In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints. The interest in insect navigation from diverse disciplines such as psychology and engineering is to a large extent because performance is achieved with such limited brain power. Desert ants are particularly impressive navigators, able to rapidly learn long, visually guided foraging routes. Their elegant behaviours provide inspiration to biomimetic engineers and for psychologists demonstrate the minimal mechanistic requirements for complex spatial behaviours. In this spirit, we have developed a parsimonious model of route navigation that captures many of the known properties of ants routes. Our model uses a neural network trained with the visual scenes experienced along a route to assess the familiarity of any view. Subsequent route navigation involves a simple behavioural routine, in which the simulated ant scans the world and moves in the most familiar direction, as determined by the network. The algorithm exhibits both place-search and route navigation using the same mechanism. Crucially, in our model it is not necessary to specify when or what to learn, nor separate routes into sequences of waypoints; thereby providing proof of concept that route navigation can be achieved without these elements. As such, we believe it represents the only detailed and complete model of insect route guidance to date.

The specific role of VEGFA-induced permeability and vascular leakage in physiology and pathology has remained unclear. Here we show that VEGFA-induced vascular leakage depends on signalling initiated via the VEGFR2 phosphosite Y949, regulating dynamic c-Src and VE-cadherin phosphorylation. Abolished Y949 signalling in the mouse mutant Vegfr2^Y949F/Y949F leads to VEGFA-resistant endothelial adherens junctions and a block in molecular extravasation. Vessels in Vegfr2^Y949F/Y949F mice remain sensitive to inflammatory cytokines, and vascular morphology, blood pressure and flow parameters are normal. Tumour-bearing Vegfr2^Y949F/Y949F mice display reduced vascular leakage and oedema, improved response to chemotherapy and, importantly, reduced metastatic spread. The inflammatory infiltration in the tumour micro-environment is unaffected. Blocking VEGFA-induced disassembly of endothelial junctions, thereby suppressing tumour oedema and metastatic spread, may be preferable to full vascular suppression in the treatment of certain cancer forms.

Computer simulations are increasingly used to monitor and predict behavior at large crowd events, such as mass gatherings, festivals and evacuations. We critically examine the crowd modeling literature and call for future simulations of crowd behavior to be based more closely on findings from current social psychological research. A systematic review was conducted on the crowd modeling literature ( N = 140 articles) to identify the assumptions about crowd behavior that modelers use in their simulations. Articles were coded according to the way in which crowd structure was modeled. It was found that 2 broad types are used: mass approaches and small group approaches. However, neither the mass nor the small group approaches can accurately simulate the large collective behavior that has been found in extensive empirical research on crowd events. We argue that to model crowd behavior realistically, simulations must use methods which allow crowd members to identify with each other, as suggested by self-categorization theory.