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.     

Automatic methods for characterization of sexual dimorphism of adult femora: distal femur

Quantifying sex differences in femoral size and shape has extensive applications in forensics and prosthesis design. By applying strong statistical techniques such as principal component analysis (PCA), certain three-dimensional (3D) morphological variations of adult femora can be quantified over various femoral sizes. Coupling this statistical approach with a novel feature generation and extraction technique, localization of statistically significant (p < 0.05) features are automatically defined and measured. Also, predefined anatomical landmarks and surgical axes have been calculated automatically. In all methods, femoral scale is controlled as a possible parameter of shape. By extensively comparing measurements across 92 male and 74 female femora, the dimorphic characteristics of the distal femur are shown. These differences have not been accounted for in many prosthetic systems and consequently these systems have limited sizing accuracy.     

髋骨闭孔沟的临床解剖学测量

目的：对闭孔沟进行解剖学观察和测量,为相关应用提供临床解剖学依据和丰富人类学数据。方法：选取结构完整、解剖标志清晰的髋骨108例,其中男性髋骨59例（左侧36例,右侧23例）,女性髋骨49例（其中左侧20例,右侧29例）,对闭孔沟进行观察并测量其长度和闭孔沟中点宽度。结果：试验测得男性左、右侧闭孔沟长度分别为31．4±3．4mm、32．1±2．4min,女性左、右侧闭孔沟长度分别为19．4±2．2mm、20．8±2．7mm;统计学分析显示同性别左、右侧差异无统计学意义（P〉0．05）,男、女性之间差异有统计学意义（P〈0．05）。男性左、右侧闭孔沟中点宽度分别为9．7±1．6mm、10．7±1．5mm,女性左、右侧闭孔沟中点宽度分别为12．1±1．7mm、11．6±2．0mm;同性别左、右侧及男、女间差异均无统计学意义（P〉0．05）。结论：男、女性闭孔沟测量数据可为闭孔处疾病的诊断和治疗提供应用解剖学的考虑依据并丰富人类学数据。     

植物木质部压力探针测定技术与方法

木质部压力探针技术是目前直接测定植物木质部导管负压的唯一手段。在结构上,木质部压力探针测定系统由精密操作装置、压力探针系统和信号采集—传输一显示系统三大部分组成。其测定原理是将毛细管探针刺入木质部导管,通过传导介质将木质部导管负压传至压力传感器,压力传感器感应压力并将压力信号输出。本文从玻璃毛细管探针的制作、去气泡水的制备以及压力探针的校准、安装、测定等方面详细介绍了木质部压力探针的使用方法和注意事项。     

Does the Spanish version of the SWLS measure the same in Spain and Peru?

AimSatisfaction with life is a measure of protection in older adults. There lies the importance of providing quality instruments. The aim of the study was to evaluate the invariance of the life satisfaction scale (SWLS) in two samples of older adults in Spain and Peru.MethodThe participants were 857 older adults in Spain (mean age = 68.23 years, SD = 5.93) and 336 older adults in Peru (average age = 72.42, SD = 7.07). All multi-group confirmatory factor analyzes were estimated in Mplus 8.0.ResultsThe results indicate the presence of a strict invariance of the one-dimensional structure of the SWLS in samples of older adults in Spain and Peru, which allows for meaningful comparisons of latent means and covariances. Comparison of latent means showed small differences in the construct between the cultural groups.ConclusionsThe SWLS is a valid instrument for intercultural comparisons between Spanish and Peruvian population. The measurement invariance assessment contributes to a better understanding of life satisfaction in populations from different cultural contexts.     

Errors-in-variables in joint population pharmacokinetic/pharmacodynamic modeling

Pharmacokinetic (PK) models describe the relationship between the administered dose and the concentration of drug (and/or metabolite) in the blood as a function of time. Pharmacodynamic (PD) models describe the relationship between the concentration in the blood (or the dose) and the biologic response. Population PK/PD studies aim to determine the sources of variability in the observed concentrations/responses across groups of individuals. In this article, we consider the joint modeling of PK/PD data. The natural approach is to specify a joint model in which the concentration and response data are simultaneously modeled. Unfortunately, this approach may not be optimal if, due to sparsity of concentration data, an overly simple PK model is specified. As an alternative, we propose an errors-in-variables approach in which the observed-concentration data are assumed to be measured with error without reference to a specific PK model. We give an example of an analysis of PK/PD data obtained following administration of an anticoagulant drug. The study was originally carried out in order to make dosage recommendations. The prior for the distribution of the true concentrations, which may incorporate an individual's covariate information, is derived as a predictive distribution from an earlier study. The errors-in-variables approach is compared with the joint modeling approach and more naive methods in which the observed concentrations, or the separately modeled concentrations, are substituted into the response model. Throughout, a Bayesian approach is taken with implementation via Markov chain Monte Carlo methods.     

Semiparametric methods for multiple exposure mismeasurement and a bivariate outcome in HIV vaccine trials

Exposure to infection information is important for estimating vaccine efficacy, but it is difficult to collect and prone to missingness and mismeasurement. We discuss study designs that collect detailed exposure information from only a small subset of participants while collecting crude exposure information from all participants and treat estimation of vaccine efficacy in the missing data/measurement error framework. We extend the discordant partner design for HIV vaccine trials of Golm, Halloran, and Longini (1998, Statistics in Medicine, 17, 2335-2352.) to the more complex augmented trial design of Longini, Datta, and Halloran (1996, Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology 13, 440-447) and Datta, Halloran, and Longini (1998, Statistics in Medicine 17, 185-200). The model for this design includes three exposure covariates and both univariate and bivariate outcomes. We adapt recently developed semiparametric missing data methods of Reilly and Pepe (1995, Biometrika 82, 299 314), Carroll and Wand (1991, Journal of the Royal Statistical Society, Series B 53, 573-585), and Pepe and Fleming (1991, Journal of the American Statistical Association 86, 108-113) to the augmented vaccine trial design. We demonstrate with simulated HIV vaccine trial data the improvements in bias and efficiency when combining the different levels of exposure information to estimate vaccine efficacy for reducing both susceptibility and infectiousness. We show that the semiparametric methods estimate both efficacy parameters without bias when the good exposure information is either missing completely at random or missing at random. The pseudolikelihood method of Carroll and Wand (1991) and Pepe and Fleming (1991) was the more efficient of the two semiparametric methods.     

Shrinkage estimation for functional principal component scores with application to the population kinetics of plasma folate

We present the application of a nonparametric method to performing functional principal component analysis for functional curve data that consist of measurements of a random trajectory for a sample of subjects. This design typically consists of an irregular grid of time points on which repeated measurements are taken for a number of subjects. We introduce shrinkage estimates for the functional principal component scores that serve as the random effects in the model. Scatterplot smoothing methods are used to estimate the mean function and covariance surface of this model. We propose improved estimation in the neighborhood of and at the diagonal of the covariance surface, where the measurement errors are reflected. The presence of additive measurement errors motivates shrinkage estimates for the functional principal component scores. Shrinkage estimates are developed through best linear prediction and in a generalized version, aiming at minimizing one-curve-leave-out prediction error. The estimation of individual trajectories combines data obtained from that individual as well as all other individuals. We apply our methods to new data regarding the analysis of the level of 14C-folate in plasma as a function of time since dosing of healthy adults with a small tracer dose of 14C-folic acid. A time transformation was incorporated to handle design irregularity concerning the time points on which the measurements were taken. The proposed methodology, incorporating shrinkage and data-adaptive features, is seen to be well suited for describing population kinetics of 14C-folate-specific activity and random effects, and can also be applied to other functional data analysis problems.     

Bayesian latent variable models for median regression on multiple outcomes

Often a response of interest cannot be measured directly and it is necessary to rely on multiple surrogates, which can be assumed to be conditionally independent given the latent response and observed covariates. Latent response models typically assume that residual densities are Gaussian. This article proposes a Bayesian median regression modeling approach, which avoids parametric assumptions about residual densities by relying on an approximation based on quantiles. To accommodate within-subject dependency, the quantile response categories of the surrogate outcomes are related to underlying normal variables, which depend on a latent normal response. This underlying Gaussian covariance structure simplifies interpretation and model fitting, without restricting the marginal densities of the surrogate outcomes. A Markov chain Monte Carlo algorithm is proposed for posterior computation, and the methods are applied to single-cell electrophoresis (comet assay) data from a genetic toxicology study.     

Semiparametric regression modeling with mixtures of Berkson and classical error, with application to fallout from the Nevada test site

We construct Bayesian methods for semiparametric modeling of a monotonic regression function when the predictors are measured with classical error. Berkson error, or a mixture of the two. Such methods require a distribution for the unobserved (latent) predictor, a distribution we also model semiparametrically. Such combinations of semiparametric methods for the dose response as well as the latent variable distribution have not been considered in the measurement error literature for any form of measurement error. In addition, our methods represent a new approach to those problems where the measurement error combines Berkson and classical components. While the methods are general, we develop them around a specific application, namely, the study of thyroid disease in relation to radiation fallout from the Nevada test site. We use this data to illustrate our methods, which suggest a point estimate (posterior mean) of relative risk at high doses nearly double that of previous analyses but that also suggest much greater uncertainty in the relative risk.