Microstructural and mechanical characteristics of PHEMA-based nanofibre-reinforced hydrogel under compression

Natural network-structured hydrogels (e.g. bacterial cellulose (BC)) can be synthesised with specific artificial hydrogels (e.g. poly(2-hydroxyethyl methacrylate)(PHEMA)) to form a tougher and stronger nanofibre-reinforced composite hydrogel, which possesses micro- and nano-porous structure. These synthetic hydrogels exhibit a number of advantages for biomedical applications, such as good biocompatibility and better permeability for molecules to pass through. In this paper, the mechanical properties of this nanofibre-reinforced hydrogel containing BC and PHEMA have been characterised in terms of their tangent modulus and fracture stress/strain by uniaxial compressive testing. Numerical simulations based on Mooney-Rivlin hyperelastic theory are also conducted to understand the internal stress distribution and possible failure of the nanofibre-reinforced hydrogel under compression. By comparing the mechanical characteristics of BC, PHEMA, and PHEMA-based nanofibre reinforced hydrogel (BC-PHEMA) under the compression, it is possible to develop a suitable scaffold for tissue engineering on the basis of fundamental understanding of mechanical and fracture behaviours of nanofibre-reinforced hydrogels

Experiment 2: Decision-making with motor noise.

<p><b>(A)</b> Learning curves for the DM+noise (red) and MO (green) tasks for each participant. The <i>R</i>² between the DM+noise and MO task is provided. <b>(B)</b> Average learning curves across 6 participants. <b>(C)</b> Error in <i>α</i> and <i>β</i> (y-axis) plotted against the number of attempts (x-axis) in the DM+noise and the MO task. Error bars in all panels represent 95%CI across participants.</p

An illustration of the model for the decision-making task and the explorative motor learning task.

<p>On each trial, a hidden target is chosen (Environment). That is, the environment is in a state, which is not directly observable. The model starts with an initial uniformly distributed belief state (illustrated with the red arrow on the top right). On each time step, given an belief, the model then chooses an action based on the belief-action value function (Action selection). Subsequently, the action is executed (Execution). Decision-making task actions are performed without motor noise; the model is able to choose the selected action accurately. Reaching actions are performed with motor noise; there is uncertainty between the selected and executed action. Once the action is executed, the environment gives observable feedback (<i>o</i>_<i>t</i>−1 = 35 in the figure). The action and observation are then used to update the belief (Bayesian belief update). The update is constrained by the fact that participants were naïve to the score function used. We modelled this uncertainty using the likelihood uncertainty parameter (Γ; <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005503#pcbi.1005503.e016" target="_blank">Eq 3</a>). A new cycle then starts with the new belief state (Bt).</p

Comparison of learning performance between the positive and negative feedback conditions.

<p>Paired t-test results on the three parameters (a,b and c in <i>y</i> = <i>ae</i>^−<i>bx</i> + <i>c</i>) between the positive and negative feedback conditions within each of the tasks.</p

The score-effect on action selection and error reduction in the decision-making task (DM) and the reaching task (MO).

<p><b>(A)</b> Action change on attempt t+1 (y-axis) following a score (points) received on attempt t (x-axis) in the DM task (red) and MO task (green). Bar plot represents average across points. <b>(B-C)</b> Error in <i>α</i> and <i>β</i> (y-axis) plotted against the number of attempts (x-axis) in the DM task (B) and MO task (C). Error bars in all panels represent 95%CI across 20 participants.</p

Predicting explorative motor learning using decision-making and motor noise

<div><p>A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant’s level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning.</p></div

Target parameters used in the MO and the DM+noise task in Experiment 2.

<p>Target parameters used in the MO and the DM+noise task in Experiment 2.</p

Behavioural learning performance for the decision-making and the reaching task.

<p><b>(A)</b> Representative participant data showing how a reaching trajectory is gradually updated to match the hidden target trajectory (red). The colours of the lines indicate the sequence of attempts (ranging from green to blue), with later attempts being closer to the target trajectory. <b>(B)</b> Learning curves (positive and negative feedback conditions) for the participants in the decision-making task (DM) and the motor learning/reaching task (MO). Points achieved (y-axis) are plotted against the number of attempts (1-25). The dark red-triangle line and dark green-circle line represent the negative conditions in the DM and MO task respectively, while the light red-triangle line and light green-circle line represent the positive conditions. Error bars indicate 95% confidence intervals (CI) across 24 participants. <b>(C-D)</b> Two representative participants in terms of their curvature exploration. The curvature parameter (x-axis) ranges from −1 to 1, where −1 = ‘curve to the left’, 1 = ‘curve to the right’, and 0 = ‘straight movement’. The participant in (C) evenly explored the curvature dimension, while the participant in (D) concentrated on straight movements with little curvature. <b>(E-F)</b> Two representative participants in terms of their error reduction in both the direction (open circle) and curvature (solid circle) dimensions plotted against the number of attempts. For the participant in (E), the error in both dimensions was reduced to a relatively low level, while for the participant in (F) the error in curvature remained high. The latter was due to the lack of exploration in the curvature dimension as shown in panel (D). <b>(G)</b> Each participant’s mean curvature across all movements during the reaching task (blue circles; the absolute values were used for the movements with negative curvature). Four participants (10,16,18,22) were identified as outliers (red crosses). The blue circles (mean curvature values) were counted as outliers if they were larger than <i>q</i>3 + 0.15(<i>q</i>3 − <i>q</i>1) or smaller than <i>q</i>1 − 0.15(<i>q</i>3 − <i>q</i>1), where <i>q</i>1 and <i>q</i>3 were the 25<i>^th</i> and 75<i>^th</i> percentiles respectively.</p

Exploration and motor noise.

<p><b>(A)</b> How movement variance in the movements changes during the course of the learning process, compared with the variance observed in the motor noise task. <b>(B)</b> Learning rate and <b>(C)</b> maximal points achieved plotted against the variance observed in the motor noise task across participants (x-axis). Each dot represents one participant, indexed with the participant ID; Red crosses in (C) are the participants who failed to explore the curvature dimension (concentrated on straight movements with little curvature) and were identified as outliers. The least-squares line (blue dash line) is with the outliers removed.</p

Action change relative to the current highest score (the reference point).

<p>Action change on attempt <i>t</i> + 1 was plotted as a function of the maximum points achieved up to <i>t</i> − 1 (the reference point) for the DM task (left panel) and MO task (right panel). If the score on attempt <i>t</i> was greater than the reference point, then the action change at <i>t</i> + 1 was considered as an action after a gain (black circle). If the score on attempt <i>t</i> was smaller than the reference point, then the action change after this score (<i>t</i> + 1) was considered as an action after a loss (black cross). Model (green) predictions are also provided. The bars represent the mean action change in the DM task (left panel) and the MO task (right panel). Error bars represent 95% CI across participants.</p