Variation in parental magmas of Mt Rouse, a complex polymagmatic monogenetic volcano in the basaltic intraplate Newer Volcanics Province, southeast Australia

Monogenetic volcanoes have long been regarded as simple in nature, involving single magma batches and uncomplicated evolutions; however, recent detailed research into individual centres is challenging that assumption. Mt Rouse (Kolor) is the volumetrically largest volcano in the monogenetic Newer Volcanics Province of southeast Australia. This study presents new major, trace and Sr–Nd–Pb isotope data for samples selected on the basis of a detailed stratigraphic framework analysis of the volcanic products from Mt Rouse. The volcano is the product of three magma batches geochemically similar to Ocean–Island basalts, featuring increasing LREE enrichment with each magma batch (batches A, B and C) but no evidence of crustal contamination; the Sr–Nd–Pb isotopes define two groupings. Modelling suggests that the magmas were sourced from a zone of partial melting crossing the lithosphere–asthenosphere boundary, with batch A forming a large volume partial melt in the deep lithosphere (1.7 GPa/55.5 km); and batches B and C from similar areas within the shallow asthenosphere (1.88 GPa/61 km and 1.94 GPa/63 km, respectively). The formation and extraction of these magmas may have been due to high deformation rates in the mantle caused by edge-driven convection and asthenospheric upwelling. The lithosphere– asthenosphere boundary is important with respect to NVP volcanism. An eruption chronology involves sequential eruption of magma batches A, C and B, followed by simultaneous eruption of batches A and B. Mt Rouse is a complex polymagmatic monogenetic volcano that illustrates the complexity of monogenetic volcanism and demonstrates the importance of combining detailed stratigraphic analysis alongside systematic geochemical sampling.

Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods which have been recently employed to analyse PNSD data, however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K-means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and Silhouette width validation values and the K-means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K-means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectra to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.

Exploring variation in measurement as a foundation for statistical thinking in the elementary school

This study was based on the premise that variation is the foundation of statistics and statistical investigations. The study followed the development of fourth-grade students' understanding of variation through participation in a sequence of two lessons based on measurement. In the first lesson all students measured the arm span of one student, revealing pathways students follow in developing understanding of variation and linear measurement (related to research question 1). In the second lesson each student's arm span was measured once, introducing a different aspect of variation for students to observe and contrast. From this second lesson, students' development of the ability to compare their representations for the two scenarios and explain differences in terms of variation was explored (research question 2). Students' documentation, in both workbook and software formats, enabled us to monitor their engagement and identify their increasing appreciation of the need to observe, represent, and contrast the variation in the data. Following the lessons, a written student assessment was used for judging retention of understanding of variation developed through the lessons and the degree of transfer of understanding to a different scenario (research question 3).

Process variability of pollutant build-up on urban road surfaces

Knowledge of the pollutant build-up process is a key requirement for developing stormwater pollution mitigation strategies. In this context, process variability is a concept which needs to be understood in-depth. Analysis of particulate build-up on three road surfaces in an urban catchment confirmed that particles <150µm and >150µm have characteristically different build-up patterns, and these patterns are consistent over different field conditions. Three theoretical build-up patterns were developed based on the size-fractionated particulate build-up patterns, and these patterns explain the variability in particle behavior and the variation in particle-bound pollutant load and composition over the antecedent dry period. Behavioral variability of particles <150µm was found to exert the most significant influence on the build-up process variability. As characterization of process variability is particularly important in stormwater quality modeling, it is recommended that the influence of behavioral variability of particles <150µm on pollutant build-up should be specifically addressed. This would eliminate model deficiencies in the replication of the build-up process and facilitate the accounting of the inherent process uncertainty, and thereby enhance the water quality predictions.

Variation in the complex carbohydrate biosynthesis loci of Acinetobacter baumannii genomes

Extracellular polysaccharides are major immunogenic components of the bacterial cell envelope. However, little is known about their biosynthesis in the genus Acinetobacter, which includes A. baumannii, an important nosocomial pathogen. Whether Acinetobacter sp. produce a capsule or a lipopolysaccharide carrying an O antigen or both is not resolved. To explore these issues, genes involved in the synthesis of complex polysaccharides were located in 10 complete A. baumannii genome sequences, and the function of each of their products was predicted via comparison to enzymes with a known function. The absence of a gene encoding a WaaL ligase, required to link the carbohydrate polymer to the lipid A-core oligosaccharide (lipooligosaccharide) forming lipopolysaccharide, suggests that only a capsule is produced. Nine distinct arrangements of a large capsule biosynthesis locus, designated KL1 to KL9, were found in the genomes. Three forms of a second, smaller variable locus, likely to be required for synthesis of the outer core of the lipid A-core moiety, were designated OCL1 to OCL3 and also annotated. Each K locus includes genes for capsule export as well as genes for synthesis of activated sugar precursors, and for glycosyltransfer, glycan modification and oligosaccharide repeat-unit processing. The K loci all include the export genes at one end and genes for synthesis of common sugar precursors at the other, with a highly variable region that includes the remaining genes in between. Five different capsule loci, KL2, KL6, KL7, KL8 and KL9 were detected in multiply antibiotic resistant isolates belonging to global clone 2, and two other loci, KL1 and KL4, in global clone 1. This indicates that this region is being substituted repeatedly in multiply antibiotic resistant isolates from these clones.

Variation in the OC locus of Acinetobacter baumannii genomes predicts extensive structural diversity in the Lipooligosaccharide

Lipooligosaccharide (LOS) is a complex surface structure that is linked to many pathogenic properties of Acinetobacter baumannii. In A. baumannii, the genes responsible for the synthesis of the outer core (OC) component of the LOS are located between ilvE and aspS. The content of the OC locus is usually variable within a species, and examination of 6 complete and 227 draft A. baumannii genome sequences available in GenBank non-redundant and Whole Genome Shotgun databases revealed nine distinct new types, OCL4-OCL12, in addition to the three known ones. The twelve gene clusters fell into two distinct groups, designated Group A and Group B, based on similarities in the genes present. OCL6 (Group B) was unique in that it included genes for the synthesis of L-Rhamnosep. Genetic exchange of the different configurations between strains has occurred as some OC forms were found in several different sequence types (STs). OCL1 (Group A) was the most widely distributed being present in 18 STs, and OCL6 was found in 16 STs. Variation within clones was also observed, with more than one OC locus type found in the two globally disseminated clones, GC1 and GC2, that include the majority of multiply antibiotic resistant isolates. OCL1 was the most abundant gene cluster in both GC1 and GC2 genomes but GC1 isolates also carried OCL2, OCL3 or OCL5, and OCL3 was also present in GC2. As replacement of the OC locus in the major global clones indicates the presence of sub-lineages, a PCR typing scheme was developed to rapidly distinguish Group A and Group B types, and to distinguish the specific forms found in GC1 and GC2 isolates.

Specificity and context in post-exercise recovery: It is not a one-size-fits-all approach

The concept of specificity of exercise prescription and training is a longstanding and widely accepted foundation of the exercise sciences. Simply, the principle holds that training adaptations are achieved relative to the stimulus applied. That is, the manipulation of training variables (e.g. intensity or loading, mode, volume and frequency) directly influences the acute training stimulus, and so the long-term adaptive response (Young et al., 2001; Bird et al., 2005). Translating this concept to practice then recommends that exercise be prescribed specific to the desired outcomes, and the more closely this is achieved, the greater the performance gain is likely to be. However, the cardiovascular and metabolic adaptations traditionally associated with long, slow distance training types, similarly achieved using high-intensity training methods (for a review see Gibala et al., 2012), highlights understanding of underlying physiology as paramount for effective training program design. Various other factors including illness, sleep and psychology also impact on the training stimulus (Halson, 2014) and must be managed collectively with appropriate post-exercise recovery to continue performance improvements and reduce overtraining and injury risks (Kenttä and Hassmén, 1998).

A laboratory assessment of the choice of vessel size under individual transferable quota regimes

This paper examines the effect of individual transferable quota regimes on technology choice, such as choice of vessel size, by using the laboratory experiment method. We find that even if vessel sizes change over time, the quota price can converge to the fundamental value conditioned on the vessels chosen. We also find that subjects choose their vessel type to maximise their profits based on the quota trading prices in the previous period. This result implies that the efficiency of quota markets in the beginning period is important because any inefficiency in quota markets may affect vessel sizes in ensuing periods. Moreover, we find that the initial allocations may significantly influence vessel sizes through two channels: first, a higher initial allocation to a subject increases the likelihood that the subject invests in a large-sized vessel; second, the quota price may be higher and more unstable under unequal allocation than under equal allocation; thus, whether the allocation is equal influences subjects' choice of vessel type. © 2014 Australian Agricultural and Resource Economics Society Inc.

Size-resolved particle distribution and gaseous concentrations by real-world road tunnel measurement

Measurements of aerosol particle number size distributions (15-700 nm), CO and NOx were performed in a bus tunnel, Australia. Daily mean particle size distributions of mixed diesel/CNG (Compressed Natural Gas) buses traffic flow were determined in 4 consecutive measurement days. EFs (Emission Factors) of Particle size distribution of diesel buses and CNG buses were obtained by MLR (Multiple Linear Regression) methods, particle distributions of diesel buses and CNG buses were observed as single accumulation mode and nuclei-mode separately. Particle size distributions of mixed traffic flow were decomposed by two log-normal fitting curves for each 30 minutes interval mean scans, all the mix fleet PSD emission can be well fitted by the summation of two log-normal distribution curves, and these were composed of nuclei mode curve and accumulation curve, which were affirmed as the CNG buses and diesel buses PN emission curves respectively. Finally, particle size distributions of diesel buses and CNG buses were quantified by statistical whisker-box charts. For log-normal particle size distribution of diesel buses, accumulation mode diameters were 74.5～87.5nm, geometric standard deviations were 1.89～1.98. As to log-normal particle size distribution of CNG buses, nuclei-mode diameters were 21～24 nm, geometric standard deviations were 1.27～1.31.

Field size consistency of nominally matched linacs

Given that there is increasing recognition of the effect that submillimetre changes in collimator position can have on radiotherapy beam dosimetry, this study aimed to evaluate the potential variability in small field collimation that may exist between otherwise matched linacs. Field sizes and field output factors were measured using radiochromic film and an electron diode, for jaw- and MLC-collimated fields produced by eight dosimetrically matched Varian iX linacs (Varian Medical Systems, Palo Alto, USA). This study used nominal sizes from 0.6×0.6 to 10×10 cm215 , for jaw-collimated fields,and from 1×1 to 10×10 cm216 , for MLC-collimated fields, delivered from a zero (head up, beam directed vertically downward) gantry angle. Differences between the field sizes measured for the eight linacs exceeded the uncertainty of the film measurements and the repositioning uncertainty of the jaws and MLCs on one linac. The dimensions of fields defined by MLC leaves were more consistent between linacs, while also differing more from their nominal values than fields defined by orthogonal jaws. The field output factors measured for the different linacs generally increased with increasing measured field size for the nominal 0.6×0.6 and 1×1 cm2 fields, and became consistent between linacs for nominal field sizes of 2×2 cm2 25 and larger. The inclusion in radiotherapy treatment planning system beam data of small field output factors acquired in fields collimated by jaws (rather than the more-reproducible MLCs), associated with either the nominal or the measured field sizes, should be viewed with caution. The size and reproducibility of the fields (especially the small fields) used to acquire treatment planning data should be investigated thoroughly as part of the linac or planning system commissioning process. Further investigation of these issues, using different linac models, collimation systems and beam orientations, is recommended.

Effect of carrier size on the dispersion of salmeterol xinafoate from interactive mixtures

The objective of this study was to determine the influence of lactose carrier size on drug dispersion of salmeterol xinafoate (SX) from interactive mixtures. SX dispersion was measured by using the fine particle fractions determined by a twin stage impinger attached to a Rotahaler1. The particle size of the lactose carrier in the SX interactive mixtures was varied using a range of commercial inhalation-grade lactoses. In addition, differing size fractions of individual lactose samples were achieved by dry sieving. The dispersion ofSXappeared to increase as the particle size of the lactose carrier decreased for the mixtures prepared from different particle size commercial samples of lactose and from different sieve fractions of the same lactose. Fine particles of lactose (<5 mm) associated with the lactose carrier were removed from the carrier surface by a wet decantation process to produce lactose samples with low but similar concentrations of fine lactose particles. The fine particle fractions of SX in mixtures prepared with the decanted lactose decreased significantly (analysis of variance, p<0.001) and the degree of dispersion became independent of the volume mean diameter of the carriers (analysis of variance, p<0.05). The dispersion behavior is therefore associated with the presence of fine adhered particles associated with the carriers and the inherent size of the carrier itself has little influence on dispersion.