Sequence diversity in the coat protein coding region of the genome RNA of Johnsongrass mosaic virus in Australia

Sequence diversity in the coat protein coding region of Australian strains of Johnsongrass mosaic virus (JGMV) was investigated. Field isolates were sampled during a seven year period from Johnsongrass, sorghum and corn across the northern grain growing region. The 23 isolates were found to have greater than 94% nucleotide and amino acid sequence identity. The Australian isolates and two strains from the U.S.A. had about 90% nucleotide sequence identity and were between 19 and 30% different in the N-terminus of the coat protein. Two amino acid residues were found in the core region of the coat protein in isolates obtained from sorghum having the Krish gene for JGMV resistance that differed from those found in isolates from other hosts which did not have this single dominant resistance gene. These amino acid changes may have been responsible for overcoming the resistance conferred by the Krish gene for JGMV resistance in sorghum. The identification of these variable regions was essential for the development of durable pathogen-derived resistance to JGMV in sorghum.

Permeation of binary gas mixtures in ultramicroporous membranes

High-quality nanometer thick ultramicroporous membranes were prepared from silica sol-gel processes and tested for the permeation of binary gas mixtures of He, H-2, CO2, and CH4 across different temperature and partial pressure regimens. Pore size distribution by molecular probing showed that the majority of pore sizes had dimensions below 2.9 Angstrom. In 50:50 binary mixtures, the fluxes of gases increased as a function of temperature, indicating an activated transport mechanism. The ultramicroporous membranes showed high selectivities at 150 degreesC for He/CO2 (30), He/CH4 (93), H-2/CO2 (10), and H-2/CH4 (9) with lower selectivities for CO2/CH4 (5). High activation energies (E-a) were observed for the permeance of 50:50 binary mixtures containing He and H-2 of 22.1-27.5 and 17.6-23.1 kJ.mol(-1), respectively. The E-a for the permeance of the total mixture approached the E-a for the permeance of the molecule with the smaller kinetic diameter (He or H-2).

No disease-associated mutations found in the coding sequence of the canine polycystic kidney disease gene 1 in Bull Terriers with polycystic kidney disease

The aim of this study was to identify possible disease-associated mutations in the canine homologue of the polycystic kidney disease gene 1 (PKD1) in Bull Terriers with autosomal dominant polycystic kidney disease. Messenger RNA was obtained from the blood or renal tissue of five Bull Terriers with the disease and four close relatives without the disease. Reverse transcription, PCR and 3' rapid amplification of cDNA ends were used to amplify the coding and 3' untranslated regions of this transcript. Comparison of PKD1 sequence between the affected and unaffected Bull Terriers, revealed six polymorphisms, but no disease-associated mutations.

Normal HPRT coding region in a male with gout due to HPRT deficiency (Brief Communication)

A deficiency of the enzyme hypoxanthine-guanine phosphoribosyltransferase (HPRT; EC 2.4.2.8) is associated with a spectrum of disease that ranges from gouty arthritis (OMIM 300323) to the more severe Lesch-Nyhan syndrome (OMIM 300322). To date, all cases of HPRT deficiency have shown a mutation within the HPRT cDNA. In the present study of an individual with gout due to HPRT deficiency, we found a normal HPRT cDNA sequence. This is the first study to provide an example of HPRT deficiency which appears to be due to a defect in the regulation of the gene. © 2005 Elsevier Inc. All rights reserved.

Single patient (n-of-1) trials with binary treatment preference

The use of a fully parametric Bayesian method for analysing single patient trials based on the notion of treatment 'preference' is described. This Bayesian hierarchical modelling approach allows for full parameter uncertainty, use of prior information and the modelling of individual and patient sub-group structures. It provides updated probabilistic results for individual patients, and groups of patients with the same medical condition, as they are sequentially enrolled into individualized trials using the same medication alternatives. Two clinically interpretable criteria for determining a patient's response are detailed and illustrated using data from a previously published paper under two different prior information scenarios. Copyright (C) 2005 John Wiley & Sons, Ltd.

The effect of aluminium content on the mechanical properties and microstructure of die cast binary magnesium-aluminium alloys

This paper investigates the relationship between mechanical properties and microstructure in high pressure die cast binary Mg-Al alloys. As-cast test bars produced using high pressure die casting have been tested in tension in order to determine the properties for castings produced using this technique. It has been shown that increasing aluminium levels results in increases in yield strength and a decrease in ductility for these alloys. Higher aluminium levels also result in a decrease in creep rate at 150 degrees C. It has also been shown that an increase in aluminium levels results in an increase in the volume fraction of eutectic Mg17Al12 in the microstructure.

Dynamic binary translation using run-time feedbacks

Dynamic binary translation is the process of translating, modifying and rewriting executable (binary) code from one machine to another at run-time. This process of low-level re-engineering consists of a reverse engineering phase followed by a forward engineering phase. UQDBT, the University of Queensland Dynamic Binary Translator, is a machine-adaptable translator. Adaptability is provided through the specification of properties of machines and their instruction sets, allowing the support of different pairs of source and target machines. Most binary translators are closely bound to a pair of machines, making analyses and code hard to reuse. Like most virtual machines, UQDBT performs generic optimizations that apply to a variety of machines. Frequently executed code is translated to native code by the use of edge weight instrumentation, which makes UQDBT converge more quickly than systems based on instruction speculation. In this paper, we describe the architecture and run-time feedback optimizations performed by the UQDBT system, and provide results obtained in the x86 and SPARC® platforms.

Experimental validation of the regulated expression of large numbers of non-coding RNAs from the mouse genome

Recent large-scale analyses of mainly full-length cDNA libraries generated from a variety of mouse tissues indicated that almost half of all representative cloned sequences did flat contain ail apparent protein-coding sequence, and were putatively derived from non-protein-coding RNA (ncRNA) genes. However, many of these clones were singletons and the majority were unspliced, raising the possibility that they may be derived from genomic DNA or unprocessed pre-rnRNA contamination during library construction, or alternatively represent nonspecific transcriptional noise. Here we Show, using reverse transcriptase-dependent PCR, microarray, and Northern blot analyses, that many of these clones were derived from genuine transcripts Of unknown function whose expression appears to be regulated. The ncRNA transcripts have larger exons and fewer introns than protein-coding transcripts. Analysis of the genomic landscape around these sequences indicates that some cDNA clones were produced not from terminal poly(A) tracts but internal priming sites within longer transcripts, only a minority of which is encompassed by known genes. A significant proportion of these transcripts exhibit tissue-specific expression patterns, as well as dynamic changes in their expression in macrophages following lipopolysaccharide Stimulation. Taken together, the data provide strong support for the conclusion that ncRNAs are an important, regulated component of the mammalian transcriptome.

Modelling predicts positive and negative interactions between three Australian tropical tree species in monoculture and binary mixture

Computer modelling promises to. be an important tool for analysing and predicting interactions between trees within mixed species forest plantations. This study explored the use of an individual-based mechanistic model as a predictive tool for designing mixed species plantations of Australian tropical trees. The 'spatially explicit individually based-forest simulator' (SeXI-FS) modelling system was used to describe the spatial interaction of individual tree crowns within a binary mixed-species experiment. The three-dimensional model was developed and verified with field data from three forest tree species grown in tropical Australia. The model predicted the interactions within monocultures and binary mixtures of Flindersia brayleyana, Eucalyptus pellita and Elaeocarpus grandis, accounting for an average of 42% of the growth variation exhibited by species in different treatments. The model requires only structural dimensions and shade tolerance as species parameters. By modelling interactions in existing tree mixtures, the model predicted both increases and reductions in the growth of mixtures (up to +/- 50% of stem volume at 7 years) compared to monocultures. This modelling approach may be useful for designing mixed tree plantations. (c) 2006 Published by Elsevier B.V.

Non-coding RNA

The term non-coding RNA (ncRNA) is commonly employed for RNA that does not encode a protein, but this does not mean that such RNAs do not contain information nor have function. Although it has been generally assumed that most genetic information is transacted by proteins, recent evidence suggests that the majority of the genomes of mammals and other complex organisms is in fact transcribed into ncRNAs, many of which are alternatively spliced and/or processed into smaller products. These ncRNAs include microRNAs and snoRNAs (many if not most of which remain to be identified), as well as likely other classes of yet-to-be-discovered small regulatory RNAs, and tens of thousands of longer transcripts (including complex patterns of interlacing and overlapping sense and antisense transcripts), most of whose functions are unknown. These RNAs (including those derived from introns) appear to comprise a hidden layer of internal signals that control various levels of gene expression in physiology and development, including chromatin architecture/epigenetic memory, transcription, RNA splicing, editing, translation and turnover. RNA regulatory networks may determine most of our complex characteristics, play a significant role in disease and constitute an unexplored world of genetic variation both within and between species.

Non-coding RNAs in the nervous system

Increasing evidence suggests that the development and function of the nervous system is heavily dependent on RNA editing and the intricate spatiotemporal expression of a wide repertoire of non-coding RNAs, including micro RNAs, small nucleolar RNAs and longer non-coding RNAs. Non-coding RNAs may provide the key to understanding the multi-tiered links between neural development, nervous system function, and neurological diseases.

Simulation of binary mixture adsorption of methane and CO2 at supercritical conditions in carbons

Knowledge of the adsorption behavior of coal-bed gases, mainly under supercritical high-pressure conditions, is important for optimum design of production processes to recover coal-bed methane and to sequester CO2 in coal-beds. Here, we compare the two most rigorous adsorption methods based on the statistical mechanics approach, which are Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) simulation, for single and binary mixtures of methane and carbon dioxide in slit-shaped pores ranging from around 0.75 to 7.5 nm in width, for pressure up to 300 bar, and temperature range of 308-348 K, as a preliminary study for the CO2 sequestration problem. For single component adsorption, the isotherms generated by DFT, especially for CO2, do not match well with GCMC calculation, and simulation is subsequently pursued here to investigate the binary mixture adsorption. For binary adsorption, upon increase of pressure, the selectivity of carbon dioxide relative to methane in a binary mixture initially increases to a maximum value, and subsequently drops before attaining a constant value at pressures higher than 300 bar. While the selectivity increases with temperature in the initial pressure-sensitive region, the constant high-pressure value is also temperature independent. Optimum selectivity at any temperature is attained at a pressure of 90-100 bar at low bulk mole fraction of CO2, decreasing to approximately 35 bar at high bulk mole fractions. (c) 2005 American Institute of Chemical Engineers.

Testing predictions of macroscopic binary diffusion coefficients using lattice models with site heterogeneity

Quantitatively predicting mass transport rates for chemical mixtures in porous materials is important in applications of materials such as adsorbents, membranes, and catalysts. Because directly assessing mixture transport experimentally is challenging, theoretical models that can predict mixture diffusion coefficients using Only single-component information would have many uses. One such model was proposed by Skoulidas, Sholl, and Krishna (Langmuir, 2003, 19, 7977), and applications of this model to a variety of chemical mixtures in nanoporous materials have yielded promising results. In this paper, the accuracy of this model for predicting mixture diffusion coefficients in materials that exhibit a heterogeneous distribution of local binding energies is examined. To examine this issue, single-component and binary mixture diffusion coefficients are computed using kinetic Monte Carlo for a two-dimensional lattice model over a wide range of lattice occupancies and compositions. The approach suggested by Skoulidas, Sholl, and Krishna is found to be accurate in situations where the spatial distribution of binding site energies is relatively homogeneous, but is considerably less accurate for strongly heterogeneous energy distributions.