Stroke-related Effects on Maximal Dynamic Hip Flexor Fatigability and Functional Implications

Introduction: Stroke-related changes in maximal dynamic hip flexor muscle fatigability may be more relevant functionally than isometric hip flexor fatigability. Methods: Ten chronic stroke survivors performed 5 sets of 30 hip flexion maximal dynamic voluntary contractions (MDVC). A maximal isometric voluntary contraction (MIVC) was performed before and after completion of the dynamic contractions. Both the paretic and nonparetic legs were tested. Results: Reduction in hip flexion MDVC torque in the paretic leg (44.7%) was larger than the nonparetic leg (31.7%). The paretic leg had a larger reduction in rectus femoris EMG (28.9%) between the first and last set of MDVCs than the nonparetic leg (7.4%). Reduction in paretic leg MDVC torque was correlated with self-selected walking speed (r2 = 0.43), while reduction in MIVC torque was not (r2 = 0.11). Conclusions: Reductions in maximal dynamic torque of paretic hip flexors may be a better predictor of walking function than reductions in maximal isometric contractions

The Stroke-related Effects of Hip Flexion Fatigue on Over Ground Walking

Individuals post stroke often rely more on hip flexors for limb advancement during walking due to distal weakness but the effects of muscle fatigue in this group is not known. The purpose of this study was to quantify how stroke affects the influence of hip flexor fatigue on over ground walking kinematics and performance and muscle activation. Ten individuals with chronic stroke and 10 without stroke (controls) participated in the study. Maximal walking speed, walking distance, muscle electromyograms (EMG), and lower extremity joint kinematics were compared before and after dynamic, submaximal fatiguing contractions of the hip flexors (30% maximal load) performed until failure of the task. Task duration and decline in hip flexion maximal voluntary contraction (MVC) and power were used to assess fatigue. The stroke and control groups had similar task durations and percent reductions in MVC force following fatiguing contractions. Compared with controls, individuals with stroke had larger percent reductions in maximal walking speed, greater decrements in hip range of motion and peak velocity during swing, greater decrements in ankle velocity and lack of modulation of hip flexor EMG following fatiguing dynamic hip flexion contractions. For a given level of fatigue, the impact on walking function was more profound in individuals with stroke than neurologically intact individuals, and a decreased ability to up regulate hip flexor muscle activity may contribute. These data highlight the importance of monitoring the effect of hip flexor muscle activity during exercise or performance of activities of daily living on walking function post stroke

Self-Learning Compensation of Hysteretic and Creep Nonlinearities in Piezoelectric Actuators

Abstract: Solid-state actuators based on active materials allow high operating frequencies with nearly unlimited displacement resolution. This predestines them, for example, for application in highly precise positioning systems. However, the nonlinear behaviour in such systems is mainly attributed to actuator transfer characteristics as a result of driving with high amplitudes. In this paper a novel self-learning compensation method based on the socalled modified Prandtl-Ishlinskii approach is presented, which allows extensive compensation of the complex solid-state actuator hysteretic and creep nonlinearities during operation. Finally, it is shown in an example involving a two-axis piezoelectric parallel-kinematic positioning system that this compensation substantially decreases the deviations of the actual output displacements from the desired displacements of the controlled system

The design, hysteresis modeling and control of a novel SMA-fishing-line actuator

Fishing line can be combined with shape memory alloy (SMA) to form novel artificial muscle actuators which have low cost, are lightweight and soft. They can be applied in bionic, wearable and rehabilitation robots, and can reduce system weight and cost, increase power-to-weight ratio and offer safer physical human-robot interaction. However, these actuators possess several disadvantages, for example fishing line based actuators possess low strength and are complex to drive, and SMA possesses a low percentage contraction and has high hysteresis. This paper presents a novel artificial actuator (known as an SMA-fishing-line) made of fishing line and SMA twisted then coiled together, which can be driven directly by an electrical voltage. Its output force can reach 2.65N at 7.4V drive voltage, and the percentage contraction at 4V driven voltage with a 3N load is 7.53%. An antagonistic bionic joint driven by the novel SMA-fishing-line actuators is presented, and based on an extended unparallel Prandtl-Ishlinskii (EUPI) model, its hysteresis behavior is established, and the error ratio of the EUPI model is determined to be 6.3%. A Joule heat model of the SMA-fishing-line is also presented, and the maximum error of the established model is 0.510mm. Based on this accurate hysteresis model, a composite PID controller consisting of PID and an integral inverse (I-I) compensator is proposed and its performance is compared with a traditional PID controller through simulations and experimentation. These results show that the composite PID controller possesses higher control precision than basic PID, and is feasible for implementation in an SMA-fishing-line driven antagonistic bionic joint

A machine learning and chemometrics assisted interpretation of spectroscopic data: a NMR-based metabolomics platform for the assessment of Brazilian propolis

In this work, a metabolomics dataset from 1H nuclear magnetic resonance spectroscopy of Brazilian propolis was analyzed using machine learning algorithms, including feature selection and classification methods. Partial least square-discriminant analysis (PLS-DA), random forest (RF), and wrapper methods combining decision trees and rules with evolutionary algorithms (EA) showed to be complementary approaches, allowing to obtain relevant information as to the importance of a given set of features, mostly related to the structural fingerprint of aliphatic and aromatic compounds typically found in propolis, e.g., fatty acids and phenolic compounds. The feature selection and decision tree-based algorithms used appear to be suitable tools for building classification models for the Brazilian propolis metabolomics regarding its geographic origin, with consistency, high accuracy, and avoiding redundant information as to the metabolic signature of relevant compounds.The work is partially funded by ERDF -European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEstOE/ EEI/UI0752/2011. RC's work is funded by a PhD grant from the Portuguese FCT ( ref. SFRH/BD/66201/2009)

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.Financial support for this investigation by National Council for Scientific and Technological Development (CNPq), Coordination for the Improvement of Higher Education Personnel (CAPES), Brazilian Biosciences National Laboratory (LNBioCNPEM/MCTI), Foundation for Support of Scientific and Technological Research in the State of Santa Catarina (FAPESC), and Portuguese Foundation for Science and Technology (FCT) is acknowledged. The research fellowship granted by CNPq to the first author is also acknowledged. The work was partially funded by a CNPq and FCT agreement through the PropMine grant

The Affective Impact of Financial Skewness on Neural Activity and Choice

Few finance theories consider the influence of “skewness” (or large and asymmetric but unlikely outcomes) on financial choice. We investigated the impact of skewed gambles on subjects' neural activity, self-reported affective responses, and subsequent preferences using functional magnetic resonance imaging (FMRI). Neurally, skewed gambles elicited more anterior insula activation than symmetric gambles equated for expected value and variance, and positively skewed gambles also specifically elicited more nucleus accumbens (NAcc) activation than negatively skewed gambles. Affectively, positively skewed gambles elicited more positive arousal and negatively skewed gambles elicited more negative arousal than symmetric gambles equated for expected value and variance. Subjects also preferred positively skewed gambles more, but negatively skewed gambles less than symmetric gambles of equal expected value. Individual differences in both NAcc activity and positive arousal predicted preferences for positively skewed gambles. These findings support an anticipatory affect account in which statistical properties of gambles—including skewness—can influence neural activity, affective responses, and ultimately, choice

New anti-perovskite-type Superconductor ZnNyNi3

We have synthesized a new superconductor ZnNyNi3 with Tc ~3 K. The crystal structure has the same anti-perovskite-type such as MgCNi3 and CdCNi3. As far as we know, this is the third superconducting material in Ni-based anti-perovskite series. For this material, superconducting parameters, lower-critical field Hc1(0), upper-critical field Hc2(0), coherence length x(0), penetration depth l(0), and Gintzburg -Landau parameter k(0) have been experimentally determined.Comment: 13 pages, 3 figures, 1 tabl

Gain and Loss Learning Differentially Contribute to Life Financial Outcomes

Emerging findings imply that distinct neurobehavioral systems process gains and losses. This study investigated whether individual differences in gain learning and loss learning might contribute to different life financial outcomes (i.e., assets versus debt). In a community sample of healthy adults (n = 75), rapid learners had smaller debt-to-asset ratios overall. More specific analyses, however, revealed that those who learned rapidly about gains had more assets, while those who learned rapidly about losses had less debt. These distinct associations remained strong even after controlling for potential cognitive (e.g., intelligence, memory, and risk preferences) and socioeconomic (e.g., age, sex, ethnicity, income, education) confounds. Self-reported measures of assets and debt were additionally validated with credit report data in a subset of subjects. These findings support the notion that different gain and loss learning systems may exert a cumulative influence on distinct life financial outcomes

Under pressure: Response urgency modulates striatal and insula activity during decision-making under risk

When deciding whether to bet in situations that involve potential monetary loss or gain (mixed gambles), a subjective sense of pressure can influence the evaluation of the expected utility associated with each choice option. Here, we explored how gambling decisions, their psychophysiological and neural counterparts are modulated by an induced sense of urgency to respond. Urgency influenced decision times and evoked heart rate responses, interacting with the expected value of each gamble. Using functional MRI, we observed that this interaction was associated with changes in the activity of the striatum, a critical region for both reward and choice selection, and within the insula, a region implicated as the substrate of affective feelings arising from interoceptive signals which influence motivational behavior. Our findings bridge current psychophysiological and neurobiological models of value representation and action-programming, identifying the striatum and insular cortex as the key substrates of decision-making under risk and urgency