The creative re-use of existing cognitive capacities may have played a significant role in the evolutionary development of the brain. There are obvious evolutionary advantages to such redeployment, and the data presented here confirm three important empirical predictions of this account of the development of cognition: (1) a typical brain area will be utilized by many cognitive functions in diverse task categories, (2) evolutionarily older brain areas will be deployed in more cognitive functions and (3) more recent cognitive functions will utilize more, and more widely scattered brain areas. These findings have implications not just for our understanding of the evolutionary origins of cognitive function, but also for the practice of both clinical and experimental neuroscience. 1 Evolution and redeployment Part of understanding the functional organization of the brain is understanding how it evolved. The current study suggests that while the brain may have originally emerged as an organ with functionally dedicated regions, the creative re-use of these regions has played a significant role in its evolutionary development. This would parallel the evolution of other capabilities wherein existing structures, evolved for other purposes, are re-used and built upon in the course of continuing evolutionary development

Efficient management of networks requires that the shortest route from one point (node) to another is known; this is termed the shortest path. It is often necessary to be able to determine alternative routes through the network, in case any part of the shortest path is damaged or busy. The k-shortest paths represent an ordered list of the alternative routes available. Four algorithms were selected for more detailed study from over seventy papers written on this subject since the 1950's. These four were implemented in the `C' programming language and, on the basis of the results, an assessment was made of their relative performance. 1 The Background The shortest path through a network is the least cost route from a given node to another given node, and this path will usually be the preferred route between those two nodes. When the shortest path between two nodes is not available for some reason, it is necessary to determine the second shortest path. If this too is not available, a thir...

The objective often explored in Web-based route planners is to find an optimal route in a network for a given mode of transport. Usually this involves searching for the cheapest route corresponding to some cost function. For many types of trips, not all desired route properties can be satisfied in this way. Following is a proposed solution for planning hiking trips. The method can easily be transferred to tourist guides in urban areas, for advice for taking a drive, or can be used in related contexts. It is anticipated that these applications will become increasingly relevant in our mobile leisure society. Planning a hiking route is based primarily on the intended length of the trip, and then on the decision about whether start and destination points need to coincide. As a result, planning trips represents neither a shortest path nor a travelling salesman problem. The problem with this approach lies in the fact that hikers require trip proposals that cannot necessarily be provided by a shortest path algorithm: round tours, routes including loops, or routes where a route segment which has to be passed in both directions are more consistent with route planning for hikers. A surprisingly simple solution for these requirements appears to be a linear dual graph, which applies a k shortest path algorithm to the linear dual graph. This paper outlines this approach and demonstrates that it achieves realistic and practical route planning.

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We describe two visual field maps, lateral occipital areas 1 (LO1) and 2 (LO2), in the human lateral occipital cortex between the dorsal part of visual area V3 and visual area V5/MT�. Each map contained a topographic representation of the contralateral visual hemifield. The eccentricity representations were shared with V1/V2/V3. The polar angle representation in LO1 extended from the lower vertical meridian (at the boundary with dorsal V3) through the horizontal to the upper vertical meridian (at the boundary with LO2). The polar angle representationinLO2wasthemirror-reversalofthatinLO1.LO1andLO2overlappedwiththeposteriorpartoftheobject-selectivelateral occipital complex and the kinetic occipital region (KO). The retinotopy and functional properties of LO1 and LO2 suggest that they correspond to two new human visual areas, which lack exact homologues in macaque visual cortex. The topography, stimulus selectivity, and anatomical location of LO1 and LO2 indicate that they integrate shape information from multiple visual submodalities in retinotopic coordinates. Key words: fMRI; homology; human; lateral occipital; retinotopy; V4; visual cortex

Transcription factors with a large number of target genes—transcription hub(s), or THub(s)—are usually crucial components of the regulatory system of a cell, and the different patterns through which they transfer the transcriptional signal to downstream cascades are of great interest. By profiling normalized abundances (AN) of basic regulatory patterns of individual THubs in the yeast Saccharomyces cerevisiae transcriptional regulation network under five different cellular states and environmental conditions, we have investigated their preferences for different basic regulatory patterns. Subgraph-normalized abundances downstream of individual THubs often differ significantly from that of the network as a whole, and conversely, certain over-represented subgraphs are not preferred by any THub. The THub preferences changed substantially when the cellular or environmental conditions changed. This switching of regulatory pattern preferences suggests that a change in conditions does not only elicit a change in response by the regulatory network, but also a change in the mechanisms by which the response is mediated. The THub subgraph preference profile thus provides a novel tool for description of the structure and organization between the large-scale exponents and local regulatory patterns.