EMG gait data from indwelling electrodes is attenuated over time and changes independent of any experimental effect

The effect of time on the validity of electromyography (EMG) signals from indwelling fine-wire electrodes has not been explored. This is important because experiments using intramuscular electrodes are often long and biochemical and mechanical factors, may impair measurement accuracy over time. Measures over extended periods might therefore be erroneous. Twelve healthy participants (age = 33 ± 8 years) walked for 50 min at a controlled speed. Fine-wire electrodes were inserted into tibialis anterior and a surface EMG sensor attached near the fine-wire insertion site. EMG signals progressively and significantly decreased with time with the fine-wire electrode, but not the surface electrode. For the fine-wire electrode, after 25 min mean amplitude had reduced by 11% (p < 0.001) and after 50 min by 16% (p < 0.001), and peak amplitude reduced 22% at 20 min (p = 0.006) and 37% at 50 min (p < 0.001). Reduced amplitude with indwelling EMG without concurrent changes in surface EMG signal suggests an important inconsistency in data from fine-wire EMG electrodes. Changes in EMG signal will occur over time independent of the experimental condition and this questions their use in experiments of more than 30 min. These results should impact on experimental study design. They also invite reinterpretation of prior literature and sensor innovation to improve measurement performance.     

颧根与侧颅底重要结构的解剖学测量

目的：通过测量颧根与侧颅底各重要结构间的距离关系,为临床侧颅底外科手术治疗提供定位参考。方法：取成人颅骨标本50例（去颅盖标本8例,整颅42例）100侧,用游标卡尺、圆规和直尺测量颧根与侧颅底重要结构的距离。结果：实验测得左右侧颧根与外耳门前缘中点、乳突尖、茎突、翼突外侧板根部、舌下神经管外口、茎乳孔、颈静脉孔外缘、颈动脉管外口后缘、棘孔、卵圆孔、破裂孔的距离分别为22.30±2.84mm和22.02±3.27mm、40.37±3.21mm和40.56±3.54mm、32.53±2.78mm和32.92±2.68mm、35.13±3.14mm和35.19±2.74mm、49.29±2.88mm和48.98±2.87mm、32.92±2.44mm和33.05±2.61mm、35.15±2.86mm和34.68±3.13mm、33.17±2.78mm和33.17±2.72mm、28.83±2.62mm和28.68±2.63mm、31.15±2.76mm和31.49±2.73mm、43.67±3.32mm和44.15±3.02mm,左右侧数据无差异（P〉0.05）。结论：颧根可以作为侧颅底外科手术的定位标志,为直视条件下经颞下等入路的侧颅底外科手术提供解剖学依据。     

Measurement error and confidence intervals for ROC curves

Measurement error in a continuous test variable may bias estimates of the summary properties of receiver operating characteristics (ROC) curves. Typically, unbiased measurement error will reduce the diagnostic potential of a continuous test variable. This paper explores the effects of possibly heterogenous measurement error on estimated ROC curves for binormal test variables. Corrected estimators for specific points on the curve are derived under the assumption of known or estimated measurement variances for individual test results. These estimators and associated confidence intervals do not depend on normal assumptions for the distribution of the measurement error and are shown to be approximately unbiased for moderate size samples in a simulation study. An application from a study of emerging imaging modalities in breast cancer is used to demonstrate the new techniques.     

A longitudinal measurement error model with a semicontinuous covariate

Covariate measurement error in regression is typically assumed to act in an additive or multiplicative manner on the true covariate value. However, such an assumption does not hold for the measurement error of sleep-disordered breathing (SDB) in the Wisconsin Sleep Cohort Study (WSCS). The true covariate is the severity of SDB, and the observed surrogate is the number of breathing pauses per unit time of sleep, which has a nonnegative semicontinuous distribution with a point mass at zero. We propose a latent variable measurement error model for the error structure in this situation and implement it in a linear mixed model. The estimation procedure is similar to regression calibration but involves a distributional assumption for the latent variable. Modeling and model-fitting strategies are explored and illustrated through an example from the WSCS.     

On the effect of misclassification on bias of perfectly measured covariates in regression

This note clarifies under what conditions a naive analysis using a misclassified predictor will induce bias for the regression coefficients of other perfectly measured predictors in the model. An apparent discrepancy between some previous results and a result for measurement error of a continuous variable in linear regression is resolved. We show that similar to the linear setting, misclassification (even when not related to the other predictors) induces bias in the coefficients of the perfectly measured predictors, unless the misclassified variable and the perfectly measured predictors are independent. Conditional and asymptotic biases are discussed in the case of linear regression, and explored numerically for an example relating birth weight to the weight and smoking status of the mother.     

Predicting and correcting bias caused by measurement error in line transect sampling using multiplicative error models

Line transect sampling is one of the most widely used methods for animal abundance assessment. Standard estimation methods assume certain detection on the transect, no animal movement, and no measurement errors. Failure of the assumptions can cause substantial bias. In this work, the effect of error measurement on line transect estimators is investigated. Based on considerations of the process generating the errors, a multiplicative error model is presented and a simple way of correcting estimates based on knowledge of the error distribution is proposed. Using beta models for the error distribution, the effect of errors and of the proposed correction is assessed by simulation. Adequate confidence intervals for the corrected estimates are obtained using a bootstrap variance estimate for the correction and the delta method. As noted by Chen (1998, Biometrics 54, 899-908), even unbiased estimators of the distances might lead to biased density estimators, depending on the actual error distribution. In contrast with the findings of Chen, who used an additive model, unbiased estimation of distances, given a multiplicative model, lead to overestimation of density. Some error distributions result in observed distance distributions that make efficient estimation impossible, by removing the shoulder present in the original detection function. This indicates the need to improve field methods to reduce measurement error. An application of the new methods to a real data set is presented.     

Parametric and Parametrically Smoothed Distribution-Free Proportional Hazard Models with Discrete Data

This paper discusses discrete time proportional hazard models and suggests a new class of flexible hazard functions. Explicitly modeling the discreteness of data is important since standard continuous models are biased; allowing for flexibility in the hazard estimation is desirable since strong parametric restrictions are likely to be similarly misleading. Simulation compare continuous and discrete models when data are generated by grouping and demonstrate that simple approximations recover underlying hazards well and outperform nonparametric maximum likelihood estimates in term of mean squared error.     

Monitoring with lichens – Conductivity methods assess salt and heavy metal damage more efficiently than chlorophyll fluorescence

In the lab, we exposed three foliose lichen species, Lobaria pulmonaria, Parmelia sulcata and Xanthoria aureola, to 0, 0.01, 0.2, and 0.6 M NaCl in combinations with copper and zinc (0, 10, 100, 500 μM). High salt concentrations adversely affected the lichen membrane integrity as measured by conductivity methods, whereas the potential photosystem II efficiency (Fv/Fm) was tolerant. High light was necessary to reduce Fv/Fm in thalli exposed to salt, whereas high light did not aggravate the conductivity. The seashore species X. aureola was much more resistant to salt than the old forest species L. pulmonaria. With respect to Cu and Zn, used concentrations had no (P. sulcata, X. aureola) or small (L. pulmonaria) effects on Fv/Fm. However, both heavy metals substantially increased conductivity in all species, consistent with membrane damage. Thus, the conductivity method detected high salt, high copper and high zinc stress much more efficiently than did the chlorophyll fluorescence method. This suggests that membrane integrity of the mycobiont is more sensitive to salt and heavy metal stress than potential photosystem II efficiency of its autotrophic partners.     

SAR measurement due to mobile phone exposure in a simulated biological media

The specific absorption rate (SAR) measurements are carried out for compliance testing of personal 3G Mobile phone. The accuracy of this experimental setup has been checked by comparing the SAR in 10 gm of simulated tissue and an arbitrary shaped box. This has been carried out using a 3G mobile Phone at 1718.5 MHz, in a medium simulating brain and muscle phantom. The SAR measurement system consists of a stepper motor to move a monopole E-field probe in two dimensions inside an arbitrary shaped box. The phantom is filled with appropriate frequency-specific fluids with measured electrical properties (dielectric constant and conductivity). That is close to the average for gray and white matters of the brain at the frequencies of interest (1718.5 MHz). Induced fields are measured using a specially designed monopole probe in its close vicinity. The probe is immersed in the phantom material. The measured data for induced fields are used to compute SAR values at various locations with respect to the mobile phone location. It is concluded that these SAR values are position dependent and well below the safety criteria prescribed for human exposure.     

Assessment of Data Quality: Errors of Measurement and Errors of Process

As projects progress from pilot studies with few simple variables and small samples, the research process as a whole becomes qualitatively more complex and subject to an array of contamination by errors and mistakes. Data usually undergo a series of manipulations (e.g., recording, computer entry, transmission) prior to final statistical analysis. The process, then, consists of numerous operations only ending with eventual statistical analysis and write-up. We present a means of estimating the impact of process error in the same terms as psychometric reliability and discuss the implications for reducing the impact of errors on overall data quality.