Stroking Parameters during Continuous and ...

696 Training & Testing

Stroking Parameters during Continuous and Intermittent Exercise in Regional-Level Competitive Swimmers

Affiliations

Key words

▶ swim technique ● ▶ endurance ● ▶ training ● ▶ lactataemia ●

M. F. M. Oliveira1, F. Caputo2, J. Dekerle3, B. S. Denadai4, C. C. Greco5 1

Human Performance Research Group, Physical Education, Florianópolis, Brazil Santa Catarina State University, Physical Education, Florianópolis, Brazil 3 University of Brighton, Chelsea School, Eastbourne, United Kingdom 4 UNESP, Human Performance Laboratory, Rio Claro, Brazil 5 UNESP, Human Performance Laboratory, Rio Claro, Brazil 2

Abstract ▼ This study aimed to determine whether maximal lactate steady state (MLSS) represents a boundary above which not only physiological but also technical changes occur. On different days, 13 male swimmers (23 ± 9 years) performed the following tests: 1) a 400-m all-out swim, to determine maximal aerobic speed (S-400); 2) a series of 30-min sub-maximal swims, to determine continuous MLSS (MLSSc), and; 3) a series of 12 × 150 s sub-maximal swims, to determine intermittent MLSS (MLSSi). Stroke rate (SR), distance per stroke cycle (DS) and stroke index (SI) were analyzed at and above (102.5 %) MLSSc and

Introduction ▼ accepted after revision November 22, 2011 Bibliography DOI http://dx.doi.org/ 10.1055/s-0031-1298003 Published online: May 16, 2012 Int J Sports Med 2012; 33: 696–701 © Georg Thieme Verlag KG Stuttgart · New York ISSN 0172-4622 Correspondence Dr. Camila Coelho Greco Human Performance Laboratory Physical Education Av 24 A 1515 Bela Vista 13506-900 Rio Claro Brazil Tel.: +55/19/3526 4338 Fax: +55/19/3526 4321 [email protected]

In swimming, both incremental and constantspeed tests have been widely used to determine the blood lactate response to exercise [20, 31, 32]. The highest exercise workload that can be maintained over time without continual blood lactate accumulation has been termed maximal lactate steady state (MLSS) [4]. Equilibrium between lactate appearance and disappearance in and out of the blood compartment occurs when exercising at or below MLSS, while a greater lactate appearance characterizes exercise above MLSS [3]. Although lactate may well increase when muscle performance declines, lactate accumulation maybe not the direct cause of fatigue [34, 38]. However, it is important to note that MLSS represents the boundary between the heavy and severe exercise intensity [8]. During exercise within severe domain, perturbations in some intramuscular fatigue-inducing factors such [PCr], [Pi] and [H + ] [46] and the depletion of the anaerobic sources yields to exhaustion [29]. Thus, MLSS differentiates conditions in which exercise tolerance is limited by stored energy (i. e., below MLSS) from others which have to be

Oliveira MFM et al. Stroking Parameters during Continuous … Int J Sports Med 2012; 33: 696–701

MLSSi. MLSSi (1.17 ± 0.09 m.s − 1) was significantly higher than MLSSc (1.13 ± 0.08 m.s − 1) while blood lactate concentration (mmol.L − 1) was similar between the 2 conditions (4.3 ± 1.1 and 4.4 ± 1.5, respectively). The increase in SR and decreases in DS and SI were significant during MLSSi, 102.5 % MLSSc and 102.5 % MLSSi. During MLSSc, DS also decreased significantly (− 3.6 %) but with no change in SR or SI. Thus, stroking technique of regional-level competitive swimmers changes over time when they swim at or above MLSS. This is the case during both continuous and intermittent swimming, despite steady state blood lactate concentrations.

terminated because of disturbance of cellular homoeostasis (i. e., above MLSS) [5]. Some studies have suggested that training at or near MLSS can improve aerobic performance in endurance athletes [7, 45]. However, in swimming, most training sessions include sets of an intermittent nature, even for endurance athletes (long-distance swimmers and triathletes). The recovery periods in between 2 repetitions of swimming workouts are likely to modify the speed value at MLSS. Beneke et al. [6] indeed demonstrated that in cycling, MLSS determined from the performance of several continuous 30-min test occurs at a lower exercise work rate (277.8 W) when compared to intermittent protocols [5-min exercise bouts interspersed with 30-s (300.4 W) or 90-s of rest (310 W)]. Thus, continuous protocols may have limited application in swimming although no study has so far investigated the effect of continuous (MLSSc) vs. intermittent (MLSSi) conditions. On top of the monitoring of metabolic responses, swimming analysis must consider the technique itself, i. e. the biomechanical aspects of the stroke (i. e. application of propulsive force and passive and active drag). Technique is indeed considered

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Authors

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Material and Methods ▼ Subjects 13 regional-level competitive male swimmers (mean ± SD; 23 ± 9 years, 1.76 ± 0.1 m and 71.3 ± 9.8 kg) volunteered and gave written informed consent to participate in the present study, which was approved by the University’s Ethics Committee of São Paulo State University. Additionally, the experiment was con-

ducted ethically according to international standards and as required by the International Journal of Sports Medicine [26]. Participants had participated in training for at least 3 years (4 training sessions a week; 17 km per week during the 2 weeks prior testing), and had competed in several regional and national level meets over middle to long distances (400–1 500 m). Their mean 400-m front crawl performance (S-400) recorded during a training session was 308.3 ± 19.7 s at the time of the study (or 69.3 ± 3.9 % of the mean speed of the short course world record). The participants were instructed to refrain from intense training sessions and to refrain from using caffeine containing food or beverages, drugs, alcohol, cigarette smoking, or any form of nicotine intake at least 24 h before the experimental sessions.

Experimental design In the first experimental session, swimmers performed a 400-m all-out swim to estimate maximal aerobic speed (S-400). This method has been shown to be valid and reliable [16, 36, 48]. Thereafter, the following swimming tests were undertaken: 1) determination of MLSSc from the performance of two to three 30-min sub-maximal constant speed swims and; 2) determination of MLSSi from the performance of two to three 30-min submaximal intermittent swims. The swimmers performed one test per day with all tests being conducted within a 28-day period. A standard warm-up was consistently performed before each test. All tests were performed in a 25 m outdoor swimming-pool (26 °C). All tests were performed using front crawl, initiated with a push start. Testing occurred at the same time of the day ( ± 2 h) to minimize the effect of circadian variation on performance.

Materials and measurements The pace of the 30-min swims was regulated by using an audible signal from an mp3 player attached to the goggles of the swimmer (Acqua Player, Mormaii, Garopaba, Brazil). Red markers were placed every 5 m on the floor of the pool and the swimmers were instructed to pass their feet across these, in time with each “beep”. The mp3 file was of 32-min duration to incorporate the break within the test for blood sampling, and was created for each swimmer according to their 400-m performance [time between 2 signals (in sec) = 5/swimming speed]. A video camera was used to film the sub-maximal constant speed tests at a rate of 60 images per second (Panasonic NVGS180, Osaka, Japan).

Continuous and intermittent maximal lactate steady state tests All participants performed a series of 30-min sub-maximal continuous swims. Based on the results of Dekerle et al. [21], the first trial was performed at 88 %S-400, with subsequent 30-min tests being swum at ± 2.5 %, depending on the [La] responses during the previous test. Blood sample collection was performed at the 10th and the 30th min of each swim and took approximately 30 s. Athletes were advised to stop for blood collection via an audible signal in the mp3 player. Earlobe capillary blood samples (25 μl) were collected in capillary tubes and subsequently analyzed for capillary blood lactate concentration ([La]) using an automated analyzer (YSI 2300, Yellow Springs, Ohio, USA). For the determination of MLSSi, the swimmers performed a series of 12 repetitions of 150-s swims interspersed with 30 s of passive recovery (5:1). This protocol was similar to the traditional endurance workouts for aerobic capacity improvement (i. e. sets of 200 m repetitions, totaling 30 min of exercise, approximately). To have greater experimental control over the

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of primary importance in swimming [12, 28, 33]. To characterize technique, indicators of motor processes such as stroke rate (SR – number of strokes per minute), distance per stroke cycle (DS – the distance the body is displaced during one complete stroke cycle) and stroke index (SI – the product of DS and speed) have been widely used. These spatial-temporal parameters represent the motor strategies adopted by the swimmers to develop a given submaximal or maximal speed. Their measurement has been shown to be highly reliable [47] and can provide useful information about the technical proficiency of a swimmer. DS is a good surrogate of propelling efficiency and the work per stroke [51] with greater DS and, consequently higher SI being evident in well-experienced swimmers for a given speed [17]. It has been demonstrated that there is a reduction in DS and an increase in SR during all-out and imposed swim paces [1, 2], which may be the result of the reduced capacity to generate force to overcome drag [18]. Therefore, a better understanding of the changes in SR, DS and SI during continuous and intermittent exercise might help coaches when monitoring their swimmers' training [1, 15]. Various factors affect these stroking parameters: speed, body composition, muscle strength physical conditioning, swimming economy and fatigue to name a few [11, 15, 25, 41, 44, 50, 51]. In cycling, fatigue has been associated with a reduction in the frequency of movements [27, 37, 53]. In swimming, studies have shown a significant reduction in SR and DS during maximal performance trials [1, 49]. During an incremental test, DS has been shown to decrease alongside an increase in SR when passing through the anaerobic threshold [31, 32]. Similar changes were observed when swimming at a constant pace above MLSS, seemingly so that swimmers can maintain their swimming speed despite the development of fatigue [20, 30–32]. Dekerle et al. [20] reported changes in the stroking parameters only when swimming above, but not at or below MLSS. These findings led the authors of these several studies [20, 30–32] to hypothesize a relationship between the changes in blood lactate concentration and the degradation in swimming technique [51]. However, a cause-effect relationship could not be demonstrated, and was only assumed. This study has been designed in an attempt to associate changes in stroke parameters (i. e. DS, SR, SI) with changes in blood lactate concentration ([La]) when swimming at (steady state) and above (non steady state) MLSS. 2 conditions were compared: continuous (MLSSc) and intermittent (MLSSi) exercise. Based on previous findings relating to the changes in stroking parameters under [La] stabilization (MLSS) or accumulation (above MLSS) [20] and based on the effect of test interruptions on the metabolic responses [6], it was hypothesized that a) the speed at MLSSi would be higher than MLSSc despite steady state [La]; b) the stroking parameters would remain stable when swimming at MLSSc and MLSSi; c) the stroking parameters would change over time when swimming above (MLSS + 2.5 %) MLSS, similarly in both conditions (MLSSc and MLSSi).

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Technical indices The time to complete 5 stroke cycles was recorded for each lap and used to calculate SR. The average of all the SR values computed before the 10th min (24–32 in total) as well as the average of all SR values computed between the 20th and 30th min of each test were used for the analysis. The speed per 25 m was recorded using a manual chronometer and DS calculated as the ratio between speed and SR. This method has been shown to be reliable and valid, although DS can be overestimated by 4.2 % on average [14]. SI is the product of speed and DS [16]. Percent

n = 13

MLSSc

MLSSi

1.13 ± 0.08 87 ± 2 4.4 ± 1.5 170 ± 10

1.17 ± 0.09* 92 ± 3* 4.3 ± 1.1 167 ± 7

Statistical analysis The values were expressed as mean ± SD. For each set of data, normal distribution (Shapiro-Wilks test) and homogeneity of variance were checked. A 3-way ANOVA with repeated measures (Bonferroni correction post-hoc test using the GreenhouseGeisser procedure) was used to determine the effect of exercise condition (continuous vs. intermittent), time (minutes 10 vs. 30) and intensity (100 % MLSS speed vs. 102.5 % MLSS speed) on [La] and DS, SR, SI. Since the percent changes in the stroking parameters between minutes 10 vs. 30 were not normally distributed, a Friedman ANOVA with Wilcoxon post-hoc test was used to depict any significant difference. Relationships between the percent changes in [La] and stroking parameters were explored using the Pearson’s product moment correlation. A significance level of 5 % was accepted (p ≤ 0.05).

Results ▼ The mean ± SD values of speed, [La], and HR are presented ▶ Table 1 for both MLSSc vs. MLSSi. The athletes were swimin ● ming faster at MLSSi (p < 0.05) but with similar average [La] and HR when compared to MLSSc (p > 0.05). S-400 was 1.29 ± 0.05 m.s − 1. ▶ Fig. 1. The stroking parameter values are presented in ●

Table 1 Comparison between MLSSc and MLSSi. Values are mean ± SD.

MLSS (m.s − 1) MLSS ( %S-400) [La] (mmol.L − 1) HR (bpm)

changes in each parameter from the 10th to the 30th min were computed for each 30-min constant-speed test.

Stroke rate

MLSS – speed at maximal lactate steady state; [La] – blood lactate concentration; HR – heart rate. *p < 0.05 significance when compared to MLSSc

There was no significant interaction effect (exercise conditionby-intensity-by-time) for SR (F = 1.017, p > 0.05). Concerning the continuous condition, there was a significant intensity-by-time

Fig. 1 Mean ± SD values of stroke rate (SR), distance per stroke cycle (DS) and stroke index (SI) obtained at the 10th and 30th min of the exercise swum continuously and intermittently at maximal lactate steady state (MLSSc and MLSSi) and 102.5 % of MLSS (102.5 % MLSSc and 102.5 % MLSSi, respectively). # p < 0.05 in relation to the value at minute 10 of the same exercise intensity; § p < 0.05 in relation to continuous exercise condition for the same moment and intensity of the exercise. N = 13.

# Stroke rate (cycle.min–1)

38

§ #

§

36

§

34 32 30 28

102.5% MLSSc

MLSSc

Distance per stroke cycle (m.cycle–1)

10th

MLSSi

102.5% MLSSi

30th

2.3 2.25 2.2

# #

2.15

#

2.1

#

2.05 2

MLSSc

102.5% MLSSc

10th 2.9

Stroke index

#

MLSSi

102.5% MLSSi

30th

#

2.7 #

# 2.5 2.3

MLSSc

102.5% MLSSc

10th

MLSSi

102.5% MLSSi

30th

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work:rest ratio we fixed the duration of repetitions. This procedure allowed us to have the same periods of blood collections and the same total duration of exercise across all participants. The speed of the first test was 5 % higher than that at MLSSc with a 2.5 % speed increase or decrease for the subsequent 30-min test, depending on the [La] responses during the previous test. Blood samples were collected at the 10th and the 30th min of exercise around MLSSi (end of the 4th and 12th repetition). For MLSSc and MLSSi determination, an increase by no more than 1 mmol.L − 1 in [La] between the minute 10 and 30 of the test was considered [5].

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12 10 8 6 4 2 0

Percentage of change (%)

b

Percentage of change (%)

MLSSc

102.5% MLSSc

MLSSi

102.5% MLSSi

12 #

10 8 6 4 2 0

c

Fig. 2 Percentages of change between the 10th and 30th min of the continuous and intermittent tests for the stroke rate (panel a), distance per stroke cycle (panel b) and stroke index (panel c). § p < 0.05 in relation to MLSSc; # p < 0.05 in relation to MLSSi. N = 13.

#§

MLSSc

102.5% MLSSc

MLSSi

102.5% MLSSi

12 10 #

8 6 4 2 0

MLSSc

102.5% MLSSc

MLSSi

interaction (F = 6.415, p < 0.05) and a significant main effect for time (F = 64.03, p < 0.01) but not intensity (F = 1.22, p > 0.05). SR increased significantly between the 10th and 30th min only during the test swum above MLSSc (p < 0.01). No significant intensity-by-time interaction was found for the intermittent condition (F = 0.043, p > 0.05) with a significant main effect for time (F = 61.59, p < 0.01) but not intensity (F = 0.79, p > 0.05). SR increased significantly between the 10th and 30th min of the test swum at (p < 0.05) and above (p < 0.05) MLSSi.

Distance per stroke There was no significant interaction effect (exercise conditionby-intensity-by-time) for DS (F = 0.948, p > 0.05). Concerning the continuous condition, there was a significant intensity-by-time interaction (F = 4.340, p < 0.05) and a significant main effect for time (F = 42.44, p < 0.01) but not intensity (F = 0.29, p > 0.05). DS reduced significantly at (p < 0.05) and above (p < 0.01) MLSSc. No significant intensity-by-time interaction was found in the intermittent condition (F = 0.070, p > 0.05) with a significant main effect for time (F = 76.02, p < 0.01) but not intensity (F = 0.07, p > 0.05). DS decreased significantly between the 10th and 30th min of the test swum at (p < 0.01) and above (p < 0.01) MLSSi.

Stroke index There was no significant interaction effect (exercise conditionby-intensity-by-time) for SI (F = 0.811, p > 0.05). For the continuous condition, there was no significant intensity-by-time interaction (F = 2.38, p > 0.05) but there was a significant main effect for time (F = 27.24, p < 0.01) but not intensity (F = 0.02, p > 0.05). SI decreased significantly between the 10th and 30th min of the test swum at 102.5 % MLSSc (p < 0.01). No significant intensity-by-time interaction was found in the intermittent condition (F = 0.20, p > 0.05) but again there was a significant main effect

102.5% MLSSi

for time (F = 71.50, p < 0.01) but not intensity (F = 0.06, p > 0.05). SI decreased significantly between the 10th and 30th min of the test swum at (p < 0.05) and above (p < 0.01) MLSSi. The percent changes in SR, DS and SI throughout the 4 tests are ▶ Fig. 2. SR, DS and SI changes from minute 10 to presented in ● 30 of the exercise were greater at 102.5 % MLSSc when compared to MLSSc (p < 0.01; p < 0.01; p < 0.05, respectively). Apart from a difference in the SR percent change between 102.5 % MLSSc and 102.5 % MLSSi (p < 0.05), no other significant difference was found. There was no significant correlation between the % change in [La] and SR (r = − 0.44 and r = − 0.18, p > 0.05), and [La] and DS (r = 0.31 and r = − 0.12, p > 0.05) at 102.5 % MLSSc and 102.5 % MLSSi, respectively.

Discussion ▼ The main findings of this study were that: a) the speed at MLSS was greater in the intermittent condition and was the result of a greater SR; b) SR increased not only during the exercise swum above continuous MLSS (102.5 %) but also during the intermittent exercise performed at MLSS. However, DS and SI decreased throughout all exercise (i. e., from 10th to 30th min), irrespective of the exercise intensity (at and above the speed corresponding to MLSS) or the exercise condition (continuous and intermittent); and c) the changes in [La] were not related to changes in the stroking parameters. Thus, the hypothesis that there was a relationship between blood lactate accumulation and stroking parameter changes was not supported by our findings for both continuous and intermittent exercise. For similar [La] levels, the speed at MLSSi was 3.24 % higher than ▶ Table 1). Thus, although the recovery period between MLSSc (● 2 repetitions was proportionally shorter (30 s) when expressed

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Percentage of change (%)

a

relative to the work duration (150 s; work:rest = 5:1), a partial resynthesis of the creatine phosphate stores alongside an increase in blood lactate removal may have contributed to the higher exercise intensity maintained while the metabolic responses were the same [39, 54]. Similar results were found by Beneke et al. [6] in cycling using work:rest ratios of 10:1 and ~3:1. However, caution should be expressed when comparing swimming with other cyclic activities because the relationship between energetic cost and speed is not linear in swimming [9], and therefore, a small increment in swimming speed requires a much greater metabolic energy turnover to be able to maintain the increased pace [22, 52]. Nevertheless, the objective of the present paper was to compare the stroking parameters during similar metabolic conditions ([La]) but at different swim speeds and this was attained successfully. As demonstrated by other authors [50], athletes swim faster (in the present study at MLSSi) thanks to higher SR. Dekerle et al. [20] have investigated the changes in the stroking parameters during long duration swims, which were performed at and above MLSS. In that study, the authors reported stability in DS (from 2.55 to 2.46 m) at MLSS speed throughout time for all athletes, but a reduction in DS above MLSS speed for the swimmers who could not maintain the pace for 30 min (from 2.76 to 2.39 m). Conversely, in the present study, during the exercise performed at and above MLSSc (102.5 % as opposed to 105 % in Dekerle et al. [20]) DS was reduced throughout the exercise with an intensity x time interaction effect (greater decrease at 102.5 % MLSSc). The difference in the present findings when compared to those reported by Dekerle et al. [20] could be explained by at least 2 factors: Firstly, the different increment in the swimming speed, which was from MLSS to MLSS + 2.5 % (~90 % S-400) in the present study rather than 5 % (~93 % S-400) in the study by Dekerle et al. [20]. Indeed, all participants completed the tests in the present study, which was not the case in Dekerle et al. [20]. It is important here to consider that in swimming the energetic cost vs. speed relationship is not linear, i. e., small speed differences may determine greater differences in metabolic and cardiovascular responses during exercise. This may help to explain why some of the swimmers did not complete the test above MLSS in the study conducted by Dekerle et al. [20]. Secondly, the contrasting findings could be explained by the standard of the swimmers tested, since the participants in the study of Dekerle et al. [20] were of higher level (MLSSc = 1.22 m.s − 1; DS = 2.64 m) when compared to those of the present study (MLSSc = 1.13 m.s − 1; DS = 2.18 m). Accordingly, SR and DS have recently been shown to stabilize at 100 % MLSSc but change at 102 % MLSSc in more experienced swimmers (MLSSc = 1.22 m.s − 1; DS = 2.42 m) [46]. Chollet et al. [13] have found that skilled swimmers were able to maintain a more constant DS throughout the race than less skilled swimmers. Thus, it could be hypothesized that technical skill may influence the DS response during swims performed around MLSS. Numerous studies have reported changes in movement patterns with the development of fatigue [23, 37, 53], which is defined as an exercise-induced reduction in the maximal capacity to generate power output [24]. In swimming, DS impairments may result from a reduced capacity to generate force to overcome drag [18]. Some studies performed under constant speed conditions have shown concomitant decreases in DS and increases in duration of the propulsive phases of a stroke cycle only when swimming above MLSS [2, 43]. Our findings suggest that the duration of

these propulsive phases would also increase during intermittent swims as long as they are performed above MLSS. Interestingly in the present study, DS also decreased over time without accumulation of [La] in the blood stream (i. e. MLSS condition). Additionally, the changes in DS and [La] were not proportional. This demonstrates that the accumulation of [La] in the blood is not “inducing” the ability to generate high muscle forces to maintain a good technical efficiency. This is a reductionist view on the mechanisms of peripheral fatigue – applied in swimming. Both central and peripheral mechanisms [10, 38] may explain the reduction in the force impulse during the propelling phase of the stroke [2]. Irrespective of the mechanisms, the recovery periods between bouts protect against swim technique impairment, since no interaction effect was found between intensity and time for DS and SR, and the change in the 2 parameters was similar between 102.5 % MLSSi and 100 % MLSSi (~4.14 %). Swimming training incorporates a high mileage performed around MLSS [40, 42]. The present findings bring some new insights to interval training prescription in swimming. Firstly, intermittent exercise allows athletes to swim faster while being under a similar metabolic stress. Their prescription in training may facilitate an increase in training loads whilst inducing identical metabolic stress [19, 35], thus possibly leading to greater aerobic adaptation, even in highly trained swimmers. Secondly, metabolic stress seems to affect DS less under this condition, enabling swimmers to develop higher DS and for longer periods during training sessions even when performed above MLSS. This should be considered by coaches and swimmers, since DS is a good surrogate of propelling efficiency and the work per stroke [51]. However, it is important to mention that the subjects did not exercise until exhaustion, which could have had an influence on the magnitude of changes in all parameters. A 5:1 intermittent MLSS set (150 s effort: 30 s passive rest) can be maintained with comparable [La] level (~4–5 mmol.L − 1) to a continuous condition, while swimming at a higher speed (+ 3 %). Interestingly, the stroke technique was changing over time in both conditions when swimming not only above (+ 2.5 %) but also at MLSS. But these changes were greater in the continuous condition. Thus, intermittent long constant-speed workouts might be prioritized in training, if the objective is to develop higher swim speeds with reduced loss of swim technique. The present findings demonstrate that in regional-level competitive swimmers the changes in stroke technique when swimming at MLSS were more dependent on exercise duration than previously thought. They can be dissociated from the changes in blood lactate concentrations using intermittent swimming.

Acknowledgements ▼ This research was supported by grants from CNPq, FAPESP and FUNDUNESP.

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