Size-corrected BMD decreases during peak linear growth: Implications for fracture incidence during adolescence

Peak adolescent fracture incidence at the distal end of the radius coincides with a decline in size-corrected BMD in both boys and girls. Peak gains in bone area preceded peak gains in BMC in a longitudinal sample of boys and girls, supporting the theory that the dissociation between skeletal expansion and skeletal mineralization results in a period of relative bone weakness. Introduction: The high incidence of fracture in adolescence may be related to a period of relative skeletal fragility resulting from dissociation between bone expansion and bone mineralization during the growing years. The aim of this study was to examine the relationship between changes in size-corrected BMD (BMDsc) and peak distal radius fracture incidence in boys and girls. Materials and Methods: Subjects were 41 boys and 46 girls measured annually (DXA; Hologic 2000) over the adolescent growth period and again in young adulthood. Ages of peak height velocity (PHV), peak BMC velocity (PBMCV), and peak bone area (BA) velocity (PBAV) were determined for each child. To control for maturational differences, subjects were aligned on PHV. BMDsc was calculated by first regressing the natural logarithms of BMC and BA. The power coefficient (pc) values from this analysis were used as follows: BMDsc = BMC/BA(pc). Results: BMDsc decreased significantly before the age of PHV and then increased until 4 years after PHV. The peak rates in radial fractures (reported from previous work) in both boys and girls coincided with the age of negative velocity in BMDsc; the age of peak BA velocity (PBAV) preceded the age of peak BMC velocity (PBMCV) by 0.5 years in both boys and girls. Conclusions: There is a clear dissociation between PBMCV and PBAV in boys and girls. BMDsc declines before age of PHV before rebounding after PHV. The timing of these events coincides directly with reported fracture rates of the distal end of the radius. Thus, the results support the theory that there is a period of relative skeletal weakness during the adolescent growth period caused, in part, by a draw on cortical bone to meet the mineral demands of the expanding skeleton resulting in a temporary increased fracture risk.

The relationship between plasma lactate parameters, W-peak and 1-h cycling performance in women

Purpose: The relationship between six descriptors of lactate increase, peak (V) over dot O-2,W-peak, and 1-h cycling performance were compared in 24 trained, female cyclists (peak (V) over dot O-2 = 48.11 +/- 6.32 mL . kg(-1) . min(-1)). Methods: The six descriptors of lactate increase were: 1) lactate threshold (LT; the power output at which plasma lactate concentration begins to increase above the resting level during an incremental exercise test), 2) LT1 (the power output at which plasma lactate increases by 1 mM or more), 3) LTD (the lactate threshold calculated by the D-max method), 4) LTMOD (the lactate threshold calculated by a modified D-max method), 5) L4 (the power output at which plasma lactate reaches a concentration of 4 mmol-L-1), and 6) LTLOG (the power output at which plasma lactate concentration begins to increase when the log([La-]) is plotted against the log (power output)). Subjects first completed a peak (V) over dot O-2 test on a cycle ergometer. Finger-tip capillary blood was sampled within 30 s of the end of each 3-min stage for analysis of plasma lactate. Endurance performance was assessed 7 d later using a 1-h cycle test (OHT) in which subjects were directed to achieve the highest possible average power output. Results: The mean power output (W) for the OHT (+/- SD) was 183.01 +/- 18.88, and for each lactate variable was: LT (138.54 +/- 46.61), LT1 (179.17 +/- 27.25), LTLOG (143.97 +/- 45.74), L4 (198.09 +/- 33.84), LTD (178.79 +/- 24.07), LTMOD (212.28 +/- 31.75). Average power output during the OHT was more strongly correlated with all plasma lactate parameters (0.61 < r < 0.84) and W-peak (r = 0.81) than with peak (V) over dot O-2 (r = 0.55). The six lactate parameters were strongly correlated with each other (0.54 < r < 0.91) and of the six lactate parameters, LTD correlated best with endurance performance (r = 0.84). Conclusions: It was concluded that plasma lactate parameters and W-peak provide better indices of endurance performance than peak (V) over dot O-2 and that, of the six descriptors of lactate increase measured in this study, LTD is most strongly related to 1-h cycling performance in trained, female cyclists.

Acute high-intensity interval training improves T-vent and peak power output in highly trained males.

This study examined the effects of four high-intensity interval-training (HIT) sessions performed over 2 weeks on peak volume of oxygen uptake (VO2peak), the first and second ventilatory thresholds (UT VT2) and peak power output (PPO) in highly trained cyclists. Fourteen highly trained male cyclists (VO2peak = 67.5 +/- 3.7 ml . kg(-1) . min(-1)) performed a ramped cycle test to determine VO2peak VT1 VT2, and PPO. Subjects were divided equally into a HIT group and a control group. The HIT group performed four HIT sessions (20 x 60 s at PPO, 120 s recovery); the V-02peak test was repeated <I wk after the HIT program. Control subjects maintained their regular training program and were reassessed under the same timeline. There was no change in V0(2peak) for either group; however, the HIT group showed a significantly greater increase in VT1, (+22% vs. -3%), VT2 (+15% vs. -1%), and PPO (+4.3 vs. -.4%) compared to controls (all P <.05). This study has demonstrated that HIT can improve VT1, VT2,, and PPO, following only four HIT sessions in already highly trained cyclists.

Reproducibility of the cycling time to exhaustion at VO(2)peak in highly trained cyclists

The purpose of the present study was to examine, in highly trained cyclists, the reproducibility of cycling time to exhaustion (T-max) at the power output equal to that attained at peak oxygen uptake ((V) over dot O(2)peak) during a progressive exercise test. Forty-three highly trained male cyclists (M +/- SD; age = 25 +/- 6yrs; weight = 75 +/- 7 kg; (V) over dot(2)peak = 64.8 +/- 5.2 ml.kg(-1) . min(-1)) performed two T-max tests one week apart. While the two measures of T-max were strongly related (r = 0.884; p < 0.001), T-max from the second test (245 +/- 57 s) was significantly higher than that of the first (237 +/- 57 s; p = 0.047; two-tailed). Within-subject variability in the present study was calculated to be 6 +/- 6%, which was lower than that previously reported for Tmax in sub-elite runners (25%). The mean T-max was significantly (p < 0.05) related to both the second ventilatory turnpoint (VT2; r = 0.38) and to (V) over dot O(2)peak (r = 0.34). Despite a relatively low within-subject coefficient of variation, these data demonstrate that the second score in a series of two T-max tests may be significantly greater than the first. Moreover the present data show that T-max in highly trained cyclists is moderately related to VT2 and (V) over dot O(2)peak.

A comparison of the cycling performance of cyclists and triathletes

The aim of this study was to compare the cycling performance of cyclists and triathletes. Each week for 3 weeks, and on different days, 25 highly trained male cyclists and 18 highly trained male triathletes performed: (1) an incremental exercise test on a cycle ergometer for the determination of peak oxygen consumption ((V) over dot O-2peak), peak power output and the first and second ventilatory thresholds, followed 15 min later by a sprint to volitional fatigue at 150% of peak power output; (2) a cycle to exhaustion test at the (V) over dot O-2peak power output; and (3) a 40-km cycle time-trial. There were no differences in (V) over dot O-2peak, peak power output, time to volitional fatigue at 150% of peak power output or time to exhaustion at (V) over dot O-2peak power output between the two groups. However, the cyclists had a significantly faster time to complete the 40-km time-trial (56:18 +/- 2:31 min:s; mean +/- s) than the triathletes (58:57 +/- 3:06 min:s; P < 0.01), which could be partially explained (r = 0.34-0.51; P < 0.05) by a significantly higher first (3.32 +/- 0.36 vs 3.08 +/- 0.36 l . min(-1)) and second ventilatory threshold (4.05 +/- 0.36 vs 3.81 +/- 0.29 l . min(-1); both P < 0.05) in the cyclists compared with the triathletes. In conclusion, cyclists may be able to perform better than triathletes in cycling time-trial events because they have higher first and second ventilatory thresholds.

Physiological responses to repeated bouts of high-intensity ultraendurance cycling - a field study case report

The present study aimed to 1) examine the relationship between laboratory-based measures and high-intensity ultraendurance (HIU) performance during an intermittent 24-h relay ultraendurance mountain bike race (similar to20 min cycling, similar to60min recovery), and 2) examine physiological and performance based changes throughout the HIU event. Prior to the HIU event, four highly-trained male cyclists (age = 24.0 +/- 2.1 yr; mass = 75.0 +/- 2.7 kg; (V)over dot O-2peak = 70 +/- 3 ml.kg(-1).min(-1)) performed 1) a progressive exercise test to determine peak Volume of oxygen uptake ((V)over dot O-2peak), peak power output (PPO), and ventilatory threshold (T-vent), 2) time-to-fatigue tests at 100% (TF100) and 150% of PPO (TF150), and 3) a laboratory simulated 40-km time trial (TT40). Blood lactate (Lac(-)), haematocrit and haemoglobin were measured at 6-h intervals throughout the HIU event, while heart rate (HR) was recorded continuously. Intermittent HIU performance, performance HR, recovery HR, and Lac declined (P < 0.05), while plasma volume expanded (P < 0.05) during the HIU event. TF100 was related to the decline in lap time (r = -0.96; P < 0.05), and a trend (P = 0.081) was found between TF150 and average intermittent HIU speed (r = 0.92). However, other measures (V)over dot O-2peak, PPO, T-vent, and TT40) were not related to HIU performance. Measures of high-intensity endurance performance (TF100, TF150) were better predictors of intermittent HIU performance than traditional laboratory-based measures of aerobic capacity.

Accuracy of SRM and power tap power monitoring systems for bicycling

Purpose: Although manufacturers of bicycle power monitoring devices SRM and Power Tap (PT) claim accuracy to within 2.5%, there are limited scientific data available in support. The purpose of this investigation was to assess the accuracy of SRM and PT under different conditions. Methods: First, 19 SRM were calibrated, raced for 11 months, and retested using a dynamic CALRIG (50-1000 W at 100 rpm). Second, using the same procedure, five PT were repeat tested on alternate days. Third, the most accurate SRM and PT were tested for the influence of cadence (60, 80, 100, 120 rpm), temperature (8 and 21degreesC) and time (1 h at similar to300 W) on accuracy. Finally, the same SRM and PT were downloaded and compared after random cadence and gear surges using the CALRIG and on a training ride. Results: The mean error scores for SRM and PT factory calibration over a range of 50-1000 W were 2.3 +/- 4.9% and -2.5 +/- 0.5%, respectively. A second set of trials provided stable results for 15 calibrated SRM after 11 months (-0.8 +/- 1.7%), and follow-up testing of all PT units confirmed these findings (-2.7 +/- 0.1%). Accuracy for SRM and PT was not largely influenced by time and cadence; however. power output readings were noticeably influenced by temperature (5.2% for SRM and 8.4% for PT). During field trials, SRM average and max power were 4.8% and 7.3% lower, respectively, compared with PT. Conclusions: When operated according to manufacturers instructions, both SRM and PT offer the coach, athlete, and sport scientist the ability to accurately monitor power output in the lab and the field. Calibration procedures matching performance tests (duration, power, cadence, and temperature) are, however, advised as the error associated with each unit may vary.

Temporal aspects of the VO2 response at the power output associated with VO2peak in well trained cyclists: Implications for interval training prescription

The power output achieved at peak oxygen consumption (VO2 peak) and the time this power can be maintained (i.e., Tmax) have been used in prescribing high-intensity interval training. In this context, the present study examined temporal aspects of the VO2 response to exercise at the cycling power that output well trained cyclists achieve their VO2 peak (i.e., Pmax). Following a progressive exercise test to determine VO2 peak, 43 well trained male cyclists (M age = 25 years, SD = 6; M mass = 75 kg SD = 7; M VO2 peak = 64.8 ml(.)kg(1.)min(-1), SD = 5.2) performed two Tmax tests 1 week apart.1. Values expressed for each participant are means and standard deviations of these two tests. Participants achieved a mean VO2 peak during the Tmax test after 176 s (SD = 40; = 74% of Tmax, SD = 12) and maintained it for 66 s (SD = 39; M = 26% of Tmax, SD = 12). Additionally they obtained mean 95 % of VO2 peak after 147 s (SD = 31; M = 62 % of Tmax, SD = 8) and maintained it for 95 s (SD = 38; M = 38 % of Tmax, SD = 8). These results suggest that 60-70% of Tmax is an appropriate exercise duration for a population of well trained cyclists to attain VO2 peak during exercise at Pmax. However due to intraparticipant variability in the temporal aspects of the VO2 response to exercise at Pmax, future research is needed to examine whether individual high-intensity interval training programs for well trained endurance athletes might best be prescribed according to an athlete's individual VO2 response to exercise at Pmax.

Muscle metabolism during constant- and alternating-intensity exercise around critical power

Few studies have focused on the metabolic responses to alternating high- and low-intensity exercise and, specifically, compared these responses to those seen during constant-load exercise performed at the same average power output. This study compared muscle metabolic responses between two patterns of exercise during which the intensity was either constant and just below critical power (CP) or that oscillated above and below CP. Six trained males (mean +/- SD age 23.6 +/- 2.6 y) completed two 30-minute bouts of cycling (alternating and constant) at an average intensity equal to 90% of CR The intensity during alternating exercise varied between 158% CP and 73% CP. Biopsy samples from the vastus lateralis muscle were taken before (PRE), at the midpoint and end (POST) of exercise and analysed for glycogen, lactate, PCr and pH. Although these metabolic variables in muscle changed significantly during both patterns of exercise, there were no significant differences (p > 0.05) between constant and alternating exercise for glycogen (PRE: 418.8 +/- 85 vs. 444.3 +/- 70; POST: 220.5 +/- 59 vs. 259.5 +/- 126mmol.kg(-1) dw), lactate (PRE: 8.5 +/- 7.7 vs. 8.5 +/- 8.3; POST: 49.9 +/- 19.0 vs. 42.6 +/- 26.6 mmol.kg(-1)dw), phosphocreatine (PRE: 77.9 +/- 11.6 vs. 75.7 +/- 16.9; POST: 65.8 +/- 12.1 vs. 61.2 +/- 12.7mmol.kg(-1)dw) or pH (PRE: 6.99 +/- 0.12 vs. 6.99 +/- 0.08; POST: 6.86 +/- 0.13 vs. 6.85 +/- 0.06), respectively. There were also no significant differences in blood lactate responses to the two patterns of exercise. These data suggest that, when the average power output is similar, large variations in exercise intensity exert no significant effect on muscle metabolism.

Thermal Cycling Stability of Silica Membranes for Gas Separation

Hydrogen is being seen as an alternative energy carrier to conventional hydrocarbons to reduce greenhouse gas emissions. High efficiency separation technologies to remove hydrogen from the greenhouse gas, carbon dioxide, are therefore in growing demand. Traditional thermodynamic separation systems utilise distillation, absorption and adsorption, but are limited in efficiency at compact scales. Molecular sieve silica (MSS) membranes can perform this separation as they have high permselectivity of hydrogen to carbon dioxide, but their stability under thermal cycling is not well reported. In this work we exposed a standard MSS membrane and a carbonised template MSS (CTMSS) membrane to thermal cycling from 100 to 450°C. The standard MSS and carbonised template CTMSS membranes both showed permselectivity of helium to nitrogen dropping from around 10 to 6 in the first set of cycles, remaining stable until the last test. The permselectivity drop was due to small micropore collapse, which occurred via structure movement during cycling. Simulating single stage membrane separation with a 50:50 molar feed of H2:CO2, H2 exiting the permeate stream would start at 79% and stabilise at 67%. Higher selectivity membranes showed less of a purity drop, indicating the margin at which to design a stable membrane separation unit for CO2 capture.

A power system control scheme based on security visualisation in parameter space

Power system real time security assessment is one of the fundamental modules of the electricity markets. Typically, when a contingency occurs, it is required that security assessment and enhancement module shall be ready for action within about 20 minutes’ time to meet the real time requirement. The recent California black out again highlighted the importance of system security. This paper proposed an approach for power system security assessment and enhancement based on the information provided from the pre-defined system parameter space. The proposed scheme opens up an efficient way for real time security assessment and enhancement in a competitive electricity market for single contingency case

Comparison of BR and QR Eigenvalue Algorithms for Power System Small Signal Stability Analysis

The BR algorithm is a novel and efficient method to find all eigenvalues of upper Hessenberg matrices and has never been applied to eigenanalysis for power system small signal stability. This paper analyzes differences between the BR and the QR algorithms with performance comparison in terms of CPU time based on stopping criteria and storage requirement. The BR algorithm utilizes accelerating strategies to improve its performance when computing eigenvalues of narrowly banded, nearly tridiagonal upper Hessenberg matrices. These strategies significantly reduce the computation time at a reasonable level of precision. Compared with the QR algorithm, the BR algorithm requires fewer iteration steps and less storage space without depriving of appropriate precision in solving eigenvalue problems of large-scale power systems. Numerical examples demonstrate the efficiency of the BR algorithm in pursuing eigenanalysis tasks of 39-, 68-, 115-, 300-, and 600-bus systems. Experiment results suggest that the BR algorithm is a more efficient algorithm for large-scale power system small signal stability eigenanalysis.

The critical power function is dependent on the duration of the predictive exercise tests chosen

The linear relationship between work accomplished (W-lim) and time to exhaustion (t(lim)) can be described by the equation: W-lim = a + CP.t(lim). Critical power (CP) is the slope of this line and is thought to represent a maximum rate of ATP synthesis without exhaustion, presumably an inherent characteristic of the aerobic energy system. The present investigation determined whether the choice of predictive tests would elicit significant differences in the estimated CP. Ten female physical education students completed, in random order and on consecutive days, five art-out predictive tests at preselected constant-power outputs. Predictive tests were performed on an electrically-braked cycle ergometer and power loadings were individually chosen so as to induce fatigue within approximately 1-10 mins. CP was derived by fitting the linear W-lim-t(lim) regression and calculated three ways: 1) using the first, third and fifth W-lim-t(lim) coordinates (I-135), 2) using coordinates from the three highest power outputs (I-123; mean t(lim) = 68-193 s) and 3) using coordinates from the lowest power outputs (I-345; mean t(lim) = 193-485 s). Repeated measures ANOVA revealed that CPI123 (201.0 +/- 37.9W) > CPI135 (176.1 +/- 27.6W) > CPI345 (164.0 +/- 22.8W) (P < 0.05). When the three sets of data were used to fit the hyperbolic Power-t(lim) regression, statistically significant differences between each CP were also found (P < 0.05). The shorter the predictive trials, the greater the slope of the W-lim-t(lim) regression; possibly because of the greater influence of 'aerobic inertia' on these trials. This may explain why CP has failed to represent a maximal, sustainable work rate. The present findings suggest that if CP is to represent the highest power output that an individual can maintain for a very long time without fatigue then CP should be calculated over a range of predictive tests in which the influence of aerobic inertia is minimised.