LHCF stabilized using a transcondylar screw and supracondylar K-wire are more likely to have major complications resulting in a poorer outcome than cases stabilized using a supracondylar screw or lateral epicondylar plate.

This paper explores the reasons why cities are often major centres of innovation, even in some less favoured regions and countries. It starts with an anatomization of the dominant factors that explain why key less favoured settings developed 'new economy' clusters through institutional interaction with Silicon Valley. The analysis concludes that public research resources and private commercialization funding are central, supported by a wide array of private but few public innovation support services. It then examines a number of cases from cities in less favoured regions and countries where innovation has occurred. The conclusions are that the research-venture capital model is present and explains innovation in each case.However, in some cases public venture capital has to substitute for private due to market failure, or a phenomenon termed 'Silicon Valley Offshore' has been induced. Cities are innovative where they concentrate the desired scienti c and investment knowledge capabilities.

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How does expertise in the analysis of particular images influence the effects of visual saliency upon attention? Expert analysts of aerial photographs and untrained viewers undertook change-detection and location memory tasks using aerial photographs with eye movements recorded throughout. Experts were more accurate in both tasks. Significant differences were also seen in the scanpaths: Untrained viewers fixated preferentially upon salient features throughout stimulus presentation whereas experts did not. However, both groups showed a strong influence of saliency in change detection and memory tasks. We interpret this apparent contradiction by: (i) assuming that the use of saliency in visual search is discretionary, and experts can use semantic information to prioritise where to fixate next; whereas, (ii) in tasks requiring spatial memory, analysis of visual saliency delivers easily acquired landmarks to reference the location of items in an image; a previously overlooked function used by expert and untrained viewers alike.How do we decide where to look in an image, and how much do bottom-up and top-down processes influence the decision? At one extreme, Itti and Koch (2000) developed a bottom-up model to predict where attention is directed on the basis of visual saliency. Saliency was defined by low-level visual characteristics of the image such as colour, intensity and orientation and the model predicted that areas of high saliency should attract the viewer's attention. Thus fixations are directed first to the region of highest saliency, then to the area of next highest saliency and so on in a quasi-automatic way.However, it is clear that visual search is open to top-down cognitive influences. When we inspect a scene, we direct our attention to the most likely locations rather than to the most visually salient objects in view (e.g. Torralba, Oliva, Castelhano, & Henderson, 2006). Underwood, Jebbett, and Roberts (2004) have also demonstrated the selective nature of searching for information in pictures in a task in which participants judged whether a sentence correctly representing a scene. Compared to seeing the picture first, participants viewing the image after the sentence made fewer fixations overall and these fixations were guided to the objects described in the sentence. The influence of saliency on early fixations is also diminished in tasks when participants search for a specific object; in which case participants are less likely to be distracted by conspicuous non-targets (Underwood, APPLIED COGNITIVE PSYCHOLOGY

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