Submovement Composition of Head Movement

Limb movement is smooth and corrections of movement trajectory and amplitude are barely noticeable midflight. This suggests that skeletomuscular motor commands are smooth in transition, such that the rate of change of acceleration (or jerk) is minimized. Here we applied the methodology of minimum-jerk submovement decomposition to a member of the skeletomuscular family, the head movement. We examined the submovement composition of three types of horizontal head movements generated by nonhuman primates: head-alone tracking, head-gaze pursuit, and eye-head combined gaze shifts. The first two types of head movements tracked a moving target, whereas the last type oriented the head with rapid gaze shifts toward a target fixed in space. During head tracking, the head movement was composed of a series of episodes, each consisting of a distinct, bell-shaped velocity profile (submovement) that rarely overlapped with each other. There was no specific magnitude order in the peak velocities of these submovements. In contrast, during eye-head combined gaze shifts, the head movement was often comprised of overlapping submovements, in which the peak velocity of the primary submovement was always higher than that of the subsequent submovement, consistent with the two-component strategy observed in goal-directed limb movements. These results extend the previous submovement composition studies from limb to head movements, suggesting that submovement composition provides a biologically plausible approach to characterizing the head motor recruitment that can vary depending on task demand.     

Characterizing and Predicting Submovements during Human Three-Dimensional Arm Reaches

We have demonstrated that 3D target-oriented human arm reaches can be represented as linear combinations of discrete submovements, where the submovements are a set of minimum-jerk basis functions for the reaches. We have also demonstrated the ability of deterministic feed-forward Artificial Neural Networks (ANNs) to predict the parameters of the submovements. ANNs were trained using kinematic data obtained experimentally from five human participants making target-directed movements that were decomposed offline into minimum-jerk submovements using an optimization algorithm. Under cross-validation, the ANNs were able to accurately predict the parameters (initiation-time, amplitude, and duration) of the individual submovements. We also demonstrated that the ANNs can together form a closed-loop model of human reaching capable of predicting 3D trajectories with VAF >95.9% and RMSE ≤4.32 cm relative to the actual recorded trajectories. This closed-loop model is a step towards a practical arm trajectory generator based on submovements, and should be useful for the development of future arm prosthetic devices that are controlled by brain computer interfaces or other user interfaces.     

Learning new gait patterns is enhanced by specificity of training rather than progression of task difficulty

The use of motor learning strategies may enhance rehabilitation outcomes of individuals with neurological injuries (e.g., stroke or cerebral palsy). A common strategy to facilitate learning of challenging tasks is to use sequential progression – i.e., initially reduce task difficulty and slowly increase task difficulty until the desired difficulty level is reached. However, the evidence related to the use of such sequential progressions to improve learning is mixed for functional skill learning tasks, especially considering situations where practice duration is limited. Here, we studied the benefits of sequential progression using a functional motor learning task that has been previously used in gait rehabilitation. Three groups of participants (N = 43) learned a novel motor task during treadmill walking using different learning strategies. Participants in the specific group (n = 21) practiced only the criterion task (i.e., matching a target template that was scaled-up by 30%) throughout the training. Participants in the sequential group (n = 11) gradually progressed to the criterion task (from 3% to 30% in increments of 3%), whereas participants in the random group (n = 11) started at 3% and progressed in random increments (involving both increases and decreases in task difficulty) to the criterion task. At the end of training, kinematic tracking performance on the criterion task was evaluated in all participants both with and without visual feedback. Results indicated that the tracking error was significantly lower in the specific group, and no differences were observed between the sequential and the random progression groups. The findings indicate that the amount of practice in the criterion task is more critical than the difficulty and variations of task practice when learning new gait patterns during treadmill walking.     

Avoiding Spurious Submovement Decompositions II: A Scattershot Algorithm

Evidence for the existence of discrete submovements underlying continuous human movement has motivated many attempts to “extract” them. Although they produce visually convincing results, all of the methodologies that have been employed are prone to produce spurious decompositions. In previous work, a branch-and-bound algorithm for submovement extraction, capable of global nonlinear minimization, and hence, capable of avoiding spurious decompositions, was presented [Rohrer and Hogan (Biol Cybern 39:190–199, 2003)]. Here, we present a scattershot-type global nonlinear minimization algorithm that requires approximately four orders of magnitude less time to compute. A sensitivity analysis reveals that the scattershot algorithm can reliably detect changes in submovement parameters over time, e.g., over the course of neuromotor recovery.     

Perturbation-based Markovian Transmission Model for Probing Allosteric Dynamics of Large Macromolecular Assembling: A Study of GroEL-GroES

Large macromolecular assemblies are often important for biological processes in cells. Allosteric communications between different parts of these molecular machines play critical roles in cellular signaling. Although studies of the topology and fluctuation dynamics of coarse-grained residue networks can yield important insights, they do not provide characterization of the time-dependent dynamic behavior of these macromolecular assemblies. Here we develop a novel approach called Perturbation-based Markovian Transmission (PMT) model to study globally the dynamic responses of the macromolecular assemblies. By monitoring simultaneous responses of all residues (>8,000) across many (>6) decades of time spanning from the initial perturbation until reaching equilibrium using a Krylov subspace projection method, we show that this approach can yield rich information. With criteria based on quantitative measurements of relaxation half-time, flow amplitude change, and oscillation dynamics, this approach can identify pivot residues that are important for macromolecular movement, messenger residues that are key to signal mediating, and anchor residues important for binding interactions. Based on a detailed analysis of the GroEL-GroES chaperone system, we found that our predictions have an accuracy of 71–84% judged by independent experimental studies reported in the literature. This approach is general and can be applied to other large macromolecular machineries such as the virus capsid and ribosomal complex.     

Fatigue Effect on Low-Frequency Force Fluctuations and Muscular Oscillations during Rhythmic Isometric Contraction

Continuous force output containing numerous intermittent force pulses is not completely smooth. By characterizing force fluctuation properties and force pulse metrics, this study investigated adaptive changes in trajectory control, both force-generating capacity and force fluctuations, as fatigue progresses. Sixteen healthy subjects (20–24 years old) completed rhythmic isometric gripping with the non-dominant hand to volitional failure. Before and immediately following the fatigue intervention, we measured the gripping force to couple a 0.5 Hz sinusoidal target in the range of 50–100% maximal voluntary contraction. Dynamic force output was off-line decomposed into 1) an ideal force trajectory spectrally identical to the target rate; and 2) a force pulse trace pertaining to force fluctuations and error-correction attempts. The amplitude of ideal force trajectory regarding to force-generating capacity was more suppressed than that of the force pulse trace with increasing fatigue, which also shifted the force pulse trace to lower frequency bands. Multi-scale entropy analysis revealed that the complexity of the force pulse trace at high time scales increased with fatigue, contrary to the decrease in complexity of the force pulse trace at low time scales. Statistical properties of individual force pulses in the spatial and temporal domains varied with muscular fatigue, concurrent with marked suppression of gamma muscular oscillations (40–60 Hz) in the post-fatigue test. In conclusion, this study first reveals that muscular fatigue impairs the amplitude modulation of force pattern generation more than it affects the amplitude responsiveness of fine-tuning a force trajectory. Besides, motor fatigue results disadvantageously in enhancement of motor noises, simplification of short-term force-tuning strategy, and slow responsiveness to force errors, pertaining to dimensional changes in force fluctuations, scaling properties of force pulse, and muscular oscillation.     

Age-related changes in the spatiotemporal structure of simple cyclical graphic movements performed at the maximal rate: I. The increase in the movement rate is accounted for by reduction of the number of submovements in a movement cycle

Right-handed subjects from four age groups, including children aged 5–6, 8–9, and 11–12 years and adults, performed simple cyclical graphic movements of different degrees of coordination and serial complexity at the maximum possible rate with the right and left hands holding the stylus either with their fingers or in the fist. The period of the cyclical movement decreased with age, the decrease depending on which hand (the right or the left) was used and how the stylus was held. The frequency of submovements only slightly depended on the age, was the same for the right and left hands, but did not depend on the type of movement or the way of holding the stylus. The age-related increase in the maximal rate of graphic movements may have been almost entirely accounted for by a decrease in the number of submovements constituting the movement cycle. The results are discussed in terms of the notion of submovements as “elementary units” of graphic movements.     

Level of Satisfaction of Older Persons with Their General Practitioner and Practice: Role of Complexity of Health Problems

Background

Satisfaction is widely used to evaluate and direct delivery of medical care; a complicated relationship exists between patient satisfaction, morbidity and age. This study investigates the relationships between complexity of health problems and level of patient satisfaction of older persons with their general practitioner (GP) and practice.

Methods and Findings

This study is embedded in the ISCOPE (Integrated Systematic Care for Older Persons) study. Enlisted patients aged ≥75 years from 59 practices received a written questionnaire to screen for complex health problems (somatic, functional, psychological and social). For 2664 randomly chosen respondents (median age 82 years; 68% female) information was collected on level of satisfaction (satisfied, neutral, dissatisfied) with their GP and general practice, and demographic and clinical characteristics including complexity of health problems. Of all participants, 4% was dissatisfied with their GP care, 59% neutral and 37% satisfied. Between these three categories no differences were observed in age, gender, country of birth or education level. The percentage of participants dissatisfied with their GP care increased from 0.4% in those with 0 problem domains to 8% in those with 4 domains, i.e. having complex health problems (p<0.001). Per additional health domain with problems, the risk of being dissatisfied increased 1.7 times (95% CI 1.4–2.14; p<0.001). This was independent of age, gender, and demographic and clinical parameters (adjusted OR 1.4, 95% CI 1.1–1.8; p = 0.021).

Conclusion

In older persons, dissatisfaction with general practice is strongly correlated with rising complexity of health problems, independent of age, demographic and clinical parameters. It remains unclear whether complexity of health problems is a patient characteristic influencing the perception of care, or whether the care is unable to handle the demands of these patients. Prospective studies are needed to investigate the causal associations between care organization, patient characteristics, indicators of quality, and patient perceptions.     

BMI as a Mediator of the Relationship between Muscular Fitness and Cardiometabolic Risk in Children: A Mediation Analysis

Objective

Muscular fitness levels have been associated with cardiometabolic risk in children, although whether body weight acts as a confounder or as an intermediate variable in this relationship remains controversial. The aim of this study was to examine whether the association between muscular fitness and cardiometabolic risk factors is mediated by body mass index (BMI).

Design and Methods

Cross-sectional study using a sample of 1158 schoolchildren aged 8-11 years from the province of Cuenca, Spain. We measured anthropometrics and biochemical variables and we calculated a muscular fitness index as the sum of z-scores of handgrip dynamometry/weight and standing long jump, and we estimated a previously validated cardiometabolic risk index (CMRI). Linear regression models were fitted for mediation analysis to assess whether the association between muscular fitness and cardiometabolic risk was mediated by BMI.

Results

Children with normal weight (NW) had a better cardiometabolic risk profile than their overweight (OW) or obese (OB) peers after controlling for muscular fitness. Marginal estimated mean±SE values for NW, OW and OB categories of CMRI were -0.75±0.06<0.84±0.10<2.18±0.16 in boys and -0.73±0.06<0.96±0.10<2.71±0.17 in girls, both p<0.001. Children with higher levels of muscular fitness had a better cardiometabolic risk profile (CMRI marginal estimated mean±SE 1.04±0.13>0.05±0.09>-1.16±0.13 for lower, middle and upper quartiles of muscular fitness in boys and 1.01±0.16>0.10±0.09>-1.02±0.15 in girls, both p<0.001), but differences disappeared when controlling for BMI. BMI acted as a full mediator between muscular fitness and most cardiometabolic risk factors (Sobel test z=-11.44 for boys; z=-11.83 for girls; p<0.001 in CMRI mediation model) and as a partial mediator in the case of waist circumference (Sobel test z=-14.86 for boys; z=-14.51 for girls; p<0.001).

Conclusions

BMI mediates the association between muscular fitness and cardiometabolic risk in schoolchildren. Overall, good muscular fitness is associated with lower cardiometabolic risk, but particularly when accompanied by normal weight.     

Use of Learning Media by Undergraduate Medical Students in Pharmacology: A Prospective Cohort Study

The ubiquity of the internet and computer-based technologies has an increasing impact on higher education and the way students access information for learning. Moreover, there is a paucity of information about the quantitative and qualitative use of learning media by the current student generation. In this study we systematically analyzed the use of digital and non-digital learning resources by undergraduate medical students. Daily online surveys and semi-structured interviews were conducted with a cohort of 338 third year medical students enrolled in a general pharmacology course. Our data demonstrate a predominant use of digital over non-digital learning resources (69 ± 7% vs. 31 ± 7%; p < 0.01) by students. Most used media for learning were lecture slides (26.8 ± 3.0%), apps (22.0 ± 3.7%) and personal notes (15.5 ± 2.7%), followed by textbooks (> 300 pages) (10.6 ± 3.3%), internet search (7.9 ± 1.6%) and e-learning cases (7.6 ± 3.0%). When comparing learning media use of teaching vs. pre-exam self-study periods, textbooks were used significantly less during self-study (-55%; p < 0.01), while exam questions (+334%; p < 0.01) and e-learning cases (+176%; p < 0.01) were utilized more. Taken together, our study revealed a high prevalence and acceptance of digital learning resources by undergraduate medical students, in particular mobile applications.     

Learning to Attend to Threat Accelerates and Enhances Memory Consolidation

Practice on a procedural task involves within-session learning and between-session consolidation of learning, with the latter requiring a minimum of about four hours to evolve due to involvement of slower cellular processes. Learning to attend to threats is vital for survival and thus may involve faster memory consolidation than simple procedural learning. Here, we tested whether attention to threat modulates the time-course and magnitude of learning and memory consolidation effects associated with skill practice. All participants (N = 90) practiced in two sessions on a dot-probe task featuring pairs of neutral and angry faces followed by target probes which were to be discriminated as rapidly as possible. In the attend-threat training condition, targets always appeared at the angry face location, forming an association between threat and target location; target location was unrelated to valence in a control training condition. Within each attention training condition, duration of the between-session rest interval was varied to establish the time-course for emergence of consolidation effects. During the first practice session, we observed robust improvement in task performance (online, within-session gains), followed by saturation of learning. Both training conditions exhibited similar overall learning capacities, but performance in the attend-threat condition was characterized by a faster learning rate relative to control. Consistent with the memory consolidation hypothesis, between-session performance gains (delayed gains) were observed only following a rest interval. However, rest intervals of 1 and 24 hours yielded similar delayed gains, suggesting accelerated consolidation processes. Moreover, attend-threat training resulted in greater delayed gains compared to the control condition. Auxiliary analyses revealed that enhanced performance was retained over several months, and that training to attend to neutral faces resulted in effects similar to control. These results provide a novel demonstration of how attention to threat can accelerate and enhance memory consolidation effects associated with skill acquisition.     

How Random Is Social Behaviour? Disentangling Social Complexity through the Study of a Wild House Mouse Population

Out of all the complex phenomena displayed in the behaviour of animal groups, many are thought to be emergent properties of rather simple decisions at the individual level. Some of these phenomena may also be explained by random processes only. Here we investigate to what extent the interaction dynamics of a population of wild house mice (Mus domesticus) in their natural environment can be explained by a simple stochastic model. We first introduce the notion of perceptual landscape, a novel tool used here to describe the utilisation of space by the mouse colony based on the sampling of individuals in discrete locations. We then implement the behavioural assumptions of the perceptual landscape in a multi-agent simulation to verify their accuracy in the reproduction of observed social patterns. We find that many high-level features – with the exception of territoriality – of our behavioural dataset can be accounted for at the population level through the use of this simplified representation. Our findings underline the potential importance of random factors in the apparent complexity of the mice''s social structure. These results resonate in the general context of adaptive behaviour versus elementary environmental interactions.     

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children’s social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a “mental model” of the robot, tailoring the tutoring to the robot’s performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot’s bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.     

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9

Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug–drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.     

Asymptotic oscillations in the tracking behaviour of the fly Musca domestica

From recent theoretical work (Poggio and Reichardt, 1981), high frequency oscillations are expected in the angular trajectory of houseflies tracking a moving target if the target's retinal position controls the flight torque by means of a stronger optomotor response to progressive than to regressive motion. Experiments designed to test this conjecture have shown that (a) asymptotic non-decaying oscillations are found in the torque of female houseflies tracking targets moving at constant angular velocity; (b) the magnitude of the oscillations grows monotonically with mean retinal excentricity of the target; (c) the period of the oscillation is around 180–200 ms. The experimental findings are consistent with the hypothesis that a progressive-regressive mechanism plays a significant role in the tracking behaviour of female houseflies. From this phenomenological point of view a flicker mechanism that is active only for nonzero motion is equivalent to a progressive-regressive system. The relatively long period of the oscillation requires more complex reaction dynamics than a pure single dead-time delay. As a specific example we show that a model where the reaction to progressive motion is sticky, holding for a longish time after the ending of the stimulus, is consistent with the experimental data.     

Avoiding spurious submovement decompositions: a globally optimal algorithm

Evidence for the existence of discrete submovements underlying continuous human movement has motivated many attempts to extract them. Although they produce visually convincing results, all of the methodologies that have been employed are prone to produce spurious decompositions. Examples of potential failures are given. A branch-and-bound algorithm for submovement extraction, capable of global nonlinear minimization (and hence capable of avoiding spurious decompositions), is developed and demonstrated.     

Robot Guided ‘Pen Skill’ Training in Children with Motor Difficulties

Motor deficits are linked to a range of negative physical, social and academic consequences. Haptic robotic interventions, based on the principles of sensorimotor learning, have been shown previously to help children with motor problems learn new movements. We therefore examined whether the training benefits of a robotic system would generalise to a standardised test of ‘pen-skills’, assessed using objective kinematic measures [via the Clinical Kinematic Assessment Tool, CKAT]. A counterbalanced, cross-over design was used in a group of 51 children (37 male, aged 5–11 years) with manual control difficulties. Improved performance on a novel task using the robotic device could be attributed to the intervention but there was no evidence of generalisation to any of the CKAT tasks. The robotic system appears to have the potential to support motor learning, with the technology affording numerous advantages. However, the training regime may need to target particular manual skills (e.g. letter formation) in order to obtain clinically significant improvements in specific skills such as handwriting.     

Adherent Use of Digital Health Trackers Is Associated with Weight Loss

We study the association between weight fluctuation and activity tracking in an on-line population of thousands of individuals using digital health trackers (1,749 ≤ N ≤ 14,411, depending on the activity tracker considered) with millions of recorded activities (119,292 ≤ N ≤ 2,221,382) over the years 2013–2015. In a first between-subject analysis, we found a positive association between activity tracking frequency and weight loss. Users who log food with moderate frequency lost an additional 0.63% (CI [0.55, 0.72]; p < .001) of their body weight per month relative to low frequency loggers. Frequent workout loggers lost an additional 0.38% (CI [0.20, 0.56]; p < .001) and frequent weight loggers lost an additional 0.40% (CI [0.33, 0.47]; p < .001) as compared to infrequent loggers. In a subsequent within-subject analysis on a subset of the population (799 ≤ N ≤ 6,052) with sufficient longitudinal data, we used fixed effect models to explore the temporal relationship between a change in tracking adherence and weight change. We found that for the same individual, weight loss is significantly higher during periods of high adherence to tracking vs. periods of low adherence: +2.74% of body weight lost per month (CI [2.68, 2.81]; p < .001) during adherent weight tracking, +1.35% per month (CI [1.26, 1.43]; p < .001) during adherent food tracking, and +0.60% per month (CI [0.44, 0.76]; p < .001) during adherent workout tracking. The findings suggest that adherence to activity tracking can be utilized as a convenient real-time predictor of weight fluctuations, enabling large-scale, personalized intervention strategies.