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The impact of a voluntary alcohol and other drug diversion program on reoffending, imprisonment, and health
Full text linkBackground: The Magistrates Early Referral into Treatment (MERIT) program is a voluntary, pre-plea diversion program for defendants appearing in the New South Wales (NSW), Australia, Local Court who have issues related to their alcohol and other drug (AOD) use. Methods: Matched treatment and comparison groups were created using propensity score matching. The outcomes examined were AOD-related hospital admissions, AOD-related Emergency Department (ED) admissions, ED admissions (general), hospital admission (general), ambulance callouts, AOD related deaths, and deaths from any cause, as well as reoffending and imprisonment. Differences between outcomes were analysed using Cox regression (health outcomes), negative binomial regression (reoffending) and logistic regression (imprisonment). Results: Survival times for participants in the MERIT program were significantly shorter for all health outcomes except one (death). At the 12-month mark, MERIT participants offended 21 per cent less frequently than comparison group participants (IRR: 0.793. CI: 0.748–0.841). This gap increased to 27 per cent after 24 months (IRR: 0.870. CI: 0.829–0.912). At the conclusion of criminal proceedings participants in the MERIT program were significantly less likely to receive a prison sentence (OR: 0.728. CI: 0.674–0.787) or to die (OR: 0.674. CI: 0.502–0.904) Conclusion: The Magistrates’ Early Referral Into Treatment Program appears to be an effective way of reducing the short-term risk of re-offending, imprisonment, and death
Health-related inequality trends in 21st century Australia: How changes in life, health, and work expectancy differ by area socioeconomic status
Full text linkIt is well accepted that the rich are healthier and live longer than the poor, in what is described as the social gradient of health. However, estimates of the magnitude of this relationship vary, and there is limited evidence on whether the social gradient is becoming steeper or shallower as lifespans lengthen. Furthermore, while literature on health and work is extensive, there has been little research combining evidence on lifespan, health-span, and work-span, with very few studies documenting trends for population subgroups despite continued policy interest in extending working lives. The present thesis contributes to these fields by estimating trends for Australia, using an ecological approach, making use of a large mostly death-registry- and Census-derived dataset. The analysis tracks changes in areas over time in pooled, random effects, and fixed effects models by area income and a socioeconomic index. The findings suggest that the social gradients for life and health expectancy are continuing to steepen. In addition, as work expectancies increase, the evidence here points to a decline in healthy retirement. Notably, the lack of improvements in health among poor women and the lack of improvements in the security and physical intensity of jobs among poor men are reducing the length of healthy retirements and potentially inhibiting the prospects of extending working lives for these groups. Overall, the thesis suggests that even in a country with a relatively efficient healthcare system, robust social safety net, and increasing lifespans, the trends related to life, health, work, and retirement are increasingly favouring more advantaged groups
Enhancing the functionality of pea protein for plant-based milk applications
Full text linkPlant proteins generally require structural modification to improve their functions in food systems. Beyond functionality, their digestibility and flavour notably impact the market appeal of plant-based products. This study aims to improve the solubility/emulsification of pea protein through thermal modification and polysaccharide incorporation to fabricate a stable and digestible pea milk system, as well as a pea milk powder system with reduced beany flavour and lipid oxidation.
Firstly, pea protein concentrate (PPC) and isolate (PPI) were extracted from the pre-roasted pea seeds. Short-term pre-roasting (150 °C for 10 and 20 min) significantly altered the tertiary structures of pea proteins to increase the solubility of PPC and PPI by ~12% at pH 7 and enhanced the emulsion ability index (EAI), while retaining the protein extraction efficiency.
Subsequently, a pea milk system was formulated by pre-roasting-treated pea proteins (150 °C for 10 min) and polysaccharides. Anionic gellan gum demonstrated a longer-term stabilisation effect on pea protein-based emulsions than neutral guar gum, due to its electrostatic repulsion with pea proteins, and stronger steric hindrance induced by thicker adsorbed interfacial layers as detected by a quartz crystal microbalance with dissipation (QCM-D). The addition of polysaccharides prevented oil droplets from flocculation during in vitro digestion, enhancing the final proteolysis.
Lastly, spray-dried pea milk powder was prepared based on the formulation of gellan gum-added pea milk, with maltodextrin added as a bulking agent. It was shown that pre-heating the aqueous phase of the feed emulsion at 95 °C for 15 min promoted the release of beany compounds in pea proteins by rearranging protein structures due to protein unfolding and disulphide bonding. Moreover, this treatment facilitated moisture evaporation and oil phase encapsulation during spray drying, and inactivated the lipoxygenase in powders, thereby inhibiting the formation of beany molecules and lipid oxidation.
Overall, it was found that controlled thermal treatments and polysaccharide addition induced the alteration of pea protein structures by exposing buried hydrophobic cores, leading to changes in non-covalent/covalent interactions such as hydrogen bonds and disulphide linkages. These strategies could enhance the functionality, digestibility and flavour of pea protein in pea milk/milk powder systems, for potential applications in legume-based dairy alternatives
Optical Nanopore Blockade Sensors for Multiplexed Detection of Proteins
No full textEnduring challenges for quantitative analysis in nanopore sensing are the detection of low biomarker concentrations in reasonable time frames and the detection of multiple biomarkers in the same sample. Herein we report an optical blockade nanopore sensor strategy that can detect more than a protein at femtomolar concentrations in rapid time (approximately 12 min). This is done using a nanopore array functionalized with an aptamer that can bind to two different but related target proteins. The assay then monitors two different colors of fluorescent particles modified with antibodies specific to the protein of interest, as they block the nanopores using a wide-field microscope. By distinguishing specific and nonspecific blockade events for each nanoparticle based on whether they can be easily pulled out of the nanopores using an electric field, we can simultaneously quantify the concentrations of vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF) down to femtomolar concentrations
Global projections of hail hazard frequency under climate change
Full text linkHail can injure people and damage infrastructure, with hailstorms a driving cause of insured losses. Hailstorms are expected to be affected by global warming, primarily via changes to atmospheric instability, wind shear, and the height of the melting level. However, the nuances of expected changes remain uncertain and are generally only studied regionally, partly because global climate models typically lack the fine grid spacing required to explicitly resolve hailstorms. Here, we show global projections using an ensemble of four hail proxies to estimate hail-prone conditions occurrence frequency in eight global climate models. We use a temperature-based framework and show projected changes in global hail hazard frequency in scenarios with two and three degrees of warming over a recent historical period. By analysing changes in the "ingredients" for the proxies we can determine which factors are most pertinent to the changes in hail-prone conditions. Under global warming, the multi-model multi-proxy results show general poleward shifts in hail-prone condition frequency, and shifts from the warm season to the cool season in many regions. The results reinforce the benefit of using proxies designed specifically for hail for such studies, since some more general thunderstorm proxies neglect the effects of temperature and can show significantly different results. Finally, we use our results to analyse changes in hail exposure to various crops worldwide. This work encompasses the first global projections for severe storms using proxies specifically designed for hailstorms
Reevaluating Network Connectivity: The Critical Role of the Relative Size of Largest Connected Component (RSLCC)
Full text linkIn network structure analysis, metrics such as Isolated Node Ratio (INR), Network Efficiency (NE), Network Clustering Coefficient (NCC), Betweenness Centrality (BC), and Closeness Centrality (CC) are used as quantitative tools to measure network connectivity. However, there is another metric that is often easily overlooked and underestimated: the Relative Size of the Largest Connected Component (RSLCC). Our research not only proves that this metric is underestimated but also designs seven methods to predict its value, achieving a Deep Neural Network (DNN) prediction accuracy of more than 99%. DNN has demonstrated excellent predictive performance in large-scale networks, while Random Forest Regression (RFR) has proven to be highly effective and the fastest method for small-scale networks. The results of this research can be applied to any network. In a disaster scenario, whether it involves a physical network or a virtual abstract network. We illustrate the practical application of these insights using a case study of transportation network disaster rescue. Detailed steps are provided to explain how to implement these research findings in real-world scenarios, demonstrating the feasibility of applying this research to actual emergency situations
Tired? Think twice: The role of repetitive negative thinking in fatigue in the general population
Full text linkFatigue is an inevitable part of the human experience, with fatigue amongst the most frequent complaints received by health professionals. Despite its prevalence, the aetiology of fatigue remains poorly understood. Physiological factors are often perceived as the primary cause of fatigue, yet studies report these factors do not fully account for fatigue. Emerging evidence suggests repetitive negative thinking (RNT) is associated with fatigue, however, the majority of these studies are correlational, cross-sectional, and conducted in clinical populations. Thus, the central aim of this thesis was to better understand the relationship between RNT and fatigue in the general population, with the goal of determining if there is a causal relationship between the two. Chapter 2 demonstrated that RNT and fatigue were positively associated, such that RNT predicted various aspects of the fatigue experience, even when controlling for alternative causal explanations. Chapter 3 revealed that the positive association between RNT and fatigue was maintained when measured longitudinally and prospectively through ecological momentary assessment. Importantly, this design established temporal precedence within the RNT-fatigue relationship, such that greater RNT preceded increases in fatigue, and vice versa. Chapter 4 then explored causality via manipulating RNT. Chapter 4 showed that an intervention which reduced RNT led to reductions in fatigue, relative to a treatment-as-usual control group, and this effect was mediated by reductions in RNT. Chapter 5 aimed to replicate this effect using validated measures of fatigue in a sample with elevated levels of fatigue. While reductions in RNT were observed in the intervention group (who completed scheduled problem-solving), compared to the control group, there was no effect of the intervention on fatigue. This thesis provides strong evidence for the relationship between RNT and fatigue in the general population. Specifically, that this relationship exists across samples varying on psychological characteristics, across different measures and subcomponents of RNT, and when measured longitudinally. Importantly, this thesis presents novel evidence that RNT may be causally related to fatigue in the general population. While further research is required to replicate this causal link, this thesis establishes a strong foundation from which to further investigate the multifactorial aetiology of fatigue
Effects of surface morphologies on boiling heat transfer in droplet impingement on superheated surfaces
Full text linkA phase change spray cooling system is an important engineering application of droplet impingement on superheated surfaces. This work studies the impacts of different morphologies on boiling heat transfer during droplet impingement on superheated surfaces using a three-dimensional hybrid approach: the multiphase pseudopotential lattice Boltzmann method for multiphase flows and the finite difference method for heat transfer. Simulations are conducted by varying boiling number (Bo) from 0.0023 to 0.0460 at an initial Reynolds number of 100 for four morphologies: single-concave, rectangular-groove, wavy-groove, and multi-concave. For single-concave morphology, the ratio of concave diameter to droplet diameter ( D c / d ) is examined with values of 1.0, 2.0, 3.0, and 3.3. In the other morphologies, cross sections are evaluated with two widths: 0.333 d and 0.667 d , with identical depths. The results show that the thermal performance of the single-concave morphology is mainly affected by D c / d . The curved geometry gives the single-concave morphology superiority in boiling heat transfer compared to other morphologies studied in the range 0.0023 < B o < 0.0389 at D c / d = 2.0 . The curved surface controls the bounce of droplets at high Bo, allowing them to deposit smoothly with a large exposed contact area, and achieve an efficient cooling effect. However, for 0.0389 ≤ B o , superiority in boiling heat transfer is achieved by the multi-concave morphology, where full film boiling does not occur. The thermal performance of other morphologies is primarily influenced by the cross-sectional width. At a width of 0.667 d , the wavy-groove morphology provides comparable performance to the multi-concave morphology within 0.0023 < B o < 0.0184 , while the multi-concave morphology achieves higher boiling heat transfer at 0.0184 ≤ B o . Conversely, a smaller width of 0.333 d significantly reduces heat transfer. This occurs because the rapid surface isolation hinders droplet access to the heated surface base. Furthermore, the rectangular-groove morphology provides the worst thermal performance due to the restrictions against penetration and smooth deposition over the superheated surface. Thermal and hydrodynamic analysis discovers the significance of the single-concave morphology in enhancing the boiling heat transfer in spray cooling systems
Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings
Full text linkPurpose: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimation, and identification of anemia-related retinal features using fundus images. Methods: The dataset included 2265 participants aged 40 years and above from a population-based study in South India. The dataset included ocular and systemic clinical parameters, dilated retinal fundus images, and hematological data such as complete blood counts and Hb concentration levels. Eighty percent of the dataset was used for algorithm development and 20% for validation. A deep-convolutional neural network, utilizing VGG16, ResNet50, and InceptionV3 architectures, was trained to predict anemia and estimate Hb levels. Sensitivity, specificity, and accuracy were calculated, and receiver operating characteristic (ROC) curves were generated for comparison with clinical anemia data. GradCAM saliency maps highlighted regions linked to anemia and image processing techniques to quantify anemia-related features. Results: For predicting anemia, the InceptionV3 model demonstrated the best performance, achieving 98% accuracy, 99% sensitivity, 97% specificity, and an area under the curve (AUC) of 0.98 (95% confidence interval [CI] = 0.97–0.99). For estimating Hb levels, the mean absolute error for the InceptionV3 model was 0.58 g/dL (95% CI = 0.57–0.59 g/dL). The model focused on the area around the optic disc and the neighboring retinal vessels, revealing that anemic subjects exhibited significantly increased vessel tortuosity and reduced vessel density (P < 0.001), with variable effects on vessel thickness. Conclusions: The InceptionV3 model accurately predicted anemia and Hb levels, highlighting the potential of deep learning and vessel analysis for noninvasive anemia detection. Translational Relevance: The proposed method offers the possibility to quantitatively predict hematological parameters in a noninvasive manner