Action for Rehabilitation from Neurological Injury (ARNI): A pragmatic study of functional training for stroke survivors

This article has been made available through the Brunel Open Access Publishing Fund. Copyright @ 2013 Cherry Kilbride et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This study evaluated the effectiveness of a twelve-week community-based functional training on measures of impairment, activity and participation in a group of stroke survivors. Isometric strength of the knee musculature, Centre-Of-Pressure (COP) based measures of balance, Berg Balance Scale (BBS), 10 m walk test, and the Subjective Index of Physical and Social Out come (SIPSO), were recorded at baseline, post-intervention, and after twelve weeks (follow-up). Exercise instructors delivered training once a week in a group format at a community centre. Significant improvement was noted in the BBS (p < 0.002), and 10 m walk speed (p = 0.03) post intervention which remained unchanged at follow-up. Total SIPSO score improved significantly post-intervention (p = 0.044). No other significant differences and no adverse effects were observed. It is possible that functional training provided more opportunity for the improvement of dynamic aspects of balance control that could be captured by the BBS but not with the traditional measures of balance using COP data. Results also suggest positive effects on the level of participation, and lack of association between measures of impairment and activity. Community based functional training could be effective and used to extend access to rehabilitation services beyond the acute and sub-acute stages after stroke.London Borough of Hillingdo

On the Computational Complexity of Non-dictatorial Aggregation

We investigate when non-dictatorial aggregation is possible from an algorithmic perspective, where non-dictatorial aggregation means that the votes cast by the members of a society can be aggregated in such a way that the collective outcome is not simply the choices made by a single member of the society. We consider the setting in which the members of a society take a position on a fixed collection of issues, where for each issue several different alternatives are possible, but the combination of choices must belong to a given set $X$ of allowable voting patterns. Such a set $X$ is called a possibility domain if there is an aggregator that is non-dictatorial, operates separately on each issue, and returns values among those cast by the society on each issue. We design a polynomial-time algorithm that decides, given a set $X$ of voting patterns, whether or not $X$ is a possibility domain. Furthermore, if $X$ is a possibility domain, then the algorithm constructs in polynomial time such a non-dictatorial aggregator for $X$. We then show that the question of whether a Boolean domain $X$ is a possibility domain is in NLOGSPACE. We also design a polynomial-time algorithm that decides whether $X$ is a uniform possibility domain, that is, whether $X$ admits an aggregator that is non-dictatorial even when restricted to any two positions for each issue. As in the case of possibility domains, the algorithm also constructs in polynomial time a uniform non-dictatorial aggregator, if one exists. Then, we turn our attention to the case where $X$ is given implicitly, either as the set of assignments satisfying a propositional formula, or as a set of consistent evaluations of an sequence of propositional formulas. In both cases, we provide bounds to the complexity of deciding if $X$ is a (uniform) possibility domain.Comment: 21 page

Line formation in solar granulation: I. Fe line shapes, shifts and asymmetries

Realistic ab-initio 3D, radiative-hydrodynamical convection simulations of the solar granulation have been applied to FeI and FeII line formation. In contrast to classical analyses based on hydrostatic 1D model atmospheres the procedure contains no adjustable free parameters but the treatment of the numerical viscosity in the construction of the 3D, time-dependent, inhomogeneous model atmosphere and the elemental abundance in the 3D spectral synthesis. However, the numerical viscosity is introduced purely for numerical stability purposes and is determined from standard hydrodynamical test cases with no adjustments allowed to improve the agreement with the observational constraints from the solar granulation. The non-thermal line broadening is mainly provided by the Doppler shifts arising from the convective flows in the solar photosphere and the solar oscillations. The almost perfect agreement between the predicted temporally and spatially averaged line profiles for weak Fe lines with the observed profiles and the absence of trends in derived abundances with line strengths, seem to imply that the micro- and macroturbulence concepts are obsolete in these 3D analyses. Furthermore, the theoretical line asymmetries and shifts show a very satisfactory agreement with observations with an accuracy of typically 50-100 m/s on an absolute velocity scale. The remaining minor discrepancies point to how the convection simulations can be refined further.Comment: Accepted for A&

Gaussian process autoregression for simultaneous proportional multi-modal prosthetic control with natural hand kinematics

Matching the dexterity, versatility, and robustness of the human hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic hands. Most of the commercially available prosthetic hands use muscle-related signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole hand. Here, in contrast, we aim to offer users flexible control of individual joints of their artificial hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 joints of the hand in a continuous manner-much like we control our natural hands. Toward this end, we instructed six able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g., grasping) and isometric force tasks (e.g., squeezing). We recorded the electromyographic and mechanomyographic activities of five extrinsic muscles of the hand in the forearm, while simultaneously monitoring 11 joints of hand and fingers using a sensorized data glove that tracked the joints of the hand. Instead of learning just a direct mapping from current muscle activity to intended hand movement, we formulated a novel autoregressive approach that combines the context of previous hand movements with instantaneous muscle activity to predict future hand movements. Specifically, we evaluated a linear vector autoregressive moving average model with exogenous inputs and a novel Gaussian process (gP) autoregressive framework to learn the continuous mapping from hand joint dynamics and muscle activity to decode intended hand movement. Our gP approach achieves high levels of performance (RMSE of 8°/s and ρ = 0.79). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated degrees of freedom of a hand. This novel undersensored control is enabled through the combination of nonlinear autoregressive continuous mapping between muscle activity and joint angles. The system evaluates the muscle signals in the context of previous natural hand movements. This enables us to resolve ambiguities in situations, where muscle signals alone cannot determine the correct action as we evaluate the muscle signals in their context of natural hand movements. gP autoregression is a particularly powerful approach which makes not only a prediction based on the context but also represents the associated uncertainty of its predictions, thus enabling the novel notion of risk-based control in neuroprosthetics. Our results suggest that gP autoregressive approaches with exogenous inputs lend themselves for natural, intuitive, and continuous control in neurotechnology, with the particular focus on prosthetic restoration of natural limb function, where high dexterity is required for complex movements

Choosing the lesser of two evils, the better of two goods: Specifying the roles of ventromedial prefrontal cortex and dorsal anterior cingulate in object choice

The ventromedial prefrontal cortex (vmPFC) and dorsal anterior cingulate cortices (ACd) are considered important for reward-based decision making. However, work distinguishing their individual functional contributions has only begun. One aspect of decision making that has received little attention is that making the right choice often translates to making the better choice. Thus, response choice often occurs in situations where both options are desirable (e.g., choosing between mousse au chocolat or crème caramel cheesecake from a menu) or, alternatively, in situations where both options are undesirable. Moreover, response choice is easier when the reinforcements associated with the objects are far apart, rather than close together, in value. We used functional magnetic resonance imaging to delineate the functional roles of the vmPFC and ACd by investigating these two aspects of decision making: (1) decision form (i.e., choosing between two objects to gain the greater reward or the lesser punishment), and (2) between-object reinforcement distance (i.e., the difference in reinforcements associated with the two objects). Blood oxygen level-dependent (BOLD) responses within the ACd and vmPFC were both related to decision form but differentially. Whereas ACd showed greater responses when deciding between objects to gain the lesser punishment, vmPFC showed greater responses when deciding between objects to gain the greater reward. Moreover, vmPFC was sensitive to reinforcement expectations associated with both the chosen and the forgone choice. In contrast, BOLD responses within ACd, but not vmPFC, related to between-object reinforcement distance, increasing as the distance between the reinforcements of the two objects decreased. These data are interpreted with reference to models of ACd and vmPFC functioning

Permutation modules for the symmetric group

In this paper we present a general method for computing the irreducible components of the permutation modules of the symmetric groups over a field $F$ of characteristic 0. We apply this machinery to determine the decomposition into irreducible submodules of the $F[S_n]$-permutation module on the right cosets of the normaliser in $S_n$ of the subgroup generated by a permutation of type $(3,3)$

The 2A Majorana representations of the Harada-Norton group.

We show that all 2 A -Majorana representations of the Harada- Norton group F 5 have the same shape. If R is such a representation, we determine, using the theory of association schemes, the dimension and the irreducible constituents of the linear span U of the Majorana axes. Finally, we prove that, if R is based on the (unique) embedding of F 5 in the Monster, U is closed under the algebra product

Impact of pairing correlations on the chemical composition of the inner crust of a neutron star

We investigate the impact of the role of pairing correlation on the energy per particles of Wigner-Seitz cells in the inner crust of a neutron star. In particular, we compare some common approximations done to treat pairing effects and we estimate the possible error. To reduce the computational cost of the calculations required to determine the chemical composition of the crust, we present a new numerical method based on Gaussian Emulator Process