Intention Tremor and Deficits of Sensory Feedback Control in Multiple Sclerosis: a Pilot Study

Background Intention tremor and dysmetria are leading causes of upper extremity disability in Multiple Sclerosis (MS). The development of effective therapies to reduce tremor and dysmetria is hampered by insufficient understanding of how the distributed, multi-focal lesions associated with MS impact sensorimotor control in the brain. Here we describe a systems-level approach to characterizing sensorimotor control and use this approach to examine how sensory and motor processes are differentially impacted by MS. Methods Eight subjects with MS and eight age- and gender-matched healthy control subjects performed visually-guided flexion/extension tasks about the elbow to characterize a sensory feedback control model that includes three sensory feedback pathways (one for vision, another for proprioception and a third providing an internal prediction of the sensory consequences of action). The model allows us to characterize impairments in sensory feedback control that contributed to each MS subject’s tremor. Results Models derived from MS subject performance differed from those obtained for control subjects in two ways. First, subjects with MS exhibited markedly increased visual feedback delays, which were uncompensated by internal adaptive mechanisms; stabilization performance in individuals with the longest delays differed most from control subject performance. Second, subjects with MS exhibited misestimates of arm dynamics in a way that was correlated with tremor power. Subject-specific models accurately predicted kinematic performance in a reach and hold task for neurologically-intact control subjects while simulated performance of MS patients had shorter movement intervals and larger endpoint errors than actual subject responses. This difference between simulated and actual performance is consistent with a strategic compensatory trade-off of movement speed for endpoint accuracy. Conclusions Our results suggest that tremor and dysmetria may be caused by limitations in the brain’s ability to adapt sensory feedback mechanisms to compensate for increases in visual information processing time, as well as by errors in compensatory adaptations of internal estimates of arm dynamics

Constraining new physics with fiducial measurements at the LHC

As Run 2 of the LHC completes, a vast set of particle collision data has been recorded by the experimental collaborations on the ring. This has enabled the collaborations to perform many measurements of fiducial particle collision properties, which have been found to be in good agreement with predictions from the Standard Model of particle physics. This collected data has also been used to perform many searches for a variety hypothesised extensions to the Standard Model, which have thus far not observed any significant sign of new physics. In this thesis contributions to the precision measurement program within the ATLAS collaboration at the LHC are presented. These contributions are primarily made to the measurement of detector corrected observables sensitive to large imbalances of momentum observed in the transverse plane. Additionally the opportunity of using such precision fiducial measurements to understand the nature of physics beyond the Standard Model is explored. This is found to give rise to interesting, competitive and complementary information to that derived from the dedicated searches. This work has led to the release of a publicly available program that can be used to automatically confront a hypothesised physics model with precision LHC measurement data. This program is called CONTUR and applications of this to a variety of hypothesised physics models are presented

Combining Parameterizations, Sobolev Methods and Shape Hessian Approximations for Aerodynamic Design Optimization

Aerodynamic design optimization, considered in this thesis, is a large and complex area spanning different disciplines from mathematics to engineering. To perform optimizations on industrially relevant test cases, various algorithms and techniques have been proposed throughout the literature, including the Sobolev smoothing of gradients. This thesis combines the Sobolev methodology for PDE constrained flow problems with the parameterization of the computational grid and interprets the resulting matrix as an approximation of the reduced shape Hessian. Traditionally, Sobolev gradient methods help prevent a loss of regularity and reduce high-frequency noise in the derivative calculation. Such a reinterpretation of the gradient in a different Hilbert space can be seen as a shape Hessian approximation. In the past, such approaches have been formulated in a non-parametric setting, while industrially relevant applications usually have a parameterized setting. In this thesis, the presence of a design parameterization for the shape description is explicitly considered. This research aims to demonstrate how a combination of Sobolev methods and parameterization can be done successfully, using a novel mathematical result based on the generalized Faà di Bruno formula. Such a formulation can yield benefits even if a smooth parameterization is already used. The results obtained allow for the formulation of an efficient and flexible optimization strategy, which can incorporate the Sobolev smoothing procedure for test cases where a parameterization describes the shape, e.g., a CAD model, and where additional constraints on the geometry and the flow are to be considered. Furthermore, the algorithm is also extended to One Shot optimization methods. One Shot algorithms are a tool for simultaneous analysis and design when dealing with inexact flow and adjoint solutions in a PDE constrained optimization. The proposed parameterized Sobolev smoothing approach is especially beneficial in such a setting to ensure a fast and robust convergence towards an optimal design. Key features of the implementation of the algorithms developed herein are pointed out, including the construction of the Laplace-Beltrami operator via finite elements and an efficient evaluation of the parameterization Jacobian using algorithmic differentiation. The newly derived algorithms are applied to relevant test cases featuring drag minimization problems, particularly for three-dimensional flows with turbulent RANS equations. These problems include additional constraints on the flow, e.g., constant lift, and the geometry, e.g., minimal thickness. The Sobolev smoothing combined with the parameterization is applied in classical and One Shot optimization settings and is compared to other traditional optimization algorithms. The numerical results show a performance improvement in runtime for the new combined algorithm over a classical Quasi-Newton scheme

Uniqueness of Cumulative Causation in J. R. Commons\u27 Institutional Economics

This article demonstrates the uniqueness of cumulative causation in J. R. Commons\u27 Institutional Economics (IE). It highlights the following three points. First, it establishes causation with trans-action as the focus of both the institutional change theory and the value theory. Second, it defines conflict as instances of creativity and innovation, that is, momentum for institutional evolution. Third, conflicts puts a \u27will\u27 in trans-actions, which drives the evolution of institutions and values. This article emphasizes that the will is exercised actively during negotiations, that is, will in trans-action is not a standalone authoritative will. Thus, cumulative causation in IE indicates the possibilities of bringing about institutional evolution through the will in transaction