A biologically inspired neural network controller for ballistic arm movements

<p>Abstract</p> <p>Background</p> <p>In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented.</p> <p>Methods</p> <p>The developed system is composed of three main computational blocks: 1) a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2) a pulse generator, which is responsible for the creation of muscular synergies; and 3) a limb model based on two joints (two degrees of freedom) and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans.</p> <p>Results</p> <p>The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians.</p> <p>Curvature values are similar to those encountered in experimental measures with humans.</p> <p>The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector.</p> <p>Conclusion</p> <p>The proposed system has been shown to properly simulate the development of internal models and to control the generation and execution of ballistic planar arm movements. Since the neural controller learns to manage movements on the basis of kinematic information and arm characteristics, it could in perspective command a neuroprosthesis instead of a biomechanical model of a human upper limb, and it could thus give rise to novel rehabilitation techniques.</p

Union inclusiveness and temporary agency workers: the role of power resources and union ideology

This article investigates the determinants of union inclusiveness towards agency workers in Western Europe, using an index which combines unionization rates with dimensions of collective agreements covering agency workers. Using fuzzy-set Qualitative Comparative Analysis, we identify two combinations of conditions leading to inclusiveness: the ‘Northern path’ includes high union density, high bargaining coverage and high union authority, and is consistent with the power resources approach. The ‘Southern path’ combines high union authority, high bargaining coverage, statutory regulations of agency work and working-class orientation, showing that ideology rather than institutional incentives shapes union strategies towards the marginal workforce

Human Milk Protein Production in Xenografts of Genetically Engineered Bovine Mammary Epithelial Stem Cells

BACKGROUND: In the bovine species milk production is well known to correlate with mammary tissue mass. However, most advances in optimizing milk production relied on improvements of breeding and husbandry practices. A better understanding of the cells that generate bovine mammary tissue could facilitate important advances in milk production and have global economic impact. With this possibility in mind, we show that a mammary stem cell population can be functionally identified and isolated from the bovine mammary gland. We also demonstrate that this stem cell population may be a promising target for manipulating the composition of cow's milk using gene transfer. METHODS AND FINDINGS: We show that the in vitro colony-forming cell assay for detecting normal primitive bipotent and lineage-restricted human mammary clonogenic progenitors are applicable to bovine mammary cells. Similarly, the ability of normal human mammary stem cells to regenerate functional bilayered structures in collagen gels placed under the kidney capsule of immunodeficient mice is shared by a subset of bovine mammary cells that lack aldehyde dehydrogenase activity. We also find that this activity is a distinguishing feature of luminal-restricted bovine progenitors. The regenerated structures recapitulate the organization of bovine mammary tissue, and milk could be readily detected in these structures when they were assessed by immunohistochemical analysis. Transplantation of the bovine cells transduced with a lentivirus encoding human β-CASEIN led to expression of the transgene and secretion of the product by their progeny regenerated in vivo. CONCLUSIONS: These findings point to a common developmental hierarchy shared by human and bovine mammary glands, providing strong evidence of common mechanisms regulating the maintenance and differentiation of mammary stem cells from both species. These results highlight the potential of novel engineering and transplant strategies for a variety of commercial applications including the production of modified milk components for human consumption

Recursive Cluster Elimination Based Support Vector Machine for Disease State Prediction Using Resting State Functional and Effective Brain Connectivity

Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification.In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE) algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects.The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the clinicians in performing more accurate diagnosis of diseases in situations where in non-neuroimaging biomarkers may be unable to perform differential diagnosis with certainty

Recessive mutations in muscle-specific isoforms of FXR1 cause congenital multi-minicore myopathy

FXR1 is an alternatively spliced gene that encodes RNA binding proteins (FXR1P) involved in muscle development. In contrast to other tissues, cardiac and skeletal muscle express two FXR1P isoforms that incorporate an additional exon-15. We report that recessive mutations in this particular exon of FXR1 cause congenital multi-minicore myopathy in humans and mice. Additionally, we show that while Myf5-dependent depletion of all FXR1P isoforms is neonatal lethal, mice carrying mutations in exon-15 display non-lethal myopathies which vary in severity depending on the specific effect of each mutation on the protein

Anodal Transcranial Direct Current Stimulation Reduces Psychophysically Measured Surround Suppression in the Human Visual Cortex

Transcranial direct current stimulation (tDCS) is a safe, non-invasive technique for transiently modulating the balance of excitation and inhibition within the human brain. It has been reported that anodal tDCS can reduce both GABA mediated inhibition and GABA concentration within the human motor cortex. As GABA mediated inhibition is thought to be a key modulator of plasticity within the adult brain, these findings have broad implications for the future use of tDCS. It is important, therefore, to establish whether tDCS can exert similar effects within non-motor brain areas. The aim of this study was to assess whether anodal tDCS could reduce inhibitory interactions within the human visual cortex. Psychophysical measures of surround suppression were used as an index of inhibition within V1. Overlay suppression, which is thought to originate within the lateral geniculate nucleus (LGN), was also measured as a control. Anodal stimulation of the occipital poles significantly reduced psychophysical surround suppression, but had no effect on overlay suppression. This effect was specific to anodal stimulation as cathodal stimulation had no effect on either measure. These psychophysical results provide the first evidence for tDCS-induced reductions of intracortical inhibition within the human visual cortex

Transcranial direct current stimulation of the prefrontal cortex modulates working memory performance: combined behavioural and electrophysiological evidence

The present study demonstrates that tDCS can alter WM performance by modulating the underlying neural oscillations. This result can be considered an important step towards a better understanding of the mechanisms involved in tDCS-induced modulations of WM performance, which is of particular importance, given the proposal to use electrical brain stimulation for the therapeutic treatment of memory deficits in clinical settings