Sprinting with prosthetic versus biological legs: insight from experimental data.
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ABSTRACT:
SUBMITTER: Beck ON
PROVIDER: S-EPMC8728174 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This paper proposes an extremum seeking controller (ESC) for simultaneously tuning the feedback control gains of a knee-ankle powered prosthetic leg using continuous-phase controllers. Previously, the proportional gains of the continuous-phase controller for each joint were tuned manually by trial-and-error, which required several iterations to achieve a balance between the prosthetic leg tracking error performance and the user's comfort. In this paper, a convex objective function is developed, which incorporates these two goals. We present a theoretical analysis demonstrating that the quasi-steady-state value of the objective function is independent of the controller damping gains. Furthermore, we prove the stability of error dynamics of continuous-phase controlled powered prosthetic leg along with ESC dynamics using averaging and singular perturbation tools. The developed cost function is then minimized by ESC in real-time to simultaneously tune the proportional gains of the knee and ankle joints. The optimum of the objective function shifts at different walking speeds, and our algorithm is suitably fast to track these changes, providing real-time adaptation for different walking conditions. Benchtop and walking experiments verify the effectiveness of the proposed ESC across various walking speeds.
| S-EPMC7546444 | biostudies-literature
Project description:Powered lower-limb assistive devices, such as prostheses and exoskeletons, are a promising option for helping mobility-impaired individuals regain functional gait. Gait phase prediction plays an important role in controlling these devices and evaluating whether the device generates a gait similar to that of individuals with intact limbs. This study proposes a gait phase prediction method based on a deep neural network (DNN). The long short-term memory (LSTM)-based model predicts a continuous gait phase from the 250 ms history of the vertical load, thigh angle, knee angle, and ankle angle, commonly available on powered lower-limb assistive devices. One unified model was trained using publicly available benchmark datasets containing intact limb gaits for level-ground walking (LGW) and ascending stairs (SA). A phase prediction error of 1.28% for all benchmark datasets was obtained. The model was subsequently applied to a state machine-controlled powered prosthetic leg dataset collected from four individuals with unilateral transfemoral amputation. The gait phase prediction results (a phase prediction error of 5.70%) indicate that the model trained on benchmark data can be used for a system not included in the training dataset with no post-processing, such as model adaptation. Furthermore, it provided information regarding evaluation of the controller: whether the prosthetic leg generated normal gait. In conclusion, the proposed gait phase prediction model will facilitate efficient gait prediction and evaluation of controllers for powered lower-limb assistive devices.
| S-EPMC9849563 | biostudies-literature
Project description:Study objectivesTo compare pregabalin versus placebo and pramipexole for reducing restless legs syndrome (RLS)-related sleep disturbance.DesignRandomized, double-blinded, crossover trial.SettingTwenty-three US sleep centers.ParticipantsEighty-five individuals with moderate to severe idiopathic RLS and associated sleep disturbance.InterventionsParticipants were randomized across 6 treatment sequences comprising three 4-week periods on pregabalin 300 mg/day (n = 75), pramipexole 0.5 mg/day (n = 76), or placebo (n = 73).Measurements and resultsPolysomnography was conducted over 2 nights at the end of each period. Primary (wake after sleep onset [WASO], pregabalin vs placebo) and key secondary endpoints were analyzed for statistical significance, with descriptive statistics for other endpoints. Pregabalin improved sleep maintenance, demonstrated by reductions in WASO (-27.1 min vs placebo [P < 0.0001]; -26.9 vs pramipexole) and number of awakenings after sleep onset (-2.7 vs placebo; -7.9 vs pramipexole [P < 0.0001]) by polysomnography, and an increase in subjective total sleep time (30.8 min vs placebo [P < 0.0001]; 26.8 vs pramipexole). Pregabalin also increased slow wave sleep duration (20.9 min vs placebo; 32.1 vs pramipexole [P < 0.0001]). Reduction in periodic limb movement arousal index (PLMAI) with pregabalin was similar to pramipexole and greater than placebo (-3.7 PLMA/h [P < 0.0001]), although reduction in total PLM in sleep was less than for pramipexole.ConclusionsThis study demonstrated improvements in objective and subjective measures of sleep maintenance and sleep architecture with pregabalin compared with placebo and pramipexole. Effects of pregabalin on periodic limb movement arousal index were comparable to pramipexole.Trial registrationClinicalTrials.gov identifier, NCT00991276; http://clinicaltrials.gov/show/NCT00991276.
| S-EPMC4044751 | biostudies-literature
Project description:Chromatin immunoprecipitation (ChIP) followed by microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) allows genome-wide discovery of protein-DNA interactions such as transcription factor bindings and histone modifications. Previous reports only compared a small number of profiles, and little has been done to compare histone modification profiles generated by the two technologies or to assess the impact of input DNA libraries in ChIP-seq analysis. Here, we performed a systematic analysis of a modENCODE dataset consisting of 31 pairs of ChIP-chip/ChIP-seq profiles of the coactivator CBP, RNA polymerase II (RNA PolII), and six histone modifications across four developmental stages of Drosophila melanogaster.Both technologies produce highly reproducible profiles within each platform, ChIP-seq generally produces profiles with a better signal-to-noise ratio, and allows detection of more peaks and narrower peaks. The set of peaks identified by the two technologies can be significantly different, but the extent to which they differ varies depending on the factor and the analysis algorithm. Importantly, we found that there is a significant variation among multiple sequencing profiles of input DNA libraries and that this variation most likely arises from both differences in experimental condition and sequencing depth. We further show that using an inappropriate input DNA profile can impact the average signal profiles around genomic features and peak calling results, highlighting the importance of having high quality input DNA data for normalization in ChIP-seq analysis.Our findings highlight the biases present in each of the platforms, show the variability that can arise from both technology and analysis methods, and emphasize the importance of obtaining high quality and deeply sequenced input DNA libraries for ChIP-seq analysis.
| S-EPMC3053263 | biostudies-literature
Project description:The sequencing of the full transcriptome (RNA-seq) has become the preferred choice for the measurement of genome-wide gene expression. Despite its widespread use, challenges remain in RNA-seq data analysis. One often-overlooked aspect is normalization. Despite the fact that a variety of factors or 'batch effects' can contribute unwanted variation to the data, commonly used RNA-seq normalization methods only correct for sequencing depth. The study of gene expression is particularly problematic when it is influenced simultaneously by a variety of biological factors in addition to the one of interest. Using examples from experimental neuroscience, we show that batch effects can dominate the signal of interest; and that the choice of normalization method affects the power and reproducibility of the results. While commonly used global normalization methods are not able to adequately normalize the data, more recently developed RNA-seq normalization can. We focus on one particular method, RUVSeq and show that it is able to increase power and biological insight of the results. Finally, we provide a tutorial outlining the implementation of RUVSeq normalization that is applicable to a broad range of studies as well as meta-analysis of publicly available data.
| S-EPMC4652761 | biostudies-literature
Project description:BackgroundThe kinetic modeling of biological systems is mainly composed of three steps that proceed iteratively: model building, simulation and analysis. In the first step, it is usually required to set initial metabolite concentrations, and to assign kinetic rate laws, along with estimating parameter values using kinetic data through optimization when these are not known. Although the rapid development of high-throughput methods has generated much omics data, experimentalists present only a summary of obtained results for publication, the experimental data files are not usually submitted to any public repository, or simply not available at all. In order to automatize as much as possible the steps of building kinetic models, there is a growing requirement in the systems biology community for easily exchanging data in combination with models, which represents the main motivation of KiMoSys development.DescriptionKiMoSys is a user-friendly platform that includes a public data repository of published experimental data, containing concentration data of metabolites and enzymes and flux data. It was designed to ensure data management, storage and sharing for a wider systems biology community. This community repository offers a web-based interface and upload facility to turn available data into publicly accessible, centralized and structured-format data files. Moreover, it compiles and integrates available kinetic models associated with the data.KiMoSys also integrates some tools to facilitate the kinetic model construction process of large-scale metabolic networks, especially when the systems biologists perform computational research.ConclusionsKiMoSys is a web-based system that integrates a public data and associated model(s) repository with computational tools, providing the systems biology community with a novel application facilitating data storage and sharing, thus supporting construction of ODE-based kinetic models and collaborative research projects.The web application implemented using Ruby on Rails framework is freely available for web access at http://kimosys.org, along with its full documentation.
| S-EPMC4236735 | biostudies-literature
Project description:BackgroundTraumatic arm amputations can be treated with replantation or surgical formalization of the stump with or without subsequent prosthetic fitting. In the literature, many authors suggest the superiority of replantation. This systematic review compared available literature to analyze whether replantation is functionally and psychologically more profitable than formalization and prosthetic fitting in patients with traumatic arm amputation.MethodsFunctional outcome and satisfaction levels were recorded of patients with amputation levels below elbow, through elbow, and above elbow.ResultsFunctional outcomes of 301 replantation patients and 172 prosthesis patients were obtained. In the replantation group, good or excellent functional scores were reported in 39% of above elbow, 55% of through elbow, and 50% of below elbow amputation cases. Nearly 100% of patients were satisfied with the replanted limb. In the prosthesis group, full use of the prosthesis was attained in 48% of above elbow and in 89% of below elbow amputation patients. Here, 29% of patients elected not to use the prosthesis for reasons including pain and functional superfluity. In both replantation patients and prosthesis wearers, a below elbow amputation yielded better functional results than higher amputation levels.ConclusionsReplantation of a traumatically amputated arm leads to good function and higher satisfaction rates than a prosthesis, regardless of the objective functional outcome. Sensation and psychological well-being seem the two major advantages of replantation over a prosthesis. The current review of the available literature shows that in carefully selected cases replantation could be the preferred option of treatment.
| S-EPMC4560425 | biostudies-literature
Project description:Biofilm removal efficacy of vortexing alone was compared with the standard vortexing-sonication procedure. Among 135 removed prostheses, 35 were diagnosed with infection and 100 with aseptic failure. At a cutoff of ≥ 50 CFU/ml, sonication was more sensitive than vortexing (60% versus 40%, P = 0.151), while the specificity was 99% for both methods.
| S-EPMC3553884 | biostudies-literature
Project description:In this article, three different data sets are presented to evaluate a representative of openly accessible 3D printed prosthetic hand. The first data set includes grasping force measurements of human hand and low-cost 3D printed hand. Three grasping functions were evaluated, spherical, cylindrical, and precision grasps. The experimental test was performed using a wearable tactile sensor. The second data set includes the numerical analysis of prosthetic fingers made from Acrylonitrile Butadiene Styrene (ABS) and Polylactic Acid (PLA) materials under different carrying loads. The numerical analyses were carried out by LS-DYNA software. The files can be used for the prosthetic fingers' evaluation and for the selection of suitable material. The third data set includes the experimental tensile test of ABS and PLA materials. The mechanical properties were calculated from the results, which can be used in the design and fabrication of products from these materials. All the datasets are available from Harvard Dataverse: https://doi.org/10.7910/DVN/GCPAIL.
| S-EPMC6610678 | biostudies-literature
Project description:UnlabelledParametric statistical methods are common in human pain research. They require normally distributed data, but this assumption is rarely tested. The current study analyzes the appropriateness of parametric testing for outcomes from the cold pressor test (CPT), a common human experimental pain test. We systematically reviewed published CPT studies to quantify how often researchers test for normality and how often they use parametric versus nonparametric tests. We then measured the normality of CPT data from 7 independent small to medium cohorts and 1 study of >10,000 subjects. We then examined the ability of 2 common mathematical transformations to normalize our skewed data sets. Lastly, we performed Monte Carlo simulations on a representative data set to compare the statistical power of the parametric t-test versus the nonparametric Wilcoxon Mann-Whitney test. We found that only 39% of published CPT studies (47/122) mentioned checking data distribution, yet 72% (88/122) used parametric statistics. Furthermore, among our 8 data sets, CPT outcomes were virtually always nonnormally distributed, and mathematical transformations were largely ineffective in normalizing them. The simulations demonstrated that the nonparametric Wilcoxon Mann-Whitney test had greater statistical power than the parametric t-test for all scenarios tested: For small effect sizes, the Wilcoxon Mann-Whitney test had up to 300% more power.PerspectiveThese results demonstrate that parametric analyses of CPT data are routine but incorrect and that they likely increase the chances of failing to detect significant between-group differences. They suggest that nonparametric analyses become standard for CPT studies and that assumptions of normality be routinely tested for other types of pain outcomes as well.
| S-EPMC4456202 | biostudies-literature