training by path-cost propagation

In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost function. Experiments on public datasets have shown that the proposed approach can obtain similar accuracy to the traditional one but with faster data training. © 2012 ICPR Org Committee.

Role of Training and Detraining on Inflammatory and Metabolic Profile in Infarcted Rats: Influences of Cardiovascular Autonomic Nervous System

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A path- and label-cost propagation approach to speedup the training of the optimum-path forest classifier

In general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies that make effort to employ machine learning algorithms in the context of big data classification problems. The research on this area ranges from Graphics Processing Units-based implementations to mathematical optimizations, being the main drawback of the former approaches to be dependent on the graphic video card. Here, we propose an architecture-independent optimization approach for the optimum-path forest (OPF) classifier, that is designed using a theoretical formulation that relates the minimum spanning tree with the minimum spanning forest generated by the OPF over the training dataset. The experiments have shown that the approach proposed can be faster than the traditional one in five public datasets, being also as accurate as the original OPF. (C) 2014 Elsevier B. V. All rights reserved.

Low-intensity resistance training attenuates dexamethasone-induced atrophy in the flexor hallucis longus muscle

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)