Kevin Hayes

Functional Data Analysis of Running Kinematics in Chronic Achilles Tendon Injury

PURPOSE Chronic Achilles tendon (AT) injuries are common, but kinematic studies confirming the exact mechanisms of injury and how orthoses are effective are lacking. Existing analysis often relies on discrete measures and provides an incomplete analysis because many of the data are discarded. Functional data analysis (FDA) views the entire dataset as a function, thus retaining the main features of the curve. This study uses FDA to examine the mechanisms of chronic AT injury and the functional effects of orthoses. METHODS Twelve subjects with a history of chronic AT injury and 12 controls ran on a treadmill with and without customized orthoses. Three-dimensional kinematic data were obtained using Qualisys motion capture systems operating at 200 Hz. Ankle dorsiflexion (ADF), knee flexion (KF), eversion (EV), calcaneal, and leg abduction angles were calculated across stance. These angle data were represented as functions, and functional principal components were extracted to describe the factors accounting for variation in the data. These components were compared in AT versus control groups and orthoses versus no-orthoses conditions. RESULTS Kinematic differences were observed, with the AT group showing greater EV, ADF, and KF during stance, whereas orthoses reduced ADF but increased EV. Different patterns of frontal plane variation distinguished between groups and conditions. CONCLUSION Results provided additional information about movement patterns compared to traditional approaches and identified the first half of stance as the most relevant period in injury occurrence. The study showed evidence that variability is related to the presence of injury in this clinical population. Further FDA focusing on within-subject variation is recommended to gain greater insight into the role of variability in injury occurrence.

Movement variability and skills monitoring in sports

The aim of this paper was to present a review on the role that movement variability (MV) plays in the analysis of sports movement and in the monitoring of the athletes skills. MV has been traditionally considered an unwanted noise to be reduced, but recent studies have re-evaluated its role and have tried to understand whether it may contain important information about the neuro-musculo-skeletal organisation. Issues concerning both views of MV, different approaches for analysing it and future perspectives are discussed. Information regarding the nature of the MV is vital in the analysis of sports movements/motor skills, and the way in which these movements are analysed and the MV subsequently quantified is dependent on the movement in question and the issues the researcher is trying to address. In dealing with a number of issues regarding MV, this paper has also raised a number of questions which are still to be addressed.

Residuals for the linear model with general covariance structure

A general theory is presented for residuals from the general linear model with correlated errors. It is demonstrated that there are two fundamental types of residual associated with this model, referred to here as the marginal and the conditional residual. These measure respectively the distance to the global aspects of the model as represented by the expected value and the local aspects as represented by the conditional expected value. These residuals may be multivariate. Some important dualities are developed which have simple implications for diagnostics. The results are illustrated by reference to model diagnostics in time series and in classical multivariate analysis with independent cases.

Temporal trends in misclassification patterns of measured and self-report based body mass index categories--findings from three population surveys in Ireland.

BackgroundAs the use of self-reported data to classify obesity continues, the temporal change in the accuracy of self-report measurement when compared to clinical measurement remains unclear. The objective of this study was to examine temporal trends in misclassification patterns, as well as sensitivity and specificity, of clinically measured versus self-report based body mass index (BMI) from three national lifestyle surveys over a 10-year period.MethodsThe Surveys of Lifestyle Attitudes and Nutrition (SLÁN) were interview based cross-sectional survey/measurements involving nationally representative samples in 1998, 2002 and 2007. Data from a subsample of both self-reported and measured height and weight were available from 66 men and 142 women in 1998, 147 men and 184 women in 2002 and 909 men and 1128 women in 2007. Respondents were classified into the BMI categories normal (< 25 kg m-2), overweight (25- < 30 kg m-2) and obese (≥ 30 kg m-2).ResultsUnderreporting of BMI increased across the three surveys (14%→21%→24%; p = 0.002). Sensitivity scores for the normal category exceeded 94% in all three surveys but decreased for the overweight (75%→68%→66%) and obese categories (80%→64%→53%). Simultaneously, specificity levels remained high.ConclusionsBMI values based on self-reported determinations of height and weight in population samples are underestimating the true prevalence of the obesity epidemic and this underestimation is increasing with time. The decreased sensitivity and consistently high specificity scores in the obese category across time, highlights the limitation of self-report based BMI classifications and the need for simple, readily comprehensible indicators of obesity.

Cation Diffusion Facilitator family: Structure and function.

The Cation Diffusion Facilitators (CDFs) form a family of membrane‐bound proteins capable of transporting zinc and other heavy metal ions. Involved in metal tolerance/resistance by efflux of ions, CDF proteins share a two‐modular architecture consisting of a transmembrane domain (TMD) and C‐terminal domain (CTD) that protrudes into the cytoplasm. Discovery of a Zn2+ and Cd2+ CDF transporter from a marine bacterium Maricaulis maris that does not possess the CTD questions current perceptions regarding this family of proteins. This article describes a new, CTD‐lacking subfamily of CDFs and our current knowledge about this family of proteins in the view of these findings.

Height and Weight Bias: The Influence of Time

Background We have previously identified in a study of both self-reported body mass index (BMI) and clinically measured BMI that the sensitivity score in the obese category has declined over a 10-year period. It is known that self-reported weight is significantly lower that measured weight and that self-reported height is significantly higher than measured height. The purpose of this study is to establish if self-reported height bias or weight bias, or both, is responsible for the declining sensitivity in the obese category between self-reported and clinically measured BMI. Methods We report on self-reported and clinically measured height and weight from three waves of the Surveys of Lifestyle Attitudes and Nutrition (SLÁN) involving a nationally representative sample of Irish adults. Data were available from 66 men and 142 women in 1998, 147 men and 184 women in 2002 and 909 men and 1128 women in 2007. Respondents were classified into BMI categories normal (<25 kg/m2), overweight (25–<30 kg/m2) and obese (≥30 kg/m2). Results Self-reported height bias has remained stable over time regardless of gender, age or clinical BMI category. Self-reported weight bias increases over time for both genders and in all age groups. The increased weight bias is most notable in the obese category. Conclusions BMI underestimation is increasing across time. Knowledge that the widening gap between self-reported BMI and measured BMI is attributable to an increased weight bias brings us one step closer to accurately estimating true obesity levels in the population using self-reported data.

Functional data analysis of knee joint kinematics in the vertical jump

Abstract Understanding of the motor development process is usually based on descriptive studies using either cross‐sectional or longitudinal designs. These data typically consist of sets of measurements on groups of individuals gathered during the performance of a single task. A natural approach is to represent the set of measurements for an individual as a single entity, a function. In practice, however, this approach is seldom applied. Typically, the analysis proceeds by reducing what are intrinsically functional responses to a single summary measurement and then using this to draw conclusions. As a result, many potentially informative data are ignored. Functional data analysis (FDA) is an emerging field in statistics that focuses on treating an entire sequence of measurements for an experimental unit as a single function. Therefore, functional data analysis appears to be inherently suitable for analysing kinematic data. In this paper, the key concepts and procedures of functional data analysis are introduced and illustrated using data obtained from a cross‐sectional study on the development of the vertical jump.

Functional data analysis of joint coordination in the development of vertical jump performance

Mastery of complex motor skills requires effective development of inter-segment coordination patterns. These coordination patterns can be described and quantified using various methods, including descriptive angle–angle diagrams, conjugate cross-correlations, vector coding, normalized root mean squared error techniques and, as in this study, functional data analysis procedures. Lower limb kinematic data were obtained for 49 children performing the vertical jump. Participants were assigned to developmental stages using the criteria of Gallahue and Ozmun (2002). Inter-segment joint coordination data consisting of pairs of joint angle–time data were smoothed using B-splines and the resulting bivariate functions were analysed using functional principal component analysis and stepwise discriminant analysis. The results of the analysis showed that the knee–hip joint coordination pattern was most effective at discriminating between developmental stages. The results provide support for the application of functional data analysis techniques in the analysis of joint coordination or time series type data.

Common functional principal components analysis: A new approach to analyzing human movement data

In many human movement studies angle-time series data on several groups of individuals are measured. Current methods to compare groups include comparisons of the mean value in each group or use multivariate techniques such as principal components analysis and perform tests on the principal component scores. Such methods have been useful, though discard a large amount of information. Functional data analysis (FDA) is an emerging statistical analysis technique in human movement research which treats the angle-time series data as a function rather than a series of discrete measurements. This approach retains all of the information in the data. Functional principal components analysis (FPCA) is an extension of multivariate principal components analysis which examines the variability of a sample of curves and has been used to examine differences in movement patterns of several groups of individuals. Currently the functional principal components (FPCs) for each group are either determined separately (yielding components that are group-specific), or by combining the data for all groups and determining the FPCs of the combined data (yielding components that summarize the entire data set). The group-specific FPCs contain both within and between group variation and issues arise when comparing FPCs across groups when the order of the FPCs alter in each group. The FPCs of the combined data may not adequately describe all groups of individuals and comparisons between groups typically use t-tests of the mean FPC scores in each group. When these differences are statistically non-significant it can be difficult to determine how a particular intervention is affecting movement patterns or how injured subjects differ from controls. In this paper we aim to perform FPCA in a manner allowing sensible comparisons between groups of curves. A statistical technique called common functional principal components analysis (CFPCA) is implemented. CFPCA identifies the common sources of variation evident across groups but allows the order of each component to change for a particular group. This allows for the direct comparison of components across groups. We use our method to analyze a biomechanical data set examining the mechanisms of chronic Achilles tendon injury and the functional effects of orthoses.

Characterization, correction and de novo assembly of an Oxford Nanopore genomic dataset from Agrobacterium tumefaciens

The MinION is a portable single-molecule DNA sequencing instrument that was released by Oxford Nanopore Technologies in 2014, producing long sequencing reads by measuring changes in ionic flow when single-stranded DNA molecules translocate through the pores. While MinION long reads have an error rate substantially higher than the ones produced by short-read sequencing technologies, they can generate de novo assemblies of microbial genomes, after an initial correction step that includes alignment of Illumina sequencing data or detection of overlaps between Oxford Nanopore reads to improve accuracy. In this study, MinION reads were generated from the multi-chromosome genome of Agrobacterium tumefaciens strain LBA4404. Errors in the consensus two-directional (sense and antisense) “2D” sequences were first characterized by way of comparison with an internal reference assembly. Both Illumina-based correction and self-correction were performed and the resulting corrected reads assembled into high-quality hybrid and non-hybrid assemblies. Corrected read datasets and assemblies were subsequently compared. The results shown here indicate that both hybrid and non-hybrid methods can be used to assemble Oxford Nanopore reads into informative multi-chromosome assemblies, each with slightly different outcomes in terms of contiguity and accuracy.