Molecular investigation of the microbial populations of the pink sugarcane mealybug, Saccharicoccus sacchari

In an attempt to better understand the microbial diversity and endosymbiotic microbiota of the pink sugarcane mealybug (PSMB) Saccharicoccus sacchari Cockerell (Homoptera: Pseudococcidae), culture-independent approaches, namely PCR, a 16S rDNA clone library, and temperature gradient gel electrophoresis (TGGE) were used. Previous work has indicated that the acetic acid bacteria Gluconacetobacter sacchari, Gluconacetobacter diazotrophicus, and Gluconacetobacter liquefaciens represent only a small proportion of the microbial community of the PSMB. These findings were supported in this study by TGGE, where no bands representing G. sacchari, G. diazotrophicus, and G. liquefaciens on the acrylamide gel could be observed following electrophoresis, and by a 16S rDNA clone library study, where no clones with the sequence of an acetic acid bacterium were found. Instead, TGGE revealed that the mealybug microbial community was dominated by beta- and gamma-Proteobacteria. The dominant band in TGGE gels found in a majority of the mealybug samples was most similar, according to BLAST analysis, to the beta-symbiont of the craw mealybug Antonina crawii and to Candidatus Tremblaya princeps, an endosymbiont from the mealybug Paracoccus nothofagicola. The sequences of other dominant bands were identified as gamma-Proteobacteria, and were most closely related to uncultured bacterial clones obtained from soil samples. Mealybugs collected from different areas in Queensland, Australia, were found to produce similar TGGE profiles, although there were a few exceptions. A 16S rDNA clone library based on DNA extracted from a mealybug collected from sugarcane in the Burdekin region in Queensland, Australia, indicated very low levels of diversity among mealybug microbial populations. All sequenced clones were most closely related to the same members of the gamma-Proteobacteria, according to BLAST analysis.

Better prediction of protein contact number using a support vector regression analysis of amino acid sequence

Background: Protein tertiary structure can be partly characterized via each amino acid's contact number measuring how residues are spatially arranged. The contact number of a residue in a folded protein is a measure of its exposure to the local environment, and is defined as the number of C-beta atoms in other residues within a sphere around the C-beta atom of the residue of interest. Contact number is partly conserved between protein folds and thus is useful for protein fold and structure prediction. In turn, each residue's contact number can be partially predicted from primary amino acid sequence, assisting tertiary fold analysis from sequence data. In this study, we provide a more accurate contact number prediction method from protein primary sequence. Results: We predict contact number from protein sequence using a novel support vector regression algorithm. Using protein local sequences with multiple sequence alignments (PSI-BLAST profiles), we demonstrate a correlation coefficient between predicted and observed contact numbers of 0.70, which outperforms previously achieved accuracies. Including additional information about sequence weight and amino acid composition further improves prediction accuracies significantly with the correlation coefficient reaching 0.73. If residues are classified as being either contacted or non-contacted, the prediction accuracies are all greater than 77%, regardless of the choice of classification thresholds. Conclusion: The successful application of support vector regression to the prediction of protein contact number reported here, together with previous applications of this approach to the prediction of protein accessible surface area and B-factor profile, suggests that a support vector regression approach may be very useful for determining the structure-function relation between primary sequence and higher order consecutive protein structural and functional properties.

Hydrothermal Stability Analysis of Carbonised Template Molecular Sieve Silica Membranes

For fuel cell CO clean up application, the presence of water with silica membranes greatly reduces their selectivity to CO. We show results of a new functional carbonised template membrane of around 13nm thickness which offered hydrothermal stability with no compromise to the membrane’s H2/CO permselectivity of 16. Lost permeance was also regenerated.

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

Analysis of Wolbachia protein synthesis in Drosophila in vivo

Intracellular Wolbachia infections are extremely common in arthropods and exert profound control over the reproductive biology of the host. However, very little is known about the underlying molecular mechanisms which mediate these interactions with the host. We examined protein synthesis by Wolbachia in a Drosophila host in vivo by selective metabolic labelling of prokaryotic proteins and subsequent analysis by 1D and 2D gel electrophoresis. Using this method we could identify the major proteins synthesized by Wolbachia in ovaries and testes of flies. Of these proteins the most abundant was of low molecular weight and showed size variation between Wolbachia strains which correlated with the reproductive phenotype they generated in flies. Using the gel systems we employed it was not possible to identify any proteins of Wolbachia origin in the mature sperm cells of infected flies.

16S rRNA phylogenetic analysis of the bacterial endosymbionts associated with cytoplasmic incompatibility in insects

Bacterial endosymbionts of insects have long been implicated in the phenomenon of cytoplasmic incompatibility, in which certain crosses between symbiont-infected individuals lead to embryonic death or sex ratio distortion. The taxonomic position of these bacteria has, however, not been known with any certainty. Similarly, the relatedness of the bacteria infecting various insect hosts has been unclear. The inability to grow these bacteria on defined cell-free medium has been the major factor underlying these uncertainties. We circumvented this problem by selective PCR amplification and subsequent sequencing of the symbiont 16S rRNA genes directly from infected insect tissue. Maximum parsimony analysis of these sequences indicates that the symbionts belong in the α-subdivision of the Proteobacteria, where they are most closely related to the Rickettsia and their relatives. They are all closely related to each other and are assigned to the type species Wolbachia pipientis. Lack of congruence between the phylogeny of the symbionts and their insect hosts suggests that horizontal transfer of symbionts between insect species may occur. Comparison of the sequences for W. pipientis and for Wolbachia persica, an endosymbiont of ticks, shows that the genus Wolbachia is polyphyletic. A PCR assay based on 16S primers was designed for the detection of W. pipientis in insect tissue, and initial screening of insects indicates that cytoplasmic incompatibility may be a more general phenomenon in insects than is currently recognized.

Algorithms for sequence analysis via mutagenesis

Despite many successes of conventional DNA sequencing methods, some DNAs remain difficult or impossible to sequence. Unsequenceable regions occur in the genomes of many biologically important organisms, including the human genome. Such regions range in length from tens to millions of bases, and may contain valuable information such as the sequences of important genes. The authors have recently developed a technique that renders a wide range of problematic DNAs amenable to sequencing. The technique is known as sequence analysis via mutagenesis (SAM). This paper presents a number of algorithms for analysing and interpreting data generated by this technique.

Equational Reasoning as a Tool for Data Analysis

A combination of deductive reasoning, clustering, and inductive learning is given as an example of a hybrid system for exploratory data analysis. Visualization is replaced by a dialogue with the data.

Design, Science and Conceptual Analysis

Philosophers expend considerable effort on the analysis of concepts, but the value of such work is not widely appreciated. This paper principally analyses some arguments, beliefs, and presuppositions about the nature of design and the relations between design and science common in the literature to illustrate this point, and to contribute to the foundations of design theory.

XSophe-Sophe-XeprView (R). A computer simulation software suite (v. 1.1.3) for the analysis of continuous wave EPR spectra

The XSophe-Sophe-XeprView((R)) computer simulation software suite enables scientists to easily determine spin Hamiltonian parameters from isotropic, randomly oriented and single crystal continuous wave electron paramagnetic resonance (CW EPR) spectra from radicals and isolated paramagnetic metal ion centers or clusters found in metalloproteins, chemical systems and materials science. XSophe provides an X-windows graphical user interface to the Sophe programme and allows: creation of multiple input files, local and remote execution of Sophe, the display of sophelog (output from Sophe) and input parameters/files. Sophe is a sophisticated computer simulation software programme employing a number of innovative technologies including; the Sydney OPera HousE (SOPHE) partition and interpolation schemes, a field segmentation algorithm, the mosaic misorientation linewidth model, parallelization and spectral optimisation. In conjunction with the SOPHE partition scheme and the field segmentation algorithm, the SOPHE interpolation scheme and the mosaic misorientation linewidth model greatly increase the speed of simulations for most spin systems. Employing brute force matrix diagonalization in the simulation of an EPR spectrum from a high spin Cr(III) complex with the spin Hamiltonian parameters g(e) = 2.00, D = 0.10 cm(-1), E/D = 0.25, A(x) = 120.0, A(y) = 120.0, A(z) = 240.0 x 10(-4) cm(-1) requires a SOPHE grid size of N = 400 (to produce a good signal to noise ratio) and takes 229.47 s. In contrast the use of either the SOPHE interpolation scheme or the mosaic misorientation linewidth model requires a SOPHE grid size of only N = 18 and takes 44.08 and 0.79 s, respectively. Results from Sophe are transferred via the Common Object Request Broker Architecture (CORBA) to XSophe and subsequently to XeprView((R)) where the simulated CW EPR spectra (1D and 2D) can be compared to the experimental spectra. Energy level diagrams, transition roadmaps and transition surfaces aid the interpretation of complicated randomly oriented CW EPR spectra and can be viewed with a web browser and an OpenInventor scene graph viewer.

Experimental Analysis of Froude Number Effect on Air Entrainment in the Hydraulic Jump

A hydraulic jump is characterized by strong energy dissipation and mixing, large-scale turbulence, air entrainment, waves and spray. Despite recent pertinent studies, the interaction between air bubbles diffusion and momentum transfer is not completely understood. The objective of this paper is to present experimental results from new measurements performed in rectangular horizontal flume with partially-developed inflow conditions. The vertical distributions of void fraction and air bubbles count rate were recorded for inflow Froude number Fr1 in the range from 5.2 to 14.3. Rapid detrainment process was observed near the jump toe, whereas the structure of the air diffusion layer was clearly observed over longer distances. These new data were compared with previous data generally collected at lower Froude numbers. The comparison demonstrated that, at a fixed distance from the jump toe, the maximum void fraction Cmax increases with the increasing Fr1. The vertical locations of the maximum void fraction and bubble count rate were consistent with previous studies. Finally, an empirical correlation between the upper boundary of the air diffusion layer and the distance from the impingement point was provided.

Analysis of multiple gas solid reactions

Multiple gas solid reactions involving one solid and N gaseous reactants are investigated in this study by using a matched asymptotic expansion technique. Two cases are particularly studied. In the first case all N chemical reaction rates are faster than the diffusion rate. While in the second case only M (M < N) chemical reaction rates are faster than the diffusion rate and the rates of the remaining (N-M) chemical reactions are comparable to that of diffusion. For these two cases the solid concentration profile behaves like a travelling wave. In the first case the wave front velocity is contributed linearly by all gaseous reactants (additive law) while in the second case this law does not hold.

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

Comparison of BR and QR Eigenvalue Algorithms for Power System Small Signal Stability Analysis

The BR algorithm is a novel and efficient method to find all eigenvalues of upper Hessenberg matrices and has never been applied to eigenanalysis for power system small signal stability. This paper analyzes differences between the BR and the QR algorithms with performance comparison in terms of CPU time based on stopping criteria and storage requirement. The BR algorithm utilizes accelerating strategies to improve its performance when computing eigenvalues of narrowly banded, nearly tridiagonal upper Hessenberg matrices. These strategies significantly reduce the computation time at a reasonable level of precision. Compared with the QR algorithm, the BR algorithm requires fewer iteration steps and less storage space without depriving of appropriate precision in solving eigenvalue problems of large-scale power systems. Numerical examples demonstrate the efficiency of the BR algorithm in pursuing eigenanalysis tasks of 39-, 68-, 115-, 300-, and 600-bus systems. Experiment results suggest that the BR algorithm is a more efficient algorithm for large-scale power system small signal stability eigenanalysis.

Clustering Process Analysis in a Large-Size Dropshaft and in a Hydraulic Jump

Recent efforts in the characterization of air-water flows properties have included some clustering process analysis. A cluster of bubbles is defined as a group of two or more bubbles, with a distinct separation from other bubbles before and after the cluster. The present paper compares the results of clustering processes two hydraulic structures. That is, a large-size dropshaft and a hydraulic jump in a rectangular horizontal channel. The comparison highlighted some significant differences in clustering production and structures. Both dropshaft and hydraulic jump flows are complex turbulent shear flows, and some clustering index may provide some measure of the bubble-turbulence interactions and associated energy dissipation.