Quantitative proteomics using uniform 15N-labeling, mascot and the trans-proteomic pipeline

Stable isotope labeling combined with MS is a powerful method for measuring relative protein abundances, for instance, by differential metabolic labeling of some or all amino acids with 14N and 15N in cell culture or hydroponic media. These and most other types of quantitative proteomics experiments using high-throughput technologies, such as LC-MS/MS, generate large amounts of raw MS data. This data needs to be processed efficiently and automatically, from the mass spectrometer to statistically evaluated protein identifications and abundance ratios. This paper describes in detail an approach to the automated analysis of uniformly 14N/15N-labeled proteins using MASCOT peptide identification in conjunction with the trans-proteomic pipeline (TPP) and a few scripts to integrate the analysis workflow. Two large proteomic datasets from uniformly labeled Arabidopsis thaliana were used to illustrate the analysis pipeline. The pipeline can be fully automated and uses only common or freely available software.

The automation and evaluation of nested clade phylogeographic analysis

Nested clade phylogeographic analysis (NCPA) is a popular method for reconstructing the demographic history of spatially distributed populations from genetic data. Although some parts of the analysis are automated, there is no unique and widely followed algorithm for doing this in its entirety, beginning with the data, and ending with the inferences drawn from the data. This article describes a method that automates NCPA, thereby providing a framework for replicating analyses in an objective way. To do so, a number of decisions need to be made so that the automated implementation is representative of previous analyses. We review how the NCPA procedure has evolved since its inception and conclude that there is scope for some variability in the manual application of NCPA. We apply the automated software to three published datasets previously analyzed manually and replicate many details of the manual analyses, suggesting that the current algorithm is representative of how a typical user will perform NCPA. We simulate a large number of replicate datasets for geographically distributed, but entirely random-mating, populations. These are then analyzed using the automated NCPA algorithm. Results indicate that NCPA tends to give a high frequency of false positives. In our simulations we observe that 14% of the clades give a conclusive inference that a demographic event has occurred, and that 75% of the datasets have at least one clade that gives such an inference. This is mainly due to the generation of multiple statistics per clade, of which only one is required to be significant to apply the inference key. We survey the inferences that have been made in recent publications and show that the most commonly inferred processes (restricted gene flow with isolation by distance and contiguous range expansion) are those that are commonly inferred in our simulations. However, published datasets typically yield a richer set of inferences with NCPA than obtained in our random-mating simulations, and further testing of NCPA with models of structured populations is necessary to examine its accuracy.

Effects of straw and silicon soil amendments on some foliar and stem-base diseases in pot-grown winter wheat

Four foliar and two stem-base pathogens were inoculated onto wheat plants grown in different substrates in pot experiments. Soils from four different UK locations were each treated in three ways: (i) straw incorporated in the field at 10 t ha−1 several months previously; (ii) silicon fertilization at 100 mg L−1 during the experiment; and (iii) no amendments. A sand and vermiculite mix was used with and without silicon amendment. The silicon treatment increased plant silica concentrations in all experiments, but incorporating straw was not associated with raised plant silica concentrations. Blumeria graminis and Puccinia recondita were inoculated by shaking infected plants over the test plants, followed by suitable humid periods. The silicon treatment reduced powdery mildew (B. graminis) substantially in sand and vermiculite and in two of the soils, but there were no effects on the slight infection by brown rust (P. recondita). Phaeosphaeria nodorum and Mycosphaerella graminicola were inoculated as conidial suspensions. Leaf spot caused by P. nodorum was reduced in silicon-amended sand and vermiculite; soil was not tested. Symptoms of septoria leaf blotch caused by M. graminicola were reduced by silicon amendment in a severely infected sand and vermiculite experiment but not in soil or a slightly infected sand and vermiculite experiment. Oculimacula yallundae (eyespot) and Fusarium culmorum (brown foot rot) were inoculated as agar plugs on the stem base. Severity of O. yallundae was reduced by silicon amendment of two of the soils but not sand and vermiculite; brown foot rot symptoms caused by F. culmorum were unaffected by silicon amendment. The straw treatment reduced severity of powdery mildew but did not detectably affect the other pathogens. Both straw and silicon treatments appeared to increase plant resistance to all diseases only under high disease pressure.

Quantitative proteomics using uniform N-15-labeling, MASCOT, and the trans-proteomic pipeline

Stable isotope labeling combined with MS is a powerful method for measuring relative protein abundances, for instance, by differential metabolic labeling of some or all amino acids with N-14 and N-15 in cell culture or hydroponic media. These and most other types of quantitative proteomics experiments using high-throughput technologies, such as LC-MS/MS, generate large amounts of raw MS data. This data needs to be processed efficiently and automatically, from the mass spectrometer to statistically evaluated protein identifications and abundance ratios. This paper describes in detail an approach to the automated analysis of Uniformly N-14/N-15-labeled proteins using MASCOT peptide identification in conjunction with the trans-proteomic pipeline (TPP) and a few scripts to integrate the analysis workflow. Two large proteomic datasets from uniformly labeled Arabidopsis thaliana were used to illustrate the analysis pipeline. The pipeline can be fully automated and uses only common or freely available software.

A molecular and structural characterization of senescing Arabidopsis siliques and comparison of transcriptional profiles with senescing petals and leaves

Senescence of plant organs is a genetically controlled process that regulates cell death to facilitate nutrient recovery and recycling, and frequently precedes, or is concomitant with, ripening of reproductive structures. In Arabidopsis thaliana, the seeds are contained within a silique, which is itself a photosynthetic organ in the early stages of development and undergoes a programme of senescence prior to dehiscence. A transcriptional analysis of the silique wall was undertaken to identify changes in gene expression during senescence and to correlate these events with ultrastructural changes. The study revealed that the most highly up-regulated genes in senescing silique wall tissues encoded seed storage proteins, and the significance of this finding is discussed. Global transcription profiles of senescing siliques were compared with those from senescing Arabidopsis leaf or petal tissues using microarray datasets and metabolic pathway analysis software (MapMan). In all three tissues, members of NAC and WRKY transcription factor families were up-regulated, but components of the shikimate and cell-wall biosynthetic pathways were down-regulated during senescence. Expression of genes encoding ethylene biosynthesis and action showed more similarity between senescing siliques and petals than between senescing siliques and leaves. Genes involved in autophagy were highly expressed in the late stages of death of all plant tissues studied, but not always during the preceding remobilization phase of senescence. Analyses showed that, during senescence, silique wall tissues exhibited more transcriptional features in common with petals than with leaves. The shared and distinct regulatory events associated with senescence in the three organs are evaluated and discussed.

Viability of Salmonella enterica subsp. enterica during the preparation and cold storage of Egyptian soft cheeses and ice-cream

Changes occurring in the viability of Salmonella enterica subsp. enterica during the preparation and cold storage of Domiati cheese, Kariesh cheese and ice-cream were examined. A significant decrease in numbers was observed after whey drainage during the manufacture of Domiati cheese, but Salmonella remained viable for 13 weeks in cheeses prepared from milks with between 60 and 100 g/L NaCl; the viability declined in Domiati cheese made from highly salted milk during the later stages of storage. The method of coagulation used in the preparation of Kariesh cheese affected the survival time of the pathogen, and it varied from 2 to 3 weeks in cheeses made with a slow-acid coagulation method to 4-5 weeks for an acid-rennet coagulation method. This difference was attributed to the higher salt-in-moisture levels and lower pH values of Kariesh cheese prepared by the slow-acid coagulation method. A slight decrease in the numbers of Salmonella resulted from ageing ice-cream mix for 24 h at 0degreesC, but a greater reduction was evident after one day of frozen storage at -20degreesC. The pathogen survived further frozen storage for four months without any substantial change in numbers.

Disorder and dissolution enhancement: Deposition of ibuprofen on to insoluble polymers

Microcrystalline cellulose (MCC) and cross-linked polyvinylpyrrolidone (PVP-CL) were examined as polymeric carriers to support amorphous ibuprofen (IB). Drug/cartier systems were prepared as physical mixes, and drug was loaded onto the polymers by hot mix and solvent deposition methods. The systems were examined using differential scanning calorimetry (DSC), X-ray powder diffractometry (XRD) and by dissolution testing. PVP-CL reduced drug crystallinity more than MCC and, surprisingly, even very simple mixing of ibuprofen with PVP-CL induced disordering of the drug. Increased ibuprofen dissolution rates were achieved with both polymers, in the order of solvent deposition > hot mixes > physical mixes. The increased dissolution rates could be attributed to a combination of faster dissolution from amorphous ibuprofen, microcrystalline drug deposition on carrier surfaces and polymer swelling. However, no clear relationship was observed between ibuprofen dissolution rates (using first order, Higuchi or Hixson-Crowell relationships) and drug crystallinity. (C) 2005 Elsevier B.V. All rights reserved.

Case Study: Interacting with Volumetric Medical Datasets in Networked CAVE Environments

Virtual Reality (VR) is widely used in visualizing medical datasets. This interest has emerged due to the usefulness of its techniques and features. Such features include immersion, collaboration, and interactivity. In a medical visualization context, immersion is important, because it allows users to interact directly and closelywith detailed structures in medical datasets. Collaboration on the other hand is beneficial, because it gives medical practitioners the chance to share their expertise and offer feedback and advice in a more effective and intuitive approach. Interactivity is crucial in medical visualization and simulation systems, because responsiveand instantaneous actions are key attributes in applications, such as surgical simulations. In this paper we present a case study that investigates the use of VR in a collaborative networked CAVE environment from a medical volumetric visualization perspective. The study will present a networked CAVE application, which has been built to visualize and interact with volumetric datasets. We will summarize the advantages of such an application and the potential benefits of our system. We also will describe the aspects related to this application area and the relevant issues of such implementations.

Using a RESTful messaging and registry system to support a range a distributed applications

Tycho was conceived in 2003 in response to a need by the GridRM [1] resource-monitoring project for a ldquolight-weightrdquo, scalable and easy to use wide-area distributed registry and messaging system. Since Tycho's first release in 2006 a number of modifications have been made to the system to make it easier to use and more flexible. Since its inception, Tycho has been utilised across a number of application domains including widearea resource monitoring, distributed queries across archival databases, providing services for the nodes of a Cray supercomputer, and as a system for transferring multi-terabyte scientific datasets across the Internet. This paper provides an overview of the initial Tycho system, describes a number of applications that utilise Tycho, discusses a number of new utilities, and how the Tycho infrastructure has evolved in response to experience of building applications with it.

Copyright and research: an archivangelist's perspective

Conflation of academic copyright issues with respect to books (whether text books, research monographs or popularisations) and research articles, is rife in the academic publishing industry. A charitable interpretation is that this is because to publishers they are all effectively the same: a product produced for commercial benefit. An uncharitable interpretation is that this is a classic Fear Uncertainty and Doubt approach, in an attempt to delay the inevitable move to Open Access (OA) to research articles. To authors, however, research articles and books are generally very different things. Research articles are produced without the expectation of direct financial return, whereas books generally include some consideration of financial return. Taylor’s “Copyright and research: an academic publisher’s perspective” (SCRIPT-ed 4:2) falls wholesale into this mental trap and in particular his lauding of the position paper of the Association of American Professional and Scholarly Publishers, shows a lack of understanding of the continuing huge loss to scholarship of a lack of OA to research articles. It should be regarded as a categorical imperative for scholars to embrace OA to research articles.

Clustering quality and topology preservation in fast learning SOMs

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper, we describe Fast Learning SOM (FLSOM) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that FLSOM also improves the quality of the map by providing better clustering quality and topology preservation of multidimensional input data. Several tests have been carried out on different multidimensional datasets, which demonstrate better performances of the algorithm in comparison with the original SOM.

Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.