Global adaptation of spring bread and durum wheat lines near-isogenic for major reduced height genes

The effect of major dwarfing genes, Rht-B1 and Rht-D1, in bread (Triticum aestivum L.) and durum (Triticum turgidum L. var. durum) wheats varies with environment. Six reduced-height near-isogenic spring wheat lines, included in the International Adaptation Trial (IAT), were grown in 81 trials around the world. Of the 56 IAT trials yielding > 3 Mg ha(-1), the mean yield of semidwarfs was significantly greater than tails in 54% of trials; in the 27 trials yielding < 3 Mg ha-1, semidwarfs were superior in only 24%. Sixteen pairs of semidwarf-tall near-isolines were grown in six managed drought environment trials (DETs) in northwestern Mexico. In these trials, semidwarfs outyielded talls in all but the most droughted environment (2.5 Mg ha(-1)). The effect of the height alleles varied with genetic background and environment. For both yield and height, variance components for allele and environment by allele interaction were larger than those for genetic background and genetic background by environment. Pattern analysis showed that tall and semidwarf lines had similar adaptation to stressed environments (< 2.8 Mg ha(-1), low rainfall), while semidwarfs yielded more in less stressed environments (> 4.3 Mg ha(-1), high rainfall). The best adapted near-isogenic pair had a Kauz background, where the tall was only 16% taller than the dwarf. In the Kauz-derived pair, the semidwarf outyielded the tall in only 13% of trials with no differences in low yielding trials. This supports the idea that '' short talls '' may be useful in marginal environments (yield < 3 Mg ha(-1)).

Reply to Paparazzo

We would like to reply to the comments made by Paparazzo on our recent paper [1] on the “effect of curve fitting parameters on quantitative analysis of Fe0.94O and Fe2O3 using XPS”. There have been many studies on the characterisation of the properties of iron oxide surfaces. The main purpose of writing the paper was to demonstrate the extent to which the selection of input parameters for curve fitting can affect the results of the quantitative analysis, and to use this analysis to develop more consistent, reproducible and quantitative methods of analysis of these data.

Using generative models for handwritten digit recognition

We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable B-splines with Gaussian ``ink generators'' spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maximization (EM) algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages. (1) After identifying the model most likely to have generated the data, the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style. (2) During the process of explaining the image, generative models can perform recognition driven segmentation. (3) The method involves a relatively small number of parameters and hence training is relatively easy and fast. (4) Unlike many other recognition schemes it does not rely on some form of pre-normalization of input images, but can handle arbitrary scalings, translations and a limited degree of image rotation. We have demonstrated our method of fitting models to images does not get trapped in poor local minima. The main disadvantage of the method is it requires much more computation than more standard OCR techniques.

A hierarchical latent variable model for data visualization

Visualization has proven to be a powerful and widely-applicable tool the analysis and interpretation of data. Most visualization algorithms aim to find a projection from the data space down to a two-dimensional visualization space. However, for complex data sets living in a high-dimensional space it is unlikely that a single two-dimensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allows the complete data set to be visualized at the top level, with clusters and sub-clusters of data points visualized at deeper levels. The algorithm is based on a hierarchical mixture of latent variable models, whose parameters are estimated using the expectation-maximization algorithm. We demonstrate the principle of the approach first on a toy data set, and then apply the algorithm to the visualization of a synthetic data set in 12 dimensions obtained from a simulation of multi-phase flows in oil pipelines and to data in 36 dimensions derived from satellite images.

Hierarchical GTM: constructing localized non-linear projection manifolds in a principled way

It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets, and therefore a hierarchical visualization system is desirable. In this paper we extend an existing locally linear hierarchical visualization system PhiVis ¸iteBishop98a in several directions: bf(1) We allow for em non-linear projection manifolds. The basic building block is the Generative Topographic Mapping (GTM). bf(2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. bf(3) Using tools from differential geometry we derive expressions for local directional curvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the ancestor visualization plots which are captured by a child model. We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set and apply our system to two more complex 12- and 18-dimensional data sets.

What does the study of the spatial patterns of pathological lesions tell us about the pathogenesis of neurodegenerative disorders?

Discrete pathological lesions, which include extracellular protein deposits, intracellular inclusions and changes in cell morphology, occur in the brain in the majority of neurodegenerative disorders. These lesions are not randomly distributed in the brain but exhibit a spatial pattern, that is, a departure from randomness towards regularity or clustering. The spatial pattern of a lesion may reflect pathological processes affecting particular neuroanatomical structures and, therefore, studies of spatial pattern may help to elucidate the pathogenesis of a lesion and of the disorders themselves. The present article reviews first, the statistical methods used to detect spatial patterns and second, the types of spatial patterns exhibited by pathological lesions in a variety of disorders which include Alzheimer's disease, Down syndrome, dementia with Lewy bodies, Creutzfeldt-Jakob disease, Pick's disease and corticobasal degeneration. These studies suggest that despite the morphological and molecular diversity of brain lesions, they often exhibit a common type of spatial pattern (i.e. aggregation into clusters that are regularly distributed in the tissue). The pathogenic implications of spatial pattern analysis are discussed with reference to the individual disorders and to studies of neurodegeneration as a whole.

Microscopy imaging of liposomes:from coverslips to environmental SEM

A plethora of techniques for the imaging of liposomes and other bilayer vesicles are available. However, sample preparation and the technique chosen should be carefully considered in conjunction with the information required. For example, larger vesicles such as multilamellar and giant unilamellar vesicles can be viewed using light microscopy and whilst vesicle confirmation and size prior to additional physical characterisations or more detailed microscopy can be undertaken, the technique is limited in terms of resolution. To consider the options available for visualising liposome-based systems, a wide range of microscopy techniques are described and discussed here: these include light, fluorescence and confocal microscopy and various electron microscopy techniques such as transmission, cryo, freeze fracture and environmental scanning electron microscopy. Their application, advantages and disadvantages are reviewed with regard to their use in analysis of lipid vesicles.

Hippocampal pathology in progressive supranuclear palsy (PSP): a quantitative study of 8 cases

Objective: To quantify the neuronal and glial cell pathology in the hippocampus and the parahippocampal gyrus (PHG) of 8 cases of progressive supranuclear palsy (PSP). Material: tau-immunolabeled sections of the temporal lobe of 8 diagnosed cases of PSP. Method: The densities of lesions were measured in the PHG, CA sectors of the hippocampus and the dentate gyrus (DG) and studied using spatial pattern analysis. Results: Neurofibrillary tangles (NFT) and abnormally enlarged neurons (EN) were most frequent in the PHG and in sector CA1 of the hippocampus, oligodendroglial inclusions (“coiled bodies”) (GI) in the PHG, subiculum, sectors CA1 and CA2, and neuritic plaques (NP) in sectors CA2 and CA4. The DG was the least affected region. Vacuolation and GI were observed in the alveus. No tufted astrocytes (TA) were observed. Pathological changes exhibited clustering, the lesions often exhibiting a regular distribution of the clusters parallel to the tissue boundary. There was a positive correlation between the degree of vacuolation in the alveus and the densities of NFT in CA1 and GI in CA1 and CA2. Conclusion: The pathology most significantly affected the output pathways of the hippocampus, lesions were topographically distributed, and hippocampal pathology may be one factor contributing to cognitive decline in PSP.

A quantitative study of the pathological changes in the cerebellum in 15 cases of variant Creutzfeldt–Jakob disease (vCJD)

Aims: To determine in the cerebellum in variant Creutzfeldt–Jakob disease (vCJD): (i) whether the pathology affected all laminae; (ii) the spatial topography of the pathology along the folia; (iii) spatial correlations between the pathological changes; and (iv) whether the pathology was similar to that of the common methionine/methionine Type 1 subtype of sporadic CJD. Methods: Sequential cerebellar sections of 15 cases of vCJD were stained with haematoxylin and eosin, or immunolabelled with monoclonal antibody 12F10 against prion protein (PrP) and studied using spatial pattern analysis. Results: Loss of Purkinje cells was evident compared with control cases. Densities of the vacuolation and the protease-resistant form of prion protein (PrPSc) (diffuse and florid plaques) were greater in the granule cell layer (GL) than the molecular layer (ML). In the ML, vacuoles and PrPSc plaques occurred in clusters regularly distributed along the folia with larger clusters of vacuoles and diffuse plaques in the GL. There was a negative spatial correlation between the vacuoles and the surviving Purkinje cells in the ML. There was a positive spatial correlation between the vacuoles and diffuse PrPSc plaques in the ML and GL. Conclusions: (i) all laminae were affected by the pathology, the GL more severely than the ML; (ii) the pathology was topographically distributed along the folia especially in the Purkinje cell layer and ML; (iii) pathological spread may occur in relation to the loop of anatomical connections involving the cerebellum, thalamus, cerebral cortex and pons; and (iv) there were pathological differences compared with methionine/methionine Type 1 sporadic CJD.

Spatial topography of the neurofibrillary tangles in cortical and subcortical regions in progressive supranuclear palsy

Objective: To study the topography of neurofibrillary tangles (NFT) in cortical and subcortical areas in progressive supranuclear palsy (PSP). Methods: Pattern analysis was carried out on tau-positive NFT in eight PSP cases. Results: Of the areas studied, NFT were randomly distributed in 68%, regularly distributed in 3%, and clustered in 29%. A regular distribution of clusters was more frequent in cortical than subcortical areas. Conclusion: NFT topography in subcortical areas was similar to inclusions in the synucleinopathy multiple system atrophy (MSA) but in cortical areas was comparable to other tauopathies. © 2006 Elsevier Ltd. All rights reserved.

Multiple system atrophy (MSA): Topographic distribution of the alpha-synuclein-associated pathological changes

To study the topographic distribution of the pathology in multiple system atrophy (MSA). Pattern analysis was carried out using a-synuclein immunohistochemistry in 10 MSA cases. The glial cytoplasmic inclusions (GCI) were distributed randomly or in large clusters. The neuronal inclusions (NI) and abnormal neurons were distributed in regular clusters. Clusters of the NI and abnormal neurons were spatially correlated whereas the GCI were not spatially correlated with either the NI or the abnormal neurons. The data suggest that the GCI represent the primary change in MSA and the neuronal pathology develops secondary to the glial pathology.

Differences in the density and spatial distribution of florid and diffuse plaques in variant Creutzfeldt-Jakob disease (vCJD)

Objective: To determine whether in cases of variant Creutzfeldt-Jakob disease (vCJD), the florid-type plaques are derived from the diffuse plaques or whether the 2 plaque types develop independently. Material: Blocks of frontal, parietal, occipital and temporal neocortex and cerebellar cortex from 11 cases of vCJD. Method: The density, distribution and spatial pattern of the florid and diffuse plaques were determined in each brain region using spatial pattern analysis. Results: The density of the diffuse plaques was significantly greater than that of the florid plaques in most areas. The ratio of the diffuse to florid plaques varied between brain regions and was maximal in the molecular layer of the cerebellum. The densities of the florid and diffuse plaques were positively correlated in the parietal cortex, occipital cortex, the inferior temporal gyrus and the dentate gyrus. Plaque densities were not related to disease duration. In the cerebral cortex, the diffuse plaques were more commonly evenly distributed or occurred in large clusters along the cortex parallel to the pia mater compared with the florid plaques which occurred more frequently in regularly distributed clusters. Conclusion: The florid plaques may not be derived from the diffuse plaques, the 2 plaque types appearing to develop independently with unique factors involved in their pathogenesis.

Spatial patterns of the vacuolation in subcortical white matter in sporadic Creutzfeldt-Jakob disease (sCJD)

OBJECTIVE: To study the spatial patterns of the vacuolation ("spongiform change") in the subcortical white matter in the "classical" form of sporadic Creutzfeldt-Jakob disease (sCJD). MATERIAL: Frontal, parietal, occipital and temporal lobes of 11 cases of sCJD. METHOD: Spatial patterns were studied across the white matter at the base of the gyri using spatial pattern analysis. RESULTS: In the white matter of all gyri studied, vacuoles were aggregated into clusters, 50 to > 800 microm in diameter and in 22/37 (59%) of gyri, the clusters of vacuoles exhibited a regular distribution across the base of the gyri. In the remaining gyri, the vacuoles were aggregated into large clusters, at least 400 microm or 800 microm in diameter, but without evidence of a regular distribution. In a significant proportion of gyri, the spatial patterns of the vacuolation were similar to those reported previously for spongiform change and prion protein (PrP) deposits in the corresponding grey matter. CONCLUSIONS: Degeneration of the white matter and the formation of clusters of vacuoles may occur before the degeneration of the grey matter or could be a consequence of pathology affecting the cortico-cortical pathways.

Diffuse beta-amyloid (A beta) deposits and neurons: in situ secretion or diffusion of A beta?

The association between diffuse-type beta -amyloid (AP) deposits and neuronal cell bodies in Alzheimer's disease (AD) and Down's syndrome (DS) could result from the secretion of AP from clusters of neurons in situ or the diffusion of A beta from cell processes, glial cells or blood vessels. To decide between these hypotheses, spatial pattern analysis was used to study the relationship between the degree of clustering of neuronal cell bodies and the presence of diffuse deposits in the temporal lobe of patients with DS. Significant clustering of neuronal cell bodies was present in 17/24 (71%) of brain areas studied. in addition, in 23/24 (96%) of brain areas, there was a positive correlation between the presence of diffuse deposits and the density of neurons. Hence, the data support the hypothesis that diffuse deposits develop in situ mainly as a result of the secretion of A beta by local clusters of neurons rather than by significant diffusion. Furthermore, the size of a diffuse deposit is likely to be determined by the number of neurons within a cluster which secrete A beta. The number and density of neurons could also be a factor determining the evolution of a diffuse into a mature amyloid deposit.

Bayesian classification with Gaussian processes

We consider the problem of assigning an input vector to one of m classes by predicting P(c|x) for c=1,...,m. For a two-class problem, the probability of class one given x is estimated by s(y(x)), where s(y)=1/(1+e-y). A Gaussian process prior is placed on y(x), and is combined with the training data to obtain predictions for new x points. We provide a Bayesian treatment, integrating over uncertainty in y and in the parameters that control the Gaussian process prior the necessary integration over y is carried out using Laplace's approximation. The method is generalized to multiclass problems (m>2) using the softmax function. We demonstrate the effectiveness of the method on a number of datasets.