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.

Attenuation of Ischemia-Induced Cellular and Behavioral Deficits by X Chromosome-Linked Inhibitor of Apoptosis Protein Overexpression in the Rat Hippocampus

Abstract: Transient forebrain ischemia produced by four-vessel occlusion (4-VO) triggers the delayed death of CA1 neurons in the hippocampus, resulting in behavioral deficits of spatial learning performance. We demonstrate that CA1 neuronal loss induced by 4-VO (12 min) is preceded by a selective and marked elevation of catalytically active caspase-3 in these neurons, indicative of apoptosis. Virally mediated overexpression of the anti-apoptotic gene X chromosome-linked inhibitor of apoptosis protein (XIAP) prevented both the production of catalytically active caspase-3 and degeneration of CA1 neurons after transient forebrain ischemia. CA1 neurons protected in this manner appeared to function normally, as assessed by immunohistochemical detection of the neuronal activity marker nerve growth factor inducible-A and by spatial learning performance in the Morris water maze. These findings indicate that caspase-3 activation is a key event in ischemic neuronal death and that blockade of this event by XIAP overexpression permits CA1 neurons to survive and operate properly after an ischemic insult.

Impact analysis and change management of UML models

Abstract: The use of Unified Modeling Language (UML) analysis/design models on large projects leads to a large number of interdependent UML diagrams. As software systems evolve, those diagrams undergo changes to, for instance, correct errors or address changes in the requirements. Those changes can in turn lead to subsequent changes to other elements in the UML diagrams. Impact analysis is then defined as the process of identifying the potential consequences (side-effects) of a change, and estimating what needs to be modified to accomplish a change. In this article, we propose a UML model-based approach to impact analysis that can be applied before any implementation of the changes, thus allowing an early decision-making and change planning process. We first verify that the UML diagrams are consistent (consistency check). Then changes between two different versions of a UML model are identified according to a change taxonomy, and model elements that are directly or indirectly impacted by those changes (i.e., may undergo changes) are determined using formally defined impact analysis rules (written with Object Constraint Language). A measure of distance between a changed element and potentially impacted elements is also proposed to prioritize the results of impact analysis according to their likelihood of occurrence. We also present a prototype tool that provides automated support for our impact analysis strategy, that we then apply on a case study to validate both the implementation and methodology.

The Attitudes of Incest Offenders - Sexual Entitlement and Acceptance of Sex With Children

Abstract: The attitudes of child molesters often are considered to contribute to their offenses, and the accurate identification of these cognitive distortions can be important to assessment and treatment. Through the administration of a newly created questionnaire, the attitudes of 50 male incest offenders were compared with those of 25 male batterers and 25 men not seeking treatment. Compared to the other groups, the incest offenders showed deviant attitudes in three domains: (a) endorsing attitudes supportive of male sexual privilege (sexual entitlement), (b) perceiving children to be sexually attractive and sexually motivated, and (c) minimizing the harm caused by sexual abuse of children. Implications of the findings for theories of father-daughter incest are discussed.

Multiinnovation Least-Squares Identification for System Modeling

Abstract: A multiinnovation least-squares (MILS) identification algorithm is presented for linear regression models with unknown parameter vectors by expanding the innovation length in the traditional recursive least-squares (RLS) algorithm from the viewpoint of innovation modification. Because the proposed MILS algorithm uses p innovations (not only the current innovation but also past innovations) at each iteration (with the integer p > 1 being an innovation length), the accuracy of parameter estimation is improved, compared with that of the RLS algorithm. Performance analysis and simulation results show that the proposed MILS algorithm is consistently convergent. Moreover, a new interval-varying MILS algorithm is proposed, for which the key is to dynamically change the interval in order to deal with cases where some measurement data are missing. Furthermore, an auxiliary-model-based MILS algorithm is derived for pseudolinear models corresponding to output error moving average systems with colored noises. Finally, the proposed algorithms are applied to model an experimental water level control system.