The margin for error when releasing the high bar for dismounts

In Men's Artistic Gymnastics the current trend in elite high bar dismounts is to perform two somersaults in an extended body shape with a number of twists. Two techniques have been identified in the backward giant circles leading up to release for these dismounts (J. Biomech. 32 (1999) 811). At the Sydney 2000 Olympic Games 95% of gymnasts used the "scooped" backward giant circle technique rather than the "traditional" technique. It was speculated that the advantage gained from the scooped technique was an increased margin for error when releasing the high bar. A four segment planar simulation model of the gymnast and high bar was used to determine the margin for error when releasing the bar in performances at the Sydney 2000 Olympic Games. The eight high bar finalists and the three gymnasts who used the traditional backward giant circle technique were chosen for analysis. Model parameters were optimised to obtain a close match between simulated and actual performances in terms of rotation angle (1.2 degrees ), bar displacements (0.014 m) and release velocities (2%). Each matching simulation was used to determine the time window around the actual point of release for which the model had appropriate release parameters to complete the dismount successfully. The scooped backward giant circle technique resulted in a greater margin for error (release window 88-157 ms) when releasing the bar compared to the traditional technique (release window 73-84 ms).     

Sodium channel gating: no margin for error

Voltage-gated Na(+) channels are the workhorses of spike generation and propagation in excitable cells. Mutations in Na(+) channel genes have been identified in disorders causing episodic dysfunction of heart, skeletal muscle, and brain. Lossin and colleagues from Al George's lab report in this issue of Neuron that three missense mutations of SCN1A found in a dominant epilepsy syndrome disrupt inactivation, thereby producing small persistent inward Na(+) currents that may result in hyperexcitability and seizures.     

Optimization of backward giant circle technique on the asymmetric bars

The release window for a given dismount from the asymmetric bars is the period of time within which release results in a successful dismount. Larger release windows are likely to be associated with more consistent performance because they allow a greater margin for error in timing the release. A computer simulation model was used to investigate optimum technique for maximizing release windows in asymmetric bars dismounts. The model comprised four rigid segments with the elastic properties of the gymnast and bar modeled using damped linear springs. Model parameters were optimized to obtain a close match between simulated and actual performances of three gymnasts in terms of rotation angle (1.5 degrees ), bar displacement (0.014 m), and release velocities (<1%). Three optimizations to maximize the release window were carried out for each gymnast involving no perturbations, 10-ms perturbations, and 20-ms perturbations in the timing of the shoulder and hip joint movements preceding release. It was found that the optimizations robust to 20-ms perturbations produced release windows similar to those of the actual performances whereas the windows for the unperturbed optimizations were up to twice as large. It is concluded that robustness considerations must be included in optimization studies in order to obtain realistic results and that elite performances are likely to be robust to timing perturbations of the order of 20 ms.     

Mechanical energetic processes during the giant swing exercise before dismounts and flight elements on the high bar and the uneven parallel bars.

The purposes of this study were as follows: (1) To study the energy exchange between the body of the gymnast and the high bar and uneven parallel bars during forward and backward giant swings. (2) To examine the differences between the mechanical energy produced and the mechanical energy absorbed by the muscles during forward and backward giant swings on the high bar and the uneven parallel bars. The data were gathered during the gymnastic world championships in 1994. The experimental set up consisted of two video cameras (50 Hz) and two force measurement bars (500 Hz). A total of 101 giant swings before dismounts and flight elements performed by 33 male and 34 female gymnasts were analyzed. There are characteristically two main phases during forward and backward giant swings before dismounts and flight elements. During the first phase energy is transferred from the gymnast's body into the bar. During this phase of the backward giant swing the energy of the system decreases because the amount of energy decrease of the gymnast's body is more than the energy transferred into the high bar. An exception can be seen during the giant swings in which the gymnast used the power technique. During forward giant swings the energy of the system increases during the first phase. This occurs through active flexion of the hipjoint which produced the extra muscular energy. During the second phase energy is transferred from the bar back into the gymnast's body whose total energy increases. An increase in the energy of the system can only be achieved through muscular work. During the second phase of the backward giant swing the energy of the system increases. The forward giant swings performed on the uneven parallel bars showed a large energy loss during this phase. The energy deficit seen during the first phase of the backward giant swing can be improved by using the power technique. To achieve this the athlete must be in a bent position at the start of the giant swing exercise. Through extension at the shoulder and hip joints muscular energy can be put into the system.     

Bias in error estimation when using cross-validation for model selection

Background  

Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate. We have evaluated the validity of using the CV error estimate of the optimized classifier as an estimate of the true error expected on independent data.     

A correction for estimating error when using the Local Pooled Error Statistical Test

Jain et al. introduced the Local Pooled Error (LPE) statistical test designed for use with small sample size microarray gene-expression data. Based on an asymptotic proof, the test multiplicatively adjusts the standard error for a test of differences between two classes of observations by pi/2 due to the use of medians rather than means as measures of central tendency. The adjustment is upwardly biased at small sample sizes, however, producing fewer than expected small P-values with a consequent loss of statistical power. We present an empirical correction to the adjustment factor which removes the bias and produces theoretically expected P-values when distributional assumptions are met. Our adjusted LPE measure should prove useful to ongoing methodological studies designed to improve the LPE's; performance for microarray and proteomics applications and for future work for other high-throughput biotechnologies. AVAILABILITY: The software is implemented in the R language and can be downloaded from the Bioconductor project website (http://www.bioconductor.org).     

The reaction of osteoclasts when releasing osteocytes from osteocytic lacunae in the bone during bone modeling

Osteocytes are released from the osteocytic lacunae when osteoclasts resorb the bone matrix during bone modeling and remodeling. It remains unknown how osteoclasts react when releasing osteocytes during bone modeling, and the fate of these released osteocytes is also unclear. Femoral mid-shafts of 2-day-old kittens were sectioned into serial 0.5 microm-thick semithin or 0.1 microm-thick ultrathin sections, and examined by light microscopy (LM) and transmission electron microscopy (TEM). The sections showed many osteoclasts at the endosteum but there were no osteoblasts. There were many half-released, fully released, half-exposed, and fully exposed osteocytes on the bone surfaces. Many cell-like structures were seen in the cell bodies of osteoclasts by LM, and some semithin sections were re-sectioned into ultrathin sections for re-observation by TEM. By TEM, these were determinated to be mononuclear cells. The serial ultrathin sections showed that the mononuclear cells appeared to be engulfed in osteoclasts on one section but that the cell was connected with the bone surface of the osteocytic lacuna on another section. These results show that the mononuclear cells in the osteoclasts were osteocytes. The present study suggests that osteoclasts engulf some osteocytes but do not engulf others when releasing osteocytes during bone modeling.     

The mammalian Golgi regulates numb signaling in asymmetric cell division by releasing ACBD3 during mitosis

Mammalian neural progenitor cells divide asymmetrically to self-renew and produce a neuron by segregating cytosolic Numb proteins primarily to one daughter cell. Numb signaling specifies progenitor over neuronal fates but, paradoxically, also promotes neuronal differentiation. Here we report that ACBD3 is a Numb partner in cell-fate specification. ACBD3 and Numb proteins interact through an essential Numb domain, and the respective loss- and gain-of-function mutant mice share phenotypic similarities. Interestingly, ACBD3 associates with the Golgi apparatus in neurons and interphase progenitor cells but becomes cytosolic after Golgi fragmentation during mitosis, when Numb activity is needed to distinguish the two daughter cells. Accordingly, cytosolic ACBD3 can act synergistically with Numb to specify cell fates, and its continuing presence during the progenitor cell cycle inhibits neuron production. We propose that Golgi fragmentation and reconstitution during cell cycle differentially regulate Numb signaling through changes in ACBD3 subcellular distribution and represent a mechanism for coupling cell-fate specification and cell-cycle progression.     

Repeated probit regression when covariates are measured with error

This paper develops a model for repeated binary regression when a covariate is measured with error. The model allows for estimating the effect of the true value of the covariate on a repeated binary response. The choice of a probit link for the effect of the error-free covariate, coupled with normal measurement error for the error-free covariate, results in a probit model after integrating over the measurement error distribution. We propose a two-stage estimation procedure where, in the first stage, a linear mixed model is used to fit the repeated covariate. In the second stage, a model for the correlated binary responses conditional on the linear mixed model estimates is fit to the repeated binary data using generalized estimating equations. The approach is demonstrated using nutrient safety data from the Diet Intervention of School Age Children (DISC) study.     

Small area estimation when auxiliary information is measured with error

Small area estimation methods typically combine direct estimatesfrom a survey with predictions from a model in order to obtainestimates of population quantities with reduced mean squarederror. When the auxiliary information used in the model is measuredwith error, using a small area estimator such as the Fay–Herriotestimator while ignoring measurement error may be worse thansimply using the direct estimator. We propose a new small areaestimator that accounts for sampling variability in the auxiliaryinformation, and derive its properties, in particular showingthat it is approximately unbiased. The estimator is appliedto predict quantities measured in the U.S. National Health andNutrition Examination Survey, with auxiliary information fromthe U.S. National Health Interview Survey.     

Allowing for never and episodic consumers when correcting for error in food record measurements of dietary intake

Food records, including 24-hour recalls and diet diaries, are considered to provide generally superior measures of long-term dietary intake relative to questionnaire-based methods. Despite the expense of processing food records, they are increasingly used as the main dietary measurement in nutritional epidemiology, in particular in sub-studies nested within prospective cohorts. Food records are, however, subject to excess reports of zero intake. Measurement error is a serious problem in nutritional epidemiology because of the lack of gold standard measurements and results in biased estimated diet-disease associations. In this paper, a 3-part measurement error model, which we call the never and episodic consumers (NEC) model, is outlined for food records. It allows for both real zeros, due to never consumers, and excess zeros, due to episodic consumers (EC). Repeated measurements are required for some study participants to fit the model. Simulation studies are used to compare the results from using the proposed model to correct for measurement error with the results from 3 alternative approaches: a crude approach using the mean of repeated food record measurements as the exposure, a linear regression calibration (RC) approach, and an EC model which does not allow real zeros. The crude approach results in badly attenuated odds ratio estimates, except in the unlikely situation in which a large number of repeat measurements is available for all participants. Where repeat measurements are available for all participants, the 3 correction methods perform equally well. However, when only a subset of the study population has repeat measurements, the NEC model appears to provide the best method for correcting for measurement error, with the 2 alternative correction methods, in particular the linear RC approach, resulting in greater bias and loss of coverage. The NEC model is extended to include adjustment for measurements from food frequency questionnaires, enabling better estimation of the proportion of never consumers when the number of repeat measurements is small. The methods are applied to 7-day diary measurements of alcohol intake in the EPIC-Norfolk study.     

A potential source of error when estimating ecological production of fishes using back-calculations

Synopsis Fish production estimates determined from weights back-calculated to the time of annulus formation were found to underestimate actual production by 12 to 44%. It is suggested that production should either be estimated from weights at the time of collection or a correction factor should be employed when back-calculating weights.     

On responder analyses when a continuous variable is dichotomized and measurement error is present

In clinical studies results are often reported as proportions of responders, i.e. the proportion of subjects who fulfill a certain response criterion is reported, although the underlying variable of interest is continuous. In this paper, we consider the situation where a subject is defined as a responder if the (error-free) continuous measurements post-treatment are below a certain fraction of (error-free) continuous measurements obtained pre-treatment. Focus is on the one-sample case, but an extension to the two-sample case is also presented. The bias of different estimates for the proportion of responders is derived and compared. In addition, an asymptotically unbiased ML-type estimate for the proportion of responders is presented. The results are illustrated using data obtained in a clinical study investigating pre-menstrual dysphoric disorder (PMDD).     

The Youden Index and the optimal cut-point corrected for measurement error

Random measurement error can attenuate a biomarker's ability to discriminate between diseased and non-diseased populations. A global measure of biomarker effectiveness is the Youden index, the maximum difference between sensitivity, the probability of correctly classifying diseased individuals, and 1-specificity, the probability of incorrectly classifying health individuals. We present an approach for estimating the Youden index and associated optimal cut-point for a normally distributed biomarker that corrects for normally distributed random measurement error. We also provide confidence intervals for these corrected estimates using the delta method and coverage probability through simulation over a variety of situations. Applying these techniques to the biomarker thiobarbituric acid reaction substance (TBARS), a measure of sub-products of lipid peroxidation that has been proposed as a discriminating measurement for cardiovascular disease, yields a 50% increase in diagnostic effectiveness at the optimal cut-point. This result may lead to biomarkers that were once naively considered ineffective becoming useful diagnostic devices.