Transcriptional dysregulation of coding and non-coding genes in cellular models of Huntington's disease

HD (Huntington's disease) is a late onset heritable neurodegenerative disorder that is characterized by neuronal dysfunction and death, particularly in the cerebral cortex and medium spiny neurons of the striatum. This is followed by progressive chorea, dementia and emotional dysfunction, eventually resulting in death. HD is caused by an expanded CAG repeat in the first exon of the HD gene that results in an abnormally elongated polyQ (polyglutamine) tract in its protein product, Htt (Huntingtin). Wild-type Htt is largely cytoplasmic; however, in HD, proteolytic N-terminal fragments of Htt form insoluble deposits in both the cytoplasm and nucleus, provoking the idea that mutHtt (mutant Htt) causes transcriptional dysfunction. While a number of specific transcription factors and co-factors have been proposed as mediators of mutHtt toxicity, the causal relationship between these Htt/transcription factor interactions and HD pathology remains unknown. Previous work has highlighted REST [RE1 (repressor element 1)-silencing transcription factor] as one such transcription factor. REST is a master regulator of neuronal genes, repressing their expression. Many of its direct target genes are known or suspected to have a role in HD pathogenesis, including BDNF (brain-derived neurotrophic factor). Recent evidence has also shown that REST regulates transcription of regulatory miRNAs (microRNAs), many of which are known to regulate neuronal gene expression and are dysregulated in HD. Thus repression of miRNAs constitutes a second, indirect mechanism by which REST can alter the neuronal transcriptome in HD. We will describe the evidence that disruption to the REST regulon brought about by a loss of interaction between REST and mutHtt may be a key contributory factor in the widespread dysregulation of gene expression in HD.

On the 'special' status of emotional faces... Comment on Yang, Hong, and Blake (2010)

A wealth of literature suggests that emotional faces are given special status as visual objects: Cognitive models suggest that emotional stimuli, particularly threat-relevant facial expressions such as fear and anger, are prioritized in visual processing and may be identified by a subcortical “quick and dirty” pathway in the absence of awareness (Tamietto & de Gelder, 2010). Both neuroimaging studies (Williams, Morris, McGlone, Abbott, & Mattingley, 2004) and backward masking studies (Whalen, Rauch, Etcoff, McInerney, & Lee, 1998) have supported the notion of emotion processing without awareness. Recently, our own group (Adams, Gray, Garner, & Graf, 2010) showed adaptation to emotional faces that were rendered invisible using a variant of binocular rivalry: continual flash suppression (CFS, Tsuchiya & Koch, 2005). Here we (i) respond to Yang, Hong, and Blake's (2010) criticisms of our adaptation paper and (ii) provide a unified account of adaptation to facial expression, identity, and gender, under conditions of unawareness

Visual Object Recognition Using Deformable Models of Vehicles

This workshop paper reports recent developments to a vision system for traffic interpretation which relies extensively on the use of geometrical and scene context. Firstly, a new approach to pose refinement is reported, based on forces derived from prominent image derivatives found close to an initial hypothesis. Secondly, a parameterised vehicle model is reported, able to represent different vehicle classes. This general vehicle model has been fitted to sample data, and subjected to a Principal Component Analysis to create a deformable model of common car types having 6 parameters. We show that the new pose recovery technique is also able to operate on the PCA model, to allow the structure of an initial vehicle hypothesis to be adapted to fit the prevailing context. We report initial experiments with the model, which demonstrate significant improvements to pose recovery.

A review and classification of the existing models of cyanobacteria

Bloom-forming and toxin-producing cyanobacteria remain a persistent nuisance across the world. Modelling of cyanobacteria in freshwaters is an important tool for understanding their population dynamics and predicting bloom occurrence in lakes and rivers. In this paper existing key models of cyanobacteria are reviewed, evaluated and classified. Two major groups emerge: deterministic mathematical and artificial neural network models. Mathematical models can be further subcategorized into those models concerned with impounded water bodies and those concerned with rivers. Most existing models focus on a single aspect such as the growth of transport mechanisms, but there are a few models which couple both.