Computed wall stress may predict the growth of abdominal aortic aneurysm

Growth rate of abdominal aortic aneurysm (AAA) is thought to be an important indicator of the potential risk of rupture. Wall stress is also thought to be a trigger for its rupture. However, stress change during the expansion of an AAA is unclear. Forty-four patients with AAAs were included in this longitudinal follow-up study. They were assessed by serial abdominal ultrasonography and computerized tomography (CT) scans if a critical size was reached or a rapid expansion occurred. Patient-specific 3-dimensional AAA geometries were reconstructed from the follow-up CT images. Structural analysis was performed to calculate the wall stresses of the AAA models at both baseline and final visit. A non-linear large-strain finite element method was used to compute the wall stress distribution. The average growth rate was 0.66cm/year (range 0-1.32 cm/year). A significantly positive correlation between shoulder tress at baseline and growth rate was found (r=0.342; p=0.02). A higher shoulder stress is associated with a rapidly expanding AAA. Therefore, it may be useful for estimating the growth expansion of AAAs and further risk stratification of patients with AAAs.

Structural analysis and magnetic resonance imaging predict plaque vulnerability: A study comparing symptomatic and asymptomatic individuals

Background: More than half of all cerebral ischemic events are the result of rupture of extracranial plaques. The clinical determination of carotid plaque vulnerability is currently based solely on luminal stenosis; however, it has been increasingly suggested that plaque morphology and biomechanical stress should also be considered. We used finite element analysis based on in vivo magnetic resonance imaging (MRI) to simulate the stress distributions within plaques of asymptomatic and symptomatic individuals. Methods: Thirty nonconsecutive subjects (15 symptomatic and 15 asymptomatic) underwent high-resolution multisequence in vivo MRI of the carotid bifurcation. Stress analysis was performed based on the geometry derived from in vivo MRI of the carotid artery at the point of maximal stenosis. The finite element analysis model considered plaque components to be hyperelastic. The peak stresses within the plaques of symptomatic and asymptomatic individuals were compared. Results: High stress concentrations were found at the shoulder regions of symptomatic plaques, and the maximal stresses predicted in this group were significantly higher than those in the asymptomatic group (508.2 ± 193.1 vs 269.6 ± 107.9 kPa; P = .004). Conclusions: Maximal predicted plaque stresses in symptomatic patients were higher than those predicted in asymptomatic patients by finite element analysis, suggesting the possibility that plaques with higher stresses may be more prone to be symptomatic and rupture. If further validated by large-scale longitudinal studies, biomechanical stress analysis based on high resolution in vivo MRI could potentially act as a useful tool for risk assessment of carotid atheroma. It may help in the identification of patients with asymptomatic carotid atheroma at greatest risk of developing symptoms or mild-to-moderate symptomatic stenoses, which currently fall outside current clinical guidelines for intervention.

Using Alkanes to Predict Voluntary Intake, Faecal Output and Digestibility of Buffel Grass Hay Fed to Steers

The cuticular waxes of forage plants contain long chain n-alkanes with odd carbon chain lengths in the range C25-C37 which are quantitatively recovered in faeces. When these concentrations are used with the concentrations of administered synthetic even chain length alkanes, the voluntary intake (VI), faecal output (FO) and digestibility (DMD) of forages can be estimated (Dove and Mayes 1991, 1996).

Potential of VIS-NIR Spectroscopy to predict perceived ‘muddy’ taint in australian farmed barramundi

Sensory analysis of food involves the measurement, interpretation and understanding of human responses to the properties of food perceived by the senses such as sight, smell, and taste (Cozzolino et al. 2005). It is important to have a quantitative means for assessing sensory properties in a reasonable way, to enable the food industry to rapidly respond to the changing demands of both consumers and the market. Aroma and flavour are among the most important properties for the consumer, and numerous studies have been performed in attempts to find correlations between sensory qualities and objective instrumental measurements. Rapid instrumental methods such as near infrared spectroscopy (NIR) might be advantageous to predict quality of different foods and agricultural products due to the speed of analysis, minimum sample preparation and low cost. The advantages of such technologies is not only to assess chemical structures but also to build an spectrum, characteristic of the sample, which behaves as a “finger print” of the sample.

Exploring the potential of VIS-NIR spectroscopy to predict sensory properties of foods

Sensory analysis of food involves the measurement, interpretation and understanding of human responses to the properties of food perceived by the senses such as sight, smell, and taste (Cozzolino et al. 2005). It is important to have a quantitative means for assessing sensory properties in a reasonable way, to enable the food industry to rapidly respond to the changing demands of both consumers and the market. Aroma and flavour are among the most important properties for the consumer and numerous studies have been performed in attempts to find correlations between sensory qualities and objective instrumental measurements. Rapid, non-destructive instrumental methods such as near infrared spectroscopy (NIR) might be advantageous to predict quality of food and agricultural products due to the speed of analysis, minimum sample preparation and low cost. The advantages of such technologies are not only to assess chemical structures but also to build a spectrum, characteristic of the sample, which behaves as a “finger print”.

The use of heat-sums to predict harvest dates following potassium chlorate (KClO3) application in longan.

The ability to initiate and manipulate flowering with KClO3 allows flowering of longan, to be triggered outside of the normal flowering season (July-September) in Australia. Fruit maturity following normal flowering will occur approximately six-eight months (180-220 days) from flowering, depending on variety. Out of season flowering will result in differing times to maturity due to different temperature regimes during the maturity period. Knowing how long fruit will take to mature from different KClO3 application dates is potentially a valuable tool for growers to use as it would allow them to time their applications with market opportunities, e.g. Chinese New Year, periods of low volumes or periods of high prices. A simple heat-sum calculation was shown to reliably quantify fruit maturity periods, 2902 and 3432 growing degree days for Kohala and Biew Kiew respectively. Growers can use heat-sum as a predictive tool to allow for efficient planning of harvesting, packaging and freight requirements.

Using a process-based model, ParopSys, to predict the impact of climate change on the eucalypt plantation pest Paropsis atomaria.

The eucalypt leaf beetle, Paropsis atomaria Olivier, is an increasingly important pest of eucalypt plantations in subtropical eastern Australia. A process-based model, ParopSys, was developed using DYMEXTM and was found to accurately predict the beetle populations. Climate change scenarios within the latest Australian climate model forecast range were run in ParopSys at three locations to predict changes in beetle performance. Relative population peaks of early generations did not change but shifted to earlier in the season. Temperature increases of 1.0 to 1.5 ºC or greater predicted an extra generation of adults at Gympie and Canberra, but not for Lowmead, where increased populations of late season adults were observed under all scenarios. Furthermore, an additional generation of late-larval stages was predicted at temperature increases of greater than 1.0 ºC at Lowmead. Management strategies to address these changes are discussed, as are requirements to improve the predictive capacity of the model.

Development of a computer code to predict cooling of die and billet in forging

A hot billet in contact with relatively cold dies undergoes rapid cooling in the forging operation. This may give rise to unfilled cavities, poor surface finish and stalling of the press. A knowledge of billet-die temperatures as a function of time is therefore essential for process design. A computer code using finite difference method is written to estimate such temperature histories and validated by comparing the predicted cooling of an integral die-billet configuration with that obtained experimentally.

Modelling temperature, photoperiod and vernalization responses of Brunonia australis (Goodeniaceae) and Calandrinia sp (Portulacaceae) to predict flowering time

Crop models for herbaceous ornamental species typically include functions for temperature and photoperiod responses, but very few incorporate vernalization, which is a requirement of many traditional crops. This study investigated the development of floriculture crop models, which describe temperature responses, plus photoperiod or vernalization requirements, using Australian native ephemerals Brunonia australis and Calandrinia sp. A novel approach involved the use of a field crop modelling tool, DEVEL2. This optimization program estimates the parameters of selected functions within the development rate models using an iterative process that minimizes sum of squares residual between estimated and observed days for the phenological event. Parameter profiling and jack-knifing are included in DEVEL2 to remove bias from parameter estimates and introduce rigour into the parameter selection process. Development rate of B. australis from planting to first visible floral bud (VFB) was predicted using a multiplicative approach with a curvilinear function to describe temperature responses and a broken linear function to explain photoperiod responses. A similar model was used to describe the development rate of Calandrinia sp., except the photoperiod function was replaced with an exponential vernalization function, which explained a facultative cold requirement and included a coefficient for determining the vernalization ceiling temperature. Temperature was the main environmental factor influencing development rate for VFB to anthesis of both species and was predicted using a linear model. The phenology models for B. australis and Calandrinia sp. described development rate from planting to VFB and from VFB to anthesis in response to temperature and photoperiod or vernalization and may assist modelling efforts of other herbaceous ornamental plants. In addition to crop management, the vernalization function could be used to identify plant communities most at risk from predicted increases in temperature due to global warming.

Relative peripheral hyperopia does not predict development and progression of myopia in children

Purpose To test the hypothesis that relative peripheral hyperopia predicts development and progression of myopia. Methods Refraction along the horizontal visual field was measured under cycloplegia at visual field angles of 0°, ±15°, and ±30° at baseline, 1 and 2 years in over 1700 initially 7-year-old Chinese children, and at baseline and 1 year in over 1000 initially 14-year olds. One refraction classification for central refraction was “nonmyopia, myopia” (nM, M), consisting of nM greater than −0.50 diopters (D; spherical equivalent) and M less than or equal to −0.50 D. A second classification was “hyperopia, emmetropia, low myopia, and moderate/high myopia” (H, E, LM, MM) with H greater than or equal to +1.00 D, E, −0.49 to +0.99 D, LM, −2.99 to −0.50 D, and MM less than or equal to −3.00 D. Subclassifications were made on the basis of development and progression of myopia over the 2 years. Changes in central refraction over time were determined for different groups, and relative peripheral refraction over time was compared between different subgroups. Results Simple linear regression of central refraction as a function of relative peripheral refraction did not predict myopia progression as relative peripheral refraction became more hyperopic: relative peripheral hyperopia and relative peripheral myopia predicted significant myopia progression for 0% and 35% of group/visual field angle combinations, respectively. Subgroups who developed myopia did not have more initial relative peripheral hyperopia than subgroups who did not develop myopia. Conclusions Relative peripheral hyperopia does not predict development nor progression of myopia in children. This calls into question the efficacy of treatments that aim to slow progression of myopia in children by “treating” relative peripheral hyperopia.

Early childhood profiles of sleep problems and self-regulation predict later school adjustment

Background Children’s sleep problems and self-regulation problems have been independently associated with poorer adjustment to school, but there has been limited exploration of longitudinal early childhood profiles that include both indicators. Aims This study explores the normative developmental pathway for sleep problems and self-regulation across early childhood, and investigates whether departure from the normative pathway is associated with later social-emotional adjustment to school. Sample This study involved 2880 children participating in the Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC) – Infant Cohort from Wave 1 (0-1 years) to Wave 4 (6-7 years). Method Mothers reported on children’s sleep problems, emotional, and attentional self-regulation at three time points from birth to 5 years. Teachers reported on children’s social-emotional adjustment to school at 6-7 years. Latent profile analysis was used to establish person-centred longitudinal profiles. Results Three profiles were found. The normative profile (69%) had consistently average or higher emotional and attentional regulation scores and sleep problems that steadily reduced from birth to 5. The remaining 31% of children were members of two non-normative self-regulation profiles, both characterised by escalating sleep problems across early childhood and below mean self-regulation. Non-normative group membership was associated with higher teacher-reported hyperactivity and emotional problems, and poorer classroom self-regulation and prosocial skills. Conclusion Early childhood profiles of self-regulation that include sleep problems offer a way to identify children at risk of poor school adjustment. Children with escalating early childhood sleep problems should be considered an important target group for school transition interventions.

Learning from the past to predict the future: an historical analysis of grass invasions in northern Australia

An important focus of biosecurity is anticipating future risks, but time lags between introduction, naturalisation, and (ultimately) impact mean that future risks can be strongly influenced by history. We conduct a comprehensive historical analysis of tropical grasses (n = 155) that have naturalised in Australia since European settlement (1788) to determine what factors shaped historical patterns of naturalisation and future risks, including for the 21 species that cause serious negative impacts. Most naturalised species were from the Old World (78 %), were introduced for use in pasture (64.5 %), were first recorded prior to 1940 (84.5 %) and naturalised before 1980 (90.3 %). Patterns for high-impact species were similar, with all being first recorded in Australia by 1940, and only seven naturalised since then-five intentionally introduced as pasture species. Counter to expectations, we found no evidence for increased naturalisation with increasing trade, including for species introduced unintentionally for which the link was expected to be strongest. New pathways have not emerged since the 1930s despite substantial shifts in trading patterns. Furthermore, introduction and naturalisation rates are now at or approaching historically low levels. Three reasons were identified: (1) the often long lag phase between introduction and reported naturalisation means naturalisation rates reflect historical trends in introduction rates; (2) important introduction pathways are not directly related to trade volume and globalisation; and (3) that species pools may become depleted. The last of these appears to be the case for the most important pathway for tropical grasses, i.e. the intentional introduction of useful pasture species. Assuming that new pathways don't arise that might result in increased naturalisation rates, and that current at-border biosecurity practices remain in place, we conclude that most future high-impact tropical grass species are already present in Australia. Our results highlight the need to continually test underlying assumptions regarding future naturalisation rates of high-impact invasive species, as conclusions have important implications for how best to manage future biosecurity risks.

When did you last predict a good idea? Exploring the case of assessing creativity through learning outcomes

It has been noted elsewhere that an idea is acknowledged to be creative if it is novel, or surprising and adaptive. So how does that fit with education's desire to measure student performance against fixed, consistent and predicted learning outcomes? This study explores practical measures and theoretical constructs that address the dearth of teaching, learning and assessment strategies to enhance creative capacity in enterprise and entrepreneurship education. It is argued that inappropriate assessment strategies can be significant inhibitors of the creativity of students and teachers. Referring to the broader discipline of 'design', as defined by Bruce and Besant (2002) – the application of human creativity to a purpose – both broad employer satisfaction with education and fast growing economic success are found (DCMS, 2014). As predictable assessment outcomes equal predictable students, these understandings can inform educators who wish to map and develop enhanced creative endeavours such as opportunity recognition, communication and innovation.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.