Kinematic variability in running: a caution regarding use of the spanning set measure

The spanning set technique quantifies intertrial variability as the span between polynomial curves representing upper and lower standard deviation curves of a repeated movement. This study aimed to assess the validity of the spanning set technique in quantifying variability and specifically to determine its sensitivity to variability presented at different phases of a movement cycle. Knee angle data were recorded from a male participant completing 12 overground running trials. Variability was added to each running trial at five different phases of the running stride. Ten variability magnitudes were also used to assess the effect of variability magnitude on the spanning set measure. Variability was quantified in all trials using mean deviation and the spanning set measure. Results of a repeated-measures ANOVA showed significant differences between the spanning set score for trials using different phases of added variability. In contrast, mean deviation values showed no difference related to the phase of added variability. Therefore, the spanning set technique cannot be recommended as a valid measure of intertrial movement variability.     

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data

Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package.     

The relationship between smoothness and performance during the practice of a lower limb obstacle avoidance task

The relationship between performance (movement time) and smoothness was examined as subjects (n = 8) practiced a simple lower limb obstacle avoidance task. Smoothness was quantified by endpoint 3D jerk-cost, partitioned into magnitudinal and directional components. Data were collected with two WATSMART cameras at a sampling rate of 200 Hz for three sets of two trial blocks, including trials 1, 2, 13, 14, 25, and 26. Ten practice trials were performed between blocks of recorded trials. A DLT method was used to reconstruct 3D position coordinates of the fifth metatarsal of the subject's right (dominant) foot, considered to be the endpoint. After the data were smoothed with a fourth order, zero lag Butterworth filter, the time period was normalized so that a comparison of jerk-cost could be made between trials. Very rapid decreases in both movement time and jerk-cost measures were followed by gradual decreases, indicating that the movement became smoother as performance improved. Correlation coefficients between movement time and the various components of jerkcost ranged from 0.70 to 0.78, supporting the hypothesis that moving more smoothly enables a person to move more rapidly during an obstacle avoidance task.     

Generation of “Virtual” Control Groups for Single Arm Prostate Cancer Adjuvant Trials

It is difficult to construct a control group for trials of adjuvant therapy (Rx) of prostate cancer after radical prostatectomy (RP) due to ethical issues and patient acceptance. We utilized 8 curve-fitting models to estimate the time to 60%, 65%, … 95% chance of progression free survival (PFS) based on the data derived from Kattan post-RP nomogram. The 8 models were systematically applied to a training set of 153 post-RP cases without adjuvant Rx to develop 8 subsets of cases (reference case sets) whose observed PFS times were most accurately predicted by each model. To prepare a virtual control group for a single-arm adjuvant Rx trial, we first select the optimal model for the trial cases based on the minimum weighted Euclidean distance between the trial case set and the reference case set in terms of clinical features, and then compare the virtual PFS times calculated by the optimum model with the observed PFSs of the trial cases by the logrank test. The method was validated using an independent dataset of 155 post-RP patients without adjuvant Rx. We then applied the method to patients on a Phase II trial of adjuvant chemo-hormonal Rx post RP, which indicated that the adjuvant Rx is highly effective in prolonging PFS after RP in patients at high risk for prostate cancer recurrence. The method can accurately generate control groups for single-arm, post-RP adjuvant Rx trials for prostate cancer, facilitating development of new therapeutic strategies.     

Effects of low-intensity resistance exercise under acute systemic hypoxia on hormonal responses

Previous studies have shown that low-intensity resistance exercises with vascular occlusion and slow movement effectively increase muscular size and strength. Researchers have speculated that local hypoxia by occlusion and slow movement may contribute to such adaptations via promoting anabolic hormone secretions by the local accumulation of metabolites. In this study, we determined the effects of low-intensity resistance exercise under acute systemic hypoxia on metabolic and hormonal responses. Eight male subjects participated in 2 experimental trials: (a) low-intensity resistance exercise while breathing normoxic air (normoxic resistance exercise [NR]), (b) low-intensity resistance exercise while breathing 13% oxygen (hypoxic resistance exercise [HR]). The resistance exercises (bench press and leg press) consisted of 14 repetitions for 5 sets at 50% of maximum strength with 1 minute of rest between sets. Blood lactate (LA), serum growth hormone (GH), norepinephrine (NE), testosterone, and cortisol concentrations were measured before normoxia and hypoxia exposures; 15 minutes after the exposures; and at 0, 15, and 30 minutes after the exercises. The LA levels significantly increased after exercises in both trials (p ≤ 0.05). The area under the curve for LA after exercises was significantly higher in the HR trial than in the NR trial (p ≤ 0.05). The GH significantly increased only after the HR trial (p ≤ 0.05). The NE and testosterone significantly increased after the exercises in both trials (p ≤ 0.05). Cortisol did not significantly change in both trials. These results suggest that low-intensity resistance exercise in the hypoxic condition caused greater metabolic and hormonal responses than that in the normoxic condition. Coaches may consider low-intensity resistance exercise under systemic hypoxia as a potential training method for athletes who need to maintain muscle mass and strength during the long in-season.     

Reliability of an exercise protocol designed to evaluate resistance exercise performance

The purpose of the present investigation was to evaluate the reliability a protocol used to assess short-term resistance exercise performance. Six men participated in this investigation after giving their consent. Subjects (N = 6) performed 6 sets of leg extensions at 80% of 10 repetition maximum (RM). Ten repetitions were performed during the first 3 sets; during the last 3 sets subjects exercised to fatigue. Ninety seconds of seated passive recovery separated each set. Subsequently, 2 experimental trials were conducted in which the exercise protocol was identical to the familiarization trial. There was a significant decline in performance from set 4 (13.5 +/- 0.9 reps) to set 5 (11.9 +/- 0.8 reps) and set 4 to set 6 (10.8 +/- 1.0 reps), suggesting that the protocol did induce fatigue. The intraclass correlations were 0.992, 0.992, and 0.993 for the fourth, fifth, and sixth sets, respectively. The average coefficients of variation for the fourth, fifth, and sixth sets were 6.7, 2.7, and 7.1%, respectively. These data suggest that the resistance training protocol used in this investigation is reliable and may be useful in evaluating interventions designed to improve fatigue resistance.     

Advantage of single-trial models for response to selection in wheat breeding multi-environment trials

An investigation was conducted to evaluate the impact of experimental designs and spatial analyses (single-trial models) of the response to selection for grain yield in the northern grains region of Australia (Queensland and northern New South Wales). Two sets of multi-environment experiments were considered. One set, based on 33 trials conducted from 1994 to 1996, was used to represent the testing system of the wheat breeding program and is referred to as the multi-environment trial (MET). The second set, based on 47 trials conducted from 1986 to 1993, sampled a more diverse set of years and management regimes and was used to represent the target population of environments (TPE). There were 18 genotypes in common between the MET and TPE sets of trials. From indirect selection theory, the phenotypic correlation coefficient between the MET and TPE single-trial adjusted genotype means [r _p(MT)] was used to determine the effect of the single-trial model on the expected indirect response to selection for grain yield in the TPE based on selection in the MET. Five single-trial models were considered: randomised complete block (RCB), incomplete block (IB), spatial analysis (SS), spatial analysis with a measurement error (SSM) and a combination of spatial analysis and experimental design information to identify the preferred (PF) model. Bootstrap-resampling methodology was used to construct multiple MET data sets, ranging in size from 2 to 20 environments per MET sample. The size and environmental composition of the MET and the single-trial model influenced the r _p(MT). On average, the PF model resulted in a higher r _p(MT) than the IB, SS and SSM models, which were in turn superior to the RCB model for MET sizes based on fewer than ten environments. For METs based on ten or more environments, the r _p(MT) was similar for all single-trial models.Communicated by H.C. Becker     

Clinical trials in developing countries: Discussions at the '9th International Symposium on Long Term Clinical Trials', London, UK, 19-20 June 2000

This symposium provided a useful forum for the discussion of issues relating to the design and conduct of clinical trials. There is a need for greater awareness of the complexity of modern day trials, in which a host of statistical, logistical, regulatory and ethical issues are involved. Issues discussed ranged from the effect of sample size on the outcome, and subgroup analysis, to defining and maintaining discrete endpoints. Some useful debate centred on the use of meta-analysis and the current limitations of combining information from different data sets. This brought up the subjects of trial registries and raw data repositories for all clinical trials. Progress and relevance of the Cochrane collaboration were reviewed. The economics of clinical trials was another important topic. Regulatory issues such as the role of data and safety monitoring boards (DSMB) and the guidelines in place for effective data monitoring and progress analysis were discussed. Representatives of government organisations and industry gave both European and American perspectives. This report however focuses specifically on the section devoted to the subject of clinical trials in developing countries.     

Adaptation to sudden unexpected loading of the low back--the effects of repeated trials

The purpose of this study was to investigate short-term changes in reactions to sudden unexpected loading of the low back. The study utilized a set-up where a horizontal force of 58 N pointing forward suddenly was applied to the upper part of the subject's trunk. EMG activity from the erector spinae muscles and trunk movement data were recorded during 10 trials for 19 subjects. The analysis included EMG reaction time, mean rectified EMG amplitude during the period 50-250 ms after the sudden loading, and time elapsed until stopping of the forward movement of the trunk (stopping time). Reaction time means ranged from 66 to 97 ms (79+/-9 ms), and no difference was found between the trials. Conversely, the mean stopping time for the first trial (468 ms) was significantly higher than for trials 3-10 (359- 371 ms), and the average EMG amplitude during the period 50-250 ms after the sudden loading was lower for the first trial. This study showed that some subjects adapted to sudden unexpected loadings of the low back through a reduction in stopping time and a progression in EMG response during the first few trials. This possible adaptation to repeated trials have been overlooked in previous studies.     

Evaluation of alternative technical markers for the pelvic coordinate system

In this study, we evaluated alternative technical markers for the motion analysis of the pelvic segment. Thirteen subjects walked eight times while tri-dimensional kinematics were recorded for one stride of each trial. Five marker sets were evaluated, and we compared the tilt, obliquity, and rotation angles of the pelvis segment: (1) standard: markers at the anterior and posterior superior iliac spines (ASIS and PSIS); (2) markers at the PSIS and at the hip joint centers, HJCs (estimated by a functional method and described with clusters of markers at the thighs); (3) markers at the PSIS and HJCs (estimated by a predictive method and described with clusters of markers at the thighs); (4) markers at the PSIS and HJCs (estimated by a predictive method and described with skin-mounted markers at the thighs based on the Helen-Hayes marker set); (5) markers at the PSIS and at the iliac spines. Concerning the pelvic angles, evaluation of the alternative technical marker sets evinced that all marker sets demonstrated similar precision across trials (about 1°) but different accuracies (ranging from 1° to 3°) in comparison to the standard marker set. We suggest that all the investigated marker sets are reliable alternatives to the standard pelvic marker set.     

Normalizing temporal patterns to analyze sit-to-stand movements by using registration of functional data

Functional data analysis techniques provide an alternative way of representing movement and movement variability as a function of time. In particular, the registration of functional data provides a local normalization of time functions. This normalization transforms a set of curves, records of repeated trials, yielding a new set of curves that only vary in terms of amplitude. Therefore, main events occur at the "same time" for all transformed curves and interesting features of individual recordings remain after averaging processes. This paper presents an application of the registration process to the analysis of the vertical forces exerted on the ground by both feet during the sit-to-stand movement. This movement is particularly interesting in functional evaluations related to balance control, lower extremity dysfunction or low-back pain.     

Analysis of yield and oil from a series of canola breeding trials. Part I. Fitting factor analytic mixed models with pedigree information

In this paper multiplicative mixed models have been used for the analysis of multi-environment trial (MET) data for canola oil and grain yield. Information on pedigrees has been included to allow for the modelling of additive and nonadditive genetic effects. The MET data set included a total of 19 trials (synonymous with sites or environments), which were sown across southern Australia in 2007 and 2008. Each trial was designed as a p-rep design using DiGGeR with the default prespecified spatial model. Lines in their first year of testing were unreplicated, whereas there were two or three replications of advanced lines or varieties. Pedigree information on a total of 578 entries was available, and there were 69 entries that had unknown pedigrees. The degree of inbreeding varied from 0 (55 entries) to nearly fully inbred (337 entries). Subsamples of 2 g harvested grain were taken from each plot for determination of seed oil percentage by near infrared reflectance spectroscopy. The MET analysis for both yield and oil modelled genetic effects in different trials using factor analytic models and the residual plot effects for each trial were modelled using spatial techniques. Models in which pedigree information was included provided significantly better fits to both yield and oil data.     

A comparative study of onion varieties in relation to bolting and yield when grown from sets

Two trials are described, each of twenty-five varieties tested during two seasons and the intervening winter storage period. Sets were produced from seed in the-first season and planted to produce mature bulbs in the second. In the second seasons of both trials, the effects of time of planting of sets were also investigated as part of a factorial experiment. In the later trial the effects of set size were also tested. Data are presented of varietal differences in bolting, yield, earliness and of behaviour of the sets in storage. The effects of set size and of late planting on these characteristics are also recorded. Some practical recommendations are made: thus, among those tested, varieties were found which bolted little and gave high yields when grown from sets, but it is emphasized that names of varieties as listed in seedmen's catalogues may not be reliable. Efforts are being made to maintain and improve by selection the most satisfactory of these strains. Late planting of sets is not recommended, for, although bolting was effectively controlled by this means, the yields were much reduced. Plants grown from large sets tended to bolt more than those from small, as has been shown by earlier workers; while on the contrary the yields from large and small sets were on the average alike. Comparing different varieties, the highest gross yields of all were produced from the large sets of non-bolting varieties; but for highest yields of bulbs free from flower stalks small sets should be used and this is advisable for all varieties. During storage the large sets lost less percentage weight than the small but they sprouted much more, and this is considered the more serious defect. The storage data demonstrate an additional disadvantage of late planting, for this involves longer storage and both sprouting and weight losses increase rapidly during late spring.     

Haptic Exploratory Behavior During Object Discrimination: A Novel Automatic Annotation Method

In order to acquire information concerning the geometry and material of handheld objects, people tend to execute stereotypical hand movement patterns called haptic Exploratory Procedures (EPs). Manual annotation of haptic exploration trials with these EPs is a laborious task that is affected by subjectivity, attentional lapses, and viewing angle limitations. In this paper we propose an automatic EP annotation method based on position and orientation data from motion tracking sensors placed on both hands and inside a stimulus. A set of kinematic variables is computed from these data and compared to sets of predefined criteria for each of four EPs. Whenever all criteria for a specific EP are met, it is assumed that that particular hand movement pattern was performed. This method is applied to data from an experiment where blindfolded participants haptically discriminated between objects differing in hardness, roughness, volume, and weight. In order to validate the method, its output is compared to manual annotation based on video recordings of the same trials. Although mean pairwise agreement is less between human-automatic pairs than between human-human pairs (55.7% vs 74.5%), the proposed method performs much better than random annotation (2.4%). Furthermore, each EP is linked to a specific object property for which it is optimal (e.g., Lateral Motion for roughness). We found that the percentage of trials where the expected EP was found does not differ between manual and automatic annotation. For now, this method cannot yet completely replace a manual annotation procedure. However, it could be used as a starting point that can be supplemented by manual annotation.     

OpenCyto: An Open Source Infrastructure for Scalable,Robust, Reproducible,and Automated,End-to-End Flow Cytometry Data Analysis

Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.

This is a PLOS Computational Biology Software Article.

     

Study protocol of a mixed-methods evaluation of a cluster randomized trial to improve the safety of NSAID and antiplatelet prescribing: data-driven quality improvement in primary care

ABSTRACT: BACKGROUND: Trials of complex interventions are criticized for being 'black box', so the UK Medical Research Council recommends carrying out a process evaluation to explain the trial findings. We believe it is good practice to pre-specify and publish process evaluation protocols to set standards and minimize bias. Unlike protocols for trials, little guidance or standards exist for the reporting of process evaluations. This paper presents the mixed-method process evaluation protocol of a cluster randomized trial, drawing on a framework designed by the authors.Methods/designThis mixed-method evaluation is based on four research questions and maps data collection to a logic model of how the data-driven quality improvement in primary care (DQIP) intervention is expected to work. Data collection will be predominately by qualitative case studies in eight to ten of the trial practices, focus groups with patients affected by the intervention and quantitative analysis of routine practice data, trial outcome and questionnaire data and data from the DQIP intervention. DISCUSSION: We believe that pre-specifying the intentions of a process evaluation can help to minimize bias arising from potentially misleading post-hoc analysis. We recognize it is also important to retain flexibility to examine the unexpected and the unintended. From that perspective, a mixed-methods evaluation allows the combination of exploratory and flexible qualitative work, and more pre-specified quantitative analysis, with each method contributing to the design, implementation and interpretation of the other.As well as strengthening the study the authors hope to stimulate discussion among their academic colleagues about publishing protocols for evaluations of randomized trials of complex interventions.Data-driven quality improvement in primary care trial registrationClinicalTrials.gov: NCT01425502.     

Retrospective analysis of the relationships among the test environments of the Southern Queensland sugarcane breeding programme

Repeatability of aspects of genotype by environment (GxE) interactions is an important factor to be assessed in designing more efficient selection programmes. Sugar yield data from multi environment trials (METs) which were part of the sugarcane breeding programme in southern Queensland were analysed. Data were obtained from 71 environments consisting of trials planted from 1986 to 1989. Retrospective analysis on these data was conducted to assess the repeatability of the clone by environment (CxE) interactions over locations and years. This analysis focussed on identifying similarities among test environments in the way they discriminated among clones for sugar yield. Analyses of variance and pattern analyses on environments over years based on standardised data were conducted. The pattern analyses were done sequentially according to the accumulated data sets over years. Squared Euclidean distances among environments were averaged over data sets and years before pattern analyses across the data sets were conducted. A graphical methodology was developed to present the results of the cumulative historical analysis. CxE interactions of a magnitude which affected selection decisions were present in each data set studied. Pattern analyses on cumulative data sets identified environmental groupings that were based on geographical positions. Each location generated a different pattern of discrimination among the clones. These results emphasised the importance of clone by location (CxL) interactions in southern Queensland and the need to concentrate more on testing across locations than on ratooning ability within a location. The classifications identified similarities among ratoon crops within a location, differences among locations and differences between ratoon crops and their plant crop (PC). This suggested that some aspects of CxL and clone by crop-year (CxY) interactions were repeatable across years. The potential applications of these results to increase efficiency of the sugarcane breeding programme, such as the possibility of applying indirect selection among environments generating similar discrimination among clones, are discussed.Abbreviations GxE Genotype-by-environment interactions - METs multi-environment trials - CxE clone-by-en-vironment interactions - CxL clone-by-location interactions - PC plant crop - CxY clone-by-crop-year interactions - CxLxF clone-by-location-by-crop-year interactions - SYT substation yield trials - BSES Bureau of SugarExperiment Stations     

Analyzing multi-environment variety trials using randomization-derived mixed models

Of interest is the analysis of results of a series of experiments repeated at several environments with the same set of plant varieties. Suppose that the experiments, multi-environment variety trials, are all conducted in resolvable incomplete block (IB) designs. Following the randomization approach adopted in Caliński and Kageyama (2000, Lecture Notes in Statistics, 150), two models for analyzing such trial data can be considered. One is derived under a complete additivity assumption, the other takes into account possible different responses of the varieties to variable environmental conditions. The analysis under the first, the standard model, does not provide answers to questions related to the performance of the individual varieties at different environments. These can be considered when using the more general second model. The purpose of this article is to devise interesting parameter estimation and hypothesis testing procedures under that more realistic model. Its application is illustrated by a thorough analysis of a set of data from a winter wheat series of trials.     

Accuracy and precision of a wrist movement when vibrotactile prompts inform movement speed

Abstract

Purpose: This study investigated the effect of movement speed on task accuracy and precision when participants were provided temporally oriented vibrotactile prompts. Materials and methods: Participants recreated a simple wrist flexion/extension movement at fast and slow speeds based on target patterns conveyed via vibrating motors affixed to the forearm. Each participant was given five performance-blinded trials to complete the task at each speed. Movement accuracy (root mean square error) and precision (standard deviation) were calculated for each trial in both the spatial and temporal domains. Results: 28 participants completed the study. Results showed temporal accuracy and precision improved with movement speed (both: fast?>?slow, p?<?0.01), while all measures improved across trials (temporal accuracy and precision: trial 1?<?all other trials, p?<?0.05; spatial accuracy: trial 1 and 2?<?all other trials, p?<?0.05; spatial precision: trial 1?<?all other trials, p?<?0.05). Conclusions: Overall, temporal and spatial results indicate participants quickly recreated and maintained the desired pattern regardless of speed. Additionally, movement speed seems to influence movement accuracy and precision, particularly within the temporal domain. These results highlight the potential of vibrotactile prompts in rehabilitation paradigms aimed at motor re-education.