Carleton University

About: Carleton University is a education organization based out in Ottawa, Ontario, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 15852 authors who have published 39650 publications receiving 1106610 citations.

Hypersensitivity of DJ-1-deficient mice to 1-methyl-4-phenyl-1,2,3,6-tetrahydropyrindine (MPTP) and oxidative stress

Abstract: Mutations of the DJ-1 (PARK7) gene are linked to familial Parkinson's disease. We used gene targeting to generate DJ-1-deficient mice that were viable, fertile, and showed no gross anatomical or neuronal abnormalities. Dopaminergic neuron numbers in the substantia nigra and fiber densities and dopamine levels in the striatum were normal. However, DJ-1-/- mice showed hypolocomotion when subjected to amphetamine challenge and increased striatal denervation and dopaminergic neuron loss induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyrindine. DJ-1-/-embryonic cortical neurons showed increased sensitivity to oxidative, but not nonoxidative, insults. Restoration of DJ-1 expression to DJ-1-/- mice or cells via adenoviral vector delivery mitigated all phenotypes. WT mice that received adenoviral delivery of DJ-1 resisted 1-methyl-4-phenyl-1,2,3,6-tetrahydropyrindine-induced striatal damage, and neurons overexpressing DJ-1 were protected from oxidative stress in vitro. Thus, DJ-1 protects against neuronal oxidative stress, and loss of DJ-1 may lead to Parkinson's disease by conferring hypersensitivity to dopaminergic insults.

The estimation of the mean squared error of small-area estimators

Abstract: Small-area estimation has received considerable attention in recent years because of a growing demand for reliable small-area statistics. The direct-survey estimators, based only on the data from a given small area (or small domain), are likely to yield unacceptably large standard errors because of small sample size in the domain. Therefore, alternative estimators that borrow strength from other related small areas have been proposed in the literature to improve the efficiency. These estimators use models, either implicitly or explicitly, that connect the small areas through supplementary (e.g., census and administrative) data. For example, simple synthetic estimators are based on implicit modeling. In this article, three small-area models, of Battese, Harter, and Fuller (1988), Dempster, Rubin, and Tsutakawa (1981), and Fay and Herriot (1979), are investigated. These models are all special cases of a general mixed linear model involving fixed and random effects, and a small-area mean can be expr...

Handbook of computational geometry

Abstract: reface. List of contrinbutors. 1. Davenport-Schinzel sequences and their geometric applications (P.K. Agarwal and M. Sharir). 2. Arrangements and their applications (P.K. Agarwal and M. Sharir). 3. Discrete geometric shapes: Matching, interpolation, and approximation (H. Alt and L.J. Guibas). 4. Deterministic parallel computational geometry (M.J. Attalah and D.Z. Chen). 5. Voronoi diagrams (F. Aurenhammer and R. Klein). 6. Mesh generation (M. Bern and P. Plassmann). 7. Applications of computational geometry to geographic information systems (L. de Floriani, P. Magillo and E. Puppo). 8. Making geometry visible: An introduction to the animation of geometric algorithms (A. Hausner and D.P. Dobkin). 9. Spanning trees and spanners (D. Eppstein). 10. Geometric data structures (M.T. Goodrich and K. Ramaiyer). 11. Polygon decomposition (J.M. Keil). 12. Link distance problems (A. Maheshwari, J.-R. Sack and H. N. Djidjev). 13. Derandomization in computational geometry (J. Matousek). 14. Robustness and precision issues in geometric computation (S. Schirra). 15. Geometric shortest paths and network optimization (J.S.B. Mitchell). 16. Randomizedalgorithms in computaional geometry (K. Mulmuley).

Variance estimation for complex surveys using replication techniques

Abstract: The analysis of survey data requires the application of special methods to deal appropriately with the effects of the sample design on the properties of estimators and test statistics. The class of replication techniques represents one approach to handling this problem. This paper discusses the use of these techniques for estimating sampling variances, and the use of such variance estimates in drawing inferences from survey data. The techniques of the jackknife, balanced repeated replication (balanced half-samples), and the bootstrap are described, and the properties of these methods are summarized. Several examples from the literature of the use of replication in analysing large complex surveys are outlined.