777,567 research outputs found

    Recognition memory in amnestic-mild cognitive impairment: insights from event-related potentials

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    Episodic memory loss is the hallmark cognitive dysfunction associated with Alzheimer’s disease (AD). Amnestic mild cognitive impairment (a-MCI) frequently represents a transitional stage between normal aging and early AD. A better understanding of the qualitative features of memory loss in a-MCI may have important implications for predicting those most likely to harbor AD-related pathology and for disease monitoring. Dual process models of memory argue that recognition memory is subserved by the dissociable processes of recollection and familiarity. Work studying recognition memory in a-MCI from this perspective has been controversial, particularly with regard to the integrity of familiarity. Event-related potentials (ERPs) offer an alternative means for assessing these functions without the associated assumptions of behavioral estimation methods. ERPs were recorded while a-MCI patients and cognitively normal (CN) age-matched adults performed a recognition memory task. When retrieval success was measured (hits versus correct rejections) in which performance was matched by group, a-MCI patients displayed similar neural correlates to that of the CN group, including modulation of the FN400 and the late positive complex (LPC) which are thought to index familiarity and recollection, respectively. Alternatively, when the integrity of these components was measured based on retrieval attempts (studied versus unstudied items), a-MCI patients displayed a reduced FN400 and LPC. Furthermore, modulation of the FN400 correlated with a behavioral estimate of familiarity and the LPC with a behavioral estimate of recollection obtained in a separate experiment in the same individuals, consistent with the proposed mappings of these indices. These results support a global decline of recognition memory in a-MCI, which suggests that the memory loss of prodromal AD may be qualitatively distinct from normal aging

    E-infrastructures fostering multi-centre collaborative research into the intensive care management of patients with brain injury

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    Clinical research is becoming ever more collaborative with multi-centre trials now a common practice. With this in mind, never has it been more important to have secure access to data and, in so doing, tackle the challenges of inter-organisational data access and usage. This is especially the case for research conducted within the brain injury domain due to the complicated multi-trauma nature of the disease with its associated complex collation of time-series data of varying resolution and quality. It is now widely accepted that advances in treatment within this group of patients will only be delivered if the technical infrastructures underpinning the collection and validation of multi-centre research data for clinical trials is improved. In recognition of this need, IT-based multi-centre e-Infrastructures such as the Brain Monitoring with Information Technology group (BrainIT - www.brainit.org) and Cooperative Study on Brain Injury Depolarisations (COSBID - www.cosbid.de) have been formed. A serious impediment to the effective implementation of these networks is access to the know-how and experience needed to install, deploy and manage security-oriented middleware systems that provide secure access to distributed hospital based datasets and especially the linkage of these data sets across sites. The recently funded EU framework VII ICT project Advanced Arterial Hypotension Adverse Event prediction through a Novel Bayesian Neural Network (AVERT-IT) is focused upon tackling these challenges. This chapter describes the problems inherent to data collection within the brain injury medical domain, the current IT-based solutions designed to address these problems and how they perform in practice. We outline how the authors have collaborated towards developing Grid solutions to address the major technical issues. Towards this end we describe a prototype solution which ultimately formed the basis for the AVERT-IT project. We describe the design of the underlying Grid infrastructure for AVERT-IT and how it will be used to produce novel approaches to data collection, data validation and clinical trial design is also presented

    A generic framework for video understanding applied to group behavior recognition

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    This paper presents an approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior. This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence. First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm. A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language. The group events recognition approach is successfully validated on 4 camera views from 3 datasets: an airport, a subway, a shopping center corridor and an entrance hall.Comment: (20/03/2012

    Group context learning for event recognition

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    We address the problem of group-level event recognition from videos. The events of interest are defined based on the motion and interaction of members in a group over time. Example events include group formation, dispersion, fol-lowing, chasing, flanking, and fighting. To recognize these complex group events, we propose a novel approach that learns the group-level scenario context from automatically extracted individual trajectories. We first perform a group structure analysis to produce a weighted graph that repre-sents the probabilistic group membership of the individuals. We then extract features from this graph to capture the mo-tion and action contexts among the groups. The features are represented using the “bag-of-words ” scheme. Finally, our method uses the learned Support Vector Machine (SVM) to classify a video segment into the six event categories. Our implementation builds upon a mature multi-camera multi-target tracking system that recognizes the group-level events involving up to 20 individuals in real-time. 1

    MHC Class I Bound to an Immunodominant Theileria parva Epitope Demonstrates Unconventional Presentation to T Cell Receptors

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    T cell receptor (TCR) recognition of peptide-MHC class I (pMHC) complexes is a crucial event in the adaptive immune response to pathogens. Peptide epitopes often display a strong dominance hierarchy, resulting in focusing of the response on a limited number of the most dominant epitopes. Such T cell responses may be additionally restricted by particular MHC alleles in preference to others. We have studied this poorly understood phenomenon using Theileria parva, a protozoan parasite that causes an often fatal lymphoproliferative disease in cattle. Despite its antigenic complexity, CD8+ T cell responses induced by infection with the parasite show profound immunodominance, as exemplified by the Tp1214–224 epitope presented by the common and functionally important MHC class I allele N*01301. We present a high-resolution crystal structure of this pMHC complex, demonstrating that the peptide is presented in a distinctive raised conformation. Functional studies using CD8+ T cell clones show that this impacts significantly on TCR recognition. The unconventional structure is generated by a hydrophobic ridge within the MHC peptide binding groove, found in a set of cattle MHC alleles. Extremely rare in all other species, this feature is seen in a small group of mouse MHC class I molecules. The data generated in this analysis contribute to our understanding of the structural basis for T cell-dependent immune responses, providing insight into what determines a highly immunogenic p-MHC complex, and hence can be of value in prediction of antigenic epitopes and vaccine design
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