Application of a new discreet form of Gauss’ theorem for measuring volume

Volume measurements are useful in many branches of science and medicine. They are usually accomplished by acquiring a sequence of cross sectional images through the object using an appropriate scanning modality, for example x-ray computed tomography (CT), magnetic resonance (MR) or ultrasound (US). In the cases of CT and MR, a dividing cubes algorithm can be used to describe the surface as a triangle mesh. However, such algorithms are not suitable for US data, especially when the image sequence is multiplanar (as it usually is). This problem may be overcome by manually tracing regions of interest (ROIs) on the registered multiplanar images and connecting the points into a triangular mesh. In this paper we describe and evaluate a new discreet form of Gauss’ theorem which enables the calculation of the volume of any enclosed surface described by a triangular mesh. The volume is calculated by summing the vector product of the centroid, area and normal of each surface triangle. The algorithm was tested on computer-generated objects, US-scanned balloons, livers and kidneys and CT-scanned clay rocks. The results, expressed as the mean percentage difference ± one standard deviation were 1.2 ± 2.3, 5.5 ± 4.7, 3.0 ± 3.2 and −1.2 ± 3.2% for balloons, livers, kidneys and rocks respectively. The results compare favourably with other volume estimation methods such as planimetry and tetrahedral decomposition.

Gender differences in recreational and transport cycling : a cross-sectional mixed-methods comparison of cycling patterns, motivators, and constraints

Background: Gender differences in cycling are well-documented. However, most analyses of gender differences make broad comparisons, with few studies modeling male and female cycling patterns separately for recreational and transport cycling. This modeling is important, in order to improve our efforts to promote cycling to women and men in countries like Australia with low rates of transport cycling. The main aim of this study was to examine gender differences in cycling patterns and in motivators and constraints to cycling, separately for recreational and transport cycling. Methods: Adult members of a Queensland, Australia, community bicycling organization completed an online survey about their cycling patterns; cycling purposes; and personal, social and perceived environmental motivators and constraints (47% response rate). Closed and open-end questions were completed. Using the quantitative data, multivariable linear, logistic and ordinal regression models were used to examine associations between gender and cycling patterns, motivators and constraints. The qualitative data were thematically analysed to expand upon the quantitative findings. Results: In this sample of 1862 bicyclists, men were more likely than women to cycle for recreation and for transport, and they cycled for longer. Most transport cycling was for commuting, with men more likely than women to commute by bicycle. Men were more likely to cycle on-road, and women off-road. However, most men and women did not prefer to cycle on-road without designed bicycle lanes, and qualitative data indicated a strong preference by men and women for bicycle-only off-road paths. Both genders reported personal factors (health and enjoyment related) as motivators for cycling, although women were more likely to agree that other personal, social and environmental factors were also motivating. The main constraints for both genders and both cycling purposes were perceived environmental factors related to traffic conditions, motorist aggression and safety. Women, however, reported more constraints, and were more likely to report as constraints other environmental factors and personal factors. Conclusion: Differences found in men’s and women’s cycling patterns, motivators and constraints should be considered in efforts to promote cycling, particularly in efforts to increase cycling for transport.

National survey of viruses in pastures of subterranean clover. I. Incidence of four viruses assessed by ELISA

A nationwide survey was made of the time-course incidence of alfalfa mosaic virus (AMV), clover yellow vein virus (CYVV), subterranean clover mottle virus (SCMoV) and subterranean clover red leaf virus (SCRLV) in improved pastures in southern regions of Australia. Averaged over all states, the highest mean incidence recorded for samples infected with individual viruses in either winter or spring was 9.4% for AMV, 5.7% for CYVV, 10.9% for SCMoV and 7.5% for SCRLV. For AMV and SCRLV, there was an increasing trend from spring 1984 to spring 1986. A similar increasing trend for SCMoV was more evident in winter than in spring. For CYVV, no time-course pattern was evident. Results support the proposition that viruses contribute significantly to "clover-decline', a well-known problem in pastures of Trifolium subterraneum. -from Authors

Ability of recombinant human bone morphogenetic protein 2 to enhance bone healing in the presence of tobramycin : evaluation in a rat segmental defect model

Objective To determine whether locally applied tobramycin influences the ability of recombinant human bone morphogenetic protein 2 (rhBMP-2) to heal a segmental defect in the rat femur. Methods The influence of tobramycin on the osteogenic differentiation of mesenchymal stem cells was first evaluated in vitro. For the subsequent, in vivo experiments, a 5-mm segmental defect was created in the right femur of each of 25 Sprague-Dawley rats and stabilized with an external fixator and four Kirschner wires. Rats were divided in four groups: empty control, tobramycin (11 mg)/absorbable collagen sponge, rhBMP-2 (11 μg)/absorbable collagen sponge, and rhBMP-2/absorbable collagen sponge with tobramycin. Bone healing was monitored by radiography at 3 and 8 weeks. Animals were euthanized at 8 weeks and the properties of the defect were compared with the intact contralateral femur. Bone formation in the defect region was assessed by dual-energy x-ray absorptiometry, microcomputed tomography, histology, and mechanical testing. Results Tobramycin exerted a dose-dependent inhibition of alkaline phosphatase induction and calcium deposition by mesenchymal stem cells cultured under osteogenic conditions. The inhibition was reversed in the presence of 500 ng/mL of rhBMP-2. Segmental defects in the rat femora failed to heal in the absence of rhBMP-2. Tobramycin exerted no inhibitory effects on the ability of rhBMP-2 to heal these defects and increased the bone area of the defects treated with rhBMP-2. Data obtained from all other parameters of healing, including dual-energy x-ray absorptiometry, microcomputed tomography, histology, and mechanical testing, were unaffected by tobramycin. Conclusions Although our in vitro results suggested that tobramycin inhibits the osteogenic differentiation of mesenchymal stem cells, this could be overcome by rhBMP-2. Tobramycin did not impair the ability of rhBMP-2 to heal critical-sized femoral defects in rats. Indeed, bone area was increased by nearly 20% in the rhBMP-2 group treated with tobramycin. This study shows that locally applied tobramycin can be used in conjunction with rhBMP-2 to enhance bone formation at fracture sites.

Transient gene expression in Medicago truncatula leaves via agroinfiltration

Transient expression is a powerful method for the functional characterization of genes. In this chapter, we outline a protocol for the transient expression of constructs in Medicago truncatula leaves using Agrobacterium tumefaciens infiltration. Using quantitative real-time PCR we demonstrate that the infiltration of a construct containing the LEGUME ANTHOCYANIN PRODUCTION 1 (LAP1) transcription factor results in the strong upregulation of key biosynthetic genes and the accumulation of anthocyanin pigment in the leaves after just 3 days. Thus, this method provides a rapid and powerful way to the discovery of downstream targets of M. truncatula transcription factors.

Artificial neural networks to predict activity type and energy expenditure in youth

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. Purpose: The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods: One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GTIM on the right hip, and (V) Over dotO(2) was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square en-or (RMSE). Results: As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. Conclusions: ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

An upstream open reading frame is essential for feedback regulation of ascorbate biosynthesis in arabidopsis

Ascorbate (vitamin C) is an essential antioxidant and enzyme cofactor in both plants and animals. Ascorbate concentration is tightly regulated in plants, partly to respond to stress. Here, we demonstrate that ascorbate concentrations are determined via the posttranscriptional repression of GDP-l-galactose phosphorylase (GGP), a major control enzyme in the ascorbate biosynthesis pathway. This regulation requires a cis-acting upstream open reading frame (uORF) that represses the translation of the downstream GGP open reading frame under high ascorbate concentration. Disruption of this uORF stops the ascorbate feedback regulation of translation and results in increased ascorbate concentrations in leaves. The uORF is predicted to initiate at a noncanonical codon (ACG rather than AUG) and encode a 60- to 65-residue peptide. Analysis of ribosome protection data from Arabidopsis thaliana showed colocation of high levels of ribosomes with both the uORF and the main coding sequence of GGP. Together, our data indicate that the noncanonical uORF is translated and encodes a peptide that functions in the ascorbate inhibition of translation. This posttranslational regulation of ascorbate is likely an ancient mechanism of control as the uORF is conserved in GGP genes from mosses to angiosperms.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.

The emerging world of small ORFs

Small open reading frames (sORFs) are an often overlooked feature of plant genomes. Initially found in plant viral RNAs and considered an interesting curiosity, an increasing number of these sORFs have been shown to encode functional peptides or play a regulatory role. The recent discovery that many of these sORFs initiate with start codons other than AUG, together with the identification of functional small peptides encoded in supposedly noncoding primary miRNA transcripts (pri-miRs), has drastically increased the number of potentially functional sORFs within the genome. Here we review how advances in technology, notably ribosome profiling (RP) assays, are complementing bioinformatics and proteogenomic methods to provide powerful ways to identify these elusive features of plant genomes, and highlight the regulatory roles sORFs can play.