Suitability of Arabidopsis thaliana as a model for host plant-Plutella xylostella-Cotesia plutellae interactions

Successful pest management is often hindered by the inherent complexity of the interactions of a pest with its environment. The use of genetically characterized model plants can allow investigation of chosen aspects of these interactions by limiting the number of variables during experimentation. However, it is important to study the generic nature of these model systems if the data generated are to be assessed in a wider context, for instance, with those systems of commercial significance. This study assesses the suitability of Arabidopsis thaliana (L.) Heynh. (Brassicaceae) as a model host plant to investigate plant-herbivore-natural enemy interactions, with Plutella xylostella (L.) (Lepidoptera: Plutellidae), the diamondback moth, and Cotesia plutellae (Kurdjumov) (Hymenoptera: Braconidae), a parasitoid of P. xylostella. The growth and development of P. xylostella and C. plutellae on an A. thaliana host plant (Columbia type) were compared to that on Brassica rapa var. pekinensis (L.) (Brassicaceae), a host crop that is widely cultivated and also commonly used as a laboratory host for P. xylostella rearing. The second part of the study investigated the potential effect of the different A. thaliana background lines, Columbia and Landsberg (used in wider scientific studies), on growth and development of P. xylostella and C. plutellae. Plutella xylostella life history parameters were found generally to be similar between the host plants investigated. However, C. plutellae were more affected by the differences in host plant. Fewer adult parasitoids resulted from development on A. thaliana compared to B. rapa, and those that did emerge were significantly smaller. Adult male C. plutellae developing on Columbia were also significantly smaller than those on Landsberg A. thaliana.

Bayesian analysis of an inverse Gaussian correlated frailty model

In survival analysis frailty is often used to model heterogeneity between individuals or correlation within clusters. Typically frailty is taken to be a continuous random effect, yielding a continuous mixture distribution for survival times. A Bayesian analysis of a correlated frailty model is discussed in the context of inverse Gaussian frailty. An MCMC approach is adopted and the deviance information criterion is used to compare models. As an illustration of the approach a bivariate data set of corneal graft survival times is analysed. (C) 2006 Elsevier B.V. All rights reserved.

Benchmarking consensus model quality assessment for protein fold recognition

Background: Selecting the highest quality 3D model of a protein structure from a number of alternatives remains an important challenge in the field of structural bioinformatics. Many Model Quality Assessment Programs (MQAPs) have been developed which adopt various strategies in order to tackle this problem, ranging from the so called "true" MQAPs capable of producing a single energy score based on a single model, to methods which rely on structural comparisons of multiple models or additional information from meta-servers. However, it is clear that no current method can separate the highest accuracy models from the lowest consistently. In this paper, a number of the top performing MQAP methods are benchmarked in the context of the potential value that they add to protein fold recognition. Two novel methods are also described: ModSSEA, which based on the alignment of predicted secondary structure elements and ModFOLD which combines several true MQAP methods using an artificial neural network. Results: The ModSSEA method is found to be an effective model quality assessment program for ranking multiple models from many servers, however further accuracy can be gained by using the consensus approach of ModFOLD. The ModFOLD method is shown to significantly outperform the true MQAPs tested and is competitive with methods which make use of clustering or additional information from multiple servers. Several of the true MQAPs are also shown to add value to most individual fold recognition servers by improving model selection, when applied as a post filter in order to re-rank models. Conclusion: MQAPs should be benchmarked appropriately for the practical context in which they are intended to be used. Clustering based methods are the top performing MQAPs where many models are available from many servers; however, they often do not add value to individual fold recognition servers when limited models are available. Conversely, the true MQAP methods tested can often be used as effective post filters for re-ranking few models from individual fold recognition servers and further improvements can be achieved using a consensus of these methods.

Three-level main-effects designs exploiting prior information about model uncertainty

To explore the projection efficiency of a design, Tsai, et al [2000. Projective three-level main effects designs robust to model uncertainty. Biometrika 87, 467-475] introduced the Q criterion to compare three-level main-effects designs for quantitative factors that allow the consideration of interactions in addition to main effects. In this paper, we extend their method and focus on the case in which experimenters have some prior knowledge, in advance of running the experiment, about the probabilities of effects being non-negligible. A criterion which incorporates experimenters' prior beliefs about the importance of each effect is introduced to compare orthogonal, or nearly orthogonal, main effects designs with robustness to interactions as a secondary consideration. We show that this criterion, exploiting prior information about model uncertainty, can lead to more appropriate designs reflecting experimenters' prior beliefs. (c) 2006 Elsevier B.V. All rights reserved.

Apical leaf necrosis and leaf nitrogen dynamics in diseased leaves: a model study

Apical leaf necrosis is a physiological process related to nitrogen (N) dynamics in the leaf. Pathogens use leaf nutrients and can thus accelerate this physiological apical necrosis. This process differs from necrosis occurring around pathogen lesions (lesion-induced necrosis), which is a direct result of the interaction between pathogen hyphae and leaf cells. This paper primarily concentrates on apical necrosis, only incorporating lesion-induced necrosis by necessity. The relationship between pathogen dynamics and physiological apical leaf necrosis is modelled through leaf nitrogen dynamics. The specific case of Puccinia triticina infections on Triticum aestivum flag leaves is studied. In the model, conversion of indirectly available N in the form of, for example, leaf cell proteins (N-2(t)) into directly available N (N-1(t), i.e. the form of N that can directly be used by either pathogen or plant sinks) results in apical necrosis. The model reproduces observed trends of disease severity, apical necrosis and green leaf area (GLA) and leaf N dynamics of uninfected and infected leaves. Decreasing the initial amount of directly available N results in earlier necrosis onset and longer necrosis duration. Decreasing the initial amount of indirectly available N, has no effect on necrosis onset and shortens necrosis duration. The model could be used to develop hypotheses on how the disease-GLA relation affects yield loss, which can be tested experimentally. Upon incorporation into crop simulation models, the model might provide a tool to more accurately estimate crop yield and effects of disease management strategies in crops sensitive to fungal pathogens.

A parametric model for the interval censored survival times of Acacia mangium plantation in a spacing trial

Survival times for the Acacia mangium plantation in the Segaliud Lokan Project, Sabah, East Malaysia were analysed based on 20 permanent sample plots (PSPs) established in 1988 as a spacing experiment. The PSPs were established following a complete randomized block design with five levels of spacing randomly assigned to units within four blocks at different sites. The survival times of trees in years are of interest. Since the inventories were only conducted annually, the actual survival time for each tree was not observed. Hence, the data set comprises censored survival times. Initial analysis of the survival of the Acacia mangium plantation suggested there is block by spacing interaction; a Weibull model gives a reasonable fit to the replicate survival times within each PSP; but a standard Weibull regression model is inappropriate because the shape parameter differs between PSPs. In this paper we investigate the form of the non-constant Weibull shape parameter. Parsimonious models for the Weibull survival times have been derived using maximum likelihood methods. The factor selection for the parameters is based on a backward elimination procedure. The models are compared using likelihood ratio statistics. The results suggest that both Weibull parameters depend on spacing and block.

The seasonal forecast of electricity demand: A hierarchical Bayesian model with climatological weather generator

In this paper we focus on the one year ahead prediction of the electricity peak-demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast. Copyright (C) 2006 John Wiley & Sons, Ltd.

Diagnosis of variability and trends in a global precipitation dataset using a physically motivated statistical model

A physically motivated statistical model is used to diagnose variability and trends in wintertime ( October - March) Global Precipitation Climatology Project (GPCP) pentad (5-day mean) precipitation. Quasi-geostrophic theory suggests that extratropical precipitation amounts should depend multiplicatively on the pressure gradient, saturation specific humidity, and the meridional temperature gradient. This physical insight has been used to guide the development of a suitable statistical model for precipitation using a mixture of generalized linear models: a logistic model for the binary occurrence of precipitation and a Gamma distribution model for the wet day precipitation amount. The statistical model allows for the investigation of the role of each factor in determining variations and long-term trends. Saturation specific humidity q(s) has a generally negative effect on global precipitation occurrence and with the tropical wet pentad precipitation amount, but has a positive relationship with the pentad precipitation amount at mid- and high latitudes. The North Atlantic Oscillation, a proxy for the meridional temperature gradient, is also found to have a statistically significant positive effect on precipitation over much of the Atlantic region. Residual time trends in wet pentad precipitation are extremely sensitive to the choice of the wet pentad threshold because of increasing trends in low-amplitude precipitation pentads; too low a choice of threshold can lead to a spurious decreasing trend in wet pentad precipitation amounts. However, for not too small thresholds, it is found that the meridional temperature gradient is an important factor for explaining part of the long-term trend in Atlantic precipitation.

The role of satellite image-processing for national-scale estimates of gene flow from genetically modified crops: rapeseed in the UK as a model

1.There is concern over the possibility of unwanted environmental change following transgene movement from genetically modified (GM) rapeseed Brassica napus to its wild and weedy relatives. 2. The aim of this research was to develop a remote sensing-assisted methodology to help quantify gene flow from crops to their wild relatives over wide areas. Emphasis was placed on locating sites of sympatry, where the frequency of gene flow is likely to be highest, and on measuring the size of rapeseed fields to allow spatially explicit modelling of wind-mediated pollen-dispersal patterns. 3. Remote sensing was used as a tool to locate rapeseed fields, and a variety of image-processing techniques was adopted to facilitate the compilation of a spatially explicit profile of sympatry between the crop and Brassica rapa. 4. Classified satellite images containing rapeseed fields were first used to infer the spatial relationship between donor rapeseed fields and recipient riverside B. rapa populations. Such images also have utility for improving the efficiency of ground surveys by identifying probable sites of sympatry. The same data were then also used for the calculation of mean field size. 5. This paper forms a companion paper to Wilkinson et al. (2003), in which these elements were combined to produce a spatially explicit profile of hybrid formation over the UK. The current paper demonstrates the value of remote sensing and image processing for large-scale studies of gene flow, and describes a generic method that could be applied to a variety of crops in many countries. 6.Synthesis and applications. The decision to approve or prevent the release of a GM cultivar is made at a national rather than regional level. It is highly desirable that data relating to the decision-making process are collected at the same scale, rather than relying on extrapolation from smaller experiments designed at the plot, field or even regional scale. It would be extremely difficult and labour intensive to attempt to carry out such large-scale investigations without the use of remote-sensing technology. This study used rapeseed in the UK as a model to demonstrate the value of remote sensing in assembling empirical information at a national level.

A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data

We describe a general likelihood-based 'mixture model' for inferring phylogenetic trees from gene-sequence or other character-state data. The model accommodates cases in which different sites in the alignment evolve in qualitatively distinct ways, but does not require prior knowledge of these patterns or partitioning of the data. We call this qualitative variability in the pattern of evolution across sites "pattern-heterogeneity" to distinguish it from both a homogenous process of evolution and from one characterized principally by differences in rates of evolution. We present studies to show that the model correctly retrieves the signals of pattern-heterogeneity from simulated gene-sequence data, and we apply the method to protein-coding genes and to a ribosomal 12S data set. The mixture model outperforms conventional partitioning in both these data sets. We implement the mixture model such that it can simultaneously detect rate- and pattern-heterogeneity. The model simplifies to a homogeneous model or a rate- variability model as special cases, and therefore always performs at least as well as these two approaches, and often considerably improves upon them. We make the model available within a Bayesian Markov-chain Monte Carlo framework for phylogenetic inference, as an easy-to-use computer program.

Some new three-level orthogonal main effects plans robust to model uncertainty

In this paper, we list some new orthogonal main effects plans for three-level designs for 4, 5 and 6 factors in IS runs and compare them with designs obtained from the existing L-18 orthogonal array. We show that these new designs have better projection properties and can provide better parameter estimates for a range of possible models. Additionally, we study designs in other smaller run-sizes when there are insufficient resources to perform an 18-run experiment. Plans for three-level designs for 4, 5 and 6 factors in 13 to 17 runs axe given. We show that the best designs here are efficient and deserve strong consideration in many practical situations.

A model for the evolution of pathogen-induced leaf shedding

A size-structured plant population model is developed to study the evolution of pathogen-induced leaf shedding under various environmental conditions. The evolutionary stable strategy (ESS) of the leaf shedding rate is determined for two scenarios: i) a constant leaf shedding strategy and ii) an infection load driven leaf shedding strategy. The model predicts that ESS leaf shedding rates increase with nutrient availability. No effect of plant density on the ESS leaf shedding rate is found even though disease severity increases with plant density. When auto-infection, that is increased infection due to spores produced on the plant itself, plays a key role in further disease increase on the plant, shedding leaves removes disease that would otherwise contribute to disease increase on the plant itself. Consequently leaf shedding responses to infections may evolve. When external infection, that is infection due to immigrant spores, is the key determinant, shedding a leaf does not reduce the force of infection on the leaf shedding plant. In this case leaf shedding will not evolve. Under a low external disease pressure adopting an infection driven leaf shedding strategy is more efficient than adopting a constant leaf shedding strategy, since a plant adopting an infection driven leaf shedding strategy does not shed any leaves in the absence of infection, even when leaf shedding rates are high. A plant adopting a constant leaf shedding rate sheds the same amount of leaves regardless of the presence of infection. Based on the results we develop two hypotheses that can be tested if the appropriate plant material is available.

Design and analysis of climate model experiments for the efficient estimation of anthropogenic signals

Presented herein is an experimental design that allows the effects of several radiative forcing factors on climate to be estimated as precisely as possible from a limited suite of atmosphere-only general circulation model (GCM) integrations. The forcings include the combined effect of observed changes in sea surface temperatures, sea ice extent, stratospheric (volcanic) aerosols, and solar output, plus the individual effects of several anthropogenic forcings. A single linear statistical model is used to estimate the forcing effects, each of which is represented by its global mean radiative forcing. The strong colinearity in time between the various anthropogenic forcings provides a technical problem that is overcome through the design of the experiment. This design uses every combination of anthropogenic forcing rather than having a few highly replicated ensembles, which is more commonly used in climate studies. Not only is this design highly efficient for a given number of integrations, but it also allows the estimation of (nonadditive) interactions between pairs of anthropogenic forcings. The simulated land surface air temperature changes since 1871 have been analyzed. The changes in natural and oceanic forcing, which itself contains some forcing from anthropogenic and natural influences, have the most influence. For the global mean, increasing greenhouse gases and the indirect aerosol effect had the largest anthropogenic effects. It was also found that an interaction between these two anthropogenic effects in the atmosphere-only GCM exists. This interaction is similar in magnitude to the individual effects of changing tropospheric and stratospheric ozone concentrations or to the direct (sulfate) aerosol effect. Various diagnostics are used to evaluate the fit of the statistical model. For the global mean, this shows that the land temperature response is proportional to the global mean radiative forcing, reinforcing the use of radiative forcing as a measure of climate change. The diagnostic tests also show that the linear model was suitable for analyses of land surface air temperature at each GCM grid point. Therefore, the linear model provides precise estimates of the space time signals for all forcing factors under consideration. For simulated 50-hPa temperatures, results show that tropospheric ozone increases have contributed to stratospheric cooling over the twentieth century almost as much as changes in well-mixed greenhouse gases.

Hydrogen-bonded dimer can mediate supramolecular beta-sheet formation and subsequent amyloid-like fibril formation: a model study

FT-IR data of six terminally blocked tripeptides containing Acp (epsilon-aminocaproic acid) reveals that all of them form supramolecular beta-sheets in the solid state. Single crystal X-ray diffraction studies of two peptides not only support this data but also disclose the fact that the supramolecular beta-sheet formation is initiated via dimer formation. The Scanning Electron Microscopic images of all peptides exhibit amyloid-like fibrils that show green birefringence after binding with Congo red, which is a characteristic feature of many neurodegenerative disease causing amyloid fibrils. (C) 2004 Elsevier Ltd. All rights reserved.

Self assembly of a model amphiphilic phenylalanine peptide/polyethylene glycol block copolymer in aqueous solution

There has been great interest recently in peptide amphiphiles and block copolymers containing biomimetic peptide sequences due to applications in bionanotechnology. We investigate the self-assembly of the peptide-PEG amphiphile FFFF-PEG5000 containing the hydrophobic sequence of four phenylalanine residues conjugated to PEG of molar mass 5000. This serves as a simple model peptide amphiphile. At very low concentration, association of hydrophobic aromatic phenylalanine residues occurs, as revealed by circular dichroism and UV/vis fluorescence experiments. A critical aggregation concentration associated with the formation of hydrophobic domains is determined through pyrene fluorescence assays. At higher concentration, defined beta-sheets develop as revealed by FTIR spectroscopy and X-ray diffraction. Transmission electron microscopy reveals self-assembled straight fibril structures. These are much shorter than those observed for amyloid peptides, the finite length may be set by the end cap energy due to the hydrophobicity of phenylalanine. The combination of these techniques points to different aggregation processes depending on concentration. Hydrophobic association into irregular aggregates occurs at low concentration, well-developed beta-sheets only developing at higher concentration. Drying of FFFF-PEG5000 solutions leads to crystallization of PEG, as confirmed by polarized optical microscopy (POM), FTIR and X-ray diffraction (XRD). PEG crystallization does not disrupt local beta-sheet structure (as indicated by FTIR and XRD). However on longer lengthscales the beta-sheet fibrillar structure is perturbed because spheruilites from PEG crystallization are observed by POM. (C) 2009 Elsevier B.V. All rights reserved.