Phase resetting as a mechanism of ERP generation: evidence from the power spectrum

A Neural Mass model is coupled with a novel method to generate realistic Phase reset ERPs. The power spectra of these synthetic ERPs are compared with the spectra of real ERPs and synthetic ERPs generated via the Additive model. Real ERP spectra show similarities with synthetic Phase reset ERPs and synthetic Additive ERPs.

Comparative genome analysis provides insights into the evolution and adaptation of Pseudomonas syringae pv. aesculi on Aesculus hippocastanum.

A recently emerging bleeding canker disease, caused by Pseudomonas syringae pathovar aesculi (Pae), is threatening European horse chestnut in northwest Europe. Very little is known about the origin and biology of this new disease. We used the nucleotide sequences of seven commonly used marker genes to investigate the phylogeny of three strains isolated recently from bleeding stem cankers on European horse chestnut in Britain (E-Pae). On the basis of these sequences alone, the E-Pae strains were identical to the Pae type-strain (I-Pae), isolated from leaf spots on Indian horse chestnut in India in 1969. The phylogenetic analyses also showed that Pae belongs to a distinct clade of P. syringae pathovars adapted to woody hosts. We generated genome-wide Illumina sequence data from the three E-Pae strains and one strain of I-Pae. Comparative genomic analyses revealed pathovar-specific genomic regions in Pae potentially implicated in virulence on a tree host, including genes for the catabolism of plant-derived aromatic compounds and enterobactin synthesis. Several gene clusters displayed intra-pathovar variation, including those encoding type IV secretion, a novel fatty acid biosynthesis pathway and a sucrose uptake pathway. Rates of single nucleotide polymorphisms in the four Pae genomes indicate that the three E-Pae strains diverged from each other much more recently than they diverged from I-Pae. The very low genetic diversity among the three geographically distinct E-Pae strains suggests that they originate from a single, recent introduction into Britain, thus highlighting the serious environmental risks posed by the spread of an exotic plant pathogenic bacterium to a new geographic location. The genomic regions in Pae that are absent from other P. syringae pathovars that infect herbaceous hosts may represent candidate genetic adaptations to infection of the woody parts of the tree.

Confocal imaging of pseudomonas syringae pv. phaseolicola colony development in bean reveals reduced multiplication of strains containing the genomic island PPHGI-1.

Pseudomonas syringae pv. phaseolicola is the seed borne causative agent of halo blight in the common bean Phaseolus vulgaris. Pseudomonas syringae pv. phaseolicola race 4 strain 1302A contains the avirulence gene hopAR1 (located on a 106-kb genomic island, PPHGI-1, and earlier named avrPphB), which matches resistance gene R3 in P. vulgaris cultivar Tendergreen (TG) and causes a rapid hypersensitive reaction (HR). Here, we have fluorescently labeled selected Pseudomonas syringae pv. phaseolicola 1302A and 1448A strains (with and without PPHGI-1) to enable confocal imaging of in-planta colony formation within the apoplast of resistant (TG) and susceptible (Canadian Wonder [CW]) P. vulgaris leaves. Temporal quantification of fluorescent Pseudomonas syringae pv. phaseolicola colony development correlated with in-planta bacterial multiplication (measured as CFU/ml) and is, therefore, an effective means of monitoring Pseudomonas syringae pv. phaseolicola endophytic colonization and survival in P. vulgaris. We present advances in the application of confocal microscopy for in-planta visualization of Pseudomonas syringae pv. phaseolicola colony development in the leaf mesophyll to show how the HR defense response greatly affects colony morphology and bacterial survival. Unexpectedly, the presence of PPHGI-1 was found to cause a reduction of colony development in susceptible P. vulgaris CW leaf tissue. We discuss the evolutionary consequences that the acquisition and retention of PPHGI-1 brings to Pseudomonas syringae pv. phaseolicola in planta.

Isolation and characterisation of EfeM, a periplasmic component of the putative EfeUOBM iron transporter of Pseudomonas syringae pv. syringae

The EfeM protein is a component of the putative EfeUOBM iron-transporter of Pseudomonas syringae pathovar syringae and is thought to act as a periplasmic, ferrous-iron binding protein. It contains a signal peptide of 34 amino acid residues and a C-terminal 'Peptidase_M75' domain of 251 residues. The C-terminal domain contains a highly conserved 'HXXE' motif thought to act as part of a divalent cation-binding site. In this work, the gene (efeM or 'Psyr_3370') encoding EfeM was cloned and over-expressed in Escherichia coli, and the mature protein was purified from the periplasm. Mass spectrometry confirmed the identity of the protein (M(W) 27,772Da). Circular dichroism spectroscopy of EfeM indicated a mainly alpha-helical structure, consistent with bioinformatic predictions. Purified EfeM was crystallised by hanging-drop vapor diffusion to give needle-shaped crystals that diffracted to a resolution of 1.6A. This is the first molecular study of a peptidase M75 domain with a presumed iron transport role.

Computer generation and quantitative morphometric analysis of virtual neurons

An important goal in computational neuroanatomy is the complete and accurate simulation of neuronal morphology. We are developing computational tools to model three-dimensional dendritic structures based on sets of stochastic rules. This paper reports an extensive, quantitative anatomical characterization of simulated motoneurons and Purkinje cells. We used several local and global algorithms implemented in the L-Neuron and ArborVitae programs to generate sets of virtual neurons. Parameters statistics for all algorithms were measured from experimental data, thus providing a compact and consistent description of these morphological classes. We compared the emergent anatomical features of each group of virtual neurons with those of the experimental database in order to gain insights on the plausibility of the model assumptions, potential improvements to the algorithms, and non-trivial relations among morphological parameters. Algorithms mainly based on local constraints (e.g., branch diameter) were successful in reproducing many morphological properties of both motoneurons and Purkinje cells (e.g. total length, asymmetry, number of bifurcations). The addition of global constraints (e.g., trophic factors) improved the angle-dependent emergent characteristics (average Euclidean distance from the soma to the dendritic terminations, dendritic spread). Virtual neurons systematically displayed greater anatomical variability than real cells, suggesting the need for additional constraints in the models. For several emergent anatomical properties, a specific algorithm reproduced the experimental statistics better than the others did. However, relative performances were often reversed for different anatomical properties and/or morphological classes. Thus, combining the strengths of alternative generative models could lead to comprehensive algorithms for the complete and accurate simulation of dendritic morphology.

Generation, description and storage of dendritic morphology data

It is generally assumed that the variability of neuronal morphology has an important effect on both the connectivity and the activity of the nervous system, but this effect has not been thoroughly investigated. Neuroanatomical archives represent a crucial tool to explore structure–function relationships in the brain. We are developing computational tools to describe, generate, store and render large sets of three–dimensional neuronal structures in a format that is compact, quantitative, accurate and readily accessible to the neuroscientist. Single–cell neuroanatomy can be characterized quantitatively at several levels. In computer–aided neuronal tracing files, a dendritic tree is described as a series of cylinders, each represented by diameter, spatial coordinates and the connectivity to other cylinders in the tree. This ‘Cartesian’ description constitutes a completely accurate mapping of dendritic morphology but it bears little intuitive information for the neuroscientist. In contrast, a classical neuroanatomical analysis characterizes neuronal dendrites on the basis of the statistical distributions of morphological parameters, e.g. maximum branching order or bifurcation asymmetry. This description is intuitively more accessible, but it only yields information on the collective anatomy of a group of dendrites, i.e. it is not complete enough to provide a precise ‘blueprint’ of the original data. We are adopting a third, intermediate level of description, which consists of the algorithmic generation of neuronal structures within a certain morphological class based on a set of ‘fundamental’, measured parameters. This description is as intuitive as a classical neuroanatomical analysis (parameters have an intuitive interpretation), and as complete as a Cartesian file (the algorithms generate and display complete neurons). The advantages of the algorithmic description of neuronal structure are immense. If an algorithm can measure the values of a handful of parameters from an experimental database and generate virtual neurons whose anatomy is statistically indistinguishable from that of their real counterparts, a great deal of data compression and amplification can be achieved. Data compression results from the quantitative and complete description of thousands of neurons with a handful of statistical distributions of parameters. Data amplification is possible because, from a set of experimental neurons, many more virtual analogues can be generated. This approach could allow one, in principle, to create and store a neuroanatomical database containing data for an entire human brain in a personal computer. We are using two programs, L–NEURON and ARBORVITAE, to investigate systematically the potential of several different algorithms for the generation of virtual neurons. Using these programs, we have generated anatomically plausible virtual neurons for several morphological classes, including guinea pig cerebellar Purkinje cells and cat spinal cord motor neurons. These virtual neurons are stored in an online electronic archive of dendritic morphology. This process highlights the potential and the limitations of the ‘computational neuroanatomy’ strategy for neuroscience databases.

The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: a case study over the UK

Over recent years there has been an increasing deployment of renewable energy generation technologies, particularly large-scale wind farms. As wind farm deployment increases, it is vital to gain a good understanding of how the energy produced is affected by climate variations, over a wide range of time-scales, from short (hours to weeks) to long (months to decades) periods. By relating wind speed at specific sites in the UK to a large-scale climate pattern (the North Atlantic Oscillation or "NAO"), the power generated by a modelled wind turbine under three different NAO states is calculated. It was found that the wind conditions under these NAO states may yield a difference in the mean wind power output of up to 10%. A simple model is used to demonstrate that forecasts of future NAO states can potentially be used to improve month-ahead statistical forecasts of monthly-mean wind power generation. The results confirm that the NAO has a significant impact on the hourly-, daily- and monthly-mean power output distributions from the turbine with important implications for (a) the use of meteorological data (e.g. their relationship to large scale climate patterns) in wind farm site assessment and, (b) the utilisation of seasonal-to-decadal climate forecasts to estimate future wind farm power output. This suggests that further research into the links between large-scale climate variability and wind power generation is both necessary and valuable.

Generation of inertia-gravity waves in the rotating thermal annulus by a localised boundary layer instability

Waves with periods shorter than the inertial period exist in the atmosphere (as inertia-gravity waves) and in the oceans (as Poincaré and internal gravity waves). Such waves owe their origin to various mechanisms, but of particular interest are those arising either from local secondary instabilities or spontaneous emission due to loss of balance. These phenomena have been studied in the laboratory, both in the mechanically-forced and the thermally-forced rotating annulus. Their generation mechanisms, especially in the latter system, have not yet been fully understood, however. Here we examine short period waves in a numerical model of the rotating thermal annulus, and show how the results are consistent with those from earlier laboratory experiments. We then show how these waves are consistent with being inertia-gravity waves generated by a localised instability within the thermal boundary layer, the location of which is determined by regions of strong shear and downwelling at certain points within a large-scale baroclinic wave flow. The resulting instability launches small-scale inertia-gravity waves into the geostrophic interior of the flow. Their behaviour is captured in fully nonlinear numerical simulations in a finite-difference, 3D Boussinesq Navier-Stokes model. Such a mechanism has many similarities with those responsible for launching small- and meso-scale inertia-gravity waves in the atmosphere from fronts and local convection.

Changes in race-specific virulence in pseudomonas syringae pv. phaseolicola are Associated with a chimeric transposable element and rare deletion events in a plasmid-borne pathogenicity island

Virulence for bean and soybean is determined by effector genes in a plasmid-borne pathogenicity island (PAI) in race 7 strain 1449B of Pseudomonas syringae pv. phaseolicola. One of the effector genes, avrPphF, confers either pathogenicity, virulence, or avirulence depending on the plant host and is absent from races 2, 3, 4, 6, and 8 of this pathogen. Analysis of cosmid clones and comparison of DNA sequences showed that the absence of avrPphF from strain 1448A is due to deletion of a continuous 9.5-kb fragment. The remainder of the PAI is well conserved in strains 1448A and 1449B. The left junction of the deleted region consists of a chimeric transposable element generated from the fusion of homologs of IS1492 from Pseudomonas putida and IS1090 from Ralstonia eutropha. The borders of the deletion were conserved in 66 P. syringae pv. phaseolicola strains isolated in different countries and representing the five races lacking avrPphF. However, six strains isolated in Spain had a 10.5-kb deletion that extended 1 kb further from the right junction. The perfect conservation of the 28-nucleotide right repeat of the IS1090 homolog in the two deletion types and in the other 47 insertions of the IS1090 homolog in the 1448A genome strongly suggests that the avrPphF deletions were mediated by the activity of the chimeric mobile element. Our data strongly support a clonal origin for the races of P. syringae pv. phaseolicola lacking avrPphF.