Comparison of the power spectral changes of the voluntary surface electromyogram and M wave during intermittent maximal voluntary contractions.

INTRODUCTION: To compare the power spectral changes of the voluntary surface electromyogram (sEMG) and of the compound action potential (M wave) in the vastus medialis and vastus lateralis muscles during fatiguing contractions. METHODS: Interference sEMG and force were recorded during 48 intermittent 3-s isometric maximal voluntary contractions (MVC) from 13 young, healthy subjects. M waves and twitches were evoked using supramaximal femoral nerve stimulation between the successive MVCs. Mean frequency (F mean), and median frequency were calculated from the sEMG and M waves. Muscle fiber conduction velocity (MFCV) was computed by cross-correlation. RESULTS: The power spectral shift to lower frequencies was significantly greater for the voluntary sEMG than for the M waves (P < 0.05). Over the fatiguing protocol, the overall average decrease in MFCV (~25 %) was comparable to that of sEMG F mean (~22 %), but significantly greater than that of M-wave F mean (~9 %) (P < 0.001). The mean decline in MFCV was highly correlated with the mean decreases in both sEMG and M-wave F mean. CONCLUSIONS: The present findings indicated that, as fatigue progressed, central mechanisms could enhance the relative weight of the low-frequency components of the voluntary sEMG power spectrum, and/or the end-of-fiber (non-propagating) components could reduce the sensitivity of the M-wave spectrum to changes in conduction velocity.

Quantitative integration of high-resolution hydrogeophysical data through Monte-Carlo-type conditional simulations

Geophysical techniques can help to bridge the inherent gap with regard to spatial resolution and the range of coverage that plagues classical hydrological methods. This has lead to the emergence of the new and rapidly growing field of hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters and their inherent trade-off between resolution and range the fundamental usefulness of multi-method hydrogeophysical surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative integration of the resulting vast and diverse database in order to obtain a unified model of the probed subsurface region that is internally consistent with all available data. To address this problem, we have developed a strategy towards hydrogeophysical data integration based on Monte-Carlo-type conditional stochastic simulation that we consider to be particularly suitable for local-scale studies characterized by high-resolution and high-quality datasets. Monte-Carlo-based optimization techniques are flexible and versatile, allow for accounting for a wide variety of data and constraints of differing resolution and hardness and thus have the potential of providing, in a geostatistical sense, highly detailed and realistic models of the pertinent target parameter distributions. Compared to more conventional approaches of this kind, our approach provides significant advancements in the way that the larger-scale deterministic information resolved by the hydrogeophysical data can be accounted for, which represents an inherently problematic, and as of yet unresolved, aspect of Monte-Carlo-type conditional simulation techniques. We present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to corresponding field data collected at the Boise Hydrogeophysical Research Site near Boise, Idaho, USA.

Measurement of the mechanical power of walking by satellite positioning system (GPS).

PURPOSE: This descriptive article illustrates the application of Global Positioning System (GPS) professional receivers in the field of locomotion studies. The technological challenge was to assess the external mechanical work in outdoor walking. METHODS: Five subjects walked five times during 5 min on an athletic track at different imposed stride frequency (from 70-130 steps x min(-1)). A differential GPS system (carrier phase analysis) measured the variation of the position of the trunk at 5 Hz. A portable indirect calorimeter recorded breath-by-breath energy expenditure. RESULTS: For a walking speed of 1.05 +/- 0.11 m x s(-1), the vertical lift of the trunk (43 +/- 14 mm) induced a power of 46.0 +/- 20.4 W. The average speed variation per step (0.15 +/- 0.03 m x s(-1)) produced a kinetic power of 16.9 +/- 7.2 W. As compared with commonly admitted values, the energy exchange (recovery) between the two energy components was low (39.1 +/- 10.0%), which induced an overestimated mechanical power (38.9 +/- 18.3 W or 0.60 W x kg(-1) body mass) and a high net mechanical efficiency (26.9 +/- 5.8%). CONCLUSION: We assumed that the cause of the overestimation was an unwanted oscillation of the GPS antenna. It is concluded that GPS (in phase mode) is now able to record small body movements during human locomotion, and constitutes a promising tool for gait analysis of outdoor unrestrained walking. However, the design of the receiver and the antenna must be adapted to human experiments and a thorough validation study remains to be conducted.

3D noise power spectrum applied on clinical mdct scanners: effects of reconstruction algorithms and reconstruction filters

The noise power spectrum (NPS) is the reference metric for understanding the noise content in computed tomography (CT) images. To evaluate the noise properties of clinical multidetector (MDCT) scanners, local 2D and 3D NPSs were computed for different acquisition reconstruction parameters.A 64- and a 128-MDCT scanners were employed. Measurements were performed on a water phantom in axial and helical acquisition modes. CT dose index was identical for both installations. Influence of parameters such as the pitch, the reconstruction filter (soft, standard and bone) and the reconstruction algorithm (filtered-back projection (FBP), adaptive statistical iterative reconstruction (ASIR)) were investigated. Images were also reconstructed in the coronal plane using a reformat process. Then 2D and 3D NPS methods were computed.In axial acquisition mode, the 2D axial NPS showed an important magnitude variation as a function of the z-direction when measured at the phantom center. In helical mode, a directional dependency with lobular shape was observed while the magnitude of the NPS was kept constant. Important effects of the reconstruction filter, pitch and reconstruction algorithm were observed on 3D NPS results for both MDCTs. With ASIR, a reduction of the NPS magnitude and a shift of the NPS peak to the low frequency range were visible. 2D coronal NPS obtained from the reformat images was impacted by the interpolation when compared to 2D coronal NPS obtained from 3D measurements.The noise properties of volume measured in last generation MDCTs was studied using local 3D NPS metric. However, impact of the non-stationarity noise effect may need further investigations.

Simulated-annealing-based conditional simulation for the local-scale characterization of heterogeneous aquifers

Simulated-annealing-based conditional simulations provide a flexible means of quantitatively integrating diverse types of subsurface data. Although such techniques are being increasingly used in hydrocarbon reservoir characterization studies, their potential in environmental, engineering and hydrological investigations is still largely unexploited. Here, we introduce a novel simulated annealing (SA) algorithm geared towards the integration of high-resolution geophysical and hydrological data which, compared to more conventional approaches, provides significant advancements in the way that large-scale structural information in the geophysical data is accounted for. Model perturbations in the annealing procedure are made by drawing from a probability distribution for the target parameter conditioned to the geophysical data. This is the only place where geophysical information is utilized in our algorithm, which is in marked contrast to other approaches where model perturbations are made through the swapping of values in the simulation grid and agreement with soft data is enforced through a correlation coefficient constraint. Another major feature of our algorithm is the way in which available geostatistical information is utilized. Instead of constraining realizations to match a parametric target covariance model over a wide range of spatial lags, we constrain the realizations only at smaller lags where the available geophysical data cannot provide enough information. Thus we allow the larger-scale subsurface features resolved by the geophysical data to have much more due control on the output realizations. Further, since the only component of the SA objective function required in our approach is a covariance constraint at small lags, our method has improved convergence and computational efficiency over more traditional methods. Here, we present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on a synthetic data set, and then applied to data collected at the Boise Hydrogeophysical Research Site.

Beyond Conditionality versus Cooperation: Power and Resistance in the Case of EU Mobility Partnerships and Swiss Migration Partnerships

Migration partnerships (MPs) have become a key instrument in global migration governance. In contrast to traditional unilateral approaches, MPs emphasize a more comprehensive and inclusive tackling of migration issues between countries of origin, transit, and destination. Due to this cooperation-oriented concept, most of the existing studies on MPs neglect power questions within partnerships in line with the official discourse, reflecting a broader trend in the international migration governance literature. Others take an instrumentalist view in analysing the power of partnerships or focus on soft power. Illustrated with the examples of the European Mobility Partnerships (EU MPs) and the Swiss Migration Partnerships (CH MPs), we conduct an analysis based on a concept of productive power drawing on post-structural and post-colonial insights. Our main argument is that in contrast to their seemingly consent-oriented and technical character, MPs are sites of intense (discursive) struggles, and (re-)produce meanings, subjects, and resistances. A productive power analysis allows us to move beyond the dichotomy in the literature between coercion and cooperation, as well as between power and resistance more broadly.

Monte Carlo simulation of a clearance box monitor used for nuclear power plant decommissioning.

When decommissioning a nuclear facility it is important to be able to estimate activity levels of potentially radioactive samples and compare with clearance values defined by regulatory authorities. This paper presents a method of calibrating a clearance box monitor based on practical experimental measurements and Monte Carlo simulations. Adjusting the simulation for experimental data obtained using a simple point source permits the computation of absolute calibration factors for more complex geometries with an accuracy of a bit more than 20%. The uncertainty of the calibration factor can be improved to about 10% when the simulation is used relatively, in direct comparison with a measurement performed in the same geometry but with another nuclide. The simulation can also be used to validate the experimental calibration procedure when the sample is supposed to be homogeneous but the calibration factor is derived from a plate phantom. For more realistic geometries, like a small gravel dumpster, Monte Carlo simulation shows that the calibration factor obtained with a larger homogeneous phantom is correct within about 20%, if sample density is taken as the influencing parameter. Finally, simulation can be used to estimate the effect of a contamination hotspot. The research supporting this paper shows that activity could be largely underestimated in the event of a centrally-located hotspot and overestimated for a peripherally-located hotspot if the sample is assumed to be homogeneously contaminated. This demonstrates the usefulness of being able to complement experimental methods with Monte Carlo simulations in order to estimate calibration factors that cannot be directly measured because of a lack of available material or specific geometries.

The branch-site test of positive selection is surprisingly robust but lacks power under synonymous substitution saturation and variation in GC

Positive selection is widely estimated from protein coding sequence alignments by the nonsynonymous-to-synonymous ratio omega. Increasingly elaborate codon models are used in a likelihood framework for this estimation. Although there is widespread concern about the robustness of the estimation of the omega ratio, more efforts are needed to estimate this robustness, especially in the context of complex models. Here, we focused on the branch-site codon model. We investigated its robustness on a large set of simulated data. First, we investigated the impact of sequence divergence. We found evidence of underestimation of the synonymous substitution rate for values as small as 0.5, with a slight increase in false positives for the branch-site test. When dS increases further, underestimation of dS is worse, but false positives decrease. Interestingly, the detection of true positives follows a similar distribution, with a maximum for intermediary values of dS. Thus, high dS is more of a concern for a loss of power (false negatives) than for false positives of the test. Second, we investigated the impact of GC content. We showed that there is no significant difference of false positives between high GC (up to similar to 80%) and low GC (similar to 30%) genes. Moreover, neither shifts of GC content on a specific branch nor major shifts in GC along the gene sequence generate many false positives. Our results confirm that the branch-site is a very conservative test.

Accuracy of indirect estimation of power output from uphill performance in cycling.

PURPOSE: To use measurement by cycling power meters (Pmes) to evaluate the accuracy of commonly used models for estimating uphill cycling power (Pest). Experiments were designed to explore the influence of wind speed and steepness of climb on accuracy of Pest. The authors hypothesized that the random error in Pest would be largely influenced by the windy conditions, the bias would be diminished in steeper climbs, and windy conditions would induce larger bias in Pest. METHODS: Sixteen well-trained cyclists performed 15 uphill-cycling trials (range: length 1.3-6.3 km, slope 4.4-10.7%) in a random order. Trials included different riding position in a group (lead or follow) and different wind speeds. Pmes was quantified using a power meter, and Pest was calculated with a methodology used by journalists reporting on the Tour de France. RESULTS: Overall, the difference between Pmes and Pest was -0.95% (95%CI: -10.4%, +8.5%) for all trials and 0.24% (-6.1%, +6.6%) in conditions without wind (<2 m/s). The relationship between percent slope and the error between Pest and Pmes were considered trivial. CONCLUSIONS: Aerodynamic drag (affected by wind velocity and orientation, frontal area, drafting, and speed) is the most confounding factor. The mean estimated values are close to the power-output values measured by power meters, but the random error is between ±6% and ±10%. Moreover, at the power outputs (>400 W) produced by professional riders, this error is likely to be higher. This observation calls into question the validity of releasing individual values without reporting the range of random errors.

Childhood cancer and nuclear power plants in Switzerland: a census-based cohort study.

BACKGROUND: Previous studies on childhood cancer and nuclear power plants (NPPs) produced conflicting results. We used a cohort approach to examine whether residence near NPPs was associated with leukaemia or any childhood cancer in Switzerland. METHODS: We computed person-years at risk for children aged 0-15 years born in Switzerland from 1985 to 2009, based on the Swiss censuses 1990 and 2000 and identified cancer cases from the Swiss Childhood Cancer Registry. We geo-coded place of residence at birth and calculated incidence rate ratios (IRRs) with 95% confidence intervals (CIs) comparing the risk of cancer in children born <5 km, 5-10 km and 10-15 km from the nearest NPP with children born >15 km away, using Poisson regression models. RESULTS: We included 2925 children diagnosed with cancer during 21 117 524 person-years of follow-up; 953 (32.6%) had leukaemia. Eight and 12 children diagnosed with leukaemia at ages 0-4 and 0-15 years, and 18 and 31 children diagnosed with any cancer were born <5 km from a NPP. Compared with children born >15 km away, the IRRs (95% CI) for leukaemia in 0-4 and 0-15 year olds were 1.20 (0.60-2.41) and 1.05 (0.60-1.86), respectively. For any cancer, corresponding IRRs were 0.97 (0.61-1.54) and 0.89 (0.63-1.27). There was no evidence of a dose-response relationship with distance (P > 0.30). Results were similar for residence at diagnosis and at birth, and when adjusted for potential confounders. Results from sensitivity analyses were consistent with main results. CONCLUSIONS: This nationwide cohort study found little evidence of an association between residence near NPPs and the risk of leukaemia or any childhood cancer.