Treatment of Parkinson’s Disease:Early, Late, and Combined

Medical therapy in de novo Parkinson’s disease typically starts with a dopamine agonist or levodopa in combination with a decarboxylase inhibitor or if symptoms are still very mild with a MAO-B inhibitor. When patients do not (or no longer) respond satisfactorily to these initial therapies, different drugs can be initiated or combined (i.e., “add-on” treatments). These add-on therapies not only comprise oral agents but also intra-jejunal and intra-cutaneous treatments and functional neurosurgical procedures. This chapter starts with the treatment of de novo Parkinson’s disease whereafter indications and expected effects of the different “add-on” therapies will be described. The “add-on” therapies will be described in a hierarchical way and treatment algorithms will be provided based on prevailing symptoms including non-motor symptoms. The symptoms that will be discussed are: (1) bradykinesia and “wearing-OFF, " (2) tremor at rest, (3) dyskinesia, (4) gait and postural symptoms including freezing of gait, and (5) important non-motor symptoms. Finally, a comprehensive add-on treatment algorithm will be provided that takes into account non-motor symptoms that may limit the efficacy and tolerability of the different add-on therapies.</p

On the Efetov-Wegner terms by diagonalizing a Hermitian supermatrix

The diagonalization of Hermitian supermatrices is studied. Such a change of coordinates is inevitable to find certain structures in random matrix theory. However it still poses serious problems since up to now the calculation of all Rothstein contributions known as Efetov-Wegner terms in physics was quite cumbersome. We derive the supermatrix Bessel function with all Efetov-Wegner terms for an arbitrary rotation invariant probability density function. As applications we consider representations of generating functions for Hermitian random matrices with and without an external field as integrals over eigenvalues of Hermitian supermatrices. All results are obtained with all Efetov-Wegner terms which were unknown before in such an explicit and compact representation.Comment: 23 pages, PACS: 02.30.Cj, 02.30.Fn, 02.30.Px, 05.30.Ch, 05.30.-d, 05.45.M

Introductory clifford analysis

In this chapter an introduction is given to Clifford analysis and the underlying Clifford algebras. The functions under consideration are defined on Euclidean space and take values in the universal real or complex Clifford algebra, the structure and properties of which are also recalled in detail. The function theory is centered around the notion of a monogenic function, which is a null solution of a generalized Cauchy–Riemann operator, which is rotation invariant and factorizes the Laplace operator. In this way, Clifford analysis may be considered as both a generalization to higher dimension of the theory of holomorphic functions in the complex plane and a refinement of classical harmonic analysis. A notion of monogenicity may also be associated with the vectorial part of the Cauchy–Riemann operator, which is called the Dirac operator; some attention is paid to the intimate relation between both notions. Since a product of monogenic functions is, in general, no longer monogenic, it is crucial to possess some tools for generating monogenic functions: such tools are provided by Fueter’s theorem on one hand and the Cauchy–Kovalevskaya extension theorem on the other hand. A corner stone in this function theory is the Cauchy integral formula for representation of a monogenic function in the interior of its domain of monogenicity. Starting from this representation formula and related integral formulae, it is possible to consider integral transforms such as Cauchy, Hilbert, and Radon transforms, which are important both within the theoretical framework and in view of possible applications

De Noordzee: een waardevol archief onder water. Meer dan 100 jaar onderzoek van strandvondsten en vondsten uit zee in België: een overzicht

De Noordzee kan beschouwd worden als een waardevol en speciaal archief, met heel wat interessante informatie over het verleden. De zone beneden de laagwaterlijn behoort tot het domein van de subtidale archeologie of de archeologie van het subgetijdengebied van de Noordzee. De zone tussen de hoog- en de laagwaterlijn behoort tot de intertidale archeologie of de archeologie van het intergetijdengebied van de Noordzee. In het eerste deel van deze studie wordt kort de geschiedenis van het onderzoek in deze beide zones geschetst. Daarna wordt in een tweede deel een chronologisch overzicht gegeven van de resultaten van het onderzoek en dit vanaf het ontstaan van de archeologie als wetenschappelijke discipline. In dit tweede deel wordt ook een klein aantal tot nu toe ongepubliceerde vondsten opgenomen van buiten het Belgische deel van de Noordzee. De reden hiervoor is zowel pragmatisch als inhoudelijk. Enerzijds worden deze vondsten geregistreerd samen met de andere vondsten, ze bevinden zich immers samen in de bestudeerde collecties. Anderzijds dragen ze ook inhoudelijk bij tot een beter inzicht in de genese van het hele zuidelijke Noordzeegebied, waarvan de zone onder Belgisch toezicht deel uitmaakt. Verder dienen in dit tweede deel ook een aantal vraagstellingen en onderzoeksstrategieën als basis voor de globale discussie in het derde deel van deze bijdrage. De bijdrage wordt ten slotte afgesloten met een zo volledig mogelijke bibliografie over het onderzoek in het Belgische deel van de Noordzee inclusief de stranden

Fast Gaussian Pairwise Constrained Spectral Clustering

International audienceWe consider the problem of spectral clustering with partial supervision in the form of must-link and cannot-link constraints. Such pairwise constraints are common in problems like coreference resolution in natural language processing. The approach developed in this paper is to learn a new representation space for the data together with a dis-tance in this new space. The representation space is obtained through a constraint-driven linear transformation of a spectral embedding of the data. Constraints are expressed with a Gaussian function that locally reweights the similarities in the projected space. A global, non-convex optimization objective is then derived and the model is learned via gradi-ent descent techniques. Our algorithm is evaluated on standard datasets and compared with state of the art algorithms, like [14,18,31]. Results on these datasets, as well on the CoNLL-2012 coreference resolution shared task dataset, show that our algorithm significantly outperforms related approaches and is also much more scalable