From filters to features:Scale-space analysis of edge and blur coding in human vision

To make vision possible, the visual nervous system must represent the most informative features in the light pattern captured by the eye. Here we use Gaussian scale-space theory to derive a multiscale model for edge analysis and we test it in perceptual experiments. At all scales there are two stages of spatial filtering. An odd-symmetric, Gaussian first derivative filter provides the input to a Gaussian second derivative filter. Crucially, the output at each stage is half-wave rectified before feeding forward to the next. This creates nonlinear channels selectively responsive to one edge polarity while suppressing spurious or "phantom" edges. The two stages have properties analogous to simple and complex cells in the visual cortex. Edges are found as peaks in a scale-space response map that is the output of the second stage. The position and scale of the peak response identify the location and blur of the edge. The model predicts remarkably accurately our results on human perception of edge location and blur for a wide range of luminance profiles, including the surprising finding that blurred edges look sharper when their length is made shorter. The model enhances our understanding of early vision by integrating computational, physiological, and psychophysical approaches. © ARVO

Perceived speed at low luminance: Lights out for the Bayesian observer?

To account for perceptual bias, Bayesian models use the precision of early sensory measurements to weight the influence of prior expectations. As precision decreases, prior expectations start to dominate. Important examples come from motion perception, where the slow-motion prior has been used to explain a variety of motion illusions in vision, hearing, and touch, many of which correlate appropriately with threshold measures of underlying precision. However, the Bayesian account seems defeated by the finding that moving objects appear faster in the dark, because most motion thresholds are worse at low luminance. Here we show this is not the case for speed discrimination. Our results show that performance improves at low light levels by virtue of a perceived contrast cue that is more salient in the dark. With this cue removed, discrimination becomes independent of luminance. However, we found perceived speed still increased in the dark for the same observers, and by the same amount. A possible interpretation is that motion processing is therefore not Bayesian, because our findings challenge a key assumption these models make, namely that the accuracy of early sensory measurements is independent of basic stimulus properties like luminance. However, a final experiment restored Bayesian behaviour by adding external noise, making discrimination worse and slowing perceived speed down. Our findings therefore suggest that motion is processed in a Bayesian fashion but based on noisy sensory measurements that also vary in accuracy

Coexpression analysis of large cancer datasets provides insight into the cellular phenotypes of the tumour microenvironment

Background: Biopsies taken from individual tumours exhibit extensive differences in their cellular composition due to the inherent heterogeneity of cancers and vagaries of sample collection. As a result genes expressed in specific cell types, or associated with certain biological processes are detected at widely variable levels across samples in transcriptomic analyses. This heterogeneity also means that the level of expression of genes expressed specifically in a given cell type or process, will vary in line with the number of those cells within samples or activity of the pathway, and will therefore be correlated in their expression.Results: Using a novel 3D network-based approach we have analysed six large human cancer microarray datasets derived from more than 1,000 individuals. Based upon this analysis, and without needing to isolate the individual cells, we have defined a broad spectrum of cell-type and pathway-specific gene signatures present in cancer expression data which were also found to be largely conserved in a number of independent datasets.Conclusions: The conserved signature of the tumour-associated macrophage is shown to be largely-independent of tumour cell type. All stromal cell signatures have some degree of correlation with each other, since they must all be inversely correlated with the tumour component. However, viewed in the context of established tumours, the interactions between stromal components appear to be multifactorial given the level of one component e.g. vasculature, does not correlate tightly with another, such as the macrophage

A logic-based diagram of signalling pathways central to macrophage activation.

BACKGROUND: The complex yet flexible cellular response to pathogens is orchestrated by the interaction of multiple signalling and metabolic pathways. The molecular regulation of this response has been studied in great detail but comprehensive and unambiguous diagrams describing these events are generally unavailable. Four key signalling cascades triggered early-on in the innate immune response are the toll-like receptor, interferon, NF-kappaB and apoptotic pathways, which co-operate to defend cells against a given pathogen. However, these pathways are commonly viewed as separate entities rather than an integrated network of molecular interactions. RESULTS: Here we describe the construction of a logically represented pathway diagram which attempts to integrate these four pathways central to innate immunity using a modified version of the Edinburgh Pathway Notation. The pathway map is available in a number of electronic formats and editing is supported by yEd graph editor software. CONCLUSION: The map presents a powerful visual aid for interpreting the available pathway interaction knowledge and underscores the valuable contribution well constructed pathway diagrams make to communicating large amounts of molecular interaction data. Furthermore, we discuss issues with the limitations and scalability of pathways presented in this fashion, explore options for automated layout of large pathway networks and demonstrate how such maps can aid the interpretation of functional studies

Saccadic compensation for reflexive optokinetic nystagmus just as good as compensation for volitional pursuit

The natural viewing behavior of moving observers ideally requires target-selecting saccades to be coordinated with automatic gaze-stabilizing eye movements such as optokinetic nystagmus. However, it is unknown whether saccade plans can compensate for reflexive movement of the eye during the variable saccade latency period, and it is unclear whether reflexive nystagmus is even accompanied by extraretinal signals carrying the eye movement information that could potentially underpin such compensation. We show that saccades do partially compensate for optokinetic nystagmus that displaces the eye during the saccade latency period. Moreover, this compensation is as good as for displacements due to voluntary smooth pursuit. In other words, the saccade system appears to be as well coordinated with reflexive nystagmus as it is with volitional pursuit, which in turn implies that extraretinal signals accompany nystagmus and are just as informative as those accompanying pursuit

A preference for visual speed during smooth pursuit eye movement

Does the preference for visual speed extend to motion perception when the eye moves? Current evidence from psychophysics and neuroscience is limited to small patches of image motion and stationary fixation. Active observers, however, are more likely to use large patches of retinal flow and extra-retinal signals accompanying eye movement to judge motion. We therefore investigated whether speed remains a primary dimension during smooth pursuit using a ‘discrimination-contour’ technique. Our results showed that observers struggled most when trying to discriminate pursued stimuli that travelled at the same speed but moved over different distances and durations. This remained the case when retinal flow was added, and when we isolated trials in which extra-retinal signals were the only salient cue to motion. Our results suggest that preferential sensitivity for visual speed is quite general, supported by the many different types of motion mechanism used by active observers

Two-dimensional analysis of smooth pursuit eye movements reveals quantitative deficits in precision and accuracy

Purpose: Small moving targets are followed by pursuit eye movements, with success ubiquitously defined by gain. Gain quantifies accuracy, rather than precision, and only for eye movements along the target trajectory. Analogous to previous studies of fixation, we analyzed pursuit performance in two dimensions as a function of target direction, velocity, and amplitude. As a subsidiary experiment, we compared pursuit performance against that of fixation. Methods: Eye position was recorded from 15 observers during pursuit. The target was a 0.4° dot that moved across a large screen at 8°/s or 16°/s, either horizontally or vertically, through peak-to-peak amplitudes of 8°, 16°, or 32°. Two-dimensional eye velocity was expressed relative to the target, and a bivariate probability density function computed to obtain accuracy and precision. As a comparison, identical metrics were derived from fixation data. Results: For all target directions, eye velocity was less precise along the target trajectory. Eye velocities orthogonal to the target trajectory were more accurate during vertical pursuit than horizontal. Pursuit accuracy and precision along and orthogonal to the target trajectory decreased at the higher target velocity. Accuracy along the target trajectory decreased with smaller target amplitudes. Conclusions: Orthogonal to the target trajectory, pursuit was inaccurate and imprecise. Compared to fixation, pursuit was less precise and less accurate even when following the stimulus that gave the best performance. Translational Relevance: This analytical approach may help the detection of subtle deficits in slow phase eye movements that could be used as biomarkers for disease progression and/or treatment

Relationship between the tissue-specificity of mouse gene expression and the evolutionary origin and function of the proteins.

BACKGROUND: The combination of complete genome sequence information with expression data enables us to characterize the relationship between a protein's evolutionary origin or functional category and its expression pattern. In this study, mouse proteins were assigned into functional and phyletic groups and the gene expression patterns of the different protein groupings were examined by microarray analysis in various mouse tissues. RESULTS: Our results suggest that the proteins that are universally distributed in all tissues are predominantly enzymes and transporters. In contrast, the tissue-specific set is dominated by regulatory proteins (signal transduction and transcription factors). An increased tendency to tissue-specificity is observed for metazoan-specific proteins. As the composition of the phyletic groups highly correlates with that of the functional groups, the data were tested in order to determine which of the two factors -- function or phyletic age -- is dominant in shaping the expression profile of a protein. The observed differences in expression patterns of genes between functional groups were found mainly to reflect their different phyletic origin. The connection between tissue specificity and phyletic age cannot be explained by the recent rate of evolution. Finally, although metazoan-specific proteins tend to be tissue-specific compared with phyletically conserved proteins present in all domains of life, many such 'universal' proteins are also tissue-specific. CONCLUSION: The minimal cellular transcriptome of the metazoan cell differs from that of the ancestral unicellular eukaryote: new functions were added (metazoan-specific proteins), whilst other functions became specialized and no longer took place in all cells (tissue-specific pre-metazoan proteins).RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Relationship between the tissue-specificity of mouse gene expression and the evolutionary origin and function of the proteins

BACKGROUND: The combination of complete genome sequence information with expression data enables us to characterize the relationship between a protein's evolutionary origin or functional category and its expression pattern. In this study, mouse proteins were assigned into functional and phyletic groups and the gene expression patterns of the different protein groupings were examined by microarray analysis in various mouse tissues. RESULTS: Our results suggest that the proteins that are universally distributed in all tissues are predominantly enzymes and transporters. In contrast, the tissue-specific set is dominated by regulatory proteins (signal transduction and transcription factors). An increased tendency to tissue-specificity is observed for metazoan-specific proteins. As the composition of the phyletic groups highly correlates with that of the functional groups, the data were tested in order to determine which of the two factors - function or phyletic age - is dominant in shaping the expression profile of a protein. The observed differences in expression patterns of genes between functional groups were found mainly to reflect their different phyletic origin. The connection between tissue specificity and phyletic age cannot be explained by the recent rate of evolution. Finally, although metazoan-specific proteins tend to be tissue-specific compared with phyletically conserved proteins present in all domains of life, many such 'universal' proteins are also tissue-specific. CONCLUSION: The minimal cellular transcriptome of the metazoan cell differs from that of the ancestral unicellular eukaryote: new functions were added (metazoan-specific proteins), whilst other functions became specialized and no longer took place in all cells (tissue-specific pre-metazoan proteins)