Spatial prediction of N2O emissions in pasture : a Bayesian model averaging analysis

Nitrous oxide (N2O) is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O - environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based) on spatial prediction of N2O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA) based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m(2) field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR) model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N2O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N2O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N2O emissions across this study region.

Bcl-2 genes and growth factors in the pathology of ischaemic acute renal failure

For the past decade, an attempt has been made by many research groups to define the roles of the growing number of Bcl-2 gene family proteins in the apoptotic process. The Bcl-2 family consists of pro-apoptotic (or cell death) and anti-apoptotic (or cell survival) genes and it is the balance in expression between these gene lineages that may determine the death or survival of a cell. The majority of studies have analysed the role/s of the Bcl-2 genes in cancer development. Equally important is their role in normal tissue development, homeostasis and non-cancer disease states. Bcl-2 is crucial for normal development in the kidney, with a deficiency in Bcl-2 producing such malformation that renal failure and death result. As a corollary, its role in renal disease states in the adult has been sought. Ischaemia is one of the most common causes of both acute and chronic renal failure. The section of the kidney that is most susceptible to ischaemic damage is the outer zone of the outer medulla. Within this zone the proximal tubules are most sensitive and often die by necrosis or desquamate. In the distal nephron, apoptosis is the more common form of cell death. Recent results from our laboratory have indicated that ischaemia-induced acute renal failure is associated with up-regulation of two anti-apoptotic Bcl-2 proteins (Bcl-2 and Bcl-XL) in the damaged distal tubule and occasional up-regulation of Bax in the proximal tubule. The distal tubule is a known reservoir for several growth factors important to renal growth and repair, such as insulin-like growth factor-1 (IGF-1) and epidermal growth factor (EGF). One of the likely possibilities for the anti-cell death action of the Bcl-2 genes is that the protected distal cells may be able to produce growth factors that have a further reparative or protective role via an autocrine mechanism in the distal segment and a paracrine mechanism in the proximal cells. Both EGF and IGF-1 are also up-regulated in the surviving distal tubules and are detected in the surviving proximal tubules, where these growth factors are not usually synthesized. As a result, we have been using in vitro methods to test: (i) the relative sensitivities of renal distal and proximal epithelial cell populations to injury caused by mechanisms known to act in ischaemia-reperfusion; (ii) whether a Bcl-2 anti-apoptotic mechanism acts in these cells; and (iii) whether an autocrine and/or paracrine growth factor mechanism is initiated. The following review discusses the background to these studies as well as some of our preliminary results.

In vivo and in vitro models demonstrate a role for caveolin-1 in the pathogenesis of ischaemic acute renal failure

Caveolae and their proteins, the caveolins, transport macromolecules; compartmentalize signalling molecules; and are involved in various repair processes. There is little information regarding their role in the pathogenesis of significant renal syndromes such as acute renal failure (ARF). In this study, an in vivo rat model of 30 min bilateral renal ischaemia followed by reperfusion times from 4 h to 1 week was used to map the temporal and spatial association between caveolin-1 and tubular epithelial damage (desquamation, apoptosis, necrosis). An in vitro model of ischaemic ARF was also studied, where cultured renal tubular epithelial cells or arterial endothelial cells were subjected to injury initiators modelled on ischaemia-reperfusion (hypoxia, serum deprivation, free radical damage or hypoxia-hyperoxia). Expression of caveolin proteins was investigated using immunohistochemistry, immunoelectron microscopy, and immunoblots of whole cell, membrane or cytosol protein extracts. In vivo, healthy kidney had abundant caveolin-1 in vascular endothelial cells and also some expression in membrane surfaces of distal tubular epithelium. In the kidneys of ARF animals, punctate cytoplasmic localization of caveolin-1 was identified, with high intensity expression in injured proximal tubules that were losing basement membrane adhesion or were apoptotic, 24 h to 4 days after ischaemia-reperfusion. Western immunoblots indicated a marked increase in caveolin-1 expression in the cortex where some proximal tubular injury was located. In vitro, the main treatment-induced change in both cell types was translocation of caveolin-1 from the original plasma membrane site into membrane-associated sites in the cytoplasm. Overall, expression levels did not alter for whole cell extracts and the protein remained membrane-bound, as indicated by cell fractionation analyses. Caveolin-1 was also found to localize intensely within apoptotic cells. The results are indicative of a role for caveolin-1 in ARF-induced renal injury. Whether it functions for cell repair or death remains to be elucidated.

Development and validation of anthropometric prediction equations for estimation of body fat in Indonesian men

Body composition of 292 males aged between 18 and 65 years was measured using the deuterium oxide dilution technique. Participants were divided into development (n=146) and cross-validation (n=146) groups. Stature, body weight, skinfold thickness at eight sites, girth at five sites, and bone breadth at four sites were measured and body mass index (BMI), waist-to-hip ratio (WHR), and waist-to-stature ratio (WSR) calculated. Equations were developed using multiple regression analyses with skinfolds, breadth and girth measures, BMI, and other indices as independent variables and percentage body fat (%BF) determined from deuterium dilution technique as the reference. All equations were then tested in the cross-validation group. Results from the reference method were also compared with existing prediction equations by Durnin and Womersley (1974), Davidson et al (2011), and Gurrici et al (1998). The proposed prediction equations were valid in our cross-validation samples with r=0.77- 0.86, bias 0.2-0.5%, and pure error 2.8-3.6%. The strongest was generated from skinfolds with r=0.83, SEE 3.7%, and AIC 377.2. The Durnin and Womersley (1974) and Davidson et al (2011) equations significantly (p<0.001) underestimated %BF by 1.0 and 6.9% respectively, whereas the Gurrici et al (1998) equation significantly (p<0.001) overestimated %BF by 3.3% in our cross-validation samples compared to the reference. Results suggest that the proposed prediction equations are useful in the estimation of %BF in Indonesian men.

Blast response and failure analysis of pile foundations subjected to surface explosion

This paper presents the response of pile foundations to ground shocks induced by surface explosion using fully coupled and non-linear dynamic computer simulation techniques together with different material models for the explosive, air, soil and pile. It uses the Arbitrary Lagrange Euler coupling formulation with proper state material parameters and equations. Blast wave propagation in soil, horizontal pile deformation and pile damage are presented to facilitate failure evaluation of piles. Effects of end restraint of pile head and the number and spacing of piles within a group on their blast response and potential failure are investigated. The techniques developed and applied in this paper and its findings provide valuable information on the blast response and failure evaluation of piles and will provide guidance in their future analysis and design.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

A randomized controlled trial of self-management programme improves health-related outcomes of older people with heart failure

Aims This paper is a report on the effectiveness of a self-management programme based on the self-efficacy construct, in older people with heart failure. Background Heart failure is a major health problem worldwide, with high mortality and morbidity, making it a leading cause of hospitalization. Heart failure is associated with a complex set of symptoms that arise from problems in fluid and sodium retention. Hence, managing salt and fluid intake is important and can be enhanced by improving patients' self-efficacy in changing their behaviour. Design Randomized controlled trial. Methods Heart failure patients attending cardiac clinics in northern Taiwan from October 2006–May 2007 were randomly assigned to two groups: control (n = 46) and intervention (n = 47). The intervention group received a 12-week self-management programme that emphasized self-monitoring of salt/fluid intake and heart failure-related symptoms. Data were collected at baseline as well as 4 and 12 weeks later. Data analysis to test the hypotheses used repeated-measures anova models. Results Participants who received the intervention programme had significantly better self-efficacy for salt and fluid control, self-management behaviour and their heart failure-related symptoms were significantly lower than participants in the control group. However, the two groups did not differ significantly in health service use. Conclusion The self-management programme improved self-efficacy for salt and fluid control, self-management behaviours, and decreased heart failure-related symptoms in older Taiwanese outpatients with heart failure. Nursing interventions to improve health-related outcomes for patients with heart failure should emphasize self-efficacy in the self-management of their disease.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

The effect of a supervised exercise training programme on sleep quality in recently discharged heart failure patients

Background Sleep disturbances, including insomnia and sleep-disordered breathing, are a common complaint in people with heart failure and impair well-being. Exercise training (ET) improves quality of life in stable heart failure patients. ET also improves sleep quality in healthy older patients, but there are no previous intervention studies in heart failure patients. Aim The aim of this study was to examine the impact of ET on sleep quality in patients recently discharged from hospital with heart failure. Methods This was a sub-study of a multisite randomised controlled trial. Participants with a heart failure hospitalisation were randomised within six weeks of discharge to a 12-week disease management programme including exercise advice (n=52) or to the same programme with twice weekly structured ET (n=54). ET consisted of two one-hour supervised aerobic and resistance training sessions, prescribed and advanced by an exercise specialist. The primary outcome was change in Pittsburgh Sleep Quality Index (PSQI) between randomisation and week 12. Results At randomisation, 45% of participants reported poor sleep (PSQI≥5). PSQI global score improved significantly more in the ET group than the control group (–1.5±3.7 vs 0.4±3.8, p=0.03). Improved sleep quality correlated with improved exercise capacity and reduced depressive symptoms, but not with changes in body mass index or resting heart rate. Conclusion Twelve weeks of twice-weekly supervised ET improved sleep quality in patients recently discharged from hospital with heart failure.

Games and entertainment software

Games and the broader interactive entertainment industry are the major ‘born global/born digital’ creative industry. The videogame industry (formally referred to as interactive entertainment) is the economic sector that develops, markets and sells videogames to millions of people worldwide. There are over 11 countries with revenues of over $1 billion. This number was expected to grow 9.1 per cent annually to $48.9 in 2011 and $68 billion in 2012, making it the fastest-growing component of the international media sector (Scanlon, 2007; Caron, 2008).

Practical Advice to Entrepreneurs Series by ACE Adjunct Professor Dean Shepherd: Practical advice on learning the most from project failure

The author, Dean Shepherd, is of entrepreneurship—how entrepreneurs think, decide to act, and feel. He recently realized that while his publications in academic journals have implications for entrepreneurs, those implications have remained relatively hidden in the text of the articles and hidden in articles published in journals largely inaccessible to those involved in the entrepreneurial process. This series is designed to bring the practical implications of his research to the forefront.

Practical Advice to Entrepreneurs Series by ACE Adjunct Professor Dean Shepherd: Practical advice for coping with business failure

The author, Dean Shepherd, is of entrepreneurship—how entrepreneurs think, decide to act, and feel. He recently realized that while his publications in academic journals have implications for entrepreneurs, those implications have remained relatively hidden in the text of the articles and hidden in articles published in journals largely inaccessible to those involved in the entrepreneurial process. This series is designed to bring the practical implications of his research to the forefront.

Sensitive methods for studying the environmental performance of protective coatings

The safe working lifetime of a structure in a corrosive or other harsh environment is frequently not limited by the material itself but rather by the integrity of the coating material. Advanced surface coatings are usually crosslinked organic polymers such as epoxies and polyurethanes which must not shrink, crack or degrade when exposed to environmental extremes. While standard test methods for environmental durability of coatings have been devised, the tests are structured more towards determining the end of life rather than in anticipation of degradation. We have been developing prognostic tools to anticipate coating failure by using a fundamental understanding of their degradation behaviour which, depending on the polymer structure, is mediated through hydrolytic or oxidation processes. Fourier transform infrared spectroscopy (FTIR) is a widely-used laboratory technique for the analysis of polymer degradation and with the development of portable FTIR spectrometers, new opportunities have arisen to measure polymer degradation non-destructively in the field. For IR reflectance sampling, both diffuse (scattered) and specular (direct) reflections can occur. The complexity in these spectra has provided interesting opportunities to study surface chemical and physical changes during paint curing, service abrasion and weathering, but has often required the use of advanced statistical analysis methods such as chemometrics to discern these changes. Results from our studies using this and related techniques and the technical challenges that have arisen will be presented.

Is age a factor in the success or failure of remote monitoring in heart failure? Telemonitoring and structured telephone support in elderly heart failure patients

Background There are few data regarding the effectiveness of remote monitoring for older people with heart failure. We conducted a post-hoc sub-analysis of a previously published large Cochrane systematic review and meta-analysis of relevant randomized controlled trials to determine whether structured telephone support and telemonitoring were effective in this population. Methods A post hoc sub-analysis of a systematic review and meta-analysis that applied the Cochrane methodology was conducted. Meta-analyses of all-cause mortality, all-cause hospitalizations and heart failure-related hospitalizations were performed for studies where the mean or median age of participants was 70 or more years. Results The mean or median age of participants was 70 or more years in eight of the 16 (n=2,659/5,613; 47%) structured telephone support studies and four of the 11 (n=894/2,710; 33%) telemonitoring studies. Structured telephone support (RR 0.80; 95% CI=0.63-1.00) and telemonitoring (RR 0.56; 95% CI=0.41-0.76) interventions reduced mortality. Structured telephone support interventions reduced heart failure-related hospitalizations (RR 0.81; 95% CI=0.67-0.99). Conclusion Despite a systematic bias towards recruitment of individuals younger than the epidemiological average into the randomized controlled trials, older people with heart failure did benefit from structured telephone support and telemonitoring. These post-hoc sub-analysis results were similar to overall effects observed in the main meta-analysis. While further research is required to confirm these observational findings, the evidence at hand indicates that discrimination by age alone may be not be appropriate when inviting participation in a remote monitoring service for heart failure.