Pseudomonas syringae pv. phaseolicola: from ‘has bean’ to supermodel

Pseudomonas syringae pv. phaseolicola causes halo blight of the common bean, Phaseolus vulgaris, worldwide and remains difficult to control. Races of the pathogen cause either disease symptoms or a resistant hypersensitive response on a series of differentially reacting bean cultivars. The molecular genetics of the interaction between P. syringae pv. phaseolicola and bean, and the evolution of bacterial virulence, have been investigated in depth and this research has led to important discoveries in the field of plant-microbe interactions. In this review, we discuss several of the areas of study that chart the rise of P. syringae pv. phaseolicola from a common pathogen of bean plants to a molecular plant-pathogen supermodel bacterium. Taxonomy: Bacteria; Proteobacteria, gamma subdivision; order Pseudomonadales; family Pseudomonadaceae; genus Pseudomonas; species Pseudomonas syringae; Genomospecies 2; pathogenic variety phaseolicola. Microbiological properties: Gram-negative, aerobic, motile, rod-shaped, 1.5 µm long, 0.7-1.2 µm in diameter, at least one polar flagellum, optimal temperatures for growth of 25-30 °C, oxidase negative, arginine dihydrolase negative, levan positive and elicits the hypersensitive response on tobacco. Host range: Major bacterial disease of common bean (Phaseolus vulgaris) in temperate regions and above medium altitudes in the tropics. Natural infections have been recorded on several other legume species, including all members of the tribe Phaseoleae with the exception of Desmodium spp. and Pisum sativum. Disease symptoms: Water-soaked lesions on leaves, pods, stems or petioles, that quickly develop greenish-yellow haloes on leaves at temperatures of less than 23 °C. Infected seeds may be symptomless, or have wrinkled or buttery-yellow patches on the seed coat. Seedling infection is recognized by general chlorosis, stunting and distortion of growth. Epidemiology: Seed borne and disseminated from exudation by water-splash and wind occurring during rainfall. Bacteria invade through wounds and natural openings (notably stomata). Weedy and cultivated alternative hosts may also harbour the bacterium. Disease control: Some measure of control is achieved with copper formulations and streptomycin. Pathogen-free seed and resistant cultivars are recommended. Useful websites: Pseudomonas-plant interaction http://www.pseudomonas-syringae.org/; PseudoDB http://xbase.bham.ac.uk/pseudodb/; Plant Associated and Environmental Microbes Database (PAMDB) http://genome.ppws.vt.edu/cgi-bin/MLST/home.pl; PseudoMLSA Database http://www.uib.es/microbiologiaBD/Welcome.html.

The core-periphery model with three regions and more

We determine the properties of the core-periphery model with three regions and compare our results with those of the standard 2-region model. The conditions for the stability of dispersion and concentration are established. As in the 2-region model, dispersion and concentration can be simultaneously stable. We show that the 3-region (2-region) model favours the concentration (dispersion) of economic activity. Furthermore, we provide some results for the n-region model. We show that the stability of concentration of the 2-region model implies that of any model with an even number of regions.

Technological change and the challenges for regional development: building social capital in less-favoured regions

The relevance of regional policy for less favoured regions (LFRs) reveals itself when policy-makers must reconcile competitiveness with social cohesion through the adaptation of competition or innovation policies. The vast literature in this area generally builds on an overarching concept of ‘social capital’ as the necessary relational infrastructure for collective action diversification and policy integration, in a context much influenced by a dynamic of industrial change and a necessary balance between the creation and diffusion of ‘knowledge’ through learning. This relational infrastructure or ‘social capital’ is centred on people’s willingness to cooperate and ‘envision’ futures as a result of “social organization, such as networks, norms and trust that facilitate action and cooperation for mutual benefit” (Putnam, 1993: 35). Advocates of this interpretation of ‘social capital’ have adopted the ‘new growth’ thinking behind ‘systems of innovation’ and ‘competence building’, arguing that networks have the potential to make both public administration and markets more effective as well as ‘learning’ trajectories more inclusive of the development of society as a whole. This essay aims to better understand the role of ‘social capital’ in the production and reproduction of uneven regional development patterns, and to critically assess the limits of a ‘systems concept’ and an institution-centred approach to comparative studies of regional innovation. These aims are discussed in light of the following two assertions: i) learning behaviour, from an economic point of view, has its determinants, and ii) the positive economic outcomes of ‘social capital’ cannot be taken as a given. It is suggested that an agent-centred approach to comparative research best addresses the ‘learning’ determinants and the consequences of social networks on regional development patterns. A brief discussion of the current debate on innovation surveys has been provided to illustrate this point.

Does regional infrastructure investment stimulate extra housing and commercial building supply? Long-run evidence across the English regions

This paper uses long-term regional construction data to investigate whether increases infrastructure investment in the English regions leads to subsequent rises in housebuilding and new commercial property, using time series modeling. Both physical (roads and harbours) and social infrastructure (education and health) impacts are investigated across nine regions in England. Significant effects for physical infrastructure are found across most regions and, also, some evidence of a social infrastructure effect. The results are not consistent across regions, which may be due to geographical differences and to network and diversionary effects. However, the results do suggest that infrastructure does have some impact but follows differential lag structures. These results provide a test of the hypothesis of the economic benefits of infrastructure investment in an approach that has not been used before.

Style analysis in real estate markets: beyond the sectors and regions dichotomy

While style analysis has been studied extensively in equity markets, applications of this valuable tool for measuring and benchmarking performance and risk in a real estate context are still relatively new. Most previous real estate studies on this topic have identified three investment categories (rather than styles): sectors, administrative regions and economic regions. However, the low explanatory power reveals the need to extend this analysis to other investment styles. We identify four main real estate investment styles and apply a multivariate model to randomly generated portfolios to test the significance of each style in explaining portfolio returns. Results show that significant alpha performance is significantly reduced when we account for the new investment styles, with small vs. big properties being the dominant one. Secondly, we find that the probability of obtaining alpha performance is dependent upon the actual exposure of funds to style factors. Finally we obtain that both alpha and systematic risk levels are linked to the actual characteristics of portfolios. Our overall results suggest that it would be beneficial for real estate fund managers to use these style factors to set benchmarks and to analyze portfolio returns.

The relative importance of sectors v's regions in determining property returns

A number of studies have investigated the benefits of sector versus regional diversification within a real estate portfolio without explicitly quantify the relative benefits of one against the other. This paper corrects this omission by adopting the approach of Heston and Rouwenhorst (1994) and Beckers, Connor and Curds (1996) on a sample of 187 property data points using annual data over the period 1981-1995. The general conclusion of which is the sector diversification explains on average 22% of the variability of property returns compared with 8% for administratively defined regions. A result in line with previous work. Implying that sector diversification should be the first level of analysis in constructing and managing the real estate portfolio. However, unlike previous work functionally defined regions provide less of an explanation of regional diversification than administrative regions. Which may be down to the weak definition of economic regions employed in this study.

Automatic prediction of functional site regions in low-resolution protein structures

World-wide structural genomics initiatives are rapidly accumulating structures for which limited functional information is available. Additionally, state-of-the art structural prediction programs are now capable of generating at least low resolution structural models of target proteins. Accurate detection and classification of functional sites within both solved and modelled protein structures therefore represents an important challenge. We present a fully automatic site detection method, FuncSite, that uses neural network classifiers to predict the location and type of functionally important sites in protein structures. The method is designed primarily to require only backbone residue positions without the need for specific side-chain atoms to be present. In order to highlight effective site detection in low resolution structural models FuncSite was used to screen model proteins generated using mGenTHREADER on a set of newly released structures. We found effective metal site detection even for moderate quality protein models illustrating the robustness of the method.